17-i

CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION................................................................................................17-1

17.2 NON-REGULATORY POLICIES........................................................................17-1

17.2.1 Methodology ........................................................................................................17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies.................................................17-3

17.2.3 Policy Interactions................................................................................................17-4

17.3 ASSUMPTIONS REGARDING NON-REGULATORY POLICIES ....................17-4

17.3.1 No New Regulatory Action...................................................................................17-4

17.3.2 Consumer Rebates................................................................................................17-4

17.3.2.1 Methodology ...................................................................................... 17-5

17.3.2.2 Analysis ............................................................................................. 17-6

17.3.3 Consumer Tax Credits ..........................................................................................17-9

17.3.4 Manufacturer Tax Credits...................................................................................17-10

17.3.5 Voluntary Energy Efficiency Targets..................................................................17-11

17.3.6 Early Replacement..............................................................................................17-12

17.3.7 Bulk Government Purchases...............................................................................17-14

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES ..................................17-15

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards .....................................17-1

Table 17.2.1 Efficiency Levels in Proposed Standards for Residential Clothes 

Washers.......................................................................................................17-4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Residential Clothes 

Washers (2010$)..........................................................................................17-7

Table 17.3.2 Market Penetrations in 2015 Attributable to Rebates for Residential 

Clothes Washers ..........................................................................................17-8

Table 17.3.3 Market Penetrations in 2015 Attributable to Consumer Tax Credits for 

Residential Clothes Washers......................................................................17-10

Table 17.3.4 Market Penetrations in 2015 Attributable to Manufacturer Tax Credits 

for Residential Clothes Washers.................................................................17-11

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Residential Front-Loading 

Clothes Washers that Meet the Proposed Consensus Standards (TSL 3).....17-17

Table 17.4.2 Impacts of Non-Regulatory Alternatives for Residential Top-Loading 

Clothes Washers that Meet the Proposed Consensus Standards (TSL 3).....17-18

Table 17.4.3 Impacts of Non-Regulatory Alternatives for All Residential Clothes 

Washers that Meet the Proposed Consensus Standard (TSL 3) ...................17-19

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curve for Front-Loading Clothes Washers.....................17-7

Figure 17.3.2 Market Penetration Curve for Top-Loading Clothes Washers.......................17-8
17-ii

Figure 17.4.1 Market Penetration of Front-Loading Clothes Washers Meeting Target 

Efficiency Levels in Various Policy Cases ................................................. 17-16

Figure 17.4.2 Market Penetration of Top-Loading Clothes Washers Meeting Target 

Efficiency Levels in Various Policy Cases ................................................. 17-16
17-1

CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that energy conservation 

standards for consumers of residential clothes washers constitute an “economically significant 

regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 

51735 (October 4, 1993). Under 10 CFR part 430, subpart C, appendix A, section III.12, DOE 

committed to evaluating non-regulatory alternatives to proposed standards by performing a 

regulatory impact analysis (RIA). 61 FR 36978 (November 15, 1996). This RIA, which DOE has 

prepared pursuant to E.O. 12866, evaluates potential non-regulatory alternatives, comparing the 

costs and benefits of each to those of the proposed standards. 58 FR 51735, 51741. As noted in

E.O. 12866, this RIA is subject to review by the Office of Management and Budget’s Office of 

Information and Regulatory Affairs. 58 FR 51735, 51740.

For this RIA, DOE used an integrated model built on the national impacts analysis (NIA)

model discussed in chapter 10. DOE studied the impacts of non-regulatory policies for the two 

product classes that are the subject of this rulemaking: front-loading and top-loading clothes 

washers. DOE identified six non-regulatory policy alternatives that potentially could achieve the 

same energy efficiency levels as the proposed consensus standards. The non-regulatory policy 

alternatives are listed in Table 17.1.1. DOE evaluated each alternative in terms of its ability to 

achieve significant energy savings at a reasonable cost, and compared the effectiveness of each 

to the effectiveness of the proposed standards. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Early Replacement

Bulk Government Purchases

Sections 17.2 and 17.3 describe DOE’s analysis of the six policies listed above. Sections 

17.4 and 17.5 present the results of the analysis.

17.2 NON-REGULATORY POLICIES

This section describes the methods DOE used to analyze the energy savings and costeffectiveness of the six non-regulatory policy alternatives (excluding the alternative of no new 

regulatory action) for residential clothes washers. This section also describes the assumptions 

underlying the analysis. 
17-2

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy 

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17-A, section 17-A.3, documents DOE’s use of the integrated NIA-RIA model.

DOE quantified the effect of each alternative on the purchase of products that meet target 

levels, which are defined as the efficiency levels in the proposed consensus standards. After 

establishing the assumptions underlying each alternative, DOE revised inputs to the NIA-RIA

model. The primary revised inputs were market shares of products meeting target efficiency 

levels and product replacement rates. The shipments of products for any given year reflect a 

distribution of efficiency levels. DOE assumed that the proposed standards would affect 100 

percent of the shipments of products that do not meet 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 

residential clothes washers attributable to each policy alternative. 

Increasing the efficiency of a product often increases its average installed cost. Operating 

costs generally decrease, however, 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 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.

