CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

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

17.2 NON-REGULATORY POLICIES.......................................................................... 17-2

17.2.1 METHODOLOGY .................................................................................................. 17-2

17.2.2 ASSUMPTIONS REGARDING NON-REGULATORY POLICIES..................... 17-3

17.2.3 POLICY INTERACTIONS..................................................................................... 17-4

17.3 NON-REGULATORY POLICY ASSUMPTIONS ................................................ 17-4

17.3.1 NO NEW REGULATORY ACTION ..................................................................... 17-4

17.3.2 CONSUMER REBATES......................................................................................... 17-4

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

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

17.3.3 CONSUMER TAX CREDITS ................................................................................ 17-9

17.3.4 MANUFACTURER TAX CREDITS.................................................................... 17-13

17.3.5 VOLUNTARY ENERGY EFFICIENCY TARGETS .......................................... 17-14

17.3.6 EARLY REPLACEMENT.................................................................................... 17-16

17.3.7 BULK GOVERNMENT PURCHASES................................................................ 17-20

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES................................... 17-21

LIST OF TABLES

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

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes 

Washers Equipment Classes........................................................................... 17-3

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

Washer ............................................................................................................ 17-7

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for 

Commercial Clothes Washers......................................................................... 17-9

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 

to 2011 .......................................................................................................... 17-10

Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes 

Washers Attributed to Tax Credits, Rebates and other Market Effects........ 17-12

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for 

Commercial Clothes Washers....................................................................... 17-13

Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits 

for Commercial Clothes Washers................................................................. 17-14

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy 

Efficiency Targets for Commercial Clothes Washers .................................. 17-15

17-i
Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for 

Commercial Clothes Washers....................................................................... 17-19

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government 

Purchasing of Commercial Clothes Washers................................................ 17-21

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

Clothes Washers (TSL 2).............................................................................. 17-23

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

Clothes Washers (TSL 2).............................................................................. 17-24

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers....................... 17-8

Figure 17.3.2 Base Case and Policy Case Shipments Projections for the Early 

Replacement Policy Case (thousand units)................................................... 17-19

Figure 17.4.1 Market Penetration of Front-Loading Commercial Clothes Washers 

Meeting the Target Level in Policy Cases (TSL 2)...................................... 17-22

Figure 17.4.2 Market Penetration of Top-Loading Commercial Clothes Washers 

Meeting the Target Level in Policy Cases (TSL 2)...................................... 17-22

17-ii
CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action

described in the Federal Register notice associated with this TSD constitutes an “economically 

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

Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies 

to provide “an assessment, including the underlying analysis, of costs and benefits of potentially 

effective and reasonably feasible alternatives to the planned regulation, identified by the agencies 

or the public (including improving the current regulation and reasonably viable non-regulatory 

actions), and an explanation why the planned regulatory action is preferable to the identified 

potential alternatives.” 58 FR 51735, 51741.

To conduct this analysis, DOE used an integrated National Impact Analysis (NIA)-RIA

model built on the NIA model discussed in Chapter 11 for its analysis. 

DOE identified six non-regulatory policy alternatives that possibly could provide 

incentives for the same energy efficiency levels as the ones in the trial standard levels (TSL) for 

the commercial clothes washers 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 

selected 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 discuss the analysis of the six selected policies listed above

(excluding the alternative of no new regulatory action). Section 17.4 presents the results of the 

policy alternatives.

17-1
17.2 NON-REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the non-regulatory policy alternatives for commercial clothes washers. This 

section also describes the assumptions underlying the analysis. 

17.2.1 METHODOLOGY

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

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

alternative. Chapter 11 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17-A, discusses the NIA-RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the 

efficiency levels corresponding to the levels set for each TSL. After establishing the quantitative 

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

spreadsheet model. The primary model inputs revised were market shares of equipment meeting 

the target efficiency levels set for each TSL. The shipments of equipment for any given year 

reflect a distribution of efficiency levels. DOE assumed, for each TSL, that new energy 

efficiency standards would affect 100 percent of the shipments of equipment that did not meet 

the TSL target level in the base case,a whereas the non-regulatory policies would affect a smaller 

percentage of those shipments. DOE made certain assumptions about the percentage of 

shipments affected by each alternative policy. DOE used those percentages to calculate the 

shipment-weighted average energy consumption and costs of commercial clothes washers

attributable to each policy alternative. 

Increasing the efficiency of equipment often increases its average installed cost. 

However, operating costs generally decrease because energy consumption declines. DOE 

therefore calculated an NPV for each non-regulatory alternative in the same way it did for the 

selected standards. In some policy scenarios, increases in total installed cost are mitigated by 

government rebates or tax credits. Because DOE assumed that consumers would re-pay credits 

and rebates in some way (such as additional taxes), DOE did not include rebates or tax credits as 

a consumer benefit when calculating national NPV. DOE’s analysis also excluded any 

administrative costs for the non-regulatory policies; including such costs would decrease the

NPVs slightly.

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

• National Energy Savings (NES), given in quadrillion Btus (quads), describes the 

cumulative national primary energy saved over the lifetime of equipment purchased 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.

17-2

 
during the 30-year analysis period starting in the effective date of the policy (2018-

2047).

