TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

RESIDENTIAL DEHUMIDIFIERS

May 2015

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

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

and by staff members of 

Navigant Consulting, Inc.

and

Ernest Orlando Lawrence Berkeley National Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

1.2 OVERVIEW OF STANDARDS FOR RESIDENTIAL DEHUMIDIFIERS ................. 1-1

1.3 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ................... 1-2

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

LIST OF TABLES

Table 1.3.1 Analyses Under the Process Rule .................................................................. 1-3
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

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

analyses and results supporting the information presented in the notice of proposed rulemaking 

(NOPR) for residential dehumidifiers. This NOPR TSD reports on the NOPR analyses conducted 

in support of the NOPR. 

1.2 OVERVIEW OF STANDARDS FOR RESIDENTIAL DEHUMIDIFIERS

The Energy Policy and Conservation Act (EPCA) of 1975, Pub. L. 94-163 (42 United 

States Code (U.S.C.) 6291–6309), established an energy conservation program for major 

household appliances. The National Energy Conservation Policy Act of 1978 (NECPA), Pub. L. 

95-619, amended EPCA to add Part Ca

 of Title III (42 U.S.C. 6311–6317), which established an 

energy conservation program for certain industrial equipment. Additional amendments to EPCA 

give U.S. Department of Energy (DOE) the authority to regulate the energy efficiency of several 

products, including residential dehumidifiers—the products that are the focus of this document. 

The amendments to EPCA in the Energy Policy Act of 2005 (EPACT 2005) (P.L. 109-58), 

established energy conservation standards for residential dehumidifiersb manufactured as of 

October 1, 2007. (Section 135(c)(4)) EPACT 2005 also required that DOE issue a final rule by 

October 1, 2009, to determine whether these standards should be amended. (Id.) Compliance 

with any amended standards would be required for dehumidifiers manufactured as of October 1, 

2012. (Id.) In the event that DOE did not publish a final rule, EPACT 2005 specified a new set of 

amended standards with a compliance date of October 1, 2012. (Id.) 

DOE issued an advance notice of proposed rulemaking (ANOPR) to consider energy 

conservation standards for dehumidifiers and other products. 72 FR 64432 (Nov. 15, 2007). The 

Energy Independence and Security Act of 2007 (EISA 2007), Pub. L 110-140 subsequently 

amended EPCA to prescribe new energy conservation standards for dehumidifiers manufactured 

on or after October 1, 2012. DOE codified the EISA 2007 standards at 10 Code of Federal 

Regulations (CFR) 430.32(v)(2). 74 FR 12058 (Mar. 23, 2009). 

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

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

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

a Part C has been redesignated Part A-1 in the United States Code for editorial reasons.

b Dehumidifiers are defined as self-contained, electrically operated, and mechanically encased assemblies consisting 

of: (1) a refrigerated surface (evaporator) that condenses moisture from the atmosphere; (2) a refrigerating system, 

including an electric motor; (3) an air-circulating fan; and (4) a means for collecting or disposing of the condensate. 

(42 U.S.C. 6291(34))
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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)): 

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).

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

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

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))

Any new or amended standard must be designed to achieve significant additional conservation of 

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

determine whether economic justification exists, DOE must review comments on the proposal 

and determine that the benefits of the proposed standard exceed its burdens to the greatest extent 

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

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 NOPR stage. The second notice is the NOPR, which 
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presents a discussion of comments received in response to the 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.

In August 2012, DOE published a notice of public meeting and availability of the 

framework document. 77 FR 49739 (Aug. 17, 2009) The framework document, Energy 

Conservation Standards Rulemaking Framework Document for Residential Dehumidifiers, 

describes the procedural and analytical approaches DOE anticipated using to evaluate the 

establishment of amended energy conservation standards for these products. This document is 

available at: http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0027-0003.

Subsequently, DOE held a public meeting on September 24, 2012, (“September 2012 

public meeting”) to discuss procedural and analytical approaches to the rulemaking. In addition, 

DOE used the public meeting to inform and facilitate involvement of interested parties in the 

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

different analyses, such as the engineering analysis and the consumer economic analyses (i.e., 

the life-cycle cost (LCC) and payback period (PBB) analyses), the methods proposed for 

conducting them, and the relationships among the various analyses.

Table 1.3.1 Analyses Under the Process Rule

Preliminary Analyses 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

Markups analysis Emissions analysis

Energy use analysis Emissions Monetization Impact

analysis

Life-cycle cost and payback period 

analysis

Utility impact analysis

Shipments analysis Employment impact analysis

National impact analysis Regulatory impact analysis

Preliminary manufacturer impact analysis

During the September 2012 public meeting, interested parties commented about 

numerous issues relating to each one of the analyses listed in Table 1.3.1. Comments from 

interested parties submitted during the framework document comment period elaborated on the 

issues raised during the public meeting. DOE attempted to address these issues during its 
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preliminary analyses and summarized the comments and DOE’s responses in chapter 2 of the 

preliminary TSD.

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

interviews with manufacturers of the residential dehumidifiers considered in this rulemaking as 

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

of products, 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 preliminary 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 preliminary manufacturer impact 

analysis (chapter 12). 

DOE developed spreadsheets for the engineering, LCC, PBP (chapter 8), and national 

impact analyses (chapter 10) for each product. For each product, DOE developed an LCC 

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

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

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

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

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

DOE website for residential dehumidifiers at: 

http://www1.eere.energy.gov/buildings/appliance_standards/residential/dehumidifiers.html.

On May 21, 2014, DOE published the NOPM and availability of the preliminary TSD. 77 

FR 29380. The preliminary TSD provides technical analyses and results that support the 

information presented in the preliminary NOPM and the executive summary for residential 

heating products. The preliminary TSD also provides a detailed description of all of the analyses 

discussed in the paragraphs above. The preliminary TSD is available at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0027-0015. 

Following publication of the NOPM and the preliminary TSD, DOE held a public 

meeting on June 13, 2014, to facilitate discussion about the preliminary analyses that were 

performed for the NOPM and described in the preliminary TSD. In addition to the public 

meeting, a written comment period was open until July 21, 2014, to allow interested parties to 

provide new comments or elaborate on any comments made at the public meeting. 

After receiving these comments, DOE revised the preliminary analyses for the NOPR 

phase of this rulemaking based on the feedback from interested parties. DOE organized and held 

a second round of interviews with manufacturers to gather additional feedback on the analyses 

and as part of the manufacturer impact analysis conducted for the NOPR phase of the 

rulemaking. 
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In addition to revising the various preliminary analyses, DOE also performed a consumer 

subgroup analysis, manufacturer impact analysis, utility impact analysis, employment impact 

analysis, and regulatory impact analysis for the NOPR stage of this rulemaking. 

1.4 STRUCTURE OF THE DOCUMENT

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

TSD consists of 17 chapters (including an environmental assessment and regulatory impact 

analysis) 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 Use Analysis: discusses the process used for generating energyuse estimates for the considered products as a function of standard 

levels.

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

standards on individual customers and users of the products and 

compares the LCC and PBP of products with and without higher 

efficiency standards.

Chapter 9 Shipments Analysis: discusses the methods used for forecasting 

shipments with and without higher efficiency standards, including how 

product purchase decisions are economically influenced and how DOE 

models this relationship with econometric equations. 
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Chapter 10 National Impact Analysis: Discusses the methods used for forecasting 

national energy consumption and national economic impacts based on 

annual product shipments and estimates of future product energy 

efficiency distributions in the absence and presence of amended energy 

conservation standards. 

Chapter 11 Consumer 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 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 3A AHAM Data Submittal

Appendix 5A Engineering Analysis Interview Guide

Appendix 6A Detailed Data for Product Price Markups

Appendix 7A Housing Variables 

Appendix 7B Weather Station Data Mapping to RECS Households 

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

Appendix 8B Uncertainty and Variability in LCC Analysis for Dehumidifiers

Appendix 8C Lifetime Distributions

Appendix 8D Distributions for Discount Rates 

Appendix 9A Relative Price Elasticity of Demand for Appliances 
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Appendix 10A User Instructions for National Impact Analysis Spreadsheet Model

Appendix 10B Full-Fuel-Cycle Multipliers

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

Product Price Forecasts

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

Growth Scenarios 

Appendix 12A Manufacturer Impact Analysis Interview Guide 

Appendix 12B Government Regulatory Impact Model Overview 

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

Order 12866 

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

Analysis Under Executive Order 12866 

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

TABLE OF CONTENTS

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

2.2 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 USE ANALYSIS ............................................................................................ 2-6

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

2.7.1 Inputs to First Costs ............................................................................................. 2-8

2.7.2 Inputs to Operating Cost ...................................................................................... 2-8

2.7.3 Other Inputs ......................................................................................................... 2-9

2.8 SHIPMENTS ANALYSIS .............................................................................................. 2-9

2.9 NATIONAL IMPACT ANALYSIS .............................................................................. 2-10

2.9.1 National Energy Savings Analysis..................................................................... 2-11

2.9.2 Net Present Value Analysis ............................................................................... 2-11

2.10 CONSUMER SUBGROUP ANALYSIS ...................................................................... 2-12

2.11 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-12

2.12 EMISSIONS ANALYSIS .............................................................................................. 2-12

2.13 MONETIZING REDUCED CO2 AND OTHER EMISSIONS .................................... 2-14

2.14 UTILITY IMPACT ANALYSIS ................................................................................... 2-15

2.15 EMPLOYMENT IMPACT ANALYSIS ....................................................................... 2-16

2.16 REGULATORY IMPACT ANALYSIS ....................................................................... 2-16

REFERENCES .......................................................................................................................... 2-17
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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

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

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

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

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

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

conservation standards for residential dehumidifiers. 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. 
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Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process 

The analyses performed as part of this notice of proposed rulemaking (NOPR) and 

reported in this NOPR technical support document (NOPR TSD) are listed below.

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

existing technology options, including prototype designs. 
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• A screening analysis to review each technology option and determine if it is 

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

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

safety.

• An engineering analysis to develop cost-efficiency relationships that show the 

manufacturer’s cost of achieving increased efficiency. 

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

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

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

a representative set of users.

• A life-cycle cost (LCC) and payback period (PBP) analysis to calculate the savings in 

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

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

from imposition of a standard. 

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

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

manufacturer cash flows.

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

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

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

• An LCC subgroup analysis to evaluate variations in customer characteristics that might 

cause a standard to disproportionately affect particular customer subpopulations. 

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

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

and manufacturing capacity.

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

the environment. 

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

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

oil utilities.

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

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

conservation standards that potentially could achieve substantially the same regulatory 

goal at a lower cost.

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

Energy Conservation Standards Rulemaking Framework Document for Residential 

Dehumidifiers (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 17, 2012. 77 FR 49739. 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-0027-0003. 

 DOE received numerous comments from interested parties regarding DOE’s analytical 

approach. In the preliminary analysis, DOE: (1) summarized the key comments received from 

interested parties and describes DOE’s responses to those comments; (2) summarized any 

significant changes in the analytical approach made since publishing the framework document; 

and (3) explained in further detail each of the issues for which DOE sought public comment in 

the executive summary. DOE announced the availability of the preliminary TSD in a Federal 

Register notice published on May 22, 2014. 79 FR 29380. The preliminary TSD is available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0027-0015. 

The following sections provide a general description of the different analytical 

components of the rulemaking analytical plan. DOE has used the most reliable data available at 

the time of each analysis in this rulemaking. All data will be available for public review. DOE 

welcomes and will consider any submissions of additional data during the rulemaking process. 
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2.2 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant product markets and 

existing technology options, including working 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 NOPR TSD 

provides additional details on the market and technology assessment. 

2.2.2 Technology Assessment

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

prototype designs as inputs to determine what technologies manufacturers 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. Because many options for improving product efficiency are available in existing units, 

product literature and direct examination provided additional information. Chapter 3 of the 

NOPR TSD includes the detailed list of all 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 the engineering analysis, DOE further considered 

efficiency enhancement options that it did not screen out in the screening analysis. Chapter 4 of 

the NOPR TSD contains details on the screening analysis. 
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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 of the NOPR 

TSD. 

2.5 MARKUPS ANALYSIS

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

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

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

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

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

in the retailer or distributor sales price. 

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

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

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

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

TSD provides details on DOE’s development of markups for residential dehumidifiers.

2.6 ENERGY USE ANALYSIS

DOE establishes the annual energy consumption of a product and assesses the energysavings potential of various product efficiencies. As part of the energy use analysis, certain 
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engineering assumptions may be required regarding product application, including how often the 

product is operated and under what conditions. DOE uses the annual energy consumption and 

energy-savings potential in the LCC and PBP analyses to establish the savings in consumer 

operating costs at various product efficiency levels. 

DOE used the Energy Information Administration’s (EIA) 2009 Residential Energy 

Consumption Survey (RECS 2009) to establish a sample of households using dehumidifiers for 

each dehumidifier product class.1 The RECS data provides information on dehumidifier 

ownership and frequency of dehumidifier use by monthly range in each household. The survey 

also includes household information such as the physical characteristics of housing units, 

household demographics, information about cooling products, and other relevant data. DOE used 

the household samples and publically available dehumidifier energy use data to determine 

dehumidifier annual energy consumption, but also as the basis for conducting the LCC and PBP 

analysis. See chapter 7 of the NOPR TSD for more information. 

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

In determining whether an energy efficiency standard is economically justified, DOE 

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

amended standards on individual consumers usually includes a reduction in operating cost and an 

increase in purchase cost. DOE used the following two metrics to measure consumer impacts:

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

the life of the appliance or product, including purchase and operating costs. The latter 

consist of maintenance, repair, and energy costs. Future operating costs are discounted to 

the time of purchase and summed over the lifetime of the appliance or product.

• PBP (payback period) measures the amount of time it takes consumers to recover the 

assumed higher purchase price of a more energy-efficient product through reduced 

operating costs.

DOE analyzed the net effect of potential dehumidifier standards on consumers by 

calculating the LCC and PBP. Inputs to the LCC calculation include the installed cost to the 

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

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

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

costs.

DOE performed the LCC and PBP analyses using a spreadsheet model combined with 

Crystal Ball (a commercially-available software program used to conduct stochastic analysis 

using Monte Carlo simulation and probability distributions) to account for uncertainty and 

variability among the input variables. Each Monte Carlo simulation consists of 10,000 LCC and 

PBP calculations. The model performs each calculation using input values that are either 

sampled from probability distributions and household samples or characterized with single point 

values. The analytical results include a distribution of 10,000 data points showing the range of 

LCC savings and PBPs for a given efficiency level relative to the base case efficiency forecast. 

In performing an iteration of the Monte Carlo simulation for a given consumer, product 
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efficiency is chosen based on its probability. If the chosen product efficiency is greater than or 

equal to the efficiency of the standard level under consideration, the LCC and PBP calculation 

reveals that a consumer is not impacted by the standard level. By accounting for consumers who 

already purchase more-efficient products, DOE avoids overstating the potential benefits from 

increasing product efficiency.

DOE is also required to perform a PBP analysis to determine whether the rebuttable 

presumption of economic justification applies (where the higher installed cost of more energyefficient equipment is less than three times the value of the lowered operating costs in the first 

year of the energy conservation standard). (42 U.S.C. 6295(o)(2)(B)(iii)) The results of this 

analysis serve as the basis for DOE to evaluate the economic justification for a potential standard 

level (thereby supporting or rebutting the results of any NOPR determination of economic 

justification). 

2.7.1 Inputs to First Costs

Installation Costs 

Typically, small incremental changes in product efficiency incur little or no change in 

installation costs over baseline products. Based on available information, DOE did not include 

any installation costs from either a portable or whole-home dehumidifier with an increased 

product efficiency.

Product Costs

To calculate the product costs paid by dehumidifier purchasers, DOE multiplied the 

manufacturing product costs (MPCs) developed from the engineering analysis by industry 

markups to derive manufacturers’ selling prices (MSPs). The MSPs in turn are multiplied by 

supply chain markups (along with sales taxes) to estimate the initial cost to the consumer. DOE 

used the supply chain markups that include separate markups on the baseline MSP and the 

incremental cost of each higher efficiency level considered.

2.7.2 Inputs to Operating Cost

Energy Prices 

DOE derived average monthly electricity prices for the 27 geographic areas in RECS 

2009 by using the latest data from EIA. DOE assigned an appropriate energy price to each 

household in the sample, depending on its location. For future prices, DOE used the projected 

annual changes in average residential electricity prices in EIA’s 2015 Annual Energy Outlook

(AEO 2015).

Maintenance and Repair Costs 

Typically, small incremental changes in product efficiency incur little or no change in 

installation, repair and maintenance costs over baseline products. Having no information to 

conclude otherwise, DOE did not include any maintenance or repair costs from either a portable 

or whole-home dehumidifier with an increased product efficiency. 

Product Lifetime
2-9 

Product lifetime is the age at which an appliance is retired from service. Based on 

information from the American Council for an Energy Efficiency Economy and the Northeast 

Energy Star Lighting and Appliance data, DOE identified an average dehumidifier lifetime of 11 

years for portable dehumidifiers. DOE estimated lifetime for whole-home dehumidifiers based 

on data from central air conditioner information and identified an average lifetime of 19 years. 

DOE characterized dehumidifier survival functions using Weibull distributions. 

2.7.3 Other Inputs

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

The discount rate used in the LCC analysis represents the rate from an individual consumer’s 

perspective.a

 Much of the data used for determining consumer discount rates comes from the 

Federal Reserve Board’s triennial Survey of Consumer Finances.

b

To estimate the share of consumers affected by a standard at a particular efficiency level, 

DOE’s LCC and PBP analysis considers the projected distribution (i.e., market shares) of 

product efficiencies that consumers will purchase in the first compliance year under the base case 

(the case without amended energy conservation standards). 

2.8 SHIPMENTS ANALYSIS

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

and also in its manufacturer impact analysis. DOE developed these shipment forecasts based on 

an analysis of key market drivers for each product. 

DOE estimated portable dehumidifier shipments by projecting shipments in two market 

segments: (1) replacements; (2) homeowners that did not previously have a dehumidifier, i.e., 

first time owners. 

To project portable dehumidifier replacement shipments, DOE developed retirement 

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

in the stock. The existing stock of products is tracked by vintage and developed from historical 

shipments data. To project shipments to the first time owner market, DOE calibrated the 

estimated shipments with the historical data by introducing into the model a market segment 

identified as existing households without dehumidifiers. DOE estimated that 0.34 percent of 

existing households without a dehumidifier would annually purchase this product over the period 

2019–2048. DOE estimated whole-home shipments at 1 percent of the total portable 

dehumidifier shipment volume. See chapter 9 of this NOPR TSD for more details regarding the 

projection of dehumidifier shipments. 

 

a The consumer discount rate differs from the discount rates used in the national impact analysis, which are intended 

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

consumption.

b Available at http://www.federalreserve.gov/econresdata/scf/scfindex.htm. 
2-10 

2.9 NATIONAL IMPACT ANALYSIS

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

costs and savings expected to result from new or amended energy conservation standards at 

specific efficiency levels. DOE determined the NPV and NES for the standard levels considered 

for the dehumidifier product classes analyzed. DOE prepared a Microsoft Excel spreadsheet that 

uses typical values (as opposed to probability distributions) as inputs. To assess the effect of 

input uncertainty on NES and NPV results, DOE has developed its spreadsheet model to conduct 

sensitivity analyses by running scenarios on specific input variables. 

Analyzing impacts of potential energy conservation standards for dehumidifiers requires 

comparing projections of U.S. energy consumption with amended energy conservation standards 

against projections of energy consumption without amended standards. The forecasts include 

projections of annual appliance shipments, the annual energy consumption of new appliances, 

and the purchase price of new appliances. 

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

case (without new standards) and each of the standards cases. The forecasted efficiencies 

represent the annual shipment-weighted energy efficiency of the products under consideration 

during the forecast period (i.e., from the assumed compliance date of a new standard to 30 years 

after compliance is required). 

DOE developed a distribution of efficiencies in the base case for 2019 (the assumed 

compliance date for amended standards) for each dehumidifier product class. In each standards 

case, a “roll-up” scenario approach was applied to establish the efficiency distribution for 2019. 

Under the “roll-up” scenario, product efficiencies in the base case that do not meet the standard 

level under consideration would “roll-up” to meet the new standard level; and product 

efficiencies above the standard level under consideration would not be affected. In addition to a 

“roll-up” scenario, DOE developed a shift scenario. In the shift scenario DOE applied an annual 

growth rate in average energy efficiency, as it is done in the base case. To develop standards case 

forecasted shipments-weighted integrated energy factors (SWIEFs), DOE developed growth 

trends for each trial standard level that maintained the same per-unit average total installed cost 

difference for the year 2019 between the base case and each standards case over the entire 

forecast period (2019–2048). DOE’s approach for developing standards case SWIEFs in this 

manner assumes that the rate of adoption of more efficient products under the standards case can 

occur only at a rate which ensures that the average total installed cost difference between the 

standards case and base case over the entire forecast period is held constant. Because the total 

installed cost versus efficiency relationship for each product class demonstrates an increasing 

cost rate for more efficient products, the SWIEF growth rate for each standards case is lower 

than the SWIEF growth rate for the base case.

DOE assumed that energy efficiencies for all dehumidifier product classes will increase at 

a rate of 0.25 percent per year in absence of standard. The growth rates in the standard cases are 

slightly lower than in the base case. Note that for the standards cases, the efficiency trend does 

not increase past the max tech level.
2-11 

2.9.1 National Energy Savings Analysis 

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

annual energy consumption per unit; (2) shipments; (3) product or equipment stock; (4) national 

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

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

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

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

case (without new efficiency standards) and for each higher efficiency standard. DOE estimated 

energy consumption and savings based on site energy and converted the electricity consumption 

and savings to source (primary) energy using annual conversion factors derived from the most 

recent version of the National Energy Modeling System (NEMS). Cumulative energy savings are 

the sum of the NES for each year over the timeframe of the analysis. Chapter 10 of this NOPR

TSD presents primary energy savings for the considered efficiency levels.

2.9.2 Net Present Value Analysis

The parameters for determining NPV are the present value of costs and the present value 

of savings. The inputs for the present value of costs and the present value of savings include (1) 

total annual installed cost; (2) total annual savings in operating costs; (3) a discount factor to 

calculate the present value of costs and savings; DOE determined the net savings for each year as 

the difference between the base case and each standards case in terms of total savings in 

operating costs versus total increases in installed costs. DOE calculated savings over the lifetime 

of products shipped in the forecast period. DOE calculated NPV as the difference between the 

present value of operating cost savings and the present value of total installed costs. DOE used a 

discount factor based on real discount rates of 3 and 7 percent to discount future costs and 

savings to present values.

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

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

effect). Because the 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 expresses savings in operating costs as decreases associated with the lower energy 

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

in operating costs are the product of savings per unit and the number of units of each vintage that 

survive in a given year. 

DOE used the most recent available edition of EIA’s Annual Energy Outlook (AEO 2015) 

as the default source of projections for future energy prices and total housing stock. It will also 

calculate the NPV assuming higher and lower economy growth scenarios from the AEO. 

DOE uses the 3 percent and 7 percent real discount rates in accordance with guidance 

provided by the Office of Management and Budget (OMB) to Federal agencies on the 
2-12 

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

and Measuring Benefits and Costs”). 

2.10 CONSUMER SUBGROUP ANALYSIS

During the NOPR stage of this rulemaking, DOE conducted a consumer subgroup 

analysis. A consumer subgroup comprises a subset of the population that may be affected 

disproportionately by new or revised energy conservation standards (e.g., low-income 

consumers, seniors). The purpose of a subgroup analysis is to determine the extent of any such 

disproportional impacts. More information can be found in chapter 11 of the NOPR TSD. 

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 NOPR TSD.

2.12 EMISSIONS ANALYSIS

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

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

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

emissions impacts in production activities (extracting, processing, and transporting fuels) that 

provide the energy inputs to power plants. These are referred to as “upstream” emissions. 

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

Statement of Policy (76 FR 51282 (Aug. 18, 2011)), the FFC analysis includes impacts on 

emissions of methane and nitrous oxide, both of which are recognized as greenhouse gases. 

DOE primarily conducted the emissions analysis using emissions factors for CO2 and 

most of the other gases derived from data in the latest version of EIA’s Annual Energy Outlook 
2-13 

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

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

EIA prepares the Annual Energy Outlook using the National Energy Modeling System 

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

regulations on emissions. The text below refers to AEO 2014, which generally represents current 

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

implementing regulations were available as of October 31, 2015. 

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

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that operates along 

with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was 

remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit), but it remained in effect.d

 On July 6, 2011 EPA issued a replacement for CAIR, the 

Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (Aug. 8, 2011). On August 21, 2012, the 

D.C. Circuit issued a decision to vacate CSAPR.e The court ordered EPA to continue 

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

The attainment of emissions caps is typically flexible among EGUs and is enforced 

through the use of emissions allowances and tradable permits. Under existing EPA regulations, 

any excess SO2 emissions allowances resulting from the lower electricity demand caused by the 

adoption of an efficiency standard could be used to permit offsetting increases in SO2 emissions 

by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about 

the effects of efficiency standards on SO2 emissions covered by the existing cap-and-trade 

system, but it concluded that no reductions in power sector emissions would occur for SO2 as a 

result of standards. 

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride (HCl) as a surrogate for acid gas 

 

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

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

2008).

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

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

further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's 

methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other 

downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip op. at 32 

(U.S. April 29, 2014). Because DOE is using emissions factors based on AEO 2014, the analysis assumes that 

CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the 

purpose of DOE's analysis of SO2 emissions.
2-14 

hazardous air pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as 

an alternative equivalent surrogate standard for acid gas HAP. The same controls are used to 

reduce HAP and non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control 

technologies installed on coal-fired power plants to comply with the MATS requirements for 

acid gas. AEO 2014 assumes that, in order to continue operating, coal plants must have either 

flue gas desulfurization or dry sorbent injection systems installed by 2016. Both technologies, 

which are used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, 

emissions will be far below the cap that would be established by CAIR, so it is unlikely that 

excess SO2 emissions allowances resulting from the lower electricity demand would be needed 

or used to permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE 

believes that energy efficiency standards will reduce SO2 emissions in 2016 and beyond. 

CAIR established a cap on NOX emissions in eastern States and the District of Columbia. 

Energy conservation standards are expected to have little or no physical effect on these emissions 

in those States covered by CAIR because excess NOX emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOX emissions. 

However, standards would be expected to reduce NOX emissions in the States not affected by the 

caps, so DOE estimated NOX emissions reductions from potential standards in the States where 

emissions are not capped.

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

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

estimated mercury emissions reduction using emissions factors based on AEO 2014, which 

incorporates the MATS. 

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

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

plants. DOE is investigating the possibility of using other methods to estimate reduction in PM 

emissions due to standards. The great majority of ambient PM associated with power plants is in 

the form of secondary sulfates and nitrates, which are produced at a significant distance from 

power plants by complex atmospheric chemical reactions that often involve the gaseous 

emissions of power plants, mainly SO2 and NOx. The monetary benefits that DOE estimates for 

reductions in NOx emissions resulting from standards are in fact primarily related to the health 

benefits of reduced ambient PM. Further detail is provided in chapter 13 of the NOPR TSD. 

2.13 MONETIZING REDUCED CO2 AND OTHER EMISSIONS

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

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

considered. 

To estimate the monetary value of benefits resulting from reduced emissions of CO2, 

DOE used the most current Social Cost of Carbon (SCC) values developed and/or agreed to by 

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

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

agricultural productivity loss, human health effects, property damage from sea level rise, and 
2-15 

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

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

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

estimates of the social benefits of reductions in GHG emissions. 

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

previous report in 2013.g

 The most recent estimates of the SCC in 2015, expressed in 2013$, are 

$12.0, $40.5, $62.4, and $119 per metric ton of CO2 avoided. For emissions reductions that occur 

in later years, these values grow in real terms over time. Additionally, the interagency group 

determined that a range of values from 7 percent to 23 percent should be used to adjust the global 

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

benefits of reducing CO2 emissions. 

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

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

values, DOE discounted the values in each of the four cases using the discount rates that had 

been used to obtain the SCC values in each case. 

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

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

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

DOE also estimated the potential monetary benefit of reduced NOX emissions resulting 

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

stationary sources range from $476 to $4,893 per ton in 2013$.h DOE calculated monetary 

benefits using a medium value for NOX emissions of $2,684 per short ton (2013$), and real 

discount rates of 3 percent and 7 percent. 

 

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

monetized estimates of SO2 and Hg reduction in this rulemaking. Further detail on the emissions 

monetization is provided in chapter 14 of the NOPR TSD. 

2.14 UTILITY IMPACT ANALYSIS

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

generation that result for each trial standard level (TSL). The utility impact analysis is based on 

output of the DOE/ EIA’s NEMS. NEMS is a public domain, multi-sectored, partial equilibrium 

model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce an energy forecast 

for the United States, the AEO. The EIA publishes a reference case, which incorporates all 

 

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

Interagency Working Group on Social Cost of Carbon, United States Government; revised November 2013.

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

h U.S. Office of Management and Budget, Office of Information and Regulatory Affairs, 2006 Report to Congress 

on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal Entities, 

Washington, DC.
2-16 

existing energy-related policies at the time of publication, and a variety of side cases which 

analyze the impact of different policies, energy price and market trends. As of 2014, DOE is 

using a new methodology based on results published for the AEO 2014 Reference case and a set 

of side cases that implement a variety of efficiency-related policies. Further detail is provided in 

chapter 15 of the NOPR TSD. 

2.15 EMPLOYMENT IMPACT ANALYSIS

The adoption of energy conservation standards can affect employment both directly and 

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

produce the covered products. DOE evaluates direct employment impacts in the MIA. Indirect 

employment impacts may result from expenditures shifting between goods (the substitution 

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

to standards. DOE defines indirect employment impacts from standards as net jobs eliminated or 

created in the general economy as a result of increased spending driven by increased product 

prices and reduced spending on energy. 

Indirect employment impacts are investigated in the employment impact analysis using 

the Pacific Northwest National Laboratory’s “Impact of Sector Energy Technologies” (ImSET) 

model.i

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

to estimate the employment and income effects of energy-saving technologies in buildings, 

industry, and transportation. Compared with simple economic multiplier approaches, ImSET 

allows for more complete and automated analysis of the economic impacts of energy 

conservation investments. Further detail is provided in chapter 16 of the NOPR TSD. 

2.16 REGULATORY IMPACT ANALYSIS

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

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

RIA addresses the potential for non-regulatory approaches to supplant or augment energy 

conservation standards in order to improve the energy efficiency or reduce the energy 

consumption of the product covered under this rulemaking. DOE recognizes that voluntary or 

other non-regulatory efforts by manufacturers, utilities, and other interested parties can 

substantially affect energy efficiency or reduce energy consumption. DOE bases its assessment 

on the actual impacts of any such initiatives to date, but also considers information presented by 

interested parties regarding the impacts existing initiatives might have in the future. Further 

detail is provided in chapter 17 of the NOPR TSD. 

 

i

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

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

www.pnl.gov/main/publications/external/technical_reports/PNNL-18412.pdf).
2-17 

REFERENCES

1

 “Residential Energy Consumption Survey: 2009 RECS Survey Data,” 2013. U.S. Department 

of Energy: Energy Information Administration. Available online at: 

http://www.eia.gov/consumption/residential/data/2009/. (Accessed November 30, 2013).
3-i 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

3.2 PRODUCT DEFINITION ............................................................................................... 3-1

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

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

3.5 MANUFACTURER TRADE GROUPS ......................................................................... 3-5

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

3.6.1 Manufacturers and Market Shares ....................................................................... 3-6

3.6.2 Mergers and Acquisitions .................................................................................... 3-7

3.6.3 Small Business Impacts ....................................................................................... 3-8

3.6.4 Distribution Channels .......................................................................................... 3-8

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

3.7.1 Federal Energy Conservation Standards .............................................................. 3-9

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

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

3.9 HISTORICAL SHIPMENTS......................................................................................... 3-11

3.9.1 Unit Shipments................................................................................................... 3-11

3.9.2 Value of Shipments ............................................................................................ 3-13

3.10 MARKET SATURATION ............................................................................................ 3-15

3.11 PRODUCT RETAIL PRICES ....................................................................................... 3-16

3.12 INDUSTRY COST STRUCTURE ................................................................................ 3-18

3.13 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES .......................... 3-21

3.14 TECHNOLOGY ASSESSMENT .................................................................................. 3-23

3.14.1 Dehumidifier Operations and Components ....................................................... 3-23

3.14.2 Dehumidifier Technology Options .................................................................... 3-25

3.14.3 Energy Efficiency .............................................................................................. 3-33

REFERENCES .......................................................................................................................... 3-34

LIST OF TABLES

Table 3.7.1 Energy Conservation Standards for Residential Dehumidifiers Established by 

EPACT 2005 .................................................................................................. 3-9

Table 3.7.2 Energy Conservation Standards for Residential Dehumidifiers Established by 

EISA 2007 .................................................................................................... 3-10

Table 3.7.3 Canadian Regulations for Residential Dehumidifiers ............................................ 3-10

Table 3.8.1 ENERGY STAR Qualifying Criteria for Dehumidifiers ........................................ 3-11

Table 3.9.1 Industry Shipments of Dehumidifiers (Domestic and Import), , ............................ 3-12

Table 3.9.2 Dehumidifier Capacity-Specific Shipments (Domestic and Import) ...................... 3-13

Table 3.9.3 Annual Shipment Value of Small Electric Household Appliances, , , ..................... 3-14

Table 3.9.4 Annual Shipment Value of Small Electromechanical Household Appliances, , , 

, .................................................................................................................... 3-14
3-ii

Table 3.9.5 Annual Shipment Value of Air Conditioning Equipment, , , .................................. 3-15

Table 3.9.6 Disposition of Previous Dehumidifiers................................................................... 3-15

Table 3.10.1 Percentage of U.S. Households with Dehumidifiers, ........................................... 3-16

Table 3.12.1 Small Electrical Appliance Manufacturing Industry Employment and 

Earnings ....................................................................................................... 3-18

Table 3.12.2 Air-Conditioning and Warm Air Heating Equipment Manufacturing 

Industry Employment and Earnings............................................................. 3-19

Table 3.12.3 Small Electrical Appliance Manufacturing Industry Materials and Wages 

Cost .............................................................................................................. 3-19

Table 3.12.4 Air-Conditioning and Warm Air Heating Equipment Manufacturing 

Industry Materials and Wages Cost ............................................................. 3-20

Table 3.12.5 Industry Cost Structure Using SEC Data, Average 2006–2012 ........................... 3-20

Table 3.13.1 Small Electrical Appliance Manufacturing Industry Inventory Levels ................ 3-21

Table 3.13.2 Air-Conditioning and Warm Air Heating Equipment Manufacturing 

Industry Inventory Levels ............................................................................ 3-22

Table 3.13.3 Full Production Capacity Utilization Rates, ......................................................... 3-23

Table 3.14.1 Technology Options for Dehumidifiers ....................................................... 3-25

LIST OF FIGURES

Figure 3.6.1 Estimated U.S. Market Share for Residential Portable Dehumidifiers ................... 3-7

Figure 3.11.1 Portable Residential Dehumidifier Retail Price versus Capacity ........................ 3-17

Figure 3.11.2 Whole-Home and High-Capacity Portable Residential Dehumidifier Retail 

Price versus Capacity ................................................................................... 3-17

Figure 3.14.1 Dehumidifier Operation Schematic ..................................................................... 3-24

Figure 3.14.2 DOE-Certified Dehumidifiers, DOE Standards, and ENERGY STAR 

Qualification Criteria ................................................................................... 3-33
3-1 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the residential dehumidifier industry in the United 

States. The 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 

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 DEFINITION

The EPACT 2005 (P.L. 109-58) amended the EPCA, Pub. L. 94-163, 42 U.S.C. 6291 et 

seq. in relevant part to establish the definition of a dehumidifier as “a self-contained, electrically 

operated, and mechanically encased assembly consisting of - 

A. a refrigerated surface (evaporator) that condenses moisture from the atmosphere;

B. a refrigerating system, including an electric motor;

C. an air-circulating fan; and

D. means for collecting or disposing of the condensate. 

42 U.S.C. 6291(34) 

On May 21, 2014 DOE published a test procedure NOPR in which it proposed definitions 

for portable and whole-home dehumidifiers, in addition to other clarifications and corrections to 

the current dehumidifier test procedure. 79 FR 29272. On February 4, 2015, DOE published a 

supplemental notice of proposed rulemaking (SNOPR), in which the previous definition 

proposals were maintained and DOE further proposed additional modifications and clarifications 

to the test procedure. 80 FR 5994. In the final rule for the test procedure rulemaking, DOE 

established the test procedure currently found at 10 CFR part 430, subpart B, appendix X1 

(appendix X1) as well as the following product definitions in 10 CFR 430.2: 

Dehumidifier means product, other than a portable air conditioner, room air conditioner, 

or packaged terminal air conditioner, that is a self-contained, electrically operated, and 

mechanically encased assembly consisting of— 

1) A refrigerated surface (evaporator) that condenses moisture from the atmosphere; 

2) A refrigerating system, including an electric motor;

3) An air-circulating fan; and

4) A means for collecting or disposing of the condensate. 

Portable dehumidifier means a dehumidifier designed to operate within the dehumidified 

space without the attachment of additional ducting, although means may be provided for optional 

duct attachment.

Whole-home dehumidifier means a dehumidifier designed to be installed with ducting to 

deliver return process air to its inlet and to supply dehumidified process air from its outlet to one 

or more locations in the dehumidified space. 
3-2 

Refrigerant-desiccant dehumidifier means a whole-home dehumidifier that removes 

moisture from the process air by means of a desiccant material in addition to a refrigeration 

system.

3.3 PRODUCT CLASSES

 When evaluating and establishing energy conservation standards, DOE generally divides 

covered products into product classes by the type of energy used or by capacity or other 

performance-related features that affect efficiency. Different energy conservation standards may 

apply to different product classes. (42 U.S.C. 6295(q)) 

For residential dehumidifiers, the EISA 2007, Pub. L 110-140 amendments to EPCA 

established product classes based on the capacity of the unit as measured in pints of water 

extracted per day (pints/day), for dehumidifiers manufactured on or after October 1, 2012. (42 

U.S.C. 6295(cc)(2)):a

• Less than 35.00 pints/day 

• 35.01 to 45.00 pints/day 

• 45.01 to 54.00 pints/day 

• 54.01 to 75.00 pints/day 

• Greater than 75.00 pints /day 

 Among residential dehumidifiers there are also two general types, differentiated by the 

primary installation configuration: portable dehumidifiers and whole-home dehumidifiers, as 

described in section 3.2. Portable dehumidifiers are the most common type of dehumidifier sold 

in the United States, representing more than 95 percent of residential dehumidifier shipments. 

Consumers typically purchase portable dehumidifiers to reduce the relative humidity in one room 

or area of a living space less than 2,500 square feet, and may move these units from room to 

room to selectively reduce humidity where necessary. These units may also be located in an 

unconditioned space where moisture control is desired, such as a basement. Portable units 

currently on the market have rated capacities ranging from 25 pints/day to more than 120 

pints/day, as determined by the test procedure at 10 CFR part 430, subpart B, appendix X 

(appendix X). Portable units are standalone appliances that are designed to operate independent 

of any other air treatment devices, and do not require attachment to ducting, although certain 

models may have optional provisions to do so (i.e., “convertible” units). 

Whole-home dehumidifiers are designed to be attached to ducting that supplies 

conditioned air to multiple or large living spaces in a residence and that returns humid air to the 

 

a For standards effective October 1, 2007, EPACT 2005, in section 135(c), specified five product classes for 

dehumidifiers based on capacity as measured by the test procedure at appendix X: 25.00 pints/day or less, 25.01–

35.00 pints/day, 35.01–54.00 pints/day, 54.01–74.99 pints/day, and 75.00 pints/day or more. EISA 2007, in section 

311(a)(1), prescribed a new set of standards for dehumidifiers to take effect on October 1, 2012. In providing a new 

set of standards, EISA 2007 consolidated the two smallest product classes (25.00 pints/day or less and 25.01–35.00 

pints/day) and subdivided the 35.01–54.00 pints/day product class into two product classes: 35.01–45.00 pints/day 

and 45.01–54.00 pints/day.
3-3 

dehumidifier inlet. Whole-home dehumidifiers are often installed in conjunction with an existing 

heating, ventilation, or central air-conditioning (HVAC) system, and may utilize certain 

components of the HVAC equipment such as the air-handling blower, but can operate 

independently as well. Whole-home dehumidifiers typically use the same dehumidification 

system as portable units; however, to effectively dehumidify a large area, these units are 

manufactured with larger components than portable dehumidifiers, and may include additional 

features, such as pre-coolers or desiccant wheels, which may be difficult to incorporate into 

portable units due to volume and weight constraints. Whole-home product capacities range from 

approximately 65 pints/day to more than 200 pints/day. However, the current DOE dehumidifier 

test procedure at appendix X does not require testing these units with ducting in place, as they 

would be installed in the field. The lack of ducting allows higher airflow through the 

dehumidifier than would be experienced in real-world installations, which in turn affects the 

measured capacity and energy efficiency. Accordingly, the newly established appendix X1 test 

procedure includes provisions to require testing the energy use of whole-home dehumidifiers in 

dehumidification mode using a representative ducted configuration. 

In the preliminary analysis for this rulemaking, DOE considered product classes that 

address both portable and whole-home dehumidifiers. DOE used the current product classes 

established by EISA 2007 as the basis for the analysis for portable dehumidifiers, with the 

capacities adjusted to account for the test procedure updates in appendix X1. In particular, 

appendix X1 requires that testing for portable dehumidifiers be conducted at an ambient 

temperature of 65 degrees Fahrenheit (°F) instead of the current 80 °F. DOE considered how the 

change in ambient temperatures would affect measured product capacities, and adjusted the 

capacity ranges in each of the portable product classes accordingly. 

DOE also conducted its preliminary analysis on two whole-home dehumidifier product 

classes. DOE separated these two product classes based on case volume, one for products with 

volumes less than or equal to 8.0 cubic feet and another for products with volumes greater than 

8.0 cubic feet, because it determined that capacity did not inherently impact efficiency for these 

products but that case volume affected consumer utility in terms of potential installation 

configurations. 

 DOE considered the following dehumidifier product classes in the preliminary analysis:

• Portable, less than 20.00 pints/day 

• Portable, 20.01 to 30.00 pints/day 

• Portable, 30.01 to 35.00 pints/day 

• Portable, 35.01 to 45.00 pints/day 

• Portable, 45.01 or more pints /day 

• Whole-home, case volume less than or equal to 8.0 cubic feet 

• Whole-home, case volume greater than 8.0 cubic feet 

In the analysis for this NOPR, DOE combined four of the portable dehumidifier product 

classes considered in the preliminary analysis into two product classes due to similarities and 

trends observed in performance at these four product classes. Chapter 5 of this NOPR TSD 
3-4 

includes more information about the portable dehumidifier product classes proposed in the 

NOPR, and listed below: 

• Portable, less than 30.00 pints/day 

• Portable, 30.01 to 45.00 pints/day 

• Portable, 45.01 or more pints /day 

• Whole-home, case volume less than or equal to 8.0 cubic feet 

• Whole-home, case volume greater than 8.0 cubic feet 

3.4 PRODUCT TEST PROCEDURES

EPACT 2005 amended EPCA to specify that the test criteria used under the ENERGY 

STARb

 program must serve as the basis for the DOE test procedure for dehumidifiers. (EPACT 

2005, section 135(b); 42 U.S.C. 6293(b)(13)) Prior to October 2012, the ENERGY STAR test 

criteria required that American National Standards Institute (ANSI)/ AHAM Standard DH-1-

2003, Dehumidifiers, be used to measure energy use while the Canadian Standards Association 

(CAN/CSA) standard CAN/CSA-C749-1994 (R2005), Performance of Dehumidifiers, be used to 

calculate the energy factor (EF). DOE adopted these test criteria, along with related definitions 

and tolerances, as its test procedure for dehumidifiers in 2006 at 10 CFR part 430, subpart B, 

appendix X (appendix X). 71 FR 71340, 71347, 71366, 713667-68 (Dec. 8, 2006). 

On October 31, 2012, DOE published a final rule to establish a new test procedure for 

dehumidifiers that references ANSI/AHAM Standard DH-1-2008, Dehumidifiers (ANSI/AHAM 

DH-1-2008), rather than the ENERGY STAR test criteria, and establishes a new energy 

efficiency metric, integrated energy factor (IEF), which incorporates measures of energy use in 

active mode, standby mode, and off mode. 77 FR 65995 (Oct. 31, 2012). The new DOE test 

procedure was codified at that time at 10 CFR part 430, subpart B, appendix X1 (appendix X1). 

On February 7, 2014, DOE published a final rule removing the existing test procedure at 

appendix X and redesignating the test procedure at appendix X1 as appendix X. Manufacturers

are currently required to test using only the active mode provisions in the redesignated appendix 

X to determine compliance with existing dehumidifier energy conservation standards. Appendix 

X must be used in its entirety if manufacturers make representations of standby mode or off 

mode energy use. 79 FR 7366 (Feb. 7, 2014). 

On May 21, 2014, DOE published a test procedure NOPR (the May 2014 Test Procedure 

NOPR) in which it proposed to establish a new dehumidifier test procedure at appendix X1, 

which would include: (1) dehumidification mode test conditions requiring a lower ambient 

temperature to more accurately reflect conditions during consumer use; (2) a measure of fan-only 

mode energy consumption for dehumidifiers that operate the blower continuously or cyclically 

when the ambient air relative humidity is below the humidity setpoint, rather than enter off-cycle 

mode; (3) new definitions for portable, whole-home, and refrigerant-desiccant dehumidifiers; (4) 

 

b For more information, please visit http://www.energystar.gov/. 
3-5 

testing methodology and calculations for whole-home dehumidifiers; (5) additional clarifications 

and editorial corrections. 79 FR 29271. 

On February 4, 2015, DOE subsequently published a SNOPR (the February 2015 Test 

Procedure SNOPR), to propose additional changes to the test procedure proposed in the May 

2014 Test Procedure NOPR, including: (1) various adjustments and clarifications to the wholehome dehumidifier test setup and conduct; (2) a method to determine whole-home dehumidifier 

case volume; (3) a revision to the method for measuring energy use in fan-only operation; (4) a 

clarification to the relative humidity and capacity equations in ANSI/AHAM DH-1-2008; and (5) 

additional technical corrections and clarifications. 80 FR 5994. 

In response to the May 2014 Test Procedure NOPR, June 2014 public meeting, and 

February 2015 Test Procedure SNOPR, DOE received comments from interested parties related 

to the test procedure. In the final rule that concluded the recent test procedure rulemaking, DOE 

addressed those comments and made certain modifications to its previous proposals. The final 

rule incorporated the proposed amendments to appendix X and established the new test 

procedure in appendix X1. The analysis conducted in support of this NOPR is based on 

capacities and efficiencies determined according to the test procedure in appendix X1. 

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 dehumidifier 

industry, DOE researched various associations available to manufacturers, suppliers, and users of 

such equipment. DOE also used the member lists of these groups in the construction of an 

exhaustive database containing domestic manufacturers.

AHAMc

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

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

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

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

In addition, AHAM conducts other market and consumer research studies. AHAM also develops 

and maintains technical standards for various appliances to provide uniform, repeatable 

procedures for measuring specific product characteristics and performance features.

3.6 MANUFACTURER INFORMATION

The following section details information regarding domestic manufacturers of 

dehumidifiers, including estimated market shares (section 3.6.1), industry mergers and 

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

distribution channels (section 3.6.4). 

 

c For more information, please visit www.aham.org. 
3-6 

3.6.1 Manufacturers and Market Shares

The majority of residential dehumidifiers are manufactured overseas by three original 

equipment manufacturers (OEMs). These products are then imported to the United States and 

sold under a variety of brands belonging to both appliance manufacturers and importers. 

Additionally, some foreign OEMs sell dehumidifiers directly into the U.S. market under their 

own brands. 

For residential dehumidifiers, DOE estimates that there are approximately 25 entities 

selling dehumidifiers in the United States, 17 of which sell portable dehumidifiers. The 

remaining 8 entities sell whole-home dehumidifiers. Table 3.6.1 lists these manufacturers and 

importers. 

Table 3.6.1 Residential Dehumidifier Original Equipment Manufacturers and Importers

Portable Dehumidifiers Whole-Home Dehumidifiers**

Desert Aire* Aprilaire*

Gree Electric Appliances Inc. of 

Zhuhai/SoleusAir* Lennox 

Hisense Kelon Electrical Holdings Limited* Munters*

Haier America Trading LLC* NovelAire*

LG Electronics, Inc.* Therma-Stor*

Midea USA Inc.* Williams Furnace Company*

Therma-Stor* The General Filters, Inc.

Crosley Healthy Air & Water Systems LLC

Danby Products Inc.

De'Longhi America

Friedrich Air Conditioning Co.

GE Appliances

Heat Controller

Oscar Air

Osram Sylvania

Perfect Aire LLC

Whynter LLC

* Original equipment manufacturers

**Some of these manufacturers and importers also sell high-capacity portable dehumidifiers with construction

similar to that of their whole-home products.

Using publicly available data, DOE estimated the market shares of entities responsible 

for the sale of residential portable dehumidifiers in the United States. DOE estimates that over 50 

percent of residential portable dehumidifier market share is held by Midea USA, Inc.d, 

 

d The U.S. division of Guangdong Midea Electric Appliances Co. Ltd., based in China.
3-7 

De'Longhi Americae, Danby Products Inc., and GE Appliances. Figure 3.6.1 illustrates the 

estimated market shares for the residential portable dehumidifier market in the United States. 

Figure 3.6.1 Estimated U.S. Market Share for Residential Portable Dehumidifiers

 The majority of the whole-home dehumidifier segment is held by Therma-Stor LLC and 

Aprilaire (Research Products Corporation). Other producers of whole-home dehumidifiers 

include Munters, NovelAire, and Williams Furnace Company. 

3.6.2 Mergers and Acquisitions

 As described in Section 3.6.1, the major manufacturers and importers of residential 

dehumidifiers sold into the U.S. market include Midea, Gree, Haier, De’Longhi S.p.A., Danby 

Products Limited, and GE Appliances. 

 

Recent merger and acquisition activities relating to the U.S. residential dehumidifier

market include the joint venture formed between Chinese manufacturer Gree and U.S.-based 

SoleusAir in 2011, which led to the creation of Gree USA, headquartered in City of Industry, 

CA. Gree USA manufactures its own brands of HVAC products and OEM private labels, and 

sells directly to wholesalers. This collaboration has opened the way for Gree to have a presence 

in the United States.

1

 Also in 2011, LG Electronics acquired LS Mtron’s Air-Conditioning unit, 

and in November 2012, Corinthian Capital Group acquired Friedrich Air Conditioning Co. from 

U.S. Natural Resources Inc.2

 Finally, in September of 2014, Electrolux bought GE’s appliances 

 

e The U.S. division of De’Longhi S.p.A., based in Italy.
3-8 

business for $3.3 billion. This move will double Electrolux’s annual appliance sales in North 

America, to over $10 billion.3

3.6.3 Small Business Impacts

 DOE considers the possible impact of energy conservation standards on small businesses. 

The products covered by this rulemaking are classified under the North American Industry 

Classification System (NAICS) codes 333415: Air-Conditioning and Warm Air Heating 

Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing and 335210: 

Small Electrical Appliance Manufacturing. The Small Business Association (SBA) defines a 

small business as a company that has fewer than 750 employees for both NAICS codes. The 750-

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

subsidiaries. Using this classification in conjunction with information from industry databases, 

the SBA member directory, and reports from vendors such as Dun & Bradstreet, DOE has 

identified five small business manufacturers which are based in the United States and produce 

one or more of the covered products. Further analysis of potential impacts on this manufacturer 

subgroup can be found in section VI.B of the NOPR notice and chapter 12 of this NOPR TSD. 

3.6.4 Distribution Channels

 Understanding the distribution channels through which residential dehumidifiers are sold 

is an important facet of the market assessment, because it helps to define the constraints or 

motivators manufacturers face from its customer base. DOE gathered information regarding the 

distribution channels for dehumidifiers from publicly available sources, as well as from 

preliminary interviews with manufacturers. 

Because major OEMs of residential dehumidifiers are based overseas, the distribution 

channel for portable dehumidifiers is often multi-tiered. Typically, foreign OEMs sell their 

products to a sourcing company with a greater U.S. presence, which in turn sells the products to 

retailers. For a segment of the market, OEMs sell their products under their own brands either to 

distributors or directly to retailers. In either arrangement, these retailers include large discount 

stores, home improvement stores, and department stores, and to a lesser extent independent 

appliance retailors, internet retailers, membership warehouse clubs, electronics stores, and office 

supply stores.4 The AHAM 2003 Fact Book reports that home improvement stores claimed 

nearly one out of every four dollars spent on appliances in 2003.5 

 The distribution channel for whole-home dehumidifiers differs from that of the portable 

dehumidifiers. The majority of whole-dehumidifiers are sold directly to home builders or 

contractors, as they are intended to be integrated into a residence’s HVAC system, and as such 

require system design and more complex installation. 

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation 

standards for dehumidifiers. Section 3.7.1 discusses Federal energy conservation standards, and 
3-9 

section 3.7.2 reviews standards in Canada that may impact the companies servicing the North 

American market. 

3.7.1 Federal Energy Conservation Standards 

On August 8, 2005 EPACT 2005 established energy conservation standards for several 

residential and commercial products, including residential dehumidifiers. Section 135(c)(4) of 

EPACT 2005 amends section 325 of EPCA, 42 U.S.C. 6295, to add subsection (cc) for 

dehumidifiers. This subsection establishes energy conservation standards for dehumidifiers based 

on the unit’s EF measured in liters (L) of water removed per kilowatt-hour (kWh) for product 

classes based on the unit’s capacity to extract moisture from the surrounding air (in pints/day). 

These Federally mandated standards took effect for dehumidifiers manufactured after October 1, 

2007. Table 3.7.1 provides the EPACT 2005 standards for residential dehumidifiers. 

 

Table 3.7.1 Energy Conservation Standards for Residential Dehumidifiers Established by 

EPACT 2005 

Dehumidifier Capacity Standards Effective October 1, 2007

EF (L/kWh)

25.00 pints/day or less 1.00

25.01–35.00 pints/day 1.20

35.01–54.00 pints/day 1.30

54.01–74.99 pints/day 1.50

75.00 pints/day or more 2.25

In addition, EPACT 2005 required that DOE issue a final rule for dehumidifiers to 

determine whether these standards should be amended by October 1, 2009. (EPACT 2005, 

section 135(c)(4)). In the event that DOE did not publish a final rule, EPACT 2005 specified a 

new set of amended standards with a compliance date of October 1, 2012. (Id.) 

 DOE issued an advance notice of proposed rulemaking (ANOPR) to consider energy 

conservation standards for dehumidifiers and other products (hereafter referred to as the “2007 

ANOPR”). 72 FR 64432 (Nov. 15, 2007). EISA 2007 subsequently amended section 325(cc) of 

EPCA to prescribe new energy conservation standards for dehumidifiers manufactured on or 

after October 1, 2012. DOE codified the EISA 2007 standards at 10 CFR 430.32(v)(2). 74 FR 

12058 (Mar. 23, 2009). Table 3.7.2 summarizes the October 1, 2012, standards prescribed by 

EISA 2007. 
3-10 

Table 3.7.2 Energy Conservation Standards for Residential Dehumidifiers Established by 

EISA 2007 

Dehumidifier Capacity Standards Effective October 1, 2012

EF (L/kWh)

35.00 pints/day or less 1.35

35.01–45.00 pints/day 1.50

45.01–54.00 pints/day 1.60

54.01–75.00 pints/day 1.70

75.01 pints/day or more 2.50

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

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

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

3.7.2 Canadian Energy Conservation Standards 

Canada’s Energy Efficiency Regulations (hereafter “Canada’s Regulations”) mandate 

minimum energy conservation standards for dehumidifiers. Canada’s Regulations refer to 

CAN/CSA-C749-07 (2007), “Performance of Dehumidifiers,” for determining compliance with 

Canada’s Regulations.f Canada’s Regulations are comparable to DOE standards effective as of 

October 1, 2012, except capacity is expressed in terms of liters/day instead of pints/day, as seen 

in Table 3.7.3. 

Table 3.7.3 Canadian Regulations for Residential Dehumidifiers

Dehumidifier Capacity Regulations Effective October 1, 2012

EF (L/kWh)

16.6 liters/day or less 1.35

16.6 – 21.3 liters/day 1.50

21.3 – 25.5 liters/day 1.60

25.5 – 35.5 liters/day 1.70

Greater than 35.5 liters/day 2.50

 

f For more information, please visit: http://oee.nrcan.gc.ca/regulations/products/14452. 
3-11 

3.8 VOLUNTARY PROGRAMS

DOE reviewed several voluntary programs promoting energy efficient appliances and 

found that ENERGY STAR is the primary voluntary program that establishes energy efficiency 

criteria for dehumidifiers in the United States. ENERGY STAR, a voluntary labeling program 

backed by the U.S. Environmental Protection Agency (EPA) and DOE, identifies energyefficient 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 should 

meet or exceed the ENERGY STAR levels. ENERGY STAR specifications exist for several 

products, including dehumidifiers. 

On October 1, 2012, the current ENERGY STAR dehumidifier qualifying criteria 

became effective. The ENERGY STAR criteria divide products into two classes: less than 75 

pints/day and greater than or equal to 75 pints/day, as measured by appendix X. The efficiency 

qualification criteria are listed in Table 3.8.1. 

Table 3.8.1 ENERGY STAR Qualifying Criteria for Dehumidifiers

Dehumidifier Capacity

Qualification Criteria Effective October 1, 

2012

EF (L/kWh)

Less than 75 pints/day 1.85

75 pints/day or greater 2.80

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 dehumidifier shipment trends and the value of 

these shipments. Knowledge of such trends will be used during the shipments analysis (chapter 9 

of this NOPR TSD). 

3.9.1 Unit Shipments

AHAM’s 2005 Fact Book provides annual unit shipments for dehumidifiers from 1995 to 

2005. In response to a data request, AHAM provided additional shipment information for 2006 

through 2011. Table 3.9.1 presents the annual shipments of dehumidifiers for the 17-year period 

from 1995 to 2011. The complete AHAM data submittal is included in appendix 3A of this TSD. 
3-12 

Table 3.9.1 Industry Shipments of Dehumidifiers (Domestic and Import)6, 7, 8

Year

Shipments 

(Thousands)

2013 1,385

2012 1,168

2011 1,368

2010 1,552

2009 1,700

2008 1,558

2007 2,004

2006 1,456

2005 1,957

2004 1,672

2003 1,311

2002 799

2001 806

2000 975

1999 950

1998 1,031

1997 820

1996 977

1995 1,003

In its data submittal, AHAM also provided capacity-specific shipment data for 

dehumidifiers. Table 3.9.2 presents a breakdown of the shipments of units greater than 35 

pints/day and less than 35 pints/day, as measured by appendix X, from 1999 to 2011. DOE notes 

that Federal energy conservation standards took effect in 2007, and ENERGY STAR 

qualification criteria took effect in both 2006 and 2008. The implementation of these programs 

could have caused some of the year-to-year variation in shipments observed during that time 

period. 
3-13 

Table 3.9.2 Dehumidifier Capacity-Specific Shipments (Domestic and Import)9

Shipments 

(Thousands)

Year

Less than 

35 

pints/day

More than 

35 

pints/day

2011 384 984

2010 409 1,143

2009 472 1,227

2008a 527 1,031

2007 603 1,401

2006b 395 1,061

2005 406 1,551

2004 584 1,089

2003 353 958

2002 236 563

2001 260 545

2000 281 695

1999 294 656

a) ENERGY STAR criteria (effective June 1, 2008)

b) ENERGY STAR criteria (effective October 1, 2006)

3.9.2 Value of Shipments

Table 3.9.3 provides the value of shipments for the manufacturers in the NAICS category 

of small electric household appliances excluding fans (product class code 3352114) from 2003 to 

2010. The values are based on data from the U.S. Census Bureau’s Current Industrial Reportsg

(CIR) and Annual Survey of Manufacturersh (ASM). This NAICS category includes companies 

primarily engaged in manufacturing small electric household appliances such as coffee makers, 

toaster ovens, portable room heaters, mixers, air purifiers, food processors, and portable 

dehumidifiers. The U.S. Census Bureau reports all shipment values in nominal dollars, i.e., 2010 

data are expressed in 2010 dollars and 2009 data are expressed in 2009 dollars. Using the Gross 

Domestic Product Implicit Price Deflator (GDPIPD) published by the U.S. Bureau of Economic 

Analysisi

, DOE converted each year’s value of shipments to 2013 dollars. 

 

g Available online at www.census.gov/manufacturing/cir/index.html h Available online at www.census.gov/manufacturing/asm/index.html

i

Available online at www.bea.gov/itable/ 
3-14 

Table 3.9.3 Annual Shipment Value of Small Electric Household Appliances10, 11, 12, 

Annual Shipment Value

($ millions)

Year Nominal 

Dollars

2013

Dollars

2011 1,043 1,078

2010 1,107 1,167

2009 1,337 1,427

2008 1,098 1,181

2007 1,115 1,223

2006 1,206 1,358

2005 1,338 1,552

2004 1,254 1,502

2003 1,080 1,329

Table 3.9.4 provides the annual shipment value for the NAICS product class for “Small 

Electromechanical Household Appliances” (product class code 3352114150), which includes 

cordless household food preparation appliances, portable dehumidifiers, air purifiers, and other 

small appliances, from 2006 to 2010 based upon data from the U.S. Census Bureau’s CIR and 

ASM. The U.S. Census Bureau shipment values are expressed in nominal dollars. DOE used the 

GDPIPD to convert each year’s value of shipments to 2013 dollars.

Table 3.9.4 Annual Shipment Value of Small Electromechanical Household 

Appliances13, 14, 15, 16, 17

Annual Shipment 

Value ($ millions)

Year Nominal 

Dollars

2013 

Dollars

2010 632 666 

2009 653 697 

2008 558 600 

2007 595 652 

2006 731 823 

Table 3.9.5 provides the annual shipment value for the NAICS product class for “airconditioning and warm air heating equipment and commercial and industrial refrigeration 

equipment manufacturer” (product class code 333415), which includes various air-conditioning 

equipment, refrigerated drinking fountains, whole-home humidifying equipment, and whole 

home dehumidifiers, and others from 2007 to 2011 based upon data from the U.S. Census 
3-15 

Bureau’s CIR and ASM. The U.S. Census Bureau shipment values are expressed in nominal 

dollars. DOE used the GDPIPD to convert each year’s value of shipments to 2013 dollars.

Table 3.9.5 Annual Shipment Value of Air Conditioning Equipment18, 19, 20, 21

Annual Shipment 

Value ($ millions)

Year Nominal 

Dollars

2013 

Dollars

2011 28,407 29,348 

2010 26,823 28,284 

2009 25,688 27,418 

2008 28,774 30,945 

2007 28,939 31,733 

According to data presented in the AHAM 2003 Fact Book, many old appliances are still 

being used after consumers purchase new units of same product. Table 3.9.6 presents the various 

methods by which consumers dispose of their older dehumidifiers. 

Table 3.9.6 Disposition of Previous Dehumidifiers22

Product Kept It

Left with 

Previous 

Home

Sold / 

Gave 

Away

Recycling 

Facility

Left at 

Curb for 

Disposal

Retailer 

Took 

Away

Dehumidifiers 18% 14% 23% 13% 24% 4%

3.10 MARKET SATURATION

AHAM’s 2005 Fact Book and the January 2010 Appliance Market Research Report

present the market saturation for dehumidifiers. The market saturation of dehumidifiers has 

gradually increased and doubled since 1982. However, from 1990 through 2001 the market 

saturation decreased by 2 percent. For the 6 years from 2003 through 2008, the market saturation 

remained constant at around 20 percent. Table 3.10.1 presents the percentage of U.S. households 

with dehumidifiers. 
3-16 

Table 3.10.1 Percentage of U.S. Households with Dehumidifiers23, 24

Year 

% of U.S. 

Households

2008 19

2007 19

2006 20 

2005 20

2004 20

2003 19 

2002 16

2001 15

1990 17 

1982 11

3.11 PRODUCT RETAIL PRICES 

 For the preliminary analysis, DOE used the DOE Compliance Certification Management 

System (CCMS)jdatabase, along with the California Energy Commission (CEC)

k and ENERGY 

STARl product databases, to identify a total of 214 portable and whole-home models of 

residential dehumidifiers on the market in the United States at that time, which encompassed 56 

different brands. DOE collected consumer retail price data for these products from the websites 

of seven types of retailers: home improvement stores, discount retail stores, discount department 

stores, national office supply stores, national hardware stores, manufacturer websites, and online 

appliance retailers. 

Figure 3.11.1 and Figure 3.11.2 summarize the data collected by DOE. These figures 

suggest that retail price is positively related to capacity for both portable and whole-home 

dehumidifiers. The consumer retail prices for portable dehumidifiersm ranged from $128 to $410, 

with an average of $240 (this is the average across all portable models and does not reflect the 

shipment-weighted average). Portable dehumidifiers are available with capacities ranging from 

30 pints/day to 71 pints/day,n as measured by the current dehumidifier test procedure, with an 

average capacity of 52 pints/day. 

 The consumer retail prices for whole-home and high-capacity portable (greater than 75 

pints/day capacity) dehumidifiers ranged from $1,000 to $5,499, with a model-based average of 

$1,979. Whole-home and high-capacity portable units range in capacity from 61 pints/day to 205 

pints/day, with an average of 112 pints/day. 

 

j

For more information, please visit www.regulations.doe.gov

k CEC appliance efficiency database available online at: http://www.energy.ca.gov/appliances/database/. l

ENERGY STAR database available online at http://www.energystar.gov/productfinder/product/certifieddehumidifiers/results m High-capacity portable dehumidifiers (rated capacities greater than 75 pints/day) are excluded from these statistics. n A 74 pint/day portable unit is also commercially available, but was excluded as an outlier from this retail price 

summary.
3-17 

Figure 3.11.1 Portable Residential Dehumidifier Retail Price versus Capacity

Figure 3.11.2 Whole-Home and High-Capacity Portable Residential Dehumidifier Retail 

Price versus Capacity
3-18 

 Other factors besides capacity and product type, such as efficiency, ENERGY STAR 

rating, and retailer, also may impact residential dehumidifier price. 

3.12 INDUSTRY COST STRUCTURE

 DOE developed the cost structure for two industry classifications associated with the 

residential dehumidifier industry from publicly available information from the ASM and 

Economic Census and the U.S. Securities and Exchange Commission (SEC) 10-K reports filed 

by publicly-owned manufacturers. Table 3.12.1 presents the small electrical appliance 

manufacturing industry (NAICS code 33521) employment levels and earnings from 2002–2011. 

The statistics illustrate a steady decline in the number of production and non-production workers 

in the industry since 2002, except during the period from 2005 to 2007 in which there was a 

slight increase. 

 

DOE converted the payroll data to constant 2013 dollars using the GDPIPD published by 

the U.S. Bureau of Economic Analysiso

. Table 3.12.1 shows that as industry employment levels 

decline, the industry payroll in constant 2013 dollars also decreases from 2002 to 2011, with a 

slight rebound from 2005 to 2007. The percent decrease in total industry employees tracks 

relatively closely with the percent decrease (increase) in payroll for all employees. 

Table 3.12.1 Small Electrical Appliance Manufacturing Industry Employment and 

Earnings

25

Year Production 

Workers 

All 

Employees

Total 

Payroll 

(2013 $ 

Mil)

2011 5,713 7,619 348

2010 6,383 8,536 385

2009 7,609 10,072 434

2008 9,905 12,809 546

2007 10,816 13,502 577

2006 9,870 12,715 533

2005 9,680 13,297 538

2004 11,770 15,533 636

2003 12,682 16,752 685

2002 14,293 18,781 761

Table 3.12.2 presents the employments levels and payroll for NAICS code 333415, 

corresponding with “air-conditioning and warm air heating equipment and commercial and 

industrial refrigeration equipment manufacturing”p, which includes whole-home dehumidifiers. 

 

o Available online at http://www.bea.gov/national/nipaweb/SelectTable.asp p Four out of five small business manufacturers identified and discussed in section 3.6.3 of this chapter are classified 

under this NAICS code.
3-19 

Both employment and earnings statistics show a decline between 2007 and 2009 with levels 

remaining largely flat thereafter through 2011. 

Table 3.12.2 Air-Conditioning and Warm Air Heating Equipment Manufacturing Industry 

Employment and Earnings26

Year Production 

Workers 

All 

Employees

Total 

Payroll 

(2013 $ 

Mil)

2011 62,009 83,969 $3,764

2010 61,380 83,054 $3,979

2009 60,041 86,454 $3,913

2008 70,787 96,610 $4,324

2007 74,728 101,485 $4,423

2006 74,909 102,354 $4,525

2005 76,011 98,097 $4,423

Table 3.12.3 presents the costs of materials and industry payroll as a percentage of value 

of shipments from 2002–2011 for the small electrical appliance manufacturing industry. The cost 

of materials as a percentage of value of shipments has remained fairly constant over the 10-year 

period, with some notable fluctuations, particularly an increase from 2002 to 2003 and a decrease 

between 2008 and 2010. DOE notes that fluctuations in raw material costs are common from 

year to year. The payroll for both production and non-production workers as a percentage of 

value of shipments has remained relatively stable since 2002. 

Table 3.12.3 Small Electrical Appliance Manufacturing Industry Materials and Wages 

Cost27

Cost as a Percentage of Value of 

Shipments (%)

Year Materials

Payroll for 

Production 

Workers

Payroll for 

All Other 

Employees 

2011 53% 7% 5%

2010 49% 6% 5%

2009 51% 6% 5%

2008 54% 7% 5%

2007 55% 8% 5%

2006 54% 8% 5%

2005 53% 7% 5%

2004 52% 7% 5%

2003 52% 8% 5%

2002 47% 8% 5%
3-20 

Table 3.12.4 shows the cost of materials and industry payroll as a percentage of value of 

shipments for the air-conditioning and warm air heating equipment and commercial and 

industrial refrigeration equipment manufacturing industry from 2005–2011. Material prices as a 

percentage of value of shipments have remained relatively constant over the 5-year period, with 

fluctuations from year to year. The cost of payroll for production workers as a percentage of 

value of shipments has decreased slightly since 2005. Finally, the cost of non-production payroll 

has remained relatively constant over the 7-year period, with fluctuations from year to year. DOE 

notes that, overall, wages and cost of materials combined represent approximately the same

percentage of the total shipments value for the air-conditioning and warm air heating equipment 

industry and for the small electrical appliance industry. 

Table 3.12.4 Air-Conditioning and Warm Air Heating Equipment Manufacturing Industry 

Materials and Wages Cost28

Cost as a Percentage of Value of 

Shipments (%)

Year Materials

Payroll for 

Production 

Workers

Payroll for 

All Other 

Employees 

2011 55% 8% 5%

2010 53% 8% 6%

2009 55% 8% 6%

2008 55% 8% 5%

2007 56% 8% 5%

2006 53% 9% 5%

2005 54% 9% 5%

Table 3.12.5 presents the industry cost structure derived from SEC 10-K reports of 

publicly-owned dehumidifier manufacturers. DOE averaged the financial data from 2006–2012 

of U.S.-based appliance manufacturers to obtain an industry average. Each financial statement 

entry is presented as a percentage of total revenues.

Table 3.12.5 Industry Cost Structure Using SEC Data, Average 2006–2012 

Financial Statement Entry Percent of 

Revenues

Cost of sales 71.7%

EBIT 7.5%

Selling, general and administrative 20.9%

Capital expenditure 2.7%

Research and development 1.3%

Depreciation and amortization 2.5%

Net plant, property and equipment 13.4%

Working capital 11.28%

A detailed financial analysis is presented in the manufacturer impact analysis (MIA, 

chapter 12 of this NOPR TSD). This analysis identifies key financial inputs including cost of 

capital, working capital, depreciation, capital expenditures, etc. 
3-21 

3.13 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES

Table 3.13.1 and Table 3.13.2 show the year-end inventory for the small electrical 

appliance manufacturing and air-conditioning and warm air heating industries, according to the 

ASM. The trend in the value of end-of-year inventory in dollars for the small electrical appliance 

industry was relatively variable between 2005 and 2011, notably increasing 34 percent from 

2006 to 2007, and decreasing 22 percent between 2008 and 2009. The decrease in inventories of 

small electrical appliance manufacturers from 2007 to 2009 aligns with the end of the 2007 to 

2009 recession, and was likely a reaction to the decline in new orders during that period. From 

2005 to 2011, inventories as a percentage of the value of shipments tracked ending inventories 

fairly closely until 2009, when it began to increase steadily, indicating that small electrical 

appliance shipments have decreased in recent years. For the air-conditioning and warm air 

heating equipment manufacturing industry, the value of the end-of-year inventories was less 

volatile than that for the small electrical appliance manufacturing industry, increasing from 2005 

to 2007 and decreasing from 2007 to 2011. Inventory as a percentage of the value of shipments 

followed a similar trend, increasing from 2005 to 2007 and decreasing thereafter. 

Table 3.13.1 Small Electrical Appliance Manufacturing Industry Inventory Levels29

Year

End-of-Year 

Inventory 

(2013 $ Mil)

EOY 

inventory as 

% of 

Shipments 

Value

2011 424 14.1%

2010 457 13.0%

2009 457 11.4%

2008 583 13.1%

2007 586 13.2%

2006 436 10.5%

2005 439 9.9%
3-22 

Table 3.13.2 Air-Conditioning and Warm Air Heating Equipment Manufacturing Industry 

Inventory Levels30

Year

End-ofYear 

Inventory 

(2013 $ 

Mil)

EOY 

inventory 

as % of 

Shipments 

Value

2011 2,667 9%

2010 2,775 9%

2009 2,888 10%

2008 3,277 10%

2007 3,523 11%

2006 3,458 11%

2005 3,307 10%

DOE obtained full production capacity utilization rates from the U.S. Census 

Bureau’s Survey of Plant Capacity from 2004–2006. After 2006, the Census 

Bureau discontinued this survey, and began a new Quarterly Survey of Plant 

Capacity Utilization. However, this survey does not collect utilization data 

beyond the 4 digit NAICS codes for the “all household appliances”q and 

“ventilation, heating, air-conditioning, and commercial refrigeration equipment”r

industries. Table 3.13.3 presents utilization rates for these umbrella industries. 

Full production capacity is defined as the maximum level of production an 

establishment could attain under normal operating conditions.s In the Survey of 

Plant Capacity reports, the full production utilization rate is a ratio of the actual 

level of operations to the full production capacity. The full production utilization 

rate for all household appliances shows fairly steady utilization between 74 and 

77 percent from 2004 through 2007, with a significant decrease to less than 60 

percent from 2008 through 2009, and then a partial rebound from 2010 to 2012. 

Plant capacity utilization for ventilation, heating, air-conditioning, and 

commercial refrigeration manufacturers reached a peak in 2008, at 67 percent, 

and hit a low of 55 percent in 2012. 

 

q

 “All Home Appliances” is the umbrella NAICS category 3352 that includes NAICS code 33521 for “Small 

Electrical Appliances.” r “Ventilation, heating, air-conditioning, and commercial refrigeration equipment” is the umbrella NAICS category 

3334 that includes NAICS code 333415 for “air-conditioning and warm air heating equipment and commercial and 

industrial refrigeration equipment.” s For more information, please visit: http://www.census.gov/manufacturing/capacity/definitions/index.html
3-23 

Table 3.13.3 Full Production Capacity Utilization Rates31, 32

Plant Capacity Utilization Rates (%)

Year

All 

Household 

Appliances

Ventilation, heating, airconditioning, and 

commercial refrigeration 

equipment mfg.

2012 65% 55%

2011 62% 60%

2010 64% 60%

2009 58% 60%

2008 59% 67%

2007 76% 66%

2006 77% 62%

2005 74% 66%

2004 76% 61%

3.14 TECHNOLOGY ASSESSMENT 

 This section provides a technology assessment for dehumidifiers. Contained in this 

technology assessment are details about product characteristics and operation (section 3.14.1), an 

examination of possible technological improvements (section 3.14.2), and a characterization of 

the product efficiency levels currently commercially available (section 3.14.3).

3.14.1 Dehumidifier Operations and Components

Dehumidifiers are refrigeration-based appliances that enable homeowners to reduce 

indoor relative humidity (RH). RH is defined as the amount of water vapor present in the air 

compared to the maximum amount of water vapor the air can hold at that temperature. A 

desirable indoor RH is typically between 30 and 60 percent. 

Dehumidifiers contain refrigeration systems that remove latent heat, and therefore 

moisture, from ambient air. Components of the refrigeration system include an evaporator, an 

expansion valve or capillary tube, a condenser, and a compressor. 

Refrigeration-based dehumidifiers operate as follows:

1. A circulating fan draws air into the dehumidifier via an intake vent, or in the case of 

whole-home dehumidifiers, via an inlet air duct, typically in the front or on the sides 

of the unit; 

2. The air is pulled across an evaporator heat exchanger that is cooled by an electricallypowered vapor compression refrigeration system; 

3. The evaporator cools the air, and moisture from the air condenses on the surface of 

the evaporator and drips either into a bin or out a drain; and
3-24 

4. The drier air is then typically pulled over a warm condenser heat exchanger and exits 

the dehumidifier via an outlet grille, or in the case of whole-home dehumidifiers, via 

an outlet air duct, typically on the top or sides of the unit. 

This process is illustrated in Figure 3.14.1. 

Figure 3.14.1 Dehumidifier Operation Schematic

 When the surface temperature of the evaporator is lower than the dew point of the air 

passing over it, the removal of latent heat occurs through condensation. All residential portable 

dehumidifiers are equipped with an automatic shut-off function that halts operation of the device 

once the condensate collection tank is full. They may also include a direct drain hose connection 

and occasionally an internal pump for direct drainage of condensate into a sump pump or floor 

drain. Whole-home dehumidifiers typically only include a direct drain hose connection and no 

internal condensate collection tank. This allows for continuous operation without the need for 

user intervention to periodically empty the condensate collection tank.

There is a wide scope of control strategies, ranging from simple on/off mechanical 

humidistats and single-speed fans to electronic controllers that use multiple sensors, liquidsolenoid controls, and other devices to maximize unit performance. All dehumidifiers sense the 

evaporator coil or liquid line temperature to prevent icing, though some units may include 

sensors in multiple locations to avoid ice accumulation. 

The electrically-powered components of the refrigeration-based dehumidifier system 

include the fan motor, the compressor that powers the refrigeration system, any electronic 

sensors or controls, and a (optional) built-in sump pump or similar accessory.

Humid Air Dry Air

Evaporator Condenser

Compressor

Condensate 

Bin

Fan

Expansion

Humid Air Dry Air

Evaporator Condenser

Compressor

Condensate 

Bin

Fan

Expansion
3-25 

3.14.2 Dehumidifier Technology Options

In order to gain a deeper understanding of the technological improvements used to 

increase the efficiency of dehumidifiers, DOE identified several possible technologies and 

examined the most common improvements used in today’s market. 

 DOE identified design options to improve dehumidifier efficiency during the preliminary 

analysis. DOE relied on previous rulemaking TSDs and information gathered during testing and 

teardowns to develop the list of technology options. DOE again considered these technology 

options in this NOPR analysis. For more details on the reverse-engineering teardown activities, 

see chapter 5 of this NOPR TSD.

 

The technology options for dehumidifiers are listed in Table 3.14.1. They are features 

that can be incorporated into the design of a dehumidifier to improve its efficiency. Based on 

product literature research, stakeholder interviews, and teardown analysis, DOE has identified 

compressor, heat exchanger, and fan motor improvements as the most common ways by which 

manufacturers may improve the energy efficiency of their dehumidifiers as measured by the 

DOE test procedure. 

Table 3.14.1 Technology Options for Dehumidifiers

1. Built-in hygrometer/humidistat

2. Improved compressor efficiency

3. Improved condenser and evaporator 

performance

4. Improved controls

5. Improved defrost methods

6. Improved demand-defrost controls

7. Improved fan and fan-motor efficiency

8. Improved flow-control devices

9. Low-standby-loss electronic controls

10. Washable air filters

11. Pre-cooling air-to-air heat exchanger

12. Heat pipes

13. Improved refrigeration system insulation

14. Refrigerant-desiccant systems

Built-in hygrometer/humidistat

All portable dehumidifiers subjected to DOE teardowns featured some type of built-in 

humidity controller. For all units in the teardown sample, DOE observed electronic controls, but 

DOE is aware that certain units may still feature electromechanical humidistats. The humidistat 

cycles the compressor and fan power supply as a function of relative humidity. Both electronic 

and electromechanical controllers measure the expansion of a reference material as a function of 

relative humidity. DOE notes that whole-home dehumidifiers are often designed to be used in 

conjunction with a remote humidistat. The humidistat is placed in the portion of the home that 

requires dehumidification, and cycles the whole-home dehumidifier compressor and fan power 
3-26 

supply as needed. Although this control is external to the unit, DOE does not expect significant 

differences in efficiency or operation when compared to the built-in controls on portable units. 

Improved compressor efficiency

Most dehumidifier manufacturers incorporate rotary R-410A compressors into their units. 

“Inertia” compressors, scroll compressors, and variable-speed compressors all have higher 

efficiencies than the traditional rotary compressors used in dehumidifiers. However, finding a 

suitable high-efficiency compressor at the capacities and price points needed for a dehumidifier 

is a challenge.

The “inertia” compressor is a technology that allows reciprocating compressors to

approach an energy efficiency ratio (EER) of 12.0, where EER represents the cooling capacity in 

Btu/hr divided by the input wattage of the compressor. “Inertia” compressors utilize lightweight, 

responsive valve technology and an innovative refrigerant flow path to reduce losses and 

improve cylinder volumetric efficiency. 

Scroll compressors require high precision to produce their internal components and are 

typically found in higher-efficiency central air-conditioning systems. Scroll compressors 

compress gas in a fundamentally different manner from traditional compressors — between two 

spirals, one fixed and one nutating. Scroll compression is inherently more efficient than 

traditional compression methods. 

Both inertia and scroll compressors are, however, substantially larger, heavier, and 

sometimes noisier than their rotary counterparts, and, as such, are not well-suited for use in 

residential dehumidifiers.

Variable-speed compressors are typically implemented through the use of an electronic 

control that varies the input frequency of the power supply for the compressor motor. Variablespeed compressors enable modulation of the refrigeration-system cooling power beyond simple 

on/off control, allowing the dehumidifier to better match the compressor power to the load, 

increasing compression efficiency. Variable-speed compressor technology has not yet been 

implemented in residential dehumidifiers, and it is therefore difficult to predict the energy 

efficiency improvements that could be achieved through its use. However, DOE expects that a 

variable-speed compressor in a dehumidifier could provide more precise control of the 

evaporator coil temperature to ensure more efficient latent heat removal, especially at low 

temperatures where ice buildup is prevalent. 

Improved condenser and evaporator performance 

Improving the overall heat transfer capability of the dehumidifier condenser and 

evaporator coils would result in improved efficiency of the refrigeration system. DOE notes that 

many factors contribute to heat exchanger performance, including size, number of fins, type of 

fins, number of tube passes, etc. DOE notes that almost all dehumidifier models in its teardown 

sample had similarly constructed heat exchangers with “slit” aluminum fins for more turbulent 
3-27 

airflow, roughly 20 fins per inch, and internally-rifled copper tubing for optimized refrigerant 

heat exchange, although the heat exchangers did vary in size from unit to unit. 

Improved controls 

Manufacturers have increasingly adopted digital controllers for dehumidifiers due to 

consumer demand and because such controllers allow dehumidifiers to better respond to 

changing environmental conditions. Unlike the snap-action switches and fixed-RH 

electromechanical controllers they replace, digital controllers can measure multiple inputs and 

respond to present conditions as well as trends. 

Digital controllers can use varying approaches, from rigid decision trees to fuzzy logic. 

During the reverse-engineering process, DOE observed that most electronic control boards were 

purchased parts that had been mass-customized for the specific dehumidifier. One advantage of 

digital controllers is the ease with which they can be reprogrammed, allowing a manufacturer to 

use one control board for its entire dehumidifier line, yet optimize its responses per dehumidifier 

model. 

 It is difficult to predict the amount of energy savings that could be achieved through the 

implementation of improved controllers because the current DOE test procedure evaluates 

continuous dehumidifier operation at constant ambient conditions. 

Improved defrost methods 

As the air drawn into the dehumidifier is cooled, water vapor condenses on the surface of 

the evaporator coil. In some cases, typically when the ambient air is typically below 65 °F, this 

water can freeze as it collects and form a growing layer of frost. The frost reduces cooling 

performance by increasing the thermal resistance to heat transfer from the coil to the air and by 

obstructing air flow. Both the method by which defrost is performed and control of the defrost 

cycle can lead to substantial energy savings.

Many dehumidifiers incorporate defrost technology. In general, two types of defrost 

mechanisms are available: passive defrost and active defrost. According to market research and 

investigative testing, most dehumidifiers that feature defrost technology use active defrost. This 

is especially true for dehumidifiers that are designed and marketed for low-temperature operation

(i.e., for use in basements). 

Dehumidifiers using passive defrost or off-cycle mode monitor the temperature of the 

evaporator and shut both the compressor and fan off if that temperature drops below the freezing 

point of water. This is not technically “defrost,” as it actually prevents the formation of frost 

rather than eliminating frost after it forms. It is, however, an effective method of preventing the 

dehumidifier from operating under low-efficiency frosted conditions. This method is simple to 

implement and incurs no additional energy expenditure. 

Active defrost in dehumidifiers is conducted in one of three ways: fan-only defrost, 

electric defrost, or hot-gas defrost. 
3-28 

Fan-only defrost involves shutting off the refrigeration system while keeping the aircirculation system running. The relatively warm ambient air melts the frost layer, and the 

dehumidifier subsequently resumes operation. Fan-only defrost may allow much of the frozen 

condensate to be reabsorbed into the ambient air as it melts, compromising the effectiveness of 

the dehumidifier. 

Electric defrost involves melting frost by briefly activating an electric resistance heater, 

which is in contact with or near the evaporator. The heater melts frost quickly but consumes 

significant energy. Hot-gas defrost uses the hot compressor discharge gas to warm the evaporator 

from the refrigerant side. Electricity usage is reduced in comparison to the electric defrost 

method; however, this method necessitates more complicated piping and control than electric 

defrost systems. Neither of these defrost technologies were identified in dehumidifiers through 

market research, and were not present in any units that were disassembled during DOE’s 

teardowns. 

Defrost methods are not currently captured as part of the DOE test procedures because 

units are tested in dehumidification mode at 80 °F conditions where icing is unlikely to occur. At 

the 65 °F test conditions, which DOE established in appendix X1, however, certain

dehumidifiers in DOE’s test sample exhibited operational patterns that were indicative of fanonly defrost. For those units, actual energy savings will be a function of ambient conditions and 

usage patterns. 

Improved demand-defrost controls 

In all active-defrost systems, control of the defrost cycle may lead to substantial energy 

savings. Defrost-cycle control involves management of the initiation and termination of defrost 

cycles, and thereby management of the frequency and duration of defrost cycles. Two different 

defrost-cycle control designs are available: timer-controlled and temperature-sensor-controlled. 

In a time-based defrost system, cycles are completely scheduled, and initiation and 

termination are timer-controlled. Cycles are initiated at regular intervals and terminated after a 

fixed amount of time or in response to low ambient temperatures. In these systems, cycle 

frequency and duration are not responsive to actual frost conditions. Under timer control, the 

frequency of defrost cycles is determined by the amount of time the manufacturer expects it to 

take for a large frost layer to develop in the worst-case scenario, and the cycle duration is long 

enough to ensure that the frost layer completely melts. Timer-based defrost can lead to 

unnecessarily frequent and unnecessarily long defrost cycles under anything but worst-case 

conditions. 

Sensor-controlled defrost occurs as-needed based on evaporator coil temperatures. 

Defrost cycles are initiated in response to freezing temperatures at the evaporator coil or other 

area in the dehumidifier, and are terminated when the coil temperature reaches a value indicating 

complete defrost. This type of defrost control saves energy relative to timer-controlled defrost by 

varying cycle duration based upon defrost requirements. As with all defrost design options, 

actual energy savings will be a function of ambient conditions and usage patterns. 
3-29 

Improved fan and fan-motor efficiency

The air-circulation system of a residential dehumidifier usually consists of a permanent 

split capacity (PSC) fan motor that drives either propeller-style blades or a tangential “squirrel 

cage” fan. These motors run on line voltage and typically feature one or two speeds. Multiple fan 

speeds are usually found on higher-end units. Some unit controllers can modulate fan speeds 

(high/low) whereas less complex models utilize a user-set switch.

Dehumidifiers are typically built using product platforms, where one enclosure serves 

multiple dehumidification capacities. As a result, the designers make tradeoffs to accommodate a 

wide range of capacities (35 to 65 pints/day, for example) within a single enclosure. While 

efficiency improvements could be achieved by optimizing fan blades for specific dehumidifier 

models, such a design change would add complexity to the manufacturing process by requiring a 

wider scope of fan blades to be stocked. Such steps would likely increase inventory, reduce fan 

blade purchase volumes and hence manufacturers see this as a relatively costly design option in 

relation to the efficiency benefit. Therefore, quantifying the efficiency improvements to the 

dehumidifier’s air-circulation system is restricted to analyzing the efficiency improvements to 

the fan motor only. 

In a PSC motor, the start-up winding is electrically connected in parallel with the main 

winding and in series with a capacitor. At start-up, the interactions between the magnetic field 

generated by the start-up winding and that generated by the main winding induce rotation. As the 

capacitor charges, the current flowing through the start-up winding decreases and the start-up 

winding becomes an auxiliary winding after the motor reaches running speed. Consequently, the 

current to the start-up winding is cut off once the capacitor is fully charged and the motor 

reaches steady-state speed. Because of this, PSC motors are substantially more efficient than 

their shaded-pole counterparts, with motor efficiencies ranging from 60 to 65 percent.

33 Like 

shaded-pole motors, PSC motors are produced in large quantities and are relatively 

inexpensive.34 

Electric motors with even higher efficiencies can be implemented by switching to 

permanent-magnet motors, which come in many varieties. The most widely-known variety is the 

electronically-commutated motor (ECM)t, though DC-motors can also be used. Permanent 

magnet motors are less noisy and substantially more efficient than either shaded-pole or PSC 

motors. ECM motors convert single-phase AC input power into three-phase power, and have 

motor efficiencies approaching 80 percent.35 However, ECM motors can weigh twice as much as 

equivalent PSC motors, potentially necessitating a redesign of the dehumidifier fan-motor 

chassis. In addition, ECM motors are complex, are not currently produced in large volumes, and 

can cost from 2.5 to 5 times as much as a PSC motor.36

Improved flow-control devices

 

t

Also known as brushless permanent magnet (BPM) motors or electronically-commutated permanent magnet 

(ECPM) motors. 
3-30 

Nearly all portable dehumidifiers use capillary-tube expansion valves for flow control. 

The capillary-tube expansion valve is a pressure-reducing device that consists of a smalldiameter line that connects the outlet of the condenser to the inlet of the evaporator. It is 

designed to provide optimum energy characteristics at one design point. If sized properly, the 

capillary-tube expansion valve compensates automatically for load and system variations and 

gives acceptable performance over a wide range of operating conditions. Because ambient 

temperature and humidity vary, however, dehumidifiers sometimes operate under conditions 

outside of the target conditions, leading to reduced efficiency. 

The thermostatic expansion valve (TXV) — a flow-control alternative to the capillary 

tube — is commonly used in higher-efficiency central air-conditioning systems. TXVs regulate 

the flow of liquid refrigerant entering the evaporator in response to the superheat of the 

refrigerant leaving it. TXVs can adapt better to changes in operating conditions such as those due 

to variations in ambient temperature, which affect the condensing temperature. As a result, TXVs 

can lead to a somewhat increased seasonal operating efficiency.

Electronic expansion valves (EEVs) are similar to TXVs, but unlike TXVs, they can be 

actively controlled. While a TXV relies on a single temperature sensor for feedback, digital 

controllers can use multiple sensors for feedback control and respond using multiple approaches. 

For example, besides modulating the refrigerant flow, the controller may also vary the fan speed 

to optimize efficiency under varying conditions. As with TXVs, EEVs can use the superheat 

control method to regulate refrigerant flow. Other methods, such as controlling compressor 

discharge temperature, can also be used. 

During the reverse-engineering analysis, DOE did not observe any units with either 

TXVs or EEVs. Given the cost of TXVs and EEVs, it is unlikely that manufacturers would 

implement them in residential dehumidifiers. Additionally, because dehumidifiers are tested 

during continuous operation under constant ambient conditions, the test procedure may not 

capture efficiency gains associated with these technologies. 