 

a The base case for the NIA is a market-weighted average of units that operate at several efficiency levels.
17-3

The following are key measures for evaluating the impact of each alternative. 

• National energy savings (NES), given in quadrillion British thermal units (quads), 

describes the cumulative national primary energy savings for products bought from 

the effective date of the policy (2015) through the end of the analysis period (2044).

• Net present value (NPV) represents the value in 2010$ (discounted to 2011) of net 

monetary savings from products bought from the effective date of the policy (2015) 

through the end of the analysis period (2044). 

• DOE calculated the NPV as the difference between the present value of installed 

product costs and operating expenditures in the base case and the present value of 

those costs in each policy case. DOE calculated operating expenses (including energy

costs) for the life of the product.

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 consumers’ responses to a program. Because the 

projected effects depend on assumptions regarding the rate of consumer participation, they are 

subject to more uncertainty than are the impacts of mandatory standards, which DOE assumes 

will meet with full compliance. To increase the robustness of the analysis, DOE conducted a 

literature review regarding each non-regulatory policy and consulted with recognized experts to 

gather information on similar incentive programs that have been implemented in the United 

States. By studying experiences with various programs, DOE sought to make credible 

assumptions regarding potential market impacts. Section 17.3 presents the sources DOE relied on 

in developing assumptions about each alternative policy and reports DOE’s conclusions as they 

affected the modeling of each policy.

Each non-regulatory policy that DOE considered would improve the average efficiency 

of new residential clothes washers relative to their base-case efficiency scenarios (which involve 

no new regulatory action). The analysis considered that each alternative policy would induce 

consumers to purchase units having the same (target) efficiency levels as required by the 

proposed standards. As opposed to the standards case, however, the policy cases may not lead to 

100 percent market penetration of units that meet target levels.

Table 17.2.1 shows the efficiency levels stipulated in the proposed standards for 

residential clothes washers.
17-4

Table 17.2.1 Efficiency Levels in Proposed Standards for Residential Clothes Washers

Front-Loading Top-Loading

TSL IMEF* IWF† IMEF IWF

Consensus

(3) 1.84 4.73 1/1/2015 1.29 1/1/2015 8.44

1/1/2018 1.57 1/1/2018 6.47 * IMEF = integrated modified energy factor. † IWF = integrated water factor.

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2015—through the end of the analysis period, which is 2044. 

17.2.3 Policy Interactions 

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as early replacement implemented with consumer rebates, or early replacement 

implemented with bulk government purchases. However, DOE made conservative assumptions 

to avoid double-counting policy impacts. The resulting impacts may not be added; the combined 

effect of several or all policies cannot be inferred from summing their results. 

Section 17.4 presents graphs that illustrate the market penetration estimated under each 

non-regulatory policy, along with tables that summarize the impacts of the non-regulatory policy 

alternatives for residential clothes washers.

17.3 ASSUMPTIONS REGARDING NON-REGULATORY POLICIES

The following subsections describe DOE’s analysis of the impacts of the six nonregulatory policy alternatives. Because the alternative of No New Regulatory Action does not 

affect energy use or NPV, but essentially represents the NIA base case, DOE did not analyze that 

alternative further. DOE developed estimates of the market penetration of high-efficiency 

products both with and without each of the non-regulatory policy alternatives. 

17.3.1 No New Regulatory Action 

The case in which no new regulatory action is taken with regard to the energy efficiency 

of residential clothes washers constitutes the base case, as described in Chapter 10, National 

Impact Analysis. The base case provides the basis of comparison for all other policies. By 

definition, no new regulatory action yields zero energy savings and an NPV of zero dollars.

17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide consumers 

with financial incentives, in the form of rebates, for purchasing energy-efficient appliances. This 

policy provides a consumer rebate for purchasing clothes washers that operate at (or above) the 

target efficiencies stipulated in the proposed standards.
17-5

17.3.2.1 Methodology

To inform its estimate of the market impacts of consumer rebates, DOE researched utility 

and agency rebate programs for residential clothes washers nationwide. DOE also reviewed the 

current State Energy Efficient Appliance Rebate Program (SEEARP) funded by the American 

Recovery and Reinvestment Act (ARRA).

1, b

SEEARP may be considered a combination of a 

consumer rebate and an early replacement program, with intention to induce appliance sales 

during the economic recession. DOE analyzed summary material from DOE on SEEARP rebates 

for clothes washers.

2

DOE also incorporated previous research that disaggregated the effect of rebates 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.

3, 4 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 policy would encompass both direct price effects and announcement effects, and 

that half the increase in market penetration associated with the policy would be due to the direct 

price effect and half to the announcement effect.

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. This study, performed by XENERGY,

Inc.,c summarized experiences with various utility rebate programs.53 XENERGY’s analytical 

method utilized 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, 6, 7, 8, 9, 10, 11

9

used different economic parameters (e.g., payback period), expert surveys, 

or model calibration based on specific utility program data rather than multi-utility data. Some 

models used in evaluating programs were so client-specific that generic relationships between 

economic parameters and consumer response could not be established. DOE decided that the 

most appropriate method for this RIA analysis was the XENERGY approach of penetration 

curves based on B/C ratio, which incorporates lifetime operating cost savings.

