1

The benefits of energy efficiency standards and how policies 

may accelerate declines in appliance costs 

R D Van Buskirk1, C L S Kantner1, B F Gerke1, L-B Desroches1 and S Chu2 

1

 Environmental Energy Technologies Division, Lawrence Berkeley National 

Laboratory, 1 Cyclotron Road, Berkeley, California, U.S.A. 94720-8136 

2

 Department of Physics and Department of Molecular and Cellular Physiology, 

Varian Bldg., Room 252, Stanford University, Stanford, CA, U.S.A. 94305-4060 

E-mail: RDVanBuskirk@lbl.gov

Abstract. We perform a retrospective analysis of multi-decade trends in price and life-cycle 

cost for home appliances in periods with and without energy-efficiency standards. In contrast to 

the classical picture of the impact of efficiency standards, the introduction and updating of 

appliance standards is not associated with a long-term increase in purchase price; rather, qualityadjusted prices undergo a continued or accelerated long-term decline. We also show that the 

incremental price of efficiency improvements has declined much faster than the baseline product 

price. Both observations are consistent with an alternative picture of energy efficiency standards in 

which such policies increase long term rates of learning-by-doing for both price and life-cycle 

cost.

Subject Classification Number, MSC2010: 91B84 

Keywords: appliance standards, energy efficiency, technological learning, life-cycle cost

 
2

1. Introduction 

Energy efficiency (EE) standards are widely viewed as a key policy tool for reducing energy 

consumption, saving money, and mitigating climate change [1]. There has been substantial debate, 

however, in both the academic literature and the political discourse regarding the impact of standards on 

both the life-cycle cost of ownership (LCC) and on the purchase price of home appliances. Many studies 

argue that appliance standards achieve a net neutral or beneficial economic impact by decreasing the 

operating energy costs more than the increase in purchase price expected to result from compliance with 

the standard [2, 3, 4, 5, 6]. Other papers assert that such mandates as EE standards are inefficient and 

costly means of decreasing CO2 emissions based on assumed parameters [7] or are likely to lead to 

expensive products that may force budget-constrained consumers to choose products of lower quality or 

reduced features [8]. There also exists a fairly extensive literature on innovation that may be induced by 

regulation. This literature discusses a diverse range of cases where regulation may have caused private 

firms to create innovations that would not have occurred in the absence of regulation [9]. 

Over the past quarter century, the academic debate on the costs and benefits of EE standards has been 

based on short-term snapshots of appliance market conditions and has not addressed the impacts of EE 

standards on innovation. From this short-term perspective—which assumes that innovation effects are not 

significant—the primary problems addressed by the regulation are market failures associated with 

environmental externalities, imperfect information, principal-agent issues, liquidity constraints, or 

behavioral anomalies [10]. Energy policies, such as minimum EE standards and labeling policies (e.g., 

ENERGY STAR Program), are devised to address these market failures and informational problems that 

lead to socially suboptimal outcomes. In the classical regulatory impact analysis picture, labels and 

mandates bring the market to a new equilibrium where appliances have higher prices and lower operating 

costs. The lower operating costs and internalized market externalities of the more efficient appliances are 

thought to justify the higher purchase prices paid by consumers. 

Recent empirical work with appliance market data suggests that price-efficiency relationships observed in 

appliance markets may be far from static [11]. Some studies argue that the prices decline faster than 

forecast in regulatory analyses due to technological innovation [12]. Additionally, a recent study of the 

2004 and 2007 US EE standards for clothes washers finds that the rate of decline in appliance prices 

appears to have accelerated after the EE standards came into effect [13]. This observation is largely 

inconsistent with the static picture of EE standards impact that is traditionally used in policy analysis 

which would have predicted a price increase. As a growing number of observers examine the 

retrospective data on appliance price changes associated with EE standards, there appears to be an 

expanding body of evidence suggesting that the traditional static picture of EE standards impacts on costs 

may be inconsistent with observed market behavior [14]. 