Low-standby-loss electronic controls

 Electronic controls may consume power even when the dehumidifier is not performing its 

intended function. Depending on the implementation of the controller, standby power is required 

to enable the electronic controls to detect user input without the user first having to turn on a 

mechanical power switch or to enable displays, illuminate switches, etc. Reducing the standby 

power consumption of electronic controls will reduce the annual energy consumption of the 

dehumidifier, but will not impact the energy consumption of the dehumidifier during operation in 

dehumidification mode. 

Washable air filters

The build-up of dust particles on the evaporator coil can lower the heat-transfer capability 

of the component. To minimize this possibility, most dehumidifiers incorporate an air filter at the 

air-intake vent. However, these filters can become clogged, obstructing air flow through the 

dehumidifier and reducing system performance. To prevent this, most manufacturers design the 
3-31 

air filters to be removable and washable, which allows the consumer to clean the air filters on a 

regular basis. It is difficult to predict the amount of energy savings that could be realized with the 

addition of washable air filters, as it is dependent on the specific dehumidifier model and use 

characteristics, and on the degree to which the consumer takes advantage of this feature. 

Additionally, efficiency testing is often conducted on new dehumidifiers, so there would be 

likely be no clear efficiency impact due to dust build up in either the evaporator or in the air 

filter. 

Pre-cooling air-to-air heat exchanger 

The efficiency of a refrigeration-based dehumidification system may be increased by precooling air prior to the evaporator inlet. With pre-cooled inlet air, the refrigeration system may 

consume less energy to maintain the proper evaporator temperature while removing additional 

condensate from the air. DOE found that many high-capacity dehumidifiers (greater than 75 

pints/day) incorporate an air-to-air heat exchanger that transfers heat from the intake air to the air 

immediately exiting the evaporator. The energy savings that can be realized from this technology 

is dependent on a unit’s specific airflow and component configuration. Based on DOE’s analysis 

of existing models with and without air-to-air heat exchangers, DOE estimates that this 

technology option may result in a 10 to 25 percent reduction in active dehumidification mode 

energy use. 

Heat pipes

Another method for increasing the efficiency of a refrigeration-based dehumidifier is to 

include a heat pipe system that acts as a pre-cooler for air entering the evaporator. A heat pipe 

system consists of a set of additional coils installed on each side of the evaporator. The coils are 

connected via a sealed refrigerant system, which is independent of the primary refrigeration 

system in the dehumidifier. The goal of the heat pipe is to passively transfer heat from the 

incoming air stream to the air stream exiting the evaporator.

The air entering the dehumidifier is pre-cooled by the refrigerant in the coil in front of the 

evaporator. The refrigerant in this section of the heat pipe evaporates as it gains heat from the 

inlet airstream and passively flows to the coil behind the evaporator. The pre-cooled air then 

passes through the evaporator, where the temperature of the air is decreased even further to 

condense moisture. Air exiting the evaporator is at a low temperature, so this air stream gains 

heat from the warmer refrigerant in the heat pipe coil behind the evaporator. As the refrigerant in 

this coil loses heat to the airstream, it condenses back to a liquid and passively flows back to the 

coil in front of the evaporator. As a result of this system, the evaporator is required to remove 

less heat from the incoming airstream, and less energy is required for the dehumidifier 

refrigeration system. 

DOE was unable to identify any residential dehumidifiers that use heat pipes to boost 

performance by pre-cooling the inlet evaporator air. DOE is aware that units existed on the 

market until 2010, at which point the manufacturer chose to discontinue the product line for 

reasons unknown to DOE. DOE is, however, aware of packaged terminal air conditioners 

(PTACs) available on the market that incorporate heat pipe systems. The manufacturer for these 
3-32 

products has provided limited information on their performance, including moisture removal. 

Based on a comparison of these products to similar PTACs without heat pipes, DOE estimates 

that heat pipes could produce as much as a 25-percent improvement in efficiency. DOE notes, 

however, that this estimate is based on products optimized for removing sensible heat from the 

airstream, and may not apply to dehumidifiers optimized for removing latent heat. DOE also 

notes that heat pipes may disrupt airflow to the evaporator. This could limit the effective heat 

transfer in the evaporator, decreasing or eliminating any efficiency gains associated with precooling the inlet air.

Improved refrigeration system insulation

 While conducting teardowns, DOE found that all products included refrigeration system 

insulation to a certain extent. The most commonly insulated parts of the system were the 

evaporator outlet and compressor inlet. Insulation helps contain heat loses throughout the 

refrigeration system and therefore improves the overall energy efficiency of the dehumidifier, 

but it is difficult to estimate the extent of the energy savings associated with improved insulation. 

 DOE also observed that whole-home dehumidifiers and the high-capacity portable 

dehumidifiers also typically featured insulated cases. However, DOE expects case insulation to 

primarily improve a unit’s noise performance rather than its energy efficiency.

Refrigerant-desiccant systems

DOE is aware of certain products available on the market that meet the definition of 

whole-home dehumidifier, but also incorporate a desiccant system to aid in moisture removal. 

These dehumidifiers employ a combined moisture removal approach, where some of the 

moisture in the process air (i.e., the air that is supplied from and returned to the conditioned 

space) is condensed on the evaporator, while additional moisture is removed via a porous 

desiccant material that adsorbs moisture when damp air passes through or over it. The desiccant 

material is typically configured in a circular or wheel structure. A portion of the wheel adsorbs 

moisture from the process air entering the unit, which is then delivered to the conditioned space. 

As the wheel rotates, the moisture in that segment is released into a separate heated reactivation 

air stream and exhausted out of the home. In addition to removing some moisture from the 

process air directly, the refrigeration system boosts the temperature of the reactivation air to 

more effectively remove moisture from the desiccant wheel, and cools the incoming air to 

improve the adsorptivity of the desiccant material.

The appendix X1 test procedure includes provisions to account for the unique setup and 

operation of these units compared to the typical refrigerant-based dehumidifiers. In particular, 

refrigerant-desiccant dehumidifiers require a separate inlet and exhaust duct for the intake of 

reactivation air that removes moisture from the desiccant wheel and discharge of the moist air to 

the outdoors, so not all moisture that is removed from the conditioned space will be accounted 

for by measurement of the condensate collected. DOE expects that refrigerant-desiccant systems 

can offer unique utility in certain conditions, particularly in very low-temperature installations 

where typical dehumidifiers may run into frosting issues. While refrigerant-desiccant 

dehumidifiers may provide additional utility compared to typical dehumidifiers under these 
3-33 

lower temperature operating conditions, under the representative ambient conditions in appendix 

X1, DOE did not observe an efficiency improvement associated with refrigerant-desiccant 

systems.

3.14.3 Energy Efficiency

In preparation for the screening and engineering analyses, DOE gathered data on the 

energy efficiency of dehumidifiers currently available in the marketplace. This data is taken from 

DOE’s CCMS database. Figure 3.14.2 plots the EF versus the capacity of each certified 

dehumidifier along with the current energy conservation standards and ENERGY STAR criteria 

for dehumidifiers, effective as of October 2012.u

Figure 3.14.2 DOE-Certified Dehumidifiers, DOE Standards, and ENERGY STAR 

Qualification Criteria37

 

u For more information, please visit www.energystar.gov. 
3-34 

REFERENCES

1 “SoleusAir, Gree Electronic Appliances Merge,” Air Conditioning, Heating & 

Refrigeration News Weekly, April 4, 2011. 

2 “Corinthian Chills Out With Acquisition of Friedrich Air Conditioning,” Dow Jones LBO 

Wire, November 29, 2012. 

3 “Electrolux takes on Whirlpool in U.S. with $3.3 billion GE Appliances buy,” Reuters, 

September 8, 2014, http://www.reuters.com/article/2014/09/08/us-general-electricdivestiture-idUSKBN0H30AG20140908 (Accessed October 13, 2014)

4 Consumer Reports, “Dehumidifier Buying Guide,” Available online 

at:http://www.consumerreports.org/cro/dehumidifiers/buying-guide.htm. (Accessed 

October 13, 2014) 

5 Association of Home Appliance Manufacturers (AHAM) Fact Book 2003. 

6 AHAM Fact Book 2005. 

7 Data Submittal by AHAM. December 21, 2012. 

8 “Appliance Design.” Volume 62, No. 3 (March 2014). Shipments data available online 

at: 

http://www.appliancedesign.com/ext/resources/files/ShipmentsForecasts/2014/03_march

_Shipments.pdf. (Accessed October 13, 2014) 

9 Ibid.

10 U.S. Census Bureau. 2007. “Table 1 - Value of Shipments of Electric Housewares and 

Fans by Class of Product: 2003 to 2007,” 2007 Annual Survey of Manufactures (ASM), 

May 2008. Available online at: 

http://www.census.gov/manufacturing/cir/historical_data/ma335e/ma335e07.xls

(Accessed October 13, 2014). 

11 U.S. Census Bureau. 2010. “Table 1 - Value of Shipments of Electric Housewares and 

Fans by Class of Product: 2010 to 2006” 2010 ASM, May 2011. Available online at: 

http://www.census.gov/manufacturing/cir/historical_data/ma335e/ma335e10.xls

(Accessed October 13, 2014). 

12 U.S. Census Bureau. 2011. “Annual Survey of Manufactures: Value of Products 

Shipments: Value of Shipments for Product Classes: 2011 and 2010”, 2011 ASM, 

November 2012. Available online at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2011_31VS101&prodType=table. (Accessed October 13, 2014) 
3-35 

13 U.S. Census Bureau. 2007. “Table 2 - Quantity and Value of Shipments of Electric 

Housewares and Fans by Product: 2007 and 2006” 2007 ASM, May 2008. Available 

online at: 

http://www.census.gov/manufacturing/cir/historical_data/ma335e/ma335e07.xls

(Accessed October 13, 2014). 

14 U.S. Census Bureau. 2008. “Table 2 - Quantity and Value of Shipments of Electric 

Housewares and Fans by Product: 2008 and 2007” 2008 ASM, May 2009. Available 

online at: 

http://www.census.gov/manufacturing/cir/historical_data/ma335e/ma335e08.xls

(Accessed October 13, 2014). 

15 U.S. Census Bureau. 2009. “Table 2 - Quantity and Value of Shipments of Electric 

Housewares and Fans by Product: 2009 and 2008” 2009 ASM, April 2010. Available 

online at: 

http://www.census.gov/manufacturing/cir/historical_data/ma335e/ma335e09.xls

(Accessed October 13, 2014). 

16 Ibid.

17 U.S. Census Bureau. 2011. 2010 Current Industrial Report – Major Household 

Appliances (CIR), May 2011. Available online at: 

www.census.gov/manufacturing/cir/historical_data/ma335f/ma335f10.xls (Accessed 

October 13, 2014). 

18 U.S. Census Bureau. 2011. “Annual Survey of Manufactures: Value of Products 

Shipments: Value of Shipments for Product Classes: 2011 and 2010”, November 2012. 

Available online at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2011_31VS101&prodType=table. (Accessed October 13, 2014). 

19 U.S. Census Bureau. 2010. “Annual Survey of Manufactures: Value of Products 

Shipments: Value of Shipments for Product Classes: 2010 and 2009”, November 2011. 

Available online at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2010_31VS101&prodType=table. (Accessed October 13, 2014). 

20 U.S. Census Bureau. 2009. “Annual Survey of Manufactures: Value of Products 

Shipments: Value of Shipments for Product Classes: 2009 and 2008”. Available online 

at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2009_31VS101&prodType=table. (Accessed October 13, 2014). 

21 U.S. Census Bureau. 2008. “Annual Survey of Manufactures: Value of Products 

Shipments: Value of Shipments for Product Classes: 2008 and 2007”. Available online 

at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2008_31VS101&prodType=table. (Accessed October 13, 2014). 
3-36 

22 AHAM Fact Book. 2003. 

23 Ibid.

24 “U.S. Appliance Industry: Market Share, Life Expectancy & Replacement Market, and 

Saturation Levels”. Appliance Magazine Market Research Report, January 2010. 

25 U.S. Census Bureau. 2002–2011. “Annual Survey of Manufactures: General Statistics: 

Statistics for Industry Groups and Industries,” ASM. Available online at: 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_

2011_31GS101&prodType=table. (Accessed November 30, 2013). 

26 Ibid. 

27 Ibid.

28 Ibid.

29 Ibid.

30 Ibid.

31 U.S. Census Bureau, Survey of Plant Capacity, 2004 to 2006. Available online at: 

http://www.census.gov/manufacturing/capacity/historical_data/index.html. (Accessed 

November 30, 2013). 

32 U.S. Census Bureau, Quarterly Survey of Plant Capacity Utilization, 2007 to 2012. 

Available online at: http://www.census.gov/manufacturing/capacity/. (Accessed 

November 30, 2013). 

33 P. Simmons. 2003. “The Art of Providing Savings.” Appliance Magazine, October 

2003. 

34 U.S. DOE. 1997. Op. cit.

35 Simmons. 2003. Op. cit.

36 Ibid.

37 DOE CCMS Database. Available online at http://www.regulations.doe.gov/certificationdata/CCMS-41431694081.html (Accessed October 13, 2014). 
4-i 

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 DISCUSSION OF DESIGN OPTIONS .......................................................................... 4-1

4.2.1 Screened-Out Design Options .............................................................................. 4-1

4.2.2 Remaining Design Options .................................................................................. 4-3

LIST OF TABLES

Table 4.2.1 Retained Design Options for Dehumidifiers ............................................................ 4-4
4-1 

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

 This chapter discusses the screening analysis conducted by the DOE of the design options 

identified in the market and technology assessment for dehumidifiers (chapter 3 of this NOPR 

TSD). In the market and technology assessment, DOE presented an initial list of technologies 

that can be used to reduce energy consumption for dehumidifiers. The goal of the screening 

analysis is to identify any design options that will be eliminated from further consideration in the 

rulemaking analyses.

The candidate design options are assessed based on DOE analysis as well as inputs from 

interested parties including manufacturers, trade organizations, and energy efficiency advocates. 

Design options that are judged to be viable approaches for improving energy efficiency are 

retained as inputs to the subsequent engineering analysis. Design options that are not 

incorporated in commercial products or in working prototypes, or that fail to meet certain criteria 

as to practicability to manufacture, install and service, as to impacts on product utility or 

availability, or as to health or safety will be eliminated from consideration in accordance with 

Energy Conservation Program for Consumer Products: Procedures for Consideration of New or 

Revised Energy Conservation Standards for Consumer Products. (61 FR 36974, section 4(a)(4) 

and 5(b)). The rationale for either screening out or retaining each design option is detailed in the 

following sections. 

4.2 DISCUSSION OF DESIGN OPTIONS

For dehumidifiers, the screening criteria specified in section 4.1 were applied to the 

design options to either retain or eliminate each technology from the engineering analysis. 

4.2.1 Screened-Out Design Options

The technologies identified in the market and technology assessment were evaluated 

pursuant to the criteria set out in the Energy Policy and Conservation Act, as amended (EPCA or 

the Act). (42 U.S.C. 6291–6309) EPCA provides criteria for prescribing new or amended 

standards, which will achieve the maximum improvement in energy efficiency the Secretary of 

Energy determines is technologically feasible. (42 U.S.C. 6295(o)(2)(A)) It also establishes 

guidelines for determining whether a standard is economically justified. (42 U.S.C. 

6295(o)(2)(B)) In view of the EPCA requirements for determining whether a standard is 

technologically feasible and economically justified, appendix A to subpart C of Title 10 Code of 

Federal Regulations part 430 (10 CFR part 430), Procedures, Interpretations and Policies for 

Consideration of New or Revised Energy Conservation Standards for Consumer Products (the 

“Process Rule”), sets forth procedures to guide DOE in the consideration and promulgation of 

new or revised product efficiency standards under EPCA. These procedures elaborate on the 

statutory criteria provided in 42 U.S.C. 6295 and in part eliminate problematic technologies early 

in the process of revising an energy efficiency standard. Under the guidelines, DOE eliminates 

from consideration technologies that present unacceptable problems with respect to the following 

four factors: 
4-2 

(1) Technological feasibility. If it is determined that a technology has not been 

incorporated in commercial products or in working prototypes, then that technology will not be 

considered further. 

(2) Practicability to manufacture, install, and service. If it is determined that mass 

production of a technology in commercial products and reliable installation and servicing of the 

technology could not be achieved on the scale necessary to serve the relevant market at the time 

of the effective date of the standard, then that technology will not be considered further. 

(3) Impacts on product utility to consumers. If a technology is determined to have 

significant adverse impact on the utility of the product to significant subgroups of consumers, or 

results in the unavailability of any covered product type with performance characteristics 

(including reliability), features, size, capacities, and volumes that are substantially the same as 

products generally available in the United States at the time, it will not be considered further. 

(4) Safety of technologies. If it is determined that a technology will have significant 

adverse impacts on health or safety, it will not be considered further. 

 The following sections detail the design options that were screened out for this 

rulemaking, and the reasons why they were eliminated. 

Pre-cooling Air-to-Air Heat Exchangers (for Portable Dehumidifiers up to 45 Pints/Day)

a

DOE is aware that certain whole-home dehumidifiers and portable dehumidifiers with 

capacities greater than 45 pints per day (pints/day) incorporate pre-cooling air-to-air heat 

exchangers, and thus DOE is retaining this technology as a design option for these product 

classes.

 However, based on teardowns and research, DOE determined that portable dehumidifiers 

with capacities up to 45 pints/day have little room to incorporate additional components within 

the product case. Adding pre-cooling air-to-air heat exchangers to these products would require 

increases in case size to accommodate the additional heat exchanger, which would also increase 

product weight at the expense of consumer utility. 

 DOE observed that the pre-cooling air-to-air heat exchangers in high-capacity portable

dehumidifiers and whole-home dehumidifiers typically occupied a volume of more than three 

times the combined evaporator and condenser volume. Although no low-capacity portable units 

on the market incorporate a pre-cooling air-to-air heat exchanger, DOE estimates that to achieve 

similar relative gains in efficiency as seen in larger units, portable units would incorporate a 

similar pre-cooling air-to-air heat exchanger size in proportion to the evaporator and condenser. 

DOE expects that with the addition of an effective pre-cooling air-to-air heat exchanger, case 

sizes would, at a minimum, roughly double for portable dehumidifiers up to 45 pints/day. 

 

a As measured by DOE’s dehumidifier test procedure at 10 CFR part 430, subpart B, appendix X1.
4-3 

DOE believes the increased size and weight associated with incorporating a pre-cooling 

air-to-air heat exchanger in portable dehumidifiers with capacities up to 45 pints/day would have 

an adverse impact on product utility to consumers. Because this design option would result in the 

unavailability of products with the same size and volume as products currently available on the 

market, DOE screened out pre-cooling air-to-air heat exchangers as a design option for portable 

dehumidifiers with capacities up to 45 pints/day. 

Heat Pipes (for Portable Dehumidifiers up to 45 Pints/Day)

 DOE identified heat pipes as a potential technology to increase dehumidifier efficiency. 

Heat pipes perform a similar function as pre-cooling air-to-air heat exchangers; lowering the 

inlet air temperature to increase the efficiency of the refrigeration system, except that heat pipes 

use a phase-change fluid to transfer heat between the two air streams. Similar to the discussion 

above for pre-cooling air-to-air heat exchangers, DOE determined that the additional heat 

exchangers and fluid tubing for heat pipes would require increases in case size, overall weight, 

and cost for portable dehumidifiers up to 45 pints/day capacity. DOE is not aware of any units 

available on the market that incorporate the heat pipe design option; however, DOE expects the 

increases in case size necessary to accommodate heat pipes to be on the same order of magnitude 

as for pre-cooling air-to-air heat exchangers.

DOE believes the increased size and weight for portable dehumidifiers up to 45 pints/day 

capacity incorporating heat pipes would have an adverse impact on product utility to consumers. 

Because this design option would result in the unavailability of products with the same size and 

volume as products currently available on the market, DOE screened out heat pipes as a design 

option for portable dehumidifiers with capacities up to 45 pints/day. 

 However, DOE has retained heat pipes as a design option for whole-home and portable 

dehumidifiers with greater than 45 pints/day capacity. DOE notes that many of these products 

already use larger case sizes to accommodate pre-cooling air-to-air heat exchangers. Products 

incorporating heat pipes would likely require case volumes similar to the products available on 

the market that include pre-cooling air-to-air heat exchangers. Because heat pipes would not 

likely impact consumer utility for whole-home and portable dehumidifiers with greater than 45 

pints/day capacity, DOE has retained this design option for these product classes. 

4.2.2 Remaining Design Options

Table 4.2.1 lists the design options for dehumidifiers that were retained by DOE for at 

least one of the analyzed product classes. Each of these technologies will be evaluated further in 

the subsequent engineering analysis. DOE has retained each of these design options, with 

limitations to specific product class as appropriate, because they either are or have previously 

been available in commercially available equipment and also meet the criteria listed in section 

4.2.1 relating to product utility, availability, and impacts on health and safety. 
4-4 

Table 4.2.1 Retained Design Options for Dehumidifiers

1. Built-in hygrometer/humidistat

2. Improved compressor efficiency

3. Improved condenser performance 

4. Improved controls

5. Improved defrost methods

6. Improved demand-defrost controls

7. Improved evaporator performance 

8. Improved fan and fan-motor efficiency 

9. Improved flow-control devices

10. Low-standby-loss electronic controls 

11. Washable air filters

12. Pre-cooling air-to-air heat exchanger (highcapacity portable and whole-home dehumidifiers)

13. Heat pipes (high-capacity portable and wholehome dehumidifiers)

14. Improved refrigeration system insulation

15. Refrigerant-desiccant systems
5-i 

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 PRODUCT CLASSES ANALYZED .............................................................................. 5-2

5.3 EFFICIENCY LEVELS................................................................................................... 5-4

5.3.1 Baseline Efficiency Levels .................................................................................. 5-5

5.3.2 Incremental Efficiency Levels ............................................................................. 5-7

5.3.2.1 Portable Dehumidifiers .............................................................................. 5-7

5.3.2.2 Whole-Home Dehumidifiers ...................................................................... 5-8

5.4 METHODOLOGY OVERVIEW .................................................................................. 5-10

5.4.1 Review of Previous Technical Support Documents and Models....................... 5-10

5.4.2 Manufacturer Interviews .................................................................................... 5-10

5.4.3 Product Testing .................................................................................................. 5-11

5.4.4 Product Teardowns ............................................................................................ 5-11

5.4.4.1 Selection of Units ..................................................................................... 5-11

5.4.4.2 Generation of Bill of Materials ................................................................ 5-12

5.4.4.3 Cost Structure of the Spreadsheet Models ............................................... 5-12

5.4.4.4 Cost Model and Definitions ..................................................................... 5-13

5.4.4.5 Cost Model Assumptions ......................................................................... 5-14

5.5 ANALYSIS AND RESULTS ........................................................................................ 5-14

5.5.1 Manufacturer Interviews .................................................................................... 5-14

5.5.1.1 Product Classes ........................................................................................ 5-15

5.5.1.2 Design Features of Current Baseline Products ......................................... 5-15

5.5.1.3 Proposed Incremental Efficiency Levels .................................................. 5-15

5.5.1.4 Design Options Required to Meet Each Efficiency Level ....................... 5-16

5.5.1.5 Performance at Reduced Ambient Temperatures ..................................... 5-16

5.5.1.6 Impact on Consumer Utility ..................................................................... 5-16

5.5.1.7 Installation and Repair Costs as a Function of Efficiency ....................... 5-17

5.5.2 Product Testing .................................................................................................. 5-17

5.5.2.1 Product Selection ...................................................................................... 5-17

5.5.2.2 Test Approach and Results ....................................................................... 5-17

5.5.3 Product Teardowns ............................................................................................ 5-21

5.5.3.1 Baseline Construction .............................................................................. 5-21

5.5.3.2 Design Options to Reach Higher Efficiency Levels ................................ 5-22

5.5.4 Numerical Model ............................................................................................... 5-28

5.5.4.1 Summary .................................................................................................. 5-28

5.5.4.2 Analysis Method ...................................................................................... 5-29

5.5.5 Cost Estimates .................................................................................................... 5-31

5.5.5.1 Cost Estimates .......................................................................................... 5-32
5-ii

LIST OF TABLES

Table 5.3.1.1 Dehumidifier Baseline Unit Efficiencies Based on EISA 2007 Standards ........... 5-5

Table 5.3.1.2 Preliminary Analysis Portable Dehumidifier Baseline Efficiency Levels ............. 5-6

Table 5.3.1.3 Preliminary Analysis Whole-Home Dehumidifier Baseline Efficiency 

Levels .................................................................................................................... 5-6

Table 5.3.1.4 NOPR Analysis Portable Dehumidifier Baseline Efficiency Levels ..................... 5-6

Table 5.3.1.5 NOPR Analysis Whole-Home Dehumidifier Baseline Efficiency Levels ............ 5-7

Table 5.3.2.1 Portable Dehumidifier Efficiency Levels for the Preliminary Analysis ................ 5-7

Table 5.3.2.2 Portable Dehumidifier Efficiency Levels for the NOPR Analysis ........................ 5-8

Table 5.3.2.3 Whole-Home Dehumidifier Efficiency Levels for the Preliminary Analysis ....... 5-9

Table 5.3.2.4 Whole-Home Dehumidifier Efficiency Levels for the NOPR Analysis.............. 5-10

Table 5.4.4.1 Major Manufacturing Processes .......................................................................... 5-13

Table 5.5.2.1 Estimated Performance at Appendix X1 Conditions ........................................... 5-20

Table 5.5.5.1 Preliminary Analysis Manufacturer Production Costs (2012$) ........................... 5-32

Table 5.5.5.2 NOPR Analysis Manufacturer Production Costs (2013$) ................................... 5-32

LIST OF FIGURES

Figure 5.4.4.1 Manufacturing Cost Assessment Stages ............................................................. 5-12

Figure 5.5.2.1 Measured EF at 80 °F and 65 °F for Portable Dehumidifiers with 

Capacities Up to 75 pints/day (under Appendix X) ........................................... 5-18

Figure 5.5.2.2 Measured EF at 80 °F and 65 °F for Whole-Home Dehumidifiers .................... 5-19

Figure 5.5.2.3 Tested and Estimated Performance at Appendix X1 Conditions ....................... 5-21

Figure 5.5.4.1 R-410A Compressor Performance Characteristics ............................................. 5-30

Figure 5.5.5.1 R-410A Compressor Cost from Room Air Conditioner Energy 

Conservation Standards Direct Final Rule Analysis .......................................... 5-31
5-1 

CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis, the DOE performed an engineering analysis 

based on the remaining design options. The engineering analysis consists of estimating the 

energy consumption and costs of dehumidifiers at various levels of increased efficiency. This 

section provides an overview of the engineering analysis (section Chapter 5), discusses product 

classes (section 5.2), establishes baseline and incremental efficiency levels (section 5.3), explains 

the methodology used during data gathering (section 5.4) and discusses the analysis and results 

(section 5.5). 

The primary inputs to the engineering analysis are baseline information from the market 

and technology assessment (chapter 3 of this NOPR TSD) and technology options from the 

screening analysis (chapter 4). Additional inputs were determined through teardown analysis and 

manufacturer interviews. The primary output of the engineering analysis is a set of costefficiency curves. In the subsequent markups analysis (chapter 6), DOE determined customer 

(i.e., product purchaser) prices by applying distribution markups, sales tax and contractor 

markups. After applying these markups, the cost-efficiency curves serve as the input to the 

building energy-use and end-use load characterization (chapter 7), and the LCC and PBP

analyses (chapter 8). 

 DOE typically structures its engineering analysis around one of three methodologies. 

These are: (1) the design-option approach, which calculates the incremental costs of adding 

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

the relative costs of achieving increases in energy efficiency levels, without regard to the 

particular design options used to achieve such increases; and/or (3) the reverse-engineering or 

cost-assessment approach, which involves a “bottom-up” manufacturing cost assessment based 

on a detailed bill of materials (BOM) derived from teardowns of the product or equipment being 

analyzed. Deciding which methodology to use for the engineering analysis depends on the 

covered product, the design options under study, and any historical data that DOE can draw on. 

 In the preliminary engineering analysis, DOE used a hybrid approach combining aspects 

of all three analysis methods described in the paragraph above. The efficiency-level approach for 

residential dehumidifiers, combined with the cost-assessment approach, allowed DOE to develop 

a cost for each product analyzed. DOE estimated that the costs for these products reflected the 

costs for typical units at their respective efficiency levels. This approach involved physically 

disassembling commercially available products, consulting with outside experts, reviewing 

publicly available cost and performance information, and modeling equipment cost. To ensure 

that DOE’s analysis covered the entire range of capacities and efficiencies available on the 

market, DOE relied on the design-option approach to determine what changes would be needed 

for a particular unit to meet each incrementally higher efficiency level.

For this NOPR, DOE followed the same general approach as for the preliminary 

engineering analysis, but modified the analysis based on comments from interested parties and to 

reflect the most current available information. 
5-2 

5.2 PRODUCT CLASSES ANALYZED

The Energy Policy Act of 2005 (EPACT 2005), Pub. L. 109-58 (42 U.S.C. 6291–6309), 

amended the Energy Policy and Conservation Act (EPCA) of 1975, Pub. L. 94-163 (42 United 

States Code (U.S.C.) 6291–6309) to establish energy conservation standards for dehumidifiers 

manufactured as of October 1, 2007. (Section 135(c)(4)) These standards specified five product 

classes: 

• 25.00 pints per day (pints/day) or less; 

• 25.01–35.00 pints/day; 

• 35.01–54.00 pints/day; 

• 54.01–74.99 pints/day; and 

• 75.00 or more pints/day. 

The Energy Independence and Security Act of 2007 (EISA 2007), Pub. L. 110-140 (EISA 

2007), in section 311(a)(1), amended EPCA to prescribe a new set of standards for 

dehumidifiers, which took effect on October 1, 2012. DOE codified the EISA 2007 standards at 

10 CFR 430.32(v)(2). 74 FR 12058 (Mar. 23, 2009). These updated standards consolidated the 

two smallest product classes (25.00 pints/day or less and 25.01–35.00 pints/day) and subdivided 

the 35.01–54.00 pints/day product class into two product classes as follows: 

• Up to 35.00 pints/day; 

• 35.01 to 45.00 pints/day; 

• 45.01 to 54.00 pints/day; 

• 54.01 to 75.00 pints/day; and

• Greater than 75.00 pints/day. 

In the preliminary analysis for this rulemaking, DOE considered portable dehumidifier 

product classes that were based on the existing product classes, but with capacities adjusted for 

the lower ambient temperature for testing as proposed in the test procedure NOPR published in 

the Federal Register on May 21, 2014 (May 2014 Test Procedure NOPR, 79 FR 29271), as 

follows: 

• 20.00 pints/day or less;

• 20.01 to 30.00 pints/day; 

• 30.01 to 35.00 pints/day; 

• 35.01 to 45.00 pints/day; and

• 45.01 or more pints /day. 

DOE considered separate portable product classes for the preliminary analysis because

manufacturers typically offer multiple products over a range of capacities, but these products are 

generally constructed on one or two standardized chassis. These standardized chassis and case

sizes may limit a manufacturer’s ability to optimize blower or heat exchanger configuration for a 

particular capacity, and significant changes in efficiency may require a manufacturer to move to 

a larger case. By maintaining product classes over a range of capacities in the preliminary 

analysis, DOE considered the ability that manufacturers have to adjust capacity within a given 
5-3 

case or chassis in order to meet the minimum energy conservation standards. Moving to a single 

portable dehumidifier product class may force manufacturers to limit the range of products 

available on the market, or require a substantial investment to establish new production lines for 

product classes in which their existing range of chassis sizes would not allow the standards to be 

met. 

DOE also considered whole-home dehumidifiers as a separate product class for the 

preliminary analysis. DOE further divided the whole-home product class into two separate 

product classes by case volume: (1) less than or equal to 8.0 cubic feet (ft3

), and (2) greater than

8.0 ft3. During interviews conducted in support of the preliminary analysis, manufacturers 

indicated that various installation locations, including attics, crawl spaces, utility closets, and 

others, impose case size restrictions for whole-home dehumidifiers. These spaces each impose 

different size restrictions on the dehumidifier floor footprint and overall case volume. The 

proposed product class case volume differentiation is intended to capture the various applications 

for these whole-home products where installation location may impose certain size restrictions 

and associated performance restrictions. Based on teardowns and market research, DOE 

concluded that 8.0 ft3 is an appropriate threshold case volume for models that represent different 

utility in terms of installation location. 

In response to the proposed product classes on which DOE based the preliminary 

analysis, several interested parties commented that DOE should not consider separate product 

classes for portable units with capacities less than or equal to 45 pints/day because units in this 

product class are all capable of achieving similar maximum efficiencies, and because there 

should be no product class differentiation based on product chassis size. Other interested parties 

commented that DOE should maintain several portable product classes to consider the 

appropriate efficiency levels for each capacity and to account for unique performance and costs 

associated with each capacity range. DOE considered these comments, and proposes in this 

NOPR to classify portable products into three product classes based on capacity, as measured by 

the test procedure at 10 CFR part 430, subpart B, appendix X1 (appendix X1), as follows: 

• 30.00 pints/day or less; 

• 30.01 to 45.00 pints/day; and

• 45.01 pints/day or more. 

DOE’s considered several factors in proposing this revised classification. Although 

portable dehumidifiers within the first two product classes are able to reach similar maximum 

efficiencies when tested under the currently applicable test procedure at 10 CFR part 430, 

subpart B, appendix X (appendix X), DOE observed that testing according to the newly 

established appendix X1 resulted in lower maximum efficiencies for products within the lowestcapacity portable product class. These results suggest an inherent relationship between capacity 

and efficiency at the lower ambient test temperature specified in appendix X1 that is not apparent 

at the ambient temperature specified in appendix X. 

In addition, product sizes and weights vary between products currently available on the 

market. Lower-capacity units typically use a smaller chassis that limits the sizes of internal 

components such as heat exchangers. In its test and teardown sample, DOE observed that 
5-4 

products with capacities below 30 pints/daya and typically on the smaller chassis had an average 

weight of 33 pounds. Portable dehumidifiers with capacities between 30 pints/day and 45 

pints/day and in larger product cases had an average weight of 45 pounds. DOE concluded that 

the 12-pound average increase in product weight in moving to a larger case would result in less 

portable units (i.e., more difficulty moving the unit within the home), which would negatively 

impact consumer utility.

DOE also observed no key difference in product characteristics for the separate product 

classes initially analyzed for the preliminary analysis that DOE is proposing to combine into a 

single product class in this NOPR. The units in the 20.00 pints/day or less and 20.01 to 30.00 

pints/day product classes had similar sizes and weights, and were able to achieve similar 

efficiency levels under both appendix X and appendix X1 testing. Similarly, units in the 30.01 to 

35.00 pints/day and 35.01 to 45.00 pints/day product classes had similar construction and 

measured efficiencies. For this NOPR analysis, DOE proposes combing the four lowest-capacity 

portable product classes analyzed in the preliminary analysis into two: 30.00 pints/day or less 

and 30.01 to 45.00 pints/day. DOE proposes maintaining the 45.01 pints/day or more product 

class as considered in the preliminary analysis because the larger chassis size and weight 

typically associated with these products would allow for consideration of certain design options, 

such as inlet pre-cooling heat exchangers, that would not be feasible in lower-capacity portable 

dehumidifiers. 

For whole-home dehumidifiers, DOE maintained the product class differentiation based 

on products that may be installed in space-constrained locations. Many of the design options 

associated with improving efficiencies for these products, such as larger heat exchangers or an 

inlet pre-cooling heat exchanger, require increasing the unit case volume. Whole-home units that 

are not space constrained may incorporate all of these design options and reach higher 

efficiencies. DOE observed that products available on the market with case volumes greater than 

8.0 ft3

 are able to incorporate additional design options and reach higher efficiencies than 

products with volumes at or less than 8.0 ft3

. DOE also expects that products with volumes of 8.0 

ft3 or less would be able to meet consumers’ needs for space-constrained installations. DOE 

notes that switching to a capacity-based product class differentiation, as proposed for portable 

dehumidifier product classes, could result in products with smaller case sizes necessary for 

certain installations being eliminated from the market because lower capacity units would require 

a larger case volume to incorporate all available design options and maximize heat exchanger 

sizes to reach high efficiencies. For these reasons, DOE proposes in this NOPR to maintain the 

two whole-home dehumidifier product classes based on case volume: (1) less than or equal to 8.0 

ft3, and (2) greater than 8.0 ft3. 

5.3 EFFICIENCY LEVELS

For dehumidifiers, energy conservation standard levels are currently defined by the 

energy factor (EF) for each product class. However, EPCA, as amended by EISA 2007, requires 

 

a For consistency, all capacities presented in the remainder of this chapter are expressed in pints/day as measured 

according to the test procedure in appendix X1 except where otherwise noted.
5-5 

that any final rule establishing or revising a standard for a covered product, adopted after July 1, 

2010, shall incorporate standby mode and off mode energy use into a single amended or new 

standard, if feasible. If not feasible, the Secretary shall prescribe within the final rule a separate

standard for standby mode and off mode energy consumption. (42 U.S.C. 6295(gg)) 

The appendix X1 dehumidifier test procedure defines an integrated energy factor (IEF) 

metric that combines active mode energy consumption with low-power mode energy 

consumption, which includes standby mode or off mode energy consumption. In accordance with 

the EISA 2007 requirements, this NOPR analysis was conducted using efficiency levels based on 

IEF as measured by appendix X1. 

5.3.1 Baseline Efficiency Levels

Typically, a baseline unit is a unit that just meets current energy conservation standards 

and provides basic consumer utility. DOE analyzed the baseline units for each product class in 

the engineering analysis, and the subsequent LCC and PBP analyses. To determine energy 

savings and changes in price, DOE compared more energy-efficient units to the baseline unit. 

Table 5.3.1.1 summarizes the October 1, 2012, energy conservation standards prescribed 

by EISA 2007, with product class capacities measured according to appendix X. Each energy 

efficiency level is expressed as a minimum EF, which is defined in liters per kilowatt-hour 

(L/kWh). 

Table 5.3.1.1 Dehumidifier Baseline Unit Efficiencies Based on EISA 2007 Standards 

Product Class 

(Capacity) EF (L/kWh)

35.00 pints/day or less 1.35

35.01–45.00 pints/day 1.50

45.01–54.00 pints/day 1.60

54.01–75.00 pints/day 1.70

75.01 pints/day or more 2.50

For the preliminary analysis, DOE conducted its analysis on efficiency levels defined by 

IEF rather than EF. In addition to considering standby mode and off mode energy use, DOE 

adjusted IEF based on additional changes to the dehumidifier test procedure as proposed in the 

May 2014 Test Procedure NOPR. These included accounting for energy consumption in fan-only 

mode, modifying the active mode test conditions required in appendix X, and establishing

separate installation requirements and test conditions for whole-home dehumidifiers. Based on 

the conversion from EF to IEF and on the proposed amendments in the May 2014 Test Procedure 

NOPR, DOE developed adjusted product classes and IEF baseline efficiency levels for the 

preliminary analysis, as shown in Table 5.3.1.2 and Table 5.3.1.3. 
5-6 

Table 5.3.1.2 Preliminary Analysis Portable Dehumidifier Baseline Efficiency Levels

Product Class

(Capacity)

IEF

(L/kWh)

20.00 pints/day or less 0.77

20.01 – 30.00 pints/day 0.80

30.01 – 35.00 pints/day 0.94

35.01 – 45.00 pints/day 1.00

45.01 pints/day or more 2.07

Table 5.3.1.3 Preliminary Analysis Whole-Home Dehumidifier Baseline Efficiency Levels

Product Class 

(Case Volume)

IEF

(L/kWh)

Less than or equal to 8.0 ft3 1.10

Greater than 8.0 ft3 1.68

 Testing according to appendix X1 would not substantively change the IEF metric from 

the May 2014 Test Procedure NOPR or February 2015 Test Procedure SNOPR proposals for 

portable dehumidifiers. Therefore, for this NOPR, DOE maintained the portable dehumidifier 

baseline efficiencies determined for the preliminary analysis, with updates to reflect the 

combined product classes as discussed in section 5.2 of this chapter. DOE set the baseline 

efficiency level for the combined product classes at the lower of the two baseline IEF levels 

considered in the preliminary analysis for the two previously separate product classes because 

that IEF would be based on the minimum energy conservation standard currently applicable for 

any product within the combined product classes. Table 5.3.1.4 presents the portable 

dehumidifier baseline efficiency levels used in this NOPR analysis.

Table 5.3.1.4 NOPR Analysis Portable Dehumidifier Baseline Efficiency Levels

Product Class

(Capacity)

IEF

(L/kWh)

30.00 pints/day or less 0.77

30.01 – 45.00 pints/day 0.94

45.01 pints/day or more 2.07

 Appendix X1 does contain revisions to the whole-home testing conditions compared to 

those proposed in the May 20014 Test Procedure NOPR based on feedback from interested 

parties and available whole-home usage data. Appendix X1 requires an ambient dry-bulb 

temperature of 73 degrees Fahrenheit (°F) instead of 65 °F as proposed in the May 2014 Test 

Procedure NOPR. On the same basis, DOE also reduced the external static pressure requirements 

in the test ducting from the 0.5 inches of water column proposed in the May 2014 Test Procedure 

NOPR to 0.20 inches of water column. These test procedure changes would increase wholehome dehumidifier IEFs from the efficiency levels considered in the preliminary analysis. Using 

a combination of additional whole-home dehumidifier testing at the adjusted test conditions, 

interpolation of previous test data at 65 °F and 80 °F dry-bulb ambient temperatures, and data for 

a range of external static pressures, DOE established updated baseline efficiency levels for the 
5-7 

two whole-home dehumidifier product classes that are consistent with the test procedure in 

appendix X1, as shown in Table 5.3.1.5. 

Table 5.3.1.5 NOPR Analysis Whole-Home Dehumidifier Baseline Efficiency Levels

Product Class 

(Case Volume)

IEF

(L/kWh)

Less than or equal to 8.0 ft3 1.77

Greater than 8.0 ft3 2.41

5.3.2 Incremental Efficiency Levels

DOE analyzed several efficiency levels beyond the baseline for both portable and wholehome dehumidifiers, and developed incremental manufacturing cost data at each of these levels

in this engineering analysis. 

5.3.2.1 Portable Dehumidifiers 

In the preliminary analysis, DOE established the first efficiency level beyond the baseline 

by assuming manufacturers would remove fan-only mode from the baseline products. DOE 

determined higher incremental efficiency levels by identifying relevant EF levels (e.g., 

ENERGY STAR and maximum available) based on the appendix X test procedure, and then 

converting to IEF based on the test procedure proposals in the May 2014 Test Procedure NOPR. 

Table 5.3.2.1 shows the incremental efficiency levels analyzed for each portable dehumidifier 

product class in the preliminary analysis. 

Table 5.3.2.1 Portable Dehumidifier Efficiency Levels for the Preliminary Analysis

Efficiency

Level

Efficiency Level 

Source

IEF Efficiency Levels (L/kWh)

20.00 

pints/day 

or less

20.01–

30.00 

pints/day

30.01–

35.00 

pints/day

35.01–

45.00 

pints/day

45.01 

pints/day 

or more

Baseline Current Baseline with 

Fan-only Mode 0.77 0.80 0.94 1.00 2.07 

1 Current Baseline with 

no Fan-only Mode 1.10 1.10 1.20 1.30 2.40 

2 Gap Fill 1 1.20 1.20 1.40 1.40 2.80 

3 Gap Fill 2/

Maximum Available 1.30 1.30 1.60 1.60 3.52 

4 Maximum Available 1.42 1.52 1.75 1.75 N/A

 DOE received comments in response to the preliminary analysis stating that DOE should 

establish the max-tech level based on the maximum efficiency that is technologically feasible
5-8 

rather than the maximum efficiency available. After further review, DOE determined that 

dehumidifiers commercially available at this time may not incorporate all design options that are 

technologically feasible, and therefore revised the max-tech efficiency levels to incorporate 

additional design options beyond those observed in its test sample. For a full description of the 

retained design options to meet the max-tech efficiency levels, see section 5.5.3.2. DOE then 

modeled the increased efficiency associated with these new max-tech levels.

For the NOPR analysis, another key change to the efficiency levels considered for the 

preliminary analysis was to combine the previous four lowest capacity portable product classes 

into two, as discussed in section 5.2. The two portable product classes from the preliminary 

analysis with capacities less than 30.00 pints/day each have three identical intermediate 

efficiency levels, and thus these same intermediate levels were maintained for the single 

combined product class. For the combined 30.01 to 45.00 pints/day product class, the IEF for 

Efficiency Level 1 was 1.20 L/kWh and 1.30 L/kWh for the 30.01 to 35.00 pints/day and 35.01-

45.00 pints/day product classes, respectively. DOE selected the IEF of 1.20 L/kWh for 

Efficiency Level 1 of the combined product class because this represents the baseline IEF with 

no fan-only mode; therefore, DOE concluded it would be appropriate to maintain the lower of 

the two IEFs at this level for the combined product class. Efficiency Level 2 and Efficiency 

Level 3 were identical for the two previous product classes and thus were also maintained for the 

combined one. 

Based on these revisions to the preliminary analysis, DOE conducted the NOPR analysis 

based on the portable dehumidifier efficiency levels presented in Table 5.3.2.2. 

Table 5.3.2.2 Portable Dehumidifier Efficiency Levels for the NOPR Analysis

Efficiency

Level Efficiency Level Source

IEF Efficiency Levels (L/kWh)

30.00 pints/day 

or less

30.01–45.00 

pints/day

45.01 pints/day 

or more

Baseline Current Baseline with 

Fan-only Mode 0.77 0.94 2.07 

1 Current Baseline with no 

Fan-only Mode 1.10 1.2 2.40 

2 Gap Fill 1 1.20 1.4 2.80 

3 Gap Fill 2/

Max-Tech 1.30 1.6 3.66 

4 Max-Tech 1.57 1.8 N/A

5.3.2.2 Whole-Home Dehumidifiers

In the preliminary analysis, DOE developed incremental whole-home dehumidifier 

efficiency levels based on IEF according to the procedure proposed in the May 2014 Test 

Procedure NOPR. DOE selected efficiency levels based on the range of efficiencies observed 
5-9 

during investigative testing according to the May 2014 Test Procedure NOPR proposal. Unlike 

portable dehumidifiers, DOE did not observe any fan-only operation for whole-home 

dehumidifiers, so Efficiency Level 1 in the preliminary analysis represented an improvement in 

dehumidification mode efficiency rather than elimination of operation in fan-only mode. Table 

5.3.2.3 includes the efficiency levels used as the basis of the preliminary analysis for the two 

whole-home dehumidifier product classes.

Table 5.3.2.3 Whole-Home Dehumidifier Efficiency Levels for the Preliminary Analysis

Efficiency 

Level Efficiency Level Source

IEF Efficiency Levels (L/kWh)

Less than or equal 

to 8.0 ft3 (Case 

Volume)

Greater than 8.0 ft3

(Case Volume)

Baseline Minimum Available 1.10 1.68 

1 Gap Fill 1 1.40 1.90 

2 Gap Fill 2/Maximum 

Available 1.59 2.80 

3 Maximum Available N/A 3.41 

 As discussed in section 5.3.1, appendix X1 requires whole-home testing conditions of 73 

°F ambient dry-bulb temperature and external pressure of 0.20 inches of water column, instead of 

the 65 °F and 0.50 inches of water column proposed in the May 2014 Test Procedure NOPR (79 

FR 29278, 29288). Accordingly, DOE adjusted the preliminary analysis IEF efficiency levels for 

this NOPR analysis.

Similar to portable dehumidifiers, DOE additionally revised the max-tech level from the 

maximum available to the maximum IEF that DOE concluded is technologically feasible. DOE 

determined that whole-home dehumidifiers commercially available at this time may not 

incorporate all design options that are technologically feasible, and therefore revised the maxtech efficiency levels to incorporate additional design options beyond those observed in its test 

sample. For a full description of the retained design options to meet the max-tech efficiency 

levels, see section 5.5.3.2. DOE then modeled the increased efficiency associated with these new 

max-tech levels.

Table 5.3.2.4 shows the efficiency levels DOE considered in this NOPR analysis based 

on the revisions to the preliminary analysis. 
5-10 

Table 5.3.2.4 Whole-Home Dehumidifier Efficiency Levels for the NOPR Analysis

Efficiency 

Level Efficiency Level Source

IEF Efficiency Levels (L/kWh)

Less than or equal 

to 8.0 ft3 (Case 

Volume)

Greater than 8.0 ft3

(Case Volume)

Baseline Minimum Available 1.77 2.41

1 Gap Fill 1 2.09 2.70

2 Gap Fill 2/Max-Tech 2.53 3.52

3 Max-Tech N/A 4.50

5.4 METHODOLOGY OVERVIEW

DOE relied on multiple sources of information for this engineering analysis. These 

sources include a review of TSDs from previous rulemakings, manufacturer interviews, internal 

product testing, and product teardowns. 

5.4.1 Review of Previous Technical Support Documents and Models

 DOE reviewed previous rulemaking TSDs to assess their applicability to the current 

standard setting process for residential dehumidifiers. These previous rulemaking TSDs served 

as a source for design options and energy consumption analysis, in addition to other sources. For 

dehumidifiers, the previous rulemaking TSD was developed in support of an advance notice of 

proposed rulemaking (ANOPR) published in 2007. 72 FR 64432 (Nov. 15, 2007). For this 

rulemaking, DOE developed a preliminary TSD available May 22, 2014. 79 FR 29380. 

5.4.2 Manufacturer Interviews

DOE understands that there is variability among manufacturers in baseline units, design 

strategies, and cost structures. To better understand and explain these variances, DOE conducted 

manufacturer interviews. These confidential interviews provided a deeper understanding of the 

various combinations of technologies used to increase residential dehumidifier efficiency, and 

their associated manufacturing costs. DOE conducted interviews prior to the preliminary analysis 

stage of this rulemaking, and conducted an additional round of interviews in advance of this 

NOPR analysis. This allowed DOE an opportunity to receive confidential manufacturer feedback 

in response to the preliminary analysis. Sample questions from the NOPR phase interviews are 

contained in appendix 5A of this NOPR TSD. 

During the interviews, DOE also gathered information about the capital expenditures 

required to increase the efficiency of the baseline units to various efficiency levels (i.e., 

conversion capital expenditures by efficiency or energy-use level). The interviews provided 

information about the size and the nature of the capital investments. DOE also requested 

information about the depreciation method used to expense the conversion capital. The 
5-11 

preliminary manufacturer impact analysis in chapter 12 of the NOPR TSD includes a discussion 

of this information obtained during manufacturer interviews. 

5.4.3 Product Testing

Much of the analysis in this chapter incorporates data from publicly available sources 

such as the California Energy Commission (CEC), DOE Compliance Certification Management 

System (CCMS), and U.S. Environmental Protection Agency ENERGY STAR databases. 

However, DOE also conducted its own investigative testing for the following purposes: 

• Verify performance trends that are apparent in the publicly available data;

• Develop a better understanding of the design options and product features 

currently available on the market; 

• Investigate ducted performance for whole-home dehumidifiers; and

• Develop a better understanding of the operational characteristics of residential 

dehumidifiers. 

5.4.4 Product Teardowns 

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most 

accurate method for determining the production cost of a product is to disassemble representative

units piece-by-piece and estimate the material, labor, and overhead costs associated with each 

component using a process commonly called a physical teardown. A supplementary method, 

called a catalog teardown, uses published manufacturer catalogs and supplementary component 

data to estimate the major physical differences between a product that has been physically 

disassembled and another similar product. DOE performed physical teardown analysis on 

dehumidifiers in all product classes. The teardown methodology is explained in the following 

sections.

5.4.4.1 Selection of Units

DOE generally adopts the following criteria for selecting units for teardown analysis:

• The selected products should span the full range of efficiency levels for each product class 

under consideration; 

• 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.
5-12 

5.4.4.2 Generation of Bill of Materials

The end result of each teardown is a structured BOM, which describes each product part 

and its relationship to the other parts, in the estimated order of assembly. The BOMs describe 

each fabrication and assembly operation in detail, including the type of value—added equipment 

needed (e.g., stamping presses, injection molding machines, spot-welders, etc.) and the estimated

cycle times associated with each conversion step. The result is a thorough and explicit model of 

the production process. 

Materials in the BOM are divided between raw materials that require conversion steps to 

be made ready for assembly, while purchased parts are typically delivered ready for installation.

The classification into raw materials or purchased parts is based on DOE’s previous industry 

experience, recent information in trade publications, and discussions with original equipment 

manufacturers (OEMs). For purchased parts, the purchase price is based on volume-variable 

price quotations and detailed discussions with suppliers. 

For parts fabricated in-house, the prices of the underlying “raw” metals (e.g., tube, sheet 

metal) are estimated on the basis of 5-year averages to smooth out spikes in demand. Other 

“raw” materials such as plastic resins, insulation materials, etc. are estimated on a current-market 

basis. The costs of raw materials are based on manufacturer interviews, quotes from suppliers, 

secondary research, and by subscriptions to publications including the American Metals Marketb

(AMM). Past price quotes are indexed using applicable Bureau of Labor Statistics producer price 

index tables as well as AMM monthly data. 

5.4.4.3 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, component-focused 

technique for rigorously calculating the manufacturing cost of a product (direct materials, direct 

labor and some overhead costs). Figure 5.4.4.1shows the three major steps in generating the 

manufacturing cost.

Figure 5.4.4.1 Manufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment was the creation of a complete and 

structured BOM from the disassembly of the units selected for teardown. The units were 

dismantled, and each part was characterized according to weight, manufacturing processes used, 

dimensions, material, and quantity. The BOM incorporates all materials, components, and 

 

b For information on American Metals Market, please visit: www.amm.com. 
5-13 

fasteners with estimates of raw material costs and purchased part costs. Assumptions on the 

sourcing of parts and in-house fabrication were based on industry experience, information in 

trade publications, and discussions with manufacturers. Interviews and plant visits were 

conducted with manufacturers to ensure accuracy on methodology and pricing. 

Following the development of a detailed BOM, the major manufacturing processes were 

identified and developed for the spreadsheet model. Some of these processes are listed in Table 

5.4.4.1. 

Table 5.4.4.1 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 were estimated and entered 

into the BOM. Based on estimated assembly and fabrication time requirements, the labor content 

of each appliance could be estimated. For this analysis, DOE estimated labor costs based on 

typical annual wages and benefits of industry employees. 

Cycle requirements for fabrication steps were similarly aggregated by fabrication 

machine type while accounting for dedicated vs. non-dedicated machinery and/or change-over 

times (die swaps in a press, for example). Once the cost estimate for each teardown unit was 

finalized, a detailed summary was prepared for relevant components, subassemblies and 

processes. The BOM thus details all aspects of unit costs: material, labor, and overhead. 

Design options used in units subject to teardown are noted in the summary sheet of each 

cost model and are cost-estimated individually. Thus, various implementations of design options 

can be accommodated, ranging from assemblies that are entirely purchased to units that are made 

entirely from raw materials. Hybrid assemblies, consisting of purchased parts and parts made on 

site are thus also accommodated.

5.4.4.4 Cost Model and Definitions

 The cost model is based on production activities and divides factory costs into the 

following categories:

• Materials: Purchased parts (i.e., motors, valves, etc.), raw materials, (i.e., cold rolled 

steel, copper tube, etc.), and indirect materials that are used for processing and 

fabrication. 

• Labor: Fabrication, assembly, indirect, and supervisor labor. Fabrication and assembly 

labor cost are burdened with benefits and supervisory costs.
5-14 

• 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. 

5.4.4.5 Cost Model Assumptions

 As discussed in the previous section, assumptions about manufacturer practices and cost 

structure played an important role in estimating the final product cost. In converting physical 

information about the product into cost information, DOE reconstructed manufacturing processes 

for each component using internal expertise and knowledge of the methods used by the industry. 

Site visits allowed DOE to confirm its cost model assumptions through direct observation of the 

manufacturing plant, as well as through manufacturer interviews, reviews of current Bureau of 

Labor Statistics data, etc.

5.5 ANALYSIS AND RESULTS

5.5.1 Manufacturer Interviews

DOE conducted interviews with residential dehumidifier manufacturers to develop a 

better understanding of current product features and the technologies used to improve energy 
5-15 

efficiency. The manufacturers interviewed represent a wide range of U.S. market share and 

included both domestic and international companies that sell residential portable and wholehome dehumidifiers in the United States. During these interviews, DOE asked manufacturers 

questions about the following topics related to the engineering analysis: 

• Product classes

• Design features of current baseline products

• Proposed incremental efficiency levels

• Design options required to meet each efficiency level 

• Performance at reduced ambient temperatures

• Impacts on consumer utility 

• Installation and repair costs as a function of efficiency 

The discussion helped DOE understand what proposed design options have already been 

implemented and what additional design options DOE should consider. 

The discussion below represents a consolidation of the manufacturer responses.

5.5.1.1 Product Classes

 DOE asked manufacturers if the current product class divisions are appropriate. 

Manufacturers generally responded that the current product classes are too granular. 

Additionally, products with capacities near the end of the range of a product class are required to 

meet different minimum efficiency levels than similar units with slightly different capacities that 

would be classified in the adjacent product class. These products typically feature similar 

constructions and design features despite falling into separate product classes, so the units 

required to meet the higher minimum efficiency level may be penalized. Manufacturers noted 

that the product classes adjusted to the lower ambient temperature conditions result in smaller 

capacity ranges for each product class. This may force manufacturers to produce products only in 

the mid capacity for each product class due to verification concerns. 

5.5.1.2 Design Features of Current Baseline Products

DOE discussed with manufacturers the features of baseline products identified during the 

preliminary analysis. The manufacturers generally agreed with DOE’s assumptions for a baseline 

unit, discussed in section 5.5.3.1 although not all manufacturers produce products at the baseline 

efficiency level. Manufacturers indicated that baseline dehumidifiers typically include the same 

components as units at higher efficiencies, perhaps with smaller heat exchangers or less efficient 

compressors. 

5.5.1.3 Proposed Incremental Efficiency Levels

DOE asked manufacturers to comment on the efficiency levels DOE considered for the 

portable and whole-home product classes in the preliminary analysis. Manufacturers were asked 

to comment on the appropriateness of each incremental efficiency level, including the gap-fill 

levels and the max-tech levels. In general, manufacturers were not able to provide feedback on 

the proposed incremental efficiency levels due to lack of available data under the lower ambient 
5-16 

test conditions. Also, some manufacturers were concerned about meeting the maximum IEF 

levels with current materials and components. They suggested that the necessary changes are not 

completely understood due to the (at that time) proposed revisions to the test conditions. 

Manufacturers also indicated that lower-capacity products may not be able to achieve as high 

efficiencies as higher capacity products, particularly at the lower ambient test conditions.

5.5.1.4 Design Options Required to Meet Each Efficiency Level

DOE asked manufacturers to describe the changes associated with each active mode 

efficiency level relative to the baseline units in each product class. From the reverse-engineering 

analysis, DOE predicted the key design options would be increased compressor efficiency and 

heat exchanger sizes. DOE highlighted these options during discussions with manufacturers to 

determine the necessary design options to meet incrementally increased IEF levels. 

Similar to the preliminary analysis, manufacturers generally agreed with DOE’s initial 

association of design options with the efficiency levels. They confirmed that the primary changes 

required to meet higher efficiency levels will likely be more efficient compressors and optimized 

heat exchangers. Manufacturers stressed that making these changes would, for certain existing 

products, require a shift to larger cases and therefore would increase the manufacturing costs and 

also require investments to update the manufacturing facilities. Manufacturers indicated that fan 

motor improvements may result in small efficiency improvements, but at a high cost. These 

added costs would be passed down to the consumer, which could particularly impact low-income 

consumers. 

5.5.1.5 Performance at Reduced Ambient Temperatures

 Manufacturers agreed that the current test conditions specify a higher dry-bulb 

temperature than those typically seen in the field, and they generally supported the ambient drybulb temperature in of 65 °F as proposed in the May 2014 Test Procedure NOPR. Manufacturers 

further indicated that they test their units at a range of ambient temperatures, including lower 

than 65 °F to ensure products will operate acceptably in low-temperature consumer installations. 

However, manufacturers also typically indicated that a single ambient test condition is 

appropriate for the DOE test procedure to limit test burden. 

5.5.1.6 Impact on Consumer Utility

DOE asked manufacturers how these design option changes may impact consumer utility. 

Manufacturers indicated that overall performance would not be impacted; however, larger fans 

may increase the fan operating noise, and larger components and cases would increase the 

overall unit weight. Manufacturers also noted that the lower ambient test temperatures may result 

in more frost on the evaporator coil. However, DOE notes that the change in test conditions 

better reflects actual consumer use, so any adjustments to avoid frosting during the test 

procedure (such as operating the evaporator coil at a higher temperature) would likely limit 

frosting during operation in the field and may improve consumer utility. 
5-17 

5.5.1.7 Installation and Repair Costs as a Function of Efficiency

Manufacturers generally indicated that typical product lifetime was the same for all 

dehumidifiers regardless of efficiency and component size. They also stated that repair and 

maintenance costs would not significantly change with improvements in efficiency. 

5.5.2 Product Testing

Prior to the preliminary analysis, DOE conducted extensive testing on dehumidifiers in

each product class, including refrigerant-based and refrigerant-desiccant whole-home 

dehumidifiers. These units were tested under varying conditions and test setups to investigate 

how units perform under the lower ambient dry-bulb temperatures and the whole-home test 

setup. DOE used the data obtained during testing to identify appropriate test conditions and 

setups as discussed in the May 2014 Test Procedure NOPR. For the preliminary engineering 

analysis, DOE used the data to determine appropriate product classes, efficiency levels, and the 

design changes necessary to achieve those levels. 

For portable dehumidifiers, the test data used in support of the preliminary analysis 

remain applicable for this NOPR analysis. DOE notes that the test procedure revisions between 

the May 2014 Test Procedure NOPR proposal and appendix X1 would not substantively affect 

the capacities or efficiencies considered in the preliminary analysis. For whole-home 

dehumidifiers, DOE revised the test conditions proposed in the May 2014 Test Procedure NOPR 

to a higher inlet air dry-bulb temperature (73 °F rather than 65 °F) and lower external static 

pressure (0.20 inches of water column rather than 0.5 inches of water column). To consider the 

effects of these changes on whole-home dehumidifier performance, DOE conducted additional 

testing on 5 whole-home units. 

5.5.2.1 Product Selection

 For the portable dehumidifiers, DOE tested a total of 24 portable units with rated 

capacities up to 75 pints/day (as measured according to appendix X), 13 large-capacity (i.e., 

portable dehumidifiers with rated capacities greater than 75 pints/day, as measured according to 

appendix X) and refrigerant-based whole-home units, and 2 refrigerant-desiccant whole-home 

dehumidifiers. The test units spanned the range of capacities and efficiencies available on the 

market from multiple manufacturers. The test results informed both the proposals presented in 

the May 2014 Test Procedure NOPR and in the preliminary engineering analysis. 

 For the additional whole-home dehumidifier testing conducted in support of this NOPR 

engineering analysis, DOE selected 5 units covering a range of capacities, efficiencies,

configurations, and manufacturers. One of the 5 selected units was a refrigerant-desiccant 

dehumidifier. 

5.5.2.2 Test Approach and Results

A detailed description of the test approach and results for the testing conducted in support 

of the preliminary analysis is included in chapter 5 of the preliminary TSD and in the May 2014 

Test Procedure NOPR. Figure 5.5.2.1 presents a summary of the portable dehumidifier test 

results from this testing at the appendix X ambient dry-bulb temperature (80 °F) and the 
5-18 

appendix X1 ambient dry-bulb temperature (65 °F). The results are presented in terms of EF 

rather than IEF in order to isolate the effects of ambient temperature on dehumidification mode 

energy use.

Figure 5.5.2.1 Measured EF at 80 °F and 65 °F for Portable Dehumidifiers with Capacities 

Up to 75 pints/day (under Appendix X)

 As discussed in section 5.2, one reason for maintaining separate portable dehumidifier 

product classes for products with capacities less than 45 pints/day is that the maximum 

efficiencies observed for products with capacities of 30 pints/day or less are lower than those for 

products with capacities between 30 and 45 pints/day. Figure 5.5.2.1 shows that portable 

dehumidifiers are generally able to reach the same maximum efficiencies when tested under the 

appendix X conditions, but the lower ambient temperature in appendix X1 results in a greater 

decrease in efficiency for lower capacity units when tested under the new test procedure. 

Figure 5.5.2.2 shows the test results from the preliminary analysis and May 2014 Test 

Procedure NOPR testing for whole-home dehumidifiers. The units were tested according to the 

ducted test setup proposed in the May 2014 Test Procedure NOPR with inlet air dry-bulb 

temperatures of 80 °F and 65 °F and external static pressure of 0.5 inches of water column. As 

observed with the portable units, both capacity and EF are reduced at lower ambient 

temperatures.

 

0.75

0.95

1.15

1.35

1.55

1.75

1.95

2.15

0 20 40 60 80

EF (L/kWh)

Product Capacity (pints/day)

80 F

65 F
5-19 

Figure 5.5.2.2 Measured EF at 80 °F and 65 °F for Whole-Home Dehumidifiers

 As discussed in section 5.3, appendix X1 requires the whole-home units to be tested at an 

inlet air dry-bulb temperature of 73 °F and external static pressure of 0.20 inches of water 

column. DOE interpolated the preliminary analysis test results to these conditions to estimate 

performance at the conditions included in appendix X1. Table 5.5.2.1 presents the estimated 

performance at 73 °F, in addition to the measured performance at 80 °F and 65 °F dry-bulb 

temperature and 0.5 inches of water column, adjusted to reflect an external static pressure of 0.2 

inches of water column. As expected, the estimated performance data fall between the test results 

from the two tested conditions. 

0.5

1

1.5

2

2.5

3

3.5

0 50 100 150 200 250

EF (L/kWh)

Product Capacity (pints/day)

80 F

65 F
5-20 

Table 5.5.2.1 Estimated Performance at Appendix X1 Conditions 

Test 

Sample

Capacity (pints/day) EF (L/kWh)

80 °F

(Measured)

73 °F

(Estimated)

65 °F

(Measured)

80 °F

(Measured)

73 °F

(Estimated)

65 °F

(Measured)

1 100.90 80.76 53.61 2.74 2.53 2.04

2 146.10 116.95 77.68 1.74 1.57 1.19

3 120.87 108.19 91.90 2.73 2.61 2.36

4 204.28 167.25 117.62 2.75 2.46 1.82

5 144.02 133.09 119.60 3.13 3.10 3.05

6 101.07 80.69 53.21 2.74 2.53 2.04

7 85.50 73.66 58.07 2.13 2.00 1.72

8 62.62 53.85 42.30 1.84 1.77 1.60

9 96.25 80.06 58.43 2.17 1.98 1.55

10 146.18 117.19 78.15 1.74 1.57 1.19

 DOE also conducted additional testing on 5 whole-home units in accordance with the 

proposals in the February 2015 Test Procedure SNOPR, the most recent test procedure at the 

time of testing. These 5 units were selected to represent the entire market, spanning the 

capacities and efficiencies available in the market and varying configurations. Of the 5 test units, 

3 were tested previously at both 80 °F and 65 °F dry-bulb temperature and 0.5 inches of water 

column, allowing a comparison of the numerically estimated results and measured data. As 

mentioned above, DOE notes that the units were tested at 0.25 inches of water column in 

accordance with the proposals in the February 2015 Test Procedure SNOPR instead of the 0.20 

inches of water column established in appendix X1. DOE subsequently made numerical 

adjustments based on previous test data at various external static pressures to present data at the 

appendix X1 conditions. These results are shown below in Figure 5.5.2.3. DOE notes that the 

difference in EF between the tested and estimated performance at the appendix X1 conditions 

ranged from 3.1 percent to 8.2 percent, with an average agreement between the average and 

estimated performance within 6 percent. In lieu of a larger set of available performance data at 

the appendix X1 conditions, DOE proposes to proceed with the estimated performance data. 
5-21 

Figure 5.5.2.3 Tested and Estimated Performance at Appendix X1 Conditions

DOE used test data when conducting teardowns and modeling to correlate efficiency 

gains with certain design features or components included in the units in the test sample. In 

particular, the testing informed the appropriate capacity and efficiency effects of the design 

features at the lower ambient temperatures because there is limited publicly available 

information regarding dehumidifier operation at these temperatures.

5.5.3 Product Teardowns 

After conducting the investigative testing for the preliminary analysis described in the 

previous section, DOE conducted teardowns on 32 out of its 39 test units, including both 

portable and whole-home dehumidifiers. The test units spanned the range of product efficiencies 

and features available on the market from multiple manufacturers. DOE relied on the 

dehumidifier teardowns to supplement the information gained through manufacturer interviews 

and to investigate performance observed during testing. Specifically, the teardowns allowed 

DOE to identify design features for improving efficiency and to develop corresponding 

manufacturing costs for products at different efficiency levels. 

 Because DOE’s teardown sample in support of the preliminary analysis included a large 

number of units that spanned a full range of capacities and efficiencies, DOE did not conduct 

additional teardowns in support of the NOPR engineering analysis. Rather, DOE used the 

teardown information gathered during the preliminary analysis to determine new manufacturing 

cost information corresponding to the revisions in proposed product classes and efficiency levels, 

as described earlier in this chapter.

5.5.3.1 Baseline Construction

 Baseline portable dehumidifier construction for products with capacities up to 45.00 

pints/day remains unchanged from the preliminary analysis. In the preliminary analysis, DOE 

observed that all of the units were housed in a plastic case with a removable bucket. They 

featured rotary R-410A compressors at the base of the unit, with the evaporator and condenser 
5-22 

housed in the top of the unit along with the fan and air filter. DOE observed that the blowers all 

used permanent split capacitor (PSC) motors. The evaporator and condenser within a unit were 

similar in construction, with similar dimensions and number of tube passes, and they were 

connected via capillary tubes. The copper tubing exiting the evaporator and entering the 

compressor was typically insulated, though the thickness and length of insulation varied among 

units. All of the units in DOE’s sample for these portable dehumidifiers with capacities up to 

45.00 pints/day featured electronic controls, although DOE is aware that certain units may still 

use electromechanical controls. 

Similarly, the baseline units for higher-capacity portable dehumidifiers (i.e., those with 

capacities greater than 45.00 pints/day and both whole-home dehumidifier product classes 

remain unchanged from the preliminary analysis. Compared to the lower-capacity portable units, 

DOE observed that the baseline units for these product classes shifted to a different design, but 

still contained most of the same general components. As expected for these product classes, the 

internal components related to the product capacity were larger than for the lower-capacity 

portable product classes. In particular, these units used higher-capacity compressors, larger heat 

exchangers, and more powerful blowers. One key difference when compared to the other 

portable product classes is the elimination of the internal condensate collection bucket. All of the 

high-capacity portable units and whole-home units in DOE’s teardown sample were designed to 

be connected to a drain. Also, the units in these product classes featured metal cases with more 

insulation than the plastic cases for the lower-capacity portable dehumidifier product classes.

DOE observed that manufacturers that produce high-capacity portable units also typically 

produce whole-home dehumidifiers. For products from the same manufacturer in the same 

capacity range, DOE observed that the high-capacity portable and whole-home products 

contained almost identical internal components. The only major differences between these 

products were the ducting attachments for whole-home units (and lack of ducting attachments for 

portable units) and the differences in controls. Whole-home products are typically controlled via 

a remote humidistat with no on-board user controls, while the portable products are controlled 

through the user interface on the unit. High-capacity portable products typically used 

electromechanical controls, unlike lower-capacity portables units, most likely due to simplicity, 

high reliability, and minimal expected consumer interaction. 

5.5.3.2 Design Options to Reach Higher Efficiency Levels

In chapter 3 of this NOPR TSD, DOE identified technology options that could potentially 

improve the efficiency of dehumidifiers. As discussed in chapter 4 of this NOPR TSD, a number 

of these technology options were eliminated from further consideration in the engineering 

analysis. DOE considered the technology options meeting all of the screening criteria in this 

engineering analysis, although not all of these design options factored into the final estimated 

incremental costs. This section explains how DOE addressed each of the technology options that 

were retained from the screening analysis. 

Retained Design Options to Meet Higher Efficiency Levels

DOE maintained the findings of the preliminary engineering analysis when estimating 

typical dehumidifier construction at higher efficiency levels. In the preliminary analysis, DOE 

identified the following key design changes to reach higher efficiency levels: improved 
5-23 

compressor efficiency, improved evaporator and condenser performance (i.e., larger heat 

exchangers), and pre-cooling air-to-air heat exchangers. After considering feedback from 

interested parties on the preliminary analysis, DOE also incorporated improved fan-motor 

efficiency in this NOPR engineering analysis.

Improved compressor efficiency 

 Improved compressor efficiency is one of the primary means for dehumidifiers to achieve 

higher efficiencies. The compressor typically represents 80 to 90 percent of a dehumidifier’s 

energy consumption, so small improvements in compressor efficiency translate directly to 

measurable improvements in IEF. DOE is aware that the rotary R-410A compressors widely 

used in dehumidifiers are available with a range of efficiencies, and manufacturers will likely 

move to a more efficient compressor of this type to achieve higher IEFs.

In chapter 3 of this NOPR TSD, DOE also identified inertia-reciprocating, scroll, and 

variable-speed compressors to further increase efficiency beyond that of traditional rotary 

compressors. DOE notes that inertia-reciprocating and scroll compressors are substantially 

larger, heavier, and noisier than rotary compressors, and therefore are not suited for residential 

dehumidifiers. Variable-speed compressors present an opportunity for a dehumidifier to adjust 

the compressor and refrigeration system operation to varying conditions. However, because the 

DOE test procedure is conducted at constant ambient conditions with continuous operation, it 

would not capture efficiency gains associated with variable-speed compressors. Therefore, DOE 

did not consider inertia-reciprocating, scroll, or variable-speed compressors in the remainder of 

its analysis.

Improved condenser and evaporator performance 

 DOE also observed during teardowns and received feedback during manufacturer 

interviews that manufacturers would likely use larger heat exchangers at the higher efficiency 

levels. Compressor efficiencies can only improve a dehumidifier’s performance to a certain 

extent for a given refrigeration system. Larger heat exchangers can help to further improve the 

performance of the refrigeration system by more effectively converting the work performed by 

the compressor into heat transfer. As more heat is absorbed in the evaporator and rejected via the 

condenser, the refrigerant cycle operates more efficiently and less compressor power is required 

to achieve a similar capacity.

 As discussed in chapter 3 of this NOPR TSD, many parameters affect a heat exchanger’s 

performance. Based on manufacturer interviews and product teardowns, DOE found that 

manufacturers likely adjust the cross-sectional area of the heat exchangers to increase heat 

transfer. DOE observed that the condenser and evaporator in all of its teardown units had similar 

construction, but the cross-sectional area was the key parameter that changed from unit to unit. 

DOE verified through modeling that increasing heat exchanger cross-sectional area resulted in 

more significant efficiency improvements compared to other possible heat exchanger changes, 

such as increasing heat exchanger depth or fin density. In its analysis, DOE assumed that 

manufacturers would rely on increased heat exchanger cross-sectional areas to improve 

condenser and evaporator performance.

Improved fan and fan-motor efficiency 
5-24 

 DOE is aware that efficiency gains may be possible through improvements to the fan and 

fan motor. DOE noted that all units in its teardown sample used PSC fan motors, which have 

improved efficiencies compared to shaded-pole motors. However, permanent-magnet motors 

would provide even higher motor efficiencies compared to PSC motors, but the overall 

improvements to IEF would be small due to the relatively small portion of energy consumed by 

the fan motor compared to the compressor. Manufacturers would also incur significant costs if 

employing permanent-magnet motors; the motors themselves cost approximately two times as 

much as a comparable PSC motor, and the different shape and weight of the motor may require 

product redesign. 

 Manufacturer interviews and product teardowns also showed that there were no 

significant changes to the blowers and fan motors at different product efficiencies. For these 

reasons, DOE did not further consider improved fan and fan-motor efficiency in developing the 

cost-efficiency relationships in the preliminary engineering analysis. However, after considering 

comments from interested parties, DOE determined that the small potential efficiency gains 

associated with improved fan motors should be incorporated into this NOPR analysis due to 

technological feasibility, as discussed in section 5.3.2. 

Pre-cooling air-to-air heat exchanger 

Pre-cooling air-to-air heat exchangers further increase the efficiency of whole-home and 

high-capacity portable dehumidifiers. By cooling the inlet air stream using the low-temperature 

air exiting the evaporator, the air-to-air heat exchanger decreases the amount of sensible heat the 

evaporator must remove from the air stream before condensation occurs. Because the heat 

exchanger uses evaporator outlet air to cool the inlet airstream, the air entering the condenser is 

at a higher temperature. The result is a slightly higher temperature exhaust air off of the 

condenser, but overall improved dehumidifier energy efficiency. 

 DOE considered pre-cooling air-to-air heat exchangers as a design option for the wholehome and high-capacity portable dehumidifier product classes in both the preliminary analysis 

and this NOPR analysis, but screened out this technology option for the lower-capacity portable

dehumidifier product classes for the reasons discussed in chapter 4 of this NOPR TSD.

Design Options Not Used to Meet Higher Efficiency Levels 

Several of the technology options identified in chapter 3 of this NOPR TSD may produce 

energy savings in certain real-world situations, but DOE did not further consider them in this 

analysis because the dehumidifier test procedure would not capture the potential improvements 

and DOE does not expect manufacturers to rely on these features to meet higher efficiency 

levels. Accordingly, DOE did not specifically consider improvements associated with these 

design options when determining the manufacturer production costs at each efficiency level.

Built-in hygrometer/humidistat 

 All portable dehumidifiers in DOE’s teardown sample featured a built-in humidity 

controller. These units all included electronic controls, but DOE is aware that certain units may 

still feature electromechanical humidistats. The humidistat cycles the compressor and fan power 

supply as a function of relative humidity. Both electronic and electromechanical controllers 

measure the expansion of a reference material as a function of relative humidity. DOE notes that 
5-25 

whole-home dehumidifiers are often designed to be used in conjunction with a remote 

humidistat. The humidistat is placed in the portion of the home that requires dehumidification, 

and cycles the whole-home dehumidifier compressor and fan power supply as needed. Although 

this control is external to the unit, DOE does not expect significant differences in efficiency or 

operation when compared to the built-in controls on portable units. 

While electronic humidistats may provide more flexible control in cycling the

refrigeration system as needed in varying conditions, DOE does not expect the type or presence 

of a hygrometer or humidistat to affect dehumidifier efficiency. The DOE test procedure requires 

continuous unit operation at constant ambient conditions, and therefore does not reflect 

performance of the humidity sensor. Because DOE does not expect the type of humidity 

controller to result in efficiency gains, and because the test procedure would not capture any 

efficiency improvements, DOE did not further consider changes to the hygrometer or humidistat 

in this analysis.

Improved controls 

Similar to the built-in hygrometer/humidistat discussed above, improved controls may 

allow dehumidifiers to better adjust their operation in response to changing conditions. Improved 

controllers may consider multiple inputs, such as ambient temperature, ambient humidity, and 

evaporator temperature, when adjusting unit operation. Because the DOE test procedure requires 

continuous unit operation at constant ambient conditions, it therefore would not reflect improved 

control schemes, and DOE did not further consider improved controls in this analysis. 

Improved defrost methods 

In chapter 3 of this NOPR TSD, DOE identified four defrost types: passive, fan-only, 

electric, and hot-gas. DOE is not aware of any units on the market that feature an electric or hotgas defrost, although there are no technical limitations that would preclude these in 

dehumidifiers. DOE also did not observe passive defrost in its testing. DOE observed that units 

typically use a fan-only defrost when necessary. 

DOE observed that some units in its test sample entered a defrost operation when tested 

in dehumidification mode at the 65 °F ambient dry-bulb temperature specified in appendix X1. 

While improved defrost methods could improve energy consumption in these units, DOE expects 

that manufacturers would likely adjust the unit controls or refrigeration system operation to 

avoid triggering defrost rather than improving performance during defrost. DOE did not observe 

different design features between the units that did or did not defrost at the reduced ambient 

temperature, so the difference in operation is likely due to different control schemes or 

refrigeration system operating parameters. Because manufacturers would likely adjust their units 

to avoid defrosts when operating at the appendix X1 test conditions, DOE did not further 

consider different defrost methods in determining how manufacturers may achieve higher 

efficiency levels. 

Improved demand-defrost controls 

Defrost controls determine if and when a defrost operation is needed and the duration of 

defrost. As described in chapter 3 of this NOPR TSD, time-based defrosts occur at regular 
5-26 

intervals for constant duration as a unit operates. Sensor-controlled defrosts occur as needed 

based on evaporator coil temperatures. DOE observed that all units in the teardown sample 

featured temperature sensors on the evaporator coil, and therefore likely employ a sensorcontrolled defrost. DOE also observed in its investigative testing that units did not enter defrost 

when tested at 80 °F ambient dry-bulb temperature, some units defrosted at 65 °F, and nearly all 

units defrosted at 55 °F. This also suggests sensor-based defrosts. 

Because DOE expects that units available on the market already feature sensor-based 

defrost control, and the test procedure likely would not capture defrost operation as described in 

the section above, DOE did not further consider this design option in this analysis. 

Improved flow-control devices 

DOE observed that all units in the teardown sample used a wound capillary tube as the 

expansion device to control refrigerant flow to the evaporator, though the length and size of 

tubing varied. In chapter 3 of this NOPR TSD, DOE describes how thermostatic expansion 

valves or electronic expansion valves would allow dehumidifiers to regulate refrigerant flow to 

the evaporator based on changes in operating conditions. 

 As discussed for previous design options, the DOE test procedure is performed under 

constant ambient conditions, and any benefit associated with a unit’s ability to adjust to varying 

ambient conditions would not be captured in the test. Therefore, DOE did not further consider 

improved flow-control devices as a design option in this analysis. 

Low-standby-loss electronic controls 

In the preliminary analysis, DOE observed that the presence of a fan-only mode had the 

most significant impact on the conversion from EF to IEF based on the calculations to 

incorporate low-power mode energy consumption as first proposed in the May 2014 Test 

Procedure NOPR. For units without a fan-only mode, DOE observed that low-power mode 

energy consumption resulted in an average decrease from EF to IEF of 0.02 L/kWh. For all units 

in the test sample, the average inactive mode or off mode power consumption was 0.9 Watts 

(W). 

 DOE observed two types of power supplies in its teardown sample: linear and switchmode. Switch-mode power supplies typically require lower standby power compared to linear 

power supplies. In the test sample, DOE found an average off-mode power of 0.4 W for switchmode power supplies compared to 1.2 W for linear power supplies. In off-cycle mode, for units 

without fan-only mode, units with switch-mode power supplies required on average 1.14 W 

compared to 1.63 W for units with linear power supplies. DOE notes that based on these average 

power inputs, the effect of changing from a linear to a switch-mode power supply has very little 

impact on IEF. For example, a baseline 50 pint/day unit with an EF of 1.6, at 80 °F ambient 

temperature, would have an IEF that rounded to 1.59 with either a linear or switch-mode power 

supply. 

 DOE also observed a range of power inputs for each power supply type. While switchmode power supplies required less power on average than linear power supplies, DOE observed 
5-27 

linear power supplies with off-mode and off-cycle mode power inputs as low as 0.51 W and 0.72 

W, respectively. 

Because of the relatively small impact of standby mode and off mode energy 

consumption on IEF, and the range of power inputs associated with each power supply, DOE did 

not consider this a specific design option in its preliminary analysis. However, because DOE 

relied on a hybrid analysis approach, including the efficiency-level approach, DOE’s cost 

estimates reflect both power supply types based on the units torn down at each efficiency level 

and product class. DOE did not use low-standby controls as a design option in the design-option 

approach portion of the preliminary analysis because of the relatively large jumps in efficiency 

between the analyzed IEF levels. DOE concluded that manufacturers would rely on other 

changes, such as improved compressor efficiency and heat exchanger optimization, to achieve 

the higher efficiencies. For this NOPR analysis, DOE maintained this approach when 

considering low-standby-loss electronic controls as a design option. 

Washable air filters 

 DOE found that all portable dehumidifiers in its test and teardown sample included 

washable air filters. Manufacturer instructions suggest that the consumer periodically clean the 

filter to minimize particulate build up and ensure optimal performance. However, because units 

typically already incorporate a washable air filter, DOE did not consider this as a design option 

to improve efficiency in this analysis. 

Heat pipes 

 As discussed in chapter 4 of this NOPR TSD, DOE screened out heat pipes as a design 

option for portable dehumidifiers with capacity up to and including 45.00 pints/day. The size and 

weight of added heat pipes would have less of an impact on consumer utility for portable units 

with capacity more than 45.00 pints/day and for whole-home dehumidifiers. 

 DOE was unable to find any residential dehumidifiers that use heat pipes to boost 

performance by pre-cooling the inlet evaporator air. DOE is aware that units incorporating heat 

pipes existed on the market until 2010, at which point the manufacturer chose to discontinue the 

product line for reasons unknown to DOE. Although heat pipes have been incorporated in the 

past, DOE does not expect manufacturers to use this design option to achieve higher efficiencies 

because pre-cooling air-to-air heat exchangers accomplish the same function of pre-cooling the 

air entering the evaporator, likely with similar efficiency improvements. However, pre-cooling 

air-to-air heat exchangers lower the temperature of the incoming air using a simpler design. They 

do not require a heat transfer liquid and can be made of less costly materials. Accordingly, the 

air-to-air heat exchangers are likely lower cost than equivalent heat pipes that accomplish the 

same function. Therefore, DOE did not further consider heat pipes in its analysis because it 

tentatively concludes that manufacturers of whole-home and higher-capacity portable 

dehumidifiers would likely use pre-cooling air-to-air heat exchangers in their place. 

Improved refrigeration system insulation 

 Through teardowns, DOE observed that manufacturers typically include insulation on the 

refrigeration system. The most commonly insulated parts of the system are the evaporator outlet 
5-28 

and compressor inlet. However, DOE did not observe that the products at higher efficiency levels 

include different insulation types or quantities. Further, manufacturers did not indicate that 

higher efficiencies could be achieved with more insulation, although some manufacturers 

commented that insulating the product’s case typically helps the unit’s noise performance. 

Because DOE did not observe a relationship between product efficiency and refrigeration system 

insulation, it did not consider this as a design option when developing cost-efficiency curves in 

this NOPR analysis. However, because DOE used a hybrid approach for this analysis, the 

estimated costs for each product torn down reflect the type and quantity of insulation included in 

those products. 

Refrigerant-desiccant systems 

DOE is aware that refrigerant-desiccant systems may offer improved performance, 

particularly in low-temperature installations where defrosting may be a concern for refrigerantonly dehumidifiers. DOE investigated the potential improvements in efficiency and capacity 

from incorporating a desiccant wheel in a whole-home dehumidifier. Based on testing, DOE did 

not observe any efficiency or capacity improvement with incorporating a desiccant wheel. 

However, DOE notes that testing under appendix X1 is conducted at an ambient temperature 

where defrosting is likely not a concern, so it may not reflect the improvements associated with a 

refrigerant-desiccant system at low temperatures. Because DOE did not observe higher 

efficiencies for refrigerant-desiccant units under representative ambient conditions, it did not 

further consider refrigerant-desiccant systems as a design option in this analysis. 

5.5.4 Numerical Model

 Although DOE tested and tore down a large sample of units from different manufacturers 

at varying capacities and efficiencies in support of the preliminary analysis, the sample did not 

cover the entire range of capacities and efficiency levels that DOE analyzed for potential 

standards. To fill in gaps in the teardown sample, DOE used a numerical model to estimate unit 

performance at different efficiency levels and capacities. DOE continued to use the modeled 

performance results in support of this NOPR analysis. 

5.5.4.1 Summary

 To conduct the energy modeling, DOE used the MarkN program developed for use in the 

most recent room air conditioner energy conservation standards direct final rule. 76 FR 22454 

(Apr. 21, 2011). The MarkN program was itself an update of an adaptation to the Oak Ridge 

National Laboratory Mark III Heat Pump program for modeling room air conditioner cooling 

performance. This program was originally used in support of the 1997 room air conditioner 

standards final rule. 62 FR 50122 (Sep. 24, 1997). The 1997 final rule and 2011 TSDc

 provide 

further details regarding the use of these programs in support of the room air conditioner 

rulemakings. 

 

c The TSD in support of the 2011 room air conditioners final rule can be found at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0010-0053
5-29 

DOE modified the room air conditioner numerical model for the dehumidifier analysis 

because both product types use similar technologies—an evaporator to absorb heat from an air 

stream, a compressor, a condenser to eject heat to an air stream, and an expansion device 

between the two heat exchangers. The only major difference in the operation of the two product 

types is the airflow through the unit. Room air conditioners have two separate air streams 

through the unit, one for the indoor air to be cooled over the evaporator and the other for the 

external air that absorbs the heat rejected by the condenser. Dehumidifiers pass the same air 

stream over the evaporator and condenser, first cooling the air on the evaporator to condense 

water, then re-heating the air over the condenser before exhausting the drier air back to the room 

or return air stream.

 To account for this difference in operation, DOE adjusted the MarkN model to set the 

condenser inlet air conditions (typically the outdoor air conditions for room air conditioners) 

equal to the evaporator outlet air conditions (typically the indoor return air conditions for room 

air conditioners). Through an iterative process, the revised program adjusts the condenser inputs 

for the MarkN program until they are within a tolerance of the evaporator outlet conditions, 

effectively modeling one air stream through both heat exchangers.

5.5.4.2 Analysis Method 

In the preliminary analysis, DOE first used the numerical model to simulate unit 

performance for the models in the test and teardown sample. The model inputs include product 

component specifications and the ambient operating conditions. DOE obtained component 

information from the product teardowns, including heat exchanger construction (number of rows, 

tube passes, tube spacing, fin spacing, etc.), refrigerant tubing (lengths and diameters of 

inlet/outlet tubes and capillary tubes), and compressor specifications. 

 The model requires detailed compressor operating information, including the mapping 

coefficients for refrigerant mass flow and power input at varying evaporator and condenser 

operating temperatures. DOE lacked this information for most units in the teardown and test 

sample. However, DOE used the data from testing to calibrate the model for each individual unit 

using a base case of compressor mapping inputs. After calibrating the model, DOE achieved 

close agreement between the model outputs and test data at both 80 °F and 65 °F ambient drybulb temperatures. 

 DOE then used the model to determine the design changes necessary to reach different 

efficiency levels or capacity ranges not covered by the models in the teardown sample. For most 

portable dehumidifiers, DOE observed that manufacturers typically use two methods to increase 

product efficiency: (1) improving the compressor efficiency, and (2) increasing the heat 

exchanger cross-sectional areas. Based on inputs from manufacturer interviews, DOE is aware 

that improving compressor efficiency is likely the first option to improve a unit’s efficiency 

before increasing the heat exchanger sizes. Increasing heat exchanger area could also require a 

change to a larger case size, which requires a higher material cost per unit, significant conversion 

costs to update the manufacturing facilities, and can increase shipping costs if fewer units can be 

transported in shipping containers. So, DOE assumed that manufacturers would first maximize 

the compressor efficiency within a unit to achieve higher efficiencies.
5-30 

To determine appropriate ranges of compressor efficiencies, DOE surveyed available 

efficiencies for rotary R-410A compressors over the range of typical compressor capacities

observed in dehumidifiers. As shown in Figure 5.5.4.1, compressors are available within this 

capacity range at rated energy efficiency ratios (EERs) ranging from roughly 8.5 to 10.5 British 

thermal units per hour per W (Btu/h/W). DOE notes that this range represents a limited number 

of compressors based on a survey of the market, not necessarily those observed during 

teardowns. DOE lacked rated information on the compressors found during teardowns, but 

expects the range presented in Figure 5.5.4.1 to be representative of compressors available to 

dehumidifier manufacturers. 

Figure 5.5.4.1 R-410A Compressor Performance Characteristics

In cases for which increasing compressor EER to 10.5 Btu/h/W did not result in a 

sufficient increase in IEF to reach a certain efficiency level, DOE additionally increased the 

condenser and evaporator cross-sectional areas in the model inputs. Improved compressor 

efficiencies and increased heat exchanger areas allowed DOE to analyze the design changes 

needed to meet each efficiency level for the lower-capacity portable and smaller-volume wholehome dehumidifier product classes. At higher efficiency levels, the highest-capacity portable and 

large-volume whole-home dehumidifier product classes incorporate inlet air-to-air heat 

exchangers. The numerical model did not incorporate a feature to simulate an inlet air-to-air heat 

exchanger, so DOE relied on unit teardowns for the analysis at these higher efficiency levels. 

For this NOPR analysis, DOE incorporated further efficiency improvements to the 

modeled results from the preliminary analysis. As discussed in section 5.5.3.2, DOE included 

improved fan-motor efficiency at the max-tech efficiency level. To implement this design 

change, DOE decreased the fan power while keeping all other dehumidifier operation constant. 

DOE estimated that the permanent-magnet motors had an 80-percent efficiency compared to 60-

percent efficiency for PSC motors.