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new products primarily 

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17-A, section 17-A.4.1, contains additional details on internal and external information diffusion.

 

b SEEARP provides Federal funding for State-run rebate programs for purchases of new ENERGY STAR-qualified 

home appliances. SEEARP was implemented in late 2009 by the 50 States and six U.S. territories, each selecting its 

own appliances, rebate levels, efficiency levels, appliance recycling requirements, and eligible populations.

c XENERGY is now owned by KEMA, Inc. (www.kema.com).
17-6

XENERGY’s model 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 measure. XENERGY then calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient products driven by consumer response to rebate programs that change the B/C 

ratio. The penetration curves depict various diffusion patterns based on perceived barriers (from 

no barriers to extremely high barriers) to consumer purchase of high-efficiency products.

DOE adjusted the XENERGY penetration curves based on expert advice founded on 

more recent utility program experience.9, 12

17.3.2.2 Analysis 

DOE also devised an interpolation method to create 

penetration curves based on relationships between the actual base-case market penetrations and 

B/C ratios for each product class. Appendix 17-A, sections 17-A.4.2 and 17-A.4.3, discuss

DOE’s method for adjusting and interpolating the curves.DOE modeled the effects of a

consumer rebate policy for residential clothes washers by determining the increases in market 

penetration of products meeting the target level relative to their market penetrations in the base 

case. It did this using the interpolated penetration curves created for each product class based on 

the XENERGY methodology to reflect the market barrier levels faced by that product class.

DOE estimated the effect of increasing the B/C ratio of each product class via a rebate 

that would pay part or all of the increased installed cost of a unit meeting the target efficiency

level compared to one meeting the baseline efficiency level.

d DOE based the rebate amounts on a 

large sample of 163 rebates for residential clothes washers initiated by 119 utilities or agencies in 

various States. Appendix 17-A, section 17-A.5.1, identifies and provides details regarding utility

rebate programs. DOE calculated the simple average of the rebate amounts for units meeting or 

exceeding the target levels in the various programs. DOE assumed that those average rebate

amounts would apply to all units that meet or exceed the target efficiency level for each product 

class. DOE assumed that rebates would remain in effect at the same levels throughout the 

forecast period (2015–2044). 

For each product class, DOE first calculated the B/C ratio without a rebate using the 

difference in total installed costs and lifetime operating cost savings between the unit meeting the 

target level and the baseline unit. It then calculated the B/C ratio given a rebate for the unit 

meeting the target efficiency level. Because the rebate reduced the incremental cost, the unit 

receiving the rebate had a larger B/C ratio. Table 17.3.1 shows the effects of consumer rebates 

on B/C ratios for both product classes. Each B/C value for units given rebates represents a 

 

d The baseline technology for each product class is defined in the engineering analysis, chapter 5, as the technology 

that represents the basic characteristics of products in that class. A baseline unit typically is one that just meets 

current Federal energy conservation standards and provides basic consumer utility. 
17-7

weighted averagee of the values for the efficiency levels at or above the target level to which the 

rebate would apply.

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Residential Clothes 

Washers (2010$)

Front-Loading Top-Loading

B/C Ratio Without Rebate 33.8 36.6

Rebate Amount $82 $61

B/C Ratio with Rebate Inf.* Inf.

Calculated Market Barrier Curve Low–Moderate X–high

* Inf. = infinite B/C ratio, which occurs when the rebate pays the full incremental cost.

DOE used the B/C ratios along with the penetration curves shown in Figures 17.3.1 and

17.3.2 to estimate the percentage of consumers who would purchase clothes washers that meet 

the target levels both with and without a rebate incentive. The curve calculated by DOE to 

represent the market behavior for front-loading clothes washers was between the low and 

moderate barriers penetration curves. For top-loading clothes washers the curve was close to the 

extremely high barriers curve.

Figure 17.3.1 Market Penetration Curve for Front-Loading Clothes Washers

 e The weighting factor is the 2015 base-case market share of each corresponding efficiency level. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

Penetration Rate

Benefit/Cost Ratio

Low Estimated Moderate No Rebate With Rebate
17-8

Figure 17.3.2 Market Penetration Curve for Top-Loading Clothes Washers

For each product class, DOE next estimated the percent increase represented by the 

change in penetration rate shown by the penetration curves. It then added that 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 case. Table 17.3.2 summarizes the market shares for 

the two product classes in 2015. DOE used the resulting increases in market shares as inputs to 

represent the policy-case scenarios in its NIA-RIA model. Appendix 17-A, section 17-A.2,

shows the annual market share increases attributable to this policy. Section 17.4 below presents 

the resulting efficiency trends for the policy case of consumer rebates for residential clothes 

washers.