• Net Present Value (NPV), represents the value of net monetary savings in 2014, 

expressed in 2013$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2018-2047). DOE calculated the NPV as 

the difference between the present value of installed equipment cost and operating 

expenditures in the base case and the present value of those costs in each policy case. 

DOE calculated operating expenses (including energy costs) for the life of equipment.

17.2.2 ASSUMPTIONS REGARDING NON-REGULATORY POLICIES

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ responses to a program. Because 

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

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

assumes will be met with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and 

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each 

alternative policy.

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

of new commercial clothes washers relative to their base case efficiency scenario (which 

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

induce consumers to purchase units having the same efficiency level as required by the selected

standards (the target level) set for each TSL. As opposed to the standards case, however, the 

policy cases may not lead to 100 percent market penetration of units that meet the target level.

Table 17.2.1 shows the efficiency level stipulated in the selected standards for 

commercial clothes washers.

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers

Equipment Classes

Equipment Type

Efficiency Levels (MEFJ2/IWF)

Baseline

FL*: EL0

TL*: EL0

TSL1 TSL2 TSL3 TSL4

FL: EL1

TL: EL1

FL: EL2

TL: EL1

FL: EL2

TL: EL2

FL: EL3

TL: EL2

Front Loading CCW 

Units 1.65/5.2 1.80/4.5 2.00/4.1 2.00/4.1 2.20/3.9

17-3
Top Loading CCW 

Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9 1.55/6.9

*FL: Front Loading. TL: Top Loading.

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

date of standards—2018—through the end of the analysis period, which is 2047. 

17.2.3 POLICY INTERACTIONS

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

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

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

implemented with bulk government purchases. However, DOE attempted to make conservative 

assumptions to avoid double-counting policy impacts. The resulting policy impacts are not 

additive; the combined effect of several or all policies cannot be inferred from summing their 

results. 

Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for the commercial clothes washer equipment classes.

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the six nonregulatory policy alternatives to the selected standards for commercial clothes washers. (Because 

the alternative of No New Regulatory Action has no energy or economic impacts, essentially 

representing the NIA base case, DOE did not perform any additional analysis for that 

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

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

17.3.1 NO NEW REGULATORY ACTION

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

of commercial clothes washers constitutes the base case, as described in Chapter 11, National 

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

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

17.3.2 CONSUMER REBATES

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

incentives in the form of rebates to consumers for purchasing energy-efficient appliances. This 

policy provides a consumer rebate for purchasing commercial clothes washers that operate at (or 

above) the same efficiency as stipulated in each TSL (target level).

17-4
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 

analysis was the XENERGY approach of penetration curves based on B/C ratio, which 

incorporates lifetime operating cost savings.

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

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

Internal sources of information encourage consumers to purchase new equipment primarily 

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

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

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

XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a policy measure. XENERGY calibrated the curves based on participation data from 

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

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8

DOE modeled the effects of a consumer rebate policy for commercial clothes washers by 

determining, for each TSL, the increase in market penetration of equipment meeting the target 

level relative to their market penetration in the base case. It used the interpolation method 

presented in Blum et al (2011)9 to create customized penetration curves based on relationships 

between actual base case market penetrations and actual B/C ratios. To inform its estimate of 

B/C ratios provided by a rebate program DOE performed a thorough nationwide search for 

existing rebate programs for commercial clothes washers. It gathered data on utility or agency 

b XENERGY is now owned by KEMA, Inc. (www.kema.com)

17-5

 
rebates throughout the nation for this equipment, and used this data to calibrate the customized 

penetration curves it developed for front-loading and top-loading commercial clothes washers so 

they can best reflect the market barrier level faced by each equipment class. Section 17.3.2.2 

shows the interpolated curves used in the analysis.

17.3.2.2 Analysis 

For the two commercial clothes washer equipment classes it analyzed, DOE estimated the 

effect of increasing its B/C ratio via a rebate that would pay all of the increased installed cost of

a unit that met the target efficiency level compared to one meeting the baseline efficiency level.

c

To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide 

search for existing rebate programs for commercial clothes washers. It gathered data from a 

sample of utility and agency rebate programs that includes 38 rebates for commercial clothes 

washers initiated by 26 utilities or agencies in various Statesd

. (Appendix 17-A, identifies the 

rebate programs.) To represent the rebate level, DOE used the simple average of the rebate 

amounts in these programs. DOE assumed that rebates would remain in effect at the same levels 

throughout the forecast period (2018–2047). 

For each of the two commercial clothes washer equipment classes, DOE first calculated 

the B/C ratio without a rebate using the difference in total installed costs (C) and lifetime 

operating cost savings (B) between the unit meeting the target level and the baseline unit. It then 

calculated the B/C ratio given a rebate for the unit meeting the target efficiency level. Because 

the rebate reduced the incremental cost, the unit receiving the rebate had a larger B/C ratio. Table 

17.3.1 shows the effect of consumer rebates on the B/C ratio for each TSL.

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

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

current Federal energy conservation standards and provides basic consumer utility. 

d Some rebates for commercial clothes washers are offered by electric and/or gas utilities, some by water utilities 

and, in some service territories, by both energy and water utilities. DOE calculated the average rebate amount from 

the total (combined) rebate amount available to the consumer.