7.5

8

8.5

9

9.5

10

10.5

11

0 5000 10000 15000 20000

Rated Compressor EER (Btu/h/W)

Rated Compressor Capacity (Btu/h)
5-31 

5.5.5 Cost Estimates

 For the models in the preliminary analysis teardown sample, DOE developed 

manufacturer cost estimates based on the method outlined in section 5.4.4. As discussed above, 

DOE did not tear down units from each manufacturer at each capacity and efficiency level. 

Instead, DOE relied on the numerical model to determine the design changes needed for a model 

in the teardown sample to reach a given efficiency or capacity level. DOE estimated that if a 

switch to a more efficient compressor was the only necessary design change, there would be no 

additional changes to the rest of the product. However, for units requiring larger heat exchangers, 

DOE scaled the size of the case and other related components to accommodate the larger coils. 

In developing its cost estimates, DOE lacked detailed compressor pricing information. 

The compressor is often the most costly component in a dehumidifier, typically representing 20 

to 30 percent of the product’s material cost. DOE requested compressor pricing during 

manufacturer interviews, but did not receive enough detailed information to develop cost 

estimates for all of the compressors found during teardowns or specified through the numerical 

model. 

DOE did, however, receive compressor pricing information from manufacturers during 

the 2011 energy conservation standards rulemaking for room air conditioners. Dehumidifiers and 

certain room air conditioners both use similar rotary compressors for R-410A refrigerant. Figure 

5.5.5.1 presents the compressor cost information gathered during the room air conditioner 

rulemaking, and presented in chapter 5 of that direct final rule TSD.

Figure 5.5.5.1 R-410A Compressor Cost from Room Air Conditioner Energy Conservation 

Standards Direct Final Rule Analysis

0

20

40

60

80

100

120

0 5000 10000 15000 20000 25000 30000

Compressor Cost ($)

Capacity (Btu/h)
5-32 

 DOE observed that certain dehumidifiers use compressors with capacities less than 5,000 

Btu/h, which are not represented by the curve. To model the costs of these lower compressor 

capacities, therefore, DOE extrapolated the curve using a linear relationship between $0 and 0 

Btu/h capacity and the endpoint of the curve at 5,000 Btu/h capacity. 

DOE used this same approach for developing cost estimates in the NOPR engineering 

analysis; however DOE updated its analysis to reflect costs in 2013 dollars and to account for the 

changes to product classes and efficiency levels as discussed earlier in this chapter.

5.5.5.1 Cost Estimates

 As discussed in the previous sections, DOE either tore down or developed models for 

units at each efficiency level for the different product classes. DOE developed manufacturing 

cost estimates for each product, and determined the incremental manufacturing cost needed to 

meet each efficiency level when compared to the baseline for a given product class. Table 5.5.5.1

presents the incremental manufacturing costs developed for the preliminary analysis. 

Table 5.5.5.1 Preliminary Analysis Manufacturer Production Costs (2012$) 

Portable Product Class Capacity

(pints/day)

Whole-Home Product 

Class Case Volume (ft3)

Efficiency Level ≤ 20.00 20.01–

30.00

30.01–

35.00

35.01–

45.00 > 45.00 ≤ 8.0 > 8.0

EL1 $- $- $- $- $38.40 $15.22 $6.14

EL2 $1.56 $1.85 $2.94 $1.98 $49.16 $76.18 $37.05

EL3 $4.64 $3.78 $8.72 $7.56 $100.13 N/A $112.01

EL4 $7.77 $10.82 $13.40 $11.24 N/A N/A N/A

 Based on the updates to the engineering analysis made for this NOPR, DOE developed 

revised incremental manufacturing costs, as presented in Table 5.5.5.2. 

Table 5.5.5.2 NOPR Analysis Manufacturer Production Costs (2013$) 

Portable Product Class Capacity

(pints/day)

Whole-Home Product Class Case 

Volume (ft3)

Efficiency Level ≤ 30.00 30.01–45.00 > 45.00 ≤ 8.0 > 8.0

EL1 $- $- $42.81 $15.30 $6.20

EL2 $1.69 $2.39 $53.66 $129.22 $37.20

EL3 $4.27 $8.07 $120.33 N/A $161.39

EL4 $19.38 $22.42 N/A N/A N/A

 DOE notes that the portable dehumidifier product classes with capacities up to and 

including 45.00 pints/day have zero incremental cost for reaching the first efficiency level. As 

discussed in section 5.3.1, the baseline IEF’s for these product classes assume the presence of fan

operation in off-cycle mode. DOE did not observe any unique design features associated with the 

presence or absence of fan operation during off-cycle mode, and expects the only cost associated 
5-33 

with eliminating fan operation to be the cost of reprogramming the unit controls. Because this is 

only a design cost, DOE does not expect any additional production cost for this change. 

Accordingly, DOE estimated zero incremental cost to reach EL1 for these product classes.
6-i 

CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 MANUFACTURER MARKUP ...................................................................................... 6-1

6.3 DISTRIBUTION CHANNELS ....................................................................................... 6-2

6.3.1 Calculating Markups ............................................................................................ 6-3

6.4 MARKUPS FOR PORTABLE DEHUMIDIFIERS ........................................................ 6-3

6.4.1 Methodology for Retailer Markups ..................................................................... 6-3

6.4.2 Derivation of Retailer Markup for Portable Dehumidifiers ................................. 6-7

6.5 MARKUPS FOR WHOLE-HOME DEHUMIDIFIERS ................................................. 6-8

6.5.1 Methodology for Wholesaler Markups .............................................................. 6-10

6.5.2 Derivation of Wholesaler Markups .................................................................... 6-10

6.5.3 Methodology for Contractor Markups ............................................................... 6-11

6.5.4 Derivation of Markups for Mechanical Contractors .......................................... 6-12

6.5.4.1 Aggregate Markups for Mechanical Contractors ..................................... 6-13

6.5.4.2 Mechanical Contractor Markups in the Replacement and First Time 

Owner Markets ......................................................................................... 6-14

6.5.5 Derivation of Markups for General Contractors ................................................ 6-15

6.6 SALES TAXES ............................................................................................................. 6-17

6.7 OVERALL MARKUPS FOR DEHUMIDIFIERS ........................................................ 6-18

REFERENCES .......................................................................................................................... 6-21

LIST OF TABLES

Table 6.4.1 Four-Firm Concentration Ratio for Major Appliance Sales in Three 

Retail Channels .............................................................................................. 6-7

Table 6.4.2 Data for Calculating Baseline Markup: Electronics and Appliance 

Stores.............................................................................................................. 6-7

Table 6.4.3 Data for Calculating Incremental Markup: Electronics and Appliance ......... 6-8

Table 6.5.1 Wholesaler Expenses and Markups ............................................................. 6-11

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Data ...... 6-13

Table 6.5.3 Baseline Markup, All Mechanical Contractors ............................................ 6-14

Table 6.5.4 Baseline Markups for Replacement and First Time Owner Markets, 

All Mechanical Contractors ......................................................................... 6-15

Table 6.5.5 Markups for the Replacement and First Time Owner Markets ................... 6-15

Table 6.5.6 General Residential Building Contractor Expenses and Markups ............... 6-16

Table 6.6.1 Average Sales Tax Rates by Census Division and Large State ................... 6-18

Table 6.7.1 Summary of Markups for Portable Dehumidifiers ...................................... 6-20

Table 6.7.2 Summary of Markups for Whole-Home Dehumidifiers .............................. 6-20
6-ii

LIST OF FIGURES

Figure 6.3.1 Distribution Channel for Portable Dehumidifiers .......................................... 6-2

Figure 6.3.2 Distribution Channels for Whole-Home Dehumidifiers ................................ 6-2
6-1 

CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

To carry out its economic analyses of potential new energy conservation standards for 

dehumidifiers, the DOE must determine the cost to the consumer of both baseline products (i.e., 

products not subject to new energy conservation standards) and more efficient products. There 

are two types of markups: (1) baseline markups on the direct business costs of products having 

baseline efficiency (baseline products) and (2) incremental markups on incremental product costs 

of higher-efficiency products. DOE estimated consumer prices for baseline products by applying 

a baseline markup to the manufacturer selling prices (MSP) estimated in the engineering 

analysis. For products having higher-than-baseline efficiency, DOE estimated consumer prices

by applying appropriate markups to the incremental MSP estimated in the engineering analysis. 

In the rulemaking for residential dehumidifiers, DOE is considering two product types: 

portable and whole-home dehumidifiers. The markups applied to the two product types differ 

because the products generally follow different distribution channels, as discussed below. Both 

product types, however, will receive manufacturer markups, as described in section 6.3. 

DOE has identified three product classes of portable dehumidifiers, and two of wholehome dehumidifiers. In this analysis, DOE assumed that the market saturation rate for each of 

the five dehumidifier product classes varies by the geographical regions defined in the 2009 

Residential Energy Consumption Survey (RECS 2009)

1

 and based on the U.S. population 

projection for 2019. Therefore, DOE calculated regional markups for each dehumidifier product 

class. 

6.2 MANUFACTURER MARKUP

A manufacturer applies a markup to transform production costs into a manufacturer 

selling price (MSP). DOE used the manufacturer’s cost of goods sold (CGS) and gross margin 

(GM), along with the following equation, to calculate the manufacturer markup (MUMFG).

MFG

MFG MFG

MFG CGS

CGS GM

MU

+ =

Where:

MUMFG = manufacturer’s markup, 

CGSMFG = manufacturer’s cost of goods sold (or manufacturer production cost), and

GMMFG = manufacturer’s gross margin. 

The manufacturer’s CGS plus its GM equals the MSP. Both baseline products and those 

produced under new energy conservation standards receive the same manufacturer markup. DOE 

determined the manufacturer markup for all five product classes of dehumidifiers to be 1.45. 

More detailed information on deriving manufacturer markups is described in chapter 5, 

Engineering Analysis. 
6-2 

6.3 DISTRIBUTION CHANNELS 

The final markups for determining consumer product prices depend on the type of 

distribution channels through which the products move from manufacturers to consumers. At 

each point in the distribution channel, companies mark up the price of a product to cover their 

business costs and profit margin. In the rulemaking for residential dehumidifiers, DOE is

considering two product types, portable and whole-home dehumidifiers. Given the differences in 

their applications, the two product types likely follow different distribution channels from 

manufacturer to end user. Data from the AHAM2 indicate that most portable dehumidifiers are 

sold through retail outlets, shown as in Figure 6.3.1. 

Figure 6.3.1 Distribution Channel for Portable Dehumidifiers

Because whole-home dehumidifiers usually are installed as part of the residential cooling 

and heating system, DOE assumes they follow the same distribution channel as do central air

conditioners and forced-air heating systems. There are two primary markets that determine how 

those products pass from the manufacturer to the consumer: one the replacement market and the 

other first time owners. In the distribution channel for replacements, the manufacturer generally 

sells the product to a wholesaler, who in turn sells it to a mechanical contractor, who in turn sells 

it to the consumer. The distribution channel for first time owners includes an additional step—

the general contractor, who buys the product from the mechanical contractor and installs it in the 

home for the consumer. 

Figure 6.3.2 illustrates the two primary distribution channels for whole-home 

dehumidifiers. 

Replacement: 

First Time Owner: 

Figure 6.3.2 Distribution Channels for Whole-Home Dehumidifiers

Manufacturer Retailer Consumer 

Manufacturer Wholesaler Mechanical

Contractor

Consumer

Wholesaler Mechanical 

Contractor

General 

Contractor

Manufacturer Consumer
6-3 

6.3.1 Calculating Markups 

 At each point in a distribution channel, companies mark up the price of a product to cover 

their business costs and profit margin. In financial statements, GM is the difference between the 

company revenue and the company cost of sales, or CGS. A company’s GM includes expenses 

associated with the distribution channel—overhead costs (sales, general, and administration); 

research and development; interest expenses; depreciation; taxes—and company profits. To 

cover costs and to contribute positively to company cash flow, the price of products must include 

a markup. Products command lower or higher markups depending on company expenses 

associated with the product and the degree of market competition. In formulating markups for 

dehumidifiers, DOE obtained data about the revenue, CGS, and expenses of firms that produce 

and sell portable or whole-home dehumidifiers. 

6.4 MARKUPS FOR PORTABLE DEHUMIDIFIERS

Consumers generally purchase portable dehumidifiers directly from retailers. Because 

they do not have to be installed in an air-conditioning or forced-air system, portable 

dehumidifiers incur no markups associated with contractors. 

6.4.1 Methodology for Retailer Markups 

DOE based the retailer markups for portable dehumidifiers on financial data for 

electronics and appliance stores from the 2012 U.S. Census Annual Retail Trade Survey (ARTS), 

which is the most recent survey that includes industry-wide detailed operating expenses for that 

economic sector.3

 DOE organized the financial data into statements that break down cost 

components incurred by firms in the sector. DOE assumes that the income statements faithfully 

represent the various average costs incurred by firms selling home appliances. Although 

electronics and appliance stores handle multiple commodity lines, the data provide the best 

available indication of expenses for selling portable dehumidifiers. 

 The baseline markup changes the MSP of baseline products to the retailer sales price. 

DOE considers baseline models to be products sold under current market conditions (i.e., without 

new energy conservation standards). DOE used the following equation to calculate an average

baseline markup (MUBASE) for retailers.

 

RTL

RTL RTL

BASE CGS

CGS GM MU + =

Where:

MUBASE = retailer’s baseline markup,

CGSRTL = retailer’s CGS, and 

GMRTL = retailer’s GM. 

Incremental markups are coefficients that relate the change in the MSP of higherefficiency models to the change in retailer sales price. DOE considers higher-efficiency models 
6-4 

to be products sold under market conditions having new efficiency standards. The incremental 

markup reflects the retailer’s increase in a product’s CGS because of new or amended standards.

 There is, unfortunately, a lack of empirical data regarding appliance retailer markup 

practices in response to a product’s cost increase (due to increased efficiency or other factors). 

DOE understands that real-world markup practices vary depending on the market conditions that 

retailers face and on the magnitude of the change in CGS. Pricing in retail stores also may 

involve rules of thumb that are difficult to quantify and to incorporate into DOE’s analysis.

 Given the uncertainty about actual markup practices in appliance retailing, DOE’s 

approach reflects the following key concepts. 

 

1. Changes in the efficiency of goods sold are not expected to increase economic profits. 

Thus, DOE calculates markups/gross margins to allow cost recovery for retail companies 

in the distribution channel (including changes in the cost of capital) without changes in 

company profits. 

2. Efficiency improvements affect some distribution costs but not others. DOE sets markups 

and retail 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 approach to incremental markups is described in more detail in Dale and Fujita.

4 To

estimate incremental retailer markups, DOE divides retailers’ operating expenses into two 

categories: (1) those that do not change when CGS increases because of amended efficiency 

standards (“invariant”), and (2) those that increase proportionately with CGS (“variant”). DOE 

defines invariant costs as including labor and occupancy expenses, because those costs likely 

will not increase as a result of a rise in CGS. All other expenses, as well as net profit, are 

assumed to vary in proportion to CGS. Although it is possible that some other expenses may not 

scale with CGS, DOE takes a conservative position that includes other expenses as variant costs. 

(Note: under DOE’s approach, a high fixed cost component yields a low incremental markup.) 

DOE used the following equation to calculate the incremental markup (MUINCR) for 

retailers.

RTL

RTL RTL

INCR CGS

CGS VC MU + =

Where:

MUINCR = retailer’s incremental markup, 

CGSRTL = retailer’s cost of goods sold, and 

VCRTL = retailer’s variant costs.

In developing incremental markups, DOE envisions that retailers cover costs without 

changing profits. Although retailers may be able to reap higher profits for a time, DOE’s 

approach assumes that competition in the appliance retail market, combined with relatively 

inelastic demand (i.e., the demand is not expected to decrease significantly in response to a 

relatively small increase in price), will tend to pressure retail margins back down. 
6-5 

 To measure the degree of competition in appliance retailing, DOE estimated the four-firm 

concentration ratio (FFCR) of major appliance sales in three retail channels: electronics and 

appliance stores, building materials and supplies dealers, and general merchandise stores.

The FFCR represents the market share of the four largest firms in a given sector. Generally, an 

FFCR of less than 40 percent indicates that the sector is not concentrated; an FFCR of more than 

70 percent indicates that a sector is highly concentrated.a, b

The FFCR of appliance sales within each retail channel is equal to the sector FFCR times 

the percent of total sales within each channel accounted for by major appliances. As shown in 

 

a University of Maryland University College: http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html. b Quick MBA: http://www.quickmba.com/econ/micro/indcon.shtml. 
6-6 

Table 6.4.1, appliance sales in electronics and appliance stores, household appliance stores, 

building materials and supplies dealers, and general merchandise stores have a FFCR less than

the 40-percent threshold. The electronics and appliance stores sector includes a subsector titled 

“household appliance stores.” Because that subsector includes numerous stores, it has a FFCR of 

only 21.3 percent. 
6-7 

Table 6.4.1 Four-Firm Concentration Ratio for Major Appliance Sales in Three Retail 

Channels 

Sector FFCR (% of 

Sector Sales)

Percent of Sales 

Accounted for by 

Major Appliances (%)

FFCR

(% of Major 

Appliance Sales)

Electronics and appliance 

stores 46.3 42.1 19.5 

Subsector: household 

appliance stores 21.3 37.1 7.9 

Building materials and 

supplies dealers 45.9 17.0 7.8 

General merchandise stores 73.2 31.6 23.1

Source: U.S. Economic Census. Establishment and Firm Size (Including Legal Form of Organization). 2007.

*Note: It is assumed that major appliance sales are uniformly distributed within all firms in each sector.

6.4.2 Derivation of Retailer Markup for Portable Dehumidifiers

The 2012 ARTS data for electronics and appliance stores provide total sales data and 

detailed operating expenses. To construct a complete data set for estimating markups, DOE 

needed to estimate CGS and GM. The most recent 2012 ARTS publishes a separate document 

containing historical sales and gross margin from 1993 to 2012 for household appliance stores. 

DOE took the GM as a percent of sales reported for 2012 and combined that percent with

detailed operating expenses data from 2012 ARTS to construct a complete income statement for 

electronics and appliance stores to estimate both baseline and incremental markups. Table 6.4.2

shows the calculation of the baseline retailer markup.

Table 6.4.2 Data for Calculating Baseline Markup: Electronics and Appliance Stores

Business Item Amount ($1,000,000)

Sales 102,998

Cost of goods sold (CGS) 73,946

Gross margin (GM) 29,052

Baseline markup = (CGS+GM)/CGS 1.39 Source: U.S. Census, 2012 Annual Retail Trade Survey. 

Table 6.4.3 shows the breakdown of operating expenses for electronics and appliance 

stores based on the 2012 ARTS data. The incremental markup is calculated as 1.13. 
6-8 

Table 6.4.3 Data for Calculating Incremental Markup: Electronics and Appliance

Business Item Amount 

($1,000,000)

Sales 102,998

Cost of goods sold (CGS) 73,946

Gross margin (GM) 29,052

Labor & Occupancy Expenses (invariant)

Annual payroll 11,371

Employer costs for fringe benefit 2,023

Contract labor costs, including temporary help 209

Purchased utilities, total 529

Cost of purchased repair and maintenance services 386

Cost of purchased professional and technical services 1,117

Purchased communication services 362

Lease and rental payments 3,166

Taxes and license fees (mostly income taxes) 451

Subtotal: 19,617

Other Operating Expenses & Profit (variant)

Expensed equipment 75

Cost of purchased packaging and containers 47

Other materials and supplies not for resale 463

Cost of purchased transportation, shipping, and warehousing services 567

Cost of purchased advertising and promotional services 1,961

Cost of purchased software 122

Cost of data processing and other purchased computer services, except 

communications + commissions paid 280 

Depreciation and amortization charges 1,564

Other operating expenses 2,113

Net profit before tax (operating profit) 2,243

Subtotal: 9,435

Incremental markup = (CGS + Total Other Operating Expenses and 

Profit)/CGS 1.13

 Source: U.S. Census. 2012 Annual Retail Trade Survey. 

6.5 MARKUPS FOR WHOLE-HOME DEHUMIDIFIERS

 DOE examined the manner in which wholesaler and contractor markups may change in 

response to changes in whole-home dehumidifier efficiency levels and other factors. Using the 

available data, DOE estimated that there are differences between incremental markups on 

incremental equipment costs of higher efficiency products and the baseline markup on direct 

business costs of products with baseline efficiency. Since the whole-home dehumidifiers are 

normally handled and installed by the HVAC experts, the data collected for this product are 

based on HVAC industry. 

DOE derived the wholesaler and contractor markups from three key assumptions about 

the costs associated with whole-home dehumidifiers. DOE based the wholesaler and mechanical 
6-9 

contractor markups on firm-level income statement data, and based the general contractor 

markups on U.S. Census Bureau data for the residential building construction industry. DOE 

obtained the firm income statements from the Heating, Air-conditioning & Refrigeration 

Distributors International (HARDI) 2013 Profit Report and from the Air Conditioning 

Contractors of America (ACCA) 2005 Financial Analysis.5, 6 HARDI and ACCA are trade 

associations representing wholesalers and mechanical contractors, respectively. DOE used the 

financial data from the 2007 U.S. Census of Business for developing general contractor markups 

in the same form as the income statement data for wholesalers and mechanical contractors. The 

key assumptions used to estimate markups using these financial data are: 

1. Firm income statements faithfully represent the various average costs incurred by 

firms that distribute and install whole-home dehumidifiers. 

2. Costs can be divided into two categories: (1) costs that vary in proportion to the MSP 

of dehumidifiers (variant costs); and (2) costs that do not vary with the MSP of 

dehumidifiers (invariant costs). 

3. Overall, wholesale and contractor prices for dehumidifiers vary in proportion to the 

wholesaler and contractor costs for dehumidifiers included in the income statements. 

 In support of the first assumption, the income statements divide firm costs into various 

expense categories, including direct costs to purchase or install the product, labor and occupancy 

costs, and other operating costs and profit. Although wholesalers and contractors tend to handle 

multiple commodity lines, including room air conditioners, furnaces, central air conditioners, 

heat pumps, and boilers, the HARDI and ACCA data provide the best available indication of the 

expenses associated with wholesaling or installing dehumidifiers. 

Information obtained from the trade literature, and from selected HVAC wholesalers, 

contractors, and consultants, tends to support the second assumption; this information indicates 

that wholesale and contractor markups vary according to the quantity of labor and materials used 

to distribute and install appliances. In the following discussion, DOE assumes a division of costs 

between those that do not scale with the manufacturer price (labor and occupancy expenses) and 

those that do (operating expenses and profit). 

 In support of the third assumption, the HVAC wholesaler and contractor industry is 

competitive, and consumer demand for heating and air conditioning is inelastic; that is, demand 

does not decrease significantly in response to an increase in product price. The large number of 

HVAC firms listed in the 2007 Census indicates the competitive nature of the market. The 2007 

Census lists, for example, more than 700 HVAC manufacturers,7

 5,300 wholesalers of heat 

pumps and air-conditioning equipment,8 more than 170,000 general residential contractors,

9

 and 

91,000 HVAC contractors.10 Following standard economic theory, competitive firms facing 

inelastic demand either set prices in line with costs or quickly go out of business.11

 DOE concluded that markups for more efficient products are unlikely to be proportional 

to all direct costs. When the wholesaler’s purchase price of products increases, for example, only 

a fraction of the business expenses increases, while the rest may remain relatively constant. If the 

unit price of a dehumidifier increases by 30 percent because of improved efficiency, for example,

it is unlikely that the cost of secretarial support in an administrative office also will increase by 
6-10 

30 percent. Therefore, DOE assumed that incremental markups cover only those costs that scale 

with a change in the MSP (variant costs).

6.5.1 Methodology for Wholesaler Markups 

 Applying the assumptions described above, DOE developed baseline and incremental 

markups for wholesalers using the firm income statement from the HARDI 2013Profit Report.

Appendix 6A.1 presents the HARDI itemized revenues and costs in full. Baseline markups cover 

all the wholesaler’s costs (both invariant and variant). DOE calculated the baseline markup for 

wholesalers using the following equation. 

WHOLE

WHOLE WHOLE WHOLE

WHOLE

WHOLE WHOLE

BASE CGS

CGS IVC VC

CGS

CGS GM MU + ( + ) = + =

 

Where:

MUBASE = wholesaler’s baseline markup, 

CGSWHOLE = wholesaler’s cost of goods sold, 

GMWHOLE = wholesaler’s gross margin, 

IVCWHOLE = wholesaler’s invariant costs, and

VCWHOLE = wholesaler’s variant costs.

Incremental markups are coefficients that relate the change in the MSP of more energy

efficient models, or those products that meet the requirements of new energy conservation 

standards, to the change in the wholesaler sales price. Incremental markups cover only those 

costs that scale with a change in the MSP (variant costs). DOE used the following equation to 

calculate the incremental markup (MUINCR) for wholesalers. 

WHOLE

WHOLE WHOLE

INCR CGS

CGS VC MU + =

 

Where:

MUINCR = wholesaler’s incremental markup, 

CGSWHOLE = wholesaler’s cost of goods sold, and 

VCWHOLE= wholesaler’s variant costs. 

6.5.2 Derivation of Wholesaler Markups 

 Wholesalers reported median data in the confidential survey that HARDI conducted of 

member firms.6 Table 6.5.1 summarizes the data as cost-per-dollar sales revenue and CGS. 
6-11 

Table 6.5.1 Wholesaler Expenses and Markups

Descriptions

Per Dollar

Sales Revenue

$

Per Dollar Cost 

of Goods 

$

Direct Cost of Equipment Sales: Cost of goods sold 0.739 1.000

Labor Expenses: Salaries and benefits 0.151 0.204

Occupancy Expense: Rent, maintenance, and utilities 0.035 0.047

Other Operating Expenses: Depreciation, advertising, and 

insurance.

0.052 0.070

Operating Profit 0.023 0.031

Wholesaler Baseline Markup (MUWHOLE BASE) 1.353

Incremental Markup (MUWHOLE INCR) 1.101 Source: Heating, Air Conditioning & Refrigeration Distributors International. 2013. 2013 Profit Report (2012 data).

 Based on the information in Table 6.5.1, direct product expenses (cost of goods sold) 

represent about $0.74 per dollar sales revenue, so for every $1 wholesalers take in as sales 

revenue, $0.74 is used to pay CGS. Labor expenses represent $0.15 per dollar of sales revenue; 

occupancy expenses represent $0.04; other operating expenses represent $0.05; and profit 

accounts for $0.02 per dollar sales revenue.

 DOE converted the expenses per dollar sales into expenses per dollar CGS by dividing 

each value in the first data column by $0.74 (i.e., CGS per dollar of sales revenue). The data in 

column two show that, for every $1.00 spent on products, the wholesaler allocates $0.204 to 

cover labor costs, $0.049 to cover occupancy expenses, $0.070 for other operating expenses, and 

$0.031 in profits. A total of $1.353 in sales revenue is earned for every $1.00 spent on products. 

Therefore, the baseline wholesaler markup (MUWHOLE BASE) is 1.353 ($1.353 ÷ $1.00). 

DOE also used the data in column two to estimate the wholesaler’s incremental markup. 

The incremental markup depends on which of the costs in Table 6.5.1 are variant and which are 

invariant with MSP. For example, for a $1.00 increase in MSP, if all the other costs scale with 

the MSP (i.e., all costs are variant), the increase in wholesale price will be $1.353, implying that 

the incremental markup is 1.353, or the same as the baseline markup. At the other extreme, if 

none of the other costs are variant, then a $1.00 increase in MSP will lead to a $1.00 increase in 

the wholesale price, for an incremental markup of 1.0. DOE believes that the labor and 

occupancy costs will be invariant and that the other operating costs and profit will scale with the 

MSP. In this case, for a $1.00 increase in MSP, the wholesale price will increase to match 

changes in "other" operating costs and operating profit of $0.075, which when divided by 73.9 

cents in CGS yields an increase of $0.103, for a wholesaler incremental markup (MUWHOLE INCR) of 

1.101. 

6.5.3 Methodology for Contractor Markups 

The type of financial data used to estimate markups for wholesalers is also available for 

mechanical and general contractors from the ACCA Financial Analysis5 and the 2007 Economic 

Census. To estimate to mechanical contractors for whole-home dehumidifiers, DOE collected 

financial data from the Plumbing and HVAC Contractors (NAICS 23822) series from the 2007 

Economic Census. To estimate general contractor markups for whole-home dehumidifiers, DOE 
6-12 

collected data from the Residential Building Construction series from the 2007 Economic 

Census, which is the aggregation of New Single-Family General Contractors (NAICS 236115), 

New Multifamily Housing Construction (NAICS 236116), New Housing Operative Builders

(NAICS 236117), and Residential Remodelers (NAICS 236118). ACCA financial data provide 

GM as a percent of sales for the mechanical contractor industry. Baseline markup can be derived 

using the following equation. 

(%) (%)

(%)

Sales GM

Sales MUBASE − =

 The U.S. Census data include the number of contractor establishments, payroll for 

construction workers, value of construction, cost of materials, and cost of subcontracted work at 

both state and national levels. DOE calculated the baseline markup for mechanical and general 

contractors using the following equation. 

Pay MatCost SubCost

V MU CONSTRUCT

BASE ++ =

Where:

MUBASE = baseline markup for mechanical contractor or general contractor,

VCONSTRUCT = value of construction, 

Pay = payroll for construction workers, 

MatCost = cost of materials, and

SubCost = cost of subcontracted work. 

Analogously, DOE estimated the incremental mechanical and general contractor markups 

by marking up only those variant costs that scale with a change in the MSP for more energy

efficient products. DOE categorized the Census cost data in each major category and used the 

following equation to estimate markups. 

CONT

CONT CONT

INCR CGS

CGS VC MU + =

 

Where:

MUINCR = contractor’s incremental markup,

CGSCONT = contractor’s cost of goods sold, and 

VCCONT= contractor’s variant costs.

 

6.5.4 Derivation of Markups for Mechanical Contractors

 This section describes markups for whole-home dehumidifiers applied by the mechanical 

contractor. After first presenting aggregate markups, this section divides those markups into 

categories of replacement and first time owner market. 
6-13 

6.5.4.1 Aggregate Markups for Mechanical Contractors

 The 2007 Economic Census provides a Geographic Area series for the Plumbing and 

HVAC Contractors (NAICS 23822) sector, which contains national average sales and cost data, 

including value of construction, cost of subcontract work, cost of materials, and payroll for 

construction workers. It also provides the cost breakdown of GM, including labor expenses, 

occupancy expenses, other operating expenses, and profit. The gross margin provided by the 

U.S. Census is disaggregated enough that DOE was able to determine the invariant and variant 

costs for the sector. By using the equations presented above, DOE estimated baseline and 

incremental markups, as shown in Table 6.5.2. (Appendix 6A.2 contains the full set of data.) 

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Data

Description

Mechanical Contractor 

Expenses and Revenue

Per Dollar

Sales 

Revenue ($)

Per Dollar 

Cost of 

Goods ($)

Direct cost of products (CGS) 0.68 1.00

Labor expenses (salaries [indirect] and benefits) 0.18 0.26

Occupancy expenses (rent, maintenance, and utilities) 0.02 0.03

Other operating expenses (depreciation, advertising, and 

insurance)

0.08 0.12

Net profit before taxes 0.04 0.06

Baseline markup (MUMECH BASE): revenue per $ CGS 1.48

Incremental markup (MUMECH INCR): increased revenue per $ increase 

in CGS

1.18

 Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. Construction: 

Industry series, Preliminary Detailed Statistics for Establishments. 2007.

 

 The first data column in Table 6.5.2 provides the CGS and a list of GM components as 

expenses per dollar of sales revenue. As shown in the table, the direct cost of sales represents 

about $0.68 per dollar sales revenue to the mechanical contractor, and the gross margin totals 

$0.32 per dollar sales revenue. DOE converted those expenses per dollar sales into revenue per 

dollar CGS by dividing each figure in the first data column by $0.68. For every $1.00 spent on 

products, the mechanical contractor earns $1.00 in sales revenue to cover the product cost and 

$0.48 to cover other costs. The $1.48 in sales revenue earned for every $1.00 spent on product 

costs is equivalent to a baseline markup (MUMECH CONT BASE) of 1.48 for mechanical contractors. 

 DOE used the data in column two of Table 6.5.2 to estimate incremental markups, after 

classifying the costs as either invariant or variant. At one extreme, if all of the other costs scale 

with the product cost (i.e., all costs are variant), the increase in mechanical contractor cost will 

be $1.48, implying that the incremental markup is 1.48, or the same as the baseline markup. At 

the other extreme, if none of the other costs are variant, then a $1.00 increase in product price 

will lead to a $1.00 increase in the mechanical contractor price, for an incremental markup of 

1.0. DOE believes the labor and occupancy costs are invariant and the other operating costs and 
6-14 

profit are variant. In that case, for a $1.00 increase in product cost, the mechanical contractor’s 

price will increase by $1.18, for an incremental markup (MUMECH CONT INCR) of 1.18. 

6.5.4.2 Mechanical Contractor Markups in the Replacement and First Time 

Owner Markets

 DOE derived the baseline and incremental markups for both replacement and new 

construction markets using the 2007 Economic Census industrial cost data12 supplemented with 

the ACCA 2005 financial data.5 The 2007 Economic Census provides a sufficiently detailed cost 

breakdown for the Plumbing and HVAC Contractors (NAICS 23822) sector to enable DOE to 

estimate baseline and incremental markups for mechanical contractors. The 2007 Economic 

Census does not separate the mechanical contractor market into replacement and first time owner

markets, however. In order to calculate markups for the two markets, DOE utilized 2005 ACCA 

financial data, which reports GM data for the entire mechanical contractor market and for both 

the replacement and first time owner markets. 

HVAC contractors, defined here as mechanical contractors, reported median cost data in 

the ACCA’s 2005 financial analysis of the HVAC industry. Those data are shown in Table 6.5.3. 

Table 6.5.3 Baseline Markup, All Mechanical Contractors

Description

Contractor Expenses or 

Revenue

Per Dollar

Sales Revenue

($)

Per Dollar 

Cost of Goods 

($)

Direct cost of product sales (CGS) 0.73 1.00

Gross margin (labor, occupancy, operating expenses, 

and profit) 0.27 0.37 

Revenue (baseline revenue earned per $ CGS) 1.37

Baseline markup (MUMECH CONT BASE) 1.37 Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry. 

 

Table 6.5.4 summarizes the GM and resulting baseline markup data for all mechanical 

contractors who serve the replacement and first time owner markets. 
6-15 

Table 6.5.4 Baseline Markups for Replacement and First Time Owner Markets, All 

Mechanical Contractors

Description

Contractor Expenses or Revenue by Market Type

Replacement First Time Owners

Per Dollar

Sales 

Revenue

($)

Per Dollar 

Cost of 

Goods 

($)

Per Dollar

Sales 

Revenue

($)

Per Dollar 

Cost of 

Goods 

($)

Direct cost of product sales 

(CGS) 0.703 1.000 0.745 1.000 

Gross margin (labor, occupancy, 

operating expenses, and profit) 0.297 0.422 0.255 0.342 

Baseline markup (MUMECH 

CONT BASE), revenue per $ 

CGS

NA 1.42 NA 1.34 

Difference compared to

aggregate baseline markup (%) NA 3.6 NA -2.2 

 Source: Air Conditioning Contractors of America (ACCA). 2005. Financial Analysis for the HVACR Contracting Industry. 

Using the baseline markup data from Table 6.5.4 and data from Table 6.5.3, DOE 

calculated that mechanical contractors’ baseline markups for the replacement and first time 

owner markets for dehumidifiers are 3.6 percent higher and 2.2 percent lower, respectively, than 

for mechanical contractors serving all markets. DOE applied those markup deviations derived for 

all mechanical contractors to the baseline markup of 1.48 and the incremental markup of 1.18 

estimated in Table 6.5.2. DOE assumed that the deviations apply equally to the baseline and 

incremental markups calculated from the 2007 Economic Census. The baseline and incremental 

markups for the replacement and first time owner markets served by mechanical contractors are 

shown in Table 6.5.5.

Table 6.5.5 Markups for the Replacement and First Time Owner Markets 

Market Sector Baseline Markup Incremental Markup

Replacement market 1.53 1.22

First time owner market 1.45 1.15

6.5.5 Derivation of Markups for General Contractors

 DOE derived markups for general contractors from U.S. Census Bureau data for the 

residential construction sector.13 The residential construction sector includes establishments 

engaged primarily in construction work, including new construction, additions, alterations, and 

repairs of residential buildings. The U.S. Census data for the construction sector include detailed 

statistics for establishments that have payrolls, similar to the data reported by HARDI for 

wholesalers. The primary difference is that the U.S. Census Bureau reports itemized revenues 

and expenses for the construction industry as a whole in total dollars rather than in typical values 

for an average or representative business. Because of this difference, DOE assumed that the total 

dollar values reported by the U.S. Census, once converted to a percentage basis, represent 
6-16 

revenues and expenses for an average or typical general contracting business. The first data 

column in Table 6.5.6 summarizes the expenses for general contractors in residential building 

construction as expenses per dollar sales revenue. (Table 6A.3 in appendix 6A.3 contains the full 

set of data.)

Table 6.5.6 General Residential Building Contractor Expenses and Markups

Description

General Contractor Expenses 

or Revenue

Per Dollar

Sales Revenue

($)

Per Dollar 

Cost of Goods 

($)

Direct cost of product sales (CGS) 0.68 1.00

Labor expenses (salaries [indirect] and benefits) 0.08 0.12

Occupancy expenses (rent, maintenance, and utilities) 0.01 0.01

Other operating expenses (depreciation, advertising, and 

insurance) 0.06 0.09 

Net profit before taxes 0.17 0.25

Baseline markup (MUGEN CONT BASE), revenue per dollar cost of 

goods

1.47

Incremental markup (MUGEN CONT INCR), increased revenue per dollar 

increase in CGS

1.34

 Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23: 236115-236118. Construction: Industry

series: Preliminary Detailed Statistics for Establishments: 2007. 

 As shown in the first data column, the direct cost of sales represents about $0.68 per 

dollar sales revenue to the general contractor. Labor expenses represent $0.08 per dollar sales 

revenue; occupancy expenses represent $0.01 per dollar sales revenue; other operating expenses 

represent $0.03; and profit makes up $0.20 per dollar sales revenue. 

 DOE converted the expenses per dollar sales into revenue per dollar CGS by dividing 

each value in the first data column by $0.68. The data in column two show that, for every $1.00 

spent on product costs, the general contractor earns $1.00 in sales revenue to cover the product 

cost, $0.12 to cover labor costs, $0.01 to cover occupancy expenses, $0.09 for other operating 

expenses, and $0.25 in profits. A total of $1.47 in sales revenue is earned for every $1.00 spent 

on product costs of a baseline markup (MUGEN CONT BASE) of 1.47. 

DOE used the data in column two of Table 6.5.6 to estimate incremental markups, after 

classifying the costs as either invariant or variant. At one extreme, if all of the other costs are 

variant, the increase in general contractor price would be $1.47, implying that the incremental 

markup is 1.48, or the same as the baseline markup. At the other extreme, if none of the other 

costs are variant, then a $1.00 increase in product cost leads to a $1.00 increase in the general 

contractor price, for an incremental markup of 1.0. DOE believes the labor and occupancy costs 

are invariant and the other operating costs and profit are variant. In this case, for a $1.00 increase 

in product cost, the general contractor’s price would increase by $1.34, giving the general 

contractor an incremental markup (MUGEN CONT INCR) of 1.34. 
6-17 

6.6 SALES TAXES

The sales tax comprises state and local taxes applied to the price a consumer pays for a 

product. The sales tax is a multiplicative factor that increases the consumer product price. DOE 

applied sales tax to the retail price of portable dehumidifiers and consumer price of whole-home 

dehumidifiers in the replacement market, not the new construction market. The common practice 

for selling larger appliances such as whole-home dehumidifiers in the new construction market is 

that general contractors (or builders) bear the added sales tax for the appliances, in addition to 

the cost of the units. Therefore, no specific sales tax is necessary to calculate the consumer 

product price for the first time owner market.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.14

The data represent weighted averages that include county and city rates. DOE then derived 

population-weighted average tax values for each RECS region, as shown in Table 6.6.1. 
6-18 

Table 6.6.1 Average Sales Tax Rates by Census Division and Large State 

RECS 

Region

State(s)

U.S. 

Population 

in 2019 

2014 Tax 

Rate (%) 

1 Connecticut, Maine, New Hampshire, Rhode Island, 

Vermont

8,453,982 5.13

2 Massachusetts 6,855,546 6.25

3 New York 19,576,920 8.40

4 New Jersey 9,461,635 6.95

5 Pennsylvania 12,787,354 6.40

6 Illinois 13,236,720 8.05

7 Indiana, Ohio 18,271,066 6.87

8 Michigan 10,695,993 6.00

9 Wisconsin 6,004,954 5.45

10 Iowa, Minnesota, North Dakota, South Dakota 10,353,316 6.86

11 Kansas, Nebraska 4,693,244 7.13

12 Missouri 6,199,882 7.20

13 Virginia 8,917,395 5.60

14 Delaware, District of Columbia, Maryland, West Virginia 9,742,487 5.59

15 Georgia 10,843,753 7.10

16 North Carolina, South Carolina 15,531,866 7.00

17 Florida 23,406,525 6.65

18 Alabama, Kentucky, Mississippi 12,198,158 7.25

19 Tennessee 6,780,670 9.45

20 Arkansas, Louisiana, Oklahoma 11,515,069 8.67

21 Texas 28,634,896 7.95

22 Colorado 5,278,867 6.10

23 Idaho, Montana, Utah, Wyoming 6,285,110 5.29

24 Arizona 8,456,448 7.20

25 Nevada, New Mexico 5,536,624 7.31

26 California 42,206,743 8.45

27 Oregon, Washington, Alaska, Hawaii 13,879,323 5.30

Population-weighted average 7.144

6.7 OVERALL MARKUPS FOR DEHUMIDIFIERS

 The overall markup for each distribution channel is the product of the appropriate 

markups, as well as the sales tax in the case of direct consumer purchases of portable 

dehumidifiers and replacement market of whole-home dehumidifiers. 

 DOE used the overall baseline markup to estimate the consumer product price of baseline 

models, given the manufacturer cost of baseline models. As stated previously, DOE considers 

baseline models to be products sold under current market conditions (i.e., without new energy 

conservation standards). The following equation shows how DOE used the overall baseline 

markup to determine the product price for baseline models. 
6-19 

( ) CPPBASE = COSTMFG MU MFG MUBASE ××× TaxSALES = COSTMFG × MUOVERALL _ BASE

Where:

CPPBASE = consumer product price for baseline model, 

COSTMFG = manufacturer’s cost for baseline model, 

MUMFG = manufacturer’s markup, 

MUBASE = baseline markup for portable dehumidifiers and whole-home 

dehumidifiers (replacement and new home markets), 

TaxSALES = sales tax (portable dehumidifiers and replacement market of whole-home 

dehumidifiers), and

MUOVERALL_BASE = overall baseline markup. 

Similarly, DOE used the overall incremental markup to estimate changes in the consumer 

product price given changes in the manufacturer cost resulting from an energy conservation 

standard. The total consumer product price for more energy efficient models comprises two 

components: the consumer product price of the baseline model, and the change in consumer 

product price associated with the increase in manufacturer cost to meet the new energy 

conservation standard. The following equation shows how DOE used the overall incremental 

markup to determine the consumer product price for more energy efficient models. 

( )

BASE MFG OVERALL INCR

STD MFG OVERALL BASE MFG MFG INCR SALES

CPP COST MU

CPP COST MU COST MU MU Tax

_

_

D+= ×

= × D+ ×××

Where:

CPPSTD = consumer product price for models that meet new energy conservation 

 standards, 

CPPBASE = consumer product price for baseline model, 

COSTMFG = manufacturer’s cost for baseline model, 

ΔCOSTMFG = change in manufacturer’s cost for more energy efficient models,

MUMFG = manufacturer’s markup, 

MUINCR = incremental markup for portable dehumidifier and whole-home 

dehumidifiers (replacement and new home markets),

TaxSALES = sales tax (portable dehumidifiers and replacement market of whole-home 

dehumidifiers), and

MUOVERALL_BASE = overall baseline markup (product of manufacturer markup, and 

MUOVERALL_INCR = overall incremental markup. 

National average baseline and incremental markups for both portable and whole-home 

dehumidifiers are summarized in Table 6.7.1 and Table 6.7.2, respectively. 
6-20 

Table 6.7.1 Summary of Markups for Portable Dehumidifiers

Markup Baseline Incremental

Manufacturer 1.45

Retailer 1.39 1.13

Sales tax 1.071

Overall 2.16 1.75

Table 6.7.2 Summary of Markups for Whole-Home Dehumidifiers

Markup Replacement First Time Owner

Baseline Incremental Baseline Incremental

Manufacturer 1.45 1.45

Wholesaler 1.35 1.10 1.35 1.10

Mechanical contractor 1.53 1.22 1.45 1.15

General contractor -- -- 1.47 1.34

Sales tax 1.071 -- --

Overall 3.21 2.08 4.17 2.46
6-21 

REFERENCES

1 U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey: 2009 RECS Survey Data, 2013. (Last accessed March, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/>

2 Association of Home Appliance Manufacturers, AHAM 2009 Fact Book. 2009. 

Washington, DC. 

3 U.S. Census Bureau, 2012 Annual Retail Trade Survey, Electronics and Appliance 

Stores. 2012: Washington, DC. <http://www.census.gov/retail/index.html> 

4 Dale, L. and K. Fujita, An Analysis of Price Determination and Markups in the AirConditioning and Heating Equipment Industry, January 2004. 

5 Air Conditioning Contractors of America (ACCA), Financial Analysis for the HVACR 

Contracting Industry: 2005, 2005. <http://www.acca.org/>

6 Heating, Air Conditioning & Refrigeration Distributors International 2013 Profit Report, 

<http://www.hardinet.org/Profit-Report>

7 U.S. Census Bureau, Manufacturing Industry Series: Detailed Statistics for the United 

States: 2007. Air-Conditioning and Warm Air Heating Equipment and Commercial and 

Industrial Refrigeration Equipment Manufacturing. Data set for Sector 31: EC073111, 

2007. <http://www.census.gov/econ/>

8 U.S. Census Bureau, Wholesale Trade: Industry Series: Preliminary Summary Statistics 

for the United States: 2007. HVAC Equipment Merchant Wholesaler. Data set for Sector 

42: EC0742111, 2007. <http://www.census.gov/econ/>

9 Bureau, U. S. C., Data set for Sector 23: EC0723I1: 236115 through 236118 (Residential 

Building Construction): Industry Series, Preliminary Detailed Statistics for 

Establishments: 2007. 2007<http://www.census.gov/econ/> 

10 U.S. Census Bureau, Data set for Sector 23, EC0723A1: 238220 (Plumbing, Heating and 

Air-Conditioning Contractors), Construction: Geographic Area Series, Detailed 

Statistics for Establishments, 2007. <http://www.census.gov/econ/>

11 Pindyck, R. S. and D. L. Rubinfeld, Microeconomics. 5th ed. 2000. Prentice Hall: New 

Jersey.

12 U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 

238220. Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments, 2007, 2007. <http://www.census.gov/econ/>
6-22 

13 U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: 2007. New Single-Family General Contractors, New Multifamily 

Housing Construction (Except Operative Builders), New Housing Operative Builders, 

and Residential Remodelers, 2007. <http://www.census.gov/econ/>

14 Sales Tax Clearinghouse Inc., State Sales Tax Rates Along with Combined Average City 

and County Rates, 2014. (Last accessed July 18, 2014.) <http://thestc.com/STrates.stm>
7-i 

CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 AVERAGE ANNUAL ENERGY USE EQUATION FOR DEHUMIDIFIERS ............ 7-1

7.3 INPUTS TO AVERAGE ANNUAL ENERGY USE ..................................................... 7-2

7.3.1 Capacity and Efficiency ....................................................................................... 7-2

7.3.2 Energy Use for Operating Modes ........................................................................ 7-3

7.3.2.1 Power for Dehumidification Mode ............................................................ 7-3

7.3.2.2 Fan-Only Mode Power and Standby/Off Mode Power .............................. 7-3

7.3.3 Fractions of Operating Hours by Mode ............................................................... 7-4

7.3.3.1 Fraction of Operating Hours in Dehumidification Mode ........................... 7-4

7.4 ENERGY USE BY EFFICIENCY LEVEL AND PRODUCT CLASS .......................... 7-7

7.5 VARIABILITY OF ANNUAL DEHUMIDIFIER ENERGY USE ................................ 7-8

7.5.1 Monthly Dehumidifier Usage .............................................................................. 7-9

7.5.2 Applications of Whole-Home Dehumidifiers .................................................... 7-10

7.5.3 Annual Monthly Use .......................................................................................... 7-10

7.6 LINKING RECS HOUSEHOLDS TO CLIMATE PARAMETERS ............................ 7-10

7.6.1 Derivation of Outdoor Air Temperatures .......................................................... 7-11

REFERENCES .......................................................................................................................... 7-12

LIST OF TABLES

Table 7.3.1 Efficiency Levels for Various Dehumidifier Capacities ................................ 7-3

Table 7.3.2 Standby/Off Mode and Fan-Only Mode Power Consumption by 

Dehumidifier Type ......................................................................................... 7-4

Table 7.3.3 Data on Annual Dehumidification Mode Hours ............................................ 7-5

Table 7.3.4 Fractions of Hours for Whole-Home Dehumidifiers Operated in 

Different Modes ............................................................................................. 7-7

Table 7.4.1 Portable Dehumidifiers ≤30.00 Pints/Day: Annual Energy Use by 

Efficiency Level ............................................................................................. 7-7

Table 7.4.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Annual Energy Use by 

Efficiency Level ............................................................................................. 7-7

Table 7.4.3 Portable Dehumidifiers >45.00 Pints/Day: Annual Energy Use by 

Efficiency Level ............................................................................................. 7-8

Table 7.4.4 Whole-Home Dehumidifiers ≤ 8.0 ft3 Case Volume: Annual Energy 

Use by Efficiency Level................................................................................. 7-8

Table 7.4.5 Whole-Home Dehumidifiers >8.0 ft3 Case Volume: Annual Energy 

Use by Efficiency Level................................................................................. 7-8

Table 7.5.1 Dehumidifier Use by Range of Months ....................................................... 7-10
7-ii

LIST OF FIGURES

Figure 7.5.1 Percent of Households that Have Dehumidifiers by Region and 

Presence of Basement .................................................................................... 7-9
7-1 

CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

To perform the life-cycle cost and payback period calculations described in chapter 8 of 

this NOPR TSD, DOE determined the savings in operating costs that consumers would derive 

from more efficient products. DOE used consumer energy use data, along with energy prices, to 

develop the energy cost component of consumer operating costs determined in chapter 8 of the 

NOPR TSD. (Maintenance and repair costs are the other contributors to operating costs.) This 

chapter describes how DOE determined the annual energy consumption of residential 

dehumidifiers. 

7.2 AVERAGE ANNUAL ENERGY USE EQUATION FOR DEHUMIDIFIERS

 For the previous rulemaking regarding dehumidifiers, which went into effect in 2012, 

DOE based the annual energy use of a dehumidifier on the following equation. 

( )

Eff

CAP Hours

DEHENERGY

× × = 24

473.0

 

Where:

DEHENERGY = annual energy consumption of dehumidifier (kWh/year),

CAP = capacity of dehumidifier (pints/day), 

0.473 = conversion factor for liters in a pint,

24 = number of hours in a day 

Hours = annual operating hours for dehumidifier, and 

Eff = dehumidifier efficiency (L/kWh). 

 The above equation estimates the annual energy consumption associated with a 

dehumidifier’s active mode, when the compressor operates. For the current rulemaking, DOE 

amended the above equation to accommodate three modes of dehumidifier operation: (1) 

standby/off mode, (2) fan-only mode, and (3) dehumidification (fan plus compressor) mode. 

The following equation calculates the annual energy use of dehumidifiers as the product 

of the annual hours of operation multiplied by the energy use and percentage of time spent in 

each operational mode. The capacity and energy efficiency of the dehumidifier are relevant while

the compressor is in operation. The energy used during standby mode and off mode is primarily 

for the display on the control panel and components on the control board, energy that is used 

during the other two modes as well.

( ) ( )



 





×+×+















×

×× ×



 



 = Fan Fan Stby Stby

Dehum

ENERGY X kW X kW

Eff

CAP X

Year

TotalHoursofUse DEH

24

473.0

 
7-2 

Where:

DEHENERGY = annual energy consumption of dehumidifier (kWh/year),

Year

TotalHoursofUse = number of hours the dehumidifier is used per year (at >0 W),

CAP = dehumidifier capacity (pints of condensate removed/day), 

0.473 = conversion factor for liters in a pint,

24 = number of hours in a day 

Eff = dehumidifier efficiency (liters of condensate removed/kWhDehum),

XDehum = fraction of time in dehumidification mode, 

XFan = fraction of time in fan-only mode, 

XStby = fraction of time in standby/off mode,

kWDehum= kW of dehumidification mode, 

kWFan = kW of fan-only mode, and 

kWStby = kW of standby/off mode. 

Note:

24

473.0

×

××

Eff

Cap X Dehum

 = XDehum X kWDehum Eq. 7.1 

7.3 INPUTS TO AVERAGE ANNUAL ENERGY USE

 The expanded equation for calculating average annual energy use includes parameters for 

dehumidifier capacity and efficiency, as well as fractions of operating hours and power use in 

dehumidification, fan-only, and standby/off modes. 

7.3.1 Capacity and Efficiency

Values for capacity and efficiency, presented in Table 7.3.1, were obtained from the 

engineering analysis described in chapter 5 of the NOPR TSD. The average capacity sizes were 

determined from the tear-down process for the engineering analysis. See chapter 5 of the NOPR

TSD for details.
7-3 

Table 7.3.1 Efficiency Levels for Various Dehumidifier Capacities

 

Portable Whole-Home

≤30.00 

Pints/Day

30.01–

45.00 

Pints/Day

>45.00 

Pints/Day

≤8.0 ft3

Case 

Volume 

>8.0 ft3

Case 

Volume 

Average Capacity 21 38 73 56 92 

Efficiency Level 

Baseline 0.77 0.94 2.07 1.77 2.41

1 1.10 1.20 2.40 2.09 2.70

2 1.20 1.40 2.80 2.53 3.52

3 1.30 1.60 3.66 - 4.50

4 1.57 1.80 - - -

The next sections describe dehumidifier energy use and fraction of time spent in each

mode of operation. 

7.3.2 Energy Use for Operating Modes

For determining energy use, DOE defines three modes of dehumidifier operation. 

• Dehumidification mode: is the mode in which the dehumidifier performs its primary 

function of removing moisture from the air by using a fan to draw moist air over a 

refrigerated coil. 

• Fan-only mode: is when the fan circulates air without activating the compressor. 

• Standby/off mode: standby mode facilitates the initiation of active mode via remote 

switch, internal sensor, or timer, and/or provides continuous status display, and off 

mode is the mode in which the dehumidifier is connected to power but is not in 

dehumidification mode, fan-only mode, or standby mode. 

7.3.2.1 Power for Dehumidification Mode

DOE calculated the power of the dehumidification mode (kWDehum) using the rated 

capacity and rated efficiency at each efficiency level for each product class. Calculating the 

dehumidification mode power in this manner assumes that it is the same at all temperatures and 

relative humidities, which may not be the case. As stipulated in the 2015 test procedure, the 

efficiency and capacity values were measured using a temperature of 65 degrees Fahrenheit (°F) 

and humidity set point of 60 percent for portable dehumidifiers, and a temperature of 73 degrees 

°F and humidity set point of 60 percent for whole home dehumidifiers. 

7.3.2.2 Fan-Only Mode Power and Standby/Off Mode Power

 Two recent field studies1

,

2 (Willem et al., 2013, Burke et al., 2014) measured energy use 

in three operating modes for both portable and whole-home dehumidifiers. Because DOE found 
7-4 

no other studies that disaggregated energy use by mode, DOE used the values reported in the 

Willem studies (see Table 7.3.2). The studies’ authors observed no relationship between the 

capacity of a portable dehumidifier and its fan-only mode or standby/off mode energy use.

Table 7.3.2 Standby/Off Mode and Fan-Only Mode Power Consumption by 

Dehumidifier Type 

 Source: Willem et al., 2013, Burke et al., 2014. 

7.3.3 Fractions of Operating Hours by Mode

A dehumidifier uses energy when the compressor is operating to remove moisture from 

the air. When the compressor is not operating, the dehumidifier may use energy for a fan-only 

mode that circulates air through the unit to sample the ambient relative humidity and to defrost 

the condenser coils. When neither the fan nor the compressor is operating, energy is used for 

standby mode or off mode power for functions such as keeping a user panel lit. 

7.3.3.1 Fraction of Operating Hours in Dehumidification Mode

Table 7.3.3 summarizes information on annual dehumidification mode operating hours by

portable and whole-home dehumidifiers derived from several studies and sources. Two of the 

studies utilized metered data from portable units. One study reports metered data for whole-home 

dehumidifiers. The other sources rely on power measurements and assumptions regarding usage 

to estimate values for annual energy use. 

Mode Portable (W) Whole-Home (W)

Min Median Max Min Median Max

Standby/Off 0.3 1.0 12.3 1.0 4.5 49.5

Fan-only 21.4 51.2 80.9 50.2 141.7 497.0 
7-5 

Table 7.3.3 Data on Annual Dehumidification Mode Hours 

Study/Source

Dehumidification Mode 

Operating Hours 

(For portable units unless noted)

Average Hours/Year

ADL (1998)3 1,620

AHAM (2005)4 1,095

ENERGY STAR fact sheet (2006)5 1,620

ENERGY STAR calculator (2006)6 2,851

The Cadmus Group, Inc. (2012)7 2,160

Willem (2013) 

CCB 1,136

CCDD 1,078

NCCB 1,267

NCCDD 1,785

Burke (2014) Whole-home 2,542

CCB = Climate controlled space, bucket 

catches dehumidifier condensate. 

CCDD = Climate controlled space, 

dehumidifier has a direct drain.

NCCB = Non-climate controlled space, 

bucket catches dehumidifier condensate. 

NCCDD = Non-climate controlled space, 

dehumidifier has a direct drain.

 The reports and studies are listed from oldest to most recent. The study by The Cadmus 

Group based annual average dehumidification mode hours on several months of metered data, 

extrapolating from those to an entire year. The second study that metered portable units (Willem

et al., 2013) estimated operating hours by correlating outside vapor density with compressor use. 

The Willem study found energy use depended on two factors: (1) whether the room in which the 

dehumidifier operated was climate controlled, and (2) how the condensate was removed (i.e., 

manually by emptying a bucket, or via a direct drain). The following sections describe annual 

dehumidification mode energy use for units operating in climate-controlled versus non-climate

controlled spaces and for the method of condensate removal. 

Portable Units— Minutes per Hour in Dehumidification Mode

 Four equations from Willem et al. (2013) correlate compressor run time with outdoor 

vapor density. Although individual dehumidifiers may differ greatly from the models, the 

equations describe, on average, the manner in which large numbers of dehumidifier units would 

operate. 

 Equation for climate-controlled dehumidifiers with bucket:

CCCC(BB)CCCCCCCCCCCCCCCCCCCC RRRRRR TTTT �

h �

= (0.4141 ∗ VVVVoooooo + 28.729) ∗ (−0.0005 ∗ VVVVoooooo

2 − 0.0246 ∗ VVVVoooooo

+ 0.7264)
7-6 

 Equation for climate-controlled, direct-drain dehumidifiers:

CCCC( )CCCCCCCCCCCCCCCCCCCC RRRRRR TTTT �

h �

= (−0.4966 ∗ VVVVoooooo + 46.463) ∗ (−0.0032 ∗ VVVVoooooo

2 + 0.1239 ∗ VVVVoooooo

+ 0.4914)

 Equation for non-climate controlled dehumidifiers with bucket: 

NN (BB)CCCCCCCCCCCCCCCCCCCC RRRRRR TTTT �

h �

= (−0.1186 ∗ VVVVoooooo + 49.389) ∗ (−0.0011 ∗ VVVVoooooo

2 + 0.016 ∗ VVVVoooooo

+ 0.4091)

 Equation for non-climate controlled, direct-drain dehumidifiers:

NN ( )CCCCCCCCCCCCCCCCCCCC RRRRRR TTTT �

h �

= (1.5535 ∗ VVVVoooooo + 27.778) ∗ (−0.0013 ∗ VVVVoooooo

2 + 0.0265 ∗ VVVVoooo

+ 0.5783)

Where: 

VDout = vapor density. 

Portable Units—Fraction of Hours in Fan-only Mode

Willem et al., 2013, predicted fan-only run time as a function of dehumidification mode 

time. The following equation describes that relationship as applied to a large data set. The 

equation is not meant to be representative of individual dehumidifier units because fan use can 

vary widely among dehumidifiers. 

FFFF RRRRRR TTTT �

h � = (.2518) ∗ (−0.0265 ∗ tDehum

2 + 1.6385 ∗ (tDehum) − 6.1693)

Where;

ttDDDDh uu = tt dd h uu uu �

h �

Portable Units— Fraction of Hours in Standby/Off Mode

 DOE determined the amount of time in standby/off mode by subtracting both the 

compressor and the fan-only run times from the total time of dehumidifier use. 

Whole-Home Units— Fraction of Hours in Fan-only and Standby/Off Modes

The small sample size in the whole-home dehumidifier study made it impossible to 

correlate vapor density to compressor use (Burke et al., 2014). The authors were, however, able 

to develop time fractions for all operating modes. DOE used the average amount of time a 

dehumidifier operated during a 24-hour period. As shown in Table 7.3.4, the compressors in the 

whole-home dehumidifiers in the study did not operate all day long. 
7-7 

Table 7.3.4 Fractions of Hours for Whole-Home Dehumidifiers Operated in Different 

Modes 

Mode

Percent of Operating 

Hours (%)

Mean

Dehumidification 50

Fan-only 0.2

Standby/Off 49.0

7.4 ENERGY USE BY EFFICIENCY LEVEL AND PRODUCT CLASS

DOE calculated the annual energy use for the five product classes of dehumidifiers based 

on the assumptions and findings presented in Section 7.3. To calculate annual energy use, DOE 

used Eq. 7.2 in section 7.2 and assumed an average of 5.2 months of annual usage for portable 

dehumidifiers and 5.6 months of annual usage for whole-home dehumidifiers. Efficiencies are 

given in terms of integrated energy factor, which divides the amount of condensate removed 

divided by a sum of energy use in dehumidification, fan-only, and standby/off modes. 

Table 7.4.1 Portable Dehumidifiers ≤30.00 Pints/Day: Annual Energy Use by Efficiency 

Level

Level Integrated Energy Factor 

(L/kWh)

Annual Energy Use* 

(kWh/year) 

Baseline 0.77 739.4

1 1.10 523.0

2 1.20 481.0

3 1.30 445.4

4 1.57 371.9

* Capacity = 21.0 pints/day; annual usage = 3,799 hours; XDehum = 35.3%; XFan

= 6.5%; XStby = 58.2%, WFan = 65 W; WStby = 1 W.

Table 7.4.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Annual Energy Use by 

Efficiency Level

Level Integrated Energy Factor

(L/kWh)

Annual Energy Use*

(kWh/year)

Baseline 0.94 1073.2

1 1.20 844.7

2 1.40 726.6

3 1.60 638.0

4 1.80 569.2

* Capacity = 37.5 pints/day; annual usage = 3,799 hours; XDehum = 35.3%; XFan = 

6.5%; XStby = 58.2%, WFan = 65 W; WStby = 1 W.
7-8 

Table 7.4.3 Portable Dehumidifiers >45.00 Pints/Day: Annual Energy Use by Efficiency 

Level

Level Integrated Energy Factor

(L/kWh)

Annual Energy Use*

(kWh/year) 

Baseline 2.07 944.3

1 2.40 817.0

2 2.80 702.9

3 3.66 542.0

* Capacity = 72.5 pints/day; annual usage = 3,799 hours; XDehum = 35.3%; XFan = 

6.5%; XStby = 58.2%, WFan = 65 W; WStby = 1 W.

Table 7.4.4 Whole-Home Dehumidifiers ≤ 8.0 ft3 Case Volume: Annual Energy Use by 

Efficiency Level

Level Integrated Energy Factor

(L/kWh)

Annual Energy Use*

(kWh/year)

Baseline 1.77 1289.0

1 2.09 1093.0

2 2.53 904.5

* Capacity = 56.2 pints/day; annual usage = 4,091 hours; XDehum = 50%; XFan

=0.2%; XStby = 49%; WFan = 142 W; WStby = 4.5 W.

Table 7.4.5 Whole-Home Dehumidifiers >8.0 ft3 Case Volume: Annual Energy Use by 

Efficiency Level

Level Integrated Energy Factor

(L/kWh)

Annual Energy Use*

(kWh/year) 

Baseline 2.41 1542.9

1 2.70 1378.1

2 3.52 1059.2

3 4.50 830.5

* Capacity = 91.7 pints/day; annual usage = 4,091 hours; XDehum = 50%; XFan

=0.2%; XStby = 49%; WFan = 142 W; WStby = 4.5 W.

7.5 VARIABILITY OF ANNUAL DEHUMIDIFIER ENERGY USE

The EIA performs the RECS, collecting information for developing a national database 

of characteristics of a range of representative housing units, appliance usage patterns, and 

household demographics. The RECS reports on the presence of dehumidifiers in households and 

the amount of time the dehumidifiers are used. DOE used the 2009 version of RECS to 

determine the variability of dehumidifiers’ annual energy consumption. 
7-9 

7.5.1 Monthly Dehumidifier Usage 

RECS 2009 questioned each household on two aspects of dehumidifier use: (1) ownership 

and (2) monthly use. Of the 12,083 household records contained in RECS 2009, 1,621 indicate 

use of dehumidifiers, which represents 13.2 percent of the households nationwide. RECS 2009

provides five categories for how long a household’s dehumidifier is in use (plugged in): 1 to 3, 4 

to 6, 7 to 9, 10 to 11, or 12 months. 

 Of the 1,621 households that use dehumidifiers, most are located in the Northeast or 

upper Midwest regions of the country. RECS 2009 disaggregates the households that have 

dehumidifiers into 10 regions based on the 10 Census divisions. Within each region, the data 

further show the percentages of households that do and do not have basements. The RECS data 

seem to confirm conventional wisdom that dehumidifiers are used primarily in basements (76 

percent of dehumidifiers are in households that have basements). Figure 7.5.1 shows both where 

the households that have dehumidifiers are located geographically, and where in the house the 

dehumidifier is located. The blue bars show that of all the census divisions, the East North 

Central has the highest percentage of dehumidifiers (30 percent). The red and green bars show 

the percentage of homes having basement dehumidifiers within any one census division. More 

than 90 percent of the households that have a dehumidifier in the West North Central census 

division locate it in the basement.

Figure 7.5.1 Percent of Households that Have Dehumidifiers by Region and Presence of 

Basement 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

New England Mid-Atlantic East North 

Central

West North 

Central

South Atlantic East South 

Central

West South 

Central

Mountain 

North

Mountain 

South

Pacific

with Dehumidifier Basement No Basement
7-10 

7.5.2 Applications of Whole-Home Dehumidifiers 

 Although RECS itself does not distinguish between dehumidifier types, RECS variables 

can be used to approximate which households use portable dehumidifiers, and which use wholehome dehumidifiers. DOE assumed that households use whole-home dehumidifiers if they:

• are located in the Northeast, Midwest, or South Census regions; 

• are located in the Building America Climate region categorized as “mixed humid”; 

• are single-family homes, either attached or detached; 

• have a central air conditioner, and 

• have duct work. 

7.5.3 Annual Monthly Use

RECS collected data on annual use aggregated into monthly ranges. Table 7.5.1 shows 

the weighted number of households in each group of months. The maximum number in each 

range was used to more closely approximate the usage assumptions in DOE’s dehumidifier test 

procedure. 

Table 7.5.1 Dehumidifier Use by Range of Months 

Use Range Portable Whole-Home

(Months) 

RECS

Record 

Count

Total 

Estimated 

Households

RECS

Record 

Count

Total 

Estimated

Households

1–3 708 6,479,451 6 58,569

4–6 492 4,456,581 9 99,188

7–9 116 1,056,969 2 23,390

10–11 36 382,792 -- --

12 249 2,414,801 3 32,862

Total 1,601 14,790,595 20 214,009

7.6 LINKING RECS HOUSEHOLDS TO CLIMATE PARAMETERS

 To apply dehumidifier energy use data from the Willem field study, DOE matched the 

locations of RECS households having portable dehumidifiers with those of National Climatic 

Data Center (NCDC) weather stations. The match enabled DOE to include, for each RECS 

household, vapor density, hourly temperature and relative humidity parameters that could be 

used to estimate portable dehumidifier operation and energy use for the RECS household sample. 

See appendix 7B for more details. 
7-11 

7.6.1 Derivation of Outdoor Air Temperatures

RECS 2009 reports both heating and cooling degree-days at a base temperature of 65 °F 

for each housing record, but provides no monthly data or humidity data. To obtain more precise 

temperature information for the households in the RECS sample, DOE assigned a physical 

location to each RECS household. DOE took the following steps. 

1. There are 151 NCDC weather stations that provide hourly outdoor air temperatures and 

humidity. Those weather stations also provided the 2009 heating and cooling degree-days 

at a base temperature of 65 °F. The period covered by the 2009 heating and cooling 

degree-days from the weather stations matched the period used to determine degree-days 

in RECS 2009. 

2. DOE assigned each RECS household to one of the 151 weather stations by calculating 

which weather station (within the appropriate census region or large state) best matched 

the 2009 heating and cooling degree-days in the RECS data set.

 The following equation calculates the degree-day distance between the 2009 weather 

station data and RECS 2009 data.

= �(HH 2 − HH 1)2 + (CC 2 − CC 1)2

Where:

DDD = degree-day distance,

HDD1 = heating degree-days from 2009 weather station data, 

HDD2 = heating degree-days from RECS 2009 data,

CDD1 = cooling degree-days from 2009 weather station data, and 

CDD2 = cooling degree-days from RECS 2009 data.

DOE then took the following steps to develop energy use profiles for portable 

dehumidifiers in U.S. households. 

1. Used field-metered data from 2012 and 2013 paired with NCDC weather station data to 

determine the relationship between outdoor and indoor conditions and the time lag 

between them.

2. Developed models to predict dehumidifier operation based on hourly (lagged) outdoor 

conditions. 

3. Used the models developed in step 2 and the NCDC weather data to estimate 

dehumidifiers’ hours of operation for RECS households. The dehumidifier’s relative 

humidity set point was not recorded for every field-metered dehumidifier. 

4. Calculated annual dehumidifier energy use for RECS households. 
7-12 

REFERENCES

1 Willem, H., et al. 2013. Using Field-Metered Data to Quantify Annual Energy Use of 

Residential Portable Unit Dehumidifiers. Lawrence Berkeley National Laboratory (LBNL), 

Berkeley, CA. Report LBNL-6469E. 

2 Burke, Tom, et al. 2014. Whole-Home Dehumidifiers: Field – Monitoring Study.

 Lawrence Berkeley National Laboratory, Berkeley, CA. LBNL-6777E. 

3 Zogg, R., and D. Alberino. Electricity Consumption by Small End Uses in Residential 

Buildings: Final Report. 1998. Report to Office of Building Equipment, U.S. Department of 

Energy. Arthur D. Little (ADL), Cambridge, MA. 

4 Association of Home Appliance Manufacturers (AHAM). 2006. AHAM Comments on 

Framework Document for Home Appliance Products. Docket Number EE-2006-STD-0127, 

RIN 1904-AB49. May 11. 

5 U.S. Environmental Protection Agency and U.S. Department of Energy. ENERGY STAR. 

Qualified Appliance Savings Fact Sheets, Inputs. 2006. 

6 U.S. Environmental Protection Agency and U.S. Department of Energy. ENERGY STAR. 

Savings Calculator – Dehumidifiers (Assumptions). 2006. (Accessed March 8, 2006.) 

<http://www.energystar.gov/index.cfm?c=dehumid.pr_dehumidifiers> 

7 Mattison, L., and D. Korn. 2012. Dehumidifiers: A Major Consumer of Residential 

Electricity. The Cadmus Group, Inc. August. At American Council for an Energy Efficient 

Economy, ACEEE Summer Study on Energy Efficiency in Buildings. 
8-i 

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach to Analysis ............................................................................. 8-1

8.1.2 Overview of Inputs to Analysis ........................................................................... 8-3

8.2 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ................ 8-4

8.2.1 Inputs to Total Installed Cost ............................................................................... 8-5

8.2.1.1 Forecasting Future Product Prices .............................................................. 8-6

8.2.1.2 Baseline Manufacturer Cost ....................................................................... 8-8

8.2.1.3 Incremental Manufacturer Cost by Efficiency Level ................................. 8-8

8.2.1.4 Overall Markup ........................................................................................ 8-10

8.2.1.5 Installation Costs ...................................................................................... 8-10

8.2.1.6 Total Installed Cost .................................................................................. 8-11

8.2.2 Inputs to Operating Cost .................................................................................... 8-13

8.2.2.1 Annual Operating Hours .......................................................................... 8-14

8.2.2.2 Operating Hours by Mode ........................................................................ 8-15

8.2.2.3 Power Use by Mode ................................................................................. 8-17

8.2.2.4 Residential Electricity Prices.................................................................... 8-17

8.2.2.5 Energy Price Trends ................................................................................. 8-19

8.2.2.6 Repair and Maintenance Costs ................................................................. 8-20

8.2.2.7 Product Lifetime ....................................................................................... 8-20

8.2.3 Discount Rates ................................................................................................... 8-25

8.2.4 Compliance Date of Standard ............................................................................ 8-30

8.2.5 Product Energy Efficiency in the Base Case ..................................................... 8-30

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ........... 8-32

8.3.1 Summary of Results ........................................................................................... 8-33

8.3.1.1 Distributions of Impacts ........................................................................... 8-37

8.3.1.2 Range of Impacts ...................................................................................... 8-40

8.4 REBUTTABLE PAYBACK PERIOD .......................................................................... 8-43

8.4.1 Inputs to Rebuttable Payback Period Analysis .................................................. 8-44

8.4.2 Results of Rebuttable Payback Period Analysis ................................................ 8-44

REFERENCES .......................................................................................................................... 8-46

LIST OF TABLES

Table 8.2.1 Inputs to Total Installed Cost ......................................................................... 8-5

Table 8.2.2 Baseline Manufacturer Costs ......................................................................... 8-8

Table 8.2.3 Portable Dehumidifiers ≤30.00 Pints/Day: Incremental Manufacturer 

Cost by Efficiency Level ............................................................................... 8-9

Table 8.2.4 Portable Dehumidifiers 30.01–45.00 Pints/Day: Incremental 

Manufacturer Cost by Efficiency Level ......................................................... 8-9
8-ii

Table 8.2.5 Portable Dehumidifiers >45.00 Pints/Day: Incremental Manufacturer 

Cost by Efficiency Level ............................................................................... 8-9

Table 8.2.6 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Incremental 

Manufacturer Cost by Efficiency Level ......................................................... 8-9

Table 8.2.7 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Incremental 

Manufacturer Cost by Efficiency Level ....................................................... 8-10

Table 8.2.8 Portable Dehumidifiers: Overall Markup .................................................... 8-10

Table 8.2.9 Whole-Home Dehumidifiers: Overall Markup ............................................ 8-10

Table 8.2.10 Whole-home Dehumidifiers: Baseline Installation Costs ............................ 8-11

Table 8.2.11 Portable Dehumidifiers ≤30.00 Pints/Day: Consumer Product Prices, 

Installation Costs, and Total Installed Costs ................................................ 8-11

Table 8.2.12 Portable Dehumidifiers 30.01–45.00 Pints/Day: Consumer Product 

Prices, Installation Costs, and Total Installed Costs .................................... 8-12

Table 8.2.13 Portable Dehumidifiers >45.00 Pints/Day: Consumer Product Prices, 

Installation Costs, and Total Installed Costs ................................................ 8-12

Table 8.2.14 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Consumer 

Product Prices, Installation Costs, and Total Installed Costs ....................... 8-12

Table 8.2.15 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Consumer 

Product Prices, Installation Costs, and Total Installed Costs ....................... 8-12

Table 8.2.16 Inputs to Operating Cost .............................................................................. 8-13

Table 8.2.17 Usage Data for RECS2009 Portable Dehumidifiers ................................... 8-14

Table 8.2.18 Usage Data for Whole-Home Dehumidifiers .............................................. 8-15

Table 8.2.19 Power Use by Mode ..................................................................................... 8-17

Table 8.2.20 Average Residential Electricity Prices in 2012 ........................................... 8-19

Table 8.2.21 Dehumidifiers: Product Lifetime Estimates and Sources ........................... 8-20

Table 8.2.22 Portable Dehumidifiers: Average, Minimum, and Maximum Product 

Lifetimes Used in LCC Analysis ................................................................. 8-21

Table 8.2.23 Room Air Conditioners: Product Lifetime Estimates and Sources ............. 8-22

Table 8.2.24 Room Air Conditioner Lifetime Reference Values ..................................... 8-23

Table 8.2.25 Lifetime Parameters ..................................................................................... 8-25

Table 8.2.26 Definitions of Income Groups ..................................................................... 8-25

Table 8.2.27 Types of Household Debt and Equity by Percentage Shares (%) ................ 8-27

Table 8.2.28 Data Used to Calculate Real Effective Mortgage Rates .............................. 8-28

Table 8.2.29 Average Real Effective Interest Rates for Household Debt (%) ................. 8-28

Table 8.2.30 Average Nominal and Real Interest Rates for Household Equity ............... 8-29

Table 8.2.31 Average Real Effective Discount ................................................................. 8-30

Table 8.2.32 Dehumidifiers ≤30.00 Pints/Day: Base-Case Market Shares ...................... 8-31

Table 8.2.33 Dehumidifiers 30.01–45.00 Pints/Day: Base-Case Market Shares ............. 8-31

Table 8.2.34 Dehumidifiers >45.00 Pints/Day: Base-Case Market Shares ...................... 8-32

Table 8.2.35 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Base-Case 

Market Shares .............................................................................................. 8-32

Table 8.2.36 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Base-Case 

Market Shares .............................................................................................. 8-32

Table 8.3.1 Dehumidifiers ≤30.00 Pints/Day: LCC Results ........................................... 8-34

Table 8.3.2 Dehumidifiers ≤30.00 Pints/Day: Simple PBP Results ............................... 8-34

Table 8.3.3 Dehumidifiers 30.01–45.00 Pints/Day: LCC Results .................................. 8-34
8-iii

Table 8.3.4 Dehumidifiers 30.01–45.00 Pints/Day: Simple PBP Results ...................... 8-35

Table 8.3.5 Dehumidifiers >45.00 Pints/Day: LCC Results ........................................... 8-35

Table 8.3.6 Dehumidifiers >45.00 Pints/Day: Simple PBP Results ............................... 8-35

Table 8.3.7 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): LCC Results .......... 8-36

Table 8.3.8 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Simple PBP 

Results .......................................................................................................... 8-36

Table 8.3.9 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): LCC Results .......... 8-36

Table 8.3.10 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Simple PBP 

Results .......................................................................................................... 8-37

Table 8.4.1 Rebuttable Payback Periods: Portable Dehumidifiers ................................. 8-44

Table 8.4.2 Rebuttable Payback Periods: Whole-Home Dehumidifiers ......................... 8-45

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP .............................. 8-4

Figure 8.2.1 Future Price Projection for Portable Dehumidifiers and Whole-Home 

Dehumidifier .................................................................................................. 8-8

Figure 8.2.2 Probability Distribution of Dehumidification Mode for Whole-Home 

Dehumidifiers .............................................................................................. 8-16

Figure 8.2.3 Probability Distribution of Fan-Only Mode for Whole-Home 

Dehumidifiers .............................................................................................. 8-16

Figure 8.2.4 Probability Distribution of Standby/Off Mode for Whole-Home 

Dehumidifiers .............................................................................................. 8-17

Figure 8.2.5 Residential Electricity Price Trends ............................................................. 8-20

Figure 8.2.6 Weibull Function for Lifetime of Portable Dehumidifiers .......................... 8-24

Figure 8.2.7 Weibull Function for Lifetime of Whole-Home Dehumidifiers .................. 8-24

Figure 8.3.1 Dehumidifiers ≤30.00 Pints/Day: Base-Case LCC Distribution ................. 8-37

Figure 8.3.2 Dehumidifiers 30.01–45.00 Pints/Day: Base-Case LCC Distribution......... 8-38

Figure 8.3.3 Dehumidifiers >45.00 Pints/Day: Base-Case LCC Distribution ................. 8-38

Figure 8.3.4 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Base-Case LCC 

Distribution .................................................................................................. 8-39

Figure 8.3.5 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Base-Case LCC 

Distribution .................................................................................................. 8-39

Figure 8.3.6 Dehumidifiers ≤30.00 Pints/Day: LCC Savings Distribution for 

Efficiency Level 3 ........................................................................................ 8-40

Figure 8.3.7 Dehumidifiers ≤30.00 Pints/Day: Range of Average LCC Savings ............ 8-41

Figure 8.3.8 Dehumidifiers 30.01–45.00 Pints/Day: Range of Average LCC 

Savings ......................................................................................................... 8-41

Figure 8.3.9 Dehumidifiers >45.00 Pints/Day: Range of Average LCC Savings ............ 8-42

Figure 8.3.10 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Range of 

Average LCC Savings.................................................................................. 8-42

Figure 8.3.11 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Range of 

Average LCC Savings.................................................................................. 8-43
8-1 

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter of the NOPR TSD describes the DOE’s method for analyzing the economic 

impacts of new energy conservation standards on individual consumers. The effects of standards 

on individual consumers include a change in operating expense (usually decreased) and a change 

in purchase price (usually increased). This chapter describes three metrics DOE used to 

determine the effects of standards on individual consumers of dehumidifiers. 

• Life-cycle cost (LCC) is the total consumer expense over the life of an appliance, 

including purchase price and operating costs (including energy expenditures). DOE 

discounts future operating costs to the time of purchase, and sums them over the 

lifetime of the product. 

• Payback period (PBP) measures the amount of time it takes a consumer to recover the 

assumed higher purchase price of more energy efficient products through lower 

operating costs.

• Rebuttable payback period is a special case of the PBP. Whereas LCC and PBP are 

estimated given a range of inputs that reflect field conditions, rebuttable payback 

period is based on laboratory conditions, specifically inputs to DOE’s test procedure.

 Inputs to the LCC and PBP calculations are discussed in section 8.2 of this chapter. 

Results are presented in section 8.3. The rebuttable PBP is discussed in section 8.4. Key 

variables and calculations are presented for each metric. DOE performed the calculations 

discussed herein using a series of Microsoft Excel spreadsheets, which are accessible on the 

Internet. 

(http://www1.eere.energy.gov/buildings/appliance_standards/residential/dehumidifiers.html) 

Details and instructions for using the spreadsheets are presented in appendix 8A. 

8.1.1 General Approach to Analysis 

DOE uses the following equation to calculate LCC, the total consumer expense 

throughout the life of an appliance. 

( ) ∑= +

+=

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 appliance lifetime, from year 1 to year N,

N = lifetime of the appliance in years,

OC = operating cost in dollars, 

r = discount rate, and 
8-2 

t = year for which operating cost is being determined. 

Numerically, the PBP, defined above, is the ratio of the increase in purchase cost (i.e., 

from a less energy efficient design to a more efficient design) to the decrease in annual operating 

expenditures. This type of calculation results in what is termed a simple payback period, because 

it does not take into account changes in operating expenses over time or the time value of money. 

That is, the calculation is done at an effective discount rate of zero percent. The equation for PBP 

is:

OC

IC PBP

∆

∆ =

Where:

ΔIC = difference in total installed cost between the more energy efficient design and the 

baseline design, and 

ΔOC = difference in annual operating expenses. 

Payback periods are expressed in years. Payback periods greater than the life of the 

product indicate that the increased total installed cost is not recovered through reduced operating 

expenses.

 Recognizing that several inputs to the determination of consumer LCC and PBP are 

either variable or uncertain, DOE conducted the LCC and PBP analysis by modeling both the 

uncertainty and variability of the inputs using Monte Carlo simulation and probability 

distributions. Appendix 8B provides a detailed explanation of Monte Carlo simulation and the 

use of probability distributions. DOE used Microsoft Excel spreadsheets combined with Crystal 

Ball (a commercially available add-in program) to develop LCC and PBP spreadsheet models 

that incorporate both Monte Carlo simulation and probability distributions. 

 In addition to using probability distributions to characterize several of the inputs to the 

analysis, DOE developed a sample of individual households that use dehumidifiers. By 

developing household samples, DOE was able to calculate the LCC and PBP for each household 

to account for the variability in energy consumption and/or energy price associated with a range 

of households. 

 As described in chapter 7 of this NOPR TSD, DOE used the EIA’s RECS 2009 to 

develop household samples for both portable and whole-home dehumidifiers.1 The EIA designed 

RECS 2009, which consists of 12,083 housing units, to be a national representation of household 

population in the United States. DOE used the subset of RECS 2009 records in which the 

household has a portable dehumidifier. Because RECS 2009 does not provides stock and usage 

information for whole-home dehumidifiers, DOE used some of the variables that were assigned 

to central air-conditioners as the sample variables for whole-home dehumidifiers. Refer to 

chapter 7 of this NOPR TSD for details. DOE used RECS to establish the variability of annual 

dehumidifier use and of energy prices. DOE assigned unique annual hours of operation to each 

household in the sample. The variability among households in annual dehumidifier use and/or 
8-3 

energy pricing contributes to the range of LCCs and PBPs calculated for the baseline efficiency 

level and each increased efficiency level. 

DOE displays the LCC results as distributions of impacts compared to baseline 

conditions. Results, which are presented in section 8.3, are based on 10,000 samples per Monte 

Carlo simulation run. To illustrate the implications of the analysis, DOE generated a frequency 

chart that depicts the variation in LCC for each efficiency level being considered. 

8.1.2 Overview of Inputs to Analysis 

 DOE categorizes inputs to the LCC and PBP analysis as (1) inputs for establishing the 

purchase expense, otherwise known as the total installed cost, and (2) inputs for calculating 

operating costs. The primary inputs for establishing the total installed cost are listed below.

• Baseline manufacturer cost: The costs incurred by the manufacturer to produce 

products that meet current minimum efficiency standards. 

• Standard-level manufacturer cost increases: The change in manufacturer costs 

associated with producing products that meet a given standard level. 

• Markups and sales tax: The increases associated with converting the manufacturer 

cost to a consumer product cost. 

• Installation cost: The cost to the consumer of installing the product. The installation 

cost represents all costs required to install the product other than the marked-up 

consumer product cost. The installation cost includes labor, overhead, and any 

miscellaneous materials and parts. Thus, the total installed cost equals the consumer 

product cost plus the installation cost. 

• Learning rate: The cost reduction factor associated with economies of scale and 

technology learning. 

 The primary inputs for calculating operating costs are listed below.

• Product energy consumption: The on-site energy use associated with operating a 

product. 

• Product efficiency: The product energy consumption associated with standard-level 

products (i.e., products having efficiencies greater than those of baseline products). 

• Energy prices: The prices consumers pay for energy (e.g., electricity or natural gas). 

• Energy price trends: DOE used the EIA’s AEO 2015 2

 to project energy prices. 

• Repair and maintenance costs: Repair costs are associated with repairing or replacing 

components that have failed. Maintenance costs are associated with maintaining the 

operation of the product. 

• Lifetime: The age at which the product is retired from service. 

• Discount rate: The rate at which DOE discounts future expenditures to establish their 

present value. 

 The data inputs for calculating the PBP for each TSL are the total installed cost of the 

product to the consumer for each energy efficiency level and the annual (first-year) operating 

expenditures. The inputs to total installed cost are the product cost plus the installation cost. The 
8-4 

inputs to operating costs are the first year energy cost, the annual repair cost, and the annual 

maintenance cost. The PBP uses the same inputs as the LCC analysis, except the PBP does not 

require energy price trends or discount rates. Because the PBP is what is termed a simple 

payback, the required energy price is only for the year in which a new energy efficiency standard 

takes effect. The energy price DOE uses in the PBP calculation is the price projected for that 

year. Discount rates are also not required for calculating the simple PBP.

Figure 8.1.1 depicts the relationships among inputs to the calculation of the LCC and 

PBP. In the figure, the yellow boxes indicate inputs, the green boxes indicate intermediate 

outputs, and the blue boxes indicate final outputs (the LCC and PBP). 

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP 

8.2 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

DOE gathered most of the data for performing the LCC and PBP analysis in 2014. DOE 

expresses dollar values in 2013$. 

Baseline

Manufacturer

Cost

Std-Level

Manufacturer

Cost

Manufacturer

Markup

Retailer or

Distributor

Markup

Sales Tax

Installation

Cost

Energy

Consumption

 Product

Price

Lifetime

Operating

Expense

Annual Energy

Expense

Lifetime Repair and

Maintenance

Cost

Discount Rate

Energy Price

Trends

Annual

Operating

Expense

Payback

Period

Life-Cycle

Cost

Energy Prices

Total Installed

Cost
8-5 

8.2.1 Inputs to Total Installed Cost 

DOE uses the following equation to define the total installed cost (IC). 

+= INSTCPCIC

Where:

IC = total installed cost, 

CPC = consumer product cost (i.e., consumer cost for the product only), and 

INST = consumer cost to install the product. 

The product cost depends on how the consumer purchases the product. As discussed in 

chapter 6 of this NOPR TSD, DOE defined markups and sales taxes for converting 

manufacturing costs into consumer product costs. Table 8.2.1 summarizes the inputs for 

determining total installed cost.

Table 8.2.1 Inputs to Total Installed Cost 

Baseline manufacturer cost

Standard-level manufacturer cost

Markups throughout distribution chain

Sales tax (replacement applications)

Installation cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce 

products that meet current minimum efficiency standards. Standard-level manufacturer cost

increases are the change in manufacturer cost associated with producing products that meet a 

new standard level. Markups and sales tax convert the manufacturer cost to a consumer product 

cost. The installation cost represents all costs required for the consumer to install the product, 

other than the marked-up consumer product cost. The installation cost includes labor, overhead, 

and any miscellaneous materials and parts. 

DOE calculated the IC for baseline products based on the following equation. 

MFG OVERALL BASE BASE

BASE BASE BASE

COST MU INST

IC CPC INST

= × +

+=

_

Where:

ICBASE = total installed cost for baseline model, 

CPCBASE = consumer product cost for baseline model, 

INSTBASE = installation cost for baseline model, 

COSTMFG = manufacturer cost for baseline model, and 

MUOVERALL_BASE = overall baseline markup (product of manufacturer markup, baseline 
8-6 

retailer or distributor markup, and sales tax). 

DOE used the following equation to calculate the IC for standard-level products. 

( ) ( )

( ) ( )

( ) BASE MFG OVERALL INCR STD

BASE BASE STD STD

BASE STD BASE STD

STD STD STD

IC COST MU INST

CPC INST CPC INST

CPC CPC INST INST

IC CPC INST

+= D × + D

= + + D + D

= + D + + D

+=

_

Where:

ICSTD = total installed cost for standard-level model, 

CPCSTD = consumer product cost for standard-level model, 

INSTSTD = installation cost for standard-level model, 

CPCBASE = consumer product cost for baseline model, 

ΔCPCSTD = change in product cost for standard-level model,

INSTBASE = baseline installation cost,

ΔINSTSTD = change in installation cost for standard-level model, 

ICBASE = baseline total installed cost, 

ΔCOSTMFG = change in manufacturer cost for standard-level model, and

MUOVERALL_INCR = overall incremental markup (product of manufacturer markup, incremental

retailer or distributor markup, and sales tax). 

The rest of this section provides information about each of the above input variables, 

which DOE used to calculate the IC for dehumidifiers. 

8.2.1.1 Forecasting Future Product Prices

 

 Historical price data for certain appliances and equipment that have been subject to 

energy conservation standards indicate that the assumption of constant real prices and costs may 

overestimate long-term trends in appliance and equipment prices. Economic literature and 

historical data suggest that the real costs of products may trend downward over time in response 

to “learning” or “experience” curves. 

An extensive body of literature discusses the learning or experience curve phenomenon, 

typically based on observations in the manufacturing sectora

. Based on the experience curve 

approach, the real cost of production is related to the cumulative production, or experience, of a 

product. Typically, DOE uses historical shipments data to estimate cumulative shipments 

(production). However, the historical shipment data for portable and whole-home dehumidifiers 

are too limited to construct robust cumulative production estimation for the products. Therefore, 

 

a Margaret Taylor and K. Sydny Fujita. Accounting for Technological Change in Regulatory Impact Analyses: The 

Learning Curve Technique. Lawrence Berkeley National Laboratory. April 2013. LBNL-6195E. (Available at: 

http://eetd.lbl.gov/publications/accounting-for-technological-change-0) 
8-7 

DOE used the appropriate Producer Price Index (PPI) series fit to an exponential model having 

year as the explanatory variable. In this case, the exponential function takes the form of:

YY = aa ∙ eebbbb

where Y is the dehumidifier price index, X is the time variable, a is the constant, and b is 

the slope parameter of the time variable. 