Table 17.3.2 Market Penetrations in 2015 Attributable to Rebates for Residential Clothes 

Washers

Front-Loading Top-Loading

%

Base-Case Market Share of Units that 

Meet Target Levels

%

72 13

Market Share of Units that Meet Target 

Levels with Rebates 77 33

Increased Market Share of Units that 

Meet Target Level with Rebates 5 20

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

Penetration Rate

Benefit/Cost Ratio

High Estimated xHigh No Rebate With Rebate
17-9

17.3.3 Consumer Tax Credits

DOE used its previous analysis of consumer participation in tax credits to estimate the 

effects of tax credits on consumer purchases of target-level clothes washers. In estimating the 

effects, DOE assumed the amount of the tax credits for each product class would be the same as

the corresponding rebate amounts described above. As with rebates, DOE incorporated previous 

research that disaggregated the effect of 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.3, 4 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 consumer tax credit policy, like 

the rebate policy, would encompass both direct price effects and announcement effects, and that 

half the increase in market penetration associated with the policy would be due to the direct price 

effect and half to the announcement effect.

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.13

In developing its assumptions, DOE reviewed other Federal and State tax credit programs 

offered for energy efficient appliances. The Energy Policy Act of 2005 (EPACT 2005) included 

Federal tax credits for consumers who purchased energy efficient products, including water 

heaters, furnaces, and furnace fans for new or existing homes. 14

1

Those tax credits were in effect 

in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by ARRA. , 15 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 from the 1980s. DOE did not find data specific enough to 

residential clothes washers to warrant adjusting its analysis method for the policy case of 

consumer tax credits. Appendix 17-A, section 17-A.6.1, contains more information on Federal 

consumer tax credits. 

DOE supported its approach using data from Oregon State’s tax credit program for 

energy efficient appliances. DOE reviewed its previous analysis of Oregon’s tax credits for 

clothes washers. 16 In the 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 efficiencies. This 

finding supports DOE’s 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 is in 

Appendix 17-A, section 17-A.6.3.
17-10

DOE applied the assumed 60-percent participation rate to the penetration rates estimated 

for the rebate policy to estimate penetration rates attributable to consumer tax credits. In doing 

so, DOE incorporated the assumptions for consumer response to financial incentives from the 

penetration curves selected for each product class. Table 17.3.3 summarizes DOE’s assumptions 

regarding the market penetration in 2015 of residential clothes washers that meet target 

efficiency levels given a consumer tax credit. 

Table 17.3.3 Market Penetrations in 2015 Attributable to Consumer Tax Credits for 

Residential Clothes Washers

Front-Loading

%

Top-Loading

Base-Case Market Share of Units that Meet 

Target Levels

%

72 13

Market Share of Units that Meet Target 

Levels with Consumer Tax Credits 75 25

Increased Market Share of Units that Meet 

Target Level with Consumer Tax Credits 3 12

DOE assumed that this policy would transform the market permanently, so that the 

increases in market shares seen in the first year of the program would be maintained throughout 

the forecast period. The increased market shares attributable to consumer tax credits shown in 

Table 17.3.3 were used as inputs in the NIA-RIA model. Appendix 17-A, section 17-A.2, shows

the annual increases in market shares attributable to this policy. Section 17.4 below presents the 

resulting efficiency trends for the policy case of consumer tax credits for residential clothes 

washers that meet target efficiency levels.

17.3.4 Manufacturer Tax Credits 

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce residential clothes washers that meet target efficiency levels, DOE assumed that such a 

tax credit would lower the consumer’s purchase cost by an amount equivalent to that provided by 

the consumer rebate or tax credit described above. DOE further assumed that manufacturers 

would pass on some of their reduced costs to consumers, causing a direct price effect. DOE 

assumed that no announcement effect would occur, because the program would not be visible to 

consumers.

f

3

Because the direct price effect is approximately equivalent to the announcement 

effect, 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. This assumed participation rate is equal to 30 percent of the number of consumers who 

would participate in a rebate program. DOE applied the assumption of 30-percent participation 

 

f This is a conservative assumption, because manufacturers or utility/agency efficiency programs might promote 

those models for which manufacturers increase production because of the tax credits, which in turn might induce 

some announcement effect. DOE, however, found no data on such programs on which to base an estimate of the 

magnitude of this possible announcement effect on consumer behavior.
17-11

to the penetration rates predicted for the rebate policy to estimate the effects of a manufacturer 

tax credit policy. In doing so, the Department incorporated the assumptions for consumer 

response to financial incentives from the penetration curves selected for each product class. 

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance 

Credits for manufacturers, which were mandated by EPACT 2005.17 Those tax credits were in 

effect for clothes washer models produced in 2006 and 2007 and then reinstated for 2009 and 

2010. DOE was unable to locate data from the Internal Revenue Service or other sources on 

manufacturer response to the Federal credits. Appendix 17-A, section 17-A.6.2 presents details 

of Federal manufacturer tax credits.

Table 17.3.4 summarizes DOE’s assumptions regarding the market penetration in 2015 of 

residential clothes washers meeting target efficiency levels given a manufacturer tax credit.

Table 17.3.4 Market Penetrations in 2015 Attributable to Manufacturer Tax Credits for 

Residential Clothes Washers

FrontLoading

%

TopLoading

Base-Case Market Share of Units that Meet 

Target Levels

%

72 13

Market Share of Units that Meet Target Levels 

with Manufacturer Tax Credits 73 19

Increased Market Share of Units that Meet Target 

Level with Manufacturer Tax Credits 2 6

DOE assumed that the manufacturer tax credit policy would transform the market 

permanently, so that the increases in market share seen in the first year of the program would be 

maintained throughout the forecast period. 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 17-A, section 17-A.2, shows the annual market share increases due to this policy. 