17-6

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

Washer

Trial Standard Levels

Front-Loading 1 2 3 4

B/C Ratio Without Rebate infinite 340.4 340.4 7.2

Rebate Amount (2013$) 160.47 160.47 160.47 160.47

B/C Ratio With Rebate infinite infinite infinite infinite

Estimated Market Barriers xHigh* Mod-High* Mod-High xHigh

Top-Loading 1 2 3 4

B/C Ratio Without Rebate infinite infinite 12.3 12.3

Rebate Amount (2013$) 160.47 160.47 160.47 160.47

B/C Ratio With Rebate infinite infinite infinite infinite

Estimated Market Barriers xHigh xHigh High-xHigh* High-xHigh

* xHigh: Extremely-High. Mod-High: Moderate-to-High. High-xHigh: High-to-Extremely-High

DOE used these B/C ratios along with the penetration curves shown in Figure 17.3.1 to 

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

the target level both with and without a rebate incentive.e The penetration curves calculated by 

DOE to represent the market behavior for front-loading and top-loading commercial clothes 

washers at each TSL are indicated in Table 17.3.1.

Front-Loading Top-Loading

TSL 1

e Most of the graphs presented in Figure 17.3.1 do not show the ‘No Rebate’ and ‘With Rebate’ data points as the 

B/C ratio for those data points are either infinite or substantially greater than 200. 

17-7

 
TSL 2 TSL 3 TSL 4

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers

For each equipment class, DOE next estimated the percent increases represented by the 

change in penetration rate shown on the corresponding penetration curve. It then added this

percent increase to the market share of units that meet the target level in the base case to obtain 

the market share of units that meet the target level in the rebate policy case. 

Table 17.3.2 summarizes DOE’s assumptions for commercial clothes washers regarding 

the market penetration of products in 2018 that meet the target efficiency level at each TSL given 

a consumer rebate. 

17-8
Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 31.0% 69.9% 69.9% 50.0%

Increased Market Share 0.0% 0.9% 0.9% 50.0%

Top-Loading 1 2 3 4

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 50.0% 50.0% 51.4% 51.4%

Increased Market Share 23.3% 23.3% 40.1% 40.1%

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17-A shows the annual market share 

increases due to this policy. Section 17.4 presents the resulting market penetration trends for the 

policy case of consumer rebates for commercial clothes washers.

17.3.3 CONSUMER TAX CREDITS

In preparing its assumptions to estimate the effects of tax credits on consumer purchases

of commercial clothes washers, DOE reviewed tax credit programs that have been offered at the 

Federal level for energy-efficient appliances. The Energy Policy Act of 2005 (EPACT 2005)

included Federal tax credits for consumers who purchase energy-efficient equipment.10 Those 

tax credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009–2010 by 

the American Recovery and Reinvestment Act of 2009 (ARRA), extended by Congress for 2011

with some modifications, and expired at the end of 2011.

11, 12 The American Taxpayer Relief 

Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat 

pumps, furnaces, and water heaters placed in service between January 1, 2012 and December 31, 

2013.13 DOE reviewed Internal Revenue Service data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal 

energy conservation tax credit program in place in the 1980s. However, DOE did not find data 

specific enough to commercial clothes washers to warrant its analysis for the consumer tax 

credits policy case. Appendix 17-A contains more information on Federal consumer tax credits. 

DOE estimated the effects of tax credits on consumer purchases using data from Oregon 

State’s tax credit programs for energy-efficient appliances. (Additional discussion of State tax 

credits for Oregon and other states is in Appendix 17-A.) Oregon has had consumer tax credits 

for efficient appliances for several years.f DOE reviewed Oregon’s business energy tax credit 

(BETC) for commercial clothes washers. Oregon’s BETC would typically cover 35–50 percent

f The District of Columbia (Washington D.C.) also passed a piece of legislation entitled the “Residential Energy 

Conservation Tax Credit Act Of 2005” for consumer tax credits, but did not appropriate the necessary funding 

and so the credits did not go into effect.

17-9

 
of the incremental cost of the efficient equipment.g The majority of these credits was claimed for 

laundries and rental dwellings, and included commercial clothes washers as well as other 

appliances. However, ODOE did not track the types of appliances involved nor the number of 

machines per project; the annual counts are the total number of projects, with each project 

involving one or more appliances. DOE was therefore unable to use the data to estimate market 

percentages for specific appliances, i.e. commercial clothes washers. However, DOE did note the 

trend of increasing numbers of credits claimed from 1998 through 2006 for residential clothes 

washers, which contributed to its assumptions regarding the trend line for this policy scenario.

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 to 

2011

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

Tax Credits* 8.2 12.2 16.2 19.7 18.5 23.4 25.5 23.6 23.8 23.3 29.7 24.6

Shipments** 86.4 88.3 91.0 97.7 102.0 101.8 106.5 118.2 137.6 145.9 155.1 142.1

Percentage*** 9.5 13.8 17.8 20.2 18.2 23.0 24.0 20.0 17.3 16.0 19.1 17.3

* Number of tax credits claimed (thousand units).