 To derive the exponential parameters for portable dehumidifiers and whole-home 

dehumidifiers, DOE obtained historical PPI data for “small electric household appliances” and 

for “room air conditioners and dehumidifiers” from the Bureau of Labor Statistics, respectively. 

Although the two PPI series encompass much more than portable and whole-home 

dehumidifiers, no PPI data specific to the two products were available. The PPI data reflect 

nominal prices, adjusted for changes in product quality. DOE calculated an inflation-adjusted 

(deflated) price index by dividing the PPI series by the Gross Domestic Product Chained Price 

Index for each product. The deflated price index is presented in 2012 dollar values. 

 

 For portable dehumidifiers, the regression performed as an exponential trend line fit 

results in an R-square of 0.99, which indicates a superior fit to the data. The fit results in a 2.02-

percent annual rate of price decline. The final estimated exponential function for portable 

dehumidifiers is:

YY = 6.266 × 1017 ∙ ee(−0.0204)

 For whole-home dehumidifiers, the regression performed as an exponential trend line fit 

results in an R-square of 0.96, which also indicates an excellent fit to the data. The fit results in a 

2.32-percent annual rate of price decline. The final estimated exponential function for wholehome dehumidifiers is:

YY = 2.949 × 1020 ∙ ee(−0.0235)

Based on the fitted regressions, DOE derived separate price factor indexes for portable 

and whole-home dehumidifiers for each future year in the analysis. For the LCC and PBP 

analysis, DOE renormalized the price factor index, setting 2012 equal to 1, to estimate the price 

of portable and whole-home dehumidifiers in 2019. 
8-8 

Figure 8.2.1 Future Price Projection for Portable Dehumidifiers and Whole-Home 

Dehumidifier 

8.2.1.2 Baseline Manufacturer Cost 

DOE developed the baseline manufacturer costs for all five product classes of 

dehumidifiers (described in chapter 5 of this NOPR TSD, Engineering Analysis). Baseline 

manufacturer costs are shown in Table 8.2.2. 

Table 8.2.2 Baseline Manufacturer Costs

Product Class

(Pints/Day)

Baseline Integrated 

Energy Factor (L/kWh)*

Baseline Manufacturer 

Cost (2013$)

≤ 30.00 0.77 $113.38

30.01–45.00 0.94 $136.99

> 45.00 2.07 $428.90

≤ 8.0 ft3 case volume (whole-home) 1.77 $397.75

>8.0 ft3 case volume (whole-home) 2.41 $537.96

 * L/kWh = liters (of moisture removed) per kilowatt-hour (of energy consumed). 

8.2.1.3 Incremental Manufacturer Cost by Efficiency Level

DOE used a reverse-engineering analysis to develop manufacturer cost increases 

associated with increases in dehumidifier efficiency. Refer to chapter 5 of this NOPR TSD for 

details. Table 8.2.3 through Table 8.2.7 present the incremental manufacturer costs at each 

efficiency level for all five product classes of dehumidifiers. 
8-9 

Table 8.2.3 Portable Dehumidifiers ≤30.00 Pints/Day: Incremental Manufacturer Cost 

by Efficiency Level

Efficiency

Level

Integrated Energy 

Factor 

(L/kWh)

Manufacturer Cost Increase 

(2013$)

Baseline 0.77 -

1 1.10 $0

2 1.20 $1.69

3 1.30 $4.27

4 1.57 $19.38

Table 8.2.4 Portable Dehumidifiers 30.01–45.00 Pints/Day: Incremental Manufacturer 

Cost by Efficiency Level

Efficiency

Level

Integrated Energy 

Factor (L/kWh)

Manufacturer Cost Increase 

(2013$)

Baseline 0.94 -

1 1.20 $0

2 1.40 $2.39

3 1.60 $8.07

4 1.80 $22.42

Table 8.2.5 Portable Dehumidifiers >45.00 Pints/Day: Incremental Manufacturer Cost 

by Efficiency Level

Efficiency

Level

Integrated Energy 

Factor 

(L/kWh)

Manufacturer Cost Increase 

(2013$)

Baseline 2.07 -

1 2.40 $42.81

2 2.80 $53.66

3 3.66 $120.33

Table 8.2.6 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Incremental 

Manufacturer Cost by Efficiency Level

Efficiency

Level

Integrated Energy 

Factor 

(L/kWh)

Manufacturer Cost Increase 

(2013$)

Baseline 1.77 -

1 2.09 $15.30

2 2.53 $129.22
8-10 

Table 8.2.7 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Incremental 

Manufacturer Cost by Efficiency Level

Efficiency

Level

Integrated Energy 

Factor 

(L/kWh)

Manufacturer Cost Increase 

(2013$)

Baseline 2.41 -

1 2.70 $6.20

2 3.52 $37.20

3 4.50 $161.39

8.2.1.4 Overall Markup 

The overall markup is the value determined by multiplying the manufacturer and 

retailer markups and the sales tax together to arrive at a single markup value. Table 8.2.8

and Table 8.2.9 show the overall baseline and incremental markups for portable and 

whole-home dehumidifiers, respectively. Refer to chapter 6 of this NOPR TSD for details. 

Table 8.2.8 Portable Dehumidifiers: Overall Markup 

Markup Baseline Incremental

Manufacturer 1.45

Retailer 1.39 1.13

Sales tax 1.071

Overall markup 2.16 1.75

 

Table 8.2.9 Whole-Home Dehumidifiers: Overall Markup 

Markup Replacement New Construction

Baseline Incremental Baseline Incremental

Manufacturer 1.45 1.45

Wholesaler 1.35 1.10 1.35 1.10

Mechanical contractor 1.53 1.22 1.45 1.15

General contractor -- -- 1.47 1.34

Sales tax 1.071 -- --

Overall markup 3.21 2.08 4.17 2.46

8.2.1.5 Installation Costs 

Based on the previous rulemaking conducted for dehumidifiers,3

 DOE determined that 

there are no installation costs for portable dehumidifiers. 
8-11 

DOE derived baseline installation costs for whole-home dehumidifiers from data in the 

RS Means Residential Cost Data, 2013.

4 The book estimates the labor required to install room 

air conditioners. Table 8.2.10 summarizes the nationally representative average costs associated 

with installing split-system air conditioners as presented in RS Means Residential Cost Data. 

Table 8.2.10 provides both bare costs (i.e., costs before overhead and profit (O&P)), and 

installation costs including O&P. DOE determined that installation costs would not be affected 

by increased efficiency levels. 

Table 8.2.10 Whole-home Dehumidifiers: Baseline Installation Costs 

Installation Type

Bare Costs (2013$) Including Overhead & Profit (2013$)

Material Labor Total Total Material* Labor**

Average $995 $338 $1,333 $1,663 $1,095 $568 

Average (2013$) $568

* Material costs including O&P equal bare costs plus 10% profit.

** DOE derived labor cost including O&P by subtracting materials plus O&P from total plus O&P.

Source: RS Means, Residential Cost Data. 2013.

8.2.1.6 Total Installed Cost 

Total installed cost is the sum of the consumer product cost and installation cost.

Table 8.2.11 through Table 8.2.15 present the total installed costs for each dehumidifier 

product class at each efficiency level examined. 

Table 8.2.11 Portable Dehumidifiers ≤30.00 Pints/Day: Consumer Product Prices, 

Installation Costs, and Total Installed Costs 

Efficiency

Level

Integrated 

Energy Factor 

(L/kWh)

Product Price 

(2013$)

Installation 

Cost (2013$)

Total Installed 

Cost (2013$)

Baseline 0.77 212 0.00 212

1 1.10 212 0.00 212

2 1.20 214 0.00 214

3 1.30 218 0.00 218

4 1.57 241 0.00 241
8-12 

Table 8.2.12 Portable Dehumidifiers 30.01–45.00 Pints/Day: Consumer Product Prices, 

Installation Costs, and Total Installed Costs 

Efficiency

Level

Integrated

Energy Factor 

(L/kWh)

Product

Price (2013$) 

Installation 

Cost (2013$)

Total Installed 

Cost (2013$)

Baseline 0.94 256 0.00 256

1 1.20 256 0.00 256

2 1.40 259 0.00 259

3 1.60 268 0.00 268

4 1.80 290 0.00 290

Table 8.2.13 Portable Dehumidifiers >45.00 Pints/Day: Consumer Product Prices, 

Installation Costs, and Total Installed Costs 

Efficiency

Level

Integrated 

Energy Factor 

(L/kWh)

Product

Price (2013$) 

Installation 

Cost (2013$)

Total Installed 

Cost (2013$)

Baseline 2.07 915 0.00 915

1 2.40 989 0.00 989

2 2.80 1,008 0.00 1,008

3 3.66 1,124 0.00 1,124

Table 8.2.14 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Consumer Product

Prices, Installation Costs, and Total Installed Costs 

Efficiency

Level

Integrated 

Energy Factor 

(L/kWh)

Product

Price (2013$) 

Installation 

Cost (2013$)

Total Installed 

Cost (2013$)

Baseline 1.77 1,094 568 1,662

1 2.09 1,121 568 1,689

2 2.53 1,322 568 1,890

Table 8.2.15 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Consumer Product

Prices, Installation Costs, and Total Installed Costs 

Efficiency

Level

Integrated 

Energy Factor 

(L/kWh)

Product

Price (2013$) 

Installation 

Cost (2013$)

Total Installed 

Cost (2013$)

Baseline 2.41 1,574 568 2,142

1 2.70 1,586 568 2,154

2 3.52 1,644 568 2,212

3 4.50 1,877 568 2,445
8-13 

8.2.2 Inputs to Operating Cost

DOE defines operating cost (OC) by the following equation: 

OC RCEC ++= MC

Where: 

EC = energy expenditure associated with operating the product, 

RC = repair cost associated with component failure, and 

MC = cost for maintaining product operation. 

Table 8.2.16 shows the inputs for determining annual OCs and their discounted values 

throughout the product lifetime. 

Table 8.2.16 Inputs to Operating Cost

Annual energy consumption

Energy prices and price trends

Repair and maintenance costs

 

The annual energy consumption is the site energy use associated with operating the 

product. Annual energy consumption varies with product efficiency. Energy prices are the prices 

paid by consumers for energy (e.g., electricity or natural gas). Multiplying the annual energy 

consumption by the energy price yields the annual energy cost. Repair costs are associated with 

repairing or replacing components that have failed. Maintenance costs are associated with 

maintaining the operation of the product. DOE used energy price trends to forecast energy prices 

into the future and, along with the product lifetime and discount rate, to establish the present 

value of lifetime energy costs. 

DOE used the following equation to calculate the annual OC for baseline products. 

OCBASE = AECBASE × PRICEENERGY RCBASE ++ MCBASE ( )

Where:

OCBASE = operating cost for baseline product, 

AECBASE = annual energy consumption for baseline product, 

PRICEENERGY = energy price,

RCBASE = repair cost associated with component 

failure for baseline product, and

MCBASE = cost for maintaining operation of baseline 

product. 

DOE calculated the annual OC for standard-level products based on the following 

equation. 
8-14 

OCSTD = AECSTD × PRICEENERGY RCSTD ++ MCSTD ( )

Where:

OCSTD = operating cost for standard-level product, 

AECSTD = annual energy consumption for standard-level product, 

PRICEENERGY = energy price,

RCSTD = repair cost associated with component failure for standard-level product, 

and

MCSTD = cost for maintaining operation of standard-level product. 

The rest of this section provides information about each of the above input variables that 

DOE used to calculate the OCs for all product classes of dehumidifiers. 

8.2.2.1 Annual Operating Hours

As described in chapter 7 of this NOPR TSD and in section 8.1.1, DOE utilized RECS 

2009 and the two recent field studies (Willem, et al., 2013, Burke, et al., 2014)5,6 to develop 

samples of individual households that use either portable or whole-home dehumidifiers. By 

developing a household sample for each product type, DOE was able to perform the LCC and 

PBP calculations for each household to account for the variability in both energy use and energy 

price, as shown in Table 8.2.17 and Table 8.2.18. Refer to chapter 7 of the NOPR TSD for 

details. 

Table 8.2.17 Usage Data for RECS2009 Portable Dehumidifiers 

Usage Bin

(Months)

Share of RECS Households

Percentage* (%) Number (n)

1 to 3 months 43.8 708

4 to 6 months 30.1 492

7 to 9 months 7.2 116

10 to 11 months 2.6 36

Turned on all year 16.3 249

Total 100.0 1,601

*Percentages represent weighted values.

 Source: RECS 2009. 
8-15 

Table 8.2.18 Usage Data for Whole-Home Dehumidifiers 

Usage Bin

(Months) 

Share of RECS Households

Percentage* (%) Number (n)

1 to 3 months 27.4 6

4 to 6 months 46.4 9

7 to 9 months 10.9 2

Turn on all year 15.4 3

Total 100.0 20

*Percentages represent weighted values.

 Source: RECS 2009. 

8.2.2.2 Operating Hours by Mode 

The fraction of time a portable dehumidifier spends in each mode of operation 

(standby/off, fan-only, and dehumidification) affects the unit’s energy use. To determine the 

fraction of time spent in each operational mode, DOE linked the RECS households having 

portable dehumidifiers to climate parameters. By linking the geographic locations of the RECS 

households with data on outdoor vapor density, DOE was able to calculate a sampled RECS 

household’s dehumidification operating times for each month. DOE also disaggregated the 

portable dehumidifier usage into four installation categories based on climate type and type of 

condensate removal (manual emptying of a bucket or use of a direct drain). DOE assumed that 

market shares of portable dehumidifiers are divided 15% to 85% between direct-drain units and 

units having buckets. See chapter 7 of this NOPR TSD for more information. 

To determine the fraction of time whole-home dehumidifiers spend in each mode of 

operation, DOE used the probability distribution of operating hours by mode from the Willem 

study (Burke, et al., 2014)6

. DOE did not link the RECS data with outdoor vapor density for 

whole-home dehumidifiers because the sample size from the study is insufficient to apply results

to U.S. households in general. Figure 8.2.2 through Figure 8.2.4 summarize the probability 

distributions of operating hours by mode for whole-home dehumidifiers. DOE assigned a 

probability distribution based on the frequency range for each mode. 
8-16 

Figure 8.2.2 Probability Distribution of Dehumidification Mode for Whole-Home 

Dehumidifiers

Figure 8.2.3 Probability Distribution of Fan-Only Mode for Whole-Home Dehumidifiers
8-17 

Figure 8.2.4 Probability Distribution of Standby/Off Mode for Whole-Home 

Dehumidifiers

8.2.2.3 Power Use by Mode 

To determine power use by mode for portable dehumidifiers, DOE used a single value 

from the Willem study (Willem, et al., 2013)

5 for fan-only mode and standby/off mode. To 

determine power use by mode for whole-home dehumidifiers, DOE used a range of power use 

associated with fan-only mode and standby/off mode from the Willem study (Burke, et al., 

2014)6, and assigned a probability distribution to each mode. Table 8.2.19 summarizes the power 

use by mode for portable and whole-home dehumidifiers. 

Table 8.2.19 Power Use by Mode 

Product Type Fan-Only Mode

(W)

Standby/Off Mode

(W)

Portable dehumidifier 65 1

Whole-home dehumidifier 50.2-141.7 1-4.5

DOE used the following equation to determine power use by dehumidification mode. 

kkkkDDDDhuuuu = 

CC CC ∗ 0.473

IIIIII ∗ 24

8.2.2.4 Residential Electricity Prices

DOE derived electricity prices for each of the 27 RECS Reportable Domain categories 

regions. Using those data, DOE analyzed the regional variability of electricity prices at the 

regional level. 
8-18 

 DOE used data from EIA Form 8617 to estimate electricity prices for residential 

consumers in each of the 27 geographic areas. Those data, published annually, include annual 

electricity sales in kilowatt-hours; revenues from electricity sales; and number of consumers in 

the residential, commercial, and industrial sectors for every utility that serves final consumers. 

DOE calculated average residential electricity prices in two steps.

1. For each utility, an average residential price was estimated by dividing residential 

revenues by residential sales.

2. An average regional price was calculated, weighting each utility having customers in 

a region by the total number of residential consumers served in that region. 

Table 8.2.20 shows the average residential electricity price calculated for each 

geographic region.
8-19 

Table 8.2.20 Average Residential Electricity Prices in 2012 

Geographic Area Average Price 

(2013$/kWh)

Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.164

Massachusetts $0.152

New York $0.190

New Jersey $0.161

Pennsylvania $0.131

Illinois $0.116

Indiana, Ohio $0.115

Michigan $0.144

Wisconsin $0.134

Iowa, Minnesota, North Dakota, South Dakota $0.111

Kansas, Nebraska $0.109

Missouri $0.104

Virginia $0.112

Delaware, DC, Maryland, West Virginia $0.131

Georgia $0.114

North Carolina, South Carolina $0.114

Florida $0.116

Alabama, Kentucky, Mississippi $0.106

Tennessee $0.103

Arkansas, Louisiana, Oklahoma $0.092

Texas $0.112

Colorado $0.116

Idaho, Montana, Utah, Wyoming $0.099

Arizona $0.114

Nevada, New Mexico $0.119

California $0.156

Alaska, Hawaii, Oregon, Washington $0.119

Source: EIA Form 861.

8.2.2.5 Energy Price Trends

DOE used EIA’s price forecasts to estimate future trends in electricity prices. To arrive at 

prices in future years, DOE multiplied the average prices listed in Table 8.2.20 by the forecast of 

annual average price changes based on the reference case in EIA’s AEO 2015.

2 To estimate the 

trend after 2040, DOE followed the guidance EIA previously provided to the Federal Energy 

Management Program, to use the average rate of change during 2025–2040. 
8-20 

DOE calculated LCC and PBP based on three separate projections from the AEO 2015: 

reference case, low economic growth, and high economic growth. Those three cases reflect the 

uncertainty regarding economic growth during the forecast period. Figure 8.2.5 shows the three 

projected trends in residential electricity prices based on the three AEO 2015 cases. 

Figure 8.2.5 Residential Electricity Price Trends

8.2.2.6 Repair and Maintenance Costs

DOE included no changes to repair or maintenance costs for either portable or wholehome dehumidifiers that exceed baseline efficiency.

8.2.2.7 Product Lifetime

For portable dehumidifiers, DOE considered the sources listed in Table 8.2.21 to estimate 

product lifetime.

Table 8.2.21 Dehumidifiers: Product Lifetime Estimates and Sources

Lifetime (years) Source

Mean* = 8; Low* = 5; High* = 10 Appliance Magazine (2005**) 8

10 ACEEE (2001) 9

12 Northeast Energy Star Lighting and Appliance 10

* Estimates are first-ownership length, not full product lifetime. ** Most current citation found.

 The estimates from Appliance Magazine are “based on first-owner use of the product and 

does not necessarily mean the appliance is worn out.” In other words, Appliance Magazine’s 

lifetime estimates underestimate the actual lifetime of the products in those cases where the 
8-21 

product is used by two or more users. As a result, DOE excluded Appliance Magazine as a 

source for determining the average product lifetime. To determine the average product lifetime, 

DOE calculated the average value from estimates provided by the two remaining sources listed 

in the table above. The resulting average lifetime estimate is 11 years. DOE used the low 

estimate from Appliance Magazine to establish the minimum product lifetime and a triangular 

distribution to establish the maximum product lifetime.

Table 8.2.22 Portable Dehumidifiers: Average, Minimum, and Maximum Product 

Lifetimes Used in LCC Analysis

Product

Minimum

years

Average

years

Maximum

years

Dehumidifiers 5.0 11.0 17.0

DOE assumed whole-home dehumidifiers have the same life span as residential room air

conditioners. For the sources used to develop the room air conditioner lifetime parameters, see 

Table 8.2.23. The resulting lifetime parameters derived for room air conditioners for wholehome dehumidifiers are shown in Table 8.2.24. 
8-22 

Table 8.2.23 Room Air Conditioners: Product Lifetime Estimates and Sources

Typical Lifetime or Range (years) Source

Original Sources

Average = 9; Low = 7 Appliance Magazine, September 200811

12.5 ASHRAE 200812

15 CEC 200513

12 European Rulemaking Draft Report14

Average = 15; High = 20 NRDC15

Other Sources

Lifetime Source

9 Appliance 

Magazine, 1997 ENERGY STAR Savings Calculatorb

18 EnerGuide 2005 Natural Resources Canada, 200816

15 NA New Mexico Market Assessment, Itron 200617

18 NA Nebraska Public Power District18

12 See endnote NYSERDA SBC, 200219

9 NA Regional Technical Form (Northwest), 200220

12.5 DOE TSD 1997 NCEP report, LBNL 200421

19 Aspen Memo,

2002

NYSERDA Deemed Savings Database: ENERGY 

STAR22

13 (TTW) DOE TSD 2005 NYSERDA Deemed Savings Database: ENERGY 

STAR23

Low = 8, High =16 NEMS Residential Demand Module, 200824

13 LBNL 200825

Average = 10–15, 

Low = 8-12, High 

= 14-18

LBNL 199426

10 Consortium for Energy Efficiency27

10 - 12 American Council for an Energy Efficient 

Economy, 200728

Note: NA means the data source is not stated in the reference.

 

b

 ENERGY STAR Savings Calculator, Products, Room Air Conditioners. Efficient and 

conventional models. 
8-23 

Table 8.2.24 Room Air Conditioner Lifetime Reference Values 

Product Type Minimum

(Years)

Average

(Years)

Maximum

(Years)

Room Air Conditioners 3.0 10.5 20.0

 To perform the LCC and PBP analysis, DOE developed survival functions for 

dehumidifiers. DOE estimated the percentage of appliances of a given age that would still be in 

operation in a given year. This survival function, which DOE assumed has the form of a 

cumulative Weibull distribution, provides an average and a median appliance lifetime. 

 The Weibull distribution is a probability distribution commonly used to measure failure 

rates.c Its form is similar to that of an exponential distribution, which models a fixed failure rate, 

except that a Weibull distribution allows for a failure rate that changes through time. The 

cumulative Weibull distribution takes the form:

e x

xP

β

α

θ 



 



 − −

)( = for x > θ and

P(x) = 1 for x ≤ θ.

Where:

P(x) = probability that the appliance is still in use at age x; 

x = age of appliance; 

α = scale parameter, which would be the decay length in an exponential distribution; 

β = shape parameter, which determines the way in which the failure rate changes through 

time; and

θ = delay parameter, which allows for a delay before any failures occur.

 When β = 1, the failure rate is constant over time, giving the distribution the form of a 

cumulative exponential distribution. In the case of appliances, β commonly is greater than 1, 

reflecting an increasing failure rate as appliances age. Figure 8.2.6 and Figure 8.2.7 show the 

Weibull retirement and survival functions for portable and whole-home dehumidifiers, 

respectively. The results of DOE’s analysis are shown in Table 8.2.25. 

 

c For reference on the Weibull distribution, see sections 1.3.6.6.8 and 8.4.1.3 of the National Institute of Standards 

and Technology (NIST)/SEMATECH e-Handbook of Statistical Methods. <www.itl.nist.gov/div898/handbook/> 

(Last accessed August 21, 2012.)
8-24 

Figure 8.2.6 Weibull Function for Lifetime of Portable Dehumidifiers 

Figure 8.2.7 Weibull Function for Lifetime of Whole-Home Dehumidifiers 
8-25 

Table 8.2.25 Lifetime Parameters

Product Type Average 

(Years) 

Weibull Parameters

Alpha (Scale) Beta (Shape)

Portable dehumidifiers 11.0 11.00 4.20 

Whole-home 

dehumidifiers 19.01 20.30 2.50 

8.2.3 Discount Rates

 The discount rate is the rate at which future savings and expenditures are discounted to 

establish their present value. DOE uses publicly available data (the Federal Reserve Board’s 

Survey of Consumer Finances (SCF)) to estimate a consumer’s opportunity cost of funds related 

to appliance energy cost savings and maintenance costs. The discount rate value is applied in the 

LCC to future year energy cost savings and non-energy operations and maintenance costs in 

order to present the estimated net LCC and LCC savings. DOE notes that the discount rate used 

in the LCC analysis is distinct from an implicit discount rate, as it is not used to model consumer 

purchase decisions. The opportunity cost of funds in this case may include interest payments on 

debt and interest returns on assets. 

 DOE estimates separate discount rate distributions for six income groups, divided based 

on income percentile as reported in the Federal Reserve Board’s SCF.

29 This disaggregation 

reflects the fact that low and high income consumers tend to have substantially different shares 

of debt and asset types and tend to face different rates on debts and assets. Summaries of shares 

and rates presented in this chapter are averages across the entire population. 

Table 8.2.26 Definitions of Income Groups 

Income Group Percentile of Income

1 1st to 20th

2 21st to 40th

3 41st to 60th

4 61st to 80th

5 81st to 90th

6 91th to 99th

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Shares of Debt and Asset Classes 

 DOE’s approach involved identifying all relevant household debt or asset classes in order 

to approximate a consumer’s opportunity cost of funds related to appliance energy cost savings 

and maintenance costs. The approach assumes that, in the long term, consumers are likely to 

draw from or add to their collection of debt and asset holdings approximately in proportion to 
8-26 

their current holdings when future expenditures are required or future savings accumulate. DOE 

has included several previously excluded debt types (i.e., vehicle and education loans, 

mortgages, all forms of home equity loan) in order to better account for all of the options 

available to consumers. 

The average share of total debt plus equity and the associated rate of each asset and debt 

type are used to calculate a weighted average discount rate for each SCF household (Table 

8.2.27). The household-level discount rates are then aggregated to form discount rate 

distributions for each of the six income groups. Note that previously DOE performed aggregation 

of asset and debt types over households by summing the dollar value across all households and 

then calculating shares. Weighting by dollar value gave disproportionate influence to the asset 

and debt shares and rates of higher income consumers. DOE has shifted to a household-level 

weighting to more accurately reflect the average consumer in each income group.

 DOE estimated the average percentage shares of the various types of debt and equity 

using data from the Federal Reserve Board’s SCF for 1995, 1998, 2001, 2004, 2007, and 2010.d

DOE derived the household-weighted mean percentages of each source of financing throughout 

the 5 years surveyed. DOE posits that these long-term averages are most appropriate to use in its 

analysis. 

 

d Note that two older versions of the SCF are also available (1989 and 1992); these surveys are not used in this 

analysis, because they do not provide all of the necessary types of data (e.g., credit card interest rates, etc). DOE 

feels that the 15-year span covered by the six surveys included is sufficiently representative of recent debt and 

equity shares and interest rates.
8-27 

Table 8.2.27 Types of Household Debt and Equity by Percentage Shares (%)

Type of Debt or Equity Income Group

1 2 3 4 5 6

Debt:

Mortgage 18.9 24.1 33.1 38.1 39.3 25.0

Home equity loan 3.1 3.3 2.6 3.6 4.5 7.2

Credit card 15.3 13.0 11.8 8.7 6.0 2.7

Other installment loan 25.1 20.6 17.3 13.2 9.6 4.7

Other residential loan 0.7 0.6 0.6 0.7 1.0 1.2

Other line of credit 1.6 1.5 1.3 1.5 2.1 1.8

Equity:

Savings account 18.5 16.0 12.7 10.6 10.4 7.9

Money market account 3.6 4.5 4.0 4.5 5.0 8.6

Certificate of deposit 7.0 7.8 5.5 5.0 4.4 4.2

Savings bond 1.8 1.7 1.9 2.2 1.7 1.1

Bonds 0.2 0.4 0.5 0.7 0.8 3.8

Stocks 2.3 3.1 4.4 5.7 7.6 15.8

Mutual funds 2.1 3.5 4.3 5.7 7.6 15.9

Total 100.0 100.0 100.0 100.0 100.0 100.0

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Debt 

DOE estimated interest rates associated with each type of debt. The source for interest 

rates for mortgages, loans, credit cards, and lines of credit was the Federal Reserve Board’s SCF

for 1995, 1998, 2001, 2004, 2007, and 2010, which associates an interest rate with each type of 

debt for each household in the survey. 

In calculating effective interest rates for home equity loans and mortgages, DOE 

accounted for the fact that interest on both such loans is tax deductible (Table 8.2.28). This rate 

corresponds to the interest rate after deduction of mortgage interest for income tax purposes and 

after adjusting for inflation (using the Fisher formula).e For example, a 6-percent nominal 

mortgage rate has an effective nominal rate of 4.5 percent for a household at the 25-percent 

marginal tax rate. When adjusted for an inflation rate of 2 percent, the effective real rate becomes 

2.45 percent. 

 

e Fisher formula is given by: Real Interest Rate = [(1 + Nominal Interest Rate) / (1 + Inflation Rate)] – 1.
8-28 

Table 8.2.28 Data Used to Calculate Real Effective Mortgage Rates

Year Mortgage Interest Rates in Selected Years (%)

Average 

Nominal 

Interest Rate

Inflation Rate30

Applicable 

Marginal Tax 

Rate31

Average Real Effective 

Interest Rate

1995 8.2 2.83 24.2 3.3

1998 7.9 1.56 25.0 4.3

2001 7.6 2.85 24.2 2.8

2004 6.2 2.66 20.9 2.2

2007 6.3 2.85 20.6 2.1

2010 5.7 1.64 20.0 2.9

Table 8.2.29 shows the household-weighted average effective real rates for different 

types of household debt. Because the interest rates for each type of household debt reflect 

economic conditions throughout numerous years and various phases of economic growth and 

recession, they are expected to be representative of rates in effect in 2019.

Table 8.2.29 Average Real Effective Interest Rates for Household Debt (%)

Type of Debt Income Group

1 2 3 4 5 6

Mortgage 6.6 6.2 6.1 5.2 5.0 4.0

Home equity loan 7.0 6.9 6.7 5.9 5.7 4.3

Credit card 15.2 15.0 14.5 14.2 14.0 14.5

Other installment loan 10.8 10.3 9.9 9.4 8.7 8.6

Other residential loan 9.8 10.2 8.9 8.2 7.7 7.4

Other line of credit 9.1 10.9 9.6 8.8 7.4 6.1

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Assets 

No similar rate data are available from the SCF for classes of assets, so DOE derived 

asset interest rates from various sources of national historical data (1983-2013). The interest rates 

associated with certificates of deposit,32 savings bonds,33 and bonds (AAA corporate bonds)34

were collected from Federal Reserve Board time-series data. Rates on money market accounts 

came from Cost of Savings Index data.35 Rates on savings accounts were estimated as one half of 

the rate for money market accounts, based on recent differentials between the return to each of 

these assets. The rates for stocks are the annual returns on the Standard and Poor’s.36 Rates for 

mutual funds are a weighted average of the stock rates (two-thirds weight) and the bond rates 

(one-third weight) in each year. DOE assumed rates on checking accounts to be zero. 
8-29 

 DOE adjusted the nominal rates to real rates using the annual inflation rate for each year. 

Average nominal and real interest rates for the classes of household assets are listed in Table 

8.2.30. Because the interest and return rates for each type of asset reflect economic conditions 

throughout numerous years, they are expected to be representative of rates that may be in effect 

in 2019. For each type, DOE developed a distribution of rates, as shown in appendix 8E. 

Table 8.2.30 Average Nominal and Real Interest Rates for Household Equity 

Type of Equity Average Real 

Rate 

(%)

Savings accounts 1.0

Money market accounts 1.9

Certificates of deposit 1.9

Savings bonds 3.4

Bonds 4.2

Stocks 9.4

Mutual funds 7.4

Discount Rate Calculation and Summary 

 Using the asset and debt data discussed previously, DOE calculated discount rate 

distributions for each income group as follows. First, DOE calculated the discount rate for each 

consumer in each of the six versions of the SCF, using the following formula: 

DDDDii = � SShaaaa ii,jj

jj

× RR ii,jj

Where:

DDDDii = discount rate for consumer i, 

SShaaaa ii,jj = share of asset or debt type j for consumer i, and

RR ii,jj = real interest rate or rate of return of asset or debt type j for consumer i. 

 The rate for each debt type is drawn from the SCF data for each household. The rate for 

each asset type is drawn from the distributions described previously. 

 

Once the real discount rate was estimated for each consumer, DOE compiled the 

distribution of discount rates in each survey by income group by calculating the proportion of 

consumers with discount rates in bins of 1 percent increments, ranging from 0-1 percent to 

greater than 30 percent. Giving equal weight to each survey, DOE compiled the six-survey 

distribution of discount rates. 

Table 8.2.31 presents the average real effective discount rate and its standard deviation 

for each of the six income groups. To account for variation among households, DOE sampled a 
8-30 

rate for each RECS household from the distributions for the appropriate income group. (RECS 

provides household income data.) Appendix 8F presents the full probability distributions for 

each income group that DOE used in the LCC and PBP analysis. 

Table 8.2.31 Average Real Effective Discount 

Income Group Discount Rate (%)

1 4.85

2 5.12

3 4.75

4 4.04

5 3.80

6 3.57

Overall Average 4.49

 

8.2.4 Compliance Date of Standard 

The compliance date is the future date when manufacturers must comply with a new or 

amended standard. The compliance date of the potential energy conservation standards for 

dehumidifiers manufactured in, or imported into, the United States is March 11, 2019. DOE

calculated the LCC for all consumers as if each would purchase a new product in 2019. 

8.2.5 Product Energy Efficiency in the Base Case 

To estimate the percentage of consumers who would be affected by a standard at any of 

the trial standard levels, DOE considered the projected distribution of efficiencies for products 

that consumers purchase under the base case (the case without new or amended energy 

conservation standards). DOE refers to this distribution of product efficiencies as the base-case 

efficiency distribution. Using the projected distribution of efficiencies for each product class, 

DOE randomly assigned a product efficiency to each sampled household. The energy efficiency 

distributions that DOE used in the LCC analysis are described below. For this NOPR analysis, 

DOE used the efficiency distributions calculated based on DOE’s Certification Database for 

Dehumidifiers.

37 The energy factors for dehumidifiers listed in the DOE product database are 

determined by the current test procedure which took effect in 2007 and was updated in 2014. The

current test procedure also defines IEF, which includes measures of standby mode and off mode 

energy use and is the basis of this NOPR analysis. Because the standby/off mode energy use is 

small compared to dehumidification mode energy use, DOE assumes that IEF as measured by 

the current test procedure is relatively equal to EF, and thus the base-case market shares would 

be similar. A proposed amendment to the test procedure would require dehumidification mode 

testing at an ambient temperature of 65 °F (for portable dehumidifiers) and of 73 °F (for wholehome dehumidifiers) rather than 80 °F and would include a measure of fan-only mode energy 

use. Although these changes may result in IEF values that are significantly lower than EF for 

certain dehumidifiers, DOE expects that the distribution of efficiencies among dehumidifier 

models will remain approximately the same.

 
8-31 

DOE also projected efficiencies for the base case based on assumptions regarding future 

improvements in efficiency and assumed an annual growth rate of 0.25 percent between 2014 

and 2048. Table 8.2.32 through Table 8.2.36 present market shares of the efficiency levels being 

considered for each dehumidifier product class in 2019, based on IEF measured at 80 °F and on 

IEF measured at 65 °F (for portable dehumidifiers) and at 73 °F (for whole-home dehumidifiers) 

with fan-only mode energy use included.

Table 8.2.32 Dehumidifiers ≤30.00 Pints/Day: Base-Case Market Shares

Efficiency

Level

80 °F 65 °F

Market Share 

(%) Integrated Energy 

Factor (L/kWh)

Integrated Energy 

Factor (L/kWh)

Baseline 1.05 0.77 11

1 1.50 1.10 23

2 1.70 1.20 0

3 1.85 1.30 66

4 2.01 1.57 0

Table 8.2.33 Dehumidifiers 30.01–45.00 Pints/Day: Base-Case Market Shares 

Efficiency

Level

80 °F 65 °F

Market Share 

(%) Integrated Energy 

Factor (L/kWh)

Integrated Energy 

Factor (L/kWh)

Baseline 1.29 0.94 0

1 1.60 1.20 0

2 1.85 1.40 94

3 1.95 1.60 2

4 2.13 1.80 4
8-32 

Table 8.2.34 Dehumidifiers >45.00 Pints/Day: Base-Case Market Shares 

Efficiency

Level

80 °F 65 °F

Market Share 

(%) Integrated Energy 

Factor (L/kWh)

Integrated Energy 

Factor (L/kWh)

Baseline 2.44 2.07 57

1 3.00 2.40 20

2 3.50 2.80 23

3 4.39 3.66 0

Table 8.2.35 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Base-Case Market 

Shares 

Efficiency

Level

80 °F 73 °F

Market Share 

(%) Integrated Energy 

Factor (L/kWh)

Integrated Energy 

Factor (L/kWh)

Baseline 1.90 1.77 75

1 2.20 2.09 25

2 2.67 2.53 0

Table 8.2.36 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Base-Case Market 

Shares 

Efficiency

Level

80 °F 73 °F

Market Share 

(%) Integrated Energy 

Factor (L/kWh)

Integrated Energy 

Factor (L/kWh)

Baseline 2.50 2.41 31

1 2.80 2.70 46

2 3.50 3.52 23

3 4.46 4.50 0

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

This section presents the results of the LCC and PBP analysis for all product classes of 

dehumidifiers. As discussed in section 8.1.1, DOE’s approach to the LCC analysis relied on 

developing samples of households that use each of the product classes. DOE also used 

probability distributions to characterize the uncertainty in many of the inputs to the analysis. 

DOE used Monte Carlo simulation to perform the LCC calculations for the households in the 

sample. For each set of sample households that use the product in each product class, DOE 

calculated the average LCC and LCC savings and the median and average PBP for each the 

efficiency levels. These efficiency levels are also referred to as trial standard levels (TSLs).
8-33 

DOE calculated LCC savings and PBPs relative to the base-case products that it assigned 

to sample households. For some consumers DOE assigned a base-case product that is more 

efficient than some of the TSLs. For that reason, the average LCC impacts are not equal to the 

difference between the LCC of a specific TSL and the LCC of the baseline product. DOE 

calculated the average LCC savings and the median PBP values by excluding the households that 

are not impacted by a standard at a given efficiency level. 

 LCC and PBP calculations were performed 10,000 times on the sample of consumers 

established for each product class. Each LCC and PBP calculation was performed on a single 

household selected from the sample. A household was selected based on its weight (i.e., how 

representative it was of other households in the distribution). Each LCC and PBP calculation also 

sampled from the probability distributions that DOE developed to characterize many of the 

inputs to the analysis. 

Using the Monte Carlo simulations for each TSL, DOE calculated the percent of 

consumers who experience a net LCC benefit, a net LCC cost, and no effect. DOE considered a 

consumer to receive no effect at a given standard level if DOE assigned it a baseline product 

having the same or higher efficiency than the standard level. The following sections present 

figures that illustrate the range of LCC and PBP effects among sample consumers. 

8.3.1 Summary of Results 

Table 8.3.1 through Table 8.3.10 show the LCC and simple PBP results by efficiency 

level for each dehumidifier product class. The average operating cost is the discounted sum. 
8-34 

Table 8.3.1 Dehumidifiers ≤30.00 Pints/Day: LCC Results

Efficiency 

Level

IEF* 

(L/Day) 

Average Life-Cycle Cost (2013$) 

Simple 

Payback 

Period

Installed 

Price

First Year's 

Operating 

Cost

Lifetime 

Operating 

Costs

Life-Cycle 

Cost Years

0 0.77 212 101 952 1,163 --

1 1.10 212 71 668 879 0.0 

2 1.20 214 65 612 826 0.1 

3 1.30 218 60 566 784 0.2 

4 1.57 241 50 469 710 0.6 

* IEF = Integrated energy factor.

† Discounted. 

Table 8.3.2 Dehumidifiers ≤30.00 Pints/Day: Simple PBP Results 

Efficiency Level

% of Consumers that 

Experience Net Cost

Average Savings

2013$

0 -- --

1 0 31

2 0 49

3 0 64

4 10.3 137

Table 8.3.3 Dehumidifiers 30.01–45.00 Pints/Day: LCC Results

Efficiency 

Level

IEF* 

(L/Day) 

Average Life-Cycle Cost (2013$) 

Simple 

Payback 

Period

Installed 

Price

First Year's 

Operating 

Cost

Lifetime 

Operating 

Costs

Life-Cycle 

Cost Years

0 0.94 256 145 1,361 1,617 --

1 1.20 256 114 1,067 1,323 0.0 

2 1.40 259 97 915 1,175 0.1 

3 1.60 268 85 802 1,069 0.2 

4 1.80 290 76 713 1,003 0.5 

* IEF = Integrated energy factor.

†

 Discounted. 
8-35 

Table 8.3.4 Dehumidifiers 30.01–45.00 Pints/Day: Simple PBP Results 

Efficiency Level

% of Consumers that 

Experience Net Cost

Average Savings

2013$

0 -- --

1 0 0

2 0 0

3 0.5 99

4 5.4 164

Table 8.3.5 Dehumidifiers >45.00 Pints/Day: LCC Results

Efficiency 

Level

IEF* 

(L/Day) 

Average Life-Cycle Cost (2013$) 

Simple 

Payback 

Period

Installed 

Price

First Year's 

Operating 

Cost

Lifetime 

Operating 

Costs

Life-Cycle 

Cost Years

0 2.07 915 127 1,195 2,110 -- 

1 2.40 989 110 1,032 2,021 4.3 

2 2.80 1,008 94 885 1,893 2.8 

3 3.66 1,124 72 678 1,802 3.8 

* IEF = Integrated energy factor.

†

 Discounted. 

Table 8.3.6 Dehumidifiers >45.00 Pints/Day: Simple PBP Results 

Efficiency Level

% of Consumers that 

Experience Net Cost

Average Savings

2013$

0 -- --

1 18.9 50

2 11.7 147

3 31.4 239
8-36 

Table 8.3.7 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): LCC Results

Efficiency 

Level

IEF* 

(L/Day) 

Average Life-Cycle Cost (2013$) 

Simple 

Payback 

Period

Installed 

Price

First Year's 

Operating 

Cost

Lifetime 

Operating 

Costs

Life-Cycle 

Cost Years

0 1.77 1,662 139 2,048 3,710 --

1 2.09 1,689 118 1,740 3,429 1.3 

2 2.53 1,890 98 1,444 3,334 5.5 

* IEF = Integrated energy factor.

†

 Discounted. 

Table 8.3.8 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Simple PBP Results

Efficiency Level

% of Consumers that 

Experience Net Cost

Average Savings

2013$

0 -- --

1 8.4 207

2 44.4 302

Table 8.3.9 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): LCC Results

Efficiency 

Level

IEF* 

(L/Day) 

Average Life-Cycle Cost (2013$) 

Simple 

Payback 

Period

Installed 

Price

First Year's 

Operating 

Cost

Lifetime 

Operating 

Costs

Life-Cycle 

Cost Years

0 2.41 2,142 166 2,446 4,589 --

1 2.70 2,154 149 2,188 4,342 0.7 

2 3.52 2,212 115 1,687 3,899 1.4 

3 4.50 2,445 90 1,328 3,773 4.0 

* IEF = Integrated energy factor.

†

 Discounted. 
8-37 

Table 8.3.10 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Simple PBP Results

Efficiency Level

% of Consumers that 

Experience Net Cost

Average Savings

2013$

0 -- --

1 1.4 75

2 10.7 416

3 39.9 542

8.3.1.1 Distributions of Impacts 

The figures in this section show the distribution of LCCs in the base case for each 

product class. Also presented are figures showing the distribution of LCC impacts for Efficiency 

Level 3. The figures are presented as frequency charts that show the distribution of LCCs, and 

LCC impacts with their corresponding probability of occurrence. DOE generated the figures for 

the distributions from a Monte Carlo simulation run based on 10,000 samples. 

Base-Case LCC Distributions. Figure 8.3.1 through Figure 8.3.5 show the base-case 

LCC distributions for each product class of dehumidifiers.

Figure 8.3.1 Dehumidifiers ≤30.00 Pints/Day: Base-Case LCC Distribution
8-38 

Figure 8.3.2 Dehumidifiers 30.01–45.00 Pints/Day: Base-Case LCC Distribution

Figure 8.3.3 Dehumidifiers >45.00 Pints/Day: Base-Case LCC Distribution
8-39 

Figure 8.3.4 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Base-Case LCC 

Distribution

Figure 8.3.5 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Base-Case LCC 

Distribution

Standard-Level Distribution of Impacts. Figure 8.3.6 is an example of a frequency chart that 

shows the distribution of LCC differences for the case of Efficiency Level 3 for product class 
8-40 

one (≤30.00 pints/day). In the figure, a text box next to a vertical line at a given value on the xaxis shows the mean change in LCC (a savings of $64 in the example here). The note, “Certainty 

is 100.00% from $0 to +Infinity,” means that 100 percent of owners of dehumidifier units will 

have LCC savings or not be affected by the efficiency level compared to the base case. The large 

spike in Figure 8.3.6 represents the percentage of consumers who are not affected by an increase 

in the efficiency level, i.e., consumers who already use dehumidifiers that have efficiencies 

greater than or equal to the efficiency level. Refer to section 8.2.5 on the distribution of product 

efficiencies under the base case. DOE can generate a frequency chart like the one shown in 

Figure 8.3.6 for each efficiency level and product class. 

Figure 8.3.6 Dehumidifiers ≤30.00 Pints/Day: LCC Savings Distribution for Efficiency 

Level 3 

8.3.1.2 Range of Impacts 

Figure 8.3.7 through Figure 8.3.11 show the range of LCC savings for all efficiency 

levels considered for each dehumidifier product class. For each efficiency level, the top and the 

bottom of the box indicate the 75th and 25th percentiles, respectively. The bar at the middle of the 

box indicates the median: 50 percent of households have LCC savings in excess of that value. 

The “whiskers” at the bottom and the top of the box indicate the 5th and 95th percentiles. The 

small box shows the average LCC savings for each standard level. 
8-41 

Figure 8.3.7 Dehumidifiers ≤30.00 Pints/Day: Range of Average LCC Savings 

Figure 8.3.8 Dehumidifiers 30.01–45.00 Pints/Day: Range of Average LCC Savings 
8-42 

Figure 8.3.9 Dehumidifiers >45.00 Pints/Day: Range of Average LCC Savings 

Figure 8.3.10 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Range of Average LCC 

Savings 
8-43 

Figure 8.3.11 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): Range of Average LCC 

Savings 

8.4 REBUTTABLE PAYBACK PERIOD

DOE develops rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy conservation standard is economically justified if the additional product costs 

attributed to the standard are less than three times the value of the first-year energy cost savings. 

(42 U.S.C. §6295 (o)(2)(B)(iii)) 

 The basic equation for rebuttable PBP is the same as that shown for the PBP in section 

8.1.1. Unlike the analyses described in section 8.2, however, the rebuttable PBP is not based on 

household samples and probability distributions. The rebuttable PBP is based instead on discrete, 

single-point values. For example, whereas DOE uses a probability distribution of regional energy 

prices in the distributional PBP analysis, it uses only the national average energy price to 

determine the rebuttable PBP.

 Other than the use of single-point values, the most notable difference between the 

distributional PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to 

determine a product’s annual energy consumption. DOE based the annual energy consumption 

for the rebuttable PBP on the number of operating hours per year specified in DOE’s proposed 

test procedure for dehumidifiers38. The following sections identify the differences, if any, 

between the annual energy consumptions determined by the distributional PBP and the rebuttable 

PBP for all product classes of dehumidifiers. 
8-44 

8.4.1 Inputs to Rebuttable Payback Period Analysis 

 Because inputs for determining total installed cost for calculating the distributional PBP 

were based on single-point values, only the variability and/or uncertainty in the inputs for 

determining operating cost contributed to variability in the distributional PBPs. The following 

summarizes the single-point values that DOE used in determining the rebuttable PBP. 

• Manufacturing costs, markups, sales taxes, and installation costs were based on the 

single-point values used in the distributional LCC and PBP analysis. 

• Energy prices were based on national average values for the year that new standards 

would take effect.

• An average discount rate or lifetime is not required in calculating the rebuttable PBP. 

• The effective date of any new standard is assumed to be 2019. 

8.4.2 Results of Rebuttable Payback Period Analysis 

DOE calculated rebuttable PBPs for each efficiency level relative to the distribution of 

product efficiencies estimated for the baseline. In other words, DOE did not determine the 

rebuttable PBP relative to the base case energy efficiency, but relative to the distribution of 

product energy efficiencies for the baseline (i.e., the case without new energy conservation 

standards). Table 8.4.1 and Table 8.4.2 present the rebuttable PBPs for each product class of 

dehumidifiers. 

Table 8.4.1 Rebuttable Payback Periods: Portable Dehumidifiers

≤30.00 

Pints/Day 30.01–45.00 Pints/Day >45.00 Pints/Day

IEF

(L/kWh)

PBP

(Yrs) IEF (L/kWh) PBP(Yrs) IEF (L/kWh) PBP(Yrs)

0.77 – 0.94 – 2.07 – 

1.10 0.0 1.20 0.0 2.40 5.6 

1.20 0.1 1.40 0.1 2.80 3.7 

1.30 0.2 1.60 0.3 3.66 5.0 

1.57 0.8 1.80 0.7 – – 
8-45 

Table 8.4.2 Rebuttable Payback Periods: Whole-Home Dehumidifiers

≤8.0 ft3 Case Volume >8.0 ft3 Case Volume

IEF

(L/kWh)

RPBP

(Years)

IEF

(L/kWh)

RPBP

(Years)

1.77 – 2.41 – 

2.09 2.0 2.70 1.0 

2.53 8.7 3.52 2.1 

– – 4.50 6.3 
8-46 

REFERENCES

1 DOE–EIA. Residential Energy Consumption Survey: 2009. RECS Survey Data—Public 

Use Microdata File. October 2011. EIA: Washington, D.C. (Last accessed October 18, 

2013.) <http://www.eia.gov/consumption/residential/data/2009/#microdata> 

2 DOE–EIA. Annual Energy Outlook 2015 with Projections to 2040. 2015. Washington, 

D.C. Report Number DOE/EIA-0383(2015). <http://www.eia.gov/forecasts/aeo/> 

3 DOE–Energy Efficiency and Renewable Energy (EERE). Energy Conservation Program 

for Consumer Products: Technical Support Document: Energy Efficiency Program for 

Consumer Products and Commercial and Industrial Equipment, Residential 

Dishwashers, Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. 

2009. Washington, DC. (Last accessed October 10, 2013.) 

<http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_produc

ts_final_rule_tsd.html> 

4 RS Means. Residential Cost Data, 32nd Annual Edition. 2013. Kingston, MA. 

5 Willem, Henry, et al. 2013. Using Field-Metered Data to Quantify Annual 

Energy Use of Residential Portable Unit Dehumidifiers. Lawrence Berkeley 

National Laboratory, Berkeley, CA. LBNL-6469E. 

6 Burke, Tom, et al. 2014. Whole-Home Dehumidifiers: Field – Monitoring Study. 

Lawrence Berkeley National Laboratory, Berkeley, CA. LBNL-6777E. 

7 DOE–EIA. EIA Form 861: Customer Count Data by Utility for 2012. (Last accessed 

December 5, 2013.) <http://www.eia.doe.gov/cneaf/electricity/page/eia861.html> 

8 The Life Expectancy/Replacement Picture. Appliance Magazine, September, 2005. Vol. 

62, No. 9.

9 Kubo, T., S. Nadel, and H. Sachs. Opportunities for New Appliance and Equipment 

Efficiency Standards: Energy and Economic Savings Beyond Current Standards 

Programs, September, 2001. Washington, DC: American Council for an Energy Efficient 

Economy (ACEEE). Report Number A016. <http://aceee.org/research-report/a016> 

10 Northeast Energy Star Lighting and Appliance. Dehumidifiers. Accessed June 26, 2006. 

<http://www.myenergystar.com/Dehumidifiers.aspx> 

11 The Life Expectancy/Replacement Picture. Appliance Magazine,, 2008. 64(9): pp. 65-66 

12 American Society for Heating Refrigeration and Air-Conditioning Engineers (ASHRAE), 

HVAC Systems and Applications. 2008: Atlanta, GA.
8-47 

13 California Energy Commission, Energy Demand Forecast Methods Report. 2005. 

<http://www.energy.ca.gov/2005publications/CEC-400-2005-036/CEC-400-2005-

036.PDF> 

14 Ecodesign, Preparatory study on the environmental performance of residential room air 

conditioning devices (airco and ventilation): Draft report of Task 2 (Version 6). 2008. 

<http://ec.europa.eu/enterprise/policies/sustainable-business/ecodesign/productgroups/airco-vent/files/residential_ventilation_en.pdf> 

15 National Resources Defense Council (NRDC), Out With the Old, In With the New: Why 

Refrigerator and Room Air Conditioner Programs Should Target Replacements to 

Maximize Energy Savings. 2002. <http://www.nrdc.org/air/energy/appliance/app1.pdf> 

16 (NRC), N. R. C., EnerGuide Appliance Directory 2005. 2005: pp. 13 

<http://publications.gc.ca/site/eng/269740/publication.html> 

17 ITRON, New Mexico Electric Energy Efficiency Potential Study: Final Report. 2006. 

<http://www.swenergy.org/news/news/documents/file/pnm_electric_potential_study.pdf> 

18 Nebraska Public Power District. 2008. <http://www.nppd.com/electric-usage-in-yourhome/> 

19 New York State Energy Research and Development Authority (NYSERDA), Final 

Report On The Initial Three-Year Systems Benefit Charge Program. 2002. 

20 Northwest Power and Conservation Council, Conservation Resource Comments 

Database. 2002. <http://www.nwcouncil.org/energy/rtf/supportingdata/default.htm> 

21 Rosenquist, G., Energy Efficiency Standards and Codes for Residential/Commercial 

Equipment and Buildings: Additional Opportunities, July 2004, 2004. Lawrence Berkeley 

National Laboratory. Report No. LBID-2533. This analysis was prepared for the National 

Commission on Energy Policy (NCEP) 

<http://eetd.lbl.gov/sites/all/files/energy_efficiency_standards_for_residential_and_com

mercial_equipment_additional_opportunities_lbnl56207_0.pdf> 

22 NYSERDA, New York State Deemed Savings Database. 2008.

23 NYSERDA, New York State Deemed Savings Database. 2008.

24 LBNL, NEMS Residential Demand Module, Report # DOE/EIA-0554. 2008. 

25 Meyers, S., J. McMahon, M. McNeil, and X. Liu, Realized and Prospective Impacts of 

U.S. Energy Efficiency Standards for Residential Appliances, 2008 Update, 2008. 

Lawrence Berkeley National Laboratory. Berkeley, CA. Report No. LBNL-49504. 

<http://www-library.lbl.gov/docs/LBNL/495/04/PDF/LBNL-49504.pdf> 
8-48 

26 Hanford, J. W., J. G. Koomey, L. E. Stewart, M. E. Lecar, R. E. Brown, F. X. Johnson, R. 

J. Huang, and L. K. Price, Baseline Data for the Residential Sector and Development of a 

Residential Forecasting Database, 1994. Lawrence Berkeley Laboratory. Berkeley, CA. 

Report No. LBL-33717. 

27 Consortium for Energy Efficiency, Super-Efficient Home Appliances Initiative: Room Air 

Conditioners. <http://www.cee1.org/content/advancing-efficiency-product-product> 

28 ACEEE, Consumer Guide to Home Energy Savings: Condensed Online Version: Cooling 

Equipment. 2007. <http://aceee.org/consumer/introduction-energy-efficiencyconsumers> 

29 The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 

2001, 2004, 2007, 2010. <http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html> 

30 U.S. Department of Labor - Bureau of Labor Statistics, Bureau of Labor Statistics Data, 

Prices & Living Conditions, 2012. <http://data.bls.gov> 

31 National Bureau of Economic Research, U.S. Federal and State Average Marginal 

Income Tax Rates, 2010. (Last accessed February 25, 2011.) 

<http://users.nber.org/~taxsim/marginal-tax-rates/> 

32 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis, 2013. 

<www.federalreserve.gov/releases/H15/data.htm> 

33 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

Monthly, Description: Bond buyer go 20-bond municipal bond index, 2013. 

<www.federalreserve.gov/releases/H15/data.htm> 

34 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: 

Annual, Description: Moody’s yield on seasoned corporate bonds—all industries, AAA, 

2013. <www.federalreserve.gov/releases/H15/data.htm> 

35 Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index 

History, 2013. <http://mortgage-x.com/general/indexes/default.asp> 

36 Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and 

Bills - United States, 2013. Damodaran. <http://pages.stern.nyu.edu/~adamodar/> 

37 DOE. Compliance Certification Database for Dehumidifiers. (Last accessed September 4, 

2014.) <http://www.regulations.doe.gov/certification-data/CCMS-41431694081.html>
8-49 

38 10 CFR part 430, subpart B, appendix X (“Appendix X”) 
9-i 

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL .................................................... 9-1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ............................................................................................ 9-3

9.3.2 Markets and Model Calibration ........................................................................... 9-4

9.3.2.1 Replacements ............................................................................................. 9-5

9.3.2.2 Model Calibration—Existing Households Without Appliance .................. 9-6

9.3.3 Base-Case Shipments ........................................................................................... 9-6

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS ............................. 9-8

9.5 AFFECTED STOCK ..................................................................................................... 9-11

REFERENCES .......................................................................................................................... 9-12

LIST OF TABLES

Table 9.3.1 Portable Dehumidifiers: Historical Shipments, Domestic plus Imports ........ 9-4

Table 9.3.2 Product Class Market Share of Dehumidifiers .............................................. 9-8

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change ........... 9-10

LIST OF FIGURES

Figure 9.3.1 Portable Dehumidifier: Survival and Retirement Functions .......................... 9-5

Figure 9.3.2 Whole-home Dehumidifier: Survival and Retirement Functions .................. 9-6

Figure 9.3.3 Dehumidifiers: Historical and Base-Case Shipments Projection................... 9-7

Figure 9.3.4 Dehumidifiers: Disaggregated Shipments Projection for Base Case ............. 9-8
9-1 

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

 Estimates of future product shipments are a necessary input to calculations of the NES 

and NPV, as well as to the manufacturer impact analysis. This chapter describes the data and 

methods the DOE used to project annual product shipments and presents results for 

dehumidifiers considered for this standards rulemaking. Because DOE did not have shipment 

data for each product classes, total historical shipments of portable and whole-home 

dehumidifiers were estimated and then separated by market share of each product classes. 

To project shipments for dehumidifiers, DOE used a shipments model that is calibrated 

with historical shipments data. The shipments model estimates shipments to specific market 

segments, the results for which are then aggregated to estimate total product shipments. To 

estimate the impacts of potential standard levels on product shipments, the shipments model 

accounts for the combined effects of changes in purchase price, annual operating cost on the 

consumer purchase decision. 

 The shipments model was developed as a part of the NIA spreadsheet. Appendix 10A 

discusses how to access the NIA spreadsheet and provides basic instructions for its use. 

 The rest of this chapter explains the shipments models in more detail. Section 9.2

presents the methodology behind the shipments model; section 9.3 describes the data inputs and 

model calibration; section 9.4 discusses impacts on shipments from changes in product purchase 

price; and section 9.5 discusses the affected stock.

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL

DOE developed a national stock model for estimating annual shipments for this standards 

rulemaking. The model considers market segmentation as a distinct input to the shipments 

projection. As represented by the following equation, the two primary market segments for 

dehumidifiers are installations in existing households without dehumidifiers, “first time owners,”

and replacements. 

SShiiiiDDDDDD(jj) = RR RRDDDDDD (jj) + FFFFFFDDDDDD (jj)

 Where:

ShipDEH(j) = total shipments of dehumidifiers in year j, 

RplDEH(j) = units of dehumidifiers retired and replaced in year j, and 

FTODEH(j) = shipments to existing households without dehumidifiers in year j. 

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 first time ownership. The 

models estimate shipments due to replacements using sales in previous years and assumptions 

about the lifetime of dehumidifiers. Estimated sales attributable to replacements in a given year 

therefore are equal to the total stock of the appliance minus the sum of the appliances sold in 
9-2 

previous years that remain in the stock. As described in chapter 8 of this NOPR TSD, DOE 

determined the useful service life of dehumidifiers. DOE then estimated how long the appliance 

is likely to remain in stock. The following equation represents how DOE estimated replacement 

shipments. 

)1()( ∑ ∑ )(

0

1

__

_

jStockjRpl Ship ageprobRtr

ageMax

age

j

Nj

p = p j ×

= =

 Where:

Stockp (j-1) = total stock of in-service appliances in year j-1, 

probRtr (age) = probability that an appliance of a particular age will be retired, and

N = start year for when the model begins its stock accounting (start year is 

specific to each product based on available historical shipments data). 

 Stock accounting takes product shipments, a retirement function, and initial in-service 

product stock as inputs to estimate the age distribution of in-service product stocks for all years. 

The age distribution of in-service product stocks is a key input to both the NES and NPV 

calculations—the operating costs for any year depend on the age distribution of the stock. The 

dependence of operating cost on the product age distribution occurs under a standards case 

scenario that produces increasing efficiency over time, whereby older, less efficient units may 

have higher operating costs, while younger, more-efficient units will have lower operating costs. 

 DOE estimated replacements using product retirement functions that it developed based 

on product lifetimes. DOE based the retirement function on a Weibull distribution for the 

product lifetime. The shipments model assumes that no units are retired below a minimum 

product lifetime and all units are retired before exceeding a maximum product lifetime. The 

models determine the probability of retirement at a certain age for all products using a Weibull 

equation: 

e x

xP

β

α

θ 



 



 − −

)( = for x > θ and

P(x) = 1 for x ≤ θ

Where:

P(x) = probability that the appliance is still in use at age x; 

x = appliance age;

α = scale parameter, which would be the decay length in an exponential 

distribution; 

β = shape parameter, which determines the way in which the failure rate changes 

through time; and 

θ = delay parameter, which allows for a delay before any failures occur.

 The retirement probability is the difference in the survival function from one year to 

another year. 
9-3 

DOE calculated total in-service stock of a product by integrating historical shipments 

data starting from a specific year. The start year depends on the historical data available for the 

product. As units are added to the in-service stock, some of the older ones retire and exit the 

stock. To estimate future shipments, DOE developed a series of equations that define the 

dynamics and accounting of in-service stocks. For new units, the equation is: 

)1()1,( _ agejStock == jShip

Where: 

Stock(j, age) = the population of in-service units of a particular age,

j = year for which the in-service stock is being estimated, and

Ship (j) = number of units purchased in year j. 

 The above equation states that the number of one-year-old units is simply equal to the 

number of new units purchased the previous year. The following equation describes the 

accounting of the existing in-service stock of units: 

[ )(1),()1,1( ] _ agejStock =++ agejStock × Rtr ageprob

In the above equation, as the year is incremented from j to j+1, the age is also 

incremented from age to age+1. With time, a fraction of the in-service stock is removed, that 

fraction being determined by a retirement probability function, probRtr(age), which is described 

in section 9.3. Most replacements are made when a product wears out and fails. Over time, some 

of the units will be retired and removed from the stock, triggering the shipment of a new unit. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

 The sections below describe the data inputs and market segments considered for 

dehumidifiers. 

9.3.1 Historical Shipments

For portable dehumidifiers, DOE used data on historical shipments (domestica shipments 

plus imports) to calibrate its shipments model for dehumidifiers. DOE’s sources for historical 

shipments data were (1) data provided by the AHAM for the period 1999 – 20111, (2) data 

provided by AHAM for the period 1995 – 19982, (3) data from the 2000 AHAM Factbook for 

the period 1989–19943

, and (4) data from Appliance Magazine4,5,6 for the period 1972–1988.b

Table 9.3.1 summarizes the historical data on portable dehumidifier shipments. 

 

a Domestic shipments include shipments to States and U.S. territories.

b Shipments estimates from Appliance Magazine included exports. However, DOE saw no difference between 

shipments in the AHAM Fact Book 2000, which exclude exports, and those reported in Appliance Magazine for 

almost all years. Thus, DOE made no adjustments to the Appliance Magazine shipments.
9-4 

Table 9.3.1 Portable Dehumidifiers: Historical Shipments, Domestic plus Imports

Year Shipments 

(thousands) Year Shipments (thousands) Year Shipments (thousands)

1972 461 1986 555 2000 975 

1973 646 1987 704 2001 806 

1974 586 1988 673 2002 799 

1975 392 1989 605 2003 1,311 

1976 440 1990 743 2004 1,672 

1977 314 1991 745 2005 1,957 

1978 442 1992 803 2006 1,456 

1979 685 1993 983 2007 2,004 

1980 673 1994 1,059 2008 1,558 

1981 536 1995 1,003 2009 1,700 

1982 440 1996 977 2010 1,552 

1983 437 1997 820 2011 1,368 

1984 591 1998 1,031 

1985 588 1999 950 

Source: 1999–2011: AHAM data submittal, 2012. 1995–1998: AHAM data submittal. 1989 – 1994: AHAM 

Factbook, 2000. 1972–1988: Appliance Magazine 1982, 1990, 1993. 

DOE assumed that whole-home shipments started from 2004, and the shipments 

accounted for about 1 percent of the portable dehumidifiers market. 

9.3.2 Markets and Model Calibration

The market for dehumidifiers is comprised primarily of replacement units for products 

that have been retired from service. Total dehumidifiers shipments are represented by the 

following equation:

SShiiiiDDDDDD(jj) = RR RRDDDDDD (jj) + FFFFFFDDDDDD (jj)

 Where:

ShipDEH (j) = total shipments of dehumidifiers in year j, 

RplDEH(j) = units of dehumidifiers retired and replaced in year j, and 

FTODEH(j) = shipments to existing households without dehumidifiers in year j. 

 The sections below discuss these markets in further detail. 
9-5 

9.3.2.1 Replacements

DOE used an accounting method that tracks the total stock of units by vintage to 

determine shipments to the replacement market. DOE estimated a stock of dehumidifiers by 

vintage by integrating historical shipments starting from 1972. Over time, some units are retired 

and removed from the stock, triggering the shipment of a replacement unit. Depending on the 

vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine 

when a portable dehumidifier fails, DOE used a product survival function based on a lifetime 

distribution having an average value of 11.0 years. To determine when a whole-home 

dehumidifier fails, DOE used a product survival function based on a lifetime distribution having 

an average value of 19.01 years. For a more complete discussion of dehumidifier lifetimes, refer 

to section 8.2.2.6 of chapter 8 and appendix 8C in this NOPR TSD. Figure 9.3.1 and Figure 9.3.2 

show the survival and retirement function that DOE used to estimate replacement shipments for 

each dehumidifier type. 

Figure 9.3.1 Portable Dehumidifier: Survival and Retirement Functions

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Lifetime Dist. for Portable Dehumidifiers

% of Retiring

% of Surviving
9-6 

Figure 9.3.2 Whole-home Dehumidifier: Survival and Retirement Functions

9.3.2.2 Model Calibration—Existing Households Without Appliance

 To calibrate the estimated shipments with the historical data, DOE introduced into the 

model a market segment identified as existing households without dehumidifiers, also referred 

to as FTOs. Based on the calibration, DOE estimated that 0.35 percent of existing households 

without a dehumidifier would annually purchase this product over the period 2019─2048. 

9.3.3 Base-Case Shipments

Figure 9.3.3 shows the projected shipments in the base case (i.e., the case without new 

energy efficiency standards) and the historical shipments DOE used to calibrate the projection. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Lifetime Dist. for Whole-home Dehumidifiers

% of Retiring

% of Surviving
9-7 

Figure 9.3.3 Dehumidifiers: Historical and Base-Case Shipments Projection

DOE’s base-case shipments model for dehumidifiers used the aggregate shipments, i.e. 

the shipments for all five product classes, as the basis for its projection. In other words, DOE did 

not develop a separate shipments model for each dehumidifier product class. As provided in 

Table 9.3.2, DOE assumed market shares for each of the five product classes based on the 

engineering analysis in the preliminary analysis phase of the standards rulemaking.

7 DOE used 

the average market shares over the period 2012 – 2048 to disaggregate projected shipments into 

each of the five dehumidifier product classes. 
9-8 

Table 9.3.2 Product Class Market Share of Dehumidifiers

Year

Product Class (pints/day)

≤30.00 30.01-35.00 >45.00 

≤ 8.0ft3 Case 

Volume 

(Wholehome)

>8.0ft3 Case 

Volume 

(Wholehome)

Pre-2004 57.1% 42.9% 0.0% 0.0% 0.0% 

Post-2004 52.4% 42.9% 3.7% 0.7% 0.3% 

Source: DOE’s Engineering Analysis of the Preliminary Analysis Technical Support Document for 

Residential Dehumidifiers.

Figure 9.3.4 Dehumidifiers: Disaggregated Shipments Projection for Base Case 

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS

 Economic theory suggests that, all else being equal, an increase in the price of a good 

leads to a decrease in demand for it. Because DOE projects that appliance standards often result 

in an increase in the price of the product, DOE conducted a literature review and an analysis of 

appliance price and efficiency data to estimate the effects on product shipments from increases in 

product price. DOE also considered the decreases in operating costs from higher energy 

efficiency and changes over time in household income. Appendix 9A explains the method DOE 

used to quantify the effects of the above variables. 

In the literature, DOE found only a few studies of appliance markets that are relevant to 

this rulemaking analysis and identified no studies that use time-series data of product prices and 
9-9 

shipments after 1980. The information that can be summarized from the literature suggests that 

the demand for appliances is price-inelastic. Other information in the literature suggests that 

appliances are a normal good, such that rising incomes increase the demand for appliances. 

Finally, the literature suggests that consumers use relatively high implicit discount ratesc

 when 

comparing appliance prices and operating costs. 

 DOE found insufficient data on product purchase price and operating cost to perform a 

thorough analysis of dynamic changes in the appliance market. Rather, it used purchase price and 

efficiency data specific to residential refrigerators, clothes washers, and room air conditioners 

during 1980–2002 to evaluate broad market trends and conduct simple regression analyses. The 

data indicate that there has been an increase in appliance shipments and a decrease in appliance 

purchase price and operating costs during the period. Household income also increased during 

this time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed the relative price, and used this variable in an analysis of market trends, as well 

as to conduct a regression analysis. The relative price is defined using the following expression. 

Income

PVOCPP

Income

TP RP + ==

Where:

RP = relative price,

TP = total price,

Income = household income, 

PP = appliance purchase price, and

PVOC = present value of operating cost. 

In the above equation, DOE used an implicit discount rate of 37 percent to determine the 

present value of operating costs. 

DOE’s analysis of market trendssuggests that the relative price elasticity of demand for 

the three appliances is relatively inelastic (that is, less than 1.0). DOE’s regression analysis 

suggests that the relative price elasticity of demand, averaged for the three appliances, is -0.34. 

Thus a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note 

that, because the relative price elasticity incorporates the effects of three factors (purchase price, 

operating cost, and household income), the effect of any single factor is mitigated by changes to

the other two. 

The relative price elasticity of -0.34 is consistent with estimates in the literature.

Nevertheless, DOE stresses that the measure is based on a small data set subject to simple 

statistical analysis. More importantly, the measure is based on an assumption that economic 

variables, including purchase price, operating costs, and household income, explain most of the 

trend in appliances per household in the United States since 1980. Changes in appliance quality 

 

c A high implicit discount rate with regard to operating costs means that consumers do not put much economic value 

on the operating cost savings realized from more efficient appliances. Consumers are much more concerned with 

the higher purchase prices.
9-10 

and consumer preferences may have occurred during that period, but DOE did not account for 

them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative 

price elasticity of demand provides a reasonable assessment of the impact that purchase price, 

operating cost, and household income have on product shipments. 

Because DOE’s projections of shipments and national impacts due to standards consider 

30-year period, it needed to consider how the relative price elasticity is affected once a new 

standard takes effect. DOE considered the relative price elasticity provided above to be a shortterm value. It was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short- and long-term price elasticities differ. To estimate how the 

relative price elasticity changes over time, therefore, DOE relied on a study pertaining to 

automobiles.8,9 That study showed that the automobile price elasticity of demand changes 

following a change in purchase price. With increasing years after the purchase price change, the 

price elasticity becomes more inelastic until it reaches a terminal value around the tenth year 

after the price change. Table 9.4.1 shows the relative change in the price elasticity of demand for 

automobiles over time. DOE developed a time-series of relative price elasticities for home 

appliances based on the relative change in the automobile price elasticity of demand. For years 

not shown in Table 9.4.1, DOE performed a linear interpolation to obtain the relative price

elasticity.

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change

Number of Years After Price Change

Change in elasticity 

relative to first year

1 2 3 5 10 20

1.00 0.78 0.63 0.44 0.35 0.33

Relative price elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

 Based on the following equation, DOE estimated standards case shipments by 

incorporating the impact of the relative price into the base-case shipments projection. Note that 

in the equation below, the relative price and the relative price elasticity are functions of the year, 

because both change with time. 

)( ( )( ) ( )()(1)()( ) _

_ _ _ _ jejMjNIjRpljShip RP j pSTD = BASE p + BASE p + BASE p × RP D×

Where:

ShipSTD_p(j) = total shipments of product p in year j under the standards case, 

RplBASE_p(j) = units of product p retired and replaced in year j under the base case, 

NIBASE_p(j) = number of new home installations of product p in year j under the base 

case, 

MBASE_p(j) = first-time owners market M of product p in year j under the base case, 

eRP(j)= relative price elasticity in year j (equals -0.34 for year 1), and 

ΔRP(j)= change in relative price due to a standard level in year j. 

DOE determined the standards case shipments were not affected by the relative price

impact on the base-case shipments projection. Because the incremental cost of dehumidifiers 
9-11 

meeting the trial standard levels is low relative to the operating cost savings for portable 

dehumidifiers, and its market share accounts for approximately 95 percent of the total shipments, 

DOE assumed that consumer price elatisicity for this product is zero. Therefore, shipments 

would not be affected by standards. 

9.5 AFFECTED STOCK

The affected stock is the in-service stock of a product that is affected by a standard level. 

In addition to the projection of product shipments under both the base case and the standards 

case, the affected stock (which represents the difference in the appliance stock between the base 

case and the standards case) is a key output of DOE’s shipments models. The affected stock 

quantifies the effect that new product shipments have on the appliance stock because of a 

standard level. Therefore, the affected stock consists of those in-service units that are purchased 

in or after the year the standard takes effect, as described by the following equation. 

∑

_

1

_

)()( )(

j Std yr

age

p p p Aff jShipjStock ageStock

=

= +

Where:

Aff Stockp(j) = affected stock of units of product p of all vintages that are operational in 

year j, 

Shipp(j) = shipments of product p in year j, 

Stockp(j) = stock of units of product p of all vintages that are operational in year j, 

age = age of the units (years), and 

Std_yr = effective date of the standard.

 As noted for the above equation, to calculate the affected stock, DOE must define the 

effective date of the standard. For the NES and NPV results presented in chapter 10 of this 

NOPR TSD, DOE assumed that new energy efficiency standards will become effective in 2019. 

Thus, all appliances purchased starting in 2019 are affected by the standard level. 

Because dehumidifiers meeting the standard levels have low incremental manufacturing 

costs and high operating cost savings, DOE estimated that the standards would have no impact

on shipments. Thus, for all trial standards levels, shipments are projected to be the same as in the 

base case.

 
9-12 

REFERENCES

1 AHAM. AHAM Data on Portable Dehumidifiers for DOE Efficiency Standards 

Rulemaking, November 12, 2012. Letter to Building Technologies Program, U.S. 

Department of Energy.

2 AHAM. AHAM Data on Dishwashers for Efficiency Standards Rulemaking, September 

7, 2006. Letter to Building Technologies Program, U.S. Department of Energy. 

3 AHAM. AHAM 2000 Fact Book, 2000. Washington, DC. 

4 Appliance Magazine, April, 1993. 

5 Appliance Magazine, April, 1990. 

6 Appliance Magazine, April, 1982. 

7 DOE–EERE. Energy Conservation Program for Residential Dehumidifiers: Notice of 

Public Preliminary Technical Support Document. 2014 Washington, D.C. (Last accessed 

September 10, 2014.) 

 <http://www.regulations.gov/#!docketDetail;D=EERE-2012-BT-STD-0027> 

 

8 Hymans, S. Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity. 1971. 1971(1): pp. 234–239.

9 Dale, L., and K.S. Fujita. An Analysis of the Price Elasticity of Demand of Household 

 Appliances.2008. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. 

 LBNL 326E. (Access May 2013.) 

 <www.osti.gov/bridge/servlets/purl/929429-Vm1S9X/929429.pdf> 
10-i 

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 PROJECTED EFFICIENCIES FOR BASE AND STANDARDS CASES .................. 10-2

10.3 NATIONAL ENERGY SAVINGS ............................................................................... 10-5

10.3.1 Definition ........................................................................................................... 10-5

10.3.2 Inputs to National Energy Savings .................................................................... 10-6

10.3.2.1 Shipments ................................................................................................. 10-6

10.3.2.2 Product Stock ........................................................................................... 10-6

10.3.2.3 Annual Energy Consumption per Unit ..................................................... 10-6

10.3.2.4 National Annual Energy Consumption .................................................... 10-8

10.3.2.5 Primary Energy Use Factors .................................................................... 10-9

10.3.2.6 Full-Fuel-Cycle Energy Factors ............................................................... 10-9

10.4 NET PRESENT VALUE ............................................................................................. 10-11

10.4.1 Definition ......................................................................................................... 10-11

10.4.2 Inputs to Net Present Value ............................................................................. 10-12

10.4.2.1 Total Installed Cost per Unit .................................................................. 10-12

10.4.2.2 Annual Operating Cost Savings per Unit ............................................... 10-13

10.4.2.3 Total Annual Increases in Installed Cost ................................................ 10-13

10.4.2.4 Total Annual Savings in Operating Cost ................................................ 10-14

10.4.2.5 Discount Factors ..................................................................................... 10-14

10.4.2.6 Present Value of Costs ........................................................................... 10-14

10.4.2.7 Present Value of Savings ........................................................................ 10-15

10.5 RESULTS OF CALCULATIONS .............................................................................. 10-15

10.5.1 Summary of Inputs ........................................................................................... 10-15

10.5.2 Results of National Energy Savings Calculations ........................................... 10-16

10.5.3 Annual Costs and Savings ............................................................................... 10-17

10.5.4 Results of Net Present Value Calculations ...................................................... 10-18

REFERENCES ........................................................................................................................ 10-20

LIST OF TABLES

Table 10.1.1 Trial Standard Levels for Dehumidifiers—IEF* (Units: L/kWh†) ............. 10-2

Table 10.2.1 Portable Dehumidifiers ≤30.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019 .................................................................. 10-3

Table 10.2.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Base- and StandardsCase Efficiency Distributions in 2019 ......................................................... 10-3

Table 10.2.3 Portable Dehumidifiers >45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019 .................................................................. 10-4

Table 10.2.4 Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume: Base- and 

Standards-Case Efficiency Distributions in 2019 ........................................ 10-4
10-ii

Table 10.2.5 Whole-Home Dehumidifiers >8.0 ft3 Case Volume: Base- and 

Standards-Case Efficiency Distributions in 2019 ........................................ 10-4

Table 10.3.1 Portable Dehumidifiers ≤30.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019 .................................................................. 10-7

Table 10.3.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Base- and StandardsCase Efficiency Distributions in 2019 ......................................................... 10-7

Table 10.3.3 Portable Dehumidifiers >45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019 .................................................................. 10-8

Table 10.3.4 Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume: Base- and 

Standards-Case Efficiency Distributions in 2019 ........................................ 10-8

Table 10.3.5 Whole-Home Dehumidifiers >8.0 ft3 Case Volume: Base- and 

Standards-Case Efficiency Distributions in 2019 ........................................ 10-8

Table 10.3.6 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014) ...................... 10-11

Table 10.4.1 Shipment-Weighted Average Per-Unit Total Installed Costs for Base 

and Standards Cases (2013$) ..................................................................... 10-13

Table 10.5.1 Inputs to Calculation of National Energy Savings and Net Present 

Value .......................................................................................................... 10-16

Table 10.5.2 Cumulative National Energy Savings (Unit: Quads) ................................. 10-17

Table 10.5.3 Full-Fuel-Cycle Energy Savings (Unit: Quads) ......................................... 10-17

Table 10.5.4 Cumulative NPV Results Based on 3-Percent Discount Rate (Billion, 

2013$) ........................................................................................................ 10-19

Table 10.5.5 Cumulative NPV Results Based on 7-Percent Discount Rate (Billion, 

2013$) ........................................................................................................ 10-19

LIST OF FIGURES

Figure 10.3.1 Site-to-Power Plant Energy Conversion Factors for Dehumidifiers............ 10-9

Figure 10.5.1 Non-Discounted Annual Installed Cost Increases and Annual 

Operating Cost Savings for Dehumidifiers, TSL 3 .................................... 10-18
10-1 

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter of the NOPR TSD describes the method the DOE used to estimate the

effects on national energy consumption of TSLs for dehumidifiers. DOE evaluated the following 

effects: (1) NES attributable to each potential standard; (2) the monetary value of energy savings 

to consumers of dehumidifiers; (3) increased total installed cost of the products because of 

standards; and (4) the NPV of energy savings (i.e., the difference between the operational 

savings and increased total installed costs). 

DOE determined both the NES and NPV for all the TSLs considered for the product 

classes of both portable and whole-home dehumidifiers. It performed all calculations using a 

Microsoft Excel spreadsheet model, which is accessible on the Internet 

(http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/47). The

spreadsheets, which implement the NIA model, combine the calculations for determining the 

NES and NPV with input from the shipments model (chapter 9 of the NOPR TSD). Details and 

instructions for using the NIA model are provided in appendix 10A of this NOPR TSD. 

 Chapter 9 of this NOPR TSD provides a detailed description of the shipments model that 

DOE used to project future purchases of dehumidifiers. Chapter 9 includes detailed descriptions 

of consumers’ sensitivities to total installed cost and operating cost, and how DOE captured 

those sensitivities within the model. 

 DOE analyzed the benefits and burdens of four TSLs for three product classes of portable 

dehumidifiers and two product classes of whole-home dehumidifiers. The TSLs were developed 

using combinations of efficiency levels for all five product classes that DOE analyzed. Table 

10.1.1 presents the TSLs and the corresponding efficiency levels for dehumidifiers. TSL 4 

represents the maximum technologically feasible (“max-tech”) improvement in energy efficiency 

for dehumidifiers. TSLs 2 and 3 represent intermediate efficiency levels between TSLs 1 and 4. 

TSL 1 represents the first efficiency level considered that exceeds baseline efficiency. 
10-2 

Table 10.1.1 Trial Standard Levels for Dehumidifiers—IEF* (Units: L/kWh†) 

TSL ≤30.00 

pints/day 

30.01-45.00 

pints/day

>45.00 

pints/day

Whole-home 

- ≤8.0ft^3 

Case 

Volume 

Whole-home 

- >8.0ft^3 

Case 

Volume

-- 0.77 0.94 2.07 1.77 2.41

TSL 1 1.10 1.20 2.40 2.09 2.70

TSL 2 1.20 1.40 2.80 2.09 3.52

TSL 3 1.30 1.60 2.80 2.09 3.52

TSL 4 1.57 1.80 3.66 2.53 4.50

 * IEF = Integrated energy factor, which includes energy consumed in standby, off, 

dehumidification, and fan-only 

 modes. 

 † L/kWh = Liters (of moisture removed) per kilowatt-hour (of energy consumed). 

10.2 PROJECTED EFFICIENCIES FOR BASE AND STANDARDS CASES 

 This section describes the method DOE used to project the energy efficiencies of 

dehumidifiers for the base case and for each of the trial standards cases. It provides efficiency 

distributions for all product classes of both portable and whole-home dehumidifiers. 

 A key factor in estimating NES and NPV is the trend in energy efficiency projected for 

the base case (without new standards) and each of the standards cases. In calculating the NES, 

per-unit annual energy consumption is a direct function of product efficiency. For the NPV, two 

inputs, the per-unit total installed cost and the per-unit annual operating cost, depend on 

efficiency. The first input, the per-unit total installed cost, is a direct function of efficiency. 

Because it is a function of annual energy use, the per-unit annual operating cost depends 

indirectly on product efficiency. 

 To project the base-case energy efficiency for dehumidifiers, DOE used the shipmentsweighted integrated energy factors (SWIEF) as a starting point for 2014 (see chapter 8). DOE 

also projected efficiencies for the base case based on assumptions regarding future improvements 

in efficiency and assumed an annual growth rate of 0.25 percent between 2014 and 2048. 

DOE assumed a “roll-up” scenario to establish the shipment-weighted efficiency for the 

year that standards are assumed to become effective (2019). 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. For its projected efficiencies of TSLs, in addition to a “roll-up” 

scenario, DOE developed a shift scenario. In the shift scenario DOE applies an annual growth 

rate in average energy efficiency to the SWIEF, as it is done in the base case. To develop 

standards case projected SWIEFs, DOE developed growth trends for each trial standard level that 

maintained the same per-unit average total installed cost difference for the year 2019 between the 

base case and each standards case over the entire projection period (2019–2048). DOE’s 

approach for developing standards case SWIEFs in this manner assumes that the rate of adoption 

of more efficient products under the standards case can occur only at a rate which ensures that 
10-3 

the average total installed cost difference between the standards case and base case over the 

entire projection period is held constant. Because the total installed cost versus efficiency 

relationship for each product class demonstrates an increasing cost rate for more efficient 

products, the SWIEF growth rate for each standards case is lower than the SWIEF growth rate 

for the base case. Note that for the standards cases, the efficiency trend does not increase past the 

max tech level. 

Table 10.2.1 through Table 10.2.5 show the base-case and TSL product efficiency 

distributions in 2019, based on the IEF for each of the five product classes that DOE is 

considering. The TSLs are composed of efficiency levels analyzed in the life-cycle cost and 

payback period analysis (chapter 8 of the NOPR TSD). Also included in the tables are the 

SWIEFs associated with the base case and each TSL.

 

Table 10.2.1 Portable Dehumidifiers ≤30.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019 

EL TSL

IEF

Market Shares (%)

Base 

Case

Trial Standard Level

(L/kWh) 1 2 3 4 

Baseline - 0.77 11 0 0 0 0

1 1 1.10 23 34 0 0 0

2 2 1.20 0 0 34 0 0

3 3 1.30 66 66 66 100 0

4 4 1.57 0 0 0 0 100 

SWIEF (L/kWh) 1.20 1.23 1.27 1.30 1.57 

Table 10.2.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019

EL TSL IEF

(L/kWh) 

Market Share (%)

Base 

Case

Trial Standard Level

1 2 3 4

Baseline - 0.94 0 0 0 0 0

1 1 1.20 0 0 0 0 0

2 2 1.40 94 94 94 0 0

3 3 1.60 2 2 2 96 0

4 4 1.80 4 4 4 4 100

SWIEF (L/kWh) 1.42 1.42 1.42 1.61 1.80
10-4 

Table 10.2.3 Portable Dehumidifiers >45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019

EL TSL IEF

(L/kWh)

Market Share (%)

Base 

Case

Trial Standard Level

1 2 3 4 

Baseline - 2.07 57 0 0 0 0 

1 1 2.40 20 77 0 0 0

2 2, 3 2.80 23 23 100 100 0

3 4 3.66 0 0 0 0 100

SWIEF (L/kWh) 2.30 2.49 2.80 2.80 3.66

Table 10.2.4 Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume: Base- and StandardsCase Efficiency Distributions in 2019

EL TSL IEF

(L/kWh)

Market Share (%)

Base Case 

Trial Standard Level

1 2 3 4

Baseline - 1.77 75 0 0 0 0

1 1, 2, 3 2.09 25 100 100 100 0

2 4 2.53 0 0 0 0 100

SWIEF (L/kWh) 1.85 2.09 2.09 2.09 2.53

Table 10.2.5 Whole-Home Dehumidifiers >8.0 ft3

 Case Volume: Base- and StandardsCase Efficiency Distributions in 2019 

EL TSL IEF

(L/kWh)

Market Share (%)

Base Case 

Trial Standard Level

1 2 3 4

Baseline - 2.41 31 0 0 0 0

1 1 2.70 46 77 0 0 0

2 2, 3 3.52 23 23 100 100 0

3 4 4.50 0 0 0 0 100

SWIEF (L/kWh) 2.80 2.89 3.52 3.52 4.50
10-5 

10.3 NATIONAL ENERGY SAVINGS

DOE calculated the NES associated with the difference between the base case and each 

of the potential standards cases for dehumidifiers. DOE calculated cumulative energy savings 

from 2019 to 2048. 

10.3.1 Definition

DOE calculated annual NES as the difference between two projections: a base case 

(without new standards) and a standards case (with new standards). Positive values of NES 

represent energy savings (i.e., national annual energy consumption under a standard is less than 

under the base case).

BASE STD NESy AEC AEC _ =

 

Cumulative energy savings are the sum of the national annual energy savings throughout 

the projection period, which starts in the compliance year (2019) and ends in the year when the 

last unit installed in 2048 is retired from service. The calculation is represented by the following 

equation. 

NEScumulative = ∑NES y

DOE calculated the national annual energy consumption by multiplying the number or 

stock of each product class (by vintage) by its unit energy consumption (also by vintage). The 

calculation of the national annual energy consumption is performed using the following 

equation. 

= ∑ V ×UECV AEC STOCK

DOE defined the quantities for the above expressions as follows. 

AEC = national annual energy consumption each year in quadrillion British thermal units 

(quads) summed over vintages of the product stock, STOCKV. 

NESy = national annual energy savings (quads). 

STOCKV = stock of product (millions of units) of vintage V surviving in the year for which 

DOE calculated annual energy consumption. 

UECV = annual energy consumption per product in either kilowatt-hours (kWh) or million 

Btus (MMBtu) (electricity and gas consumption are converted from site energy to 

source energy (quads) by applying a time-dependent conversion factor). 

V = year in which the product was purchased as a new unit. 

y = year in the forecast.

 The stock of a product depends on annual shipments and the lifetime of the product. As 

described in chapter 9 of the NOPR TSD, DOE projected dehumidifier shipments under the base 
10-6 

and standards cases. To avoid including savings attributable to shipments displaced because of 

standards (consumers deciding not to buy higher-priced products), DOE used the projected 

standards-case shipments and, in turn, the standards-case stock, to calculate the AEC for the base 

case.

10.3.2 Inputs to National Energy Savings 

 The inputs to the calculation of NES are:

• shipments, 

• product stock (STOCKV), 

• annual energy consumption per unit (UEC), 

• national annual energy consumption (AEC), 

• site-to-source conversion factor (src_conv), and 

• primary energy to full fuel cycle multipliers (μ).

10.3.2.1 Shipments

DOE projected shipments for the base case and all standards cases. Several factors, 

including total installed cost (purchase price plus installation costs), operating cost, and product 

lifetime, all affect projected shipments. Chapter 9 of the NOPR TSD, Shipments Analysis, 

details the method DOE used to calculate and generate the shipments projections for 

dehumidifiers. 

10.3.2.2 Product Stock

The product stock in a given year is the number of products shipped from earlier years 

that survive in that year. The NIA model tracks the number of units shipped each year. DOE

assumes that products have an increasing probability of retiring as they age. The probability of 

survival as a function of years since purchase is the survival function. Chapter 9 of the NOPR 

TSD provides additional details on the survival functions that DOE used for portable and wholehome dehumidifiers. 

10.3.2.3 Annual Energy Consumption per Unit

 DOE presented the per-unit annual energy consumption as a function of product 

efficiency in chapter 7, Energy Use Determination, and section 8.2.2 of chapter 8, Life-Cycle 

Cost and Payback Period Analysis. Because the per-unit annual energy consumption is directly 

dependent on efficiency, DOE used the base case and standards case energy efficiency 

distribution presented in section 10.2, in combination with the annual energy use data presented 

in chapter 8, to estimate the shipment-weighted average annual per-unit energy consumption 

under the base case and standards cases. 