In this paper, we investigate long-term trends in appliance prices and energy use in an effort to better 

understand interactions between policy and rates of change in appliance markets. Specifically, we 

examine the long-term dynamics of markets for efficient products and estimate long-term rates of change 

of appliance first costs and operating costs for periods both with no EE policy and with active EE 

standards and labeling programs. Our analysis considers mandatory EE standards, but the results may also 

be applicable to EE labeling programs that allow consumers to make cost-effective trade-offs between the 

price of efficiency and energy-cost savings [15]. 
3

The discussion of market failures in the literature with regard to EE technology has largely focused on 

factors that hinder consumers’ choice of efficient technologies that are cost-effective on a life-cycle-coast 

basis—the so-called energy-efficiency gap. From the long-term-dynamics perspective, an economically 

inefficient market equilibrium at a particular point in time may not be the only market failure addressed 

by regulation. Another market failure that regulation may potentially address is the well-known market 

failure regarding underinvestment in research, development and innovation [16, 17, 18]. In particular, the 

structure of the supply markets for appliances may lead to underinvestment in innovation, thus hindering 

EE in the menu of products offered to consumers. There is evidence that the supply markets for 

appliances are not perfectly competitive and there are varying levels of industry concentration, which can 

lead to price discrimination and under-investment in EE by manufacturers [19]. In a theoretical model 

[20], Fischer demonstrated that when suppliers discriminate on price, they will offer less efficient 

products to the low-income consumers and extract their entire economic surplus, while charging highincome consumers more for the higher efficiency appliances than in a perfectly competitive market. In 

this situation, minimum EE standards will increase consumer welfare. This study examines new economic 

evidence regarding rates of change in quality-adjusted product prices and EE, which may indicate that 

changes in consumer costs and welfare over the long term are driven by changes in EE policy. 

2. Life-cycle cost 

EE standards effectively mandate the manufacturing of appliances and equipment that may have a higher 

first cost but which have lower operating costs. The total consumer impacts of EE standards are estimated 

by calculating the energy-related*

 LCC of an appliance, which is a sum of the purchase price PA and the 

energy-related operating costs over time, OC(t). LCC includes a discounted sum of operating costs, 

because money spent on operating costs y years in the future is worth less than the same amount of money 

in the present which could be invested at some interest rate.†

 Assuming a yearly compound interest rate i,

the present value of an expense y years in the future is discounted to ( ) / (1 )y OC y i  , so that the total 

present value of operating costs over the appliance lifetime of L-years is given by 

1

( )

(1 )

L

L y

y

OC

i

V y P OC 

      (1)

The sum of price and present value of operating costs then provides an estimate of the LCC of that 

appliance: 

 LCC P PVOC  A L  (2) 

                                                           

*

 This analysis assumes that non energy-related operating costs (e.g., maintenance and repair costs) are independent 

of EE and therefore irrelevant to the effect of EE policy on LCC. 

†

 If operating cost is measured in inflation-adjusted dollars, the inflation-adjusted interest rate is used. We assume 

operating costs and interest are charged at the end of the year in which they occur. 
4

3. Experience curve calculations 

Experience curves (also called learning curves) can be used to describe price trends for many 

technologies‡

 by modeling price P as a power-law function of the total cumulative quantity Q of units 

deployed: P = PoQ-b where Po and b are empirically determined parameters [21, 22, 23]. Q is the 

observable variable used as proxy for cumulative manufacturing experience [24]. The parameter b is a 

parameter that describes the relation between fractional increases in experience and fractional declines in 

price. The experience curve concept is based on the empirical observation that people and organizations 

complete tasks more efficiently as they accumulate experience, thus reducing the marginal cost of 

production. However, there are other mechanisms that could also yield a correlation between price and 

cumulative experience (e.g., changes in pricing strategies or globalization of manufacturing operations 

and supply chains). In this study, we will use the phrase experience curve as shorthand to refer to any 

empirical correlation between price and cumulative experience, without necessarily linking observed 

trends to learning-by-doing processes. 

To calculate the experience curves, we estimate inflation- and quality-adjusted prices over time by 

projecting current prices into the past using product-specific price indices. Price indices provide a method 

of accounting for changes in the mix of products offered in the market, as well as the quality and features 

of those products. For example, the refrigerators that were available for sale in the 1960s are no longer 

available today, and the technologies, features and configurations of refrigerators today did not exist in 

the 1960s. Any measurement of long term price trends needs to account for these gradual but significant 

changes. 