Section 17.4 below presents the resulting efficiency trends for the policy case of manufacturer 

tax credits for residential clothes washers.

17.3.5 Voluntary Energy Efficiency Targets 

For this scenario, DOE assumed that voluntary energy efficiency targets would be 

achieved as manufacturers gradually stopped producing units that operated below target levels. 

DOE assumed that the impetus for phasing out production of low-efficiency units would be a 

program similar to the ENERGY STAR labeling program conducted by the Environmental 

Protection Agency and DOE. The program specifies the minimum energy efficiencies that

various products, including residential clothes washers, 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. 
17-12

Program efforts generally involve a combination of information dissemination and utility or 

agency rebates. Researchers have analyzed the ENERGY STAR program’s effects on sales of 

several consumer products based on State-specific data on percentages of shipments of 

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. 18, 19, 20

ENERGY STAR projects market penetration of compliant appliances and estimates the 

percentage of sales of compliant appliances that are attributable to the program. DOE estimated 

the percentage of market shares attributable to the ENERGY STAR program for residential 

clothes washers for 1997–2025.21 DOE then assumed that, beginning in 2015, an enhanced 

ENERGY STAR program for each product class would add 50 percent to the annual increases in 

market penetration shown by existing programs. From this forecast DOE calculated the annual 

percent increases in market share represented by the shipments attributed to ENERGY STAR. 

DOE added those percent increases to the market shares of residential clothes washers that meet 

target levels in the RIA base case, starting in 2015, to obtain the annual market shares of units 

meeting the target efficiency level in the voluntary efficiency targets case.

DOE estimated that the programs developed in support of voluntary efficiency targets 

would increase market shares of efficient units. Appendix 17-A, section 17-A.2, shows the 

annual increases in market share due to this policy used as inputs to the NIA-RIA model. Section 

17.4 below presents the resulting efficiency trends for the policy case of voluntary energy 

efficiency targets for clothes washers that meet target efficiency levels.

17.3.6 Early Replacement 

The non-regulatory policy of early replacement refers to a program to replace residential 

appliances before the ends of their useful lives. The purpose of such a policy is to replace old, 

inefficient units with higher efficiency units. The economic feasibility of early replacement

depends on the vintage of the unit being replaced, the installed cost of the new unit, and the 

energy cost savings. DOE examined several resources to inform its analysis of the policy. One 

report detailed the Connecticut Appliance Retirement Program (ARP), which was conducted in 

2004.22 Another was a 2006 study of the potentials for electric energy efficiency performed for 

the State of Vermont.23

The Connecticut ARP was conducted from June through December 2004 by Nexus 

Market Research, Inc., and RLW Analytics, Inc., for Northeast Utilities–Connecticut Light and 

Power and the United Illuminating Company’s State programs. The purpose of the ARP was to 

help Connecticut utility customers overcome barriers to recycling room air conditioners (RACs), 

secondary refrigerators, and freezers. The program picked up used appliances at customers’ 

homes or at turn-in events, paid participants to retire their units, and educated customers about 

the costs of operating older appliances. In addition, the program provided consumers with 

financial incentives to replace inefficient RACs with ENERGY STAR-qualified units. DOE 

considers the RAC program to most closely resemble the early replacement policy scenario for 

residential clothes washers, because consumers replaced primary units rather than retiring second 
17-13

units. Nexus/RLW used program data and surveys to estimate the number of RACs retired by 

ARP participants, the percentage of retired units that were replaced with an ENERGY STAR 

model, and the number of RACs replaced by non-participants during the program. According to 

the Nexus/RLW analysis, about 7 percent of all RACs retired during the program were retired 

through the ARP, and 63 percent of those were replaced with ENERGY STAR models. Thus the 

program resulted directly in about 4 percent of total eligible RAC consumers deciding on early 

replacement of inefficient units. 

In 2006, GDS Associates, Inc., conducted a study for the State of Vermont of the 

potential for reducing electricity use and peak demand through energy efficiency and fuel 

conversion measures. The study took an aggressive, multi-program approach, one aspect of 

which was early replacement of appliances. GDS considered that under the program residential 

appliances, including RACs, would be replaced during 4 years (2006–2009). GDS estimated 

achievable market penetrations assuming that consumers would receive a financial incentive 

equal to 50 percent of the incremental cost of each program measure. GDS assumed a maximum

80-percent penetration. For early replacement of RACs, GDS estimated a maximum achievable 

participation of 5 percent of eligible single-family or multi-family homes in the year before the 

program began (2005).

DOE also reviewed a study it had conducted in the 1990s, under EPACT 1992, which 

analyzed the effectiveness of Federal programs that supported early retirement of appliances.