 Table 10.3.1 through Table 10.3.5 present the per-unit annual energy consumption based 

on the efficiency distribution corresponding to the base case and each TSL, resprectively. 
10-7 

Table 10.3.1 Portable Dehumidifiers ≤30.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019

EL TSL

IEF

Market Shares (%)

Base 

Case

Trial Standard Level

(L/kWh) 1 2 3 4

Baseline - 0.77 11 0 0 0 0

1 1 1.10 23 34 0 0 0

2 2 1.20 0 0 34 0 0 

3 3 1.30 66 66 66 100 0 

4 4 1.57 0 0 0 0 100

SWAEU (kWh/yr) 477 454 440 428 355

Table 10.3.2 Portable Dehumidifiers 30.01–45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019

EL TSL IEF

(L/kWh) 

Market Share (%)

Base Case Trial Standard Level

1 2 3 4

Baseline - 0.94 0 0 0 0 0

1 1 1.20 0 0 0 0 0

2 2 1.40 94 94 94 0 0

3 3 1.60 2 2 2 96 0

4 4 1.80 4 4 4 4 100

SWAEU (kWh/yr) 685 685 685 604 540
10-8 

Table 10.3.3 Portable Dehumidifiers >45.00 Pints/Day: Base- and Standards-Case 

Efficiency Distributions in 2019

EL TSL IEF

(L/kWh)

Market Share (%)

Base 

Case

Trial Standard Level

1 2 3 4

Baseline - 2.07 57 0 0 0 0

1 1 2.40 20 77 0 0 0

2 2, 3 2.80 23 23 100 100 0

3 4 3.66 0 0 0 0 100

SWAEU (kWh/yr) 826 755 670 670 513

Table 10.3.4 Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume: Base- and StandardsCase Efficiency Distributions in 2019 

EL TSL IEF

(L/kWh)

Market Share (%)

Base 

Case

Trial Standard Level

1 2 3 4

Baseline - 1.77 75 0 0 0 0 

1 1, 2, 3 2.09 25 100 100 100 0 

2 4 2.53 0 0 0 0 100 

SWAEU (kWh/yr) 916 809 809 809 671 

Table 10.3.5 Whole-Home Dehumidifiers >8.0 ft3

 Case Volume: Base- and StandardsCase Efficiency Distributions in 2019 

EL TSL IEF

(L/kWh)

Market Share (%)

Base 

Case

Trial Standard Level

1 2 3 4

Baseline - 2.41 31 0 0 0 0

1 1 2.70 46 77 0 0 0

2 2, 3 3.52 23 23 100 100 0

3 4 4.50 0 0 0 0 100

SWAEU (kWh/yr) 1,000 963 784 784 617

10.3.2.4 National Annual Energy Consumption 

 The national annual energy consumption is the product of the annual energy consumption 

per unit and the number of units of each vintage (V). The calculation of AEC accounts for 

differences in unit energy consumption from year to year. DOE used the equation below (as 

presented in section 10.3.1) to calculate annual energy consumption. 

 

= ∑ V ×UECV AEC STOCK
10-9 

 To determine national annual energy consumption, DOE calculated the annual energy 

consumption at the site and then applied a conversion factor to calculate primary energy 

consumption, as described below. 

10.3.2.5 Primary Energy Use Factors

 For electricity use, the conversion from site kWh to power plant primary million Btu uses 

a marginal heat rate factor that accounts for losses associated with the generation, transmission, 

and distribution of electricity. DOE derived these marginal factors using data published with the 

EIA’s AEO2014,

1

 following the methodology outlined in appendix 15A. The factors depend on 

the sector and end-use, and also vary with time due to changes in the mix of fuels used for 

electric power generation. Figure 10.3.1 shows the site-to-power plant factors from 2019 to the 

end of the AEO analysis period (2040). For years after 2040, DOE held the factors constant and 

equal to their 2040 values. 

 For fossil fuels such as natural gas, fuel oil or propane, the site energy and primary 

energy are the same, as energy used in processing is captured in the FFC metric.

Figure 10.3.1 Site-to-Power Plant Energy Conversion Factors for Dehumidifiers

10.3.2.6 Full-Fuel-Cycle Energy Factors

The full-fuel-cycle energy use is equal to the primary energy use plus the energy 

consumed "upstream" of the site in the extraction, processing and distribution of fuels. The FFC 

energy use was calculated by applying a fuel-specific FFC energy multiplier to the primary 

energy use. DOE developed FFC multipliers using the data and projections generated by the 

NEMS used for AEO 2014. The AEO provides extensive information about the energy system, 

including projections of future oil, natural gas and coal supply, energy use for oil and gas field 

and refinery operations, and fuel consumption and emissions related to electric power 

production. This information can be used to define a set of parameters representing the energy 

intensity of energy production. The multplier for electricity represents the energy needed to 

produce and deliver the fuels that are consumed in electricity generation. The multipliers are 
10-10 

dimensionless numbers that express the upstream energy use as a percentage of the primary 

energy use.

Because the FFC energy multipliers depend on the fuel type, the FFC energy is calculated 

starting with the annual site energy numbers ASEC. The equation is: 

FFC(L,y) = ∑F ASEC(L,F,y)* h(F,y)*μ(F,y). 

Where:

ASEC = annual site energy consumption 

L = trial standard level

F = fuel type

y = analysis year

h = energy unit conversion factor 

μ= full fuel cycle multiplier

FFC = annual full fuel cycle energy consumption 

If a product uses only one fuel, then the FFC energy is equal to the primary energy APEC

multiplied by the FFC multipler μ. For products that use multiple fuels, the relationship between 

the primary energy use and the FFC energy is less straight-forward.

As with the NES, DOE calculated cumulative, national level energy savings in the fullfuel-cycle metric by calculating the difference relative to the base case and summing over the 

analysis period:

NES-FFC(L,y) = FFC(L=0,y) – FFC(L,y),

NES-FFCcum(L) = ∑y NES-FFC(L,y)

 The method used to calculate FFC energy multipliers and the derived values are 

described in appendix 10B.

Table 10.3.6 shows the FFC energy multipliers used for residential dehumidifers for 

selected years. The method used to calculate FFC energy multipliers is described in appendix 

10B. 
10-11 

Table 10.3.6 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)

Electricity 2019 2020 2025 2030 2035 2040

Upstream to Power Plants 1.043 1.044 1.045 1.046 1.047 1.047

10.4 NET PRESENT VALUE

DOE calculated the NPV of the increased product price and reduced operating cost 

associated with the difference between the base case and each potential standards case for the 

dehumidifier product classes. 

10.4.1 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, and 

PVC = present value of increased total installed costs (including purchase price and 

installation costs). 

DOE determined the PVS and PVC according to the following expressions. 

PVS ∑ ×= DFOCS yy

PVC ∑TIC ×= DFyy

Where: 

OCS = total annual savings in operating costs each year summed over vintages of the 

product stock, STOCKV; 

DF = discount factor in each year; 

TIC = total annual increases in installed cost each year summed over vintages of the 

product stock, STOCKV; and

y = year in the forecast.

 DOE calculated the total annual consumer savings in operating cost by multiplying the 

number or stock of a given product class (by vintage) by its per-unit operating cost savings (also 

by vintage). DOE calculated the total annual increases in consumer product price by multiplying 

the number or shipments of the given product class (by vintage) by its per-unit increase in 

consumer product cost (also by vintage). The calculation of total annual operating cost savings 

and total annual product price increases are represented by the following equations. 
10-12 

OCSy = ∑STOCKV ×UOCSV

TICy = ∑SHIPy×UTICy

Where:

STOCKV = stock of products of vintage V that survive in the year for which DOE calculated 

annual energy consumption, 

UOCSV = annual operating cost savings per unit of vintage V, 

V = year in which the product was purchased as a new unit; 

SHIPy = shipments of products in year y; and

UTICy = annual per-unit increase in installed product price in year y. 

DOE determined the total increased product price for each year from 2019 to 2048. DOE

determined the present value of operating cost savings for each year from 2019 to the year when 

all units purchased in 2048 will have been retired. DOE calculated costs and savings as the 

difference between a standards case and a base case without new standards. 

 DOE developed a discount factor from the national discount rate and the number of years 

between the “present” (year to which the sum is being discounted) and the year in which the 

costs and savings occur. The NPV is the sum over time of the discounted net savings. 

10.4.2 Inputs to Net Present Value

The inputs to the calculation of NPV are: 

• total installed cost per unit, 

• annual operating cost savings per unit, 

• total annual increases in product price, 

• total annual savings in operating cost, 

• discount factor, 

• present value of costs, and

• 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 and standards case) multiplied by the shipments 

forecasted in the standards case. 

The total annual operating cost savings are equal to the change in annual operating cost

(difference between base and standards case) per unit multiplied by the shipments forecasted in 

the standards case. As noted, DOE did not calculate operating cost savings using base-case 

shipments. The annual operating cost includes energy costs.

10.4.2.1 Total Installed Cost per Unit 

The average annual product cost depends directly on efficiency. DOE therefore used the 

efficiency distributions presented in 0 through Table 10.2.5, along with the product price at 
10-13 

various efficiency levels (presented in chapter 8 of the NOPR TSD), to estimate the shipmentweighted average annual product cost under the base and standards cases. Table 10.4.1 shows the 

shipment-weighted average installed cost of dehumidifiers in 2019 for the base and standards 

cases. 

Table 10.4.1 Shipment-Weighted Average Per-Unit Total Installed Costs for Base and 

Standards Cases (2013$) 

Product Class Base

Case

Trial Standard Level

1 2 3 4 

≤30.00 pints/day $216 $216 $217 $218 $241 

30.01-45.00 pints/day $260 $260 $260 $269 $290 

>45.00 pints/day $951 $994 $1,008 $1,008 $1,124 

Whole-home - <8.0ft^3 Case Volume $1,669 $1,689 $1,689 $1,689 $1,890 

Whole-home - >8.0ft^3 Case Volume $2,164 $2,167 $2,212 $2,212 $2,445 

To evaluate the effect of uncertainty regarding price trends, DOE examined the effect of 

various product price forecasts on the consumer NPV for the considered TSLs for residential 

dehumidifiers. In addition to the default price trend, DOE considered separate product price 

sensitivity cases for portable dehumidifiers and whole-home dehumidifiers. For portable 

dehumidifiers, DOE considered a case for a low price decline based on estimating an experience 

curve using Producer Price Index (PPI) data for “small electric household appliances” from 1983 

to 2012. A case for high price decline was based on the price forecast of the “furniture and 

appliances” series from AEO 2013. For whole-home dehumidifiers, a case for a low price decline 

was based on an exponential fit to the PPI from 1978 to 2012 for “air-conditioning, refrigeration, 

and forced air heating equipment.” The high price decline was based on the price forecast of the 

“furniture and appliances” series from AEO 2013. The approach used to forecast the price trends 

and the results of the sensitivity cases are described in appendix 10C of this NOPR TSD. 

10.4.2.2 Annual Operating Cost Savings per Unit

The per-unit annual operating cost includes the costs for energy, repair, and maintenance. 

As described in chapter 8 of the NOPR TSD, DOE assumed that potential standards would not 

increase maintenance and repair costs for any of the considered product classes. Therefore, DOE 

determined the per-unit annual operating cost savings based only on the energy cost savings 

attributable to a standard level. DOE determined the per-unit annual operating cost savings by 

multiplying the per-unit savings in annual energy consumption for each product class by the 

appropriate energy price. 

As described in chapter 8, DOE forecasted energy prices based on EIA’s AEO 2015. 2

The energy price trends are described in chapter 8 of this NOPR TSD. 

10.4.2.3 Total Annual Increases in Installed Cost

The total annual increase in installed cost for any given standards case is the product of 

the total per-unit increase in installed cost 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. Below is 
10-14 

the equation for calculating the increase in total annual installed cost for a given standards case 

(as introduced in section 10.4.1). 

 

TIC = ∑STOCKV ×UTICV

10.4.2.4 Total Annual Savings in Operating Cost 

 The total annual savings in operating cost for any given standards case is the product of 

the annual operating cost savings per unit attributable 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. Below is the equation for calculating the total annual operating cost savings for a given 

standards case (as introduced in section 10.4.1). 

 

OCS =∑STOCKV ×UOCSV

10.4.2.5 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 using both a 3-percent and a 7-percent real discount rate, 

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 

2014. 

10.4.2.6 Present Value of Costs

The present value of increased installed costs is the increase in annual installed cost in 

each year (i.e., the difference between a standards case and base case), discounted to the present 

and summed over the period for which DOE is considering the installation of products (that is, 

from 2019 to 2048).

The increase in total installed cost refers to both product cost and installation cost 

associated with the higher energy efficiency of products purchased in the standards case 

compared to the base case. DOE calculated annual increases in installed costs as the difference in 
10-15 

total installed cost for new products purchased each year multiplied by the shipments in the 

standards case.

10.4.2.7 Present Value of Savings 

The present value of operating cost savings is the annual operating cost savings (the 

difference between the base case and a standards case) discounted to the present and summed 

over the period from the compliance year, 2019, to the year when the last unit installed in 2048 is 

retired from service. Savings are decreases in operating costs associated with the higher energy 

efficiency of products 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 

that survive in a particular year. 

10.5 RESULTS OF CALCULATIONS

The national impact analysis (NIA) model estimates the NES and NPV attributable to a 

given trial standard level. The inputs to the NIA model were discussed 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://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/47). Details 

and instructions for using the spreadsheet are provided in appendix 10A of this NOPR TSD. 

10.5.1 Summary of Inputs

Table 10.5.1 summarizes the inputs to the NIA model. The data source for each input is 

described briefly. 
10-16 

Table 10.5.1 Inputs to Calculation of National Energy Savings and Net Present Value 

Input Data Source

Shipments Annual shipments from shipments model. (See chapter 9.)

Effective date of standard 2019

Base-case projected efficiencies

SWIEF determined in 2014 for each of the considered products

classes. Annual growth rate of 0.25 percent assumed for 

determining SWIEF between 2014 and 2048. (See section 10.2)

Standards-case efficiencies Roll-up scenario for 2019; efficiency improvement after 2019 

based on 0.25 percent (See section 10.2.)

Annual energy consumption per unit

Annual weighted-average values are a function of cost at each 

TSL. (See section 10.3.2.3.) Incorporates forecast of future 

product prices based on historical data.

Total installed cost per unit Annual weighted-average values are a function of the efficiency 

distribution. (See section 10.4.2.1.)

Energy cost per unit

Annual weighted-average values are a function of the annual 

energy consumption per unit and energy prices. (See chapter 8, 

for energy prices.)

Repair and maintenance costs per unit Annual values do not change with efficiency level.

Projection of installed cost per unit Price forecast based on historical PPI data.

Projection of energy prices 

AEO 2015 forecasts (to 2040) and extrapolation through 2048

(See chapter 8.)

Energy site-to-power plant and FFC 

conversion A time-series conversion factor derived from AEO 2014. 

Discount rate Three and seven percent real.

Present year Future costs and savings are discounted to 2014.

*Section 10.3.2.5 provides more detail on NEMS.

10.5.2 Results of National Energy Savings Calculations 

 Table 10.5.2 shows the NES results for the TSLs analyzed for dehumidifiers. NES 

results, which are cumulative to 2097, are shown as primary energy savings. DOE based the 

inputs to the NIA model on weighted-average values, yielding results that are discrete point 

values, rather than a distribution of values such as obtained in the LCC and PBP analysis

(chapter 8 of the preliminary TSD). 
10-17 

Table 10.5.2 Cumulative National Energy Savings (Unit: Quads) 

TSL ≤30.00 

Pints/Day

30.01-45.00 

Pints/Day

>45.00 

Pints/Day

≤8.0 ft3

Case 

Volume 

(WholeHome)

>8.0 ft3

Case 

Volume 

(Wholehome)

All

1 0.02 0.01 0.03 0.00 0.00 0.07 

2 0.05 0.01 0.03 0.00 0.00 0.11 

3 0.08 0.18 0.03 0.00 0.00 0.31 

4 0.33 0.34 0.06 0.01 0.01 0.75 

Table 10.5.3 Full-Fuel-Cycle Energy Savings (Unit: Quads)

TSL ≤30.00 

Pints/Day

30.01-45.00 

Pints/Day

>45.00 

Pints/Day

≤8.0 ft3

Case 

Volume 

(WholeHome)

>8.0 ft3

Case 

Volume 

(Wholehome)

All

1 0.02 0.01 0.03 0.00 0.00 0.07 

2 0.06 0.01 0.04 0.00 0.00 0.11 

3 0.09 0.19 0.04 0.00 0.00 0.32 

4 0.35 0.36 0.07 0.01 0.01 0.79 

 

10.5.3 Annual Costs and Savings 

To illustrate the basic inputs to the NPV calculations, Figure 10.5.1 presents the nondiscounted annual installed cost increases and annual operating cost savings nationwide for TSL

3 for dehumidifiers. The figure also shows the net savings, which represent the difference 

between the savings and costs for each year. The annual product cost is the increase in the total 

installed cost for products purchased each year during the projection period. 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 presented below for each TSL. 
10-18 

Figure 10.5.1 Non-Discounted Annual Installed Cost Increases and Annual 

Operating Cost Savings for Dehumidifiers, TSL 3 

10.5.4 Results of Net Present Value Calculations 

This section provides results from the calculation of NPV for the potential efficiency 

standards for dehumidifiers. Results, which are cumulative, are shown as the discounted value of 

savings. DOE based the inputs to the NIA model on weighted-average values, yielding results 

that are discrete point values, rather than a distribution of values as produced by the life-cycle 

cost and payback period analysis. 

The present value of increased total installed costs is the increase in total annual installed 

cost (i.e., the difference between the standards case and base case), discounted to the present and 

summed over the period in which DOE evaluated the impacts of standards (2019 to 2048).

Savings are decreases in operating costs associated with the higher energy efficiency of 

products purchased in a standards case as compared to the base case. Total savings in operating 

costs are the savings per unit multiplied by the number of units of each vintage (i.e., the year of 

manufacture) that survive in a particular year. For units purchased through 2048, operating costs 

include energy consumed until the last unit is retired from service. 

Table 10.5.4 and Table 10.5.5 presents NPV results for the trial standard levels 

considered for dehumidifiers. 
10-19 

Table 10.5.4 Cumulative NPV Results Based on 3-Percent Discount Rate (Billion, 2013$) 

TSL ≤30.00 

Pints/Day

30.01-45.00 

Pints/Day

>45.00 

Pints/Day

≤8.0 ft3 Case 

Volume 

(WholeHome) 

>8.0 ft3

Case 

Volume 

(WholeHome)

All

1 0.22 0.10 0.16 0.02 0.00 0.50 

2 0.44 0.10 0.20 0.02 0.02 0.78 

3 0.68 1.35 0.20 0.02 0.02 2.27 

4 2.23 2.36 0.32 0.03 0.03 4.96 

Table 10.5.5 Cumulative NPV Results Based on 7-Percent Discount Rate (Billion, 2013$) 

TSL ≤30.00 

Pints/Day

30.01-45.00 

Pints/Day

>45.00 

Pints/Day

≤8.0 ft3 Case 

Volume 

(WholeHome) 

>8.0 ft3

Case 

Volume 

(WholeHome)

All

1 0.13 0.04 0.06 0.01 0.00 0.24 

2 0.23 0.04 0.08 0.01 0.01 0.37 

3 0.33 0.60 0.08 0.01 0.01 1.04 

4 0.97 1.02 0.12 0.01 0.01 2.13 
10-20 

REFERENCES

1 U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. 2014. Washington, DC. Report No. DOE/EIA-0383(2014). April. (Last accessed 

August 21, 2014.) 

 < http://www.eia.gov/forecasts/aeo/> 

2 U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections 

to 2040. 2015. Washington, DC. Report No. DOE/EIA-0383(2015). April. (Last accessed 

April 20, 2015.) 

 < http://www.eia.gov/forecasts/aeo/> 
11-i 

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 IDENTIFIED SUBGROUPS ......................................................................................... 11-1

11.2.1 Senior-Only Households .................................................................................... 11-1

11.2.2 Low-Income Households ................................................................................... 11-1

11.3 INPUTS TO CONSUMER SUBGROUP ANALYSIS ................................................. 11-1

11.4 RESULTS ...................................................................................................................... 11-3

REFERENCES ........................................................................................................................ 11-13

LIST OF TABLES

Table 11.3.1 Household Population .................................................................................. 11-2

Table 11.3.2 Portable Dehumidifiers ≤30.00 Pints/Day: Weighted-Average Annual 

Electricity Use .............................................................................................. 11-2

Table 11.3.3 Portable Dehumidifiers 30.01–45.00 Pints/Day: Weighted-Average 

Annual Electricity Use ................................................................................. 11-2

Table 11.3.4 Portable Dehumidifiers >45.00 Pints/Day: Weighted-Average Annual 

Electricity Use .............................................................................................. 11-3

Table 11.3.5 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): WeightedAverage Annual Electricity Use .................................................................. 11-3

Table 11.3.6 Dehumidifiers >8.0 ft3 Case Volume (Whole-Home): WeightedAverage Annual Electricity Use .................................................................. 11-3

Table 11.4.1 Senior-Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers ≤30.00 Pints/Day .................. 11-4

Table 11.4.2 Senior-Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers 30.01–45.00 Pints/Day ......... 11-4

Table 11.4.3 Senior-Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers >45.00 Pints/Day .................. 11-5

Table 11.4.4 Senior-Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Dehumidifiers ≤8.0 ft3 Case Volume (WholeHome): ......................................................................................................... 11-5

Table 11.4.5 Senior-Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Dehumidifiers >8.0 ft3 Case Volume (WholeHome): ......................................................................................................... 11-6

Table 11.4.6 Senior-Only Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers ≤30.00 Pints/Day ................................................................. 11-6

Table 11.4.7 Senior-Only Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers 30.01–45.00 Pints/Day ......................................................... 11-7
11-ii

Table 11.4.8 Senior-Only Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers >45.00 Pints/Day ................................................................. 11-7

Table 11.4.9 Senior-Only Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Dehumidifiers 

≤8.0 ft3 Case Volume (Whole-Home) ......................................................... 11-7

Table 11.4.10 Senior-Only Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Dehumidifiers 

>8.0 ft3 Case Volume (Whole-Home) ......................................................... 11-8

Table 11.4.11 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers ≤30.00 Pints/Day: ................. 11-8

Table 11.4.12 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers 30.01–45.00 Pints/Day ......... 11-9

Table 11.4.13 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Portable Dehumidifiers >45.00 Pints/Day .................. 11-9

Table 11.4.14 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Dehumidifiers ≤8.0 ft3

 Case Volume (WholeHome): ....................................................................................................... 11-10

Table 11.4.15 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Dehumidifiers >8.0 ft3 Case Volume (WholeHome): ....................................................................................................... 11-10

Table 11.4.16 Low Income Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers ≤30.00 Pints/Day ............................................................... 11-11

Table 11.4.17 Low Income Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers 30.01–45.00 Pints/Day ....................................................... 11-11

Table 11.4.18 Low Income Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Portable 

Dehumidifiers >45.00 Pints/Day ............................................................... 11-11

Table 11.4.19 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Dehumidifiers ≤8.0 ft3

 Case Volume (WholeHome): ....................................................................................................... 11-12

Table 11.4.20 Low Income Households: Summary of Life-Cycle Cost Savings 

Relative to the Base Case Efficiency Distribution for Dehumidifiers 

>8.0 ft3 Case Volume (Whole-Home) ....................................................... 11-12
11-1 

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

 Chapter 8 of this TSD describes the LCC and PBP analysis that examines energy savings 

and costs impacts of energy conservation standards on the U.S. population. In analyzing the 

potential impacts of new or amended standards on consumers, the DOE further evaluates the 

impacts on identifiable groups of consumers (subgroups) that may be disproportionately affected 

by a national standard level. The consumer subgroup analysis evaluates effects by analyzing the 

LCCs and PBPs for subgroups of residential consumers. For both portable and whole-home 

dehumidifiers, DOE identified two consumer subgroups that warranted further study: (1) senioronly households and (2) low-income households. 

 DOE determined the impact on consumer subgroups for portable and whole-home 

dehumidifiers using the LCC spreadsheet model, which enables DOE to analyze the LCC for any 

subgroup by sampling only the data that apply to that subgroup. (Chapter 8 explains in detail the 

inputs to the model used in determining LCCs and PBPs.) As described in section 11.3, the 

energy use and energy price characteristics of the two subgroups (senior-only and low-income) 

differ from those for the general population. 

This chapter describes the identification of the two subgroups and gives the results of the 

LCC and PBP analysis for those subgroups. 

11.2 IDENTIFIED SUBGROUPS

 The following two sections describe how DOE defined the two consumer subgroups 

identified for further examination. 

11.2.1 Senior-Only Households

Senior-only households comprise occupants who are all at least 65 years of age. Based on 

RECS 2009, senior-only households represent 17 percent of U.S. households.1

11.2.2 Low-Income Households

As defined in the RECS survey, low-income household residents are living at or below 

the poverty line. The poverty line varies with household size, age of head of household, and 

family income. The RECS survey classifies 15 percent of the country’s households as lowincome.

11.3 INPUTS TO CONSUMER SUBGROUP ANALYSIS

Table 11.3.1 summarizes the overall household populations and the populations of 

senior-only and low-income households in RECS. Table 11.3.2 through Table 11.3.6 summarize 

the weighted-average annual energy use for the households analyzed in the consumer subgroup 

analysis. These values are compared against the weighted-average values for the national sample. 
11-2 

Table 11.3.1 Household Population 

National

Count Sum

12,083 113,616,229

Senior-Only 1,939 19,562,375

Senior-Only (%) 16.0 17.2

Low-Income 1675 16,867,387

Low-Income (%) 13.9 14.8

Table 11.3.2 Portable Dehumidifiers ≤30.00 Pints/Day: Weighted-Average Annual 

Electricity Use 

Efficiency 

Level

All

Households

SeniorOnlyLowIncome

(kWh/year)

Baseline 720 582 699

1 505 409 491

2 463 375 450

3 428 346 416

4 355 287 345

Table 11.3.3 Portable Dehumidifiers 30.01–45.00 Pints/Day: Weighted-Average Annual 

Electricity Use 

Efficiency 

Level

All

Households

SeniorOnlyLowIncome

(kWh/year)

Baseline 1,030 833 1,000

1 808 653 784

2 693 560 673

3 607 491 589

4 540 436 524
11-3 

Table 11.3.4 Portable Dehumidifiers >45.00 Pints/Day: Weighted-Average Annual 

Electricity Use 

Efficiency 

Level

All

Households

SeniorOnlyLowIncome

(kWh/year)

Baseline 905 731 878

1 781 631 758

2 670 542 650

3 513 415 498

Table 11.3.5 Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home): Weighted-Average 

Annual Electricity Use 

Efficiency 

Level

All

Households

SeniorOnlyLowIncome

(kWh/year)

Baseline 951 850 850

1 809 722 722

2 671 600 600

Table 11.3.6 Dehumidifiers >8.0 ft3

 Case Volume (Whole-Home): Weighted-Average 

Annual Electricity Use

Efficiency 

Level

All

Households

SeniorOnlyLowIncome

(kWh/year)

Baseline 1,137 1,015 1,015

1 1,016 908 908

2 784 700 700

3 617 551 551

11.4 RESULTS

Table 11.4.1 through Table 11.4.20 summarize the LCC and PBP results from DOE’s 

subgroup analysis. The results describe the financial effects of potential standards on senior-only 

and low-income households. The tables present the average installed price; average lifetime 

operating cost (discounted); average life-cycle cost; average life-cycle cost savings; percentage 

of each subgroup who are burdened with net costs, realize net savings, or are not affected; and 

the simple payback period. 
11-4 

Table 11.4.1 Senior-Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Portable Dehumidifiers ≤30.00 Pints/Day 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $212 $84 $786 $998 --

1 1 $212 $59 $552 $763 0.0

2 2 $214 $54 $506 $720 0.1

3 3 $218 $50 $468 $686 0.2

4 4 $241 $41 $388 $629 0.7

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.2 Senior-Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Portable Dehumidifiers 30.01–45.00 Pints/Day 

TSL Efficiency

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $256 $120 $1,124 $1,380 --

1 1 $256 $94 $882 $1,137 0.0

2 2 $259 $81 $756 $1,016 0.1

3 3 $268 $71 $662 $930 0.2

4 4 $290 $63 $589 $879 0.6

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 
11-5 

Table 11.4.3 Senior-Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Portable Dehumidifiers >45.00 Pints/Day 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $915 $105 $987 $1,903 --

1 1 $990 $91 $852 $1,842 5.2

2, 3 2 $1,008 $78 $731 $1,739 3.4

4 3 $1,124 $60 $560 $1,684 4.6

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.4 Senior-Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Dehumidifiers ≤8.0 ft3

 Case Volume (Whole-Home):

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $1,643 $126 $1,826 $3,469 --

1, 2, 3 1 $1,670 $107 $1,552 $3,221 1.4

4 2 $1,867 $89 $1,288 $3,155 6.0

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 
11-6 

Table 11.4.5 Senior-Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Dehumidifiers >8.0 ft3 Case Volume (Whole-Home):

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $2,125 $151 $2,181 $4,306 --

1 1 $2,136 $135 $1,950 $4,087 0.7

2, 3 2 $2,194 $104 $1,504 $3,698 1.5

4 3 $2,424 $82 $1,184 $3,609 4.4

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.6 Senior-Only Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers ≤30.00 

Pints/Day

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

 2013$ 

1 1 0 24

2 3 0 39

3 3 0 51

4 4 12.0 107

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).
11-7 

Table 11.4.7 Senior-Only Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers 30.01–45.00 

Pints/Day

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 0 0

2 3 0 0

3 3 0.5 81

4 4 6.1 130

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).

Table 11.4.8 Senior-Only Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers >45.00 

Pints/Day 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 21.6 36

2, 3 2 13.6 114

4 3 37.0 169

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).

Table 11.4.9 Senior-Only Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Dehumidifiers ≤8.0 ft3 Case Volume 

(Whole-Home)

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1, 2, 3 1 5.5 182

4 2 40.3 248

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).
11-8 

Table 11.4.10 Senior-Only Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Dehumidifiers >8.0 ft3 Case 

Volume (Whole-Home)

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 0.7 67

2, 3 2 7.5 367

4 3 35.9 457

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).

Table 11.4.11 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Portable Dehumidifiers ≤30.00 Pints/Day: 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $212 $96 $885 $1,097 --

1 1 $212 $67 $621 $833 0.0

2 2 $215 $62 $570 $784 0.1

3 3 $219 $57 $526 $745 0.2

4 4 $242 $47 $436 $678 0.6

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 
11-9 

Table 11.4.12 Low Income Households: Summary of LCC and PBP Results by Efficiency

Level for Portable Dehumidifiers 30.01–45.00 Pints/Day 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $256 $137 $1,266 $1,522 --

1 1 $256 $107 $993 $1,249 0.0

2 2 $260 $92 $851 $1,111 0.1

3 3 $269 $81 $746 $1,014 0.2

4 4 $290 $72 $663 $954 0.5

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.13 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Portable Dehumidifiers >45.00 Pints/Day 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $917 $120 $1,112 $2,029 --

1 1 $992 $104 $959 $1,951 4.5

2, 3 2 $1,011 $89 $823 $1,834 3.0

4 3 $1,127 $68 $631 $1,757 4.0

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 
11-10 

Table 11.4.14 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home):

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $1,626 $92 $1,201 $2,827 --

1, 2, 3 1 $1,653 $78 $1,021 $2,673 1.9

4 2 $1,851 $65 $847 $2,698 8.3

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.15 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Dehumidifiers >8.0 ft3 Case Volume (Whole-Home):

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback 

years Installed Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $2,102 110 $1,434 $3,536 --

1 1 $2,113 98 $1,283 $3,396 1.0

2, 3 2 $2,171 76 $989 $3,161 2.0

4 3 $2,403 60 $779 $3,182 6.0

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 
11-11 

Table 11.4.16 Low Income Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers ≤30.00 

Pints/Day

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 0 28

2 3 0 45

3 3 0 58

4 4 13.9 125

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).

Table 11.4.17 Low Income Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers 30.01–45.00 

Pints/Day

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 0 0

2 3 0 0

3 3 1.2 92

4 4 7.8 150

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).

Table 11.4.18 Low Income Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Portable Dehumidifiers >45.00 

Pints/Day 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 21.9 43

2, 3 2 15.2 133 

4 3 37.1 209

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).
11-12 

Table 11.4.19 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Dehumidifiers ≤8.0 ft3 Case Volume (Whole-Home):

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1, 2, 3 1 9.4 113

4 2 53.9 89

Note: The average LCC, LCC savings, and simple payback for each TSL are calculated assuming that all consumers 

use products with that EL. This allows the results for each TSL to be compared under the same conditions. 

Table 11.4.20 Low Income Households: Summary of Life-Cycle Cost Savings Relative to 

the Base Case Efficiency Distribution for Dehumidifiers >8.0 ft3 Case 

Volume (Whole-Home) 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings 

2013$ 

1 1 1.5 43

2, 3 2 12.2 224

4 3 48.3 204

Note: The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes households with zero LCC savings (no impact).
11-13 

REFERENCES

1 U.S. Department of Energy–Energy Information Administration. Residential Energy 

Consumption Survey, 2009 Public Use Data Files. 2013. Washington, D.C. (Last accessed 

March 25, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=characteristics> 

 

 
12-i 

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION ......................................................................................................... 12-1

12.2 METHODOLOGY ........................................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-2

12.2.2 Phase II: 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 ..................................................... 12-4

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 Consumer Confusion ......................................................................................... 12-6

12.3.2 Consumer Utility ................................................................................................ 12-6

12.3.3 Impacts on Profitability...................................................................................... 12-7

12.3.4 Impacts on Small Businesses ............................................................................. 12-7

12.4 GRIM INPUTS AND ASSUMPTIONS ........................................................................ 12-7

12.4.1 Overview of the GRIM ...................................................................................... 12-7

12.4.2 Sources for GRIM Inputs ................................................................................... 12-8

12.4.2.1 Corporate Annual Reports ........................................................................ 12-8

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 ............................................................................... 12-9

12.4.2.5 Manufacturer Interviews .......................................................................... 12-9

12.4.3 Financial Parameters ........................................................................................ 12-10

12.4.4 Corporate Discount Rate .................................................................................. 12-10

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 ....................................................... 12-15

12.4.7 Production Costs .............................................................................................. 12-15

12.4.8 Conversion Costs and Stranded Assets ............................................................ 12-17

12.4.8.1 Residential Dehumidifier Conversion Costs .......................................... 12-17

12.4.9 Markup Scenarios ............................................................................................ 12-20

12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario ................. 12-20

12.4.9.2 Preservation of Per-Unit Operating Profit Markup Scenario ................. 12-21

12.5 INDUSTRY FINANCIAL IMPACTS ........................................................................ 12-22

12.5.1 Introduction ...................................................................................................... 12-22

12.5.2 Residential Dehumidifier Industry Financial Impacts ..................................... 12-24
12-ii

12.6 IMPACTS ON SMALL BUSINESS RESIDENTIAL DEHUMIDIFIER 

MANUFACTURERS .................................................................................................. 12-26

12.7 OTHER IMPACTS ...................................................................................................... 12-28

12.7.1 Employment ..................................................................................................... 12-28

12.7.2 Production Capacity ......................................................................................... 12-31

12.7.3 Cumulative Regulatory Burden ....................................................................... 12-31

12.7.3.1 DOE Regulations for Other Products Produced by 

Residential Dehumidifier Manufacturers ............................................... 12-31

12.7.3.2 Other Regulations That Could Impact Residential 

Dehumidifiers ......................................................................................... 12-32

12.8 CONCLUSION ............................................................................................................ 12-33

LIST OF TABLES

Table 12.2.1 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 Residential Dehumidifiers ......................................... 12-13

Table 12.4.5 Total Base-Case 2019 NIA Shipments ............................................................... 12-14

Table 12.4.6 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, ≤ 

30.00 pints/day, in 2019 ............................................................................. 12-14

Table 12.4.7 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, 

30.01 to 45.00 pints/day, in 2019 ............................................................... 12-14

Table 12.4.8 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, > 

45.00 pints/day, in 2019 ............................................................................. 12-14

Table 12.4.9 Base-Case Distribution of Efficiencies for Whole-Home 

Dehumidifiers ≤ 8.0 ft3, in 2019 ................................................................ 12-15

Table 12.4.10 Base-Case Distribution of Efficiencies for Whole-Home 

Dehumidifiers > 8.0 ft3, in 2019 ................................................................ 12-15

Table 12.4.11 MPC Breakdown for Product Class 1 – Portable Dehumidifiers, ≤ 

30.00 pints/day ........................................................................................... 12-16

Table 12.4.12 MPC Breakdown for Product Class 2 - Portable Dehumidifiers, 

30.01 to 45.00 pints/day ............................................................................. 12-16

Table 12.4.13 MPC Breakdown for Product Class 3 - Portable Dehumidifiers, > 

45.00 pints/day ........................................................................................... 12-16

Table 12.4.14 MPC Breakdown for Product Class 4 - Whole-Home Dehumidifiers 

≤ 8.0 ft3 ....................................................................................................... 12-17

Table 12.4.15 MPC Breakdown for Product Class 5 - Whole-Home Dehumidifiers 

> 8.0 ft3

 ...................................................................................................... 12-17
12-iii

Table 12.4.16 Product Conversion Costs for Product Class 1 – Portable 

Dehumidifiers, ≤ 30.00 pints/day by EL .................................................... 12-18

Table 12.4.17 Product Conversion Costs for Product Class 2 - Portable 

Dehumidifiers, 30.01 to 45.00 pints/day by EL ......................................... 12-18

Table 12.4.18 Product Conversion Costs for Product Class 3 - Portable 

Dehumidifiers, > 45.00 pints/day by EL .................................................... 12-18

Table 12.4.19 Product Conversion Costs for Product Class 4 - Whole-Home 

Dehumidifiers ≤ 8.0 ft3 by EL .................................................................. 12-18

Table 12.4.20 Product Conversion Costs for Product Class 5 - Whole-Home 

Dehumidifiers > 8.0 ft3 by EL ................................................................. 12-19

Table 12.4.21 Capital Conversion Costs for Product Class 1 – Portable 

Dehumidifiers, ≤ 30.00 pints/day by EL .................................................... 12-19

Table 12.4.22 Capital Conversion Costs for Product Class 2 - Portable 

Dehumidifiers, 30.01 to 45.00 pints/day by EL ......................................... 12-19

Table 12.4.23 Capital Conversion Costs for Product Class 3 - Portable 

Dehumidifiers, > 45.00 pints/day by EL .................................................... 12-19

Table 12.4.24 Capital Conversion Costs for Product Class 4 - Whole-Home 

Dehumidifiers ≤ 8.0 ft3 by EL .................................................................. 12-19

Table 12.4.25 Capital Conversion Costs for Product Class 5 - Whole-Home 

Dehumidifiers > 8.0 ft3 by EL ................................................................. 12-20

Table 12.4.26 Industry Average Baseline Markups ................................................................. 12-20

Table 12.4.27 Preservation of Per-Unit Operating Profit Markups for Product 

Class 1: Portable Dehumidifiers, ≤ 30.00 pints/day .................................. 12-21

Table 12.4.28 Preservation of Per-Unit Operating Profit Markups for Product 

Class 2: Portable Dehumidifiers, 30.01 to 45.00 pints/day 

Residential Dehumidifiers ......................................................................... 12-21

Table 12.4.29 Preservation of Per-Unit Operating Profit Markups for Product 

Class 3: Portable Dehumidifiers, > 45.00 pints/day .................................. 12-22

Table 12.4.30 Preservation of Per-Unit Operating Profit Markups for Product 

Class 4: Whole-Home Dehumidifiers ≤ 8.0 ft3 .......................................... 12-22

Table 12.4.31 Preservation of Per-Unit Operating Profit Markups for Product 

Class 5: Whole-Home Dehumidifiers > 8.0 ft3

 .......................................... 12-22

Table 12.5.1 Manufacturer Impact Analysis for Residential Dehumidifiers – 

Preservation of Gross Margin Percentage Markup Scenario ..................... 12-24

Table 12.5.2 Manufacturer Impact Analysis for Residential Dehumidifiers – 

Preservation of Per-Unit Operating Profit Markup Scenario ..................... 12-25

Table 12.7.1 Potential Changes in the Total Number of Domestic Residential 

dehumidifier Production Workers in 2019 ................................................ 12-29

Table 12.7.2 Other DOE and Federal Actions Affecting the Residential 

Dehumidifier Industry ................................................................................ 12-32

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow ............................................................ 12-8
12-iv

Figure 12.5.1 Annual Industry Net Cash Flows for Residential Dehumidifiers 

(Preservation of Gross Margin Markup Scenario) ..................................... 12-25

Figure 12.5.2 Annual Industry Net Cash Flows for Residential Dehumidifiers 

(Preservation of Per-Unit Operating Profit Markup Scenario) .................. 12-26

Figure 12.7.1 Residential Dehumidifier Domestic Production Employment by 

Year ............................................................................................................ 12-30
12-1 

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the 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 residential 

dehumidifiers, 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 product 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 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 residential dehumidifier 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 

residential dehumidifier 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 70 percent of the residential dehumidifier 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 
12-2 

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. 

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the residential dehumidifier industry 

that built upon the market and technology assessment prepared for this rulemaking. (See chapter 

3 of this NOPR 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 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 residential dehumidifier 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 

residential dehumidifier 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 30 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 

 

a

 Available online at www.sec.gov. b

 Available online at www2.standardandpoors.com. 
12-3 

with manufacturers. DOE’s shipments analysis, presented in chapter 9 of 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 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 residential 

dehumidifiers 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 non-disclosure 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 12A of this NOPR TSD. 

12.2.3 Phase III: Subgroup Analysis

While conducting the MIA, DOE interviewed a representative cross-section of residential 

dehumidifier manufacturers. The MIA interviews broadened the discussion to include businessrelated 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 residential dehumidifier 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. 
12-4 

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. 

12.2.3.3 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 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 NAICS 

code, presented in Table 12.2.1, to determine whether any small entities would be affected by the 

rulemaking.c For the product 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 residential dehumidifiers, DOE 

used its own publically available CCMS database1 as a starting point to identify manufacturers of 

residential dehumidifiers. Additionally, DOE used public certification databases provided by the 

CEC2 and ENERGY STAR3 to identify residential dehumidifier manufacturers. DOE then 

checked this list of residential dehumidifier manufacturers against the employee limit for small 

businesses using reports from vendors such as Dun & Bradstreet. DOE also consulted publicly 

available data from the 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. 

Based on the size standards published by the SBA (65 FR 30840 (May 15, 2000), as 

amended at 67 FR 52602 (Aug. 13, 2002); 74 FR 46313 (Sep. 9, 2009)), to be categorized as a 

small business manufacturer of residential dehumidifiers under NAICS codes 333415 (“AirConditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration 

Equipment Manufacturing”) or 335210 (“Small Electrical Appliance Manufacturing”), a 

 

c The size standards are available on the SBA’s website at www.sba.gov/content/table-small-business-sizestandards.
12-5 

dehumidifier manufacturer and its affiliates may employ a maximum of 750 employees. The 

750-employee threshold includes all employees in a business’ parent company and any other 

subsidiaries. Using this classification in conjunction with a search of industry databases and the 

SBA member directory, DOE identified five manufacturers of residential dehumidifiers that 

qualify as small businesses, all of which are manufacturers of whole-home and high-capacity 

portable dehumidifiers. 

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Air-Conditioning and Warm Air Heating Equipment 

and Commercial and Industrial Refrigeration 

Equipment Manufacturing

N/A 750 333415

Small Electrical Appliance Manufacturing N/A 750 335210

The analysis of impacts on the small business manufacturer 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 residential 

dehumidifier industry. The interviews also solicited manufacturer views on changes in 

employment patterns that may result from more stringent standards. The employment impacts 

section of the interview guide focused on current employment levels associated with 

manufacturers at each production facility, expected future employment levels with and without 

amended energy conservation standards, and differences in workforce skills and issues related to 

the retraining of employees. The employment impacts are reported in section 12.7.1. 
12-6 

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects of regulation on manufacturers of 

residential dehumidifiers. These effects may be the result of other regulatory actions affecting 

residential dehumidifiers, or of amended energy conservation standards for other products and 

equipment made by the same manufacturers. DOE identified regulations relevant to residential 

dehumidifier 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 product classes under review.

12.3.1 Consumer Confusion

The majority of manufacturers interviewed emphasized concerns over the impact of new test 

conditions in the DOE dehumidifier test procedure on the rated capacity of their products. One 

manufacturer noted that lower ambient testing conditions would lead to a 60-percent to 70-

percent decrease in capacity and efficiency. Some manufacturers fear that a shift in rated 

capacity resulting from a change in test procedure will lead to confusion in the market, as 

consumers find it important to have the same apparent capacity in a replacement residential 

dehumidifier, even if it is simply a larger unit at a lower rating condition. Also, dehumidifiers 

with smaller capacities cannot reach the same efficiency as higher-capacity units due to 

limitations of the vapor-compression cycle, because the parasitic losses make it harder to 

maintain efficiency with smaller compressors. One manufacturer estimated that a multi-million 

dollar investment would be necessary to redesign products that would maintain customer 

perception of rated capacities. That manufacturer went on to note that if it is unable to produce 

comparable products at the same effective capacity, it would consider exiting the market.

Other manufacturers indicated that as product ratings are modified to reflect the test results 

at the lower ambient temperature, the whole product classification system will need to be 

revisited, which will require a substantial investment in consumer education. 

12.3.2 Consumer Utility

Multiple manufacturers interviewed expressed concerns that an amended energy 

conservation standard for residential dehumidifiers would have an adverse impact on price, noise 

level, and size, and would thus compromise consumer utility. Manufacturers are concerned that 

residential dehumidifiers would need to become physically larger to deliver the same moisture 

removal capacity to comply with new amended testing and energy conservation standards. For 

customers with space constraints, finding a product that best fits their needs may be more 

difficult under an amended standard. For example, some whole-home dehumidifiers must fit into 

a small attic or crawl space. If amended energy conservation standards for whole-home products 
12-7 

cannot be met within the size constraints associated with this type of installation, part of the 

whole-home market segment may move to portable products, reducing consumer utility by 

forcing the unit into the living space. Additionally, larger portable dehumidifiers are already 

cumbersome to move around, making them close to the limit of what is considered portable. As 

such, consumers may be forced to purchase a lower-capacity dehumidifier or alternative product. 

12.3.3 Impacts on Profitability 

During interviews, many manufacturers stated that an industry-wide price increase of 25 

percent would have major negative impacts on the portable dehumidifier market. Manufacturers 

went on to note that a price increase of 50 percent or more would cause the market to collapse 

entirely. A whole-home dehumidifier manufacturer stated that a 10-percent cost increase would 

have a significant impact on the whole-home market because any increases in manufacturer 

production costs are magnified due to the two-tiered distribution channel that is characteristic of 

the whole-home market. Among manufacturers, it was agreed that consumers find a product’s 

price to be the most important aspect when considering dehumidifier purchases. Relatedly, one 

manufacturer suggested that as prices increase, consumers may opt to rent units as needed, 

instead of buying one. Accordingly, manufacturers expect a negative impact on profitability as 

revenues decline following any amended energy conservation standard which would raise prices 

for residential dehumidifiers. Similar impacts on profitability are expected if manufacturers 

maintain current prices while absorbing the higher costs associated with the design and 

manufacture of higher efficiency products. 

12.3.4 Impacts on Small Businesses

One small manufacturer noted that it and its competitors in the whole-home segment would 

be disproportionately impacted by an amended energy conservation standard. Small business 

manufacturers have fewer human and capital resources than larger, more diversified portable unit 

manufacturers. Additionally, due the low-volume nature of the residential whole-home 

dehumidifier market, small business manufacturers of whole-home products are disadvantaged in 

achieving the scale needed to exert purchasing power in sourcing components from vendors. One 

small business manufacturer noted that its lack of influence on suppliers ultimately impacts its 

ability to compete with larger manufacturers.

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 
12-8 

a number of inputs to arrive at a series of annual cash flows, beginning with the base year, 2015, 

and continuing to 2048, 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 

conservation standards on manufacturers. Appendix 12B provides more technical details and 

user information for the GRIM. 

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, 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 

residential dehumidifiers. 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 residential dehumidifiers. These figures were later revised 

using feedback from interviews to be representative of the residential dehumidifier

manufacturing industry. DOE used corporate annual reports to derive the following initial inputs 

to the GRIM: 

• Tax rate; 

• Working capital; 
12-9 

• 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 NIA. 

The model relied on historical shipments data for residential dehumidifiers. Chapter 10 of the 

NOPR TSD describes the methodology and analytical model DOE used to forecast shipments. 

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 residential dehumidifiers. During this analysis, DOE used a manufacturing cost model 

to develop MPC estimates for residential dehumidifiers. 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 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.
12-10 

12.4.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 2006 to 2012 for 

four publicly held manufacturers of residential dehumidifiers as a starting point for determining 

the residential dehumidifier industry financial parameters. The industry financial parameters 

were determined by weighting each manufacturer’s individual financial parameters by their 

respective estimated 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 

Manufacturer 

D 

Tax Rate

(% of Taxable 

Income)

31.1 38.1 33.7 19.2 29.0

Working Capital 

(% of Revenue) 11.3 44.5 19.8 5.9 -4.5

SG&A

(% of Revenue) 20.9 30.3 26.6 19.6 16.0

R&D 

(% of Revenue) 1.3 0.0 2.2 0.7 1.8

Depreciation 

(% of Revenue) 2.5 2.0 2.8 0.7 2.9

Capital 

Expenditures 

(% of Revenue)

2.7 2.5 2.4 0.3 3.2

Net Property, Plant, 

and Equipment 

(% of Revenues)

13.4 13.6 12.9 5.8 14.7

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 residential dehumidifier manufacturing industry based 

on three representative companies, using the following formula: 
12-11 

WACC = After-Tax Cost of Debt × (Debt Ratio) + Cost of Equity × (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. 

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 four publiclyheld residential dehumidifier manufacturers. DOE determined that the industry-average cost of 

equity for the residential dehumidifier industry is 13.5 percent (see Table 12.4.2). 
12-12 

Table 12.4.2 Cost of Equity Calculation 

Parameter

Industry 

Weighted 

Average

Manufacturer 

A 

Manufacturer 

B 

Manufacturer 

C 

Manufacturer 

D 

(a) Average Beta 1.37 0.91 1.08 1.38 1.62

(b) Yield on 10 Year TBill (1928–2012) (%) 5.16

 

 

 

 

 

 

 

 

 

 

 

 

(c) Market Risk 

Premium (1928–2012) 

(%)

6.10

Cost of Equity (b) + 

[(a)*(c)] (%) 13.51

Equity/Total Capital 

(%) 78.20 86.97 72.73 86.47 73.33

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 four manufacturers by using S&P and other estimates of 

corporate credit ratings and adding the relevant spread to the risk-free rate. 

Because 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 residential dehumidifier

industry is 4.4 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 AA AA AAA BBB

(a) Yield on 10 year TBill (1927–2011) (%) 5.16 

(b) Gross Cost of Debt 

(%) 6.33 5.81 5.81 5.66 6.76

(c) Tax Rate (%) 31.09 38.05 33.70 22.38 29.01

Net Cost of Debt (b) x[1-

(c)] (%) 4.36 

Debt/Total Capital (%) 21.80 13.03 27.27 13.53 26.67
12-13 

Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated 

value for the residential dehumidifier industry’s inflation-adjusted WACC and the initial 

estimate of the discount rate is 8.4 percent. During interviews, DOE did not receive any feedback 

from manufacturers regarding this estimate of the discount rate. 

12.4.5 Trial Standard Levels 

DOE developed TSLs to analyze the impact on manufacturers of amended energy 

efficiency standards for the five product classes of residential dehumidifiers. Table 12.4.4

presents the TSLs and the corresponding product class efficiency levels based on IEF. See 

chapter 5 of this NOPR TSD for a discussion of product classes and IEF.

TSL 4 is comprised of the max-tech efficiency levels for all product classes. TSL 3 is 

comprised of the efficiency level corresponding to one below the max-tech level for all product 

classes. TSL 2 is comprised of Efficiency Level 2 for all product classes, except where 

Efficiency Level 2 is also the max-tech level (i.e. for product class 4—with only two efficiency 

levels—TSL 2 corresponds to Efficiency Level 1). TSL 1 is comprised of Efficiency Level 1 for 

each product class. 

Table 12.4.4 Trial Standard Levels for Residential Dehumidifiers 

Product class Base Case TSL 1 TSL 2 TSL 3 TSL 4

Portable ≤ 30.00 pints/day Efficiency Level Baseline EL 1 EL 2 EL 3 EL 4

IEF at 65 °F 0.77 1.10 1.20 1.30 1.57

Portable 30.01–45.00 pints/day Efficiency Level Baseline EL 1 EL 2 EL 3 EL 4

IEF at 65 °F 0.94 1.20 1.40 1.60 1.80 

Portable > 45.00 pints/day Efficiency Level Baseline EL 1 EL 2 EL 2 EL 3

IEF at 65 °F 2.07 2.40 2.80 2.80 3.66

Whole-Home ≤ 8.0 ft3 Efficiency Level Baseline EL 1 EL 1 EL 1 EL 2

IEF at 73 °F 1.77 2.09 2.09 2.09 2.53

Whole-Home > 8.0 ft3 Efficiency Level Baseline EL 1 EL 2 EL 2 EL 3

IEF at 73 °F 2.41 2.70 3.52 3.52 4.50

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 residential 

dehumidifier shipment data from the NIA. Chapter 9 of the NOPR TSD explains DOE’s 

calculations of total shipments in detail.

Table 12.4.5 shows total shipments forecasts for each product class of residential 

dehumidifiers in 2019, the year new standards for residential dehumidifiers would take effect. 
12-14 

Table 12.4.5 Total Base-Case 2019 NIA Shipments 

Product class Total Industry Shipments

Portable ≤ 30.00 pints/day 1,052,023

Portable 30.01–45.00 pints/day 857,947

Portable > 45.01 pints/day 69,664

Whole-Home ≤ 8.0 ft3 13,361

Whole-Home > 8.0 ft3 5,727

12.4.6.1 Base-Case Shipments Forecast

As part of the shipment analysis, DOE estimated the distribution of shipments by 

efficiency level for each product class of residential dehumidifiers. DOE held the base-case 

energy efficiency distribution constant throughout the forecast period. Table 12.4.6 through 

Table 12.4.10 show the base-case distributions of shipments by efficiency level estimated in the 

NIA for the residential dehumidifier product classes.

Table 12.4.6 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, ≤ 30.00 

pints/day, in 2019 

Efficiency Level Baseline EL 1 EL 2 EL 3 EL 4

IEF 0.77 1.10 1.20 1.30 1.57

% of Shipments 10.7% 22.9% 0.0% 66.4% 0.0%

Table 12.4.7 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, 30.01 to 

45.00 pints/day, in 2019 

Efficiency Level Baseline EL 1 EL 2 EL 3 EL 4

IEF 0.94 1.20 1.40 1.60 1.80

% of Shipments 0.0% 0.0% 94.3% 2.0% 3.7%

Table 12.4.8 Base-Case Distribution of Efficiencies for Portable Dehumidifiers, > 45.00 

pints/day, in 2019 

Efficiency Level Baseline EL 1 EL 2 EL 3

IEF 2.07 2.40 2.80 3.66

% of Shipments 57.1% 20.1% 22.9% 0.0%
12-15 

Table 12.4.9 Base-Case Distribution of Efficiencies for Whole-Home Dehumidifiers ≤ 8.0 

ft3, in 2019 

Efficiency Level Baseline EL 1 EL 2

IEF 1.77 2.09 2.53

% of Shipments 74.9% 25.1% 0.0%

Table 12.4.10 Base-Case Distribution of Efficiencies for Whole-Home Dehumidifiers > 8.0 

ft3

, in 2019 

Efficiency Level Baseline EL 1 EL 2 EL 3

IEF 2.41 2.70 3.52 4.50

% of Shipments 30.9% 46.3% 22.9% 0.0%

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 residential 

dehumidifiers. The 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 

product class on the compliance dated and thereafter. 

As in the shipments analysis, DOE assumed no relative price elasticity in the residential 

dehumidifier market, meaning that amended energy conservation standards that increase the first 

cost of residential dehumidifiers would lead to no change the number of total shipments.

12.4.7 Production Costs 

Changes in the MPCs of residential dehumidifiers 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 five residential dehumidifier

product 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 efficiency level. For labor, 

DOE estimated the labor contribution at each efficiency level 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 

 

d The estimated compliance date for the residential dehumidifier energy conservation standard is 2019.
12-16 

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 

residential dehumidifiers by multiplying the MPCs by the appropriate manufacturer markup for 

that product. Table 12.4.11 through Table 12.4.15 show the production cost estimates used in the 

GRIM for the representative product classes for residential dehumidifiers. 

Table 12.4.11 MPC Breakdown for Product Class 1 – Portable Dehumidifiers, ≤ 30.00 

pints/day 

EL IEF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 1.10 $98.12 $3.75 $4.05 $7.49 $113.41 1.45 $164.44 

EL2 1.20 $99.81 $3.75 $4.11 $7.43 $115.09 1.45 $166.88 

EL3 1.30 $102.37 $3.76 $4.20 $7.34 $117.66 1.45 $170.61 

EL4 1.57 $117.54 $3.73 $4.74 $6.78 $132.79 1.45 $192.54 

Table 12.4.12 MPC Breakdown for Product Class 2 - Portable Dehumidifiers, 30.01 to 

45.00 pints/day 

EL IEF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 1.20 $117.89 $4.26 $4.86 $9.38 $136.39 1.45 $197.76 

EL2 1.40 $120.30 $4.25 $4.95 $9.27 $138.77 1.45 $201.22 

EL3 1.60 $126.00 $4.25 $5.15 $9.07 $144.47 1.45 $209.48 

EL4 1.80 $140.26 $4.31 $5.66 $8.57 $158.81 1.45 $230.27 

Table 12.4.13 MPC Breakdown for Product Class 3 - Portable Dehumidifiers, > 45.00 

pints/day

EL IEF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 2.40 $312.20 $88.75 $16.83 $53.94 $471.71 1.45 $683.98 

EL2 2.80 $321.63 $90.03 $17.21 $53.68 $482.55 1.45 $699.69 

EL3 3.66 $357.42 $108.12 $19.59 $64.11 $549.24 1.45 $796.39 
12-17 

Table 12.4.14 MPC Breakdown for Product Class 4 - Whole-Home Dehumidifiers ≤ 8.0 ft3

EL IEF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 2.09 $267.49 $80.03 $14.73 $50.79 $413.04 1.45 $598.91

EL2 2.53 $369.34 $84.39 $18.80 $54.44 $526.96 1.45 $764.09

Table 12.4.15 MPC Breakdown for Product Class 5 - Whole-Home Dehumidifiers > 8.0 ft3

EL IEF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 2.70 $364.53 $103.21 $19.41 $57.01 $544.16 1.45 $789.03

EL2 3.52 $390.63 $107.70 $20.52 $56.31 $575.16 1.45 $833.98

EL3 4.50 $496.10 $120.03 $24.95 $58.28 $699.35 1.45 $1,014.06

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 Residential Dehumidifier 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 CCMS, 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 product 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 product 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 
12-18 

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 product 

class. 

Table 12.4.16 through Table 12.4.25 show DOE’s estimates of the product and capital 

conversion costs necessary for each residential dehumidifier product class at each efficiency 

level identified. None of the manufacturers interviewed provided comment regarding potential 

stranded assets.

Table 12.4.16 Product Conversion Costs for Product Class 1 – Portable Dehumidifiers, ≤ 

30.00 pints/day by EL 

EL (IEF) Product Conversion Costs (Millions 2013$)

EL1 (1.10) $0.02

EL2 (1.20) $0.02

EL3 (1.30) $9.20

EL4 (1.57) $15.20

Table 12.4.17 Product Conversion Costs for Product Class 2 - Portable Dehumidifiers, 

30.01 to 45.00 pints/day by EL 

EL (IEF) Product Conversion Costs (Millions 2013$)

EL1 (1.20) $0.03

EL2 (1.40) $0.03

EL3 (1.60) $16.00

EL4 (1.80) $26.30

Table 12.4.18 Product Conversion Costs for Product Class 3 - Portable Dehumidifiers, > 

45.00 pints/day by EL

EL (IEF) Product Conversion Costs (Millions 2013$)

EL1 (2.4) $1.43

EL2 (2.8) $2.29

EL3 (3.66) $2.33

Table 12.4.19 Product Conversion Costs for Product Class 4 - Whole-Home Dehumidifiers 

≤ 8.0 ft3 by EL 

EL (IEF) Product Conversion Costs (Millions 2013$)

EL1 (2.09) $0.56

EL2 (2.53) $1.97
12-19 

Table 12.4.20 Product Conversion Costs for Product Class 5 - Whole-Home Dehumidifiers 

> 8.0 ft3 by EL

EL (IEF) Product Conversion Costs (Millions 2013$)

EL1 (2.70) $1.88 

EL2 (3.52) $2.16 

EL3 (4.50) $2.34 

Table 12.4.21 Capital Conversion Costs for Product Class 1 – Portable Dehumidifiers, ≤ 

30.00 pints/day by EL 

EL (IEF) Capital Conversion Costs (Millions 2013$)

EL1 (1.10) $0.00 

EL2 (1.20) $0.00 

EL3 (1.30) $6.90 

EL4 (1.57) $11.30 

Table 12.4.22 Capital Conversion Costs for Product Class 2 - Portable Dehumidifiers, 30.01 

to 45.00 pints/day by EL

EL (IEF) Capital Conversion Costs (Millions 2013$)

EL1 (1.20) $0.00 

EL2 (1.40) $0.00 

EL3 (1.60) $11.90 

EL4 (1.80) $19.60 

Table 12.4.23 Capital Conversion Costs for Product Class 3 - Portable Dehumidifiers, > 

45.00 pints/day by EL

EL (IEF) Capital Conversion Costs (Millions 2013$)

EL1 (2.4) $0.48

EL2 (2.8) $0.76

EL3 (3.66) $0.78

Table 12.4.24 Capital Conversion Costs for Product Class 4 - Whole-Home Dehumidifiers 

≤ 8.0 ft3

 by EL 

EL (IEF) Capital Conversion Costs (Millions 2013$)

EL1 (2.09) $0.19

EL2 (2.53) $0.66
12-20 

Table 12.4.25 Capital Conversion Costs for Product Class 5 - Whole-Home Dehumidifiers 

> 8.0 ft3 by EL

EL (IEF) Capital Conversion Costs (Millions 2013$)

EL1 (2.70) $0.63 

EL2 (3.52) $0.72 

EL3 (4.50) $0.78

12.4.9 Markup Scenarios

MSPs include direct manufacturing production costs (i.e., labor, material, overhead, and 

depreciation estimated in DOE’s MPCs) and all non-production costs (i.e., SG&A, R&D, and 

interest), along with profit. To calculate the MSPs in the GRIM, DOE applied manufacturer 

markups to the MPCs estimated in the engineering analysis. Based on publicly available 

financial information for manufacturers of residential dehumidifiers and comments from 

manufacturer interviews, DOE assumed the industry average base-case markup on production 

costs to be 1.45. This markup takes into account the two-tiered sourcing structure of the small 

portable dehumidifier segment, in addition to the traditional one-tiered structure of the highcapacity portable and whole-home dehumidifier segment. The majority of the market for the 

lower-capacity portable product classes (product classes 1 and 2) are manufactured under 

contract by overseas OEMs. The engineering analysis, as detailed in chapter 5 of the NOPR 

TSD, estimates the cost of manufacturing at the OEM level. These production costs are marked 

up once by the OEM to the company contracting its manufacture and again by the contracting 

company who imports the product, and sells it to “big box” stores or other retailers. For the small 

portable dehumidifier segment, the industry average baseline markup breaks down as follows: 

Table 12.4.26 Industry Average Baseline Markups

OEM to Contracting Company Markup 1.20

Contracting Company to First Customer Markup 1.21

Overall OEM to First Customer Markup 1.45

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 per-unit operating profit 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. 

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 1.45 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.
12-21 

12.4.9.2 Preservation of Per-Unit Operating Profit Markup Scenario

DOE also modeled the preservation of per-unit operating profit 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 per-unit operating profit in the year after standards go into 

effect matches per-unit operating profit of the same year in the base case. This scenario 

represents a more substantial impact to the residential dehumidifier 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 per-unit operating profit they 

saw prior to new and amended standards. 

While all compliant products receive the 1.45 markup in the preservation of gross margin 

percentage markup scenario, Table 12.4.27 through Table 12.4.31 list the calibrated markups 

used in the preservation of per-unit operating profit markup scenario. 

Table 12.4.27 Preservation of Per-Unit Operating Profit Markups for Product Class 1: 

Portable Dehumidifiers, ≤ 30.00 pints/day 

EL IEF Minimally Compliant EL

EL 1 EL 2 EL 3 EL4

EL 1 1.10 1.449

EL 2 1.20 1.450 1.448

EL 3 1.30 1.450 1.450 1.449

EL 4 1.57 1.450 1.450 1.450 1.436

Table 12.4.28 Preservation of Per-Unit Operating Profit Markups for Product Class 2: 

Portable Dehumidifiers, 30.01 to 45.00 pints/day Residential Dehumidifiers 

EL IEF Minimally Compliant EL

EL 1 EL 2 EL 3 EL4

EL 1 1.20 1.450

EL 2 1.40 1.450 1.450

EL 3 1.60 1.450 1.450 1.446

EL 4 1.80 1.450 1.450 1.450 1.436
12-22 

Table 12.4.29 Preservation of Per-Unit Operating Profit Markups for Product Class 3: 

Portable Dehumidifiers, > 45.00 pints/day

EL IEF Minimally Compliant EL

EL 1 EL 2 EL 3 EL4

EL 1 2.40 1.442

EL 2 2.80 1.450 1.442

EL 3 3.66 1.450 1.450 1.429

Table 12.4.30 Preservation of Per-Unit Operating Profit Markups for Product Class 4: 

Whole-Home Dehumidifiers ≤ 8.0 ft3

EL IEF Minimally Compliant EL

EL 1 EL 2 EL 3 EL4

EL 1 2.09 1.447

EL 2 2.53 1.450 1.423

Table 12.4.31 Preservation of Per-Unit Operating Profit Markups for Product Class 5: 

Whole-Home Dehumidifiers > 8.0 ft3 

EL IEF Minimally Compliant EL

EL 1 EL 2 EL 3 EL4

EL 1 2.70 1.450

EL 2 3.52 1.450 1.445

EL 3 4.50 1.450 1.450 1.425

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 residential dehumidifier 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 residential dehumidifier

manufacturing industry, and is the sum of all annual net cash flows discounted at the industry’s 

WACC. The GRIM for the residential dehumidifier industry models cash flows from 2015 to 

2048. This timeframe models both the short-term impacts on the industry from the 

announcement of the standard until the compliance date in 2019, and a long-term assessment 

over the 30-year analysis period immediately thereafter. 
12-23 

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 residential dehumidifier industry, DOE examined the 

two markup scenarios described above: the preservation of gross margin percentage markup 

scenario and the preservation of per-unit operating profit markup scenario. DOE’s estimates of 

INPV for the full analysis period (2015–2048) for the base case and at each TSL in the standards 

case are presented in Table 12.5.1 and Table 12.5.2 below. While INPV is useful for evaluating 

the long-term effects of amended energy conservation standards, short-term changes in cash flow 

are also important indicators of the industry’s financial situation. For example, a large 

investment over one or two years could strain the industry’s 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 2015 through 2048 for the base case 

and each TSL in the standards case. 

Annual cash flows are discounted to the base year, 2015. Between 2015 and the 2019 

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 

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 per-unit operating profit scenario because this 

scenario is calibrated to have the same earnings before interest and taxes in the standards case at 
12-24 

each TSL as the base case as in the year after the standard takes effect. In this scenario 

production costs increase, but per-unit operating profit remains approximately equal to the base 

case, effectively decreasing profit margins as a percentage of revenue. 

12.5.2 Residential Dehumidifier Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for the residential 

dehumidifier manufacturing industry. Figure 12.5.1 and Figure 12.5.2 present the annual net cash 

flows for the residential dehumidifier manufacturing industry for each of the markup scenarios. 

Table 12.5.1 Manufacturer Impact Analysis for Residential Dehumidifiers – Preservation 

of Gross Margin Percentage Markup Scenario

Base Case

Trial Standard Level

1 2 3 4 

INPV (2013$ millions) 186.5 184.0 183.4 155.2 146.3 

Change in INPV (2013$ millions) - (2.5) (3.1) (31.3) (40.2)

(%) - (1.4%) (1.6%) (16.8%) (21.6%) 

*For tables in section 12.5, values in parenthesis indicate negative numbers 
12-25 

Table 12.5.2 Manufacturer Impact Analysis for Residential Dehumidifiers – Preservation 

of Per-Unit Operating Profit Markup Scenario

Base Case

Trial Standard Level

1 2 3 4 

INPV (2013$ millions) 186.5 183.5 182.1 151.6 126.8 

Change in INPV (2013$ millions) - $ 

(3.0)

$ 

(4.4)

$ 

(34.9)

$ 

(59.7)

(%) - (1.6%) (2.4%) (18.7%) (32.0%) 

*For tables in section 12.5, values in parenthesis indicate negative numbers

Figure 12.5.1 Annual Industry Net Cash Flows for Residential Dehumidifiers (Preservation 

of Gross Margin Markup Scenario)
12-26 

Figure 12.5.2 Annual Industry Net Cash Flows for Residential Dehumidifiers (Preservation 

of Per-Unit Operating Profit Markup Scenario)

12.6 IMPACTS ON SMALL BUSINESS RESIDENTIAL DEHUMIDIFIER 

MANUFACTURERS 

 As discussed in section 12.2.3.3, DOE identified five domestic small business 

manufacturers that may be disproportionately affected by any energy efficiency regulation in the 

residential dehumidifier industry. These manufacturers are focused on one specific market 

segment (high-capacity portable and whole-home dehumidifiers) and are at least one order of 

magnitude smaller than their diversified competitors. Due to this combination of market 

concentration and size, these small businesses are at risk of high disproportionate impacts, 

depending on the TSL chosen. 

DOE received feedback from small business manufacturers and OEM contractors through 

public comments and confidential interviews (see section IV.J.3 of the NOPR notice for a 

discussion of public comments and feedback received from dehumidifier manufacturers during 

the NOPR phase). These manufacturers expressed a high degree of concern relating to the 

magnitude of burdens and the disproportionate impacts that they believe will result from 

amended energy conservation standards for residential dehumidifiers. 

 Today’s proposed standards for residential dehumidifiers could cause small 

manufacturers to be at a disadvantage relative to large manufacturers. One way in which small 

manufacturers could be at a disadvantage is that they may be disproportionately affected by 

product and capital conversion costs. Product redesign, testing, and certification costs tend to be 

fixed per basic model and do not scale with sales volume. For each model, small businesses must 

make investments in research and development to redesign their products, but because they have 

lower sales volumes, they must spread these costs across fewer units. In addition, because small 
12-27 

manufacturers have fewer engineers than large manufacturers, they would need to allocate a 

greater portion of their available resources to meet a standard. Because engineers may need to 

spend more time redesigning and testing existing models as a result of the new standard, they 

may have less time to develop new products. Similarly, upfront capital investments in new 

manufacturing capital for platform redesigns, as well as depreciated manufacturing capital, can 

only be spread across a disproportionately lower volume of shipments. 

 Furthermore, smaller manufacturers may lack the purchasing power of larger 

manufacturers. For example, since fan-motor suppliers give discounts to manufacturers based on 

the number of motors they purchase, larger manufacturers may have a pricing advantage because 

they have higher-volume purchases. This purchasing power differential between high-volume 

and low-volume orders applies to other residential dehumidifier components as well, including 

compressors and heat exchangers. Some larger manufacturers of lower-capacity portable 

dehumidifiers may even manufacture heat exchangers in-house. Additionally, because small 

business manufacturers produce larger units, they require larger/custom components (e.g. larger 

compressors) than do the large manufacturers who produce lower-capacity portable products and 

who account for the majority of the dehumidifier market. Because of the low-volume nature of 

the high-capacity portable and whole-home dehumidifier market, certain technological 

improvements to components may only be developed for small portable products, or with 

significant lag time for large dehumidifier products.

To access to the capital required to cover the conversion costs associated with reaching 

the proposed standards, small business manufacturers would likely be forced to take on 

additional debt, whereas larger manufacturers of small portable products would be better 

equipped to fund purchases with existing cash flow from operations. 

In terms of impacts to small business manufacturers associated with the specific TSLs 

outline in this notice, disproportionate impacts will be greatest at TSL 1 and TSL 2, where 

relatively more high-capacity portable and whole-home dehumidifiers are at or below the 

baseline than is the case for the lower-capacity portable products. Additionally, it is assumed that 

small business manufacturers will be required to outsource the testing of their products to thirdparty testing facilities. In contrast, the large manufacturers of small portable dehumidifiers are 

assumed to have in-house testing capabilities, which significantly reduce the cost of testing. 

While the magnitude of conversion cost burden increases slightly for small business 

manufacturers at TSL 3 and TSL 4, relative to lower TSLs, disproportionate impacts decrease 

substantially, as relatively more lower-capacity portable product platforms will require 

substantial redesign. Between TSL 3 and TSL 4, TSL 3 minimizes standards compliance burdens 

for small business manufacturers relative to the burdens of high-volume portable dehumidifier 

manufacturers. 

Further detail on small business high-capacity portable and whole-home dehumidifier 

manufacturers is found in section VI.B, “Review under the Regulatory Flexibility 

Act,” of the NOPR notice.
12-28 

12.7 OTHER IMPACTS

12.7.1 Employment

For residential dehumidifiers, 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 

2015 to 2048. DOE used statistical data from the most recent U.S Census Bureau’s 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 

residential dehumidifier 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 

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 

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 residential 

dehumidifier production labor in 2019 will fall approximately within the range of $7.9 to $9.4 

million, depending on the TSL chosen. Using the $18.32 hourly wage rate including fringe 

benefits and 1,955 production hours per year per employee found in the 2011 ASM, DOE

estimates there will be 219 to 261 domestic production workers involved in the manufacturing of

residential dehumidifiers in 2019, the year in which amended standards would go into effect, for 

TSL 1 through TSL 4. In addition, DOE estimates that 78 to 93 non-production employees in the 
12-29 

United States will support residential dehumidifier production.e Approximately 3 percent of 

residential dehumidifiers sold in the United States are manufactured domestically. The 

employment tab of the residential dehumidifier 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 residential 

dehumidifier market. 

Table 12.7.1 Potential Changes in the Total Number of Domestic Residential dehumidifier

Production Workers in 2019 

Baseline TSL 1 TSL 2 TSL 3 TSL 4

Total Number of Domestic Production 

Workers in 2019 

(without changes in production locations)

214 219 222 222 261 

Potential Changes in Domestic 

Production Workers in 2019* - 5 - 

(214) 

8 - 

(214) 

8 - 

(214) 

47 - 

(214) 

*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.

 

e As defined in the 2011 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.” 
12-30 

Figure 12.7.1 Residential Dehumidifier Domestic Production Employment by Year 

Because production employment expenditures are assumed to be a fixed percentage of 

cost of goods sold and the MPCs typically increase with more efficient products, labor tracks the 

increased prices in the GRIM. As efficiency of dehumidifiers increases, so does the complexity 

of the products, generally requiring more labor to produce. However, because only 3 percent of 

residential dehumidifier manufacturing takes place domestically, employment impacts are 

expected to be minimal. DOE expects that there would be minimal employment impacts among 

domestic residential dehumidifier manufacturers for TSL 1 through TSL 3. For TSL 4, the GRIM 

predicts a 21.9-percent increase in total domestic production employment following amended 

standards based on the increase in complexity and relative price of the high-capacity portable and 

whole-home dehumidifier segment. 

During manufacturer interviews, some small businesses stated that, contrary to the above 

findings, domestic production and non-production employment in the industry may decrease as a 

result of amended standards for residential dehumidifiers. 

 

 Similarly, the above analysis does not account for the possible relocation of domestic 

jobs to lower-labor-cost countries because the potential relocation of U.S. jobs is uncertain and 

highly speculative. As mentioned above, the vast majority of residential dehumidifiers sold in the 

United States are manufactured abroad. However, approximately 100 percent of high-capacity 

portable and whole-home dehumidifiers are manufactured domestically. Feedback from 

manufacturers during NOPR interviews reveals that some domestic small businesses in the 

residential dehumidifier industry may be forced to make employment cuts or to shift production 

to new locations, including locations outside of the United States, as a result of amended energy 

conservation standards. 
12-31 

12.7.2 Production Capacity 

 As noted previously, the majority of residential dehumidifiers sold in the United States 

are not produced domestically. However, feedback from domestic manufacturers of highcapacity portable products and whole-home dehumidifiers suggested that production of these 

products could shift abroad as a result of amended energy conservation standards. This could 

lead to a permanently lower production capacity within the residential dehumidifier 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 2019 compliance date of any

amended energy conservation standards for residential dehumidifiers. In addition to amended 

energy conservation regulations, several other Federal regulations apply to residential 

dehumidifiers. While this analysis focuses on the impacts on manufacturers born of other Federal 

requirements, DOE also has described some 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. 

12.7.3.1 DOE Regulations for Other Products Produced by Residential 

Dehumidifier 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 residential dehumidifier manufacturers also produce 

other appliances and residential products. In addition to the amended energy conservation 

standards for residential dehumidifiers, 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 other DOE energy conservation 

standards that could also affect manufacturers of residential dehumidifiers in the 3 years leading 

up to and after the compliance date of any amended energy conservation standards for this 

product. 
12-32 

Table 12.7.2 Other DOE and Federal Actions Affecting the Residential Dehumidifier

Industry

Regulation

Approximate 

Compliance 

Date*

Number of Impacted 

Companies 

Estimated Total 

Industry Conversion 

Costs

Microwave Ovens 2016 4 $43.1 M

Residential Clothes Washers 2018 5 $418.5 M

Commercial Clothes Washers 2018 5 $10.2 M

Dishwashers 2019 5 N/A†

Portable Air Conditioners 2019 6 N/A†

Miscellaneous Refrigeration 2019 3 N/A†

Packaged Terminal Air 

Conditioners 2019 3 N/A†

Room Air Conditioners 2022 2 N/A†

Clothes Dryers 2022 2 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 Residential Dehumidifiers

Independent Testing and Certification

Several manufacturers were concerned that changes in the DOE energy conservation 

standards, and the resulting re-design of their equipment, would require them to re-certify all of 

their equipment with other organizations aside from DOE. This would include re-testing and 

certification by Underwriters Laboratories (UL).

Additionally, one manufacturer cited increasing requirements of “big box” retailers— 

which are a crucial component in the portable dehumidifier distribution channel—as a

cumulative burden. Retailers such as Walmart and Home Depot have adopted their own 

certification systems for suppliers in efforts relating to quality assurance and the fulfillment of 

“ethical sourcing” commitments.f,g

 

 

f https://homedepotlink.homedepot.com/enus/Related%20Documents/Import%20Supplier%20Handbook%20092811.pdf

g http://cdn.corporate.walmart.com/0e/ca/52eda3d84f828f82da0e9a02f021/standards-for-suppliersmanual_129833075555266802.pdf
12-33 

12.8 CONCLUSION

The following section summarizes the scenarios DOE believes are most likely to capture 

the range of impacts on residential dehumidifier 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.

At TSL 1, DOE estimates the impact on INPV for manufacturers of residential 

dehumidifiers to range from -$2.5 million to -$3.0 million, or a change in INPV of -1.4 percent 

to -1.6 percent under the preservation of gross margin percentage markup scenario and the 

preservation of per-unit operating profit markup scenario, respectively. At this TSL, industry free 

cash flow is estimated to decrease by approximately 11.2 percent to $14.1 million, compared to 

the base-case value of $15.8 million in the year before the compliance date (2018). 

At TSL 1, the industry as a whole is expected to incur $3.9 million in product conversion 

costs attributed to upfront research, development, testing, and certification; as well as $1.3 

million in one-time investments in property, plant and equipment (PP&E) necessary to 

manufacture redesigned platforms. The majority of industry conversion cost burden at TSL 1 

will be felt by manufacturers of high-capacity portable and whole-home dehumidifiers, as 

relatively more of these products are currently at the baseline than is the case for the lowercapacity portable products. These baseline products may necessitate complete platform 

redesigns, which involve moving to a new case size to accommodate larger heat exchangers. 

These changes require upfront capital investments for new tooling among other changes to 

manufacturing production lines. Additionally, it is assumed that manufacturers of high-capacity 

portable and whole-home dehumidifiers, the majority of which are small business manufacturers, 

will be required to outsource testing of their products to third-party testing facilities, contributing 

to greater product conversion costs. In contrast, the large manufacturers of small portable 

dehumidifiers are assumed to have in-house testing capabilities which significantly reduce the 

cost of testing. DOE’s assumptions regarding testing burdens were confirmed during 

manufacturer interviews.

At TSL 2, DOE estimates the impact on INPV for manufacturers of residential 

dehumidifiers to range from -$3.1 million to -$4.4 million, or a change in INPV of -1.6 percent 

to -2.4 percent under the preservation of gross margin percentage markup scenario and the 

preservation of per-unit operating profit markup scenario, respectively. At this TSL, industry free 

cash flow is estimated to decrease by approximately 14.4 percent to $13.6 million, compared to 

the base-case value of $15.8 million in the year before the compliance date (2018). 

At TSL 2, the industry as a whole is expected to incur $5.1 million in product conversion 

costs associated with the upfront research, development, testing, and certification; as well as $1.7 

million in one-time investments in PP&E to manufacture products requiring platform redesigns. 

Similar to TSL 1, the majority of industry conversion cost burden at TSL 2 will be felt by 

manufacturers of high-capacity portable and whole-home dehumidifiers, as relatively more 

products of these types are at the baseline than are the lower-capacity portable products, and will 

require complete platform redesigns. Platform redesigns at TSL 2 will require moving to a new 

case size to accommodate larger heat exchangers, and will necessitate upfront capital

investments for new tooling. As at TSL 1, because manufacturers of high-capacity portable and 
12-34 

whole-home dehumidifiers are largely small businesses, it is assumed that these manufacturers 

will be required to outsource testing of their products to third-party testing facilities. In contrast, 

the large manufacturers of small portable dehumidifiers are assumed to have in-house testing 

capabilities, which significantly reduce the cost of testing. DOE’s assumptions regarding testing 

burdens were confirmed during manufacturer interviews. 

At TSL 3, DOE estimates the impact on INPV for manufacturers of residential 

dehumidifiers to range from -$31.3 million to -$34.9 million, or a change in INPV of -16.8 

percent to -18.7 percent under the preservation of gross margin percentage markup scenario and 

the preservation of per-unit operating profit markup scenario, respectively. At this TSL, industry 

free cash flow is estimated to decrease by approximately 116.1 percent to -$2.5 million, 

compared to the base-case value of $15.8 million in the year before the compliance date (2018). 

At TSL 3, the industry as a whole is expected to spend $30.2 million in product 

conversion costs associated with the research and development and testing and certification, as 

well as $20.5 million in one-time investments in PP&E to manufacture redesigned platforms. 

While conversion costs remain relatively constant for manufacturers of high-capacity portable 

and whole-home dehumidifiers for TSL 1 through TSL 3, the conversion costs for manufacturers 

of lower-capacity portable products increase substantially at TSL 3, as a greater portion of these 

products will require total platform redesigns. As with the high-capacity portable and wholehome dehumidifier market segment, platform redesigns for lower-capacity portable units will 

consist of moving products to a new case size to accommodate larger heat exchangers, and in 

turn requires capital investments in new tooling for larger cases. This upfront investment is in 

addition to additional R&D and testing expenditures. Because lower-capacity portable units 

represent approximately 97 percent of the market, conversion costs associated with this segment 

have a significant impact on total industry conversion costs. 

 At TSL 4, DOE estimates the impact on INPV for manufacturers of residential 

dehumidifiers to range from -$40.2 million to -$59.7 million, or a change in INPV of -21.6 

percent to -32.0 percent under the preservation of gross margin percentage markup scenario and 

the preservation of per-unit operating profit markup scenario, respectively. At this TSL, industry 

free cash flow is estimated to decrease by approximately 186.4 percent to -$13.7 million, 

compared to the base-case value of $15.8 million in the year before the compliance date (2018). 

At TSL 4, the industry as a whole is expected to spend $48.1 million in product 

conversion costs associated with the research and development and testing and certification, as 

well as $33.1 million in one-time investments in PP&E for platform redesigns. Again, 

conversion costs remain relatively constant for manufacturers of high-capacity portable and 

whole-home dehumidifiers from TSL 1 through TSL 4. In contrast, the conversion cost burden 

for manufacturers of lower-capacity portable products increases substantially at TSL 4, as an 

increasingly larger portion of smaller portable products will require platform redesigns. Again, 

since lower-capacity portable units represent approximately 97 percent of the market, conversion 

costs associated with this segment have a significant impact on total industry conversion costs. 

Beyond the direct financial impact on manufacturers, TSL 4 may also contribute to the 

potential unavailability of products at certain capacities across the five product classes. The 

efficiencies at TSL 4 are theoretical levels that DOE determined dehumidifiers could achieve by 
12-35 

incorporating the most efficient type of each component. DOE is not aware of any dehumidifiers 

currently available on the market that achieve the TSL 4 efficiencies. To meet TSL 4, all 

products would be required to incorporate the highest efficiency compressors; however, 

manufacturers indicated that few such compressors are available in the range of compressor 

capacities suitable for residential dehumidifiers, and it is unlikely that substantially more would 

become available if standards at TSL 4 were adopted. In addition, the specific compressor 

capacities available at any given time are driven largely by the markets for other products with 

higher shipments (for example, room air conditioners), and thus dehumidifier manufacturers may 

be constrained in their design choices. Because DOE assumed manufacturers would optimize all 

components at TSL 4, including the use of larger heat exchangers and permanent-magnet blower 

motors, manufacturers would not have alternative design pathways to achieve the max-tech 

efficiency level in the absence of high efficiency compressors. Therefore, DOE expects that 

those dehumidifier platforms for which a suitable high efficiency compressor is not available 

would be unable to meet the max-tech efficiency level associated with TSL 4. While this would 

likely not eliminate entire product classes from the market, it has the potential to eliminate 

dehumidifiers of certain capacities within a given product class. The potential for this impact on 

manufacturers of high-capacity portable and whole-home dehumidifiers is exacerbated by this 

segment’s low production volumes, which encumbers manufacturers’ ability to influence the 

availability of higher efficiency components from their vendors.
12-36 

REFERENCES

1

 U.S. Department of Energy. Compliance Certification Database. Available online at: 

http://www.regulations.doe.gov/certification-data/. (Last accessed October 13, 2014). 

2

 California Energy Commission. Appliance Efficiency Database. Available online at: 

http://www.appliances.energy.ca.gov/. (Last accessed October 13, 2014). 

3

 ENERGY STAR. Residential dehumidifiers. Available online at : 

http://www.energystar.gov/productfinder/product/certified-dehumidifiers/results. (Last 

accessed October 13, 2013.) 

4

 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies, 

3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000. 
13-i 

CHAPTER 13. EMISSIONS ANALYSIS 

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-1

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS ............................................. 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-3

13.5 EMISSIONS IMPACT RESULTS ................................................................................ 13-4

REFERENCES .......................................................................................................................... 13-9

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors .................................................................... 13-3

Table 13.4.1 Electricity Upstream Emissions Factors ...................................................... 13-4

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for 

Dehumidifiers .............................................................................................. 13-5

LIST OF FIGURES

Figure 13.5.1 CO2 Total Emissions Reduction .................................................................. 13-6

Figure 13.5.2 SO2 Total Emissions Reduction ................................................................... 13-6

Figure 13.5.3 NOx Total Emissions Reduction .................................................................. 13-7

Figure 13.5.4 Hg Total Emissions Reduction .................................................................... 13-7

Figure 13.5.5 N2O Total Emissions Reduction .................................................................. 13-8

Figure 13.5.6 CH4 Total Emissions Reduction .................................................................. 13-8
13-1 

CHAPTER 13. EMISSIONS ANALYSIS 

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector and site combustion emissions 

of carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg). The 

second component estimates the impacts of a potential standard on emissions of two additional 

greenhouse gases, methane (CH4) and nitrous oxide (N2O), as well as the reductions to emissions 

of all species due to “upstream” activities in the fuel production chain. These upstream activities 

comprise extraction, processing, and transporting fuels to the site of combustion. The associated 

emissions are referred to as upstream emissions. Together, these emissions account for the FFC, 

in accordance with DOE’s FFC Statement of Policy. 76 FR 51282 (Aug. 18, 2011). 

The analysis of power sector emissions uses marginal emissions intensity factors 

calculated by DOE. As of 2014, DOE is using a new methodology based on results published for 

the AEO 2014 reference case and a set of side cases that implement a variety of efficiencyrelated policies.

1

 The new methodology is described in chapter 15 and in the report “Utility 

Sector Impacts of Reduced Electricity Demand” (Coughlin, 2014).2 Site emissions of CO2 and 

NOX are estimated using emissions intensity factors from a publication of the EPA.

3

Combustion emissions of CH4 and N2O are estimated using emissions intensity factors 

published by the EPA, GHG Emissions Factors Hub.a The FFC upstream emissions are 

estimated based on the methodology developed by Coughlin (2013).4 The upstream emissions 

include both emissions from fuel combustion during extraction, processing and transportation of 

fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH4 and CO2. 

The emissions intensity factors are expressed in terms of physical units per MWh or 

MMBtu of site energy savings. Total emissions reductions are estimated using the energy 

savings calculated in the national impact analysis (chapter 10). 

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying the physical units by the gas

global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment 

Report of the Intergovernmental Panel on Climate Change,5

 DOE used GWP values of 28 for 

CH4 and 265 for N2O.b

 

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

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

regulations on emissions. AEO 2014 generally represents current Federal and State legislation 

and final implementation regulations in place as of the end of October 2013. 

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

b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.
13-2 

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

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that that operates 

along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR 

was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP 

v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering 

CAIR. The AEO 2014 emissions factors used for the present analysis assume that CAIR remains 

a binding regulation through 2040. c 

The attainment of emissions caps is typically flexible among affected EGUs and is 

enforced through the use of emissions allowances and tradable permits. Under existing EPA 

regulations, any excess SO2 emissions allowances resulting from the lower electricity demand 

caused by the imposition of an efficiency standard could be used to permit offsetting increases in 

SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was 

uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing 

cap-and-trade system, but it concluded that no reductions in power sector emissions would occur 

for SO2 as a result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the MATS for power 

plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for 

hydrogen chloride as a surrogate for acid gas HAP, and also established a standard for SO2 (a 

non-HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The same 

controls are used to reduce HAP and non-HAP acid gas; thus, SO2 emissions will be reduced as a 

result of the control technologies installed on coal-fired power plants to comply with the MATS 

requirements for acid gas. AEO 2014 assumes that, in order to continue operating, coal plants 

must have either flue gas desulfurization or dry sorbent injection systems installed by 2016. Both 

technologies, which are used to reduce acid gas emissions, also reduce SO2 emissions. Under the 

MATS, emissions will be far below the cap established by CAIR, so it is unlikely that excess 

SO2 emissions allowances resulting from the lower electricity demand would be needed or used 

to permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE believes 

that efficiency standards will reduce SO2 emissions in 2016 and beyond. 

 

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

further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's 

methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other 

downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip op. at 32 

(U.S. April 29, 2014). Because DOE is using emissions factors based on AEO 2014, the analysis assumes that 

CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the 

purpose of DOE's analysis of SO2 emissions.
13-3 

CAIR established a cap on NOx emissions in 28 eastern States and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOx emissions in 

those States covered by CSAPR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions. 

However, standards would be expected to reduce NOx emissions in the States not affected by 

CAIR, so DOE estimated NOx emissions reductions from potential standards for those States. 

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

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

estimated mercury emissions reductions using emissions factors based on AEO 2014, which 

incorporates the MATS. 

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced 

equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a 

unit reduction to annual site electricity demand for a given end use to corresponding reductions 

to installed capacity by fuel type, fuel use for generation, and power sector emissions. Details on 

the approach used may be found in Coughlin (2014).

Table 13.3.1 presents the average power plant emissions factors for selected years. These

power plant emissions factors are derived from the emissions factors of the plant types used to 

supply electricity for homes. The average factors for each year take into account the projected 

shares of each of the sources in total electricity generation. 

Table 13.3.1 Power Plant Emissions Factors 

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 723 642 579 529 483

SO2 g/MWh 718 560 471 395 353

NOx g/MWh 574 479 419 369 334

Hg g/MWh 0.00222 0.00173 0.00145 0.00122 0.00109

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50.2 49.4 47.9 46.4 44.8

* Refers to site electricity savings. 

13.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10B. See also Coughlin (2013) and Coughlin (2014). When 

demand for a particular fuel is reduced, there is a corresponding reduction in the emissions from 

combustion of that fuel at either the building site or the power plant. The associated reduction in 

energy use for upstream activities leads to further reductions in emissions. These upstream 

emissions are defined to include the combustion emissions from the fuel used upstream, the 
13-4 

fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated 

with the fuel used on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5 percent of total CO2 emissions for natural gas 

and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal 

production. Combustion emissions of CH4 are very small, while fugitive emissions (particularly 

for gas production) may be relatively large. Hence, fugitive emissions make up over 99 percent

of total methane emissions for natural gas, about 95 percent for coal, and 93 percent for 

petroleum fuels. 

Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for 

methane from coal mining and natural gas production were estimated based on a review of recent 

studies compiled by Burnham (2011).6 This review includes estimates of the difference between 

fugitive emissions factors for conventional production of natural vs. unconventional (shale or 

tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting 

requirements for the petroleum and natural gas industries.7, 8 As more data are made available, 

DOE will continue to update these estimated emissions factors.