For US data, we use price indices published by the Bureau of Labor Statistics (BLS) and the Bureau of 

Economic Analysis. [See the Supplemental Information (SI) for additional detail regarding price indices 

and the calculation of experience curves.] The BLS provides both consumer price indices for retail 

purchase prices and producer price indices for the prices received by producers for their products. We use 

the all-items consumer price index (CPI) to adjust for inflation in our analysis. In some cases our CPI data 

is sourced indirectly from the BLS using tables published by Gordon [25]. 

In the LCC calculations, to estimate an average price in a recent year, we use the shipment-weighted 

average price obtained from the LCC spreadsheets associated with the Department of Energy’s (DOE) 

most recent rulemaking or notice of proposed rulemaking for a particular appliance. These spreadsheets 

are available on the DOE’s Appliance Standards website as part of technical support documents created 

for each rulemaking. We obtain the historical inflation- and quality-adjusted prices by multiplying this 

recent shipment-weighted price by the appropriate inflation- and quality-adjusted price index. 

We obtained data to estimate historical average energy use from a variety of different sources, including 

the Association of Home Appliance Manufacturers (AHAM), recent DOE rulemakings, DOE’s Energy 

Information Administration’s (EIA) Residential Energy Consumption Surveys [26], and the published 

literature [27]. For periods with missing or incomplete data, we interpolate from the historical trends. In 

the case of clothes washers, we also consider water usage and water efficiency when calculating operating 

costs. Water usage data were obtained from recent DOE rulemakings. 

                                                           

‡

 The experience curve literature focuses on product cost rather than price. Although price often deviates from the 

cost of production, we assume that on average the price trend is a good approximation of the cost trend. 
5

We use historical average retail prices of electricity for residential consumers from the Annual Energy 

Review published by EIA [28]. For years prior to 1960, we extrapolate the retail price of electricity using 

the CPI for electricity [29]. In the case of water, the average retail price in 2008 was obtained from a 

recent DOE rulemaking analysis, and extrapolated to prior years using the CPI for water and sewage 

maintenance. 

Cumulative unit shipments to the US market for each product are estimated using annual shipment data 

from recent DOE rulemaking analyses. Other inputs to the LCC calculation, such as the appliance lifetime 

and discount rate, are similarly estimated using data from these DOE analyses. We follow DOE’s practice 

of using a weighted-average consumer cost of capital from a variety of debt and asset classes to represent 

the discount rate, because that average represents the financial cost of money to a consumer. For a review 

and discussion of the issues surrounding discount rates and the energy efficiency gap, we recommend 

Jaffe and Stavins [30]. Depending on the rulemaking, the average discount rate for each appliance type 

was found to be 5.0 ±0.5%. For simplicity, we use an average real discount rate of 5.0% in the LCC 

calculations for all appliances. 

For refrigerators in the Netherlands, we obtained price and energy use data from Weiss et.al. [31] and 

from the German market research firm The GfK Group. These refrigerator price data are not qualityadjusted. We calculate the inflation adjustment using the Harmonized Index of Consumer Prices (HICP) 

from Eurostat [32]. We obtained nominal pre-tax electricity prices from Eurostat [33], and estimate posttax electricity prices using tax rates from the U.K. Department of Energy and Climate Change [34]. We 

use the average tax rate for 2008 and 2009 as the assumed tax rate for the analysis period. Historic 

shipments were obtained from an Ecocold report [35]. Shipments to the EU-15 nations were used as the 

basis for experience, since manufacturers tend to sell products across the European market. The lifetime 

and discount rates are assumed to be constant in time and the same as those of US refrigerators. 

The ultimate product of this data collection, processing, and integration is a set of yearly time series for 

cumulative shipments, prices, and LCCs, for four US appliances and for refrigerators in the Netherlands. 