** Shipments of clothes washers to Oregon (thousand units). 

*** Percentage of shipments to Oregon with tax credits.

Because tax credits have not been available for commercial clothes washers, DOE 

analyzed the Oregon residential tax credit data for residential clothes washers to inform the 

analysis of the tax credit policy for commercial clothes washers. DOE obtained data from the 

Oregon Department of Energy (ODOE) on the number of efficient appliances for which tax 

credits were claimed by Oregon residential taxpayers for the years 1998 through 2012, including 

those for residential clothes washers.

14 Since the tax credit for clothes washers expired at the end 

of 2011, DOE assumed that the 2012 credits in ODOE’s database were for systems sold prior to 

2012. DOE also obtained shipments data for clothes washers by state (including Oregon) for the 

years 2000 through 2012 from the Association of Home Appliance Manufacturers (AHAM).15 It 

then calculated the percentages of clothes washers sold in Oregon for which taxpayers claimed 

credits for the years 2000 through 2011. Table 17.3.3 shows the number of Oregon residential 

tax credits claimed for clothes washers as percentages of total annual shipments these claims 

represent for the years 2000–2011. DOE used those percentages to estimate the percentage of 

shipments of commercial clothes washers that would meet the efficiency level at each TSL due 

to a tax credit program for 2018 through 2029.

Not all of these high-efficiency clothes washers, however, would likely be purchased due 

to the tax credit program alone. Some taxpayers would be motivated by utility rebate incentives, 

and others would be "free riders" who would purchase efficient appliances without a tax credit or 

rebate incentive but still claimed the tax credit. To better understand this interaction between tax 

credits, rebates, and free riders/market impacts, in its previous analysis DOE reviewed a report 

g The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year 

thereafter; the taxpayer may carry the unused credit forward up to eight years. Those with eligible project costs of 

$20,000 or less may take the tax credit in one year.

17-10

 
prepared by KEMA on efficient clothes washers in the Northwest. KEMA compared the 

penetration of “ultra high efficiency” (UHE)h clothes washers in Oregon and Washington, two 

neighboring States that have similar utility rebate programs for residential clothes washers.

16 The 

study attributed the difference in market share of these UHE units (50 percent for Oregon versus 

15 percent for Washington) to the Oregon tax credits. 

DOE relied on this conclusion to estimate the increase in market share of high-efficiency 

clothes washers due to a consumer tax credit program only. For each equipment class, DOE first 

removed the potential contributions of rebates and free riders to that increase. For rebates, DOE 

used the same market penetration it estimated above for the rebate policy case; for free riders 

DOE used the base case market share. Since both the rebate policy case and the base case market 

shares are constant, the resulting percentages for front-loading and top-loading commercial 

clothes washers for the years 2018 through 2029 have different values yet same shape. They both 

present an increasing trend for the first seven years and a decreasing trend for the remaining five 

years. DOE did some interpolations to smooth the shapes of the two market penetration curves 

for the twelve initial years, and extrapolated the decreasing trend of market share for years 

beyond 2029 until that share reaches zero. Table 17.3.4 shows the percentages of increase in total 

shipments attributed to the tax credits themselves, to utility rebates, and to other market 

influences/free riders for 2018 through 2029 for each TSL.

h Units meeting specifications of minimum modified energy factor (MEFJ2) of 2.20 and maximum water factor 

(WF) of 6.5.

17-11

 
Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes Washers 

Attributed to Tax Credits, Rebates and other Market Effects 

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

TSL 1

Front-Loading

Rebates 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Market effects 2.9% 4.3% 5.5% 6.3% 5.6% 7.1% 7.4% 6.2% 5.4% 4.9% 5.9% 5.4%

Tax Credits 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

%

13.2

%

12.0

%

Top-Loading

Rebates 2.2% 3.2% 4.1% 4.7% 4.2% 5.3% 5.6% 4.7% 4.0% 3.7% 4.5% 4.0%

Market effects 2.5% 3.7% 4.8% 5.4% 4.9% 6.1% 6.4% 5.4% 4.6% 4.3% 5.1% 4.6%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

TSL 2

Front-Loading

Rebates 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2%

Market effects 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

%

13.2

%

12.0

%

Tax Credits 2.9% 4.2% 5.4% 6.1% 5.5% 6.9% 7.2% 6.0% 5.2% 4.8% 5.8% 5.2%

Top-Loading

Rebates 2.2% 3.2% 4.1% 4.7% 4.2% 5.3% 5.6% 4.7% 4.0% 3.7% 4.5% 4.0%

Market effects 2.5% 3.7% 4.8% 5.4% 4.9% 6.1% 6.4% 5.4% 4.6% 4.3% 5.1% 4.6%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

TSL 3

Front-Loading

Rebates 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2%

Market effects 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

%

13.2

%

12.0

%

Tax Credits 2.9% 4.2% 5.4% 6.1% 5.5% 6.9% 7.2% 6.0% 5.2% 4.8% 5.8% 5.2%

Top-Loading

Rebates 3.8% 5.5% 7.2% 8.1% 7.3% 9.2% 9.6% 8.0% 6.9% 6.4% 7.7% 7.0%

Market effects 1.1% 1.6% 2.0% 2.3% 2.0% 2.6% 2.7% 2.3% 1.9% 1.8% 2.2% 2.0%

Tax Credits 4.6% 6.7% 8.7% 9.8% 8.8% 11.2% 11.7% 9.7% 8.4% 7.8% 9.3% 8.4%

TSL 4

Front-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Top-Loading

Rebates 3.8% 5.5% 7.2% 8.1% 7.3% 9.2% 9.6% 8.0% 6.9% 6.4% 7.7% 7.0%

Market effects 1.1% 1.6% 2.0% 2.3% 2.0% 2.6% 2.7% 2.3% 1.9% 1.8% 2.2% 2.0%

Tax Credits 4.6% 6.7% 8.7% 9.8% 8.8% 11.2% 11.7% 9.7% 8.4% 7.8% 9.3% 8.4%

Table 17.3.5 summarizes DOE’s assumptions for commercial clothes washers regarding 

the market penetration of units in 2018 that meet the efficiency level at each TSL given a 

consumer tax credit.