For ease of application in its analysis, DOE developed all of the emissions factors using 

site (point of use) energy savings in the denominator. Table 13.4.1 presents the electricity 

upstream emissions factors for selected years. These were used to estimate the emissions 

associated with the decreased electricity use. The caps that apply to power sector NOX emissions 

do not apply to upstream combustion sources. 

Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0.000012 0.000011 0.000011 0.000010 0.000010

N2O g/MWh 0.252 0.247 0.241 0.234 0.228

CH4 g/MWh 2149 2195 2216 2248 2255

* Refers to site electricity savings.

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold in 2019-2048 for each TSL. Negative values indicate that emissions increase.
13-5 

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Dehumidifiers

TSL 1 TSL 2 TSL 3 TSL 4

Power Sector and Site Emissions

CO2 (million metric tons) 4.05 6.40 18.3 44.6

SO2 (thousand tons) 3.52 5.55 15.8 38.2

NOX (thousand tons) 3.18 5.03 14.3 34.8

Hg (tons) 0.011 0.017 0.049 0.118

CH4 (thousand tons) 0.382 0.607 1.75 4.28

N2O (thousand tons) 0.055 0.087 0.250 0.613

Upstream Emissions

CO2 (million metric tons) 0.221 0.352 1.01 2.50

SO2 (thousand tons) 0.039 0.062 0.179 0.440

NOX (thousand tons) 3.14 5.00 14.4 35.6

Hg (tons) 0.000 0.000 0.000 0.001

CH4 (thousand tons) 18.3 29.1 84.1 207

N2O (thousand tons) 0.002 0.003 0.009 0.022

Total Emissions

CO2 (million metric tons) 4.27 6.75 19.3 47.1

SO2 (thousand tons) 3.56 5.61 16.0 38.6

NOX (thousand tons) 6.33 10.0 28.8 70.4

Hg (tons) 0.011 0.017 0.049 0.119

CH4 (thousand tons) 18.7 29.8 85.9 211

N2O (thousand tons) 0.057 0.090 0.259 0.634

Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for 

each type of emission from each TSL. The reductions reflect the lifetime impacts of products 

sold in 2019-2048. 
13-6 

Figure 13.5.1 CO2 Total Emissions Reduction 

Figure 13.5.2 SO2 Total Emissions Reduction 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2019202420292034203920442049205420592064206920742079208420892094

kt

CO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0

200

400

600

800

1,000

1,200

1,400

1,600

2019202420292034203920442049205420592064206920742079208420892094

ton

SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4
13-7 

Figure 13.5.3 NOx Total Emissions Reduction 

Figure 13.5.4 Hg Total Emissions Reduction 

0

500

1,000

1,500

2,000

2,500

2019202420292034203920442049205420592064206920742079208420892094

ton

NOx Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

2019202420292034203920442049205420592064206920742079208420892094

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4
13-8 

Figure 13.5.5 N2O Total Emissions Reduction

Figure 13.5.6 CH4 Total Emissions Reduction 

0

5

10

15

20

25

2019202420292034203920442049205420592064206920742079208420892094

ton

N2OEmissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2019

2024

202920342039

2044

204920542059

2064

206920742079

2084

20892094

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4
13-9 

REFERENCES

1 Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

2040, 2014. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf> 

2 Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-Pending. 

3 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

4 Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

5 Intergovernmental Panel On Climate Change, Chapter 8. In Climate Change 2013: The 

Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report 

of the Intergovernmental Panel on Climate Change, T.F. Stocker, D. Qin, G.-K. Plattner, 

M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

Editor. 2013. Cambridge University Press: Cambridge, United Kingdom and New York, 

NY, USA

6 Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou-Rivera, Life-Cycle 

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum.

Environmental Science & Technology, 2011. 46(2): pp. 619-627 

7 U.S. Environmental Protection Agency, Fugitive Emissions Reporting from the 

Petroleum and Natural Gas Industry, 2009. U.S. Environmental Protection Agency. 

Washington, DC. 

8 U.S. Environmental Protection Agency, Oil and Natural Gas Sector: Standards of 

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, 

Background Supplemental Technical Support Document for the Final New Source 

Performance Standards, 2012. U.S. Environmental Protection Agency. Washington, DC. 

 
14-i 

CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon ........................................................................................ 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ............................................................. 14-2

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES ................................. 14-2

14.3.1 Current Approach and Key Assumptions .......................................................... 14-2

14.3.2 Limitations of Current Estimates ....................................................................... 14-4

14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.5 RESULTS ...................................................................................................................... 14-5

REFERENCES .......................................................................................................................... 14-9

LIST OF TABLES

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 

2007$ per Metric Ton) ................................................................................. 14-3

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 

2007$ per Metric Ton of CO2) ..................................................................... 14-4

Table 14.4.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating 

Units (2010$) ............................................................................................... 14-5

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Dehumidifiers ............................................................................... 14-6

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Dehumidifiers ..................................................................... 14-7

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Dehumidifiers ......................................................................................... 14-8
14-1 

CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

dehumidifiers, the DOE estimated the monetary benefits of the reduced emissions of CO2 and 

NOX that would be expected to result from each TSL considered for this rulemaking. This 

chapter summarizes the basis for the monetary values assigned to emissions and presents the 

modeled benefits of estimated reductions. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of reduced 

emissions of CO2 is what value to assign to each unit eliminated. The value must encompass a 

broad range of physical, economic, social, and political effects. Analysts developed the concept 

of the SCC to represent the broad cost or value associated with producing—or reducing—a 

quantifiable amount of CO2 emissions.

14.2.1 Social Cost of Carbon 

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. The SCC is intended to include (but is not limited 

to) changes in net agricultural productivity, human health, property damages from increased 

flood risk, and the value of ecosystem services. SCC estimates are provided in dollars per metric 

ton of carbon dioxide. A value for the domestic SCC is meant to represent the damages in the 

United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is 

meant to reflect the value of damages worldwide.

Under section 1(b)(6) of Executive Order 12866,1 agencies must, to the extent permitted 

by law, “assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the SCC estimates required by the Executive Order is to enable agencies to incorporate the 

monetized social benefits of reducing CO2 emissions into cost-benefit analyses of regulatory 

actions. The estimates are presented with an acknowledgement of the many uncertainties 

involved and with a clear understanding that they will need updating in response to increasing 

knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts 

from numerous agencies met regularly to explore the technical literature in relevant fields, 

discuss key model inputs and assumptions, and consider public comments. The primary objective 

of the process was to develop a range of SCC values using a defensible set of assumptions 

regarding model inputs that was grounded in the scientific and economic literature. In this way, 

key uncertainties and model differences transparently and consistently inform the range of SCC 

estimates developed for use in the rulemaking process.
14-2 

14.2.2 Monetizing Carbon Dioxide Emissions 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces several serious challenges. A report from the National Research 

Council2 points out that any assessment will suffer from uncertainty, speculation, and lack of 

information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the effects of changes in climate on the physical and 

biological environment, and (4) the translation of those environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change raises serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the limits of both quantification and monetization, SCC estimates can be useful 

in estimating the social benefits of reducing CO2 emissions. An agency can estimate the benefits 

from reduced (or costs from increased) emissions in any future year by multiplying the change in 

emissions in that year by the SCC values appropriate for that year. Then the net present value of 

the benefits can be calculated by multiplying each of the future benefits by an appropriate 

discount factor and summing across all affected years. This approach assumes that the marginal 

damages from increased emissions are constant for small departures from the baseline emissions 

path, an approximation that is reasonable for policies that have effects on emissions that are 

small relative to cumulative global carbon dioxide emissions. 

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES

In 2009, an interagency process was initiated to develop a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To provide consistency in 

how benefits are evaluated across Federal agencies, the Administration sought to develop a 

transparent and defensible method, specifically designed for the rulemaking process, to quantify 

avoided climate change damages from reduced CO2 emissions. The interagency group did not 

undertake any original analysis. Instead, it combined SCC estimates from the literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment was a set of five interim values: global SCC estimates for 2007 (in 

2006$) of $55, $33, $19, $10, and $5 per ton of CO2.

3

 Those interim values represented the first 

sustained interagency effort within the U.S. government to develop an SCC for use in regulatory 

analysis. The results of that preliminary effort were presented in several proposed and final rules. 

14.3.1 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened regularly to 

improve the SCC estimates. Specifically, the group considered public comments and further 

explored the technical literature in relevant fields. The interagency group relied on three 

integrated assessment models commonly used to estimate the SCC. The models are known by 

their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peerreviewed literature and were used in the most recent assessment of the Intergovernmental Panel 

on Climate Change. Each model was given equal weight in developing SCC values.
14-3 

Each model takes a slightly different approach to calculating how increases in emissions 

produce economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches taken by the 

key modelers in the field. An extensive review of the literature identified three sets of input 

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and 

discount rates. A probability distribution for climate sensitivity was specified as an input to all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from the three integrated assessment models, at discount 

rates of 2.5 percent, 3 percent, and 5 percent. The fourth value, which represents the 95th

percentile of the SCC estimate across all three models at a 3-percent discount rate, is included to 

represent larger-than-expected effects from temperature changes farther out in the tails of the 

SCC distribution. The values increase in real terms over time. Additionally, the interagency 

group determined that a range of values from 7 percent to 23 percent should be used to adjust the 

global SCC to calculate domestic effects, although preference is given to consideration of the 

global benefits of reducing CO2 emissions. Table 14.3.1 presents the values in the 2010 

interagency group report.4

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 2007$ 

per Metric Ton) 

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC values used for the analysis of the effects of potential standards for 

dehumidifiers were generated using the most recent versions of the three integrated assessment 

models that have been published in the peer-reviewed literature, as described in the 2013 update 

from the interagency working group (revised November 2013). Table 14.3.2 shows the updated 

sets of SCC estimates in 5-year increments from 2010 to 2050. The full set of annual SCC 

estimates for 2010–2050 is presented in appendix 14B of this TSD. The central value that 

emerges is the average SCC across models at a 3-percent discount rate. To capture the 
14-4 

uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes 

the importance of including all four sets of SCC values. 

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 2007$ 

per Metric Ton of CO2) 

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191 

2045 24 66 92 206

2050 26 71 97 220

14.3.2 Limitations of Current Estimates 

The interagency group recognizes that current models are imperfect and incomplete. 

Because key uncertainties remain, current SCC estimates should be treated as provisional and 

revisable. Estimates doubtless will evolve in response to improved scientific and economic 

understanding. The 2009 National Research Council report points out the tension between 

producing quantified estimates of the economic damages from an incremental ton of carbon and 

the limits of current modeling efforts. Several analytic challenges are being addressed by the 

research community, some by research programs housed in many of the Federal agencies 

participating in the interagency process. The interagency group intends to review and reconsider 

SCC estimates periodically to incorporate expanding knowledge of the science and economics of 

climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, applying the GDP price 

deflator to adjust the values to 2013$. For the four SCC values, the values of emissions in 2015 

were $12.0, $40.5, $62.4, and $119 per metric ton avoided (values expressed in 2013$). DOE 

derived values after 2050 using the relevant growth rates for 2040–2050 in the interagency 

update. 

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

for that year under each discount rate. To calculate a present value of the stream of monetary 

values, DOE discounted the values in each of the four cases using the same discount rate that had 

been used to obtain the SCC values in each case. 
14-5 

14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefits of reduced NOX emissions attributable to 

the TSLs considered for dehumidifiers. As noted in chapter 13, new or amended energy 

conservation standards would reduce NOX emissions in those States that are not affected by 

emissions caps. DOE estimated the monetized value of NOX emissions reductions resulting from 

each TSL based on estimates of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by electricity generating units. The estimates were 

developed by Krewski et al. (2009) and are reported in EPA’s Office of Air Quality Planning and 

Standards report “Technical Support Document: Estimating the Benefit per Ton of Reducing 

PM2.5 Precursors from 17 Sectors.”5 Table 14.4.1 summarizes the monetized values estimated in 

2010$ for NOX emission reductions in 2016, 2020, 2025 and 2030, at discount rates of 3 percent 

and 7 percent. DOE applied the GDP price deflator to adjust the values to 2013$. For the two 

NOX values, the values of emissions in 2016 were $5483 and $4850 per ton avoided (values 

expressed in 2013$). DOE further interpolated the values between the intervals, and extrapolated 

the values after 2030 using the relevant growth rates for 2016–2030. DOE then multiplied the 

NOX emissions reduction estimated for each year by the NOX value for that year under each 

discount rate. To calculate a present value of the stream of monetary values, DOE discounted the 

values calculated under each discount rate using the same discount rate that had been used to 

obtain the NOX values.

Table 14.4.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating Units 

(2010$) 

Year Discount Rate (%)

3 7

2016 5200 4600

2020 5400 4900

2025 5800 5200 

2030 6200 5600

DOE continues to evaluate appropriate values for monetizing avoided SO2 and Hg 

emissions. DOE did not monetize those emissions for this analysis. 

14.5 RESULTS

Table 14.5.1 presents the global values of CO2 emissions reductions for each considered 

TSL. 
14-6 

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Dehumidifiers 

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$ 

Primary Energy Emissions

1 29.5 133 210 410

2 46.2 209 330 644

3 130 593 939 1,831

4 311 1,427 2,264 4,411

Upstream Emissions

1 1.57 7.16 11.3 22.1

2 2.48 11.3 18.0 35.0

3 7.06 32.4 51.5 100

4 17 79 126 244

Full-Fuel-Cycle Emissions

1 31.1 140 221 432

2 48.6 220 348 679

3 137 625 990 1,931

4 328 1,506 2,390 4,656

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, $40.5, $62.4, and 

$119 per metric ton (2013$).

After calculating global values of CO2 emissions reductions for each considered TSL, 

DOE calculated domestic values as a range of from 7 percent to 23 percent of the global values. 

Results for domestic values are presented in Table 14.5.2. 
14-7 

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Dehumidifiers 

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$ 

Primary Energy Emissions

1 2.1 to 6.8 9.3 to 30.6 14.7 to 48.3 28.7 to 94.3

2 3.2 to 10.6 14.6 to 48.0 23.1 to 76.0 45.1 to 148.2

3 9.1 to 30.0 41.5 to 136.3 65.7 to 216.0 128.2 to 421.1

4 21.8 to 71.5 99.9 to 328.1 158.5 to 520.8 308.8 to 1014.6

Upstream Emissions

1 0.1 to 0.4 0.5 to 1.6 0.8 to 2.6 1.5 to 5.1

2 0.2 to 0.6 0.8 to 2.6 1.3 to 4.1 2.4 to 8.0

3 0.5 to 1.6 2.3 to 7.5 3.6 to 11.8 7.0 to 23.1

4 1.2 to 3.9 5.5 to 18.2 8.8 to 28.9 17.1 to 56.2

Full-Fuel-Cycle Emissions

1 2.2 to 7.2 9.8 to 32.2 15.5 to 50.9 30.2 to 99.4

2 3.4 to 11.2 15.4 to 50.6 24.4 to 80.1 47.6 to 156.3

3 9.6 to 31.6 43.8 to 143.8 69.3 to 227.8 135.2 to 444.2

4 22.9 to 75.4 105.4 to 346.3 167.3 to 549.7 325.9 to 1070.8

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, $40.5, $62.4, and 

$119 per metric ton (2013$).

Table 14.5.3 presents the present value of cumulative NOX emissions reductions for each 

TSL. Monetary values are calculated using the average dollar-per-ton values assigned to NOX

emissions at 7-percent and 3-percent discount rates. 
14-8 

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Dehumidifiers 

TSL

3% discount rate 7% discount rate

Million 2013$

Primary Energy Emissions

1 11.9 5.36

2 18.6 8.27

3 52.4 22.8

4 125 52.9

Upstream Emissions

1 11.4 4.88

2 18.0 7.58

3 51.4 21.2

4 125 49.9

Full-Fuel-Cycle Emissions

1 23.3 10.2

2 36.5 15.9

3 104 44.0

4 250 103
14-9 

REFERENCES

1 Presidential Documents--Executive Order 12866--Regulatory Planning and Review, 

October 4, 1993. <http://www.archives.gov/federal-register/executiveorders/pdf/12866.pdf> 

2 National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2010. National Academies Press. Washington, DC. 

<http://www.nap.edu/catalog.php?record_id=12794> 

3 Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, February 2010. 

United States Government. Washington, D.C. 

<http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf> 

4 Interagency Working Group on Social Cost of Carbon, Technical Update of the Social 

Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, May 

2013; revised November 2013. United States Government. Washington, D.C. 

<http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-updatesocial-cost-of-carbon-for-regulator-impact-analysis.pdf> 

5 EPA, Technical Support Document - Estimating the Benefit per Ton of Reducing PM2.5

Precursors from 17 Sectors, 2013. Office of Air and Radiation, Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. < 

http://www2.epa.gov/sites/production/files/2014-

10/documents/sourceapportionmentbpttsd.pdf> 
15-i 

CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 ALL PETROLEUM-BASED GENERATION IS ALLOCATED TO PEAK 

PERIODS; ...................................................................................................................... 15-2

15.4 NATURAL GAS GENERATION IS ALLOCATED TO ANY REMAINING 

PEAK REDUCTION; THIS IS CONSISTENT WITH THE FACT THAT OIL 

AND GAS STEAM UNITS ARE USED IN NEMS TO MEET PEAK 

DEMAND; ..................................................................................................................... 15-3

15.5 BASE-LOAD GENERATION (NUCLEAR AND COAL) IS ALLOCATED 

PROPORTIONALLY TO ALL PERIODS; .................................................................. 15-3

15.6 THE REMAINING GENERATION OF ALL TYPES IS ALLOCATED TO THE 

REMAINING OFF-PEAK AND SHOULDER REDUCTIONS 

PROPORTIONALLY. ................................................................................................... 15-3

15.7 UTILITY IMPACT RESULTS ..................................................................................... 15-4

15.7.1 Installed Capacity............................................................................................... 15-4

15.7.2 Electricity Generation ........................................................................................ 15-7

15.7.3 Results Summary ............................................................................................. 15-10

REFERENCES ........................................................................................................................ 15-11

LIST OF TABLES

Table 15.7.1 Summary of Utility Impact Results ........................................................... 15-10

LIST OF FIGURES

Figure 15.7.1 Total Electric Capacity Reduction ............................................................... 15-4

Figure 15.7.2 Coal Capacity Reduction ............................................................................. 15-5

Figure 15.7.3 Nuclear Capacity Reduction ........................................................................ 15-5

Figure 15.7.4 Gas Combined Cycle Capacity Reduction ................................................... 15-6

Figure 15.7.5 Peaking Capacity Reduction ........................................................................ 15-6

Figure 15.7.6 Renewables Capacity Reduction.................................................................. 15-7

Figure 15.7.7 Total Generation Reduction ......................................................................... 15-7

Figure 15.7.8 Coal Generation Reduction .......................................................................... 15-8

Figure 15.7.9 Nuclear Generation Reduction ..................................................................... 15-8

Figure 15.7.10 Natural Gas Combined Cycle Generation Reduction .................................. 15-9

Figure 15.7.11 Oil Generation Reduction ............................................................................ 15-9

Figure 15.7.12 Renewables Generation Reduction ............................................................ 15-10
15-1 

CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

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

and power generation that result for each TSL. 

The utility impact analysis is based on output of the DOE/ EIA’s NEMS.a NEMS is a 

public domain, multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, 

DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy 

Outlook (AEO). The EIA publishes a reference case, which incorporates all existing energyrelated policies at the time of publication, and a variety of side cases which analyze the impact of 

different policies, energy price and market trends. As of 2014, DOE is using a new methodology 

based on results published for the AEO 2014 Reference case and a set of side cases that 

implement a variety of efficiency-related policies.

2

 

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements: 

• The assumptions used in the AEO reference case and side cases are fully documented and 

receive detailed public scrutiny. 

• NEMS is updated each year, with each edition of the AEO, to reflect changes in energy 

prices, supply trends, regulations, etc. 

• The comprehensiveness of NEMS permits the modeling of interactions among the 

various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the transparency 

of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be reduced 

under the new approach. 

On the average, however, over the full analysis period, the results from the new approach 

are comparable to results from the old approach. 

15.2 METHODOLOGY 

DOE estimates the marginal impacts of reduction in energy demand on the energy supply 

sector. In principle, marginal values should provide a better estimate of the actual impact of 

energy conservation standards. 

 

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

documentation. A useful summary is National Energy Modeling System: An Overview.

1
15-2 

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (GW) may change, and the mix of capacity by fuel 

type may change. Each of these effects can vary for different types of end use. The change in 

total generating capacity is sensitive to the degree to which the end-use is peak coincident, while

the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2. 

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies); 

• Best Available Technology (highest efficiency irrespective of cost); 

• High Technology (higher penetration rates for efficiency and demand management); 

• Extended Policies (includes efficiency standards that are not in the reference). 

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to 

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period. 

3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules: 

3.1.All Petroleum-Based Generation Is Allocated To Peak Periods; 
15-3 

3.2.Natural Gas Generation Is Allocated To Any Remaining Peak Reduction; This Is 

Consistent With The Fact That Oil And Gas Steam Units Are Used In Nems To Meet 

Peak Demand;

3.3.Base-Load Generation (Nuclear And Coal) Is Allocated Proportionally To All Periods; 

3.4.The Remaining Generation Of All Types Is Allocated To The Remaining Off-Peak And 

Shoulder Reductions Proportionally. 

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types. 

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand. 

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a 

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time. 

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts. 

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Electricity 

Demand Reductions”.3
15-4 

15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types used to supply 

electricity for homes. 

15.3.1 Installed Capacity 

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. Note that a negative number means an 

increase in capacity under a TSL. 

Figure 15.7.1 Total Electric Capacity Reduction

0

100

200

300

400

500

600

700

800

900

1,000

2019202420292034203920442049205420592064206920742079208420892094

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4
15-5 

Figure 15.7.2 Coal Capacity Reduction 

Figure 15.7.3 Nuclear Capacity Reduction

0

20

40

60

80

100

120

140

160

180

200

2019202420292034203920442049205420592064206920742079208420892094

MW

Coal

TSL 1 TSL 2 TSL 3 TSL 4

0

2

4

6

8

10

12

14

16

18

2019202420292034203920442049205420592064206920742079208420892094

MW

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4
15-6 

Figure 15.7.4 Gas Combined Cycle Capacity Reduction

Figure 15.7.5 Peaking Capacity Reduction 

0

50

100

150

200

250

300

350

400

20192024202920342039

2044204920542059206420692074

2079208420892094

MW

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

0

50

100

150

200

250

2019202420292034203920442049205420592064206920742079208420892094

MW

Peaking

TSL 1 TSL 2 TSL 3 TSL 4
15-7 

Figure 15.7.6 Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each TSL by fuel type. Note that a negative number means an increase in generation under a 

TSL.

Figure 15.7.7 Total Generation Reduction

0

20

40

60

80

100

120

140

160

2019202420292034203920442049205420592064206920742079208420892094

MW

Renewable

TSL 1 TSL 2 TSL 3 TSL 4

0

500

1,000

1,500

2,000

2,500

3,000

3,500

20192024

2029

203420392044

2049

205420592064

2069

207420792084

2089

2094

GWh

Total

TSL 1 TSL 2 TSL 3 TSL 4
15-8 

Figure 15.7.8 Coal Generation Reduction 

Figure 15.7.9 Nuclear Generation Reduction

0

200

400

600

800

1,000

1,200

2019202420292034203920442049205420592064206920742079208420892094

GWh

Coal

TSL 1 TSL 2 TSL 3 TSL 4

0

20

40

60

80

100

120

140

2019202420292034203920442049205420592064206920742079208420892094

GWh

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4
15-9 

Figure 15.7.10 Natural Gas Combined Cycle Generation Reduction

Figure 15.7.11 Oil Generation Reduction

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

20192024202920342039

2044204920542059206420692074

2079208420892094

GWh

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

0

1

2

3

4

5

6

20192024

2029

20342039

20442049

20542059

2064

206920742079

2084

20892094

GWh

Oil

TSL 1 TSL 2 TSL 3 TSL 4
15-10 

Figure 15.7.12 Renewables Generation Reduction

15.3.3 Results Summary 

Table 15.7.1 presents a summary of the utility impact results for dehumidifiers. 

Table 15.7.1 Summary of Utility Impact Results

TSL 1 TSL 2 TSL 3 TSL 4

Installed Capacity Reduction (MW)

2020 15.8 24.5 61.0 128

2025 55.1 81.3 207 446

2030 75.5 113 308 691

2035 71.4 112 334 793

2040 66.6 110 343 863

Electricity Generation Reduction (GWh)

2020 74.1 115 286 599

2025 244 360 917 1,974

2030 309 462 1,258 2,825

2035 270 423 1,260 2,997

2040 234 387 1,205 3,035

0

100

200

300

400

500

600

2019202420292034203920442049205420592064206920742079208420892094

GWh

Renewable

TSL 1 TSL 2 TSL 3 TSL 4
15-11 

REFERENCES

1 U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<http://www.eia.gov/oiaf/aeo/overview/> 

2 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

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

<http://www.eia.gov/forecasts/aeo/> 

3 Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-Pending. 
16-i 

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS ............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT-TERM RESULTS ............................................................................................ 16-2

16.5 LONG-TERM RESULTS .............................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-4

LIST OF TABLES

Table 16.4.1 Dehumidifier Net National Short-term Change in Employment 

(Number of Jobs) ......................................................................................... 16-3
16-1 

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation 

or elimination resulting from possible standards, due to reallocation of the associated 

expenditures for purchasing and operating dehumidifiers. Job increases or decreases reported in 

this chapter are separate from the direct dehumidifier 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 

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

economic impacts of energy-efficiency investments in buildings.
16-2 

 In an input/output model, the level of employment in an economy is determined by the 

relationship of different sectors of the economy and the spending flows among them. Different 

sectors have different levels of labor intensity and so changes in the level of spending (e.g., due 

to the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which affects the overall level of employment.

 ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input-output sector. The model applies estimates of employment and 

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy-efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient products. The increased cost of products leads to higher employment in 

the product manufacturing sectors and lower employment in other economic sectors. Second, 

commercial firm and residential spending are redirected from utilities toward firms that supply 

production inputs. Third, 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 dehumidifier manufacturing sector 

estimated in Chapter 12 using the 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 dehumidifier standards relative to the base 

case. DOE disaggregated the impact of standards on employment into three component effects: 

increased capital investment costs, decreased energy costs, and changes in operations and 

maintenance costs. DOE presents the summary impact. 

 Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors, the dehumidifier production sector, the energy generation sector, and the general 

consumer goods sector (as mentioned above ImSET’s calculations are made at a much more 

disaggregate level). By raising energy efficiency, the rule generally increases the purchase price 

of dehumidifiers; this increase in expenditures causes an increase in employment in this sector. 

At the same time, the improvements in energy efficiency reduce consumer expenditures on 

energy, freeing up this money to be spent in other sectors. The reduction in energy demand 

causes a reduction in employment in the energy production sector. Finally, based on the net 

impact of increased expenditures on dehumidifiers and reduced expenditures on energy, 
16-3 

consumer expenditures on everything else are either positively or negatively affected, increasing 

or reducing jobs in that sector accordingly. The model also captures any indirect jobs created or 

lost by changes in consumption due to changes in employment (e.g. as more workers are hired 

they consume more goods, which generates more employment; the converse is true for workers 

laid off). 

Table 16.4.1 present the modeled net employment impact from the rule in 2019, rounded 

to the nearest ten jobs. Approximately 97 percent of dehumidifiers are imported and 3 percent 

are produced domestically. The net employment impact estimate is sensitive to assumptions 

regarding the return to the U.S. economy of money spent on imported dehumidifiers. The two 

scenarios bounding the ranges presented in Table 16.4.1 represent situations in which none of the 

money spent on imported dehumidifiers returns to the U.S. economy and all of the money spent 

on imported dehumidifiers returns to the U.S. economy. The U.S. trade deficit in recent years 

suggests that between 50 percent and 75 percent of the money spent on imported dehumidifiers 

is likely to return, with employment impacts falling within the ranges presented below. 

Table 16.4.1 Dehumidifier Net National Short-term Change in Employment (Number of 

Jobs) 

Trial 

Standard 

Level

2019 2024 

TSL 1 20 to 60 150 to 190

TSL 2 40 to 100 210 to 280

TSL 3 100 to 260 570 to 720

TSL 4 200 to 980 1,230 to 1,920

 For context, the OMB currently assumes that the unemployment rate may decline to 5.3 

percent in 2017.5 The unemployment rate in 2019 is projected to remain close to “full 

employment.” When an economy is at full employment any effects on net employment are likely 

to be transitory as workers change jobs, rather than enter or exit longer-term employment.

16.5 LONG-TERM RESULTS

Over the long term DOE expects the energy savings to consumers to increasingly 

dominate the increase in product costs, resulting in increased aggregate savings to consumers. As 

a result, DOE expects demand for energy to decline over time and demand for other goods to 

increase. Since the electricity generation and natural gas sectors are relatively capital intensive 

compared to the consumer goods sector, the net effect will be an increase in labor demand. In 

equilibrium, this should lead to upward pressure on wages and a shift in employment away from 

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 shortterm effects presented in Table 16.4.1. The ImSET model projections, assuming no price or wage 

effects until 2024, are included in the second column of Table 16.4.1. 
16-4 

REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building 

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment.

Energy Economics, 2008. 30(5): pp. 2283-2301 

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy 

Technologies, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. 

PNNL- 15273. 

3. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy 

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. 

Report No. PNNL-11884. Prepared for the U.S. Department of Energy under Contract 

DE-AC06-76RLO 1830. 

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide, 

Data Guide, 1997. Stillwater, MN. 

5. Office of Management and Budget, Fiscal Year 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> 
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 ............................................ 17-2

17.2.3 Policy Interactions ............................................................................................. 17-3

17.3 NON-REGULATORY POLICY ASSUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ............................................................................... 17-4

17.3.2 Consumer Rebates ............................................................................................. 17-4

17.3.2.1 Methodology ............................................................................................ 17-4

17.3.2.2 Analysis .................................................................................................... 17-5

17.3.3 Consumer Tax Credits ..................................................................................... 17-10

17.3.4 Manufacturer Tax Credits ................................................................................ 17-12

17.3.5 Voluntary Energy Efficiency Targets .............................................................. 17-14

17.3.6 Bulk Government Purchases ............................................................................ 17-16

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES ......................................... 17-18

REFERENCES ........................................................................................................................ 17-23

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards ................................... 17-1

Table 17.2.1 Energy Efficiency by TSL (IEF).................................................................. 17-3

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates........................................... 17-6

Table 17.3.2 Market Penetrations in 2019 Attributable to Consumer Rebates............... 17-10

Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Tax Credits ........ 17-12

Table 17.3.4 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits ... 17-13

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets for TSL 3 ....................................................................................... 17-15

Table 17.3.6 Market Penetrations in 2019 Attributable to Voluntary Energy 

Efficiency Targets ...................................................................................... 17-15

Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary 

Energy Efficiency Targets for TSL 3 ......................................................... 17-16

Table 17.3.8 Market Penetrations in 2019 Attributable to Bulk Government 

Purchases.................................................................................................... 17-18

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives for TSL 3 ....................... 17-22

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Dehumidifiers ............................................. 17-9
17-ii

Figure 17.4.1 Market Penetration of Efficient Portable Dehumidifiers ≤30.00 

Pints/Day (TSL 3) ...................................................................................... 17-19

Figure 17.4.2 Market Penetration of Efficient Portable Dehumidifiers 30.01–45.00 

Pints/Day (TSL 3) ...................................................................................... 17-19

Figure 17.4.3 Market Penetration of Efficient Portable Dehumidifiers >45.00 

Pints/Day (TSL 3) ...................................................................................... 17-20

Figure 17.4.4 Market Penetration of Efficient Whole-Home Dehumidifiers ≤8.0 ft3

Case Volume (TSL 3) ................................................................................ 17-20

Figure 17.4.5 Market Penetration of Efficient Whole-Home Dehumidifiers >8.0 ft3

Case Volume (TSL 3) ................................................................................ 17-21
17-1 

CHAPTER 17. REGULATORY IMPACT ANALYSIS 

17.1 INTRODUCTION

The DOE has determined that the regulatory action described in the Federal Register 

notice associated with this TSD constitutes an “economically significant regulatory action” under 

Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 51735 (October 4, 

1993). For such actions, E.O. 12866 requires Federal agencies to provide “an assessment, 

including the underlying analysis, of costs and benefits of potentially effective and reasonably 

feasible alternatives to the planned regulation, identified by the agencies or the public (including 

improving the current regulation and reasonably viable non-regulatory actions), and an 

explanation why the planned regulatory action is preferable to the identified potential 

alternatives.” 58 FR 51735, 51741. 

To conduct this analysis, DOE used an integrated NIA and RIA model. The NIA-RIA 

was built on the NIA model as discussed in Chapter 10. DOE identified five non-regulatory 

policy alternatives that possibly could provide incentives for the same energy efficiency levels as 

the ones in the proposed trial standard levels for the dehumidifiers that are the subject of this 

rulemaking. The non-regulatory policy alternatives are listed in Table 17.1.1, which also includes 

the “no new regulatory action” alternative. DOE evaluated each alternative in terms of its ability 

to achieve significant energy savings at a reasonable cost, and compared the effectiveness of 

each to the effectiveness of the proposed standards for the 5 product classes of dehumidifiers 

covered by this rulemaking. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Bulk Government Purchases

 

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed in Table 17.1.1

(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 dehumidifiers. 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 NES and NPV 

associated with each non-regulatory policy alternative. Chapter 10 of the TSD describes the NIA 

spreadsheet model. Appendix 17A discusses the NIA-RIA integrated model approach. 

DOE quantified the effect of each alternative on the purchase of equipment that meets the 

efficiency levels corresponding to each TSL. After establishing the quantitative assumptions 

underlying each alternative, DOE appropriately revised inputs to the NIA-RIA spreadsheet 

model. The primary model inputs revised were market shares of equipment meeting the target 

efficiency levels set for each TSL. The shipments of equipment for any given year reflect a 

distribution of efficiency levels. DOE assumed, for each TSL, that new energy efficiency 

standards would affect 100 percent of the shipments of products that did not meet the TSL target 

levels in the base case,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 dehumidifiers attributable to each policy alternative. 

Increasing the efficiency of a product often increases its average installed cost. However, 

operating costs generally decrease because energy consumption declines. DOE, therefore, 

calculated an NPV for each non-regulatory alternative in the same way it did for the proposed 

standards. In some policy scenarios, increases in total installed cost are mitigated by government 

rebates or tax credits. Because DOE assumed that consumers would re-pay credits and rebates in 

some way (such as additional taxes), DOE did not include rebates or tax credits as a consumer 

benefit when calculating national NPV. DOE’s analysis also excluded any administrative costs 

for the non-regulatory policies; including such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative. 

• National Energy Savings, given in quadrillion Btus (quads), describes the cumulative 

national energy saved over the lifetime of equipment purchased during the 30-year 

analysis period starting in the effective date of the policy (2019-2048). 

• Net Present Value, represents the value of net monetary savings in 2014, expressed in

2013$, from equipment purchased during the 30-year analysis period starting in the 

effective date of the policy (2019-2048). DOE calculated the NPV as the difference 

between the present values of installed equipment cost and operating expenditures in the 

base case and the present values of those costs in each standards case. DOE calculated 

operating expenses (including energy costs) for the life of the product. 

17.2.2 Assumptions Regarding Non-Regulatory Policies

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ response to a program. Because 

the projected effects depend on assumptions regarding the rate of consumer participation, they 

 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.
17-3 

are subject to more uncertainty than are the impacts of mandatory standards, which DOE 

assumes will be met with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE on which relied when 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 dehumidifiers relative to their base case efficiency scenario (which involves no new 

regulatory action). The analysis considered that each alternative policy would induce consumers 

to purchase units having the same technology as required by standards (the target level), 

according to the minimum energy efficiency set for each TSL. As opposed to the standards case, 

however, the policy cases may not lead to 100 percent market penetration of units that meet the 

target level. 

Table 17.2.1 shows the energy efficiencies from the technologies stipulated for 

dehumidifiers for each TSL. 

Table 17.2.1 Energy Efficiency by TSL (IEF) 

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day 1.10 1.20 1.30 1.57

Portable Dehumidifiers 30.01–45.00 Pints/Day 1.20 1.40 1.60 1.80

Portable Dehumidifiers >45.00 Pints/Day 2.40 2.80 2.80 3.66

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume 2.09 2.09 2.09 2.53

Whole-Home Dehumidifiers >8.0 ft3 Case Volume 2.70 3.52 3.52 4.50

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards, 2019, through the end of the analysis period, 2048. 

17.2.3 Policy Interactions 

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as voluntary efficiency targets implemented with consumer rebates or tax credits. However, 

DOE made conservative assumptions to avoid double-counting policy impacts. The resulting 

policy impacts are, therefore, not additive, and the combined effect of several or all policies 

cannot be inferred from summing their results. 

Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for dehumidifiers. 
17-4 

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to the standards proposed for dehumidifiers. (Because the 

alternative of “No New Regulatory Action” has no energy or economic impacts, essentially 

representing the NIA base case, DOE did not perform any additional analysis for that 

alternative.) DOE developed estimates of the market penetration of more efficient products both 

with and without each of the non-regulatory policy alternatives. 

17.3.1 No New Regulatory Action 

The case in which no new regulatory action is taken with regard to the energy efficiency 

of dehumidifiers constitutes the base case, as described in chapter 10. The base case provides the 

basis of comparison for all other policies. By definition, no new regulatory action yields zero 

energy savings for an NES and zero dollars for an NPV.

17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing energy-efficient equipment. This 

policy provides a consumer rebate for purchasing dehumidifiers that operate at the same 

efficiency levels as stipulated in each TSL. 

17.3.2.1 Methodology 

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. The study, performed by XENERGY, 

Inc.,b summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7

 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5DOE decided that the most appropriate available method for this RIA 

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 

 

b XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17-5 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17A contains additional details on internal and external information diffusion. 

XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a policy measure. XENERGY calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8

 

DOE modeled the effects of a consumer rebate policy for dehumidifiers 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

dehumidifiers. It gathered data on utility or agency rebates throughout the nation for this 

equipment, and used this data to calibrate the customized penetration curves it developed for 

each product class covered by this RIA so they can best reflect the market barrier levels that 

consumer rebates for dehumidifiers would face. Section 17.3.2.2 shows the resulting interpolated 

curves used in the analysis. 

17.3.2.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of dehumidifiers via a rebate that 

would pay – depending on the product class – part or all of the increased installed cost of units 

that meet the target efficiency levels compared to units 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 dehumidifiers. It gathered data from a sample of utility 

and agency rebate programs that includes 34 rebates for dehumidifiers initiated by 29 utilities or 

agencies in various States. DOE then estimated a market representative rebate value for all 

product classes covered by this RIA which it applied in the calculation of the B/C ratio of 

dehumidifiers under the effect of consumer rebates. (Appendix 17A identifies the rebate 

programs and details the methodology DOE used to estimate a market representative rebate 

amount.) DOE assumed that rebates would remain in effect at the same level throughout the 

forecast period (2019-2048). 

 

c The baseline technology is defined in the engineering analysis, Chapter 5, as the technology that represents the 

basic characteristics of dehumidifiers. A baseline unit typically is one that just meets current Federal energy 

conservation standards and provides basic consumer utility. 
17-6 

DOE first calculated the B/C ratio of a dehumidifier without a rebate using the difference 

in total installed costs (C) and lifetime operating cost savingsd (B) between a unit meeting the 

target level and a baseline unit. It then calculated the B/C ratio given a rebate for the unit 

meeting the target efficiency level. Because the rebate reduced the incremental cost, the unit 

receiving the rebate had a larger B/C ratio. Table 17.3.1 shows the effect of consumer rebates for 

each TSL on the B/C ratio of dehumidifiers shipped in the first year of the analysis period. 

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

B/C Ratio Without Rebate 0.0 97.7 44.0 12.1

Rebate Amount (2013$) 24.05 24.05 24.05 24.05

B/C Ratio With Rebate 0.0 infinite infinite 66.6

Estimated Market Barriers High High Low-Mod High

Portable Dehumidifiers 30.01–45.00 Pints/Day

B/C Ratio Without Rebate 0.0 90.7 33.7 14.1

Rebate Amount (2013$) 24.05 24.05 24.05 24.05

B/C Ratio With Rebate 0.0 infinite infinite 48.0

Estimated Market Barriers High No High High

Portable Dehumidifiers >45.00 Pints/Day

B/C Ratio Without Rebate 1.6 2.5 2.5 1.8

Rebate Amount (2013$) 24.05 24.05 24.05 24.05

B/C Ratio With Rebate 2.4 3.3 3.3 2.1

Estimated Market Barriers Low-Mod Low-Mod Low-Mod High

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

B/C Ratio Without Rebate 5.2 5.2 5.2 1.2

Rebate Amount (2013$) 24.05 24.05 24.05 24.05

B/C Ratio With Rebate 47.7 47.7 47.7 1.3

Estimated Market Barriers Mod Mod Mod High

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

B/C Ratio Without Rebate 10.1 4.9 4.9 1.7

Rebate Amount (2013$) 24.05 24.05 24.05 24.05

B/C Ratio With Rebate infinite 7.5 7.5 1.8

Estimated Market Barriers Low-Mod Mod Mod High

* Mod: Moderate market barriers; Low-Mod: Low-to-Moderate market barriers. 

DOE used the B/C ratio along with the customized penetration curves shown in Figure 

17.3.1 to estimate the percentage of consumers who would purchase dehumidifiers that meet the 

target levels both with and without a rebate incentive. The estimated levels of market barriers

corresponding to the penetration curves DOE calculated to represent the market behavior for

dehumidifiers at the proposed TSL are indicated (bolded) in Table 17.3.1. DOE assumed the 

estimated market barriers would remain the same over the whole analysis period.

 

d The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.
17-7 

TSL 3

Portable Dehumidifiers ≤30.00 Pints/Day Portable Dehumidifiers 30.01–45.00 Pints/Day 
17-8 

Portable Dehumidifiers >45.00 Pints/Day Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume
17-9 

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Figure 17.3.1 Market Penetration Curves for Dehumidifiers

DOE next estimated the percent increase represented by the change in penetration rate 

shown on the corresponding penetration curve. It then added this percent increase to the market 

share of units that meet the target level in the base case to obtain the market share of units that 

meet the target level in the rebate policy case. 

Table 17.3.2 summarizes DOE’s assumptions for dehumidifiers regarding the market 

penetration of products in 2019 that meet the target levels at each TSL given a consumer rebate. 
17-10 

Table 17.3.2 Market Penetrations in 2019 Attributable to Consumer Rebates

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 22.9% 0.0% 66.4% 0.0%

Policy Case Market Share 33.6% 33.6% 71.8% 39.7%

Increased Market Share 10.7% 33.6% 5.4% 39.7%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 0.0% 94.3% 2.0% 3.7%

Policy Case Market Share 0.0% 94.3% 50.0% 34.7%

Increased Market Share 0.0% 0.0% 48.0% 31.1%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 20.1% 22.9% 22.9% 0.0%

Policy Case Market Share 29.1% 30.0% 30.0% 0.2%

Increased Market Share 9.0% 7.2% 7.2% 0.2%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 25.1% 25.1% 0.0%

Policy Case Market Share 62.7% 62.7% 62.7% 0.1%

Increased Market Share 37.6% 37.6% 37.6% 0.1%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 46.3% 22.9% 22.9% 0.0%

Policy Case Market Share 70.5% 33.8% 33.8% 0.2%

Increased Market Share 24.2% 10.9% 10.9% 0.2%

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of consumer rebates for dehumidifiers. Because 

energy prices increase over the analysis period and equipment prices are held constant, the B/C 

ratios increase over time. With increasing B/C ratios and constant market barriers, the increase in 

market penetration of more efficient technologies eventually increases over the analysis period. 

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous 

analysis of consumer participation in tax credits. DOE supported its approach using data from 

Oregon State’s tax credit program for energy-efficient appliances. DOE also incorporated 

previous research that disaggregated the effect of rebates and tax credits into a direct price effect, 

which derives from the savings in purchase price, and an announcement effect, which is 

independent of the amount of the incentive.10, 11 The announcement effect derives from the 

credibility that a technology receives from being included in an incentive program, as well as 

changes in product marketing and modifications in markup and pricing. DOE assumed that the 

rebate and consumer tax credit policies would encompass both direct price effects and 

announcement effects, and that half the increase in market penetration associated with either 

policy would be due to the direct price effect and half to the announcement effect. 
17-11 

In estimating the effects of a tax credit on purchases of consumer products that meet new 

efficiency standards, DOE assumed the amount of the tax credit would be the same as the 

corresponding rebate amount discussed above. 

DOE estimated that fewer consumers would participate in a tax credit program than 

would take advantage of a rebate. Research has shown that the delay required for a consumer to 

receive a tax credit, plus the added time and cost in preparing the tax return, make a tax credit 

incentive less effective than a rebate received at the time of purchase. Based on previous 

analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a 

rebate would take advantage of a tax credit.12

In preparing its assumptions to estimate the effects of tax credits on consumer purchases 

of dehumidifiers, DOE also reviewed other tax credit programs that have been offered at both the 

Federal and State levels for energy-efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for 

consumers who purchase energy-efficient products.13 Those tax credits were in effect in 2006 

and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and 

Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 with some modifications, 

and expired at the end of 2011.14, 15 The American Taxpayer Relief Act of 2012 extended, with 

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water 

heaters placed in service between January 1, 2012 and December 31, 2013.16 DOE reviewed 

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during 

tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation 

tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

dehumidifiers to warrant adjusting its analysis method for the Consumer Tax Credits policy case. 

Appendix 17A contains more information on Federal consumer tax credits. 

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to 

provide support for its assumptions.17 In that previous analysis, DOE compared the market 

shares of ultra-high efficiency (UHE) residential clothes washers in Oregon, which offered both 

State tax credits and utility rebates, with those in Washington State, which offered only utility 

rebates during the same period. Based on this analysis, DOE estimated that in Oregon the impact 

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent 

efficiency. This finding supports its original assumption that participation in a tax credit program 

would be about 60 percent of participation in a rebate program. Additional discussion of State 

tax credits for Oregon and other states is in appendix 17A.

DOE applied the assumed 60 percent participation described above to the increase in 

penetration rates estimated for the rebate policy to estimate penetration rates attributable to 

consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to 

financial incentives from the customized penetration curves it developed for dehumidifiers (See

Figure 17.3.1). 

Table 17.3.3 summarizes DOE’s assumptions for dehumidifiers regarding the market 

penetration of products in 2019 that meet the target levels at each TSL given a consumer tax 

credit. 
17-12 

Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 22.9% 0.0% 66.4% 0.0%

Policy Case Market Share 29.3% 20.2% 69.6% 23.8%

Increased Market Share 6.4% 20.2% 3.2% 23.8%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 0.0% 94.3% 2.0% 3.7%

Policy Case Market Share 0.0% 94.3% 30.8% 22.3%

Increased Market Share 0.0% 0.0% 28.8% 18.6%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 20.1% 22.9% 22.9% 0.0%

Policy Case Market Share 25.5% 27.2% 27.2% 0.1%

Increased Market Share 5.4% 4.3% 4.3% 0.1%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 25.1% 25.1% 0.0%

Policy Case Market Share 47.7% 47.7% 47.7% 0.1%

Increased Market Share 22.6% 22.6% 22.6% 0.1%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 46.3% 22.9% 22.9% 0.0%

Policy Case Market Share 60.8% 29.4% 29.4% 0.1%

Increased Market Share 14.5% 6.6% 6.6% 0.1%

The increased market shares attributable to consumer tax credits shown in Table 17.3.3

were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of consumer tax credits for dehumidifiers that meet 

the efficiency level for the proposed TSL. Because the increase in market penetration for 

consumer tax credits is proportional to the increase in market penetration DOE calculated for 

consumer rebates, the former follows a similar increasing trend over the analysis period as the 

latter.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce dehumidifiers that meet the target efficiency levels at each TSL, DOE assumed that a 

manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to 

that provided by the consumer rebates or tax credits described above. DOE further assumed that 

manufacturers would pass on some of their reduced costs to consumers, causing a direct price 

effect. DOE assumed that no announcement effect would occur, because the program would not 

be visible to consumers.e Because the direct price effect is approximately equivalent to the 

 

e Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.
17-13 

announcement effect,10 DOE estimated that a manufacturer tax credit would induce half the 

number of consumers assumed to take advantage of a consumer tax credit to purchase more 

efficient products. Thus the assumed participation rate is equal to 30 percent of the number of 

consumers who would participate in a rebate program. 

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance 

Credits for manufacturers mandated by EPACT 2005.18 Those manufacturer tax credits have 

been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009. 

DOE was unable to locate data from the Internal Revenue Service or other sources on 

manufacturer response to the Federal credits. Appendix 17A presents details on Federal 

manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the increase in penetration 

rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In 

doing so, DOE incorporated the assumptions for consumer response to financial incentives from 

the customized penetration curves it developed for dehumidifiers. (See Figure 17.3.1) 

Table 17.3.4 summarizes DOE’s assumptions for dehumidifiers regarding the market 

penetration of products in 2019 that meet the target levels at each TSL given a manufacturer tax 

credit.

Table 17.3.4 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 22.9% 0.0% 66.4% 0.0%

Policy Case Market Share 26.1% 10.1% 68.0% 11.9%

Increased Market Share 3.2% 10.1% 1.6% 11.9%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 0.0% 94.3% 2.0% 3.7%

Policy Case Market Share 0.0% 94.3% 16.4% 13.0%

Increased Market Share 0.0% 0.0% 14.4% 9.3%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 20.1% 22.9% 22.9% 0.0%

Policy Case Market Share 22.8% 25.0% 25.0% 0.1%

Increased Market Share 2.7% 2.2% 2.2% 0.1%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 25.1% 25.1% 0.0%

Policy Case Market Share 36.4% 36.4% 36.4% 0.0%

Increased Market Share 11.3% 11.3% 11.3% 0.0%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 46.3% 22.9% 22.9% 0.0%

Policy Case Market Share 53.5% 26.1% 26.1% 0.1%

Increased Market Share 7.3% 3.3% 3.3% 0.1%
17-14 

The increased market shares attributable to a manufacturer tax credit shown in Table 

17.3.4 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of manufacturer tax credits for dehumidifiers. 

Because the increase in market penetration for manufacturer tax credits is proportional to the 

increase in market penetration DOE calculated for consumer rebates, the former follows a similar 

increasing trend over the analysis period as the latter. 

17.3.5 Voluntary Energy Efficiency Targets 

DOE assumed that voluntary energy efficiency targets would lead manufacturers of 

dehumidifiers to gradually stop producing units that operate below the efficiency levels set for 

each TSL. DOE assumed that the impetus for phasing out production of low-efficiency units 

would be a program with impacts similar to those of the ENERGY STAR labeling program 

conducted by the EPA and DOE in conjunction with industry partners. The ENERGY STAR 

program specifies the minimum energy efficiencies that various products must have to receive 

the ENERGY STAR label. ENERGY STAR encourages consumers to purchase efficient 

products via marketing that promotes consumer label recognition, various incentive programs 

that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying 

appliances. ENERGY STAR projects market penetration of compliant appliances and estimates 

the percentage of sales of compliant appliances that are attributable to the ENERGY STAR 

program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

19, 20, 21

DOE believes that informational incentive programs – like ENERGY STAR, or any other 

labeling program sponsored by industry or other organizations – are likely to reduce the market 

barriers to more efficient products over time. During the rebate analysis, when assessing the B/C 

ratio and market penetration in the base case for dehumidifiers, DOE observed that the level of 

market barriers for more efficient dehumidifiers are in the range of low-to-moderate barriers to a 

high level of market barriers. DOE estimates that voluntary energy efficiency targets could 

reduce these barriers to lower levels over 10 years. Table 17.3.5 presents the levels of market 

barriers DOE estimated for dehumidifiers in the base case and in the policy case of voluntary 

energy efficiency targets. DOE followed the methodology presented by Blum et al (2011)22 to 

evaluate the effects that such a reduction in market barriers would have on the market 

penetration of efficient dehumidifiers.

f

 The methodology relies on interpolated market 

penetration curves to calculate – given a B/C ratio – how the market penetration of more efficient 

units increases as the market barrier level to those units decreases.

 

f For the calculation of B/C ratios DOE discounted the cash flow of the operating cost savings to the purchase year 

using a 7 percent discount rate.
17-15 

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets for TSL 3 

Base Case Voluntary Energy 

Efficiency Targets

Portable Dehumidifiers ≤30.00 Pints/Day Low-Moderate Low

Portable Dehumidifiers 30.01–45.00 Pints/Day High Moderate-High

Portable Dehumidifiers >45.00 Pints/Day Low-Moderate Low

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume Moderate Low

Whole-Home Dehumidifiers >8.0 ft3 Case Volume Moderate Low

Table 17.3.6 summarizes DOE’s assumptions for dehumidifiers regarding the market 

penetration of products in 2019 that meet the target levels at each TSL given voluntary energy 

efficiency targets. Table 17.3.7 expands on Table 17.3.6 to include, for the proposed TSL, 

DOE’s assumptions regarding the market penetration of units in selected years. 

Table 17.3.6 Market Penetrations in 2019 Attributable to Voluntary Energy Efficiency 

Targets

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 22.9% 0.0% 66.4% 0.0%

Policy Case Market Share 33.6% 33.6% 67.5% 10.4%

Increased Market Share 10.7% 33.6% 1.1% 10.4%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 0.0% 94.3% 2.0% 3.7%

Policy Case Market Share 0.0% 94.3% 31.0% 12.8%

Increased Market Share 0.0% 0.0% 29.0% 9.2%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 20.1% 22.9% 22.9% 0.0%

Policy Case Market Share 21.5% 25.6% 25.6% 0.2%

Increased Market Share 1.4% 2.7% 2.7% 0.2%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 25.1% 25.1% 0.0%

Policy Case Market Share 26.7% 26.7% 26.7% 0.1%

Increased Market Share 1.6% 1.6% 1.6% 0.1%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 46.3% 22.9% 22.9% 0.0%

Policy Case Market Share 50.1% 24.6% 24.6% 0.2%

Increased Market Share 3.9% 1.7% 1.7% 0.2%
17-16 

Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary Energy 

Efficiency Targets for TSL 3 

2019 2028 2048

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 66.4% 66.4% 87.4%

Policy Case Market Share 67.5% 77.2% 87.4%

Increased Market Share 1.1% 10.8% 0.0%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 2.0% 18.1% 55.3%

Policy Case Market Share 31.0% 50.4% 55.3%

Increased Market Share 29.0% 32.3% 0.0%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 22.9% 22.9% 22.9%

Policy Case Market Share 25.6% 46.4% 56.8%

Increased Market Share 2.7% 23.5% 34.0%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 38.3% 68.5%

Policy Case Market Share 26.7% 60.8% 68.5%

Increased Market Share 1.6% 22.5% 0.0%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 22.9% 22.9% 37.6%

Policy Case Market Share 24.6% 60.0% 67.8%

Increased Market Share 1.7% 37.2% 30.2%

The increased market shares attributable to voluntary energy efficiency targets shown in 

Table 17.3.6 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual 

market share increases due to this policy for the whole forecast period. Section 17.4 presents the 

resulting market penetration trends for the policy case of voluntary energy efficiency targets for

dehumidifiers that meet the efficiency level for the proposed TSL. Because of the decrease in the 

market barriers level over the first 10 years of the analysis period, the market penetration of more 

efficient dehumidifiers significantly increases over that period. For the remaining 20 years of the 

forecast period the increase in market penetration keeps growing because, even though the 

market barriers level remains constant (at 2028 level), the increase in energy prices leads to 

increasing B/C ratios and eventually to higher market penetrations. 

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing 

large quantities of products that meet a certain, target efficiency level. Combining the market 

demands of multiple public sectors can provide a market signal to manufacturers and vendors 

that some of their largest customers seek products that meet an efficiency target at favorable 

prices. Such a program also can induce “market pull,” whereby manufacturers and vendors 

would achieve economies of scale for high efficiency products. 
17-17 

Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other products. FEMP, however, does not track purchasing data, because of the complex range of 

purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

23, 24

DOE assumed that government agencies would administer bulk purchasing programs for 

dehumidifiers. At the federal level, this type of program could modify the current FEMP 

procurement guidelines for dehumidifiers, which refer to the ENERGY STAR requirements for 

dehumidifiers.

25 DOE reviewed its own previous research on the potential for market 

transformation through bulk government purchases. Its major study analyzed several scenarios 

based on the assumption that 20 percent of Federal equipment purchases in 2000 already 

incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the 

DOE report showed energy efficient purchasing ramping up during 10 years from 20 percent to 

80 percent of all Federal purchases.26 Based on this study, DOE estimated that a bulk 

government purchase program instituted within a 10-year period would result in at least 80 

percent of government-purchased dehumidifiers meeting the target efficiency level.

DOE assumed that bulk government purchases would affect a subset of housing units for

which government agencies purchased or influenced the purchase of dehumidifiers. This subset 

would consist primarily of public housing and housing on military bases. According to the RECS 

2009, about 1.2 percent of all U.S. households with dehumidifiers are housing units in public 

housing authority.27 DOE therefore estimated that 1.2 percent of the U.S. housing units with 

dehumidifiers constitutes the market to which this policy would apply. 

DOE estimated that starting in 2019, each year of a bulk government purchase policy 

would result in an increasing percent of shipments of government-purchased units beyond the 

base case that would meet the target efficiency level. DOE estimated that within 10 years (by 

2028) bulk government purchasing programs would result in 80 percentg

 of the market for

dehumidifiers used in publicly owned houses meeting the target level. DOE modeled the bulk 

government purchase program assuming that the market share for dehumidifiers achieved in 

2028 would be at least maintained throughout the rest of the forecast period. 

Table 17.3.8 summarizes DOE’s assumptions for dehumidifiers regarding the market 

penetration of products in 2019 that meet the target levels at each TSL given bulk government 

purchases. 

 

g The 80 percent target to be achieved within 10 years may not be reached, as it is constrained by the market share 

below the target level in the base case scenario.
17-18 

Table 17.3.8 Market Penetrations in 2019 Attributable to Bulk Government Purchases

TSL 1 TSL 2 TSL 3 TSL 4

Portable Dehumidifiers ≤30.00 Pints/Day

Base-Case Market Share 22.9% 0.0% 66.4% 0.0%

Policy Case Market Share 22.9% 0.0% 66.4% 0.1%

Increased Market Share 0.0% 0.0% 0.0% 0.1%

Portable Dehumidifiers 30.01–45.00 Pints/Day

Base-Case Market Share 0.0% 94.3% 2.0% 3.7%

Policy Case Market Share 0.0% 94.3% 2.1% 3.8%

Increased Market Share 0.0% 0.0% 0.1% 0.1%

Portable Dehumidifiers >45.00 Pints/Day

Base-Case Market Share 20.1% 22.9% 22.9% 0.0%

Policy Case Market Share 20.2% 22.9% 22.9% 0.1%

Increased Market Share 0.1% 0.1% 0.1% 0.1%

Whole-Home Dehumidifiers ≤8.0 ft3 Case Volume

Base-Case Market Share 25.1% 25.1% 25.1% 0.0%

Policy Case Market Share 25.2% 25.2% 25.2% 0.1%

Increased Market Share 0.1% 0.1% 0.1% 0.1%

Whole-Home Dehumidifiers >8.0 ft3 Case Volume

Base-Case Market Share 46.3% 22.9% 22.9% 0.0%

Policy Case Market Share 46.3% 22.9% 22.9% 0.1%

Increased Market Share 0.0% 0.1% 0.1% 0.1%

The increased market shares attributable to bulk government purchases shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of bulk government purchases for dehumidifiers. 

Market penetrations slightly increase over the first 10 years of the forecast period, steady for 

some years, and follow the base case market penetration trend for the rest of the analysis period. 

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 through Figure 17.4.5 show the effects of each non-regulatory policy 

alternative on the market penetration of more efficient dehumidifiers. Relative to the base case, 

the alternative policy cases increase the market shares that meet the target level. Recall the 

proposed standards (not shown in the figures) would result in a 100-percent market penetration 

of products that meet the more efficient technology. 
17-19 

Figure 17.4.1 Market Penetration of Efficient Portable Dehumidifiers ≤30.00 Pints/Day 

(TSL 3) 

Figure 17.4.2 Market Penetration of Efficient Portable Dehumidifiers 30.01–45.00 

Pints/Day (TSL 3) 
17-20 

Figure 17.4.3 Market Penetration of Efficient Portable Dehumidifiers >45.00 Pints/Day 

(TSL 3) 

Figure 17.4.4 Market Penetration of Efficient Whole-Home Dehumidifiers ≤8.0 ft3 Case 

Volume (TSL 3) 
17-21 

Figure 17.4.5 Market Penetration of Efficient Whole-Home Dehumidifiers >8.0 ft3 Case 

Volume (TSL 3) 

Table 17.4.1 shows the national energy savings and net present value for the five nonregulatory policy alternatives analyzed in detail for dehumidifiers. The target level for each 

policy corresponds to the same efficient technology proposed for standards in TSL 3. The case in 

which no regulatory action is taken with regard to dehumidifiers 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).h The NPVs shown in Table 17.4.1 are based on two 

discount rates, 7 percent and 3 percent. 

 The policy with the highest projected cumulative energy savings is consumer rebates, 

followed by voluntary energy efficiency targets, consumer tax credits and manufacturer tax 

credits. Bulk government purchases lead to the lowest cumulative energy savings. Overall, the 

energy saving benefits from the alternative policies, range from 0.2 percent to 35 percent of the 

benefits from the proposed standards. 

 

h For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1

correspond to the case where the cash flow of the operating cost savings was discounted to the purchase year using a 

7 percent discount rate. 
17-22 

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives for TSL 3 

Policy Alternative 

Energy Savings*

quads

Net Present Value*

million 2013$

7% Disc Rate 3% Disc Rate

Consumer Rebates 0.107 (34.7%)** 361.7 789.2

Consumer Tax Credits 0.064 (20.8%) 217.0 473.5

Manufacturer Tax Credits 0.032 (10.4%) 108.5 236.8

Voluntary Energy Efficiency Targets 0.077 (25.2%) 233.0 568.0

Bulk Government Purchases 0.001 (0.2%) 2.0 4.4

Proposed Standards*** 0.307 (100.0%) 1035.2 2267.3

* For products shipped 2019-2048.

**The percentages show how the energy savings from each policy alternative compare to the (primary) 

energy savings from the proposed standards (represented in the table as 100%).

*** Refers to primary energy savings.
17-23 

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

<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 March, 2014.) <http://calmac.org/publications/PGE0264_Final_Report.pdf>

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market 

Potential Study, 2010. (Last accessed March, 2014.) <www.ameren.com/-

/media/missourisite/Files/Environment/Renewables/AmerenUEVolume2MarketResearchReport2.pdf>

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc. 

June, 2010. 

9. Blum, H., Atkinson, B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. Lawrence Berkeley National Laboratory. 

Berkeley, CA. Report No. LBNL-4749E. <http://escholarship.org/uc/item/4t32x6vr> 

10. LBNL, Energy End-Use Forecasting: Analysis of Tax Credits for Efficient Equipment, 

1998. LBNL Technology Evaluation Modeling and Assessment Group (Last accessed 

March, 2014.) <http://enduse.lbl.gov/Projects/TaxCredits.html>

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

Purchase Decisions, 1994. Prepared for Southern California Edison by Cambridge 

Systematics, Pacific Consulting Services, The Technology Applications Group, and 

California Survey Research Services. 

12. DOE: Office of Codes and Standards, Technical Support Document: Energy Efficiency 

Standards for Consumer Products: Refrigerators, Refrigerator-Freezers, and Freezers, 
17-24 

Including Environmental Assessment and Regulatory Impact Analysis, November, 1995. 

Washington, DC. Report No. DOE/EE-0064. 

13. 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

14. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed March, 2014.) <http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed March, 2014.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) <http://energytaxincentives.org/>

17. DOE - Energy Efficiency & Renewable Energy, Technical Support Document: Energy 

Efficiency Program for Consumer Products and Commercial and Industrial Equipment, 

Residential Dishwashers, Dehumidifiers, and Cooking Products, and Commercial 

Clothes Washers, 2008. Washington, DC. 

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 

2014.) <http://energytaxincentives.org/builders/appliances.php>

19. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, 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

> 

20. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

Program Evaluation Conference Proceedings. 

21. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

Evaluation Conference Proceedings. Seattle, WA. 

22. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. 

23. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008. 
17-25 

24. Responsible Purchasing Network, (Last accessed March, 2014.) 

<www.responsiblepurchasing.org/>

25. ENERGY STAR, Dehumidifiers for Consumers, (Last accessed October, 2014.) 

<http://www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel4_Paper13.pdf>

26. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

<www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel4_Paper13.pdf> 

27. DOE: EIA, Residential Energy Consumption Survey (RECS), 2009. (Last accessed 

December, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata>

 
3A-i 

APPENDIX 3A. AHAM DATA SUBMITTAL

TABLE OF CONTENTS

3A.1 RESIDENTIAL DEHUMIDIFIERS .............................................................................1

 
3A-1 

APPENDIX 3A. AHAM DATA SUBMITTAL

3A.1 RESIDENTIAL DEHUMIDIFIERS

ENGINEERING ANALYSIS DATA REQUEST SHEETS FOR RESIDENTIAL 

DEHUMIDIFIERS

Aggregated industry data is requested for Table A-1 through Table A-5. 

Table A-1 Residential Dehumidifier Shipments 

Year

Shipments, Domestic + Imports

(Units)

Portable

Whole 

Home

Under 

15 

pints/day

15 < 20 

pints/day

20 < 25 

pints/day

25 < 30 

pints/day

30 < 35 

pints/day

35 < 40 

pints/day

40 < 45 

pints/day

45 < 50 

pints/day

50 < 60 

pints/day

60 & 

Over 

pints/day

1995

1996

1997

1998

1999 0 6,825 275 222,856 63,754 41,961 307,165 0 307,324

2000 0 7,897 211 206,745 65,896 * 327,781 * 366,897 +

2001 0 7,148 4 139,389 113,647 * 335,080 * 210,382 +

2002 0 6,474 0 157,076 72,562 * 426,510 * 136,380 +

2003 0 * * 207,375 145,585 * 437,056 0 313,993 207,030

2004 0 * 0 275,343 308,644 * 679,490 0 316,460 92,552

2005 0 * * 194,598 210,906 * 517,902 * 655,926 377,503

2006 0 * * 154,708 239,977 * 157,993 295,777 315,886 291,238

2007 0 * * 248,896 353,688 * 311,645 207,846 428,241 453,255

2008 0 * * 266,320 261,102 * 180,387 * 543,683 306,811

2009 * * * 183,316 288,928 * 87,286 * 755,972 384,115

2010 * * * * 409,449 0 * 219,988 * 922,785

2011 * * * 21,170 363,233 0 * 99,802 454,096 430,190

NOTES:

* Combined with next larger range to avoid disclosure. 

+ Combined with the next smaller range to avoid disclosure.

 = AHAM does not have data for this category during the requested period. 

Domestic shipments includes units imported into the US, but does not include exports. 

Data represent estimates of total industry volume, based upon information submitted by the participants.
3A-2 

Table A-2 Shipment-Weighted Average Efficiency Data 

Year

Shipment-Weighted Average Efficiency

(EF, in liters/kWh)

Portable

Whole Home

35.00 

pints/day or 

less

35.01 to 

45.00 

pints/day

45.01 to 

54.00 

pints/day

54.01 to 

75.00 

pints/day

Greater than 

75.00 

pints/day

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

NOTES:

 = AHAM does not have data for this category during the requested period. 
3A-3 

Table A-3 Market Share Efficiency Data: Portable Dehumidifiers with Capacity up to 54.00 pints/day 

Portable, 35.00 pints/day or less Portable, 35.01 to 45.00 pints/day Portable, 45.01 to 54.00 pints/day

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

1.35 1.50 1.60

1.45 1.62 1.70

1.61 1.80

Notes:

* Total market share percentages should equal 100%.

 = AHAM does not have data for this category during the requested period.

Table A-4 Market Share Efficiency Data: Portable Dehumidifiers with Capacity Greater Than 54.00 pints/day and WholeHome Dehumidifiers

Portable, 54.01 to 75.00 pints/day Portable, greater than 75.00 pints/day Whole-Home

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

Efficiency Bins 

(EF)

Market Share for 

2010 or 2011*

1.70 2.50 2.50

1.85 2.80 2.80

2.10 3.50 3.50

2.47 4.17 4.17

Notes:

* Total market share percentages should equal 100%.

 = AHAM does not have data for this category during the requested period.
3A-4 

Table A-5 Manufacturer Cost Data: Portable Dehumidifiers

Product Class  Portable, 35.01 to 45.00 

pints/day Portable, 45.01 to 54.00 pints/day Portable, 54.01 to 75.00 pints/day

Efficiency Level 1 2 1 2 3 1 2 3 4

EF (liters/kWh) 1.50 1.62 1.60 1.70 1.80 1.70 1.85 2.10 2.47

Average Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Minimum Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Maximum Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Conversion Capital Expenditures ($, Millions)

Building CAPX

Tooling/ Equipment 

CAPX

One-Time Product Conversion Expenses ($, Millions)

R&D

Marketing

NOTES: 

 = AHAM does not have data for this category during the requested period.

# Depreciation on the conversion capital expenditure should NOT be included in the incremental overhead. 

* Incremental costs per unit should be reported relative to the baseline unit’s cost. The baseline unit complies with the federal standards for residential dehumidifiers as of October 

1, 2012.

Other Information:

1. What depreciation method would your company use to depreciate the conversion capital expenditures? _______________________________________________.

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 for 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 a product, such as supervisors, 

janitors, stockroom personnel, inspectors, 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). 

Full Production Cost = Direct Material + Direct Labor + Overhead (factory) + Depreciation

Full Cost of Product = Full Production Cost + Non-production Costs (SG&A, R&D, interest, and profit)
3A-5 

Table A-6 Manufacturer Cost Data: Whole-Home Dehumidifiers

Product Class 

 Whole Home

Efficiency Level 1 2 3 4

EF (liters/kWh) 2.50 2.80 3.50 4.17

Average Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Minimum Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Maximum Incremental Costs ($ Per Unit)*

Material

Labor

Overhead#

Conversion Capital Expenditures ($, Millions)

Building CAPX

Tooling/ 

Equipment CAPX

One-Time Product Conversion Expenses ($, Millions)

R&D

Marketing

NOTES: 

 = AHAM does not have data for this category during the requested period.

# Depreciation on the conversion capital expenditure should NOT be included in the incremental overhead. 

* Incremental costs per unit should be reported relative to the baseline unit’s cost. The baseline unit for whole-home 

dehumidifiers complies with the federal standards for residential dehumidifiers with greater than 75.00 pints/day capacity as of 

October 1, 2012.

Other Information:

2. What depreciation method would your company use to depreciate the conversion capital expenditures? 

_______________________________________________.

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 for 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 a product, such as supervisors, janitors, stockroom 

personnel, inspectors, 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).

Full Production Cost = Direct Material + Direct Labor + Overhead (factory) + Depreciation

Full Cost of Product = Full Production Cost + Non-production Costs (SG&A, R&D, interest, and profit)
5A-i 

APPENDIX 5A. ENGINEERING ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

5A.1 RESIDENTIAL DEHUMIDIFIERS .............................................................................1

 
5A-1 

APPENDIX 5A. ENGINEERING ANALYSIS INTERVIEW GUIDE

5A.1 RESIDENTIAL DEHUMIDIFIERS

Residential Dehumidifier Rulemaking 

Engineering Analysis Interview Guide for 

Residential Dehumidifiers 

August 6. 2014 
5A-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 residential portable 

and whole-home dehumidifiers. 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. 

DOE published a notice of public meeting and availability of the preliminary technical support 

document (TSD) regarding energy conservation standards for dehumidifiers on May 22, 2014. 79 

FR 29380. Based on feedback received from stakeholders, DOE is revising the preliminary 

analyses in preparation for a notice of proposed rulemaking (NOPR). DOE requests 

manufacturer feedback on a number of issues covered in the sections below in support of 

updating its analyses. 

1 PRODUCT CLASSES

As specified in the preliminary TSD, DOE conducted its analyses on the following product 

classes and efficiency levels for portable and whole-home dehumidifiers. The capacities and 

efficiency levels reflect results obtained using the test procedure proposed in the NOPR 

published in the Federal Register on May 21, 2014. 79 FR 29271.

Table 1.1 Preliminary Analysis Portable Dehumidifier Efficiency Levels at 65 °F 

Efficiency

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels (L/kWh)

20.00 

pints/day 

or less

20.01–

30.00 

pints/day

30.01–

35.00 

pints/day

35.01–

45.00 

pints/day

45.01 

pints/day 

or more

Baseline DOE Standard with Fanonly Mode 0.77 0.80 0.94 1.00 2.07 

1 DOE Standard with no 

Fan-only Mode 1.10 1.10 1.20 1.30 2.40 

2 Gap Fill 1* 1.20 1.20 1.40 1.40 2.80 

3 Gap Fill 2 1.30 1.30 1.60 1.60 3.52 

4 Maximum Available 1.42 1.52 1.75 1.75 N/A

1.1 Should DOE consider collapsing portable dehumidifiers into fewer product classes? If so, 

what capacity bins would be appropriate? 

1.2 Is there any unique consumer utility associated with portable products in the different 

capacity bins that would warrant maintaining separate product classes?
5A-3 

Table 1.2 Preliminary Analysis Whole-Home Dehumidifier Efficiency Levels 65 °F 

Efficiency 

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels 

(L/kWh)

Less than or equal 

to 8.0 ft3 (Case 

Volume)

Greater than 8.0 ft3

(Case Volume)

Baseline Minimum Available 1.10 1.68 

1 Gap Fill 1 1.40 1.90 

2 Gap Fill 2/Maximum 

Available 1.59 2.80 

3 Maximum Available N/A 3.41 

1.3 Is case volume an appropriate differentiator between whole-home dehumidifier product 

classes? If so, is 8 ft3 the appropriate cutoff point between the small and large product classes? 

1.4 If case volume is not an appropriate metric for separating whole-home dehumidifier 

product classes, are multiple product classes necessary? If so, what would be the appropriate 

product classes and differentiators? 
5A-4 

2 ENGINEERING

In the preliminary analysis, DOE developed cost estimates associated with improving product 

efficiencies beyond the baseline for the product classes listed in Table 1.1 and Table 1.2. Table 

2.1 below lists the incremental manufacturing costs determined in the preliminary analysis.

Table 2.3 Incremental Dehumidifier Manufacturing Costs at Higher Efficiencies

Portable Product Class Capacities

(pints/day)

Whole-Home Product 

Class Case Volume (ft3)

Efficiency Level ≤ 20 20.01-30 30.01-35 35.01-45 > 45 ≤ 8.0 > 8.0

EL1 $- $- $- $- $38.40 $15.22 $6.14

EL2 $1.56 $1.85 $2.94 $1.98 $49.16 $76.18 $37.05

EL3 $4.64 $3.78 $8.72 $7.56 $100.13 $112.01

EL4 $7.77 $10.82 $13.40 $11.24

2.1 Are the costs presented in Table 2.1 appropriate for the corresponding efficiency 

improvements in each product class? DOE assumed in the preliminary analysis that 

manufacturers would primarily rely on increased compressor efficiencies and improvements 

to the heat exchangers to reach higher efficiency levels.

2.2 DOE assumed in its preliminary analysis that rotary R-410A compressor efficiencies may 

reach energy efficiency ratios (EERs) up to 10.5 British thermal units per hour per Watt 

(Btu/h/W). Is this maximum EER appropriate for the entire range of rotary R-410A 

compressor capacities (i.e., <3,000 Btu/h up to roughly 15,000 Btu/h)? What price premium is 

associated with more efficient compressors?

2.3 DOE relied on information from the room air conditioners final rule published in 2011 in 

determining compressor pricing information (described in chapter 5 of the preliminary TSD). 

Are the costs shown on this curve appropriate for compressors included in dehumidifiers? 

What price-capacity trend should DOE use for low-capacity compressors (below 5,000 Btu/h) 

beyond the range of the room air conditioners pricing curve? 

2.4 What efficiency gains would be associated with moving to more efficient blower motors 

(i.e., permanent magnet motors)? What price premium would these motors have compared to 

the typical permanent-split capacitor motors used in most dehumidifiers? 

2.5 In the preliminary analysis, DOE considered improvements to the evaporator and 

condenser by increasing the cross-sectional area of these heat exchangers. What factors limit 

the extent to which manufacturers may increase the heat exchanger sizes? Would 

manufacturers consider improvements to the heat exchangers other than increasing crosssectional area (e.g., increasing the number of tube passes in the direction of the air flow)? 

2.6 For the portable product classes with capacities less than 45 pints/day, DOE assumed that 

manufacturers would improve efficiencies from the baseline to EL1 by eliminating fan-only 
5A-5 

mode. Would removing fan-only mode have any impact on consumer utility? 

2.7 Testing

2.7.a What should the inlet air condition (dry-bulb temperature and relative humidity) 

be for testing whole-home dehumidifiers? How would an inlet air temperature of 

73 °F affect measured efficiencies and capacities compared to the 65 °F used for 

the preliminary analysis?

2.7.b What should the test external static pressure be for whole-home dehumidifiers? 

How would an external static pressure of 0.25 inches of water affect measured 

efficiencies and capacities compared to 0.5 in. water as proposed in the 

preliminary analysis?

2.7.c Should the test procedure require an additional test duct for units with 

ventilation/fresh air collars or should these collars be capped closed for testing? 

2.7.d Is 65 °F the most appropriate inlet temperature condition for portable 

dehumidifiers? If not, what should it be? 

2.7.e The proposed test procedure does not account for dehumidifier performance under 

varying ambient conditions. Should DOE consider testing at multiple ambient 

conditions to assess potential efficiency improvements associated with certain 

design options (e.g., variable speed compressors, flow control devices, improved 

defrost control)? If so, what ambient conditions should be tested? 

2.7.f If multiple ambient conditions were assessed, how would the increased test 

burden associated with these additional test points impact profitability and overall 

manufacturing costs? Also, how much energy would a field unit with these design 

options save when compared with a unit without these design options. 
5A-6 

3 KEY ISSUES

3.1 Since the preliminary interview, are there any new key issues for your company 

regarding amended energy conservation standards for residential dehumidifiers?
5A-7 

4 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding manufacturer impacts at the plant or profit center level 

directly pertinent to dehumidifier 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 dehumidifier industry profit center 

will help DOE understand the probable future of the manufacturing activity with and without 

amended energy conservation standards.

4.1 What percentage of your dehumidifier manufacturing corresponds to each product class, 

in terms of both revenue and shipments within the U.S.? If known, please also indicate your 

company’s approximate market share for each product class. Please also indicate whether you 

purchase your dehumidifiers from other manufacturers, and whether the factory that supplies 

the products is located in the United States.

Table 4.4 Portable Dehumidifier U.S. Revenue and Shipment Volumes by Product Class

Product 

Class 

Number

Product 

Class

2013 

Revenue

2013 

Shipments

% 

Made

% 

Bought

% Made in 

U.S.

Market 

Share

1 

20.00 

pints/day 

or less**

2 

20.01–

30.00 

pints/day

3 

30.01–

35.00 

pints/day

4 

35.01–

45.00 

pints/day

5 

45.01 

pints/day 

or more

Table 4.2 Whole-Home Dehumidifier U.S. Revenue and Shipment Volumes by Product 

Class

Product Class 

Number 

Product 

Class

2013 

Revenue

2013 

Shipments

% 

Made

% 

Bought

% Made in 

U.S.

Market 

Share

1 

<= 8.0 ft3

(Case 

Volume)

2 

> 8.0 ft3

(Case 

Volume)
5A-8 

5 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 each product classes of residential 

dehumidifiers. DOE defines manufacturer production cost as all direct costs associated with 

manufacturing a product: direct labor, direct materials, overhead, and depreciation. The 

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 1.43 for residential dehumidifiers. 

5.1 Is the 1.43 baseline markup representative of an average industry markup? 

5.2 Please comment on the baseline markups DOE calculated as compared to your 

company’s baseline markups for the dehumidifier product classes. How would revised 

standards potentially impact your baseline markups and margins for each product class? 

Table 5.5 Portable Dehumidifier Baseline Manufacturer Markups by Product Class

Product 

Class Product Type

Estimated 

Baseline 

Markup

Manufacturer Comments or Revised Estimates

1 20.00 pints/day 

or less** 1.43

2 20.01–30.00 

pints/day 1.43

3 30.01–35.00 

pints/day 1.43

4 35.01–45.00 

pints/day 1.43

5 45.01 pints/day 

or more

1.43
5A-9 

Table 5.6 Whole-Home Dehumidifier Baseline Manufacturer Markups by Product Class

Product 

Class Product Type

Estimated 

Baseline 

Markup

Manufacturer Comments or Revised Estimates

1 <= 8.0 ft3 (Case 

Volume) 1.43

2 > 8.0 ft3

(Case Volume) 1.43

5.3 DOE is interested in understanding if efficiency is a feature that earns a premium or 

whether it would cut into profit margins (and reduce markups). Within each product class, 

would markups vary by efficiency level? If yes, please provide estimates for your markups by 

product class and efficiency level in Table 5.3 and Table 5.4. 

Table 5.7 Estimated Markups for Portable Dehumidifiers

Efficiency

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels (L/kWh)

20.00 

pints/day 

or less

20.01–

30.00 

pints/day

30.01–

35.00 

pints/day

35.01–

45.00 

pints/day

45.01 

pints/day 

or more

1 DOE Standard with no 

Fan-only Mode 

2 Gap Fill 1

3 Gap Fill 2

4 Maximum Available

Table 5.8 Estimated Markups for Whole-Home Dehumidifiers

Efficiency 

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels 

(L/kWh)

Less than or equal to 

8.0 ft3 (Case Volume)

Greater than 8.0 ft3

(Case Volume)

1 Gap Fill 1

2 Gap Fill 2/Maximum 

Available

3 Maximum Available

5.4 What factors or product attributes besides efficiency affect the profitability of 

dehumidifiers within a product class? 
5A-10 

5.5 Would you expect changes in your estimated profitability following an amended energy 

conservation standard? If so, please explain why. 
5A-11 

6 FINANCIAL PARAMETERS

DOE’s contractor has developed a draft model of the dehumidifier 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 

dehumidifier profit center. This section attempts to understand the financial parameters for 

dehumidifier manufacturing and how your company’s financial situation could differ from the 

industry aggregate picture. 

6.1 In order to accurately collect information about dehumidifier manufacturing, please 

compare your financial parameters to the GRIM parameters tabulated below.

1.

 Table 6.9 Financial Parameters for Residential Dehumidifier 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) 31.1%

Discount Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost of 

debt and return on equity)

8.4%

Working 

Capital

Current assets less current liabilities (percentage of 

revenues)

20.1%

Net PPE Net plant property and equipment (percentage of 

revenues) 13.4%

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 20.9%

R&D Research and development expenses (percentage 

of revenues) 1.3%

Depreciation Amortization of fixed assets (percentage of 

revenues) 2.5%

Capital 

Expenditures

Outlay of cash to acquire or improve capital assets 

(percentage of revenues, not including acquisition 

or sale of business units)

2.7%

Cost of Goods 

Sold

Includes material, labor, overhead, and 

depreciation (percentage of revenues) 71.7%

6.2 Relative to the sale of dehumidifiers, do you typically pay for shipping costs? Does the 

customer reimburse you for some or all of the costs associated with the shipment of your 

dehumidifier products? 

6.3 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? 
5A-12 

7 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 and 

certification, marketing and other costs for redesigning products necessitated by an 

amended energy conservation standard.

CAPITAL CONVERSION COSTS

7.1 Please provide estimates for your capital conversion costs by product class and efficiency 

level in Table 7.1 through Table 7.7. 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. 

For each of the product categories, please note which efficiency level changes could be made 

within existing platform designs and which would result in major product redesigns. Also note 

which design options would require only minor changes to production lines, and which would 

require major changes to production lines, substantial modifications to existing facilities, or 

the development of entirely new manufacturing facilities. 
5A-13 

Portable Dehumidifiers 

Table 7.10 Expected Capital Conversion Costs for Portable Dehumidifiers – 20.00 

pints/day of less

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.42

Table 7.11 Expected Capital Conversion Costs for Portable Dehumidifiers – 20.01 to 30.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.52

Table 7.12 Expected Capital Conversion Costs for Portable Dehumidifiers – 30.01 to 35.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.20

2 1.40

3 1.60

4 1.75

Table 7.13 Expected Capital Conversion Costs for Portable Dehumidifiers – 35.01 to 45.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.30

2 1.40

3 1.60

4 1.75

Table 7.14 Expected Capital Conversion Costs for Portable Dehumidifiers – 45.01 

pints/day or more 

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 2.40

2 2.80

3 3.52

4 N/A

Whole-Home Dehumidifiers 
5A-14 

Table 7.15 Expected Capital Conversion Costs for Whole-Home Dehumidifiers - Less than 

or equal to 8.0 ft3

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.40

2 1.59

3 N/A

Table 7.16 Expected Capital Conversion Costs for Whole-Home Dehumidifiers - 8.0 ft3 or 

more

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.90

2 2.80

3 3.41

PRODUCT CONVERSION COSTS

7.2 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 7.8 through 

Table 7.14 (where applicable) considering such expenses as product development expenses, 

prototyping, testing, certification, and marketing [if you have not previously certified your 

products with the DOE, please be sure to breakout an estimate of the costs associated with 

testing and certification, if possible]. In the description column, please describe the 

assumptions behind the estimates provided. 
5A-15 

Portable Dehumidifiers 

Table 7.17 Expected Product Conversion Costs for Portable Dehumidifiers – 20.00 

pints/day of less

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.42

Table 7.18 Expected Product Conversion Costs for Portable Dehumidifiers – 20.01 to 30.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.52

Table 7.19 Expected Product Conversion Costs for Portable Dehumidifiers – 30.01 to 35.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.20

2 1.40

3 1.60

4 1.75

Table 7.20 Expected Product Conversion Costs for Portable Dehumidifiers – 35.01 to 45.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.30

2 1.40

3 1.60

4 1.75

Table 7.21 Expected Product Conversion Costs for Portable Dehumidifiers – 45.01 

pints/day or more 

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 2.40

2 2.80

3 3.52

4 N/A

Whole-Home Dehumidifiers 
5A-16 

Table 7.22 Expected Product Conversion Costs for Whole-Home Dehumidifiers - Less than 

or equal to 8.0 ft3

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.40

2 1.59

3 N/A

Table 7.23 Expected Product Conversion Costs for Whole-Home Dehumidifiers - 8.0 ft3 or 

more

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.90

2 2.80

3 3.41
5A-17 

8 DIRECT EMPLOYMENT IMPACT ASSESSMENT AND MANUFACTURING

CAPACITY

8.1 Where are your dehumidifier manufacturing facilities that produce products for the 

United States located? What types of products are manufactured at each location? Please 

provide employment levels and annual shipment figures for your company’s dehumidifier 

manufacturing at each location by product class. 

Table 8.24 Dehumidifiers Employment and Shipment Volumes by Product Class*

Facility Location Product Types Manufactured Employees Annual 

Shipments

Example Sheboygan, WI Portable dehumidifiers 200 100,000 

1 

2 

3 

4

5

 *For manufacturing facilities that produce products for the U.S. 

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 How would amended energy conservation standards impact your company’s 

manufacturing capacity? How much, if any, downtime would be required? 

8.4 What percentage of your company’s overall dehumidifier sales is made within the United 

States? 

8.5 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.?
5A-18 

9 IMPACTS ON SMALL BUSINESS

9.1 The Small Business Administration (SBA) denotes a small business in the dehumidifier 

manufacturing industry as having less than 750 total employees, including the parent company 

and all subsidiaries.a By this definition, is your company considered a small business? 

9.2 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.

9.3 To your knowledge, are there any small businesses, 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. The products covered by this rulemaking are classified under NAICS codes 

333415: Air-Conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration 

Equipment Manufacturing and 335210: Small Electrical Appliance Manufacturing. To be categorized as a small 

business, a manufacturer (and its affiliates) classified by one of these NAICS codes may employ a maximum of 750

employees. The 750 employee threshold includes all employees in a business’s parent company and any other 

subsidiaries. 
6A-i

APPENDIX 6A. DETAILED DATA FOR PRODUCT PRICE MARKUPS 

TABLE OF CONTENTS

6A.1 DETAILED WHOLESALER COST DATA ......................................................... 6A-1

6A.2 DETAILED MECHANICAL CONTRACTOR DATA ......................................... 6A-2

6A.3 DETAILED GENERAL CONTRACTOR COST DATA ...................................... 6A-4

6A.4 STATE SALES TAX RATES ................................................................................ 6A-5

LIST OF TABLES

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers .................................. 6A-1

Table 6A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups .................................................................................. 6A-3

Table 6A.3.1 Residential General Contractor Expenses and Markups ............................. 6A-4

Table 6A.4.1 State Sales Tax Rates .................................................................................. 6A-5
6A-1 

APPENDIX 6A. DETAILED DATA FOR PRODUCT PRICE MARKUPS 

6A.1 DETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air-conditioning & Refrigeration Distributors 

International (HARDI) 2013 Profit Report, Table 6.5.1 of chapter 6 shows wholesaler revenues 

and costs in aggregated form. Table 6A-1 in this appendix provides the complete breakdown of 

costs and expenses. The column labeled “Scaling” in Table 6A.1.1 indicates which expenses the 

U.S. Department of Energy (DOE) assumed to scale with only the baseline markup and which 

with both the baseline and incremental markups. Only those expenses that scale with both 

baseline and incremental costs are marked up when there is an incremental change in equipment 

costs.

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers 

Item

Percent of Revenue

% Scaling

Cost of Goods Sold 73.9

Gross Margin 26.1

Payroll Expenses 15.1

Baseline

Executive Salaries & Bonuses 1.6

Branch Manager Salaries and Commissions 1.3

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2.3

Inside/Counter Sales/Wages 2.6

Purchasing Salaries/Wages 0.5

Credit Salaries/Wages 0.2

IT Salaries/Wages 0.2

Warehouse Salaries/Wages 1.4

Accounting 0.5

Delivery Salaries/Wages 0.8

All Other Salaries/Wages & Bonuses 0.8

Payroll Taxes 1.0

Group Insurance 1.0

Benefit Plans 0.4

Occupancy Expenses 3.5

Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0.3

Rent or Ownership in Real Estate 2.5
6A-2 

Item

Percent of Revenue

% Scaling

Other Operating Expenses 5.2

Baseline & Incremental

Sales Expenses (incl. advertising & promotion) 0.9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1.2

Personal Property Taxes/Licenses 0.1

Collection Expenses 0.3

Bad Debt Losses 0.2

Data Processing 0.3

All Other Operating Expenses 1.6

Total Operating Expenses 23.8

Operating Profit 2.3

Baseline & Incremental

Other Income 0.4

Interest Expense 0.4

Other Non-operating Expenses 0.0

Profit Before Taxes 2.3

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2013. 2013 Profit Report (2012 Data).

Note: The wholesaler costs and expenses are percentage values as opposed to the per-dollar of sales revenue values shown in Table 6.4.1.

6A.2 DETAILED MECHANICAL CONTRACTOR DATA

Tables 6.5.3 and Table 6.5.4 of chapter 6 provide mechanical contractor revenues and 

costs in aggregated form by ‘Cost of Goods Sold’ and ‘Gross Margin.’ The tables are based on 

data in the 2005 edition of Financial Analysis for the HVACR Contracting Industry, published 

by the Air Conditioning Contractors of America (ACCA). The ACCA report did not provide a 

more disaggregated tabulation of these costs and expenses. As in section 6A.1, the gross margin 

category was assumed to scale only with the baseline markup.

A further disaggregated breakdown of costs used to scale the incremental markup are 

shown in Table 6A.2.1 by both dollar value and percentage terms from the 2007 Census of 

Business. As the ACCA data were used to calculate the baseline markup, in Table 6A.2.1 only 

the categories in the ‘Scaling’ column that are scaled with both the baseline and incremental 

markups are marked when there is an incremental change in equipment costs.
6A-3 

Table 6A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups

Item

Dollar Value 

$1,000

Percentage

% Scaling

Total Cost of Equipment Sales 107,144,428 67.80

Total payroll, construction workers wages 31,373,558 19.85

Cost of materials, components, and supplies 59,023,964 37.35

Cost of construction work subcontracted out to others 13,646,192 8.63

Total cost of selected power, fuels, and lubricants 3,100,714 1.96

Gross Margin 50,895,129 32.20

Payroll Expenses 28,065,632 17.76

Baseline

Total payroll, other employee wages 14,041,336 8.88

Total fringe benefits 13,585,040 8.60

Temporary staff and leased employee expenses 439,256 0.28

Occupancy Expenses 3,436,208 2.17

Baseline

Rental costs of machinery and equipment 1,047,026 0.66

Rental costs of buildings 1,231,263 0.78

Communication services 640,851 0.41

Cost of repair to machinery and equipment 517,068 0.33

Other Operating Expenses 12,671,194 8.02

Baseline & 

Incremental

Purchased professional and technical services 843,641 0.53

Data processing and other purchased computer services 98,016 0.06

Expensed computer hardware and other equipment 255,474 0.16

Expensed purchases of software 64,195 0.04

Advertising and promotion services 1,018,265 0.64

All other expenses 6,944,674 4.39

Refuse removal (including hazardous waste) services 153,241 0.10

Taxes and license fees 996,138 0.63

Total depreciation ($1,000) 2,297,550 1.45

Net Profit Before Income Taxes 6,722,095 4.25

Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors: 2007. Sector 23: 238220. Construction: Geographic 

Area Series. Detailed Statistics for Establishments: 2007. 