Shipments data refer only to the US market (or the EU market for Netherlands refrigerators). Global 

shipments would be a closer indicator of cumulative experience, but provided that manufacturers’ global 

shipments grow consistently with their US/EU shipments, power-law experience curves can be accurately 

fitted using the more limited data set. We combine the price and LCC data with the shipments data to 

obtain experience curves, P(Q) and LCC(Q). 

4. Efficiency policy and changes in long term trends 

Figure 1 shows the results of fitting experience curves to price and LCC for four major US appliances 

(refrigerators, clothes washers, room air conditioners and central air conditioners) and for refrigerators in 

the Netherlands. These products were chosen because multi-decadal information was available both 

before and after the establishment of standards (“pre-standards” and “post-standards,” respectively). In all 

cases, the price and LCC generally decline with time, and a significant transition can be seen in the LCC 

decline rate parameter near the initiation of standards. A distinct acceleration of the price decline can also 

be seen for clothes washers, room air conditioners, central air conditioners, and refrigerators in the 

Netherlands. 
6

We consider the post-standards period to begin after the issuance of the first standard likely to affect the 

market under consideration. In most cases, the first standard corresponds to a federal standard, or to a 

European Union standard in the case of refrigerators in the Netherlands. We also consider California 

standards given that California is the most populous US State and therefore constitutes a significant 

fraction of the US appliance market. In some cases, California efficiency standards may have been 

effectively treated as a national standard by manufacturers. 

We fit the data with a simple mathematical model that includes two power-law experience curves with 

different decline-rate parameters b, corresponding to the periods before and after a transition year. The 

decline rates, transition year, and overall normalization are treated as free parameters, allowing us to 

calculate the transition year within a 95% confidence interval using standard maximum-likelihood 

techniques. [See the SI for further details. The SI also evaluates the sensitivity of results to variations in 

model formulation.] Table 1 provides the resulting transition years and confidence intervals of these 

experience-curve fits for US refrigerators, clothes washers, room air conditioners, central air conditioners, 

and Netherlands refrigerators, and compares them to the first year of efficiency standards for these 

products. 

Table 1. Results for least-squares, two-slope experience curve model fit of price and LCC for the 

appliances considered in this study; 95% confidence intervals were calculated from the appropriate 

distribution. For reference, the year of the first efficiency standard implementation is also shown for each 

appliance. 

 Transition Year Pre-Transition 

Decline Rate b 

Post-Transition 

Decline Rate b 

Appliance First Efficiency 

Standard Year LCC Price LCC Price LCC Price 

Refrigerators 

(US) 1977 1977.2ିଷ.ଶ

ାଶ.ଷ 1995.0ି଼.଴ାଷ.଻ 0.36ି଴.଴଼

ା଴.଴ଵ 0.84ି଴.଴଴

ା଴.଴ହ 1.25ି଴.଴ହ

ା଴.଴଴ 1.51ି଴.ଵସ

ା଴.଴ସ

Clothes 

Washers (US) 1988 1989.8ିଵ.ଶ

ାଵ.ଽ 1986.9ିଵ.ହ

ାଵ.ଷ 0.12ି଴.଴଴

ା଴.଴ସ 0.45ି଴.଴ଷା଴.଴ଷ 1.20ି଴.ଵ଴ା଴.଴ଵ 0.98ି଴.଴ଷା଴.଴଴

Room Air 

Conditioners 

(US) 

1979 1976.3ିଶ.଼

ାଵ.଼ 1972.8ିଵ.଺

ାଵ.ଷ 0.16ି଴.଴ଵ

ା଴.଴ସ 0.27ି଴.଴ସା଴.଴ଵ 0.76ି଴.଴ସା଴.଴଴ 0.75ି଴.଴ସା଴.଴ଵ

Central Air 

Conditioners 

(US) 