24

The study identified policy options for early replacement that included a direct national program;

replacement of Federally owned appliances; and promotion through equipment manufacturers, 

consumer incentives, incentives to utilities, and building regulations.g

For this RIA analysis, DOE analyzed a program to target installed units having efficiency 

levels lower than target levels and encourage their early replacement with products that perform 

at target levels. For each product class, DOE modeled the effects of the early replacement policy 

by increasing by 4 percent per year the retirement rate of units that were in the stock in the first 

year of the analysis period (2015). DOE used the 4-percent rate from the Connecticut study,

because it was based on real-world experience. DOE assumed that the early replacement 

program would continue until it had facilitated the replacement of all eligible residential clothes 

washers in the stock in the year the program began (2015). Shipments of new units in 2015 and 

beyond were not affected by the program, but remained at base-case efficiency levels. After the 

stock of inefficient units was replaced completely, the policy would produce no additional 

impacts. 

 g The analysis concluded that, although cost-effective opportunities for early replacement exist, a widespread 

Federal program was not justified economically. Because early retirement means that a unit may be replaced by an 

appliance less efficient than the eventual replacement would have been, energy savings would be less than 

anticipated. Early replacement programs also could increase long-term sales volatility by encouraging a temporary 

increase in production, followed by a lull in demand. Early replacement could be economical in localities subject to

high energy costs or environmental constraints; when replacement appliances are much more efficient than existing 

stock; or when a major technology breakthrough has occurred, creating the need for a ready market.
17-14

An early replacement policy would create a fairly immediate jump in shipments of 

products that meet target efficiency levels, leading to an immediate gain in the market share of 

efficient units compared to the base case. As opposed to the policy cases discussed previously, 

however, an early replacement policy results in market shares of efficient units returning to basecase percentages as the eligible market is depleted. In addition, because units removed early from 

the stock would have been replaced later (at the ends of their useful lives) without the program, 

the number of shipments in later years drops below the base-case shipments forecast. Section 

17.4 below presents the resulting efficiency trends for the policy case of early replacement for 

residential clothes washers.

17.3.7 Bulk Government Purchases 

DOE assumed that a policy requiring bulk government purchases would lead to Federal, 

State, and local governments purchasing products that meet target efficiency levels. Combining 

the market demands of multiple public sectors would 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 could induce “market pull,” whereby manufacturers and 

vendors would achieve economies of scale for high efficiency products.

Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other equipment. FEMP, however, does not track purchasing data, because of the complex range 

of purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible. 25, 26

DOE assumed that government agencies, such as the Department of Housing and Urban 

Development, would administer bulk purchasing programs for residential clothes washers. The 

policy also would be incorporated into the FEMP program. The FEMP program currently has 

procurement guidelines in place for residential clothes washers.

27 Federal construction 

requirements include the FEMP guidelines for installing or replacing equipment. 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.28 Based on its 

study, DOE estimated that a bulk government purchase program instituted within a 10-year 
17-15

period would result in at least 80 percent of government-purchased residential clothes washers

meeting target efficiency levels. 

DOE assumed that bulk government purchases would affect a subset of housing units for 

which government agencies purchase or influence the purchase of clothes washers. This subset 

consists primarily of public housing and housing on military bases. DOE defined this subset 

based on the American Housing Survey (AHS) for 2009, which identified 1.8 million

households, or about 1.4 percent of all U.S. households, as publicly owned housing.

29 (The AHS

reports 130.0 million U.S. households.

30) The 2005 Residential Energy Consumption Survey 

(RECS 2005) reported that 0.2 percent of publicly owned households had front-loading clothes 

washers and 40.1 percent had top-loading clothes washers.

31 RECS 2005 estimated that 0.56

percent of U.S. housing units are publicly owned households that use top-loading clothes 

washers; this constitutes the population to which this policy would apply. DOE estimated that a 

miniscule percentage of U.S. housing units represent publicly owned households that use frontloading clothes washers; for them the potential energy savings are minor. 

DOE estimated that, starting in 2015, each year of a bulk government purchase policy

would result in an increasing percent of shipments of government-purchased units beyond the 

base case would meet target efficiency levels. DOE estimated that within 10 years (by 2024),

bulk government purchasing programs would result in 80 percent of the top-loading clothes 

washer market for publicly owned housing meeting target levels. DOE modeled the bulk 

government purchase program assuming that the market share for each product achieved in 2024

would be maintained throughout the rest of the forecast period. Appendix 17-A, section 17-A.2, 

shows the annual market share increases due to this policy used as inputs to the NIA-RIA model. 

Section 17.4 below presents the resulting efficiency trends for the policy case of bulk 

government purchase of residential clothes washers.

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES 

Figures 17.4.1 and 17.4.2 show the effects of all six non-regulatory policies on market 

penetration of the two product classes of residential clothes washers. The market share of 

products that meet the target level in the base case is forecasted to be constant for both product 

classes. Relative to the base case, every policy case increases the market share of products that 

meet target efficiency levels. Recall that the proposed consensus standards (not shown in the 

figures) would result in a 100-percent market penetration of products that meet the target 

efficiency level.
17-16

Figure 17.4.1 Market Penetration of Front-Loading Clothes Washers Meeting 

Target Efficiency Levels in Various Policy Cases

Figure 17.4.2 Market Penetration of Top-Loading Clothes Washers Meeting 

Target Efficiency Levels in Various Policy Cases

Tables 17.4.1 and 17.4.2 show the national energy savings and net present values (NPVs) 

of the non-regulatory policies analyzed for residential clothes washers. The target level for each 

65.0%

70.0%

75.0%

80.0%

85.0%

90.0%

201520162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Early Replacement

Bulk Purchasing

Base Case

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

201520162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Early Replacement

Bulk Purchasing

Base Case
17-17

policy equals the efficiency level in the proposed standard. These results correspond to the 

market shares in Figures 17.4.1 and 17.4.2. The case in which no regulatory action is taken with 

regard to residential clothes washers constitutes the base case (or "No New Regulatory Action" 

scenario), in which energy savings and NPV are zero by definition. For comparison, the tables 

include the impacts of the proposed consensus standards. Energy savings are given in quads. The 

NPVs shown in Tables 17.4.1 and 17.4.2 are based on two discount rates, 7 percent and 3 

percent.