Note: Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values. This is in 

contrast to the cost per dollar of sales revenue values shown in Table 6.5.2. 
6A-4 

6A.3 DETAILED GENERAL CONTRACTOR COST DATA

Based on U.S. Department of Census data, Table 6.5.6 of chapter 6 shows residential 

building general contractor revenues and costs in aggregated form. Table 6A.3.1 shows the 

complete breakdown of costs and expenses of residential building contractor provided by the 

U.S. Department of Census. The column labeled “Scaling” indicates which expenses DOE 

assumed to scale with only the baseline markup and which are scaled with both the baseline and 

incremental markups. Only those expenses that scale with baseline and incremental costs are 

marked up when there is an incremental change in equipment costs. 

Table 6A.3.1 Residential General Contractor Expenses and Markups

Item

Dollar Value 

$1,000

Percentage 

% Scaling

Total Cost of Equipment Sales 238,431,389 67.55

Total payroll, construction workers wages 16,629,321 4.71

Cost of materials, components, and supplies 126,764,975 35.91

Cost of construction work subcontracted out to others 90,956,668 25.77

Total cost of selected power, fuels, and lubricants 4,080,425 1.16

Gross Margin 114,558,247 32.45

Payroll Expenses 28,806,792 8.16

Baseline Total payroll, other employee wages 20,843,029 5.90

Total fringe benefits 7,464,670 2.11

Temporary staff and leased employee expenses 499,093 0.14

Occupancy Expenses 3,558,796 1.01

Baseline

Rental costs of machinery and equipment 572,783 0.16

Rental costs of buildings 1,532,841 0.43

Communication services 810,436 0.23

Cost of repair to machinery and equipment 642,736 0.18

Other Operating Expenses 21,341,175 6.05

Baseline & 

Incremental

Purchased professional and technical services 1,834,816 0.52

Data processing and other purchased computer services 141,344 0.04

Expensed computer hardware and other equipment 261,701 0.07

Expensed purchases of software 105,338 0.03

Advertising and promotion services 2,544,687 0.72

All other expenses 10,840,757 3.07

Refuse removal (including hazardous waste) services 520,907 0.15

Taxes and license fees 1,791,539 0.51

Total depreciation ($1,000) 3,300,086 0.93

Net Profit Before Income Taxes 60,851,484 17.24 Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23, EC0723I1: 236115 through 236118. Construction, Industry 

Series, Preliminary Detailed Statistics for Establishments: 2007.

Note: General contractor costs and expenses are first presented as total dollar values and then converted to percentage values. This is in contrast 

to the cost per dollar of sales revenue values shown in Table 6.5.6.
6A-5 

6A.4 STATE SALES TAX RATES 

Table 6A.4.1 State Sales Tax Rates 

State

Combined State 

and Local Tax 

Rate

% State

Combined State 

and Local Tax 

Rate

% State

Combined State 

and Local Tax 

Rate

%

Alabama 8.55 Kentucky 6.00 North Dakota 5.95

Alaska 1.30 Louisiana 8.80 Ohio 7.10

Arizona 7.15 Maine 5.50 Oklahoma 8.40

Arkansas 8.90 Maryland 6.00 Oregon --

California 8.40 Massachusetts 6.25 Pennsylvania 6.35

Colorado 6.10 Michigan 6.00 Rhode Island 7.00

Connecticut 6.35 Minnesota 7.20 South Carolina 7.00

Delaware -- Mississippi 7.05 South Dakota 5.45

Dist. of Columbia 5.75 Missouri 7.40 Tennessee 9.45

Florida 6.65 Montana -- Texas 7.95

Georgia 7.05 Nebraska 6.05 Utah 6.65

Hawaii 4.35 Nevada 7.95 Vermont 6.10

Idaho 6.05 New Hampshire -- Virginia 5.60

Illinois 8.00 New Jersey 6.95 Washington 8.90

Indiana 7.00 New Mexico 6.60 West Virginia 6.05

Iowa 6.80 New York 8.45 Wisconsin 5.45

Kansas 7.90 North Carolina 6.90 Wyoming 5.50

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed on July 18, 2014). 
7A-i

APPENDIX 7A. HOUSING VARIABLES

TABLE OF CONTENTS

7A.1 INTRODUCTION .................................................................................................. 7A-1

7A.2 RECS SAMPLE DETERMINATION .................................................................... 7A-1

7A.3 RECS 2009 DATABASE VARIABLE RESPONSE CODES ............................... 7A-2

LIST OF TABLES

Table 7A.2.1 List of RECS 2009 Variables Used for Dehumidifier LCC Analysis ............. 7A-1

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life-Cycle Cost Analysis ....... 7A-2
7A-1

APPENDIX 7A. HOUSING VARIABLES

7A.1 INTRODUCTION

Using Microsoft ACCESS, DOE created a database containing a subset of the records 

and variables from DOE’s Energy Information Administration (EIA)’s RECS 2009.1 DOE used 

this RECS subset in the life-cycle cost (LCC) analysis of the Dehumidifier Rulemaking. This 

appendix explains the variable name abbreviations and provides definitions of the variable 

values. For the entire RECS 2009 dataset, refer to 

http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata. 

7A.2 RECS SAMPLE DETERMINATION

 The RECS consists of three parts: 

• Personal interviews with households for information about energy used, how it is used, 

energy-using appliances, structural features, energy efficiency measures, and 

demographic characteristics of the household. 

• Telephone interviews with rental agents for households that have any of their energy use 

included in their rent. 

• Mail questionnaires sent to energy suppliers (after obtaining permission from households) 

to collect the actual billing data on energy consumption and expenditures. 

 The subset of RECS 2009 records used to study dehumidifiers met all of the following 

criteria:

• used a dehumidifier;

• and for homes with a whole-home dehumidifier: 

o located in a mixed temperature and moist climate;

o single-family attached or detached;

o equipped with a central air conditioner; 

o equipped with duct work; 

The RECS 2009 weighting indicates how commonly each household configuration 

occurs in the general population. Table 7A.2.1 shows the RECS sample weights and criteria for 

replacements.

Table 7A.2.1 lists the variables use in the analysis. 

Table 7A.2.1 List of RECS 2009 Variables Used for Dehumidifier LCC Analysis

Variable Description

Location Variables

REGIONC Census Region
7A-2

Variable Description

DIVISION Census Division

REPORTABLE_DOMAIN Reportable states and groups of states

Climate_Region_Pub Building America Climate Region

HDD65 Heating degree days in 2009, base temperature 65F

HDD30YR Heating degree days, 30-year average 1981-2010, base 65F

CDD65 Heating degree days in 2009, base temperature 65F

CDD30YR Cooling degree days, 30-year average 1981-2010, base 65F

StationID* ID number of weather station identified with household (See 

Appendix 7-B)

Housing Unit Characteristics Variables

NWEIGHT Final sample weight

DOEID Unique identifier for each respondent

TYPEHUQ Type of housing unit

YEARMADE Year housing unit was built

NOTMOIST Use dehumidifier

USENOTMOIST Number of months in 2009 use dehumidifier

COOLTYPE Type of air conditioning equipment used

CENACHP Central air conditioner is a heat pump

CELLAR Basement in housing unit

BASEFIN Finished basement

Household Characteristics Variables

NHSLDMEM Number of household members

Seniors* Number of household members age 65 or older

POVERTY100 Household income at or below 100% of poverty line

* Not part of RECS 2009 variables.

7A.3 RECS 2009 DATABASE VARIABLE RESPONSE CODES

Table 7A.3.1 provides the response codes for all RECS 2009 variables used in the 

dehumidifier housing samples.

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life-Cycle Cost Analysis 

Variable Response Codes

CDD65 Cooling degree days in 2009, base temperature 65F

CELLAR

0

1 

-2

No

Yes

Not Applicable

CENACHP

0

1 

-2

No

Yes

Not Applicable

Climate_Region_Pub

1

2 

3 

4 

5

Very Cold/Cold

Hot-Dry/Mixed-Dry

Hot-Humid

Mixed-Humid

Marine
7A-3

Variable Response Codes

COOLTYPE

1

2 

3 

-2

Central system

Window/wall units 

Both a central system and window/wall units 

Not Applicable

DIVISION

1

2 

3 

4 

5 

6 

7 

8 

9 

10

New England Census Division (CT, MA, ME, NH, RI, VT)

Middle Atlantic Census Division (NJ, NY, PA)

East North Central Census Division (IL, IN, MI, OH, WI) 

West North Central Census Division (IA, KS, MN, MO, ND, 

NE, SD)

South Atlantic Census Division (DC, DE, FL, GA, MD, NC, 

SC, VA, WV)

East South Central Census Division (AL, KY, MS, TN)

West South Central Census Division (AR, LA, OK, TX) 

Mountain North Sub-Division (CO, ID, MT, UT, WY) 

Mountain South Sub-Division (AZ, NM, NV)

Pacific Census Division (AK, CA, HI, OR, WA)

DOEID

00001 -

12083 Unique identifier for each respondent

HDD65 Heating degree days in 2009, base temperature 65F

NOTMOIST

0

1

No

Yes

NWEIGHT Final sample weight

POVERTY100

0

1

No

Yes

REGIONC

1

2 

3 

4

Northeast Census Region

Midwest Census Region

South Census Region 

West Census Region

REPORTABLE_DOMAIN

1

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20

Connecticut, Maine, New Hampshire, Rhode Island, Vermont

Massachusetts

New York

New Jersey

Pennsylvania 

Illinois

Indiana, Ohio 

Michigan

Wisconsin

Iowa, Minnesota, North Dakota, South Dakota 

Kansas, Nebraska

Missouri 

Virginia

Delaware, District of Columbia, Maryland, West Virginia

Georgia

North Carolina, South Carolina

Florida 

Alabama, Kentucky, Mississippi

Tennessee

Arkansas, Louisiana, Oklahoma
7A-4

Variable Response Codes

21

22 

23 

24 

25 

26 

27

Texas

Colorado 

Idaho, Montana, Utah, Wyoming 

Arizona 

Nevada, New Mexico

California

Alaska, Hawaii, Oregon, Washington

Seniors

0

1

No

Yes

TYPEHUQ

1

2 

3 

4 

5

Mobile Home

Single-Family Detached

Single-Family Attached

Apartment in Building with 2 - 4 Units 

Apartment in Building with 5+ Units

USENOTMOIST

1

2 

3 

4 

5 

-2

1 to 3 months

4 to 6 months 

7 to 9 months 

10 to 11 months 

Turned on all year 

Not applicable

YEARMADE

1600 -

2009 Year housing unit was built

* Not part of RECS 2009 variables.
7A-5

REFERENCES

1 U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey: 2009 RECS Survey Data, 2013. (Last accessed March, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/>

2 U.S. Department of Energy: Energy Information Administration, Commercial Buildings 

Energy Consumption Survey, 2003. (Last accessed September, 2013.) 

<http://www.eia.doe.gov/emeu/cbecs/>
7B-i

APPENDIX 7B. WEATHER STATION DATA MAPPING TO RECS 

HOUSEHOLDS 

TABLE OF CONTENTS

7B.1 INTRODUCTION ...................................................................................................7B-1

7B.2 MAPPING METHODOLOGY ...............................................................................7B-1

7B.3 MAPPING RESULTS .............................................................................................7B-2

LIST OF TABLES

Table 7B.3.1 Weather Station Data .............................................................................7B-2
7B-1

APPENDIX 7B. WEATHER STATION DATA MAPPING TO RECS HOUSEHOLDS

7B.1 INTRODUCTION

 Energy Information Administration’s (EIA) 2009 Residential Energy Consumption 

Survey (RECS 2009)1 provide data on annual heating and cooling degree-days but not on other 

weather parameters needed for the analysis such as average monthly outdoor absolute humidity 

(AH), monthly heating degree days (HDD) and cooling degree days (CDD), and average outdoor 

temperature. 

7B.2 MAPPING METHODOLOGY

To derive the additional weather data that is needed for the analysis (e.g., AH), for each 

building in the sample, DOE developed an approach to assign a physical location to each RECS 

household. a The methodology consists of the following steps:

1. DOE assembled monthly weather data from 151 weather stations from the National 

Oceanic and Atmospheric Administration (NOAA)’s National Climatic Data Center

(NCDC) that provide the heating and cooling degree-days at base temperature 65o

F for 

year 2009 (for the RECS sample).

3 The 2009 heating and cooling degree days match the 

period used to determine the degree-days in RECS 2009. 

2. RECS reports both annual HDD and CDD to base temperature 65oF for each building 

record. DOE assigned each building to one of the 151 weather stations by calculating 

which weather station (within the appropriate region) was the closest using the root mean 

square distance of the RECS annual degree days to the weather station annual degree 

days. The following equation calculates the root mean square distance in degree days 

between the U.S. weather data and the RECS/CBECS data:

" " ( Distance = − + − HDD HDD ) (CDD CDD ) 2 1

2

2 1

2

Where:

HDD1 = heating degree days from U.S. weather data,

HDD2 = heating degree days from RECS data,

CDD1 = cooling degree days from U.S. weather data, and 

CDD2 = cooling degree days from RECS data.

 

a For confidentiality, heating and cooling degree day values were altered slightly by EIA to mask the exact 

geographic location of the housing unit.
7B-2

7B.3 MAPPING RESULTS

Table 7B.3.1 shows the imputation results for all RECS locations. Note that some U.S. 

weather station data match with several of the RECS weather data. The number of RECS 

buildings that were matched to the specified weather station is indicated in the column “Count”. 

Table 7B.3.1 shows the data matches (151 weather stations) including the outdoor AH for the 

weather stations. 

Table 7B.3.1 Weather Station Data 

WBAN State City HDD CDD Vapor Density Count

03013 CO LAMAR 5374 1003 0.0060160842 1

03031 TX ODESSA 2355 2478 0.0076450452 4

03102 CA ONTARIO 1315 1952 0.0080335282 10

03104 CA PALM SPRINGS 1067 3943 0.0066554348 1

03178 CA SAN DIEGO 1530 826 0.0093166774 1

03849 KY LONDON 4287 1039 0.0093844476 8

03856 AL HUNTSVILLE 2971 1847 0.0106559281 4

03882 FL PANAMA CITY 1235 2952 0.0143677968 4

03894 TN CLARKSVILLE 3927 1293 0.0100264573 10

03904 TX COLLEGE STATION 1475 3421 0.0119196775 1

03919 KS SALINA 5080 1307 0.008216447 1

03928 KS WICHITA 4521 1495 0.0087987799 6

03947 MO KANSAS CITY 5007 1083 0.0088161017 41

03953 AR JONESBORO 3521 1684 0.010599441 4

03963 MO JEFFERSON CITY 4507 1158 0.0089089311 6

04726 PA JOHNSTOWN 6676 263 0.0069141535 2

04781 NY ISLIP 5277 657 0.007716758 46

04841 WI SHEBOYGAN 7720 258 0.0067380115 21

04845 WI KENOSHA 7149 345 0.0070480555 21

04849 OH LORAIN/ELYRIA 5667 733 0.0075507607 8

04852 OH NEW PHILADELPHIA 5649 605 0.0075274837 3

04858 OH NEWARK 5862 518 0.0080336913 20

12815 FL ORLANDO 590 3599 0.0144723192 4

12838 FL MELBOURNE 548 3590 0.0154710453 4

12839 FL MIAMI 109 4905 0.0168885045 4

12842 FL TAMPA 488 3866 0.0152015791 4

12843 FL VERO BEACH 489 3547 0.0159311502 4

12844 FL WEST PALM BEACH 239 4234 0.0155691407 4

12849 FL FORT LAUDERDALE 120 4726 0.0165875652 4

12873 FL ST PETERSBURG/ CLEARWATER 444 3963 0.0153142247 4

12947 TX COTULLA 1089 4060 0.0122759646 1

12960 TX HOUSTON 1255 3419 0.0138533393 5

13722 NC RALEIGH/DURHAM 3126 1876 0.0099136985 4

13729 WV ELKINS 5508 420 0.007951544 50

13733 VA LYNCHBURG 4394 1025 0.0088138222 29

13739 PA PHILADELPHIA 4498 1211 0.0085328605 5

13740 VA RICHMOND 3742 1570 0.0094171582 14

13748 NC WILMINGTON 2439 2053 0.0118205767 1
7B-3

WBAN State City HDD CDD Vapor Density Count

13833 MS HATTIESBURG 1878 2617 0.0126452945 3

13837 GA AUGUSTA 2082 2444 0.0105780255 2

13841 OH WILMINGTON 5373 702 0.008214416 8

13874 GA ATLANTA 2803 1849 0.0107638781 6

13876 AL BIRMINGHAM 2612 1950 0.0110090341 2

13877 TN BRISTOL/JHNSN CTY/KNGSPRT 4244 941 0.0091840512 2

13881 NC CHARLOTTE 3325 1623 0.0099698823 13

13882 TN CHATTANOOGA 3141 1822 0.0103702599 2

13891 TN KNOXVILLE 3593 1401 0.0099456766 3

13894 AL MOBILE 1608 2680 0.0130542794 2

13897 TN NASHVILLE 3589 1553 0.0098770797 3

13963 AR LITTLE ROCK 2939 1940 0.011046741 1

13978 MS GREENWOOD 2464 2237 0.0117848858 1

13987 MO JOPLIN 4258 1349 0.0091626993 18

13989 KS EMPORIA 4939 1130 0.0084067492 14

13993 MO ST JOSEPH 5583 971 0.0081289433 29

13995 MO SPRINGFIELD 4570 1110 0.0089819159 15

14605 ME AUGUSTA 7465 275 0.0065529735 31

14710 NH MANCHESTER 6366 553 0.0068233628 17

14732 NY NEW YORK 4566 1049 0.0077815891 35

14735 NY ALBANY 6506 450 0.0070472883 14

14736 PA ALTOONA 6095 390 0.0073154231 8

14745 NH CONCORD 7350 329 0.0067044268 12

14758 CT NEW HAVEN 5339 706 0.0081707387 4

14764 ME PORTLAND 7023 304 0.0070281667 27

14765 RI PROVIDENCE 5610 591 0.0075202781 14

14768 NY ROCHESTER 6689 325 0.0071111021 69

14770 PA SELINSGROVE 5524 715 0.0077313171 4

14771 NY SYRACUSE 6516 428 0.0069783428 13

14778 PA WILLIAMSPORT 5584 659 0.0074689608 1

14794 RI WESTERLY 5721 464 0.0083001294 3

14815 MI BATTLE CREEK 6420 514 0.0072130328 19

14820 OH CLEVELAND 5742 681 0.0076627958 21

14821 OH COLUMBUS 5207 873 0.0077971836 9

14827 IN FORT WAYNE 5939 616 0.00788948 15

14850 MI TRAVERSE CITY 7517 247 0.0064113439 19

14853 MI DETROIT 6659 449 0.0071093382 19

14914 ND FARGO 9240 361 0.005866579 9

14916 ND GRAND FORKS 9736 268 0.0059222236 19

14919 ND JAMESTOWN 9644 258 0.0058707582 12

14920 WI LA CROSSE 7210 520 0.0069321426 21

14921 WI LONE ROCK 7130 378 0.0070061569 21

14925 MN ROCHESTER 7658 319 0.0066144531 38

14939 NE LINCOLN 6105 896 0.007342676 14

14950 IA OTTUMWA 6277 573 0.0079394164 11

14991 WI EAU CLAIRE 8026 329 0.0063139509 21

22010 TX DEL RIO 1265 3806 0.0105411543 1

23042 TX LUBBOCK 3151 1968 0.0071494326 1
7B-4

WBAN State City HDD CDD Vapor Density Count

23065 KS GOODLAND 5983 728 0.0063323031 1

23090 NM FARMINGTON 5225 1003 0.0041742386 1

23129 CA LONG BEACH 1125 1208 0.0098345389 4

23157 CA BISHOP 4063 1140 0.0036467675 2

23174 CA LOS ANGELES 1275 551 0.0096459357 2

23185 NV RENO 4942 1069 0.0040345727 1

23213 CA SANTA ROSA 3135 350 0.0081665898 2

23230 CA OAKLAND 2827 174 0.0085942545 2

23232 CA SACRAMENTO 2531 1344 0.0081042454 1

23234 CA SAN FRANCISCO 2584 214 0.0087316501 3

24012 ND DICKINSON 9526 183 0.0055502263 6

24023 NE NORTH PLATTE 6872 534 0.0066847 1

24033 MT BILLINGS 6695 623 0.0047439218 1

24090 SD RAPID CITY 7752 354 0.0054756864 34

24130 OR BAKER CITY 7660 192 0.0051141527 1

24143 MT GREAT FALLS 7940 288 0.0043370561 1

24153 MT MISSOULA 7492 353 0.0048594245 1

24216 CA RED BLUFF 2518 2096 0.0070521959 2

24221 OR EUGENE 5046 330 0.0076039356 1

24225 OR MEDFORD 4456 1024 0.0064759087 1

24229 OR PORTLAND 4282 628 0.0074672022 1

24232 OR SALEM 4701 454 0.0073914877 1

24234 WA SEATTLE 4706 374 0.0074312216 6

53120 CA RAMONA 2304 980 0.0076951935 1

53866 IN SHELBYVILLE 5427 732 0.0083896808 7

53867 SC COLUMBIA 2391 2329 0.0110377232 1

53872 NC MONROE 3197 1720 0.0101469551 4

53903 TX HUNTSVILLE 1637 3151 0.0125537826 8

53909 TX FORT WORTH 2220 2704 0.0106132655 3

54737 PA LANCASTER 5366 764 0.0081751608 24

54740 VT SPRINGFIELD 7388 298 0.0065290992 1

54743 NJ CALDWELL 5368 761 0.0076220114 17

54756 MA ORANGE 7079 377 0.0068937208 43

54769 MA PLYMOUTH 6050 453 0.0079864434 43

54777 MA TAUNTON 6099 468 0.0076840108 43

54781 VT BENNINGTON 7145 244 0.0068121386 9

54782 PA POTTSTOWN 4980 844 0.0082301142 19

54788 CT MERIDEN 5924 548 0.0077707976 23

93084 AZ SAFFORD 1968 2942 0.004977519 1

93134 CA LOS ANGELES 844 1371 0.0093026134 4

93193 CA FRESNO 2274 2383 0.0074362585 2

93778 PA YORK 5485 707 0.008171033 7

93780 NJ MOUNT HOLLY 4881 859 0.0085453221 17

93801 GA ROME 3155 1695 0.0107720384 26

93805 FL TALLAHASSEE 1564 2808 0.0132476954 4

93814 KY COVINGTON 4884 881 0.0086178081 3

93817 IN EVANSVILLE 4363 1281 0.0092907246 2

93821 KY LOUISVILLE 4132 1324 0.009041761 5
7B-5

WBAN State City HDD CDD Vapor Density Count

93990 KS HILL CITY 5569 1047 0.0074465493 3

93992 AR EL DORADO 2620 2112 0.011859783 1

94014 ND WILLISTON 9630 296 0.0053011177 14

94040 NE MC COOK 5994 852 0.0070509402 2

94227 WA SHELTON 5714 176 0.0072152937 2

94240 WA QUILLAYUTE 5885 42 0.0074349835 2

94741 NJ TETERBORO 4884 944 0.0077413683 17

94822 IL ROCKFORD 6656 433 0.0072514877 50

94823 PA PITTSBURGH 5627 631 0.0073981585 6

94847 MI DETROIT 6062 597 0.0072827025 19

94943 SD CHAMBERLAIN 7314 556 0.00654522 26

94950 SD MITCHELL 7295 576 0.0069465882 23

94963 MN MINNEAPOLIS 7592 544 0.0062048826 24

94971 IA ESTHERVILLE 7931 362 0.0066166922 6

94982 IA DAVENPORT 6533 542 0.0075225903 4

94988 IA MARSHALLTOWN 7105 442 0.0073512376 14

94993 SD SISSETON 8780 321 0.0062633651 5
7B-6

REFERENCES

1 U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey: 2009 RECS Survey Data, 2013. (Last accessed March, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/>

2 U.S. Department of Energy: Energy Information Administration, Commercial Buildings 

Energy Consumption Survey, 2003. (Last accessed September, 2013.) 

<http://www.eia.doe.gov/emeu/cbecs/>

3 National Oceanic And Atmospheric Administration, Degree Days Archives, 1997-

Present.

<ftp://ftp.cpc.ncep.noaa.gov/htdocs/products/analysis_monitoring/cdus/degree_days/archi

ves/>

4 American Society for Heating Refrigeration and Air-Conditioning Engineers (ASHRAE), 

ASHRAE Handbook: Fundamentals. 1993. 
8A-i

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND 

PAYBACK PERIOD SPREADSHEET 

TABLE OF CONTENTS

8A.1 DEFINITIONS ........................................................................................................ 8A-1

8A.2 BASIC INSTRUCTIONS ....................................................................................... 8A-3
8A-1

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND PAYBACK 

PERIOD SPREADSHEET

8A.1 DEFINITIONS

The interested reader can examine and reproduce detailed results of the U.S. Department 

of Energy’s (DOE’s) life-cycle cost (LCC) and payback period (PBP) analysis for residential 

dehumidifiers by using Microsoft Excel spreadsheets available on DOE’s website at

http://www1.eere.energy.gov/buildings/appliance_standards/residential/dehumidifiers.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 2010. The LCC and PBP workbook for residential dehumidifiers

comprises the following worksheets.

Summary Presents the results of an analysis in terms of average LCC, LCC

savings, and simple PBP for all dehumidifier product classes. A 

table includes, for each efficiency level considered, installed price; 

lifetime operating cost; LCC average savings; and the percentage 

of customers that would incur a net cost from each standard level. 

The user can stipulate three parameters for a simulation run: 

whether the AEO energy price trend reflects an economic case that 

is reference, low-growth, or high-growth (reference is default); the 

number of simulation runs to be performed within a range of 

1000–10,000 (10,000 is default); and equipment price trend, i.e., 

price based on PPI trend, or constant equipment price. 

LCC & Payback The LCC&Payback worksheet shows LCC and PBP calculation 

results for different efficiency levels for a single Residential

Energy Consumption Survey (RECS) 2009 household. During a 

Crystal Ball simulation, the spreadsheet records the LCC and PBP 

values for every sampled household.

Rebuttable Payback The Rebuttable Payback worksheet contains the installation costs, 

energy use calculations, and the simple PBP calculations for each 

efficiency level.

RECS Sample The RECS Sample worksheet contains the RECS 2009 household 

data for each product type. During a Crystal Ball simulation, DOE 

uses these household characteristics to determine the analysis 

parameters.
8A-2

Weather Data The Weather Data sheet contains the RECS 2009 household data 

matched with the National Oceanic and Atmospheric 

Administration’s National Climatic Data Center (NCDC) weather 

stations data. 

Vapor Density Table Calculates the average vapor density for each month. It also 

calculates the annual operating hours by mode for the sampled 

household with a portable unit dehumidifier. 

Usage Contains probability distribution of operating hours by mode for 

whole-home dehumidifiers. It also contains information of fanonly and standby power demand for each product type. 

Base-Case Efficiency Gives the market shares for efficiency levels in the base case.

Distribution

Equipment Prices Develops total installed cost for dehumidifiers in 2013$. 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 Prices Contains the regional prices in 2013$ for electricity used in the 

LCC and PBP analysis. 

Energy Price Trends Contains the electricity price trends for the reference, high, and 

low economic growth scenarios based on AEO 2015. 

Discount Rate Contains data from which an average discount rate and a 

distribution of discount rates are determined. 

Lifetime Presents the average lifetime, in years, for portable unit and wholehome dehumidifiers, the Weibull parameters used for the survival 

function, and a graph of the Weibull retirement function for 

portable unit and whole-home dehumidifiers, respectively. 

Forecast Cells Gives details regarding base-case efficiency distributions for all 

dehumidifier product classes. Median, minimum, maximum, and 

average values are given, along with 5th, 25th, 50th, 75th, and 95th

percentile values. Included are product prices and details of the 

LCC and PBP (LCC savings in terms of money, energy, and the 

percentages of customers that would experience a net cost, no 

impact, or net savings from each efficiency level). 
8A-3

8A.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. 

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 8B. UNCERTAINTY AND VARIABILITY IN LCC ANALYSIS FOR 

DEHUMIDIFIERS

TABLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 UNCERTAINTY ...........................................................................................................8B-1

8B.3 VARIABILITY ..............................................................................................................8B-1

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY .....................................8B-2

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL.........................8B-2

LIST OF FIGURES

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions .................................................................................................8B-3
8B-1 

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LCC ANALYSIS FOR 

DEHUMIDIFIERS

8B.1 INTRODUCTION

 Analysis of energy conservation standards involves calculations of impacts, for example, 

the impact of a standard on consumer life-cycle cost (LCC). In order to perform the calculation, 

the analyst must first: 1) specify the equation or model that will be used; 2) define the quantities 

in the equation; and 3) provide numerical values for each quantity. In the simplest case, the 

equation is unambiguous (contains all relevant quantities and no others), each quantity has a 

single numerical value, and the calculation results in a single value. However, unambiguity and 

precision are rarely the case. In almost all cases, the model and/or the numerical values for each 

quantity in the model are not completely known (i.e., there is uncertainty) or the model and/or 

the numerical values for each quantity in the model depend upon other conditions (i.e., there is 

variability).

 Thorough analysis involves accounting for uncertainty and variability. While the simplest 

analysis involves a single numerical value for each quantity in a calculation, arguments can arise 

about what the appropriate value is for each quantity. Explicit analysis of uncertainty and 

variability is intended to provide more complete information to the decision-making process.

8B.2 UNCERTAINTY

 When making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy actually consumed by a 

particular appliance type (such as the average U.S. water heater, direct heating equipment, or 

pool heater) is not directly recorded, but rather estimated based upon available information. Even 

direct laboratory measurements have some margin of error. When estimating numerical values 

expected for quantities at some future date, the exact outcome is rarely known in advance.

8B.3 VARIABILITY

 Variability means that different applications or situations produce different numerical 

values when calculating a quantity. Specifying an exact value for a quantity may be difficult 

because the value depends on something else. For example, water heater energy consumption 

depends upon the specific circumstances and behaviors of the occupants (e.g., number of 

persons, length and temperature of showers, etc.). Variability makes specifying an appropriate 

population value more difficult in as much as anyone value may not be representative of the 

entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of 

interest (e.g., hours of use) to other variables that are better known or easier to forecast (e.g., 

persons per household). 
8B-2 

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY 

 This section describes two approaches to uncertainty and variability: 

• scenario analysis, and 

• probability analysis. 

Scenario analysis uses a single numerical value for each quantity in a calculation, then 

changes one (or more) of the numerical values and repeats the calculation. A number of 

calculations are done, which provide some indication of the extent to which the result depends 

upon the assumptions. For example, the life-cycle cost of an appliance could be calculated for 

energy rates of 2, 8, and 14¢ per kWh. 

The advantages of scenario analysis are that each calculation is simple; a range of 

estimates is used and crossover points can be identified. (An example of a crossover point is the 

energy rate above which the life-cycle cost is reduced, holding all other inputs constant. That is, 

the crossover point is the energy rate at which the consumer achieves savings in operating 

expense that more than compensate for the increased purchase expense.) The disadvantage of 

scenario analysis is that there is no information about the likelihood of each scenario. 

 Probability analysis considers the probabilities within a range of values. For quantities 

with variability (e.g., electricity rates in different households), surveys can be used to generate a 

frequency distribution of numerical values (e.g., the number of households with electricity rates 

at particular levels) to estimate the probability of each value. For quantities with uncertainty, 

statistical or subjective measures can be used to provide probabilities (e.g., manufacturing cost to 

improve energy efficiency to some level may be estimated to be $10 ± $3). 

 The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides greater information about 

the outcome of the calculations, that is, it provides the probability that the outcome will be in a 

particular range. 

 Scenario and probability analysis provide some indication of the robustness of the policy 

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a 

wide range of possible conditions. 

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

 To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, 

and payback period (PBP) analyses, DOE used Microsoft Excel spreadsheets combined with 

Crystal Ball, a commercially available add-in, to conduct probability analyses. The probability 

analyses used Monte Carlo simulation and probability distributions. 
8B-3 

NORMAL TRIANGULAR UNIFORM

WEIBULL CUSTOM

Simulation refers to any analytical method meant to imitate a real-life system, especially 

when other analyses are too mathematically complex or too difficult to reproduce. Without the 

aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most 

likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and 

simulation to automatically analyze the effect of varying inputs on outputs of the modeled 

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly 

generates values for uncertain variables again and again to simulate a model. Monte Carlo 

simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos 

containing games of chance. Games of chance such as roulette wheels, dice, and slot machines, 

exhibit random behavior. The random behavior in games of chance is similar to how Monte 

Carlo simulation selects variable values at random to simulate a model. When you roll a die, you 

know that either a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular 

roll. It's the same with the variables that have a known range of values but an uncertain value for 

any particular time or event (e.g., equipment lifetime, discount rate, and installation cost). 

For each uncertain variable (one that has a range of possible values), possible values are 

defined with a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Types of probability distributions include those in Figure 

8B.5.1. 

 

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions

 

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling values 

from the probability distributions for the uncertain variables and using those values for the cell. 

Crystal Ball simulations can consist of as many trials (or scenarios) as desired—hundreds or 

even thousands. During a single trial, Crystal Ball randomly selects a value from the defined 
8B-4 

possibilities (the range and shape of the probability distribution) for each uncertain variable and 

then recalculates the spreadsheet. 
8C-i

APPENDIX 8C. LIFETIME DISTRIBUTIONS 

TABLE OF CONTENTS

8C.1 INTRODUCTION ...................................................................................................8C-1

8C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS .......................8C-1

LIST OF TABLES

Table 8C.2.1 Residential Dehumidifier Lifetime Parameters .............................................8C-2

LIST OF FIGURES 

Figure 8C.2.1 Fraction of Portable Unit Dehumidifier Failing ............................................8C-3

Figure 8C.2.2 Cumulative Lifetime Length of Portable Unit Dehumidifiers .......................8C-3

Figure 8C.2.3 Fraction of Whole-home Dehumidifier Failing .............................................8C-4

Figure 8C.2.4 Cumulative Lifetime Length of Whole-home Dehumidifiers .......................8C-4

 
8C-1

APPENDIX 8C. LIFETIME DISTRIBUTIONS

8C.1 INTRODUCTION

The U.S. Department of Energy (DOE) characterized the lifetime of both product types

of dehumidifiers (i.e. portable unit dehumidifiers, whole-home dehumidifiers) being considered 

for new energy efficiency standards. DOE characterized residential dehumidifier lifetimes using 

a Weibull probability distribution that ranged from the minimum to maximum lifetime estimates, 

as described in chapter 8, section 8.2.2. The Weibull distribution is recommended for evaluating

lifetime data, because it can be shaped to match low, average, and high values. The probability of 

exceeding the high value is contained in the long tail of the Weibull distribution.

1,2

8C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS

Weibull distributions utilize available data to assign low, average, and high values to a 

random variable that has unknown distribution parameters. DOE applied Weibull distributions to 

product lifetime data to derive low, average, and high lifetime values, along with a percentile

containing a high value. A similar approach is described in a technical note to the software 

Crystal Ball, which uses a most likely value in place of an average value.3

 The Weibull 

distribution can be defined as: 

 Where: 

 L = location, 

 α (alpha) = scale, and 

 β (beta) = shape. 

 The cumulative distribution therefore is: 

 Based on available data, Weibull distribution parameters are specified as follows.

1. The output deviates must be greater than the expert opinion of low value. 

2. The average, Xavg, must be equal to the average value from the available data. 

3. The high value, Xb, must correspond to some particular percentile point (e.g., 95 

percent or 90 percent). 
8C-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 (1) specifying a Weibull distribution that 

has the calculated parameters (location, scale, and shape) in an assumption cell, then (2) 

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 portable unit 

dehumidifiers, DOE used lifetime estimates from the previous DOE rulemaking for 

dehumidifiers.4 DOE estimated that portable unit dehumidifier lifetimes do not vary by capacity 

size. DOE assumed whole-home dehumidifiers have the same life span as residential room air 

conditioner. DOE therefore applied the lifetime parameters derived for room air conditioners to 

whole-home dehumidifiers.5 DOE estimated that the maximum lifetime percentile for both 

product types was 99 percent.

Table 8C.2.1 Residential Dehumidifier Lifetime Parameters

Product Type Average 

(Years)

Weibull Parameters

Alpha (Scale) Beta (Shape)

Portable dehumidifiers 11.0 11.00 4.20 

Whole-home 

dehumidifiers 19.01 20.30 2.50 

Figures 8C.2.1 to 8C.2.4 show the Weibull distribution as well as the cumulative Weibull 

distribution for each product type of residential dehumidifiers. 
8C-3

Figure 8C.2.1 Fraction of Portable Unit Dehumidifier Failing

Figure 8C.2.2 Cumulative Lifetime Length of Portable Unit Dehumidifiers

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Porbability of Failure (%)

Age of Equipment (Years)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Cumulative Porbability (%)

Age of Equipment (Years)
8C-4

Figure 8C.2.3 Fraction of Whole-home Dehumidifier Failing

Figure 8C.2.4 Cumulative Lifetime Length of Whole-home Dehumidifiers

0%

1%

1%

2%

2%

3%

3%

4%

4%

5%

5%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Porbability of Failure (%)

Age of Equipment (Years)

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 31 33 35 37 39 41 43 45

Cumulative Porbability (%)

Age of Equipment (Years)
8C-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. 

2 Karr, T. Making the Most of Life Test Data. Appliance Magazine. October 2003. (Last 

accessed March 23, 2010.) 

<www.appliancemagazine.com/ae/editorial.php?article=197&zone=215&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 December 4, 2008.) 

Available from: www.crystalball.com/support/simulation/cbl_gen_021A.html. 

4 DOE–Energy Efficiency and Renewable Energy (EERE). Energy Conservation Program for 

Consumer Products: Technical Support Document: Energy Efficiency Program for Consumer 

Products and Commercial and Industrial Equipment, Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. 2009. Washington, 

DC. (Last accessed October 10, 2013.) 

<http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products_f

inal_rule_tsd.html> 

5 DOE–EERE. Energy Conservation Program for Consumer Products. Technical Support 

Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment, Residential Clothes Dryers and Room Air Conditioners. 2011. 

Washington, D.C. (Last accessed October 25, 2013.) <http://www.regulations.gov/> 
8D-i 

APPENDIX 8D. DISTRIBUTIONS FOR DISCOUNT RATES

TABLE OF CONTENTS

8D.1 INTRODUCTION ........................................................................................................ 8D-1

8D.2 DISTRIBUTIONS OF RATES FOR DEBT CLASSES .............................................. 8D-1

8D.3 DISTRIBUTIONS OF RATES FOR EQUITY CLASSES .......................................... 8D-5

8D.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP ......................................................................................................................... 8D-9

LIST OF TABLES

Table 8D.4.1 Distribution of Real Discount Rates by Income Group ............................ 8D-10

LIST OF FIGURES

Figure 8D.2.1 Distribution of Mortgage Interest Rates ..................................................... 8D-2

Figure 8D.2.2 Distribution of Home Equity Loa Interest Rates ........................................ 8D-2

Figure 8D.2.3 Distribution of Credit Card Interest Rates .................................................. 8D-3

Figure 8D.2.4 Distribution of Installment Loan Interest Rates .......................................... 8D-3

Figure 8D.2.5 Distribution of Other Residence Loan Interest Rates ................................. 8D-4

Figure 8D.2.6 Distribution Other Lines of Credit Loan Interest Rates .............................. 8D-4

Figure 8D.3.1 Distribution of Annual Rates of Return on CDs ......................................... 8D-5

Figure 8D.3.2 Distribution of Annual Rates of Return on Savings Bonds ........................ 8D-6

Figure 8D.3.3 Distribution of Annual Rates of Return on Corporate AAA Bonds ........... 8D-6

Figure 8D.3.4 Distribution of Annual Rates of Return on Savings Accounts ................... 8D-7

Figure 8D.3.5 Distribution of Annual Rates of Return on Money Market Accounts ........ 8D-7

Figure 8D.3.6 Distribution of Annual Rates of Return on the S&P 500 ........................... 8D-8

Figure 8D.3.7 Distribution of Annual Rates of Return on Mutual Funds .......................... 8D-8

Figure 8D.4.1 Distribution of Real Discount Rates by Income Group .............................. 8D-9
8D-1 

APPENDIX 8D. DISTRIBUTIONS FOR DISCOUNT RATES

8D.1 INTRODUCTION

The U.S. Department of Energy (DOE) derived discount rates for the life-cycle cost 

(LCC) analysis using data on interest or return rates for various types of debt and equity to 

calculate a real effective discount rate for each household in the Federal Reserve Board’s Survey 

of Consumer Finances (SCF) in 1995, 1998, 2001, 2004, 2007, and 2010.1 To account for 

variation among households in rates for each of the types, DOE sampled a rate for each 

household in its building sample from a distribution of discount rates for each of six income 

groups. This appendix describes the distributions used. 

8D.2 DISTRIBUTIONS OF RATES FOR DEBT CLASSES 

Figure 8D.2.1 through Figure 8D.2.6 show the distribution of real interest rates for 

different types of household debt. The data source for the interest rates for mortgages, home 

equity loans, credit cards, installment loans, other residence loans, and other lines of credit is the 

Federal Reserve Board’s SCF in 1995, 1998, 2001, 2004, 2007, and 2010.1

 DOE adjusted the 

nominal rates to real rates using the annual inflation rate in each year. 

Using the appropriate SCF data for each year, DOE adjusted the nominal mortgage 

interest rate and the nominal home equity loan interest rate for each relevant household in the 

SCF for mortgage tax deduction and inflation. In cases where the effective interest rate is equal 

to or below the inflation rate (resulting in a negative real interest rate), DOE set the real effective 

interest rate to zero.
8D-2 

Figure 8D.2.1 Distribution of Mortgage Interest Rates 

Figure 8D.2.2 Distribution of Home Equity Loa Interest Rates

0%

5%

10%

15%

20%

25%

30%

35%

0.5%1.6%

2.5%3.5%4.4%5.4%6.3%7.4%8.5%

9.4%

10.4%11.6%12.5%13.2%14.4%16.6%22.8%

Probability

Real Interest Rate

0%

5%

10%

15%

20%

25%

30%

0.4%1.5%2.5%3.5%4.5%5.4%6.4%7.5%8.4%9.4%

10.5%11.5%12.5%13.8%14.7%15.7%16.6%17.1%22.0%

Probability

Real Interest Rate
8D-3 

Figure 8D.2.3 Distribution of Credit Card Interest Rates 

Figure 8D.2.4 Distribution of Installment Loan Interest Rates

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0.0%2.5%4.5%

6.6%

8.6%

10.5%12.5%14.7%16.4%18.4%20.4%22.7%24.7%26.8%28.5%

Probability

Real Interest Rate

0%

5%

10%

15%

20%

25%

30%

35%

40%

0.0%2.4%4.5%6.6%8.5%

10.5%12.5%14.7%16.4%18.4%20.5%22.5%24.6%26.8%28.5%

Probability

Real Interest Rate
8D-4 

Figure 8D.2.5 Distribution of Other Residence Loan Interest Rates

Figure 8D.2.6 Distribution Other Lines of Credit Loan Interest Rates

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0.2%

1.5%2.5%3.5%4.5%

5.5%6.5%7.5%8.6%9.4%

10.4%11.5%12.5%13.1%14.7%

15.7%16.6%

Probability

Real Interest Rate

0%

2%

4%

6%

8%

10%

12%

0.1%2.5%

4.5%

6.6%8.6%

10.5%12.5%14.7%16.4%18.4%20.2%22.6%25.4%

Probability

Real Interest Rate
8D-5 

8D.3 DISTRIBUTIONS OF RATES FOR EQUITY CLASSES

Figure 8D.3.1 through Figure 8D.3.7 show the distribution of real interest rates for 

different types of equity. Data for equity classes are not available from the Federal Reserve 

Board’s SCF, so DOE derived data for these classes from national-level historical data (1984-

2013). The interest rates associated with certificates of deposit (CDs),2 savings bonds,3 and 

AAA corporate bonds4 are from Federal Reserve Board time-series data. DOE assumed rates on 

checking accounts to be zero. Rates on savings and money market accounts are from Cost of 

Savings Index data.5 The rates for stocks are the annual returns on the Standard and Poor’s 

(S&P) 500.6 The mutual fund rates are a weighted average of the stock rates (two-thirds weight) 

and the bond rates (one-third weight) in each year. DOE adjusted the nominal rates to real rates 

using the annual inflation rate in each year. 

 Figure 8D.3.1 Distribution of Annual Rates of Return on CDs

-0.04

-0.02

0

0.02

0.04

0.06

0.08

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

CDs (Average of 12 and 30 month)
8D-6 

Figure 8D.3.2 Distribution of Annual Rates of Return on Savings Bonds 

Figure 8D.3.3 Distribution of Annual Rates of Return on Corporate AAA Bonds 

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

19841986198819901992

1994

199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Savings Bonds

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

AAA Corporate Bonds
8D-7 

Figure 8D.3.4 Distribution of Annual Rates of Return on Savings Accounts 

Figure 8D.3.5 Distribution of Annual Rates of Return on Money Market Accounts

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

19841986

1988199019921994

19961998200020022004200620082010

2012

Annual Rate of Return (%)

Year

Savings Accounts

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Money Market Accounts
8D-8 

Figure 8D.3.6 Distribution of Annual Rates of Return on the S&P 500 

Figure 8D.3.7 Distribution of Annual Rates of Return on Mutual Funds

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

19841986198819901992

1994

199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Stocks (S&P 500)

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

1984

198619881990

19921994

1996199820002002

20042006

200820102012

Annual Rate of Return (%)

Year

Mutual Funds
8D-9 

8D.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP

Figure 8D.4.1 and Table 8D.4.1 presents the distributions of real discount rates for each 

income group. 

Figure 8D.4.1 Distribution of Real Discount Rates by Income Group

0%

5%

10%

15%

20%

25%

0.0% 5.0% 10.0% 15.0% 20.0%

Share of Individuals in Group (%)

Real Effective Discount Rate (%)

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6
8D-10

Table 8D.4.1 Distribution of Real Discount Rates by Income Group 

DR Bin

Income Group 1 Income Group 2 Income Group 3 Income Group 4 Income Group 5 Income Group 6

(1-20 percentile) (21-40 percentile) (41-60 percentile) (61-80 percentile) (81-90 percentile) (90-99 percentile)

rate weight rate weight rate weight rate weight rate weight rate weight

0-1 0.5% 0.238 0.6% 0.152 0.6% 0.104 0.6% 0.077 0.6% 0.056 0.6% 0.057

1-2 1.6% 0.110 1.6% 0.120 1.6% 0.105 1.6% 0.146 1.6% 0.142 1.6% 0.185

2-3 2.5% 0.087 2.5% 0.112 2.6% 0.131 2.5% 0.205 2.5% 0.219 2.5% 0.207

3-4 3.5% 0.117 3.5% 0.137 3.5% 0.164 3.5% 0.173 3.5% 0.200 3.5% 0.178

4-5 4.5% 0.097 4.5% 0.113 4.5% 0.136 4.5% 0.129 4.5% 0.153 4.5% 0.144

5-6 5.5% 0.083 5.5% 0.084 5.5% 0.100 5.5% 0.093 5.5% 0.098 5.5% 0.120

6-7 6.5% 0.058 6.5% 0.062 6.5% 0.075 6.5% 0.067 6.5% 0.063 6.4% 0.079

7-8 7.5% 0.036 7.5% 0.051 7.6% 0.054 7.4% 0.041 7.4% 0.029 7.3% 0.011

8-9 8.5% 0.036 8.4% 0.039 8.4% 0.034 8.5% 0.015 8.4% 0.012 8.5% 0.005

9-10 9.5% 0.017 9.5% 0.018 9.5% 0.017 9.5% 0.010 9.5% 0.008 9.6% 0.005

10-11 10.5% 0.014 10.5% 0.019 10.5% 0.013 10.5% 0.011 10.6% 0.004 10.7% 0.004

11-12 11.5% 0.010 11.5% 0.015 11.5% 0.014 11.5% 0.007 11.4% 0.004 11.7% 0.001

12-13 12.5% 0.011 12.5% 0.012 12.5% 0.009 12.4% 0.005 12.4% 0.002 12.4% 0.002

13-14 13.6% 0.012 13.5% 0.008 13.5% 0.009 13.5% 0.004 13.5% 0.002 13.3% 0.001

14-15 14.6% 0.016 14.6% 0.014 14.6% 0.009 14.5% 0.005 14.6% 0.003 14.2% 0.001

15-16 15.5% 0.011 15.5% 0.010 15.5% 0.006 15.6% 0.004 15.6% 0.002 15.3% 0.000

16-17 16.5% 0.013 16.5% 0.009 16.5% 0.004 16.5% 0.003 16.5% 0.001 0.0% 0.000

17-18 17.5% 0.009 17.6% 0.006 17.5% 0.005 17.5% 0.003 17.6% 0.001 17.7% 0.001

18-19 18.4% 0.005 18.5% 0.005 18.6% 0.003 18.4% 0.001 18.2% 0.000 0.0% 0.000

19-20 19.4% 0.006 19.4% 0.004 19.4% 0.002 19.7% 0.000 19.7% 0.000 19.4% 0.000

20-21 20.6% 0.004 20.4% 0.002 20.5% 0.001 20.3% 0.001 20.5% 0.000 20.3% 0.000

21-22 21.4% 0.003 21.4% 0.002 21.4% 0.001 21.5% 0.001 0.0% 0.000 21.4% 0.000

22-23 22.5% 0.002 22.4% 0.001 22.6% 0.001 22.9% 0.000 22.8% 0.000 22.3% 0.000

23-24 23.6% 0.001 23.4% 0.001 23.6% 0.001 0.0% 0.000 0.0% 0.000 24.0% 0.000

24-25 24.6% 0.001 24.5% 0.000 24.6% 0.000 24.1% 0.000 24.3% 0.000 0.0% 0.000

25-26 25.4% 0.001 25.4% 0.001 25.5% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

26-27 26.5% 0.001 26.5% 0.000 26.4% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

27-28 27.8% 0.000 27.6% 0.000 27.8% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

28-29 28.2% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

29-23 29.9% 0.000 29.3% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

>30 59.1% 0.001 142.7% 0.002 0.0% 0.000 53.3% 0.000 0.0% 0.000 0.0% 0.000
8D-11

REFERENCES

1 The Federal Reserve Board, Survey of Consumer Finances 1995, 1998, 2001, 2004, 

2007, 2010. <http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html> 

2 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis, 2013.

<www.federalreserve.gov/releases/H15/data.htm>

3 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

Monthly, Description: Bond buyer go 20-bond municipal bond index, 2013.

<www.federalreserve.gov/releases/H15/data.htm>

4 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: 

Annual, Description: Moody’s yield on seasoned corporate bonds—all industries, AAA, 

2013. <www.federalreserve.gov/releases/H15/data.htm>

5 Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index 

History, 2013. <http://mortgage-x.com/general/indexes/default.asp>

6 Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and 

Bills - United States, 2013. Damodaran. <http://pages.stern.nyu.edu/~adamodar/> 
9A-i 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR 

APPLIANCES 

TABLE OF CONTENTS

9A.1 INTRODUCTION .................................................................................................. 9A-1

9A.2 LITERATURE REVIEW ....................................................................................... 9A-1

9A.2.1 Price ........................................................................................................................ 9A-1

9A.2.2 Income..................................................................................................................... 9A-1

9A.2.3 Appliance Efficiency and Discount Rates .............................................................. 9A-2

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS .................................................. 9A-2

9A.3.1 Physical Household/Appliance Variables ............................................................... 9A-3

9A.3.2 Economic variables ................................................................................................. 9A-3

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS .......................................................................................................... 9A-4

9A.4.1 Broad Trends ........................................................................................................... 9A-5

9A.4.2 Specification of Model ............................................................................................ 9A-7

9A.4.3 Discussion of Model ............................................................................................... 9A-8

9A.4.4 Analysis Results ...................................................................................................... 9A-8

9A.4.4.1 Individual Appliance Model ................................................................. 9A-9

9A.4.4.2 Combined Appliance Model ................................................................. 9A-9

9A.4.5 Additional Regression Specifications and Results ............................................... 9A-10

9A.4.5.1 Lower Consumer Discount Rate ......................................................... 9A-10

9A.4.5.2 Disaggregated Variables ..................................................................... 9A-11

9A.5 LONG RUN IMPACTS ........................................................................................ 9A-12

9A.6 SUMMARY .......................................................................................................... 9A-13

9A.7 DATA USED IN THE ANALYSIS ..................................................................... 9A-14

LIST OF TABLES

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales ................................................................ 9A-2

Table 9A.3.1 Physical Household/Appliance Variables ................................................... 9A-3

Table 9A.3.2 Economic Variables .................................................................................... 9A-4

Table 9A.4.1 Estimate of Total Price Elasticity of Demand............................................. 9A-6

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand ......... 9A-7

Table 9A.4.3 Individual Appliance Model Results .......................................................... 9A-9

Table 9A.4.4 Combined Appliance Model Result ............................................................ 9A-9

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate..................... 9A-11

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate .................... 9A-12
9A-ii

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change .............................................................................. 9A-13

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change .............................................................................. 9A-13

LIST OF FIGURES 

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements ............ 9A-4

Figure 9A.4.1 Residual Shipments and Appliance Price ................................................... 9A-6
9A-1 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES 

9A.1 INTRODUCTION

 This appendix summarizes DOE’s study of the price elasticity of demand for home 

appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular 

set of appliances because of the availability of data to determine a price elasticity. Section 9A.2 

reviews the existing economics literature describing the impact of economic variables on the sale 

of durable goods. Section 9A.3 describes the market for home appliances and the changes that 

have occurred over the past 20 years. In section 9A.4, DOE summarizes the results of its

regression analysis and presents estimates of the price elasticity of demand for the three 

appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price 

elasticity. DOE’s interpretation of its results is presented in section 9A.6. Finally, section 9A.7 

describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

 Relatively few studies measure the impact of price, income and efficiency on the sale of 

household appliances. This section briefly reviews the literature that describes the likely 

importance of these variables on the purchase of household appliances.

9A.2.1 Price

 DOE reviewed many studies that sought to measure the impact of price on sales in a 

dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of 

demand to decline over time. The author explains this as the result of buyers delaying purchases 

after a price increase but eventually making the purchase (Table 9A.2.1).1 A contrasting study of 

household white goods also prior to 1970, finds the elasticity of demand to increase over time as 

more price-conscious buyers enter the market.2

 An analysis of refrigerator market survey data 

finds that consumer purchase probability decreases with survey asking price.3 Estimates of the 

price elasticity of demand for different brands of the same product tend to vary. A review of 41 

studies of the impact of price on market share found the average price elasticity to be -1.75.4 The 

average estimate of price elasticity of demand reported in these studies is -0.33 in the appliance 

market and -0.47 in the combined automobile and appliance markets. 

9A.2.2 Income

 Higher income households are more likely to own household appliances.5 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets.1, 2

 The average income elasticity of demand is 0.50 in the appliance study 

cited in the literature review, much larger in the automobile study (Table 9A.2.1).
9A-2 

9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumers’ choice of

appliance. Typically, this impact is summarized by the implicit discount rate; that is, the rate 

consumers use to compare future savings in appliance operating costs against a higher initial 

purchase price of an appliance. One early and much cited study concludes that consumers use a 

20 percent implicit discount rate when purchasing room air conditioners (Table 9A.2.1).6 A 

survey of several studies of different appliances suggests that the consumer implicit discount rate 

has a broad range and averages about 37 percent.

7

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile 

and Appliance Sales

Durable Good Price 

Elasticity

Income 

Elasticity

Brand 

Price 

Elasticity

Implicit 

Discount 

Rate

Model Data 

Years

Time 

Period

Automobiles1 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run

Automobiles1 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run

Clothes Dryers2 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed

Room Air 

Conditioners2 -0.378 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers2 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed

Refrigerators3 -0.37 - - 39% Logit probability, survey 

data 1997 Short run

Various4 - - -1.769 - Multiplicative regression - Mixed

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey data - - 

Household 

Appliances7 - - - 37%10 Assorted - - 

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS

 In this section DOE evaluates variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical household/appliance variables and 

economic variables.
9A-3 

9A.3.1 Physical Household/Appliance Variables

Several variables influence the sale of refrigerators, clothes washers and dishwashers. 

The most important for explaining appliance sales trends are the annual number of new 

households formed (housing starts) and the number of appliances reaching the end of their 

operating life (replacements). Housing starts influence sales because new homes are often 

provided with, or soon receive, new appliances, including dishwashers and refrigerators. 

Replacements are correlated with sales because new appliances are typically purchased when old 

ones wear out. In principle, if households maintain a fixed number of appliances, shipments 

should equal housing starts plus appliance replacements. 

9A.3.2 Economic variables

 Appliance price, appliance operating cost and household income are important economic 

variables affecting shipments. Low prices and costs encourage household appliance purchases 

and a rise in income increases householder ability to purchase appliances. In principle, changes 

in economic variables should explain changes in the number of appliances per household. 

 During a 1980–2002 study period, annual shipments grew 69 percent for clothes washers, 

81 percent for refrigerators and 105 percent for dishwashers (Table 9A.3.1). This rising 

shipments trend is explained in part by housing starts, which increased 6 percent and by 

appliance replacements, which rose between 49 percent and 90 percent, depending on the 

appliance, over the period (Table 9A.3.1).a For mature markets such as these, replacements 

exceed appliance sales associated with new housing construction. 

Table 9A.3.1 Physical Household/Appliance Variables 

Shipmentsi(millions) Housing Startsii (millions) Replacementsiii (millions)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90%

i

Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine.

iiHousing Starts: Annual number of new homes constructed. Source: U.S. Census.

iiiReplacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.

 Shipments increased somewhat more rapidly than housing starts and replacements. This 

is shown by comparing the beginning and end points of lines that represent “starts plus 

replacements” (uppermost solid line in Figure 9A.3.1) and “shipments” (diamond linked line in 

Figure 9A.3.1). In 1980 the “shipment” line begins below the “starts plus replacements” line. In 

2002, the “shipments” line ends above the “starts plus replacements” line. This more rapid 

 

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes 

washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in 

section 9-A.3. The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary 

analysis. 
9A-4 

increase in shipments, compared to housing starts plus replacements, suggests that the appliance 

per household ratio increased over the study period. 

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and 

Replacements

 Economic variables, including price, cost and income, may explain this increase in 

appliances per household. Over the period, appliance prices decreased 40 percent to 50 percent, 

operating costs fell between 33 percent and 72 percent, and median household income rose 16 

percent (Table 9A.3.2). 

Table 9A.3.2 Economic Variables

Pricei(1999$) Operating Costii (1999$) Household Incomeiii (1999$)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16%

Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16%

Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16%

i

Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine.

iiOperating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book.

iiiIncome: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE

SHIPMENTS

Few data are available to estimate the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete—appliance energy use data are available 

for only 12 years of the 1980-2002 study period. Industry price data are also incomplete—

available for only 8 years of the study period for each of the appliances. 

Refrigerator Shipments

0

1

2

3

4

5

6

7

8

9

10

1980 1984 1988 1992 1996 2000

Millions 

New Housing

Retirements

Shipments

Clothes Washer Shipments

0

1

2

3

4

5

6

7

8

1980 1984 1988 1992 1996 2000

Millions 

Dishwasher Shipments

0

1

2

3

4

5

6

1980 1984 1988 1992 1996 2000

Millions 
9A-5 

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. This section begins by describing broad trends 

apparent in the economic and physical household data sets and then specifies a simple regression 

model to measure these trends, making assumptions to minimize the number of explanatory 

variables. Finally, results of the regression analysis are presented along with an estimate of the 

price elasticity of demand for appliances. In section 9-A.4.5, DOE presents the results of 

regression analysis performed with more complex models, which are used to test assumptions 

underlying the simple model. These results support the specification of the simple model and the 

price elasticity of appliance demand estimated with that model. 

9A.4.1 Broad Trends 

In this section DOE reviews trends in the physical household and economic data sets and 

posits a simple approach for estimating the price elasticity of appliance demand. As noted above, 

the physical household variables (housing starts and appliance replacements) explain most of the 

variability in appliance shipments during the study period (1980-2002).b DOE assumes the rest 

of the variability in shipments (referred to as “residual shipments”) is explained by economic 

variables. Below, DOE presents a tabular method for measuring price elasticities. 

To illustrate this tabular approach, DOE defines two new variables—residual shipments 

and total price. Residual shipments are defined as the difference between shipments and physical 

household demand (starts plus replacements). Total price, represented by the following equation, 

is defined as appliance price plus the present value of lifetime appliance operating cost:

c

 

PPTP += PVOC

where:

TP = Total price,

PP = Appliance purchase price, and

PVOC = Present value of operating cost. 

Over the study period, residual shipments increased in proportion to total shipments by 

30 percent for refrigerators, 19 percent for clothes washers, and 23 percent for dishwashers. At 

the same time, total prices declined 47 percent, 45 percent and 48 percent for refrigerators, 

clothes washers, and dishwashers, respectively. Assuming that total price explains the entire 

change in per household appliance usage, a rough estimate is calculated of the total price 

 

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two 

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer 

shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19-year operating life for refrigerators, 14-year operating life 

for clothes washers, and a 12-year operating life for dishwashers. A 37 percent discount rate is used to sum annual 

operating costs into a present value operating cost.
9A-6 

elasticity of demand equal to -0.48 for refrigerators, -0.32 for clothes washers and -0.37 for 

dishwashers (Table 9A.4.1). 

Table 9A.4.1 Estimate of Total Price Elasticity of Demand 

Residual Shipments (millions) Total Price (1999$)

Appliance 1980 2002 Difference Change 1980 2002 Change Elasticity

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

The negative correlation between total price and residual shipments suggested by these negative 

price elasticities is illustrated in a graph of residual shipments on the y-axis and total price on the 

x-axis (Figure 9A.4.1).

Yellow points are observed price data; red points are interpolated price data.

Figure 9A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining a third variable, termed 

relative price, which is calculated as total price divided by household income and represented by 

the following equation.d 

Income

TP

RP =

where:

RP = Relative price,

TP = Total price, and

 

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand 

cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an 

elasticity less negative than price elasticity alone.

Refrigerators

400

800

1200

1600

2000

-2.0 -1.0 0.0 1.0 2.0

Total Price

Residual Shipments 

Clothes Washers

400

600

800

1000

1200

-1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 

Dishwashers

200

400

600

800

1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 
9A-7 

Income = Household income. 

The percent decline in relative price for the three appliances divided by the percent 

decline in residual shipments suggests a rough estimate of relative price elasticity equal to -0.40 

for refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (Table 9A.4.2). 

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand 

Residual Shipments (millions) Relative Price (1999$)

Appliance 1980 2002 Change 1980 2002 Change Elasticity

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

9A.4.2 Specification of Model

The limited price data suggest it is appropriate to use a simple regression model to 

estimate the impact of economic variables on shipments, using few explanatory variables. The 

following equation, chosen for this analysis, includes one physical household variable (housing 

starts plus replacements) and one relative price variable (the sum of purchase price plus 

operating cost, divided by income). 

Ship cRPba [Starts +×+×+= Rplc] Eq. 9A.1

where:

Ship = Quantity of appliance sold, 

RP = Relative price,

Starts = Number of new homes, and 

Rplc = Number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with the 

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative

price elasticity of demand for the three appliances. 

 DOE used the following combined regression equation to estimate an average price 

elasticity of demand across the three appliances, using pooled data in a single regression. A 

combined regression specification is justified, given the limited data available and the similarity 

in price and shipment behavior across appliances (see Figure 9A.4.1). Thus, the model 

represented by the combined regression equation is considered the basic model in DOE’s

analysis of appliance shipments. 

 Ship cRPba [Starts ] eCWdRplc ×+×++×+×+= DW Eq. 9A.2
9A-8 

where:

CW = Quantity of clothes washers sold, and 

DW = Quantify of dishwashers sold. 

9A.4.3 Discussion of Model 

 The most important assumption used to specify this model is that changes in economic 

variables over the study period—income, price, and operating cost—are responsible for all 

observed growth in residual appliance shipments. In other words, DOE assumes no impact from 

other possible factors, such as changing consumer preferences or increases in the quality of 

appliances. This assumption seems unlikely, but without additional data, the impact of this 

assumption on the price elasticity of demand cannot be measured. DOE effectively assumes that 

changes in consumer preferences and appliance characteristics, while affecting which models are 

purchased, have relatively little impact on the total number of appliances purchased in a year. 

 Three additional assumptions used to specify this model deserve comment. The relative

price variable is specified in the model, assuming that (1) the correct implicit discount rate is 

used to combine appliance price and operating cost and that (2) rising income has the same 

impact on shipments as falling total price. The “starts + replacements” variable is specified, 

assuming (3) that starts and replacements have similar impacts on shipments. 

 To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations 9A.1 and 9A.2. The results of this analysis, 

presented in section 9A.4.5, indicate that the elasticity of relative price is fairly insensitive to 

changes in the discount rate. 

 To investigate the second and third assumptions, DOE specified a regression model 

separating income from total price and replacements from starts, thereby adding two additional 

explanatory variables to the basic model as shown in the following equation: 

baShip TP Inconec eStartd Rplc gCWf ×+×+×+×+×+×+= DW Eq. 9A.3

 The results of the regression analysis of this model are presented in section 9A.4.5. These 

results suggest that the elasticity of total price (coefficient b) is relatively insensitive to changes 

in the treatment of income and “starts + replacements” in the model. 

9A.4.4 Analysis Results

 The following sections describe results of analyses using both the individual and 

combined models for appliances and the effects of a lower consumer discount rate and 

disaggregated variables.
9A-9 

9A.4.4.1 Individual Appliance Model

 The individual appliance regression equations are specified in the following equation. 

Ship cRPba [Starts +×+×+= Rplc]

 In regression analysis of this model, the elasticity of relative price (b) is estimated to be

-0.40 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 9A.4.3), 

averaging -0.35. These elasticities are similar to those reported in the literature survey for 

appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a 

tabular approach (Table 9A.4.2). 

The estimated coefficient associated with the “starts + replacements” variable is close to 

one. A coefficient equal to one for this variable would imply that, holding economic variables 

constant, shipments increase in direct proportion to an increase in “starts + replacements.” The 

high R-squared values (above 95) and t-statistics (above 5) in the results provide a measure of 

confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individual Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78

Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model

 The combined appliance regression equation is specified in the following equation. 

Ship cRPba [Starts ] eCWdRplc ×+×++×+×+= DW

This regression analysis indicates that the model fits the existing shipments data well 

(high R-squared) and that the variables included in the model are statistically significant (Table 

9A.4.4). Estimated with this model, the elasticity of relative price is -0.34, close to the average 

value estimated in the individual appliance models (-0.35). It is also similar to elasticity 

estimates reported in the literature survey and calculated using the tabular approach in Table 

9A.4.2. 

Table 9A.4.4 Combined Appliance Model Result

Variable Coefficient t-statistic

Intercept -1.60 -15.54

Relative Price -0.34 -10.74
9A-10

Starts + Replacements 1.21 13.95

CW -0.20 -9.04

DW -0.32 -6.58

R2 0.983

Observations 69

9A.4.5 Additional Regression Specifications and Results

 As described in section 9A.4.3, DOE used three assumptions to specify its appliance 

models. The first, made to aggregate appliance price and operating cost, is that the implicit price 

variable in the basic regression model is specified using a 37 percent implicit discount rate. The 

second states that the implicit price variable is defined assuming that rising income has the same 

impact on shipments as falling total price. The third states that the “starts + replacements” 

variable is defined assuming that housing starts have a similar impact on shipments as appliance 

replacements. 

9A.4.5.1 Lower Consumer Discount Rate

 To investigate the first assumption about discount rates, DOE calculated “present value

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations 9A.1 and 9A.2. The estimated coefficient 

associated with the relative price variable in these regressions is almost identical to the 

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The 

elasticity of relative price calculated using a 20 percent discount rate is -0.33 in the combined 

regression and averages -0.35 for the three appliances (Table 9A.4.5). The elasticity of price 

calculated using a 37 percent discount rate is -0.34 in the combined regression and averages -

0.35 for the three appliances. DOE concludes from this analysis that the elasticity of relative

price is fairly insensitive to changes in the discount rate. 
9A-11

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

9A.4.5.2 Disaggregated Variables 

 To investigate the second and third assumptions, DOE constructed a regression model 

that separates income from total price and replacements from starts, thus adding two additional 

explanatory variables to the basic model (as shown earlier as Eq. 9A.3 and shown below).

baShip TP eStartdIncomec Rplc gCWf ×+×+×+×+×+×+= DW

The estimated coefficient associated with the total price variable in these regressions is 

almost identical to the coefficients estimated for the relative price variable reported above. The 

elasticity of total price in the above equation is -0.36 in the combined appliance regression and 

averages -0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on 

the model described in equation 9A.2 is -0.34 in the combined regression (Table 9A.4.4) and 

averages -0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price 

elasticity calculated in this analysis is relatively insensitive to the specification of household 

income and “starts + replacements” variables in the model.

Three Appliances

Variable Coefficient t-Stat

Intercept -1.53 -14.61

Total Price / Income -0.33 -10.69

Starts + Retirements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R2 0.982

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Total Price / Income -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts + Retirements 1.04 5.73 1.06 5.83 1.34 11.64

R2 0.953 0.950 0.977

Observations 23 23 23
9A-12

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

 As noted above in Table 9A.2.1, the literature review provides price elasticities over short 

and long time periods, also referred to as short run and long run price elasticities. As noted in the 

first two rows of Table 9A.2.1, one source (i.e., Hymans) shows that the price elasticity of 

demand is significantly different over the short run and long run for automobiles.1 Because 

DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time 

period, consideration must be given to how the relative price elasticity is affected once a new 

standard takes effect. 

 DOE considers the relative price elasticities determined above in section 9A.4 to be short 

run elasticities. DOE was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short run and long run price elasticities differ. Therefore, to estimate 

how the relative price elasticity changes over time, DOE relied on the Hymans study pertaining 

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price 

elasticity of demand changes in the years following a purchase price change. With increasing 

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal 

value around the tenth year after the price change.

Three Appliances

Variable Coefficient t-Stat

Intercept -2.92 -1.26

Income 0.58 2.92

Total Price -0.36 -7.06

Housing Starts 0.44 10.02

Retirements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R2 0.985

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18

Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R2 0.984 0.958 0.979

Observations 23 23 23
9A-13

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33 

Source: Hymans, 1971.

 Based on the relative change in the automobile price elasticity of demand shown in Table 

9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 

9A.5.2 presents the time series. 

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33 

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9A.6 SUMMARY

This appendix describes the results of a literature search, tabular analysis, and regression 

analyses of the impact of price and other variables on appliance shipments. In the literature, DOE

found only a few studies of appliance markets that are relevant to this analysis and no studies 

after 1980 using time series price and shipments data. The information that can be summarized 

from the literature suggests that the demand for appliances is price inelastic. Other information in 

the literature suggests that appliances are a normal good, such that rising incomes increase the 

demand for appliances. Finally, the literature suggests that consumers use relatively high implicit 

discount rates, when comparing appliance prices and appliance operating costs. 

There are too few price and operating cost data available to perform complex analysis of 

dynamic changes in the appliance market. In this analysis, DOE used data available for 

refrigerators, clothes washers, and dishwashers to evaluate broad market trends and perform 

simple regression analysis. 

These data indicate an increase in appliance shipments and a decline in appliance price 

and operating cost over the study period 1980-2002. Household income has also risen during this 

time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed relative price, and used that variable in a tabular analysis of market trends and a 

regression analysis.
9A-14

DOE’s tabular analysis of trends in the number of appliances per household suggests that 

the price elasticity of demand for the three appliances is inelastic. Our regression analysis of 

these same variables suggests that the relative price elasticity of demand is -0.34. The price 

elasticity is consistent with estimates in the literature. Nevertheless, DOE stresses that the 

measure is based on a small data set, using very simple statistical analysis. More important, the 

measure is based on an assumption that economic variables, including price, income and 

operating costs, explain most of the trend in appliances per household in the United States since 

1980. Changes in appliance quality and consumer preferences may have occurred during this 

period, but they are not accounted for in this analysis. 

9A.7 DATA USED IN THE ANALYSIS

• Appliance Shipments are defined as the annual number of units shipped in millions. These 

data were collected from the Association of Home Appliance Manufacturers (AHAM)

8, 9

 and 

Appliance Magazine10 as annual values for each year, 1980–2002. AHAM was used for the 

period 1989–2002 while Appliance Magazine was used for the period 1980–1988.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999 

dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected 

from AHAM Fact Books.

11 Price values for other years were interpolated from these eight 

years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25 

reports) as annual values for each year, 1980–2002.12

• Replacements, driven by equipment retirements, are estimated with the assumption that 

some fraction of sales arise from consumers replacing equipment at the end of its useful life. 

Since each appliance has a different expected lifespan (19 years for refrigerators,13 14 years 

for clothes washers,14 12 years for dishwashers15), replacements are calculated differently for 

each appliance type. Replacements are estimated as the average of shipments 14–24 years 

previous for refrigerators, 9–19 years previous for clothes washers, and 7–17 years previous 

for dishwashers. Historical shipments data were collected from AHAM and Appliance 

Magazine.

• Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in 

kilowatt-hours. Electricity consumption depends on appliance capacity and efficiency. These 

data were provided by AHAM for 1980, 1990–1997 and 1999–2002.9 Data were interpolated 

in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its 

expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are 

assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent6 and 37 
9A-15

percent16 were used, producing similar estimates of price elasticity. A study by Hausman 

recommended a discount rate of “about 20 percent” in its introduction and presented results 

ranging from 24.1 percent to 29 percent based on his calculations for room air conditioners. 

A study by Train suggests a range of implicit discount rates averaging 35 percent for 

appliances.

• Income: Median annual household income in 2003 dollars. These data were collected for 

each year, 1980–2002, from Table H-6 of the U.S. Census.17
9A-16

REFERENCES

1 Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity, 1971. 1971(1): pp. 234-239 

2 Golder, P. N. and G. J. Tellis, Beyond Diffusion: An Affordability Model of the Growth 

of New Consumer Durables. Journal of Forecasting, 1998. 17 

3 D. Revelt and K. Train, Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level. Review of Economics and Statistics, 1997 

4 Tellis, G., The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales. Journal of Marketing Research, 1988

5 U.S. Department of Energy - Energy Information Agency, The Effect of Income on 

Appliances in U.S. Households, 2002. Washington, DC. 

<http://www.eia.doe.gov/emeu/recs/appliances/appliances.html> 

6 Hausman, J., Individual discount rates and the purchase and utilization of energy using 

durables. The Bell Journal of Economics, 1979 

7 Train, K., Discount Rates in Consumers' Energy-Related Decisions. Energy: The 

International Journal, 1985. 10(12) 

8 Association of Home Appliance Manufacturers, AHAM 2000 Fact Book. 2000. 

Washington, DC. 

9 Association of Home Appliance Manufacturers, AHAM 2003 Fact Book. 2003. 

Washington, DC. 

10 Statistical Review. Appliance Magazine, April, 1990 

11 Association of Home Appliance Manufacturers, AHAM Fact Books, 1987, 1988, 1993, 

1995, 2000, and 2003. Washington, DC. 

12 U.S. Census Bureau, Current Construction Reports, C25 – New One-Family Houses 

Sold. Washington, DC. <https://www.census.gov/prod/www/construction_housing.html> 

13 Duemling, R., Energy and Resources Group, University of California. Berkeley, 1999. 

14 U.S. Department of Energy-Office of Building Research and Standards, Final Rule 

Technical Support Document (TSD): Energy Efficiency Standards for Consumer 

Products: Clothes Washers, 2000. U.S. Department of Energy. Washington, DC. Report 

No. LBNL-47462. 

<http://www.eren.doe.gov/buildings/codes_standards/reports/cwtsd/index.html> 
9A-17

15 U.S. Department of Energy-Office of Codes and Standards, Technical Support 

Document: Energy Conservation Standards for Consumer Products: Dishwashers, 

Clothes Washers, and Clothes Dryers including: Environmental Impacts, Regulatory 

Impact Analysis, December, 1990. Washington, DC. Report No. DOE/CE-0299P. 

 < http://energy.gov/sites/prod/files/nepapub/nepa_documents/RedDont/EA-0386-FEA1989.pdf > 

16 K.E. Train and T. Atherton, Rebates, loans, and customers’ choice of appliance efficiency 

level: combining stated- and revealed-preference data. The Energy Journal, 1995 

17 U.S. Census Bureau, Income, Historical Income Tables – Households, Table H-6. 

Washington, DC. 

<https://www.census.gov/hhes/www/income/data/historical/household/> 
10A-i

APPENDIX 10A. USER INSTRUCTIONS FOR NATIONAL IMPACT ANALYSIS

SPREADSHEET MODEL

TABLE OF CONTENTS

10A.1 USER INSTRUCTIONS ...................................................................................... 10A-1

10A.2 STARTUP ............................................................................................................. 10A-1

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS ........ 10A-1

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEETS ............................................................................ 10A-2
10A-1 

APPENDIX 10A. USER INSTRUCTIONS FOR NATIONAL IMPACT ANALYSIS

SPREADSHEET MODEL

10A.1 USER INSTRUCTIONS

 The results obtained in this analysis can be examined and reproduced using the Microsoft 

Excel® spreadsheets accessible on the Internet from the Department of Energy’s (DOE's) 

residential dehumidifier rulemaking page:

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=62. From 

that page, follow the links to the Preliminary Analysis phase of the rulemaking and then to the 

analytical tools. 

10A.2 STARTUP

The NIA spreadsheets enable the user to perform a National Impact Analysis (NIA) for 

residential dehumidifiers. To utilize the spreadsheet, the Department assumes that the user has 

access to a PC with a hardware configuration capable of running Windows 2010 or later. To use 

the NIA spreadsheets, the user requires Microsoft Excel® 2010 or later installed under the 

Windows operating system. 

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS

The NIA spreadsheets perform calculations to project the change in national energy use 

and net present value of financial impacts due to revised energy efficiency standards. The energy 

use and associated costs for a given standard level are determined by calculating the shipments 

and then calculating the energy use and costs for all dehumidifiers shipped under that standard. 

The differences between the standards and base case can then be compared and the overall 

energy savings and net present values determined. The NIA spreadsheets consist of the following 

worksheets: 

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 residential dehumidifiers

and the associated incremental prices. This sheet also contains 

efficiency weighted average energy use and equipment price for 

the base and standards cases for all the product classes.

Efficiency 

Distribution

This sheet contains base and standards case efficiency trends for 

each product class. 

Historical 

Shipment This sheet contains data for historical sales of the equipment. 

Base Case 

This sheet calculates the estimation of shipments for selected 

product class. The sheet starts with the stock accounting of the 

equipment and uses the survival function to calculate the surviving 
10A-2 

stock. It then performs calculations of replacements, and 

shipments going into new ownership, thus yielding total shipments 

for selected product class. It also calculates the energy 

consumption and operating cost for selected product class.

Standards Case

This sheet estimates shipments for selected product class by taking 

price elasticity into account for the standards case. It also 

calculates the energy savings and cost savings. The energy and 

cost savings in a single year are the difference between the base 

case energy use and costs for that year and the standard case 

energy use and costs in the same year.

Housing 

Projection

This sheet includes Annual Energy Outlook (AEO) projection of 

housing stocks and housing starts for residential buildings.

Electricity Prices This worksheet contains projected average electricity for the three 

economic growth scenarios.

Learning Rate It includes the learning multipliers to adjust the manufacturer’s 

cost over the entire analysis period.

Site-to-Power 

Plant

The sheet contains the marginal site-to-power plant and upstream 

to power plant conversion factors that are used in the source and 

FFC energy savings calculations, respectively.

Lifetime This sheet contains the lifetime and the retirement function for 

each product class.

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEETS

Basic instructions for operating the NIA spreadsheets are as follows:

1. Once the NIA spreadsheet file has been downloaded from the Department’s web site, 

open the file using MS Excel. Click “Enable Macro” when prompted and then click on 

the tab for the worksheet User Inputs. 

2. Use MS Excel's View/Zoom commands at the top menu bar to change the size of the 

display to make it fit your monitor.

3. The user can change the parameters in the sheet “NIA Summary”. The default 

parameters are:

(a) Discount Rate: Set to 7%. To change value, click on the drop-down arrow and 

interchange value (7% or 3%).

(b) Product Class: To change product class, click on the drop-down arrow and 

interchange value (Product Class 1, 2, 3, 4, or 5).
10A-3 

(c) Trial Standards Level: To change standards level, click on the drop-down arrow

and interchange value (TSL 1, 2, 3, or 4).

(d) Relative Price Elasticity: To change the applicable elasticity, use the drop-down 

arrow and select the desired value (No impact or RP elasticity = - 0.34) 

(e) Economic Growth: To change the value, use the drop-down arrow and select the 

desired Growth level (Reference, Low, or High).

(f) Learning Sensitivity: To change value, use the drop-down arrow and select the 

desired learning level (Default, High, or Low).

(g) Current Year: Set to 2014. To change the value, click on cell D6 and change to 

desired year.

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. 
10B-i 

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 METHODOLOGY ......................................................................................................10B-1

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE .....................................10B-3

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs ........................................10B-3

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014).............10B-3
10B-1 

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

10B.1 INTRODUCTION

 This appendix summarizes the methods the U.S. Department of Energy (DOE) used to 

calculate the full-fuel-cycle (FFC) energy savings estimated from potential standards for 

residential dehumidifiers. The FFC measure includes point-of-use (site) energy; the energy losses 

associated with generation, transmission, and distribution of electricity; and the energy 

consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s 

method of analysis previously encompassed only site energy and the energy lost through 

generation, transmission, and distribution of electricity. In 2011 DOE announced its intention, 

based on recommendations from the National Academy of Sciences, to use FFC measures of 

energy use and emissions when analyzing proposed energy conservation standards.1 This 

appendix summarizes the methods DOE used to incorporate impacts of the full fuel cycle into 

the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources 

of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to 

the heat content (British thermal units [Btu]) of the primary fuel used to produce an end-use 

service. Site energy use is defined as the energy consumed at the point of use in a house or 

establishment. When natural gas or petroleum fuels are consumed at the site (for example in an 

on-site furnace), site energy is identical to primary energy, with both equal to the heat content of 

the primary fuel consumed. 

 

For electricity generated by an off-site power plant, site energy is measured in kilowatthours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary 

energy required to generate and deliver electricity to the site. For the FFC analysis, upstream 

energy use is defined as the energy consumed in extracting, processing, and transporting or 

distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in full-fuel-cycle analysis must distinguish between electricity generated by fossil 

fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For 

the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant. 

There is no upstream component for the latter, because no fuel per se is used. 

10B.2 METHODOLOGY 

The mathematical approach to determining FCC is discussed in Coughlin (2012).2 Details 

on analyzing the fuel production chain are presented in Coughlin (2013).3 The methods used to 

calculate FFC energy use are summarized here. When all energy quantities are normalized to the 

same units, FFC energy use can be represented as the product of the primary energy use and an 

FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that 
10B-2 

represent the energy intensity and material losses at each stage of energy production. Those 

parameters depend only on physical data, so the calculations require no assumptions about prices 

or other economic factors. Although the parameter values often differ by geographic region, this 

analysis utilizes national averages. 

The fuel cycle parameters are defined as follows.

• ax is the quantity of fuel x burned per unit of electricity produced, on average, for grid 

electricity. The calculation of ax includes a factor to account for losses incurred 

through the transmission and distribution systems. 

• by is the amount of grid electricity used in producing fuel y, in MWh per physical unit 

of fuel y. 

• cxy is the amount of fuel x consumed in producing one unit of fuel y. 

• qx is the heat content of fuel x (MBtu/physical unit). 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x 

produced). 

All the parameters are calculated as functions of an annual time step; hence, when 

evaluating the effects of potential new standards, a time series of annual values is used to 

estimate the FFC energy and emissions savings in each year of the analysis period and 

cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor qx. 

To convert electricity in kWh to primary energy units, on-site electricity consumption is 

multiplied by the site-to-power-plant energy use factor, described in chapter 10. The site-topower-plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its 

generation. The multipliers are dimensionless numbers applied to primary energy savings to 

obtain the FFC energy savings. The upstream component of the energy savings is proportional to 

(μ-1). The fuel type is denoted by a subscript on the multiplier μ. 

When DOE estimates energy savings attributable to appliance standards, the method for 

performing the full-fuel-cycle analysis utilizes data and projections published in the Annual 

Energy Outlook (AEO); in the case of residential dehumidifiers, the AEO2014.

4 Table 10B.2.1

summarizes the AEO2014 data used as inputs to the calculation of various parameters. The 

column titled “AEO Table” gives the name of the table that provided the reference data.
10B-3 

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter(s) Fuel(s) AEO Table Variables

qx All Conversion factors MMBtu per physical unit

ax All

Electricity supply, disposition, 

prices, and emissions Generation by fuel type 

Energy consumption by sector 

and source

Electric energy consumption 

by the power sector

bc, cnc, cpc Coal

Coal production by region and 

type

Coal production by type and 

sulfur content

bp, cnp, cpp Petroleum

Refining industry energy 

consumption Refining-only energy use 

Liquid fuels supply and 

disposition Crude supply by source 

International liquids supply and 

disposition Crude oil imports 

Oil and gas supply Domestic crude oil production

cnn Natural gas

Oil and gas supply U.S. dry gas production

Natural gas supply, disposition, 

and prices Pipeline, lease, and plant fuel 

zx All

Electricity supply, disposition, 

prices, and emissions Power sector emissions

 The AEO2014 does not provide all the information needed to estimate total energy use in 

the fuel production chain. Coughlin (2013) describes the additional data sources needed to 

complete the analysis. The time dependence in the FFC multipliers for dehumidifiers, however, 

arises exclusively from variables taken from the AEO2014.

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE 

 FFC energy multipliers for selected years are presented in Table 10B.3.1. The 2040 value 

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014 

projection. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation throughout the forecast period. 

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014) 

2019 2020 2025 2030 2035 2040

Electricity 1.043 1.044 1.045 1.046 1.047 1.047

Natural gas 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum fuels 1.176 1.176 1.176 1.174 1.172 1.170
10B-4 

REFERENCES

 

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

Conservation Program for Consumer Products and Certain Commercial and Industrial 

Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy 

Conservation Standards Program. Federal Register. August 18, 2011. Vol. 76, no. 160: 

pp. 51281–51289. 

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012. 

37(1): pp. 698–708. (Last accessed June 22, 2014.) 

<http://www.sciencedirect.com/science/article/pii/S0360544211006803> 

3 Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

4 U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. April 2014. DOE/EIA-0383(2014). Washington, D.C. (Last accessed June 22, 

2014.) <http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf > 
10C-i

APPENDIX 10C. NATIONAL NET PRESENT VALUE OF CONSUMER BENEFITS

USING ALTERNATIVE PRODUCT PRICE FORECASTS

TABLE OF CONTENTS

10C.1 INTRODUCTION .................................................................................................10C-1

10C.2 EXPERIENCE CURVE ESTIMATION – LOW PRICE DECLINE 

SCENARIO FOR PORTABLE DEHUMIDIFIERS .............................................10C-1

10C.3 EXPONENTIAL FIT APPROACH – LOW PRICE DECLINE SCENARIO 

FOR WHOLE-HOME DEHUMIDIFIERS ...........................................................10C-4

10C.4 ANNUAL ENERGY OUTLOOK 2013 PRICE FORECAST – HIGH PRICE 

DECLINE SCENARIO FOR PORTABLE AND WHOLE-HOME 

DEHUMIDIFIERS ................................................................................................10C-6

10C.5 SUMMARY ...........................................................................................................10C-6

10C.6 RESIDENTIAL DEHUMIDIFIERS NPV RESULTS USING 

ALTERNATIVE LEARNING RATES .................................................................10C-8

LIST OF TABLES

Table 10C.5.1 Price Trend Scenarios ...............................................................................10C-6

Table 10C.6.1 Residential Dehumidifiers: Net Present Value of Consumer Impacts 

Under Alternative Product Price Forecasts (3 Percent Discount Rate) .....10C-8

Table 10C.6.2 Residential Dehumidifiers: Net Present Value of Consumer Impacts 

Under Alternative Product Price Forecasts (7 Percent Discount Rate) .....10C-9

LIST OF FIGURES

Figure 10C.2.1 Historical Real Price Index for Small Electric Household Appliances .....10C-2

Figure 10C.2.2 Historical and Projected Shipments of Portable Dehumidifiers .............10C-3

Figure 10C.2.3 Relative Price versus Cumulative Shipments of Portable 

Dehumidifiers, with Power Law Fit...........................................................10C-4

Figure 10C.3.1 Relative Price of Air-Conditioning, Refrigeration, and Forced Air 

Heating Equipment versus Year, with Exponential Fit .............................10C-5

Figure 10C.5.1 Portable Dehumidifier Price Factor Indexes for the Default Case and 

Sensitivity Cases ........................................................................................10C-7

Figure 10C.5.2 Whole-Home Dehumidifier Price Factor Indexes for the Default Case 

and Sensitivity Cases .................................................................................10C-7
10C-1

APPENDIX 10C. NATIONAL NET PRESENT VALUE OF CONSUMER BENEFITS 

USING ALTERNATIVE PRODUCT PRICE FORECASTS

10C.1 INTRODUCTION

 The NPV results presented in chapter 10 are based on future price projection derived 

from historical PPI data from the Bureau of Labor Statistics (BLS). DOE collected PPI data of 

“small electric household appliances, except fans” from 1983 to 2012 to project future price for 

portable dehumidifiers and PPI data of “room AC and dehumidifiers” from 1990 to 2009 to 

project future price for whole-home dehumidifiers. DOE also investigated the impact of different 

product price forecasts on the consumer net present value (NPV) for the trial standard levels of 

both types of dehumidifiers. For portable dehumidifiers, DOE considered two price sensitivity 

scenarios: (1) a low price decline trend based on the experience curve approach, and (2) a high 

price decline trend based on the “furniture and appliances” that was forecasted in AEO2013. 

Similarly, DOE also considered two price sensitivity scenarios for whole-home dehumidifiers: 

(1) a low price decline trend based on “air-conditioning, refrigeration, and forced air heating 

equipment” PPI, and (2) a high price decline trend based on the “furniture and appliances” that 

was forecasted in AEO2013.

10C.2 EXPERIENCE CURVE ESTIMATION – LOW PRICE DECLINE SCENARIO 

FOR PORTABLE DEHUMIDIFIERS

For this sensitivity case, DOE is using the experience curve approach to forecast future 

prices of portable dehumidifiers. 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. DOE was not 

able to apply the same shipment-based learning curve approach to whole-home dehumidifiers 

because the historical shipment data are insufficient. 

In the experience curve method, the real product price (or proxy thereof) is related to the 

cumulative production or “experience” with a product. A common functional relationship used to 

model the evolution of production costs is: 

Y = aX-b

Where: 

a = an initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 
10C-2

Y = the price as a function of cumulative production. 

Thus, as experience (production) accumulates, the cost of producing the next unit 

decreases. The percentage reduction in cost that occurs with each doubling of cumulative 

production is known as the learning or experience rate (ER), and is given by: 

ER = 1 – 2-b

 In typical experience curve formulations, the experience rate parameter is derived using 

two historical data series: price (or cost) and cumulative production, which is a function of 

shipments during a long time span.

To derive an experience rate parameter for residential portable dehumidifiers, DOE 

obtained historical Producer Price Index (PPI) data for small electric household appliances 

spanning the time period 1983-2012 from the Bureau of Labor Statistics’ (BLS). a DOE used PPI 

data for this industry as representative of residential portable dehumidifiers because PPI data 

specific to residential portable dehumidifiers are not available. An inflation-adjusted price index

for small electric household appliances was calculated by dividing the PPI series by the Gross 

Domestic Product Chained Price Index for the same years. This inflation-adjusted price index 

(shown in Figure 10C.2.1) was used in subsequent analysis steps. 

Figure 10C.2.1 Historical Real Price Index for Small Electric Household 

Appliances

 

a

 Series ID PCU33521033521014; http://www.bls.gov/ppi/ 
10C-3

DOE assembled a time-series of annual shipments from 1972-2010 for portable 

dehumidifiers, which were obtained from the AHAM Fact Book. This annual historical 

shipments data were used to estimate cumulative shipments (production) for portable 

dehumidifiers. Projected shipments after 2010 were obtained from the base case projections 

made for the NIA. Figure 10C.2.2 shows the shipments time series used in the analysis. 