17-12
Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 38.2% 72.2% 72.2% 5.2%

Increased Market Share 7.2% 3.2% 3.2% 5.2%

Top-Loading 1 2 3

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 32.0% 32.0% 16.4% 16.4%

Increased Market Share 5.2% 5.2% 5.1% 5.1%

The increased market shares attributable to consumer tax credits shown in Table 17.3.5

were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market share 

increases due to this policy. Section 17.4 presents the resulting market penetration trends for the 

policy case of consumer tax credits for commercial clothes washers that meet the efficiency level

for each TSL.

17.3.4 MANUFACTURER TAX CREDITS

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

produce commercial clothes washers that meet the target efficiency level at each TSL, DOE 

attempted to investigate manufacturer response to the Energy Efficient Appliance Credits for 

manufacturers mandated by EPACT 2005.17 Those manufacturer tax credits have been in effect 

for dishwashers, clothes washers and refrigerators produced beginning in 2009. (Appendix 17-A 

presents details on Federal manufacturer tax credits.) DOE was unable to locate data from the 

Internal Revenue Service or other sources on manufacturer response to the Federal credits.

DOE then estimated the effects of tax credits to manufacturers based on its previous 

analysis of consumer participation in tax credits. DOE incorporated previous research that 

disaggregated the effect of rebates and tax credits into a direct price effect, which derives from 

the savings in purchase price, and an announcement effect, which is independent of the amount 

of the incentive.18,19 The announcement effect derives from the credibility that a technology 

receives from being included in an incentive program, as well as changes in product marketing 

and modifications in markup and pricing. DOE assumed that rebate and consumer tax credit 

policies would encompass both direct price effects and announcement effects, and that half of the 

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

effect and half to the announcement effect. DOE then assumed that a manufacturer tax credit 

would lead manufacturers to pass on some of their reduced costs to consumers, causing a direct 

price effect. However, no announcement effect would occur because the program would not be 

17-13
visible to consumers.i

Because the direct price effect is approximately equivalent to the 

announcement effect,

18 DOE estimated that a manufacturer tax credit would induce half the 

number of consumers assumed to take advantage of a consumer tax credit to purchase more 

efficient equipment. DOE thus applied 50 percent to the increase in market penetrations 

estimated above for consumer tax credits (See Table 17.3.5) and used the resulting market 

penetrations to represent the impacts from manufacturer tax credits. Table 17.3.6 summarizes

DOE’s assumptions for commercial clothes washers regarding the market penetration of units in 

2018 meeting the target efficiency level at each TSL given a manufacturer tax credit.

Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 34.6% 70.6% 70.6% 2.6%

Increased Market Share 3.6% 1.6% 1.6% 2.6%

Top-Loading 1 2 3 4

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 29.4% 29.4% 13.8% 13.8%

Increased Market Share 2.6% 2.6% 2.6% 2.6%

The increased market shares attributable to a manufacturer tax credit shown in Table 

17.3.6 were used as inputs in the NIA-RIA model. Appendix 17-A, shows the annual market 

share increases due to this policy. Section 17.4 presents the resulting market penetration trends 

for the policy case of manufacturer tax credits for commercial clothes washers.

17.3.5 VOLUNTARY ENERGY EFFICIENCY TARGETS 

For each equipment class, DOE assumed that voluntary energy efficiency targets would 

be achieved as manufacturers gradually stopped producing units that operated below the

efficiency level set for each TSL. DOE assumed that the impetus for phasing out production of 

low-efficiency units would be a program similar to the ENERGY STAR labeling program 

conducted by the Environmental Protection Agency (EPA) and DOE. The ENERGY STAR 

program specifies the minimum energy efficiencies that various products must have to receive 

the ENERGY STAR label. ENERGY STAR encourages consumers to purchase efficient 

products via marketing that promotes consumer label recognition, various incentive programs 

that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying

appliances. ENERGY STAR projects market penetration of compliant appliances and estimates 

i

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.

17-14

 
the percentage of sales of compliant appliances that are attributable to the ENERGY STAR 

program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

20, 21, 22

DOE based its estimates of market penetration on its previous estimates of the impact of 

a voluntary energy efficiency targets program on shipments of commercial clothes washers. In 

the absence of data on the impacts of the existing ENERGY STAR program for commercial 

clothes washers, in its prior analysis23 DOE estimated the percentage of market shares 

attributable to the existing ENERGY STAR program for residential clothes washers for 1996 –

2025. DOE assumed that commercial clothes washers would experience the same trend of annual

increases in market penetration beginning in 2018 as the one presented by residential clothes 

washers starting in 1997. DOE added those percent increases to the market shares of commercial 

clothes washers that met the target level in the RIA base case for each TSL, starting in 2018, to 

obtain the annual market shares of units meeting the efficiency level set for each TSL in the 

voluntary energy efficiency targets policy case. Table 17.3.7 summarizes DOE’s assumptions for 

commercial clothes washers regarding the market penetration of units in 2018 that, given 

voluntary energy efficiency targets, would meet the target efficiency level set for each TSL.