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Residential Front-Loading 

Clothes Washers that Meet the Proposed Consensus Standards (TSL 3)

Policy Alternative

Primary

Energy

Savings

Net Present Value*

quads

7% 

discount 

rate

billion 2010$

3% 

discount 

rate

Consumer Rebates -0.076 -1.861 -3.641

Consumer Tax Credits -0.045 -1.119 -2.190

Manufacturer Tax Credits -0.023 -0.560 -1.097

Voluntary Energy Efficiency Targets 0.021 0.467 1.316

Early Replacement

Bulk Government Purchases -0.001 -0.013 -0.029

Proposed Standards -0.172 -4.137 -8.276

* For products shipped in 2015–2044.
17-18

Table 17.4.2 Impacts of Non-Regulatory Alternatives for Residential Top-Loading 

Clothes Washers that Meet the Proposed Consensus Standards (TSL 3)

Policy Alternative

Primary

Energy

Savings

Net Present Value*

quads

7% 

discount 

rate

billion 2010$

3% 

discount 

rate

Consumer Rebates 0.641 5.825 13.487

Consumer Tax Credits 0.388 3.519 8.150

Manufacturer Tax Credits 0.195 1.768 4.096

Voluntary Energy Efficiency Targets 0.183 1.151 2.851

Early Replacement

Bulk Government Purchases 0.006 0.040 0.101

Proposed Standards 2.361 20.200 48.072

* For products shipped in 2015–2044.

Note that the national energy savings for front-loading clothes washers are negative for 

the standards case and for most non-regulatory policy cases. This effect reflects the cross-price 

elasticity for total installed costs assumed in the NIA model, which results in market shifts 

between the two product classes. (See chapter 10 for more discussion on cross-price elasticity.) 

The total national energy savings for both product classes together is positive, however. Table 

17.4.3 shows the total NES and NPV for residential clothes washers for the non-regulatory 

policies and standards. Consumer rebates produce the most national energy savings of the six 

non-regulatory policies, while bulk government purchases provide the least savings.
17-19

Table 17.4.3 Impacts of Non-Regulatory Alternatives for All Residential Clothes Washers 

that Meet the Proposed Consensus Standard (TSL 3)

Policy Alternative

Primary

Energy

Savings

Net Present Value*

quads

7% 

discount 

rate

billion 2010$

3% 

discount 

rate

Consumer Rebates 0.566 3.964 9.846

Consumer Tax Credits 0.342 2.400 5.959

Manufacturer Tax Credits 0.172 1.208 2.999

Voluntary Energy Efficiency Targets 0.204 1.618 4.166

Early Replacement

Bulk Government Purchases 0.006 0.027 0.072

Proposed Standards 2.189 16.063 39.796

* For products shipped in 2015–2044.
17-20

REFERENCES

 1 American Recovery and Reinvestment Act of 2009. (Last accessed January 2011.) 

<http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>

2 U.S. Department of Energy. Approved Energy Efficient Appliance Rebate Programs. (Last 

accessed January 2011.) <http://www.energysavers.gov/financial/70022.html>

3 The Technology Evaluation Modeling and Assessment Group. Energy End-Use Forecasting:

Analysis of Tax Credits for Efficient Equipment. 1998. Lawrence Berkeley National 

Laboratory: Berkeley, CA. (Last accessed January 2011.) 

<http://enduse.lbl.gov/Projects/TaxCredits.html>

4 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. 

5 Rufo, M., and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency. 2002. XENERGY Inc.: Oakland, CA. Prepared for The Energy Foundation and 

The Hewlett Foundation. (Last accessed January 2011.) 

<http://www.ef.org/documents/Secret_Surplus.pdf>

6 ICF International. Arizona Public Service Energy Efficient Market Potential Study. 2007. ICF 

International: Fairfax, VA.

7 Mosenthal, P. Personal communication. Optimal Energy, Bristol, VT. Telephone 

conversation with Barbara Atkinson, LBNL. January 2008. 

8 Lee, A. Personal Communication. Quantec, LLC, Portland, OR. Telephone conversation 

with Barbara Atkinson, LBNL. January 2008. 

9 Rufo, M. Personal communication. Itron, Inc., Oakland, CA. Telephone conversations with 

Barbara Atkinson, LBNL. January 2008 and March 2009. 

10 Itron Inc. and KEMA Inc. California Energy Efficiency Potential Study. 2008. Itron, Inc.: 

San Diego, CA, and KEMA, Inc.: Oakland, CA. Submitted to Pacific Gas and Electric Co. 