 Figure 10C.2.2 Historical and Projected Shipments of Portable 

Dehumidifiers

To estimate an experience rate parameter, a least-squares power-law fit was performed on 

the unified price index versus cumulative shipments. See Figure 10C.2.3. The form of the fitting 

equation is: 

P(X) = PoX-b, 

 where the two parameters, b (the learning rate parameter) and Po (the price or cost of the 

first unit of production), are obtained by fitting the model to the data. DOE notes that the 

cumulative shipments on the right hand side of the equation can have a dependence on price, so 

there is an issue with simultaneity where the independent variable is not truly independent. 

DOE’s use of a simple least squares fit is equivalent to an assumption of no significant first price 

elasticity effects in the cumulative shipments variable.
10C-4

Figure 10C.2.3 Relative Price versus Cumulative Shipments of 

Portable Dehumidifiers, with Power Law Fit 

 The regression performed as power-law fit results in an R-square of 0.986, which 

indicates a great fit to the data. The parameter values obtained are:

Po = 3.301−1.462

+1.613 (95% confidence) for household laundry equipment, and 

b = 0.328±0.015 (95% confidence) for household laundry equipment. 

The estimated experience rate (defined as the fractional reduction in price expected from

each doubling of cumulative production) is 20.3−0.8

+0.8% (95% confidence). DOE then derived a 

price factor index for this scenario, and the index value in a given year is a function of the 

experience rate and the cumulative production forecast through that year, which is based on the 

shipments forecast described in chapter 9. 

10C.3 EXPONENTIAL FIT APPROACH – LOW PRICE DECLINE SCENARIO 

FOR WHOLE-HOME DEHUMIDIFIERS

 The historical shipments for whome-home dehumidifiers are not sufficient enough to 

perform an experence curve estimation. Therefore, for this scenario, DOE used an inflationadjusted “air-conditioning, refrigerationm and forced air heating equipment” Producer Price 
10C-5

Index (PPI) from 1978-2012 to fit an exponential model with year as the explanatory variable.b

DOE chose this PPI series because this is the more aggregated industry including whole-home 

dehumidifiers than the “room AC and dehumidifiers” industry used in the default price scenario. 

The PPI data reflect nominal prices, adjusted for product quality changes. An inflation-adjusted 

(deflated) price index for air-conditioning, refrigeration and forced air heating equipment was 

calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index. The 

deflated price index is now presented in 2012 dollar values. In this case, the exponential 

function takes the form of: 

YY = aa ∙ eebbbb

where Y is the “air-conditioning, refrigerationm and forced air heating equipment” price index, 

X is the time variable, a is the constant and b is the slope parameter of the time variable. 

 

To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted air-conditioning, refrigerationm and forced air heating equipment PPI versus 

year from 1978 to 2012. See Figure 10C.3.1. 

Figure 10C.3.1 Relative Price of Air-Conditioning, Refrigeration, 

and Forced Air Heating Equipment versus Year, 

with Exponential Fit

 The regression performed as an exponential trend line fit results in an R-square of 0.85, 

which indicates a reasonable fit to the data. The final estimated exponential function is:

 

b

 Series ID PCU333415333415; http://www.bls.gov/ppi/ 
10C-6

 YY = 7.393 × 106 ∙ ee(−0.0079) 

DOE then derived a price factor index for this scenario, with 2012 equal to 1, to project

prices in each future year in the analysis period considered in the NIA since 2012. The index 

value in a given year is a function of the exponential parameter and year. 

10C.4 ANNUAL ENERGY OUTLOOK 2013 PRICE FORECAST – HIGH PRICE 

DECLINE SCENARIO FOR PORTABLE AND WHOLE-HOME 

DEHUMIDIFIERS

DOE also examined a forecast based on the “chained price index—furniture and 

appliances” that was forecasted for AEO2013 out to 2040. This index is the most disaggregated 

category that includes both types of dehumidifiers. To develop an inflation-adjusted index, DOE 

normalized the above index with the “chained price index—gross domestic product” forecasted 

for AEO2013. To extend the price index beyond 2040, DOE used the average annual price 

growth rate in 2031 to 2040. 

10C.5 SUMMARY

Table 10C.5.1 shows the summary of the average annual rates of changes for the product 

price index in each scenario. Figure 10C.5.1 and Figure 10C.5.2 show the resulting price trends

for portable and whole-home dehumidifiers respectively. 

Table 10C.5.1 Price Trend Scenarios

Product Scenario Price Trend Average Annual 

Rate of Change %

Portable 

Dehumidifier Default

Exponential Fit using Small 

Electric Household Appliances 

PPI (1983 to 2012)

-2.02 

High Price 

Decline

AEO2013 “chained price index—

furniture and appliances” -2.41 

Low Price 

Decline

Experience Curve Estimation 

using Small Electric Household 

Appliances PPI (1983 to 2012)

-1.08 

Whole-Home 

Dehumidifier Default Exponential Fit using Room AC and Dehumidifiers (1990 to 2009) -2.32 

High Price 

Decline

AEO2013 “chained price index—

furniture and appliances” -2.41 

Low Price 

Decline

Exponential Fit using AirConditioning, Refrigeration, and 

Forced Air Heating Equipment 

PPI (1978 to 2012)

-0.79 
10C-7

Figure 10C.5.1 Portable Dehumidifier Price Factor Indexes for the 

Default Case and Sensitivity Cases 

Figure 10C.5.2 Whole-Home Dehumidifier Price Factor Indexes for 

the Default Case and Sensitivity Cases 
10C-8

10C.6 RESIDENTIAL DEHUMIDIFIERS NPV RESULTS USING ALTERNATIVE 

LEARNING RATES

Table 10C.6.1 Residential Dehumidifiers: Net Present Value of Consumer Impacts 

Under Alternative Product Price Forecasts (3 Percent Discount Rate) 

Price 

Trend

Product Class 

(pints/day)

Trial Standard Levels ( billion 2013$)

TSL1 TSL 2 TSL 3 TSL 4

Default

≤30.00 0.216 0.440 0.675 2.228

30.01-45.00 0.096 0.096 1.345 2.356 

>45.00 0.158 0.202 0.202 0.324

≤8.0ft3 Case Volume (Whole-home) 0.023 0.023 0.023 0.027

>8.0ft3 Case Volume (Whole-home) 0.003 0.022 0.022 0.025 

Low 

Price 

Decline

≤30.00 0.216 0.439 0.671 2.178

30.01-45.00 0.095 0.095 1.331 2.311

>45.00 0.152 0.194 0.194 0.304

≤8.0ft3 Case Volume (Whole-home) 0.023 0.023 0.023 0.022 

>8.0ft3 Case Volume (Whole-home) 0.003 0.021 0.021 0.022

High 

Price 

Decline

≤30.00 0.217 0.441 0.677 2.252 

30.01-45.00 0.096 0.096 1.352 2.377

>45.00 0.161 0.205 0.205 0.333 

≤8.0ft3 Case Volume (Whole-home) 0.023 0.023 0.023 0.030

>8.0ft3 Case Volume (Whole-home) 0.003 0.022 0.022 0.027
10C-9

Table 10C.6.2 Residential Dehumidifiers: Net Present Value of Consumer Impacts

Under Alternative Product Price Forecasts (7 Percent Discount Rate) 

Price 

Trend

Product Class 

(pints/day)

Trial Standard Levels (billion 2013$)

TSL1 TSL 2 TSL 3 TSL 4

Default

≤30.00 0.125 0.226 0.334 0.970

30.01-45.00 0.040 0.040 0.600 1.019 

>45.00 0.059 0.081 0.081 0.123

≤8.0ft3 Case Volume (Whole-home) 0.010 0.010 0.010 0.007

>8.0ft3 Case Volume (Whole-home) 0.001 0.009 0.009 0.008 

Low 

Price 

Decline

≤30.00 0.125 0.225 0.332 0.949

30.01-45.00 0.039 0.039 0.595 1.000 

>45.00 0.057 0.078 0.078 0.114

≤8.0ft3 Case Volume (Whole-home) 0.010 0.010 0.010 0.005 

>8.0ft3 Case Volume (Whole-home) 0.001 0.009 0.009 0.007 

High 

Price 

Decline

≤30.00 0.125 0.226 0.335 0.980 

30.01-45.00 0.040 0.040 0.603 1.028

>45.00 0.060 0.082 0.082 0.127

≤8.0ft3 Case Volume (Whole-home) 0.010 0.010 0.010 0.008

>8.0ft3 Case Volume (Whole-home) 0.001 0.009 0.009 0.009
10D-i

APPENDIX 10D. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE USING 

ALTERNATIVE GROWTH SCENARIOS 

TABLE OF CONTENTS

10D.1 INTRODUCTION ................................................................................................ 10D-1

10D.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO ................... 10D-2

10D.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCENARIO .................... 10D-3

LIST OF TABLES

Table 10D.2.1 Cumulative National Primary Energy Savings in Quads, High 

Economic Growth Scenario ...................................................................... 10D-2

Table 10D.2.2 Cumulative Full-Fuel Cycle Energy Savings in Quads, High Economic 

Growth Scenario ....................................................................................... 10D-3

Table 10D.2.3 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario ....................................................................................... 10D-3

Table 10D.3.1 Cumulative National Primary Energy Savings in Quads, Low 

Economic Growth Scenario ...................................................................... 10D-3

Table 10D.3.2 Cumulative Full-Fuel Cycle Energy Savings in Quads, Low Economic 

Growth Scenario ....................................................................................... 10D-4

Table 10D.3.3 Cumulative Net Present Value of Consumer Benefits, Low Economic 

Growth Scenario ....................................................................................... 10D-4

LIST OF FIGURES 

Figure 10D.1.1 Total Housing Stock Forecast Under Alternative AEO2015 Economic 

Growth Scenarios ...................................................................................... 10D-1

Figure 10D.1.2 Average Residential Electricity Price Forecasts under Alternative 

AEO2015 Economic Growth Scenarios ................................................... 10D-2
10D-1

APPENDIX 10D. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE 

USING ALTERNATIVE GROWTH SCENARIOS

10D.1 INTRODUCTION

This appendix presents national energy savings (NES) and net present value (NPV) 

results using inputs from alternative economic growth scenarios. The scenarios use the energy 

price and housing starts forecasts in the High Economic Growth case and the Low Economic 

Growth case from EIA’s Annual Energy Outlook 2015 (AEO 2015).1 

Figure 10D.1.1 and Figure 10D.1.2 show the forecasts for total housing stock and 

residential electricity prices under the different economic growth scenarios. AEO2015 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 2030–2040. 

Figure 10D.1.1 Total Housing Stock Forecast Under Alternative 

AEO2015 Economic Growth Scenarios

100

110

120

130

140

150

160

170

Total Housing Stock (millions)

Reference High Low
10D-2

Figure 10D.1.2 Average Residential Electricity Price Forecasts 

under Alternative AEO2015 Economic Growth 

Scenarios

10D.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO

Table 10D.2.1 Cumulative National Primary Energy Savings in Quads, High Economic 

Growth Scenario

TSL ≤30.00 

pints/day

30.01-45.00 

pints/day

>45.00 

pints/day

Wholehome - 

≤8.0ft^3 

Case 

Volume

Wholehome -

 >8.0ft^3 

Case 

Volume

All

1 0.02 0.01 0.03 0.00 0.00 0.07

2 0.05 0.01 0.03 0.00 0.00 0.11

3 0.09 0.19 0.03 0.00 0.00 0.31

4 0.34 0.35 0.06 0.01 0.01 0.77

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

Electricity Price Trend (2013=100)

Residential Electricity Price Projection

Reference Low High
10D-3

Table 10D.2.2 Cumulative Full-Fuel Cycle Energy Savings in Quads, High Economic 

Growth Scenario

TSL ≤30.00 

pints/day

30.01-45.00 

pints/day

>45.00 

pints/day

Wholehome - 

≤8.0ft^3 

Case 

Volume

Wholehome -

 >8.0ft^3 

Case 

Volume

All

1 0.02 0.01 0.03 0.00 0.00 0.07

2 0.06 0.01 0.04 0.00 0.00 0.11 

3 0.09 0.20 0.04 0.00 0.00 0.33

4 0.36 0.37 0.07 0.01 0.01 0.81

Table 10D.2.3 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario

Price 

Trend

Product Class 

(pints/day)

Trial Standard Levels

TSL1 TSL 2 TSL 3 TSL 4

3% 

(billion 

2013$) 

≤30.00 0.22 0.46 0.70 2.34

30.01-45.00 0.10 0.10 1.41 2.48

>45.00 0.17 0.21 0.21 0.35

≤8.0ft3 Case Volume (Whole-home) 0.02 0.02 0.02 0.03 

>8.0ft3 Case Volume (Whole-home) 0.00 0.02 0.02 0.03 

7% 

(billion 

2013$) 

≤30.00 0.13 0.23 0.34 1.01

30.01-45.00 0.04 0.04 0.62 1.06

>45.00 0.06 0.08 0.08 0.13

≤8.0ft3 Case Volume (Whole-home) 0.01 0.01 0.01 0.01 

>8.0ft3 Case Volume (Whole-home) 0.00 0.01 0.01 0.01

10D.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCENARIO

Table 10D.3.1 Cumulative National Primary Energy Savings in Quads, Low Economic 

Growth Scenario

TSL ≤30.00 

pints/day

30.01-45.00 

pints/day

>45.00 

pints/day

Wholehome - 

≤8.0ft^3 

Case 

Volume

Wholehome -

 >8.0ft^3 

Case 

Volume

All

1 0.02 0.01 0.03 0.00 0.00 0.07 

2 0.05 0.01 0.03 0.00 0.00 0.11

3 0.08 0.18 0.03 0.00 0.00 0.30

4 0.33 0.34 0.06 0.01 0.01 0.74
10D-4

Table 10D.3.2 Cumulative Full-Fuel Cycle Energy Savings in Quads, Low Economic 

Growth Scenario

TSL ≤30.00 

pints/day

30.01-45.00 

pints/day

>45.00 

pints/day

Wholehome - 

≤8.0ft^3 

Case 

Volume

Wholehome -

 >8.0ft^3 

Case 

Volume

All

1 0.02 0.01 0.03 0.00 0.00 0.07

2 0.05 0.01 0.03 0.00 0.00 0.11 

3 0.09 0.19 0.03 0.00 0.00 0.32

4 0.34 0.35 0.06 0.01 0.01 0.77

Table 10D.3.3 Cumulative Net Present Value of Consumer Benefits, Low Economic 

Growth Scenario

Price 

Trend

Product Class 

(pints/day)

Trial Standard Levels

TSL1 TSL 2 TSL 3 TSL 4

3% 

(billion 

2013$) 

≤30.00 0.21 0.42 0.65 2.11

30.01-45.00 0.09 0.09 1.28 2.24

>45.00 0.15 0.19 0.19 0.30

≤8.0ft3 Case Volume (Whole-home) 0.02 0.02 0.02 0.02 

>8.0ft3 Case Volume (Whole-home) 0.00 0.02 0.02 0.02 

7% 

(billion 

2013$) 

≤30.00 0.12 0.22 0.32 0.92

30.01-45.00 0.04 0.04 0.57 0.97

>45.00 0.06 0.08 0.08 0.11

≤8.0ft3 Case Volume (Whole-home) 0.01 0.01 0.01 0.01 

>8.0ft3 Case Volume (Whole-home) 0.00 0.01 0.01 0.01
10D-5

REFERENCES

 

1 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2015 with Projections to 2040, 2015. Washington, DC. Report Number: DOE/EIA0383(2015). 

 < http://www.eia.gov/forecasts/aeo/> 
12A-i 

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 RESIDENTIAL DEHUMIDIFIERS .............................................................................1

 
12A-1 

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

12A.1 RESIDENTIAL DEHUMIDIFIERS

Residential Dehumidifier Rulemaking 

Manufacturer Impact Analysis Interview Guide for 

Residential Dehumidifier 

August 6. 2014 
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 residential portable 

and whole-home dehumidifiers. 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. 

DOE published a notice of public meeting and availability of the preliminary technical support 

document (TSD) regarding energy conservation standards for dehumidifiers on May 22, 2014. 79 

FR 29380. Based on feedback received from stakeholders, DOE is revising the preliminary 

analyses in preparation for a notice of proposed rulemaking (NOPR). DOE requests 

manufacturer feedback on a number of issues covered in the sections below in support of 

updating its analyses. 

1 PRODUCT CLASSES

As specified in the preliminary TSD, DOE conducted its analyses on the following product 

classes and efficiency levels for portable and whole-home dehumidifiers. The capacities and 

efficiency levels reflect results obtained using the test procedure proposed in the NOPR 

published in the Federal Register on May 21, 2014. 79 FR 29271.

Table 1.1 Preliminary Analysis Portable Dehumidifier Efficiency Levels at 65 °F 

Efficiency

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels (L/kWh)

20.00 

pints/day 

or less

20.01–

30.00 

pints/day

30.01–

35.00 

pints/day

35.01–

45.00 

pints/day

45.01 

pints/day 

or more

Baseline DOE Standard with Fanonly Mode 0.77 0.80 0.94 1.00 2.07 

1 DOE Standard with no 

Fan-only Mode 1.10 1.10 1.20 1.30 2.40 

2 Gap Fill 1* 1.20 1.20 1.40 1.40 2.80 

3 Gap Fill 2 1.30 1.30 1.60 1.60 3.52 

4 Maximum Available 1.42 1.52 1.75 1.75 N/A

1.1 Should DOE consider collapsing portable dehumidifiers into fewer product classes? If so, 

what capacity bins would be appropriate? 

1.2 Is there any unique consumer utility associated with portable products in the different 

capacity bins that would warrant maintaining separate product classes?
12A-3 

Table 1.2 Preliminary Analysis Whole-Home Dehumidifier Efficiency Levels 65 °F 

Efficiency 

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels 

(L/kWh)

Less than or equal 

to 8.0 ft3 (Case 

Volume)

Greater than 8.0 ft3

(Case Volume)

Baseline Minimum Available 1.10 1.68 

1 Gap Fill 1 1.40 1.90 

2 Gap Fill 2/Maximum 

Available 1.59 2.80 

3 Maximum Available N/A 3.41 

1.3 Is case volume an appropriate differentiator between whole-home dehumidifier product 

classes? If so, is 8 ft3

 the appropriate cutoff point between the small and large product classes? 

1.4 If case volume is not an appropriate metric for separating whole-home dehumidifier 

product classes, are multiple product classes necessary? If so, what would be the appropriate 

product classes and differentiators? 
12A-4 

2 ENGINEERING

In the preliminary analysis, DOE developed cost estimates associated with improving product 

efficiencies beyond the baseline for the product classes listed in Table 1.1 and Table 1.2. Table 

2.1 below lists the incremental manufacturing costs determined in the preliminary analysis.

Table 2.3 Incremental Dehumidifier Manufacturing Costs at Higher Efficiencies

Portable Product Class Capacities

(pints/day)

Whole-Home Product 

Class Case Volume (ft3)

Efficiency Level ≤ 20 20.01-30 30.01-35 35.01-45 > 45 ≤ 8.0 > 8.0

EL1 $- $- $- $- $38.40 $15.22 $6.14

EL2 $1.56 $1.85 $2.94 $1.98 $49.16 $76.18 $37.05

EL3 $4.64 $3.78 $8.72 $7.56 $100.13 $112.01

EL4 $7.77 $10.82 $13.40 $11.24

2.1 Are the costs presented in Table 2.1 appropriate for the corresponding efficiency 

improvements in each product class? DOE assumed in the preliminary analysis that 

manufacturers would primarily rely on increased compressor efficiencies and improvements 

to the heat exchangers to reach higher efficiency levels.

2.2 DOE assumed in its preliminary analysis that rotary R-410A compressor efficiencies may 

reach energy efficiency ratios (EERs) up to 10.5 British thermal units per hour per Watt 

(Btu/h/W). Is this maximum EER appropriate for the entire range of rotary R-410A 

compressor capacities (i.e., <3,000 Btu/h up to roughly 15,000 Btu/h)? What price premium is 

associated with more efficient compressors?

2.3 DOE relied on information from the room air conditioners final rule published in 2011 in 

determining compressor pricing information (described in chapter 5 of the preliminary TSD). 

Are the costs shown on this curve appropriate for compressors included in dehumidifiers? 

What price-capacity trend should DOE use for low-capacity compressors (below 5,000 Btu/h) 

beyond the range of the room air conditioners pricing curve? 

2.4 What efficiency gains would be associated with moving to more efficient blower motors 

(i.e., permanent magnet motors)? What price premium would these motors have compared to 

the typical permanent-split capacitor motors used in most dehumidifiers? 

2.5 In the preliminary analysis, DOE considered improvements to the evaporator and 

condenser by increasing the cross-sectional area of these heat exchangers. What factors limit 

the extent to which manufacturers may increase the heat exchanger sizes? Would 

manufacturers consider improvements to the heat exchangers other than increasing crosssectional area (e.g., increasing the number of tube passes in the direction of the air flow)? 

2.6 For the portable product classes with capacities less than 45 pints/day, DOE assumed that 

manufacturers would improve efficiencies from the baseline to EL1 by eliminating fan-only 
12A-5 

mode. Would removing fan-only mode have any impact on consumer utility? 

2.7 Testing

2.7.a What should the inlet air condition (dry-bulb temperature and relative humidity) 

be for testing whole-home dehumidifiers? How would an inlet air temperature of 

73 °F affect measured efficiencies and capacities compared to the 65 °F used for 

the preliminary analysis?

2.7.b What should the test external static pressure be for whole-home dehumidifiers? 

How would an external static pressure of 0.25 inches of water affect measured 

efficiencies and capacities compared to 0.5 in. water as proposed in the 

preliminary analysis?

2.7.c Should the test procedure require an additional test duct for units with 

ventilation/fresh air collars or should these collars be capped closed for testing? 

2.7.d Is 65 °F the most appropriate inlet temperature condition for portable 

dehumidifiers? If not, what should it be? 

2.7.e The proposed test procedure does not account for dehumidifier performance under 

varying ambient conditions. Should DOE consider testing at multiple ambient 

conditions to assess potential efficiency improvements associated with certain 

design options (e.g., variable speed compressors, flow control devices, improved 

defrost control)? If so, what ambient conditions should be tested? 

2.7.f If multiple ambient conditions were assessed, how would the increased test 

burden associated with these additional test points impact profitability and overall 

manufacturing costs? Also, how much energy would a field unit with these design 

options save when compared with a unit without these design options. 
12A-6 

3 KEY ISSUES

3.1 Since the preliminary interview, are there any new key issues for your company 

regarding amended energy conservation standards for residential dehumidifiers?
12A-7 

4 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding manufacturer impacts at the plant or profit center level 

directly pertinent to dehumidifier 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 dehumidifier industry profit center 

will help DOE understand the probable future of the manufacturing activity with and without 

amended energy conservation standards.

4.1 What percentage of your dehumidifier manufacturing corresponds to each product class, 

in terms of both revenue and shipments within the U.S.? If known, please also indicate your 

company’s approximate market share for each product class. Please also indicate whether you 

purchase your dehumidifiers from other manufacturers, and whether the factory that supplies 

the products is located in the United States.

Table 4.4 Portable Dehumidifier U.S. Revenue and Shipment Volumes by Product Class

Product 

Class 

Number

Product 

Class

2013 

Revenue

2013 

Shipments

% 

Made

% 

Bought

% Made in 

U.S.

Market 

Share

1 

20.00 

pints/day 

or less**

2 

20.01–

30.00 

pints/day

3 

30.01–

35.00 

pints/day

4 

35.01–

45.00 

pints/day

5 

45.01 

pints/day 

or more

Table 4.2 Whole-Home Dehumidifier U.S. Revenue and Shipment Volumes by Product 

Class

Product Class 

Number 

Product 

Class

2013 

Revenue

2013 

Shipments

% 

Made

% 

Bought

% Made in 

U.S.

Market 

Share

1 

<= 8.0 ft3

(Case 

Volume)

2 

> 8.0 ft3

(Case 

Volume)
12A-8 

5 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 each product classes of residential 

dehumidifiers. DOE defines manufacturer production cost as all direct costs associated with 

manufacturing a product: direct labor, direct materials, overhead, and depreciation. The 

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 1.43 for residential dehumidifiers. 

5.1 Is the 1.43 baseline markup representative of an average industry markup? 

5.2 Please comment on the baseline markups DOE calculated as compared to your

company’s baseline markups for the dehumidifier product classes. How would revised 

standards potentially impact your baseline markups and margins for each product class? 

Table 5.5 Portable Dehumidifier Baseline Manufacturer Markups by Product Class

Product 

Class Product Type

Estimated 

Baseline 

Markup

Manufacturer Comments or Revised Estimates

1 20.00 pints/day 

or less** 1.43

2 20.01–30.00 

pints/day 1.43

3 30.01–35.00 

pints/day 1.43

4 35.01–45.00 

pints/day 1.43

5 45.01 pints/day 

or more

1.43
12A-9 

Table 5.6 Whole-Home Dehumidifier Baseline Manufacturer Markups by Product Class

Product 

Class Product Type

Estimated 

Baseline

Markup

Manufacturer Comments or Revised Estimates

1 <= 8.0 ft3 (Case 

Volume) 1.43

2 > 8.0 ft3

(Case Volume) 1.43

5.3 DOE is interested in understanding if efficiency is a feature that earns a premium or 

whether it would cut into profit margins (and reduce markups). Within each product class, 

would markups vary by efficiency level? If yes, please provide estimates for your markups by 

product class and efficiency level in Table 5.3 and Table 5.4. 

Table 5.7 Estimated Markups for Portable Dehumidifiers

Efficiency

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels (L/kWh)

20.00 

pints/day 

or less

20.01–

30.00 

pints/day

30.01–

35.00 

pints/day

35.01–

45.00 

pints/day

45.01 

pints/day 

or more

1 DOE Standard with no 

Fan-only Mode 

2 Gap Fill 1

3 Gap Fill 2

4 Maximum Available

Table 5.8 Estimated Markups for Whole-Home Dehumidifiers

Efficiency 

Level Efficiency Level Source

Integrated Energy Factor Efficiency Levels 

(L/kWh)

Less than or equal to 

8.0 ft3 (Case Volume)

Greater than 8.0 ft3

(Case Volume)

1 Gap Fill 1

2 Gap Fill 2/Maximum 

Available

3 Maximum Available

5.4 What factors or product attributes besides efficiency affect the profitability of 

dehumidifiers within a product class? 
12A-10 

5.5 Would you expect changes in your estimated profitability following an amended energy 

conservation standard? If so, please explain why. 
12A-11 

6 FINANCIAL PARAMETERS

DOE’s contractor has developed a draft model of the dehumidifier 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 

dehumidifier profit center. This section attempts to understand the financial parameters for 

dehumidifier manufacturing and how your company’s financial situation could differ from the 

industry aggregate picture. 

6.1 In order to accurately collect information about dehumidifier manufacturing, please 

compare your financial parameters to the GRIM parameters tabulated below.

1.

 Table 6.9 Financial Parameters for Residential Dehumidifier 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) 31.1%

Discount Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost of 

debt and return on equity)

8.4%

Working 

Capital

Current assets less current liabilities (percentage of 

revenues)

20.1%

Net PPE Net plant property and equipment (percentage of 

revenues) 13.4%

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 20.9%

R&D Research and development expenses (percentage 

of revenues) 1.3%

Depreciation Amortization of fixed assets (percentage of 

revenues) 2.5%

Capital 

Expenditures

Outlay of cash to acquire or improve capital assets 

(percentage of revenues, not including acquisition 

or sale of business units)

2.7%

Cost of Goods 

Sold

Includes material, labor, overhead, and 

depreciation (percentage of revenues) 71.7%

6.2 Relative to the sale of dehumidifiers, do you typically pay for shipping costs? Does the 

customer reimburse you for some or all of the costs associated with the shipment of your 

dehumidifier products? 

6.3 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-12 

7 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 and 

certification, marketing and other costs for redesigning products necessitated by an 

amended energy conservation standard.

CAPITAL CONVERSION COSTS

7.1 Please provide estimates for your capital conversion costs by product class and efficiency 

level in Table 7.1 through Table 7.7. 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. 

For each of the product categories, please note which efficiency level changes could be made 

within existing platform designs and which would result in major product redesigns. Also note 

which design options would require only minor changes to production lines, and which would 

require major changes to production lines, substantial modifications to existing facilities, or 

the development of entirely new manufacturing facilities. 
12A-13 

Portable Dehumidifiers 

Table 7.10 Expected Capital Conversion Costs for Portable Dehumidifiers – 20.00 

pints/day of less

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.42

Table 7.11 Expected Capital Conversion Costs for Portable Dehumidifiers – 20.01 to 30.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.52

Table 7.12 Expected Capital Conversion Costs for Portable Dehumidifiers – 30.01 to 35.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.20

2 1.40

3 1.60

4 1.75

Table 7.13 Expected Capital Conversion Costs for Portable Dehumidifiers – 35.01 to 45.00 

pints/day

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.30

2 1.40

3 1.60

4 1.75

Table 7.14 Expected Capital Conversion Costs for Portable Dehumidifiers – 45.01 

pints/day or more 

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 2.40

2 2.80

3 3.52

4 N/A

Whole-Home Dehumidifiers 
12A-14 

Table 7.15 Expected Capital Conversion Costs for Whole-Home Dehumidifiers - Less than 

or equal to 8.0 ft3

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.40

2 1.59

3 N/A

Table 7.16 Expected Capital Conversion Costs for Whole-Home Dehumidifiers - 8.0 ft3 or 

more

Efficiency

Level IEF L/kwh

Total Capital 

Conversion Costs Description

1 1.90

2 2.80

3 3.41

PRODUCT CONVERSION COSTS

7.2 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 7.8 through 

Table 7.14 (where applicable) considering such expenses as product development expenses, 

prototyping, testing, certification, and marketing [if you have not previously certified your 

products with the DOE, please be sure to breakout an estimate of the costs associated with 

testing and certification, if possible]. In the description column, please describe the 

assumptions behind the estimates provided. 
12A-15 

Portable Dehumidifiers 

Table 7.17 Expected Product Conversion Costs for Portable Dehumidifiers – 20.00 

pints/day of less

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.42

Table 7.18 Expected Product Conversion Costs for Portable Dehumidifiers – 20.01 to 30.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.10

2 1.20

3 1.30

4 1.52

Table 7.19 Expected Product Conversion Costs for Portable Dehumidifiers – 30.01 to 35.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.20

2 1.40

3 1.60

4 1.75

Table 7.20 Expected Product Conversion Costs for Portable Dehumidifiers – 35.01 to 45.00 

pints/day

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.30

2 1.40

3 1.60

4 1.75

Table 7.21 Expected Product Conversion Costs for Portable Dehumidifiers – 45.01 

pints/day or more 

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 2.40

2 2.80

3 3.52

4 N/A

Whole-Home Dehumidifiers 
12A-16 

Table 7.22 Expected Product Conversion Costs for Whole-Home Dehumidifiers - Less than 

or equal to 8.0 ft3

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.40

2 1.59

3 N/A

Table 7.23 Expected Product Conversion Costs for Whole-Home Dehumidifiers - 8.0 ft3 or 

more

Efficiency

Level IEF L/kwh

Total Product 

Conversion Costs Description

1 1.90

2 2.80

3 3.41
12A-17 

8 DIRECT EMPLOYMENT IMPACT ASSESSMENT AND MANUFACTURING

CAPACITY

8.1 Where are your dehumidifier manufacturing facilities that produce products for the 

United States located? What types of products are manufactured at each location? Please 

provide employment levels and annual shipment figures for your company’s dehumidifier 

manufacturing at each location by product class. 

Table 8.24 Dehumidifiers Employment and Shipment Volumes by Product Class*

Facility Location Product Types Manufactured Employees Annual 

Shipments

Example Sheboygan, WI Portable dehumidifiers 200 100,000 

1 

2 

3 

4

5

 *For manufacturing facilities that produce products for the U.S. 

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 How would amended energy conservation standards impact your company’s 

manufacturing capacity? How much, if any, downtime would be required? 

8.4 What percentage of your company’s overall dehumidifier sales is made within the United 

States? 

8.5 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-18 

9 IMPACTS ON SMALL BUSINESS

9.1 The Small Business Administration (SBA) denotes a small business in the dehumidifier 

manufacturing industry as having less than 750 total employees, including the parent company 

and all subsidiaries.a By this definition, is your company considered a small business? 

9.2 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.

9.3 To your knowledge, are there any small businesses, 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. The products covered by this rulemaking are classified under NAICS codes 

333415: Air-Conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration 

Equipment Manufacturing and 335210: Small Electrical Appliance Manufacturing. To be categorized as a small 

business, a manufacturer (and its affiliates) classified by one of these NAICS codes may employ a maximum of 750

employees. The 750 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 OVERVIEW

12B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards 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 2048. Computed by 

growing the Free Cash Flow in year 2048 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 2048 

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 (in 2013$ millions) 2014 2015 2016 2017 2018 2019 2020

Revenues $ 361.4 $ 383.5 $ 401.2 $ 411.7 $ 414.1 $ 422.4 $ 413.1

Total Shipments (million units) 1.668 1.775 1.870 1.942 1.986 2.004 2.004

- Materials $ 210.6 $ 222.8 $ 232.5 $ 238.1 $ 239.2 $ 245.1 $ 239.7

- Labor $ 12.1 $ 13.3 $ 14.4 $ 15.1 $ 15.4 $ 15.6 $ 15.2

- Depreciation $ 8.9 $ 9.4 $ 9.9 $ 10.1 $ 10.2 $ 10.4 $ 10.2

- Overhead $ 17.7 $ 18.9 $ 20.0 $ 20.6 $ 20.8 $ 20.3 $ 19.8

- Standard SG&A $ 86.0 $ 91.3 $ 95.5 $ 98.0 $ 98.5 $ 100.5 $ 98.3

- R&D $ 4.7 $ 5.0 $ 5.2 $ 5.4 $ 5.4 $ 5.5 $ 5.4

- Product Conversion Costs -$ -$ $ 5.9 $ 10.4 $ 13.3 $ 0.6 -$ 

- Stranded Assets -$ -$ -$ -$ -$ -$ -$ 

Earnings Before Interest and Taxes (EBIT) $ 21.4 $ 22.8 $ 17.9 $ 14.1 $ 11.2 $ 24.5 $ 24.5

Per Unit EBIT ($/unit) $ 12.86 $ 12.82 $ 9.56 $ 7.24 $ 5.67 $ 12.21 $ 12.23

EBIT/Revenues (%) 5.9% 5.9% 4.5% 3.4% 2.7% 5.8% 5.9%

- Taxes $ 6.7 $ 7.1 $ 5.6 $ 4.4 $ 3.5 $ 7.6 $ 7.6

Net Operating Profit after Taxes (NOPAT) $ 14.8 $ 15.7 $ 12.3 $ 9.7 $ 7.8 $ 16.9 $ 16.9

Cash Flow Statement 

NOPAT $ 14.8 $ 15.7 $ 12.3 $ 9.7 $ 7.8 $ 16.9 $ 16.9

+ Depreciation $ 8.9 $ 9.4 $ 9.9 $ 10.1 $ 10.2 $ 10.4 $ 10.2

+ Loss on Disposal of Stranded Assets -$ -$ -$ -$ -$ -$ -$ 

- Change in Working Capital -$ $ 2.5 $ 2.0 $ 1.2 $ 0.3 $ 0.9 $ (1.1)

Cash Flows from Operations $ 23.7 $ 22.6 $ 20.2 $ 18.6 $ 17.7 $ 26.3 $ 28.1

- Ordinary Capital Expenditures $ 9.6 $ 10.2 $ 10.6 $ 10.9 $ 11.0 $ 11.2 $ 10.9

- Capital Conversion Costs -$ -$ $ 4.1 $ 7.2 $ 9.2 -$ -$ 

Free Cash Flow $ 14.1 $ 12.5 $ 5.5 $ 0.6 $ (2.5) $ 15.1 $ 17.2

Discounted Cash Flow

Free Cash Flow $ 14.1 $ 12.5 $ 5.5 $ 0.6 $ (2.5) $ 15.1 $ 17.2

Terminal Value -$ -$ -$ -$ -$ -$ -$ 

Present Value Factor 0.000 1.000 0.922 0.851 0.784 0.723 0.667

Discounted Cash Flow -$ $ 12.5 $ 5.0 $ 0.5 $ (2.0) $ 10.9 $ 11.5

INPV at TSL 3 $ 155.8
14A-i

APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY .................................................................................. 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS ............................................ 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES .......................................................................................................... 14A-4

14A.4 APPROACH AND KEY ASSUMPTIONS ......................................................... 14A-5

14A.4.1 Integrated Assessment Models ............................................................................. 14A-5

14A.4.2 Global versus Domestic Measures of SCC ......................................................... 14A-11

14A.4.3 Valuing Non-CO2 Emissions .............................................................................. 14A-13

14A.4.4 Equilibrium Climate Sensitivity ......................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories........................................................ 14A-16

14A.4.6 Discount Rate ...................................................................................................... 14A-19

14A.5 REVISED SCC ESTIMATES ............................................................................ 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ............................................................... 14A-32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS ................................................................................... 14A-34

14A.8 CONCLUSION ................................................................................................... 14A-37

14A.9 ANNEX............................................................................................................... 14A-44

14A.9.1 Other (non-CO2) gases ........................................................................................ 14A-45

14A.9.2 Extrapolating Emissions Projections to 2300 ..................................................... 14A-47

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 14A-2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions ............................................................................................ 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios ................................................................................ 14A-18

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) ...................... 14A-29

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation ............ 14A-35

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-44

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-55

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-56
14A-ii

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-57

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............. 14A-58

LIST OF FIGURES 

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due 

to an Increase in Annual Global Temperature in the DICE, FUND, and 

PAGE models.......................................................................................... 14A-10

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 14A-11

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 14A-16

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-30

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios ........................................................ 14A-47

Figure 14A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 14A-49

Figure 14A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 14A-50

Figure 14A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 

2090-2100 is maintained through 2300) ................................................. 14A-51

Figure 14A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 14A-52

Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-53

Figure 14A.9.8 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000-

2300 (Post-2100 extrapolations assume decline in CO2/GDP growth 

rate over 2090-2100 is maintained through 2300) .................................. 14A-54

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-57
14A-1

APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

14A.1 EXECUTIVE SUMMARY

 Under Executive Order 12866, agencies are required, to the extent permitted by law, “to 

assess both the costs and the benefits of the intended regulation and, recognizing that some costs 

and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the 

social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include (but is not limited to) 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change. 

 This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process. 

 

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government.

With participation by:

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury
14A-2

 The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution.

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.b

 

 

 When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

 

b In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 
14A-3

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions. 

 For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here. 

 An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was to 

develop a range of SCC values using a defensible set of input assumptions that are grounded in 

the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

section 14-A.9 for the full range of annual SCC estimates from 2010 to 2050. 

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 
14A-4

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process. 

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

 To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 

2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(in 2007 dollars), increasing both values at 2.4 percent per year. It also included a sensitivity 

analysis at $80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages in 

the United States resulting from a unit change in carbon dioxide emissions, while a global SCC 

value is meant to reflect the value of damages worldwide. 

 A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (in 2007 dollars). In 

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified 

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean 

values were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 

percent, respectively (in 2006 dollars for 2007 emissions). 

 In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across agencies, the Administration sought to develop a transparent 

and defensible method, specifically designed for the rulemaking process, to quantify avoided 

climate change damages from reduced CO2 emissions. The interagency group did not undertake 

any original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

 The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published estimates 

produced from the most recently available versions of three integrated assessment models—

DICE, PAGE, and FUND—at approximately 3 and 5 percent discount rates. The $55 and $10 

values were derived by adjusting the published estimates for uncertainty in the discount rate 

(using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.
14A-5

These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules. 

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable, since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC. 

 The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them. 

14A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.c These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below). 

 

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).
14A-6

 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At the 

same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages. 

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships. 

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them. 

 Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g. the probability of catastrophe, and the ability to adapt to climate change 

and the physical changes it causes).

 The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.
14A-7

 The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

The DICE Model 

 The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are 

treated as analogous to investment in “natural capital.” By investing in natural capital today 

through reductions in emissions—implying reduced consumption—harmful effects of climate 

change can be avoided and future consumption thereby increased. 

 For purposes of estimating the SCC, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function links global average temperature to the 

overall impact on the world economy. It varies quadratically with temperature change to capture 

the more rapid increase in damages expected to occur under more extreme climate change, and is 

calibrated to include the effects of warming on the production of market and nonmarket goods 

and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other 

vulnerable market sectors” (based primarily on changes in energy use), human health (based on 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above.

 No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function. 

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs." 

 Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 
14A-8

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d 

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE). 

 PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

 Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

The FUND Model 

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

 

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. Specifically, 

the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing 

representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP 

trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re-calibrated the exogenous path 

of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly 

matched the EMF scenarios.
14A-9

the absolute temperature change but also on the rate of temperature change and level of regional 

income.e In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations. 

 Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

 Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions 

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

 The damage functions for the three IAMs are presented in Figure 14A.4.1 and Figure 

14A.4.2, using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (Figure 14A.4.2) and higher (Figure 14A.4.1) 

increases in global-average temperature. 

 

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al., 2006).
14A-10

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 

2100 Due to an Increase in Annual Global Temperature in the 

DICE, FUND, and PAGE modelsf

The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

 

f The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, socioeconomic, 

and emissions parameters will produce a different realization of damages for each IAM. The damage functions 

represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions, 

the damages from FUND may cross from negative to positive at less than or greater than 3 °C.
14A-11

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in 

DICE, FUND, and PAGE

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.g

 

Global SCC 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

 

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.
14A-12

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

 When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation.

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.h

 For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach. 

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions. For 

example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7-10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.i

 On the basis of this evidence, the interagency workgroup determined that a range of 

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

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

 

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time. i

Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report. 
14A-13

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach. 

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases. 

 

 In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts.

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for carbon dioxide 

emissions. 

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.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.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC): 

 

j

The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100-200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g. Hansen et al. 2007).
14A-14

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k 

For fundamental physical reasons as well as data limitations, values substantially higher 

than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is 

generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et 

al., 2007, p 799) 

 After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

Table 14A.4.1 included below gives summary statistics for the four calibrated distributions. 

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log-normal Gamma Weibull

Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102

Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5th percentile 1.72 1.49 1.37 1.13

10th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th percentile 5.86 5.14 4.93 4.69

95th percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC: 

(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”;l

 

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent 

probability.

l

Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode 

equal to 3°C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the 

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected 

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most 

consistent with a Roe and Baker distribution with the median set equal to 3 °C.
14A-15

(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and

(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 

721). 

 We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: (1) 

absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

 Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2-6.0 °C). 

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is 

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent 

probability,” it reflects a greater degree of certainty about very low values of ECS than was 

expressed by the IPCC. 
14A-16

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium 

Climate Sensitivity (°C)

 To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 14A.4.3 (above) overlays it on Figure 9.20 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.m 

14A.4.5 Socioeconomic and Emissions Trajectories

 Another key issue considered by the interagency group is how to select the set of 

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

 

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5-95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.
14A-17

 To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available. 

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 14A.4.2

below). Four of these represent potential business-as-usual (BAU) growth in population, wealth, 

and emissions and are associated with CO2 (only) concentrations ranging from 612 to 889 ppm 

in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO2e (ii.e., 

CO2-only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m2) in 2100, a lowerthan-BAU trajectory.n

 Out of the 10 models included in the EMF-22 exercise, we selected the 

trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. 

For the BAU pathways, we used the GDP, population, and emission trajectories from each of 

these four models. For the 550 ppm CO2e scenario, we averaged the GDP, population, and 

emission trajectories implied by these same four models. 

 

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.
14A-18

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

Reference Fossil and Industrial CO2 Emissions (GtCO2/yr)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (using market exchange rates in trillion 2005$)o

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socioeconomic pathways.

 

o While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP). PPP takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.
14A-19

 There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

most likely outcome span a wide range, from the more optimistic (e.g., abundant low-cost, lowcarbon energy) to more pessimistic (e.g., constraints on the availability of nuclear and 

renewables).p Second, the socioeconomic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g., MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.q We chose not to include 

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

 For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, the 

United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people 

in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the 

IMAGE, MiniCAM, and MERGE models. 

 In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Annex for 

greater detail.

14A.4.6 Discount Rate

 The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law. 

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because carbon 

dioxide emissions are long-lived, subsequent damages occur over many years. In calculating the 

SCC, we first estimate the future damages to agriculture, human health, and other market and 

non-market sectors from an additional unit of carbon dioxide emitted in a particular year in terms 

of reduced consumption (or consumption equivalents) due to the impacts of elevated 

temperatures, as represented in each of the three IAMs. Then we discount the stream of future 

damages to its present value in the year when the additional unit of emissions was released using 

 

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. q For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 concentrations in 

2100 would drop from 794 ppmv in its reference case to 762 ppmv.
14A-20

the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

 For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing those 

challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

 Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

 One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

 Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 
14A-21

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999). 

 Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 

the prescriptive approach assumes a representative agent with perfect foresight and no credit 

constraints. This is an artificial rendering of the real world that misses many of the frictions that 

characterize individuals’ lives and indeed the available descriptive evidence supports this. For 

instance, many individuals smooth consumption by borrowing with credit cards that have 

relatively high rates. Some are unable to access traditional credit markets and rely on payday 

lending operations or other high-cost forms of smoothing consumption. Whether one puts greater 

weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained 

individuals accept suggest that some account should be given to the discount rates revealed by 

their behavior. 

 We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While relying 

primarily on the descriptive approach in selecting specific discount rates, the interagency group 

has been keenly aware of the deeply normative dimensions of both the debate over discounting 

in the intergenerational context and the consequences of selecting one discount rate over another. 

Historically Observed Interest Rates

 In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

 The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 
14A-22

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

 Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into "certainty equivalents," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption. 

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate.

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

 This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).r This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.s A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t

 

 

r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

t

Cambell et al (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950 – 2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20 – 40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).
14A-23

The Ramsey Equation

 Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v

 In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market interest rate.

 A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an inter-

 

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. 
14A-24

generational setting, it may make sense to use a small positive pure rate of time 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006). 

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100. 

 Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent 

income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 

percent of their income.x

 Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent. 

 We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework. 

Accounting for Uncertainty in the Discount Rate 

 While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

uncertainty in the discount rate (e.g., the rate follows a random walk), then it will result in an 

effective (or certainty-equivalent) discount rate that declines over time. Consequently, lower 

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009). 

 

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)
14A-25

The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time. 

 While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.y

 A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

 The central value, 3 percent, is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods. 

 

y For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31 - 75; 2.5

percent for years 76 - 125; 2 percent for years 126 - 200; 1.5 percent for years 201 - 300; and 1 percent after 300 

years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time. z Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 
14A-26

 The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher. 

14A.5 REVISED SCC ESTIMATES 

 Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group: 

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds. 

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22. 

• Constant annual discount rates of 2.5, 3, and 5 percent. 

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions. 

a. In PAGE, the consumption-equivalent damages in each period are 

calculated as a fraction of the EMF GDP forecast, depending on the 

temperature in that period relative to the pre-industrial average 

temperature in each region. 

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
14A-27

c. In DICE, temperature affects both consumption and investment, so we 

first adjust the EMF GDP paths as follows: Using the Cobb-Douglas 

production function with the DICE2007 parameters, we extract the path of 

exogenous technical change implied by the EMF GDP and population 

paths, then we recalculate the baseline GDP path taking into account 

climate damages resulting from the baseline emissions path. 

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by 

model.) 

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE 

is run in 10-year time steps, FUND in annual time steps, while the time steps in 

PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions 

using the agreed upon fixed discount rates. 

7. Calculate the SCC as the net present value of the discounted path of damages 

computed in step 6, divided by the unit of carbon emissions used to shock the 

models in step 3. 

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of 

CO2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 

in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons 

anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, 

climate damages are calculated as lost consumption in each future year. 

 It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.) 

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 
14A-28

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than 

another. Because the literature shows that the SCC is quite sensitive to assumptions about the 

discount rate, and because no consensus exists on the appropriate rate to use in an 

intergenerational context, we present SCCs based on the average values across models and 

socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socioeconomic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the Annex.) 

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. 

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high-temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 14A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. As 

expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 
14A-29

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) 

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th DICE

IMAGE 10.8 35.8 54.2 70.8 

MERGE 7.5 22.0 31.6 42.1 

Message 9.8 29.8 43.5 58.6 

MiniCAM 8.6 28.8 44.4 57.9 

550 Average 8.2 24.9 37.4 50.8 

PAGE

IMAGE 8.3 39.5 65.5 142.4 

MERGE 5.2 22.3 34.6 82.4 

Message 7.2 30.3 49.2 115.6 

MiniCAM 6.4 31.8 54.7 115.4 

550 Average 5.5 25.4 42.9 104.7 

FUND

IMAGE -1.3 8.2 19.3 39.7 

MERGE -0.3 8.0 14.8 41.3 

Message -1.9 3.6 8.8 32.1 

MiniCAM -0.6 10.2 22.2 42.6 

550 Average -2.7 -0.2 3.0 19.4 

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.bb

 

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 
14A-30

The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models.

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5.1 Level of Global GDP across EMF Scenarios 

Table 14A.5.2 shows the four selected SCC values in five-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing
14A-31

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Annex. 

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050 

Average Annual 

Growth Rate (%)

5% 3% 2.5% 3.0%

Avg Avg Avg 95th

2010-2020 3.6% 2.1% 1.7% 2.2%

2020-2030 3.7% 2.2% 1.8% 2.2%

2030-2040 2.7% 1.8% 1.6% 1.8%

2040-2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis.

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in the year 2020 that are 
14A-32

calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc

14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

 Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

 Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans.

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

 Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-

 

cc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 
14A-33

impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium. 

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

 Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

 Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion. 

 

dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).
14A-34

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

 Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude 

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS

 As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems, (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming, and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

 It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 
14A-35

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14A.7.1. Ranges of probability are averaged across core experts on each 

topic. 

 As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 14A.4.1), 

so the high-end output from PAGE potentially offers the best insight into the SCC if the world 

were to experience catastrophic climate change. For instance, at the 95th percentile and a 3 

percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation 

Possible Tipping Points

Duration before

effect is fully

realized (in years)

Additional Warming by 2100 

0.5-1.5 C 1.5-3.0 C 3-5 C 

Reorganization of Atlantic Meridional 

Overturning Circulation about 100 0-18% 6-39% 18-67% 

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96% 

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88% 

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94% 

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49% 

Dieback of boreal forests about 50 13-43% 20-81% 34-91% 

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (that is, by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16-36 percent of 
14A-36

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact. 

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007). 

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009). 

 The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace.

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 
14A-37

14A.8 CONCLUSION

 The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. 

 We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 

 
14A-38

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14A-44

14A.9 ANNEX

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) 

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination. 
14A-45

14A.9.1 Other (non-CO2) gases 

 In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.

ee This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N20, SF6, and the CO2

emissions from land were replaced with the EMF values. 

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

"excess forcing" vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factorsff, decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N20, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time. 

 According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2

 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

 

ee Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial, and land CO2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4).

ff Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.
14A-46

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2, due to other residual forcing. 

 For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions: 

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter; and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above and 

remains constant over time. 

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, 

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black 

carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since the 

SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC 

projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides 

one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with 

the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.gg

 Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, the figure below shows that the sulfur 

dioxide emissions peak over the short term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.hh Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.ii The lowerbound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

 

gg AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850-2000: 

methods and results. Joint Global Research Institute, College Park, 14 pp.

ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental 

Science and Technology, 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M.

Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835-

1837.
14A-47

 With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2

. 

. 

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios 

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines 

show the median, 5th, and 95th percentile of the frequency distribution for the full 

ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) 

illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate 

the minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, 

http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-4.html. 

 Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6-7 percent (or $0.50-$3)–depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

 Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

14A.9.2 Extrapolating Emissions Projections to 2300 

 To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 
14A-48

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows: 

1. Population growth rate declines linearly, reaching zero in the year 2200. 

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300. 

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 2090-

2100 is maintained from 2100 through 2300. 

4. Net land use CO2 emissions decline linearly, reaching zero in the year 2200. 

5. Non-CO2 radiative forcing remains constant after 2100. 

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

 The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).jj The resulting range of EMF population trajectories (figure below) also encompass the 

UN medium scenario forecasts through 2300—global population of 8.5 billion by 2200, and 9 

billion by 2300. 

 Maintaining the decline in the 2090-2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

 

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300. 

 

jj United Nations. 2004. World Population to 2300. 

http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf
14A-49

Figures below show the paths of global population, GDP, fossil and industrial CO2

emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 intensity (fossil and 

industrial CO2 emissions/GDP) resulting from these assumptions. 

Figure 14A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations 

assume the population growth rate changes linearly to reach a 

zero growth rate by 2200.) 

Note: In the fifth scenario, 2000-2100 population is equal to the average of the population under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year
14A-50

Figure 14A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP 

per capita growth declines linearly, reaching zero in the year 

2300) 

Note: In the fifth scenario, 2000-2100 GDP is equal to the average of the GDP under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]
14A-51

Figure 14A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post2100 extrapolations assume growth rate of CO2 intensity 

(CO2/GDP) over 2090-2100 is maintained through 2300) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&Industrial CO2 Emissions [GtCO2/decade]

Year
14A-52

Figure 14A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero 

in the year 2200)kk

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

 

kk MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4).

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z
14A-53

Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing 

after 2100) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year
14A-54

Figure 14A.9.8 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 

2000-2300 (Post-2100 extrapolations assume decline in 

CO2/GDP growth rate over 2090-2100 is maintained through 

2300) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year
14A-55

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8
14A-56

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6
14A-57

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), 

by discount rate 

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been 

truncated at approximately the 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

14274053667992

105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%
14A-58

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate Scenario

DICE PAGE FUND

5% 

Mean 9 6.5 -1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3% 

Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50% 

Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30
14B-i 

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS 

14B.1 PREFACE ....................................................................................................................14B-1

14B.2 PURPOSE ....................................................................................................................14B-1

14B.3 SUMMARY OF MODEL UPDATES.........................................................................14B-2

14B.3.1 DICE ................................................................................................................14B-2

14B.3.1.1 Carbon Cycle Parameters .......................................................................14B-2

14B.3.1.2 Sea Level Dynamics ...............................................................................14B-3

14B.3.1.3 Re-calibrated Damage Function .............................................................14B-3

14B.3.2 FUND ...............................................................................................................14B-4

14B.3.2.1 Space Heating .........................................................................................14B-4

14B.3.2.2 Sea Level Rise and Land Loss ...............................................................14B-5

14B.3.2.3 Agriculture .............................................................................................14B-5

14B.3.2.4 Temperature Response Model ................................................................14B-6

14B.3.2.5 Methane ..................................................................................................14B-6

14B.3.3 PAGE ...............................................................................................................14B-6

14B.3.3.1 Sea Level Rise ........................................................................................14B-6

14B.3.3.2 Revised Damage Function to Account for Saturation ............................14B-7

14B.3.3.3 Regional Scaling Factors ........................................................................14B-7

14B.3.3.4 Probability of a Discontinuity ................................................................14B-7

14B.3.3.5 Adaptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes ....................................................................14B-8

14B.4 REVISED SCC ESTIMATES .....................................................................................14B-8

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

14B.6 ANNEX......................................................................................................................14B-14
14B-ii

LIST OFTABLES 

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ...........................................................................................................14B-9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-10

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) ..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16

LIST OF FIGURES 

Figure 14B.4.1 Distribution of SCC Estimates for 2010 (in 2007$ per ton CO2) ............14B-10
14B-1

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSISUNDER EXECUTIVE ORDER 12866

14B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report of the 

Interagency Working Group on the Social Cost of Carbon of the United States Government. 

Minor changes were made to the working group's report to make it more consistent with the rest 

of this technical support document. 

14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) a

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).1 E.O. 13563 commits the Administration to regulatory 

decision making “based on the best available science.”b Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available.c New 

versions of the three integrated assessment models used by the U.S. government to estimate the 

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer 

reviewed literature. While acknowledging the continued limitations of the approach taken by the 

interagency group in 2010 (documented in the original 2010 TSD), this document provides an 

update of the SCC estimates based solely on the latest peer-reviewed version of the models, 

replacing model versions that were developed up to ten years ago in a rapidly evolving field. It 

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic 

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models 

by the developers themselves in the peer-reviewed literature. The Environmental Protection 

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy 

(DOE), continues to investigate potential improvements to the way in which economic damages 

associated with changes in CO2 emissions are quantified. 

Section 14B.3 summarizes the major updates relevant to SCC estimation that are 

contained in the new versions of the integrated assessment models released since the 2010 

interagency report. Section 14B.4 presents the updated schedule of SCC estimates for 2010 – 

2050 based on these versions of the models. 

a

 In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67. b http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

c See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).1
14B-2

14B.3 SUMMARY OF MODEL UPDATES

 This section briefly summarizes changes integrated into the most recent versions of the 

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon. For 

example, both the DICE and PAGE models now include an explicit representation of sea level 

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to 

ensure damages are constrained GDP, updated regional scaling of damages, and a revised 

treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, 

the model’s simple carbon cycle has been updated to be more consistent with a relatively more 

complex climate model. The FUND model includes updated damage functions for sea level rise 

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to 

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect 

effects of methane emissions. Changes made to parts of the models that are superseded by the 

interagency working group’s modeling assumptions – regarding climate sensitivity, discounting, 

and socioeconomic variables – are not discussed. 

14B.3.1 DICE

 Changes in the DICE model relevant for the SCC estimates developed by the interagency 

working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit 

representation of sea level dynamics, and 3) a re-calibrated damage function that includes an 

explicit representation of economic damages from sea level rise. Changes were also made to 

other parts of the DICE model—including the equilibrium climate sensitivity parameter, the rate 

of change of total factor productivity, and the elasticity of the marginal utility of consumption—

but these components of DICE are superseded by the interagency working group’s assumptions 

and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008)2

and on DICE2010 in Nordhaus (2010)3 and the associated on-line appendix containing 

supplemental information. 

14B.3.1.1 Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).2d

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer version of MAGICC (Nordhaus 2010 p 2).3

 For example, in DICE2010 in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean. 

 

d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).4
14B-3

The implication of these changes for DICE2010 is in general a weakening of the ocean as 

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007. 

14B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website.e

 The average global sea level 

anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal 

expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland 

ice sheet, and 4) melting of the Antarctic ice sheet. 

 The parameters of the four components of the SLR module are calibrated to match 

consensus results from the IPCC’s Fourth Assessment Report.4,f The rise in sea level from 

thermal expansion in each time period (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900. 

 

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1 o

C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in 

each period is proportional to the difference between the previous period’s sea level anomaly and 

the equilibrium sea level anomaly, where the constant of proportionality increases with the 

temperature anomaly in the current period. 

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per 

decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C. 

14B.3.1.3 Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

 

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf.

f For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)5 and NAS (2011).6

 
14B-4

fraction of output in each period that is lost due to climate change impacts is represented as one 

minus a fraction, which is one divided by a quadratic function of the temperature anomaly, 

producing a sigmoid (“S”-shaped) function. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

 The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3

 who notes 

that “...damages in the uncontrolled (baseline) (i.e., reference) case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 o

C above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 o

C in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

14B.3.2 FUND

 FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors.g

 Notable changes, due to their impact 

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.h We discuss each of these in turn. 

14B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

 

g http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update 

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013).7 For the 

purpose of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by 

adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along 

with making minor stability improvements in the sea wall construction algorithm.

h The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not the subject of significant updates.
14B-5

has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the 

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at any level of warming will not exceed two times the 

reductions experienced at one degree of warming. Since the reduced need for space heating 

represents a benefit of climate change in the model, or a negative damage, this change will 

increase the estimated SCC. This update accounts for a significant portion of the difference in the 

expected SCC estimates reported by the two versions of the model when run probabilistically. 

14B.3.2.2 Sea Level Rise and Land Loss 

 The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

14B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 
14B-6

14B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore 

in FUND 3.8, the temperature response will typically be faster than in the previous version. The 

overall effect of this change is likely to increase estimates of the SCC as higher temperatures are 

reached during the timeframe analyzed and as the same damages experienced in the previous 

version of the model are now experienced earlier and therefore discounted less. 

14B.3.2.5 Methane

 The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8

 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

14B.3.3 PAGE

 PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10, 11, 12 A description of PAGE2002 can be found 

in Hope (2006).13

14B.3.3.1 Sea Level Rise

 While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE2009 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 
14B-7

categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

14B.3.3.2 Revised Damage Function to Account for Saturation 

 In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced.

14B.3.3.3 Regional Scaling Factors 

 As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

14B.3.3.4 Probability of a Discontinuity 

 In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated with or without a 

discontinuity occurring, rather than as an expected value. A large‐scale discontinuity becomes 

possible when the temperature rises beyond some threshold value between 2 and 4°C. The 

probability that a discontinuity will occur beyond this threshold then increases by between 10 

and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, 

the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a 

mean of 15 percent) in addition to other damages, and other regions lose an amount determined 

by the regional scaling factor. The threshold value for a possible discontinuity is lower than in 

PAGE2002, while the rate at which the probability of a discontinuity increases with the 

temperature anomaly and the damages that result from a discontinuity are both higher than in 

PAGE2002. The model assumes that only one discontinuity can occur and that the impact is 

phased in over a period of time, but once it occurs, its effect is permanent. 
14B-8

14B.3.3.5 Adaptation 

 As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to 

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. 

Similarly, adaptation is assumed to alleviate 25-50 percent of the damages from the first 0.20 to 

0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c)12 estimates 

that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea 

level rise via adaptation increase the SCC by approximately 30 percent.

14B.3.3.6 Other Noteworthy Changes

 Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

14B.4 REVISED SCC ESTIMATES

 The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD.1 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

rate remains the same. This includes the five reference scenarios based on the EMF-22 modeling 

exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth 

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent. 
14B-9

 As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central 

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values.

Table 14B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex. 

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

 The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. Figure 

14B.4.1 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution. 
14B-10

Figure 14B.4.1 Distribution of SCC Estimates for 2010 (in 2007$ per ton CO2) 

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 

through running them for a set of perturbation years out to 2050. Table 14B.4.2 illustrates how 

the growth rate for these four SCC estimates varies over time.

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050 

Average Annual 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.3% 2.4% 4.4%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.9% 1.5% 2.1%

2040-2050 2.6% 1.6% 1.3% 1.5%

 The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. 
14B-11

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

 The 2010 interagency SCC technical support report discusses a number of important 

limitations for which additional research is needed. In particular, the document highlights the 

need to improve the quantification of both non-catastrophic and catastrophic damages, the 

treatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications 

of risk aversion for SCC estimation as well as the inability to perfectly substitute between 

climate and non-climate goods at higher temperature increases, both of which have implications 

for the discount rate used. EPA, DOE, and other agencies continue to engage in long-term 

research work on modeling and valuation of climate impacts that we expect will inform 

improvements in SCC estimation in the future.
14B-12

REFERENCES 

1 Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf> 

2 Nordhaus, W., A Question of Balance. 2008. Yale University Press: New Haver. 

3 Nordhaus, W., Economic aspects of global warming in a post-Copenhagen environment.

Proceedings of the National Academy of Sciences, 2010. 107(26): pp. 11721-11726 

4 Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models 

and Their Evaluation. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University 

Press: Cambridge, United Kingdom and New York, NY, USA 

5 Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel 

and R.S.J. Tol, Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

twenty-first century. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161-181 

6 National Academy of Sciences, Climate Stabilization Targets: Emissions, 

Concentrations, and Impacts over Decades to Millennia. 2011. National Academies 

Press, Inc: Washington, DC.

7 Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change, 2013(Forthcoming) 

8 Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, 

J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van 

Dorland, Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. 

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New 

York, NY, USA

9 Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change, 

2012(Forthcoming) 
14B-13

10 Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf

11 Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School 

http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf

12 Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1108.pdf

13 Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 

 
14B-14

14B.6 ANNEX

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220
14B-15

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -14 -2 4 15 31 39 55 86 107 157

MERGE -6 1 6 14 27 35 46 70 87 141

MESSAGE -16 -5 1 11 24 31 43 67 83 126

MiniCAM Base -7 2 7 16 32 39 55 83 103 158

5th Scenario -29 -13 -6 4 16 21 32 53 69 103

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -13 -4 0 8 18 23 33 51 65 99

MERGE -7 -1 2 8 17 21 29 45 57 95

MESSAGE -14 -6 -2 5 14 18 26 41 52 82

MiniCAM Base -7 -1 3 9 19 23 33 50 63 101

5th Scenario -22 -11 -6 1 8 11 18 31 40 62
14B-16

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -4 -1 2 3 6 10 14 24

MERGE -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13
17A-i

APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ...................................................................................................... 17A-1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY....................................... 17A-2

17A.3 NIA-RIA INTEGRATED MODEL............................................................................ 17A-7

17A.4 MARKET PENETRATION CURVES ...................................................................... 17A-7

17A.4.1 Introduction ..................................................................................................... 17A-7

17A.4.2 Adjustment of XENERGY Penetration Curves ............................................ 17A-9

17A.4.3 Interpolation of Penetration Curves .............................................................. 17A-10

17A.5 CONSUMER REBATE PROGRAMS..................................................................... 17A-11

17A.6 FEDERAL AND STATE TAX CREDITS............................................................... 17A-14

17A.1.1 Federal Tax Credits for Consumers .............................................................. 17A-14

17A.6.1 Federal Tax Credits for Manufacturers......................................................... 17A-15

17A.6.2 State Tax Credits........................................................................................... 17A-16

REFERENCES ..................................................................................................................... 17A-17

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers ≤30.00 Pints/Day (TSL 3) ............ 17A-2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers 30.01-45.00 Pints/Day (TSL 3) .... 17A-3

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers >45.00 Pints/Day (TSL3) ............. 17A-3

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Whole-Home Dehumidifiers for ≤8.0 ft3 Case Volume 

(TSL 3)...................................................................................................... 17A-4

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Whole-Home Dehumidifiers >8.03 Case Volume (TSL 3). 17A-5

Table 17A.5.3 Rebates Programs for Dehumidifiers...................................................... 17A-12

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies .............................................................. 17A-9
17A-1

APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTINGMATERIALS 

17A.1 INTRODUCTION

This appendix contains sections discussing the following topics: 

• Projections of annual market share increases for the alternative policies;

• NIA-RIA Integrated Model;

• Market penetration curves used to analyze consumer rebates and voluntary energy

efficiency targets, including:

o Background material on XENERGY’s approach,

o DOE’s adjustment of these curves for this analysis, and

o The method DOE used to derive interpolated, customized curves;

• Detailed table of rebates offered for the considered product, as well as DOE’s approach

to estimate a market representative rebate value for this RIA; and

• Background material on Federal and State tax credits for appliances.
17A-2

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2.1 through Table 17A.2.5 show the annual increases in market shares of 

dehumidifiers meeting the target efficiency levels for the proposed TSL (TSL 3). DOE used 

these market share increases as inputs to the NIA-RIA spreadsheet model.

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers ≤30.00 Pints/Day (TSL 3) 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 4.8% 2.9% 1.5% 1.2% 0.02%

2020 4.8% 2.9% 1.5% 2.5% 0.03%

2021 4.8% 2.9% 1.5% 3.7% 0.05%

2022 4.8% 2.9% 1.5% 4.8% 0.06%

2023 4.8% 2.9% 1.5% 6.0% 0.08%

2024 4.8% 2.9% 1.5% 7.0% 0.10%

2025 4.8% 2.9% 1.5% 8.1% 0.11%

2026 4.8% 2.9% 1.5% 9.2% 0.13%

2027 4.8% 2.9% 1.5% 10.2% 0.15%

2028 4.8% 2.9% 1.5% 11.2% 0.16%

2029 4.8% 2.9% 1.5% 11.3% 0.16%

2030 4.8% 2.9% 1.5% 11.3% 0.16%

2031 4.8% 2.9% 1.5% 11.4% 0.16%

2032 4.8% 2.9% 1.5% 11.4% 0.16%

2033 4.8% 2.9% 1.5% 11.5% 0.16%

2034 4.8% 2.9% 1.5% 11.5% 0.16%

2035 4.8% 2.9% 1.5% 11.6% 0.16%

2036 4.8% 2.9% 1.5% 11.6% 0.16%

2037 4.8% 2.9% 1.5% 11.7% 0.16%

2038 4.8% 2.9% 1.5% 11.7% 0.16%

2039 4.8% 2.9% 1.5% 11.8% 0.16%

2040 4.8% 2.9% 1.5% 11.8% 0.16%

2041 4.8% 2.9% 1.5% 11.9% 0.16%

2042 3.0% 1.8% 0.9% 10.1% 0.14%

2043 0.0% 0.0% 0.0% 6.9% 0.10%

2044 0.0% 0.0% 0.0% 3.8% 0.06%

2045 0.0% 0.0% 0.0% 0.6% 0.03%

2046 0.0% 0.0% 0.0% 0.0% 0.00%

2047 0.0% 0.0% 0.0% 0.0% 0.00%

2048 0.0% 0.0% 0.0% 0.0% 0.00%
17A-3

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers 30.01-45.00 Pints/Day (TSL 3) 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2019 48.0% 28.8% 14.4% 32.2% 0.1%

2020 46.2% 27.7% 13.9% 33.4% 0.2%

2021 44.5% 26.7% 13.3% 34.2% 0.2%

2022 42.7% 25.6% 12.8% 34.7% 0.3%

2023 40.9% 24.5% 12.3% 34.9% 0.4%

2024 39.1% 23.5% 11.7% 35.0% 0.4%

2025 37.3% 22.4% 11.2% 34.9% 0.5%

2026 35.5% 21.3% 10.7% 34.7% 0.6%

2027 33.7% 20.2% 10.1% 34.4% 0.7%

2028 31.9% 19.1% 9.6% 34.0% 0.7%

2029 30.1% 18.0% 9.0% 32.5% 0.7%

2030 28.3% 17.0% 8.5% 30.9% 0.7%

2031 26.4% 15.9% 7.9% 29.3% 0.7%

2032 24.6% 14.8% 7.4% 27.7% 0.6%

2033 22.8% 13.7% 6.8% 26.0% 0.6%

2034 20.9% 12.6% 6.3% 24.4% 0.6%

2035 19.1% 11.5% 5.7% 22.8% 0.6%

2036 17.3% 10.4% 5.2% 21.1% 0.6%

2037 15.4% 9.2% 4.6% 19.5% 0.5%

2038 13.5% 8.1% 4.1% 17.8% 0.5%

2039 11.7% 7.0% 3.5% 16.1% 0.5%

2040 9.8% 5.9% 2.9% 14.5% 0.5%

2041 8.0% 4.8% 2.4% 12.8% 0.5%

2042 6.8% 4.1% 2.1% 11.0% 0.4%

2043 5.8% 3.5% 1.7% 9.3% 0.4%

2044 4.9% 2.9% 1.5% 7.6% 0.4%

2045 4.1% 2.5% 1.2% 5.8% 0.4%

2046 3.5% 2.1% 1.1% 4.1% 0.3%

2047 3.0% 1.8% 0.9% 2.3% 0.3%

2048 2.6% 1.6% 0.8% 0.6% 0.3%

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Portable Dehumidifiers >45.00 Pints/Day (TSL3) 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2019 7.3% 4.4% 2.2% 3.7% 0.1%

2020 7.3% 4.4% 2.2% 7.4% 0.1%

2021 7.3% 4.4% 2.2% 10.7% 0.2%

2022 7.3% 4.4% 2.2% 13.5% 0.3%

2023 7.3% 4.4% 2.2% 16.1% 0.3%
17A-4

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2024 7.3% 4.4% 2.2% 18.6% 0.4%

2025 7.3% 4.4% 2.2% 20.8% 0.5%

2026 7.3% 4.4% 2.2% 23.0% 0.5%

2027 7.3% 4.4% 2.2% 25.1% 0.6%

2028 7.3% 4.4% 2.2% 27.1% 0.7%

2029 7.3% 4.4% 2.2% 27.6% 0.7%

2030 7.3% 4.4% 2.2% 28.1% 0.7%

2031 7.3% 4.4% 2.2% 28.6% 0.7%

2032 7.3% 4.4% 2.2% 29.1% 0.7%

2033 7.3% 4.4% 2.2% 29.6% 0.7%

2034 7.3% 4.4% 2.2% 30.2% 0.7%

2035 7.3% 4.4% 2.2% 30.7% 0.7%

2036 7.3% 4.4% 2.2% 31.2% 0.7%

2037 7.3% 4.4% 2.2% 31.7% 0.7%

2038 7.3% 4.4% 2.2% 32.2% 0.7%

2039 7.3% 4.4% 2.2% 32.7% 0.7%

2040 7.3% 4.4% 2.2% 33.2% 0.7%

2041 7.3% 4.4% 2.2% 33.7% 0.7%

2042 7.3% 4.4% 2.2% 34.1% 0.7%

2043 7.3% 4.4% 2.2% 34.6% 0.7%

2044 7.3% 4.4% 2.2% 35.0% 0.7%

2045 7.3% 4.4% 2.2% 35.5% 0.7%

2046 7.3% 4.4% 2.2% 35.9% 0.7%

2047 7.3% 4.4% 2.2% 36.4% 0.7%

2048 7.3% 4.4% 2.2% 36.8% 0.7%

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Whole-Home Dehumidifiers for ≤8.0 ft3

 Case Volume (TSL 3) 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2019 35.8% 21.5% 10.7% 0.7% 0.1%

2020 34.4% 20.6% 10.3% 0.7% 0.1%

2021 34.5% 20.7% 10.3% 0.7% 0.2%

2022 34.5% 20.7% 10.4% 0.6% 0.2%

2023 34.5% 20.7% 10.4% 0.5% 0.3%

2024 34.5% 20.7% 10.4% 0.3% 0.3%

2025 34.4% 20.6% 10.3% 0.1% 0.4%

2026 33.4% 20.1% 10.0% 0.0% 0.4%

2027 32.5% 19.5% 9.7% 0.0% 0.4%

2028 31.6% 19.0% 9.5% 0.0% 0.5%

2029 30.3% 18.2% 9.1% 0.0% 0.5%
17A-5

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2030 29.0% 17.4% 8.7% 0.0% 0.5%

2031 27.6% 16.6% 8.3% 0.0% 0.4%

2032 26.2% 15.7% 7.9% 0.0% 0.4%

2033 24.9% 14.9% 7.5% 0.0% 0.4%

2034 23.6% 14.1% 7.1% 0.0% 0.4%

2035 22.3% 13.4% 6.7% 0.0% 0.4%

2036 21.0% 12.6% 6.3% 0.0% 0.4%

2037 19.8% 11.9% 5.9% 0.0% 0.3%

2038 18.6% 11.2% 5.6% 0.0% 0.3%

2039 17.5% 10.5% 5.2% 0.0% 0.3%

2040 16.4% 9.8% 4.9% 0.0% 0.3%

2041 15.3% 9.2% 4.6% 0.0% 0.3%

2042 14.4% 8.6% 4.3% 0.0% 0.2%

2043 13.5% 8.1% 4.0% 0.0% 0.2%

2044 12.6% 7.5% 3.8% 0.0% 0.2%

2045 11.8% 7.1% 3.5% 0.0% 0.2%

2046 11.0% 6.6% 3.3% 0.0% 0.2%

2047 10.2% 6.1% 3.1% 0.0% 0.2%

2048 9.6% 5.7% 2.9% 0.0% 0.1%

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Whole-Home Dehumidifiers >8.03

 Case Volume (TSL 3)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2019 10.9% 6.5% 3.3% 0.8% 0.1%

2020 10.9% 6.5% 3.3% 2.4% 0.1%

2021 10.9% 6.5% 3.3% 4.0% 0.2%

2022 10.9% 6.5% 3.3% 5.4% 0.3%

2023 10.9% 6.5% 3.3% 6.9% 0.3%

2024 10.9% 6.5% 3.3% 8.3% 0.4%

2025 10.9% 6.5% 3.3% 9.7% 0.5%

2026 10.9% 6.5% 3.3% 11.0% 0.5%

2027 10.9% 6.5% 3.3% 12.3% 0.6%

2028 10.9% 6.5% 3.3% 13.6% 0.7%

2029 10.9% 6.5% 3.3% 14.3% 0.7%

2030 10.9% 6.5% 3.3% 15.0% 0.7%

2031 10.9% 6.5% 3.3% 15.7% 0.7%

2032 10.5% 6.3% 3.2% 16.1% 0.7%

2033 9.7% 5.8% 2.9% 15.9% 0.7%

2034 8.8% 5.3% 2.6% 15.7% 0.7%

2035 7.9% 4.7% 2.4% 15.6% 0.6%
17A-6

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy 

Eff Targets

Bulk Govt 

Purchases

2036 7.0% 4.2% 2.1% 15.4% 0.6%

2037 6.1% 3.7% 1.8% 15.2% 0.6%

2038 5.2% 3.1% 1.6% 15.0% 0.6%

2039 4.3% 2.6% 1.3% 14.7% 0.6%

2040 3.4% 2.1% 1.0% 14.5% 0.6%

2041 2.5% 1.5% 0.8% 14.2% 0.6%

2042 1.6% 1.0% 0.5% 13.9% 0.6%

2043 0.7% 0.4% 0.2% 13.6% 0.6%

2044 0.0% 0.0% 0.0% 13.3% 0.5%

2045 0.0% 0.0% 0.0% 13.0% 0.5%

2046 0.0% 0.0% 0.0% 12.7% 0.5%

2047 0.0% 0.0% 0.0% 12.3% 0.5%

2048 0.0% 0.0% 0.0% 11.9% 0.5%
17A-7

17A.3 NIA-RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA-RIAa model approach that the Department 

built on the NIA model discussed in Chapter 10 and documented in Appendix 10-A.The 

resulting integrated NIA-RIA model features both the NIA and RIA inputs, analyses and results. 

It has the capability to generate results, by product class and TSL, for the mandatory standards 

and each of the RIA policies. Separate modules estimate increases in market penetration of more 

efficient equipment for consumer rebates, voluntary energy efficiency targets and bulk 

government purchases.b The consumer rebates module calculates benefit-cost (B/C) ratios and 

market barriers, and generates customized market penetration curves for each product class; the 

voluntary energy efficiency targets module relies on the market barriers calculated in the 

consumer rebates module to project a reduction in those barriers over the first ten years of the 

forecast period and estimate the market effects of such a reduction; and the bulk government 

purchases module scales down the market for dehumidifiers to housing units in public housing 

authority. A separate module summarizes the market impacts from mandatory standards and all 

policy alternatives, and an additional module produces all tables and figures presented in Chapter 

17 as well as the tables of market share increases for each policy reported in Section 17A.2 of 

this Appendix.

17A.4 MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates and Voluntary Energy Efficiency Targets policies. 

Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the 

method it used to develop interpolated penetration curves for dehumidifiers that meet the target 

efficiency levels at each TSL. The resulting curves are presented in Chapter 17. 

17A.4.1 Introduction

XENERGY, Inc.c

, developed a re-parameterized, mixed-source information diffusion 

model to estimate market impacts induced by financial incentives for purchasing energy efficient 

appliances.

1 The basic premise of the mixed-source model is that information diffusion drives 

the adoption of technology. 

Extensive economic literature describes the diffusion of new products as technologies 

evolve. Some research focuses primarily on developing analytical models of diffusion patterns 

applicable to individual consumers or to technologies from competing firms.

2, 3, 4

 One study 

records researchers’ attempts to investigate the factors that drive diffusion processes.

5 Because a 

new product generally has its own distinct characteristics, few studies have been able to 

a NIA = National Impact Analysis; RIA = Regulatory Impact Analysis

b As mentioned in Chapter 17, the increase in market penetrations for consumer tax credits and manufacturer tax 

credits are estimated as a fraction of the increase in market penetration of consumer rebates. 

c XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17A-8

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase 

new products at the same time: some act quickly after a new product is introduced; others wait 

for the product to mature. Third, diffusion processes can be characterized approximately by 

asymmetric S-curves that depict three stages of diffusion: starting, accelerating, and decreasing 

(as the adoption ceiling is approached). 

A so-called epidemic model of diffusion is used widely in marketing and social studies. 

The epidemic model assumes that (1) all consumers place identical value on the benefits of a 

new product, and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase a new product is information about the availability and 

benefits of the product. In other words, information diffusion drives consumers’ adoption of a 

new product.

3

 The model incorporates information diffusion from both internal sources (spread

by word of mouth from early adopters to prospective adopters) and external sources (the 

“announcement effect” produced by government agencies, institutions, or commercial 

advertising). The model incorporates both internal and external sources by combining a logistic 

function with an exponential function.4,5 

The relative degree of influence from the internal and external sources determines the 

general shape of the diffusion curve for a specific product.4,5 If adoption of a product is 

influenced primarily by external sources of information (the announcement effect), for instance, 

a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources 

provide immediate information exposure to a significant number of prospective adopters. In 

contrast, internal sources (such as a network of prospective adopters) are relatively small in size 

and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, 

information diffusion dominated by external sources is represented by a concave curve (the 

exponential curve in Figure 17A.4.1). If adoption of a new product is influenced most strongly 

by internal sources of information, the number of adopters increases gradually, forming a convex 

curve (the logistic curve in Figure 17A.4.1). 
17A-9

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on Adoption of 

New Technologies

17A.4.2 Adjustment of XENERGY Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later 

conducted similar California studies, DOE made some adjustments to XENERGY’s original 

implementation (penetration) curves.6 The experiences with utility programs since the 

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as 

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues 

and other non-economic factors. DOE therefore adjusted the maximum penetration parameters 

for some of the curves from 80 percent to the following levels: 

Moderate Barriers: 70% 

High Barriers: 60% 

Extremely High Barriers: 50% 

The low barriers and no barriers curves (the latter used only when a product has a very 

high base-case-market share) remained, respectively, with 80 percent and 100 percent as their 

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set 

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively, 

for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

that the policy case market share cannot be great than 100 percent, as might occur for products 

with high base case market shares of the target-level technology.
17A-10

17A.4.3 Interpolation of Penetration Curves 

As discussed above, the XENERGY penetration (implementation) curves followed a 

functional form to estimate the market implementation rate caused by energy efficiency 

measures such as consumer rebates.d The XENERGY report presents five reference market 

implementation curves that vary according to the level of market barriers to technology 

penetration.1 Such curves have been used by DOE in the Regulatory Impact Analyses for 

rulemakings for appliance energy efficiency standards to estimate market share increases in 

response to rebate programs.e

 They provide a framework for evaluating technology penetration, 

yet require matching the studied market to the curve that best represents it. This approximate 

matching can introduce some inaccuracy to the analysis. 

Blum et al (2011, Appendix A)7 presents an alternative approach to such evaluation: a 

method to estimate market implementation rates more accurately by performing interpolations of 

the reference curves. The referred report describes the market implementation rate function and 

the reference curves, the method to calibrate the function to a given market, and the limitations 

of the method. 

DOE used the above referred method to interpolate market implementation curves, to 

generate customized curves that were used to estimate the effects of consumer rebates and 

voluntary energy efficiency targets for each product class covered by this RIA. For consumer 

rebates, DOE derived such curves based on an algorithm that finds the market implementation 

curve that best fits, for the first year of the analysis period, the B/C ratio of the target efficiency 

level and the market penetration of equipment with that level of energy efficiency in the base 

case. For the analysis of voluntary energy efficiency targets, DOE departs from the market 

barriers level corresponding to the market implementation curve it derived for consumer rebates, 

to linearly decrease it over the ten initial years of the analysis period. For each year, as market 

barriers decline, the corresponding market implementation curve leads – for the same B/C ratio – 

to higher market penetrations. 

d The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. e DOE has also used this method to estimate market share increases resulting from consumer tax credit and 

manufacturer tax credit programs, since the effects of tax credits on markets are considered in this RIA proportional 

to the impacts from rebates. 
17A-11

17A.5 CONSUMER REBATE PROGRAMS

DOE performed a search for rebate programs that offered incentives for dehumidifiers. 

Some organizations nationwide, comprising electric utilities and regional agencies, offer rebate 

programs for this. Table 17A.5.3 provides the organizations’ names, states, rebate amounts, and 

program websites (as they were available in September, 2014. 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 that table. 

DOE relied on the data it gathered from the 34 rebate programs offered by the 29 

organizations listed in Table 17A.5.3 to calculate a market representative rebate amount for 

dehumidifiers. DOE calculated the market representative rebate amount for dehumidifiers as the 

simple average of the rebate values offered by the programs listed in Table 17A.5.3. The 

representative rebate is $24.05 (in 2013$).f

 

f The former $24.41, average value expressed in 2014$ was deflated to 2013$ based on the ratio of the average 2013 

CPI and the average 2014 CPI (January through September).
17A-12

Table 17A.5.1 Rebates Programs for Dehumidifiersg 

Organization State Rebate* Website

Empire AR $50 https://www.empiredistrict.com/EnergySolution

s/Electric.aspx

Bright Energy Solutions 

(Offered by 16 Utilities) IA $10 http://www.brightenergysolutions.com/municipalities/?category=home&state=ia

Cedar Falls Utilities IA $25 http://www.cfu.net/save-energy/residentialrebates.aspx

Central Iowa Power 

Cooperative (offered by 

11 utilities)

IA $25 http://www.cipco.net/members.cfm

NIPSCO IN $40 http://www.dsireusa.org/incentives/incentive.cf

m?Incentive_Code=IN54F

Cape Light Compact MA $30 http://www.capelightcompact.org/rebatesapplications/resrebates/

Mansfield Municipal 

Electric Department MA $50 http://www.mansfieldelectric.com

MuniHELPS (offered by 

3 utilities through the 

MMWEC )

MA $25 http://www.munihelps.org/energy-rebateprograms.html

MuniHELPS (offered by 

8 utilities through the 

MMWEC )

MA $20 http://www.munihelps.org/energy-rebateprograms.html

Reading Municipal Light 

Department (RMLD) MA $25 http://www.rmld.com/sites/rmld/files/file/file/rebate.pdf

Baltimore Gas & Electric 

Company (BGE) 

SmartEnergy Savers 

Program

MD $25 http://www.bgesmartenergy.com/residential/lig

hting-appliances/dehumidifiers

Be SMART Home 

Efficiency Rebate 

Program

MD $25 http://www.mdhousing.org/website/programs/B

eSmart/rebate.aspx

Delmarva Power MD $25 http://homeenergysavings.delmarva.com/applia

nce-rebate-program/overview/dehumidifiers

PotomacEdison MD $25 http://energysavemdhome.com/appliance/energy-appliance-rebates/

Coldwater Board of 

Public Utilities 

(Efficiency Smart)

MI $20 http://www.efficiencysmart.org/for-yourhome/efficient-product-rebates

g This table is based on rebate programs DOE found to be available through an extensive internet search during

September, 2014. Some of the programs referenced—and consequently their websites—may no longer be available 

by the time this document is published. 
17A-13

Organization State Rebate* Website

DTE Energy MI $25 

https://www2.dteenergy.com/wps/portal/dte/resi

dential/saveEnergy/details/rebates%20and%20o

ffers/dehumidifier%20rebate%20application/!ut

/p/b1

Energy Optimization 

(offered by 11 utilities) MI $25 http://www.michigan-energy.org

Energy Smart Program -

Bay City, Michigan MI $25 http://www.baycityenergysmart.org/apply-now---home-rebates.html

Energy Smart Program 

(offered by 15 utilities) MI $15 http://www.mienergysmart.com/zeeland.html

Lansing Board of Water 

& Light MI $25 http://www.lbwl.com/energysavers/

Alexandria Light and 

Power MN $10 http://www.alputilities.com/residential/rebates.php

Bright Energy Solutions 

(Offered by 22 Utilities) MN $10 http://www.brightenergysolutions.com/municipalities/?category=home&state=mn

Duluth Energy Efficiency 

Program (DEEP) MN $10 http://duluthenergy.org/resources/financial

Southern Minnesota 

Municipal Power Agency 

(Offered by 13 utilities)

MN $65 

http://smmpa.org/members/wells-publicutilities/home-services/energy-star%C2%AEfor-your-home-(1).aspx

Bright Energy Solutions 

(Offered by 5 Utilities) ND $10 http://www.brightenergysolutions.com/municipalities/?category=home&state=nd

Orange & Rockland NY $10 

http://www.oru.com/programsandservices/incen

tivesandrebates/greenteam/residentialprograms/

electricappliancerebate.html

American Municipal 

Power (Public Electric 

Utilities) - Efficiency 

Smart Residential 

Program

OH $20 http://www.efficiencysmart.org/for-yourhome/efficient-product-rebates

First Energy Ohio -

Residential Efficiency 

Rebates

OH $25 https://www.firstenergycorp.com/content/custo

mer/save_energy/save_energy_ohio.html

FirstEnergy (MetEdison, 

Penelec, Penn Power, 

West Penn Power)

PA $25 https://www.firstenergycorp.com/save_energy/s

ave_energy_pennsylvania.html

Bright Energy Solutions 

(Offered by 11 Utilities) SD $10 http://www.brightenergysolutions.com/municipalities/?category=home&state=sd

Efficiency Vermont VT $25 

https://www.efficiencyvermont.com/For-MyHome/ways-to-save-andrebates/Appliances/Dehumidifiers/General-Info

Eau Claire Energy WI $25 http://documents.ecec.com/documents/incentive
17A-14

Organization State Rebate* Website

Cooperative s/appliances/Incentive_Appliances.pdf

Riverland Energy 

Cooperative WI $25 http://riverlandenergy.com/content/rebates

Appalachian Power WV $25 https://www.appalachianpower.com/save/progra

ms/SmartLightingProgram.aspx

* In 2014$.

17A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

17A.1.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.8, 9 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).10 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.8, 11

The tax credit for furnace fans was $50 in 2011, after which it expired. 

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 

credit had had a notable impact on sales of higher-efficiency heating and cooling equipment. 

Some distributors combined the Federal tax credits with manufacturer rebates and utility 

program rebates for a greater consumer incentive. However, when the amount of the Federal tax 

credit was reduced, smaller utility rebate incentives had not induced the same levels of 

equipment sales increases. The decrease in incentive size from a $1,500 cap in 2009-2010 to a 

$500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a 

decline in total sales of residential HVAC products. Distributors stated that an incentive needed 

to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence 

consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline 

in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 

SEER to 2009 levels (after an increase in 2010). The large majority of distributors observed no 

impacts from the utility programs with their lower rebate amounts available in 2011. Distributors 
17A-15

also commented on the advantages of the Federal tax credit being nationwide in contrast to 

utility rebate programs that target regional markets.12, 13

In an effort to evaluate the potential impact of a Federal appliance tax credit program, 

DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed 

Form 5695, Residential Energy Credits.14 It also estimated the percentage of taxpayers with 

entries under Form 5695’s section 3, Residential energy property costs, line 3b, qualified natural 

gas, propane, or oil furnace or hot water boiler. DOE reasoned that the percentage of taxpayers 

with an entry on Line 3b could serve as a rough indication of the potential of taxpayer 

participation in a Federal tax credit program for furnaces during the initial program years. DOE 

found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 

2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those 

filing Form 5695 for any qualifying energy property expenditure (which also included 

installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 

respectively. 

DOE also reviewed data from an earlier Federal energy conservation tax credit program 

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located 

data for only the first three years of the program.

15, 16, 17 For those three years - 1979, 1980, and 

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 

percent. Given that the data from this earlier tax credit program were not disaggregated by type 

of energy property, this data series served only to indicate a possible trend of greater 

participation in the initial program year, followed by slightly smaller participation in subsequent 

years. However, DOE did not find detailed analysis of this program to indicate the possible 

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 

2005 energy tax credit program data for its first two program years. 

As discussed in Chapter 17, DOE analyzed the percentage of participation in consumer 

tax credit programs using its estimates of consumer participation in rebate programs that was 

based on benefit/cost data specific to each product class of dehumidifiers covered by this RIA. 

Hence it was difficult to compare these detailed estimates to the more general data analysis 

described above from the existing Federal tax credit program, or to use the IRS data analysis in 

its consumer tax credit analysis.

17A.6.1 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. 
17A-16

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. For 2012-

2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers 

for clothes washers and dishwashers.

11 The credit amounts applied to each unit manufactured. 

The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers was 

capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator 

(30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to 

manufacturers was capped at $25 million for 2011, with the most efficient refrigerators (35%) 

and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.20

17A.6.2 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient 

appliances for several years, and the States of Kentucky, Michigan and Indiana began offering 

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on 

taxpayer participation in credits for eligible products. (See the discussion in Chapter 17, Section 

17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon 

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.21, 22 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).21, 23 

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.24 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 

source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water 

boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water 

heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures; and 

controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to 

$150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in 

new construction or remodeling projects. The tax credit covers only that part of the cost and 

materials that exceed established standards of construction. 
17A-17

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.)

<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

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