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy Efficiency 

Targets for Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 31.5% 69.5% 69.5% 0.5%

Increased Market Share 0.5% 0.5% 0.5% 0.5%

Top-Loading 1 2 3 4

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 27.2% 27.2% 11.8% 11.8%

Increased Market Share 0.5% 0.5% 0.5% 0.5%

DOE estimated that the program developed in support of the voluntary energy efficiency 

targets policy would increase market shares of efficient units. Appendix 17-A shows the annual 

market share increases due to this policy used as inputs to the NIA-RIA model. Section 17.4

presents the resulting market penetration trends for the policy case of voluntary energy efficiency 

targets for commercial clothes washers.

17-15
17.3.6 EARLY REPLACEMENT 

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

clothes washers before the end of their useful lives. The purpose of such a policy is to replace 

old, inefficient units by units at the target efficiency level defined for each TSL. DOE examined 

several resources to inform its analysis of the policy. One report detailed the Connecticut 

Appliance Retirement Program (ARP), which was conducted in 2004.24 Another was a 2006 

study of the potentials for electric energy efficiency performed for the State of Vermont.25

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

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

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

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

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

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

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

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

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

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

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

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

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

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

replacement of inefficient units. 

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

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

measures. The study took an aggressive, multi-program approach, one aspect of which was early 

replacement of appliances. GDS considered that under the program residential appliances, 

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

market penetrations assuming that consumers would receive a financial incentive equal to 50 

percent of the incremental cost of each program measure. GDS assumed a maximum 80-percent 

penetration. For early replacement of RACs, GDS estimated a maximum achievable participation 

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

(2005).

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

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

26

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

17-16
replacement of Federally-owned appliances; and promotion through product manufacturers, 

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

Historically, DOE modeled the effects of the early replacement policy by increasing by 4

percent per year the retirement rate of units that were in the stock in the first year of the analysis 

period. DOE chose the 4-percent rate from the Connecticut study because it was based on actual 

program experience. DOE assumed that the early replacement program would continue until it 

had facilitated the replacement of all eligible units in the stock in the year the program began. 

Shipments of new units in the year the program began and beyond would not be affected by the

program, but would remain at base-case efficiency levels. After the stock of inefficient units was

completely replaced, the policy would produce no additional impacts.

For the commercial clothes washer RIA analysis, DOE analyzed a program designed to 

target installed units with efficiency levels lower than the target level set for each TSL and 

designed to encourage their early replacement with equipment that performs at that target level.

Rather than relying on a fixed rate of increase in retirements, DOE based its estimates on the 

economic feasibility of early replacements. The feasibility depends on the energy efficiency of 

the unit being replaced, the purchase cost of a new efficient unit, and the energy cost savings.

DOE performed the feasibility analysis of early replacements for front-loading and top-loading 

commercial clothes washers.

DOE started the analysis estimating the stock of existing commercial clothes washers by 

vintage for each equipment class. To estimate the stock by vintage DOE relied on the historical 

shipments from 1989 to 2011 used in the NIA (Chapter 10). DOE then used the same lifetime 

distributions used in the NIA (Chapter 10) to estimate the existing stock by vintage for each of 

the 30 years that comprise the analysis period. As a second step to this analysis, DOE estimated

historical market efficiency distributions. DOE applied the historical market efficiency 

distributions to the stock by vintage that DOE projected. It then estimated: (a) the market average 

unit lifetime energy cost of a commercial clothes washer from the existing stock, and (b) the unit 

lifetime energy cost of a replacement, efficient commercial clothes washer. DOE calculated both 

values as the discounted sum of the corresponding stream of energy costs, taking into account the 

age of the unit in the replacement year and a stream of annual probabilities that the unit will be in 

operation until the expected end of its life. The difference between the two lifetime energy costs 

DOE calculated reflects the estimated energy cost savings that a consumer is likely to enjoy from 

the early replacement of units in the existing stock by more efficient units. DOE calculated these 

savings for each of the 30 years in the analysis period for commercial clothes washers from all 

vintages in the existing stock.

j

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.

17-17

 
Savings from early replacements, however, come at a cost. Anticipating the time of 

replacement of a unit in stock either (a) prevents consumers from realizing financial benefits 

from investing the money they would pay for the new, replacement unit; or (b) imposes financial 

costs to those consumers who do not hold funds – and therefore would have to loan – to purchase 

the new, replacement unit. In either case, there is an economic value in delaying the time of 

replacement. DOE calculated such value for commercial clothes washers from all vintages in the 

existing stock and for each year in the 30-year analysis period.