CALMAC Study ID: PGE0264.01. (Last accessed January 2011.) 

<http://calmac.org/publications/PGE0264_Final_Report.pdf>

11 Global Energy Partners. AmerenUE Demand Side Management (DSM) Market Potential 

Study, Volume 2: Market Research. 2010. Global Energy Partners, LLC: Walnut Creek, CA. 
17-21

 

Report Number 1287-2. (Last Accessed January 2011.) 

<http://oa.mo.gov/purch/bids/b3z10177att2-2.pdf>

12 Coito, F. Personal communication. KEMA, Inc., Oakland, CA. E-mail to Barbara Atkinson, 

LBNL. June 2010.

13 U.S. Department of Energy–Office of Codes and Standards. Technical Support Document: 

Energy Efficiency Standards for Consumer Products: Refrigerators, Refrigerator-Freezers, 

and Freezers, Including Environmental Assessment and Regulatory Impact Analysis.

November 1995. Washington, D.C. Report No. DOE/EE-0064.

14 Energy Policy Act of 2005. 119 STAT. 594 Public Law 109–58. Section 1333, 26 USC 25C 

note. November 8, 2005. (Last accessed January 2011.) 

<http://frwebgate3.access.gpo.gov/cgibin/getdoc.cgi?dbname=109_cong_public_laws&docid=f:publ058.109.pdf>

15 Tax Incentives Assistance Project. General Information: Legislative Language & Pending 

Updates. 2009. (Last accessed January 2011.) 

<http://energytaxincentives.org/general/legislative.php>

16 U.S. Department of Energy–Office of 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, D.C.

17 Tax Incentives Assistance Project. Manufacturer Incentives, (Last accessed January 2011.) 

<http://energytaxincentives.org/builders/appliances.php>

18 Feldman S., L. Hoegfen, L.Wilson-Wright, and A. Li. Modeling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In 2005 ECEEE Summer Study: Energy Savings: 

What Works and Who Delivers? 2005. Mandelieu La Napoule, France, May 30–June 4. 

19 Rosenberg, M. The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances. In 2003 International Energy Program Evaluation Conference. 2003. Seattle, 

WA, November 20–22.

20 Titus E., and S. Fledman. Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S. In 2003 International Energy Program Evaluation Conference. 2003. 

Seattle, WA, November 20–22. 

21 Homan, G., M. Sanchez, M. C. Webber, R. Brown, and G. Homan. Calendar Year 2009 

Program Benefits for ENERGY STAR Labeled Products. 2010. Lawrence Berkeley National 

Laboratory: Berkeley, CA. Draft LBNL Report. November 15. 
17-22

 

22 Nexus and RLW Analytics. Impact, Process, and Market Study of the Connecticut Appliance 

Retirement Program: Overall Report, Final. 2005. Nexus Market Research, Inc., and RLW 

Analytics, Inc. Cambridge, MA. Submitted to Northeast Utilities–Connecticut Light and 

Power and the United Illuminating Company. (Last accessed January 2011.) 

<http://www.ctsavesenergy.org/files/Appliance%20Retirement%2012-05.pdf>

23 GDS Associates, Inc. Vermont Electric Energy Efficiency Potential Study: Final Report. , 

2007. GDS Associates, Inc.: Marietta, GA. Prepared for the Vermont Department of Public 

Service. (Last accessed January 2011.) 

<http://publicservice.vermont.gov/energy/vteefinalreportjan07v3andappendices.pdf>

24 McMahon, J. E., and J. P. Harris. Early Replacement of Appliances. 1994. Lawrence 

Berkeley National Laboratory: Berkeley, CA. Unpublished report for the U.S. Department of 

Energy.

25 Smith, N. Personal communication. National Association of State Procurement Officials, 

Lexington, KY. Telephone conversation with Barbara Atkinson, LBNL. April 2008. 

26 Responsible Purchasing Network. (Last accessed January 2011.) 

<www.responsiblepurchasing.org/>

27 Federal Energy Management Program. FEMP Designated Product: Clothes Washers. 2010. 

Version 5, June. (Last accessed January 2011.) 

<http://www1.eere.energy.gov/femp/technologies/eep_clothes_washers.html#performance>

28 Harris, J., and F. Johnson. Potential Energy, Cost, and CO2 Savings from Energy-Efficient 

Government Purchases. In ACEEE 2000 Summer Study on Energy Efficiency in Buildings. 

2000. Asilomar, CA, November 20–25. American Council for an Energy-Efficient Economy. 

Lawrence Berkeley National Laboratory Report No. LBNL-45439.

29 U.S. Department of Housing and Urban Development. A Picture of Subsidized Households–

2008. Last updated January 2010. (Last accessed January 2011.) 

<http://www.huduser.org/picture2008/index.html/>

30 U.S. Census Bureau–Housing and Household Economic Statistics Division. 2009 AHS 

National Data. 2009. (Last accessed January 2011.) 

<http://www.huduser.org/portal/datasets/ahs/ahsdata09.html>

31 U.S. Department of Energy–Energy Information Administration. Residential Energy 

Consumption Survey: 2005 Public Use Data Files. February 2009. (Last accessed January 

2011.) <http://www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html>