While the energy cost savings are an incentive for early replacing a unit in the existing 

stock (benefits from replacing), the financial costs underlying the early replacement (benefits 

from waiting) may hinder the initiative. DOE considered the net-result from the benefits from 

replacing and the benefits from waiting as an indicator of the decision that consumers would 

make towards anticipating the replacement of a front-loading and a top-loading commercial 

clothes washer in the existing stock by a more efficient unit rated at the target efficiency level set 

for each TSL. The comparison of the two benefits shows that the early replacement of a unit 

would be cost-effective for front-loading units after their seventh year of operation, and for all 

top-loading units.

Based on the stock by vintage that DOE projected over the 30-year analysis period, and 

the net-results from the benefits from replacing and the benefits from waiting for each vintage, 

DOE projected the number of potential front-loading and top-loading commercial clothes 

washers in the existing stock to be (early) replaced in each of the 30 years of the analysis period. 

For this projection DOE further considered that, because of lack of information on the benefits 

from early replacements or any transaction costs involved in the process of early replacing a unit 

in operation, not all consumers would be informed to or willing to decide for the replacement. To 

estimate the share of the market that would take such initiative DOE used the customized 

penetration curves it developed to support the analysis of the rebate policy case described above. 

DOE assumed for this policy case the same monetary incentive and market barrier level it 

estimated for each equipment class when calculating the impacts from rebates. As for the 

benefit-cost ratio, DOE used the ratio of the benefits from replacing divided by the benefits from 

waiting.

The units in the existing stock projected to be replaced early represent additional 

shipments of front-loading and top-loading units rated at the target efficient level of each TSL. 

Figure 17.3.2 shows DOE’s projections of shipments of front-loading and top-loading 

commercial clothes washers in response to an early replacement policy targeting the efficiency 

levels set for the selected TSL. An early replacement policy would create a fairly immediate 

jump in shipments of equipment that meet the target efficiency level relative to the base case. 

High-efficiency units would be brought quickly into the stock, leading to an immediate gain in 

the market share of efficient units compared to the base case. However, as opposed to the policy 

cases discussed previously, an early replacement policy results in market shares of efficient units 

returning to base-case percentages as the eligible market is depleted. Additionally, because units 

removed early from the stock would have been replaced later (at the ends of their useful lives) 

17-18
without the program, the number of shipments in later years drops slightly below the base-case 

shipments forecast. 

Front-Loading Top-Loading

Figure 17.3.2 Base Case and Policy Case Shipments Projections for the Early 

Replacement Policy Case (thousand units)

DOE used the additional shipments for early replacements to calculate the increase in 

market penetration of more efficient units that would result from an early replacement policy.

Table 17.3.8 summarizes DOE’s assumptions for commercial clothes washers regarding the 

market penetration of units in 2018 meeting the target efficiency level at each TSL given early 

replacement incentives.

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 32.9% 75.5% 75.5% 2.8%

Increased Market Share 1.9% 6.5% 6.5% 2.8%

Top-Loading 1 2 3 4

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 35.3% 35.3% 25.6% 25.6%

Increased Market Share 8.6% 8.6% 14.3% 14.3%

The resulting increased market shares attributable to early replacements shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market 

share increases due to this policy. Section 17.4 presents the resulting market penetration trends 

and cumulative savings for the policy case of early replacement for commercial clothes washers.

17-19
17.3.7 BULK GOVERNMENT PURCHASES 

Bulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of equipment that meet the target efficiency level. Combining the market 

demands of multiple public sectors also can provide a market signal to manufacturers and 

vendors that some of their largest customers seek equipment that meet an efficiency target at 

favorable prices. Such a program also can induce “market pull,” whereby manufacturers and 

vendors would achieve economies of scale for high efficiency equipment.

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

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

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

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

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

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

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

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

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

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

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

27, 28

DOE assumed that government agencies would administer bulk purchasing programs for 

commercial clothes washers. At the federal level, this type of program could modify the current 

FEMP procurement guidelines for commercial clothes washers, which refer to the ENERGY 

STAR requirements for commercial clothes washers29. DOE reviewed its own previous research 

on the potential for market transformation through bulk government purchases. Its major study 

analyzed several scenarios based on the assumption that 20 percent of Federal equipment 

purchases in 2000 already incorporated energy efficiency requirements based on FEMP 

guidelines. One scenario in the DOE report showed energy efficient purchasing ramping up 

during 10 years from 20 percent to 80 percent of all Federal purchases.30 Based on this study, 

DOE estimated that a bulk government purchase program instituted within a 10-year period 

would result in at least 80 percent of government-purchased commercial clothes washers meeting 

target efficiency levels.

To estimate the impact of a bulk government purchase program for this equipment, DOE 

first determined the shipments of commercial clothes washers that go to government-owned 

housing. A report published by Lawrence Berkeley National Laboratory (LBNL)31 in 2012

estimated that around 3000-3200 commercial clothes washers were purchased annually for 

Federal buildings from 2005 through 2011. This corresponds to an average of 1.9% of all 

shipments of commercial clothes washers in the same period. DOE applied the estimated 1.9% of 

shipments to the forecast of market penetration of more efficient front-loading and top-loading 

commercial clothes washers. DOE estimated that, for each TSL, starting in 2018 each year of a 

bulk government purchase policy would result in an increasing percent of shipments of 

17-20
government-purchased units beyond the base case that would meet target efficiency levels. DOE 

estimated that within 10 years (by the end of 2027), bulk government purchasing programs 

would result in 80 percent of the front-loading and top-loading commercial clothes washers 

market for government-owned commercial buildings meeting the target level of each TSL. DOE 

modeled the bulk government purchase program assuming that the market share for each 

equipment class achieved in 2027 would be maintained throughout the rest of the analysis

period. 

Table 17.3.9 summarizes DOE’s assumptions for commercial clothes washers regarding 

the market penetration of units in 2018 meeting, for each TSL, the target efficiency level given 

bulk government purchasing.

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing of 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3 4

Base-Case Market Share 31.0% 69.0% 69.0% 0.0%

Policy Case Market Share 31.7% 70.3% 70.3% 0.1%

Increased Market Share 0.7% 1.3% 1.3% 0.1%

Top-Loading 1 2 3 4

Base-Case Market Share 26.7% 26.7% 11.3% 11.3%

Policy Case Market Share 27.3% 27.3% 11.6% 11.6%

Increased Market Share 0.6% 0.6% 0.3% 0.3%

The resulting increased market shares attributable to bulk government purchasing shown 

in Table 17.3.9 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual 

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

17.4 below presents the resulting market penetration trends for the policy case of bulk 

government purchase of commercial clothes washers.

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 and Figure 17.4.2 show the effects of each non-regulatory policy on market 

penetration for front-loading and top-loading commercial clothes washers. Relative to the base 

case, the policy cases increase the market shares that meet the target level. Recall the selected 

standards (not shown in the figures) would result in a 100-percent market penetration of 

equipment that meets the target efficiency level.

17-21
Figure 17.4.1 Market Penetration of Front-Loading Commercial Clothes 

Washers Meeting the Target Level in Policy Cases (TSL 2)

Figure 17.4.2 Market Penetration of Top-Loading Commercial Clothes 

Washers Meeting the Target Level in Policy Cases (TSL 2)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

17-22
Table 17.4.1 and Table 17.4.2 show the national energy savings and net present value for 

six non-regulatory policies analyzed in detail for commercial clothes washers. The target level 

for each policy equals the efficiency levels proposed for standards in TSL 2. The case in which 

no regulatory action is taken with regard to commercial clothes washers constitutes the base case

(or "No New Regulatory Action" scenario), in which NES and NPV are zero by definition. For 

comparison, the tables include the impacts of the proposed standards. Energy savings are given 

in quadrillion British thermal units (quads). The NPVs shown in Table 17.4.1 and Table 17.4.2

are based on two discount rates, 7 percent and 3 percent.

The policies with the highest projected cumulative energy savings for front-loading 

commercial clothes washers are voluntary energy efficiency targets and early replacements, and 

for top-loading are consumer rebates and voluntary energy efficiency targets. Early replacements 

result in negative NES for top-loading commercial clothes washers, and negative NPV for both 

equipment classes and at both discount rates. The increase in shipments induced by the policy 

outweighs the benefits from the higher energy efficiency achieved in the policy case, in relation 

to the base case, during the first years of the analysis period. For the remaining years, the 

decrease in shipments potentiates the benefits from the higher energy efficiency. However, the 

positive effects from the last years of the analysis period do not counterbalance the negative 

effects observed over the initial years. In the case of top-loading commercial clothes washers this 

is further amplified by the shipments increase in the policy case, driven by substitution, which 

eventually results in negative NES. NPV would become zero for front-loading commercial 

clothes washers at 2.8 percent discount rate, and for top-loading at 0.7 percent discount rate.

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

Clothes Washers (TSL 2)

Policy Alternative Primary Energy 

Savings*

quads

Net Present Value*

billion 2013$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 0.0002 (2.8%)** 0.001 0.003

Consumer Tax Credits 0.0007 (10.0%) 0.006 0.011

Manufacturer Tax Credits 0.0003 (5.0%) 0.003 0.006

Voluntary Energy Efficiency Targets 0.0047 (70.0%) 0.026 0.067

Early Replacement 0.0044 (65.2%) -0.089 -0.009

Bulk Government Purchases 0.0003 (4.7%) 0.002 0.005

Proposed Standards 0.0067 (100.0%) 0.042 0.099

* For products shipped 2018 – 2047

**The percentages show how the primary energy savings from each alternative policy compare to the 

savings from the proposed standards (represented in the table as 100%).

17-23
Table 17.4.2 Impacts of Non-Regulatory Alternatives for Top-Loading Commercial 

Clothes Washers (TSL 2)

Policy Alternative Primary Energy 

Savings*

quads

Net Present Value*

billion 2013$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 0.023 (37.6%) 0.076 0.163

Consumer Tax Credits 0.006 (10.1%) 0.027 0.049

Manufacturer Tax Credits 0.003 (5.0%) 0.013 0.024

Voluntary Energy Efficiency Targets 0.020 (32.4%) 0.055 0.133

Early Replacement -0.007 (-12.4%) -0.382 -0.349

Bulk Government Purchases 0.001 (2.1%) 0.004 0.009

Proposed Standards 0.060 (100.0%) 0.201 0.433

* For products shipped 2018 – 2047

**The percentages show how the primary energy savings from each alternative policy compare to the 

savings from the proposed standards (represented in the table as 100%).

17-24
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energy_efficiency>

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

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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.

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