1979 1975.5ି଴.ଽ

ାସ.଴ 1973.9ିଶ.ଷ

ାଷ.ଵ 0.01ି଴.଴଴

ା଴.଴ଷ 0.07ି଴.଴ଷା଴.଴ଷ 0.39ି଴.଴ଷା଴.଴ଶ 0.28ି଴.଴ଷା଴.଴଴

Refrigerators 

(Netherlands) 1999 2000ିଶ଺

ାସ 2001ିଵସାଶ 0.45ି଴.ଵହ

ା଴.ଵସ 0.25ି଴.ଵସ

ା଴.ଷଶ 1.08ି଴.ଵଵା଴.ଵଵ 0.80ି଴.ଵଶା଴.ଵଶ

The correspondence between calculated transition year and first efficiency standard year can be seen in 

figure 2 with 95% confidence intervals shown as error bars. In almost all cases, the data yield a transition 

year that is close to the onset of standards, with the exception of US refrigerator purchase price. The 

confidence intervals for the Netherlands refrigerator case are relatively large because of the larger 

variance in the available price and efficiency data. 
7

Figure 1. Price and LCC trends for appliances pre-standards and post-standards. Unfilled symbols 

indicate pre-standards data, while solid symbols indicate the post-standards era. Lines indicate best-fit 

experience curves from the two-trend model. Circles indicate transition points in the decline rates. Data 

are shown for United States (A) refrigerators, (B) clothes washers, (C) room air conditioners, and (D) 

central air conditioners, as well as for (E) Netherlands refrigerators. Netherlands refrigerator price and 

LCC are shown per 100 liters of volume, and cumulative shipments are for the EU. In figures 1(A) – (D), 

Room AC

C

5 25 50 250

Central AC

D

Cumulative Shipments (millions) 

250

500

750

1000

2500

5000

7500

10000

Refrigerators

A

Price and LCC 

(2008$)

25 50 75 100 250 500

750

1000

2500

5000

7500

10000

Clothes Washers

B

Price and LCC 

(2008$)

Cumulative Shipments (millions) 

50 75 100 250 500

100

250

500

750

1000

E

 EU 15 Cumulative Shipments (millions)

Netherlands Refrigerators

Price and LCC (2008$)

 Pre-standards Price

 Post-Standards Price

 Pre-standards LCC

 Post-Standards LCC

 Two power law fit

1978

1980

1987

1990

1993

2001

1979 1981 1983 1985

1992

2005

1979

1981

2000

1990

1988

2004

1994

1999

2002

Price and LCC per 100 L volume (2008$)
8

the dashed and solid arrows represent California and Federal standards, respectively. In figure 1(E), the 

dashed arrows indicate a voluntary agreement with manufacturers, and solid arrows indicate mandatory 

standards. 

Figure 2. The calculated year of transition to an accelerated price or LCC decline, as a function of the 

year standards are first enacted. Open symbols correspond to price transition year, closed symbols to LCC 

transition year. If the change in slope of the experience curves in figure 1 occurred at the time the 

standard was introduced, the data points would lie on the dashed line.

5. Trends in the incremental price of efficiency: the case of US refrigerators 

The price and LCC trends observed in figure 1 clearly diverge from historical, pre-2011 US regulatory 

forecasts, which assumed no future price declines in the absence of new standards, and which projected 

an increase in appliance price following the introduction and subsequent updates of standards. We 

compiled historical, engineering-based estimates of the relationship between appliance price and 

efficiency that were developed in DOE regulatory analyses accompanying the consideration of new or 

updated efficiency standards for refrigerators. For refrigerators, new mandatory Federal EE standards had 

initial compliance dates of 1990, 1993, 2001 and 2014, with supporting analyses performed in 1982, 

1987, 1989, 1995, and 2010. At the time each analysis was done, the incremental cost of higher efficiency 

was based on the current market costs of introducing technologies to achieve a given efficiency. The full 

historical sequence of price-efficiency estimates for 17- to 18-cubic-foot refrigerators with top-mount 

freezers and automatic defrost is shown in figure 3(A). Note that the most energy-efficient refrigerator 

considered in 1989 had higher energy use than the least efficient refrigerator considered in 2010, and the 

estimated price of achieving that efficiency was roughly twice the price estimate of the least efficient 
9

2010 refrigerator. Over the period considered, the efficiency of the least efficient refrigerator on the 

market improved at an average rate of 4% per year while its price dropped 2.5% per year. 

Figure 3. Modeling of energy use vs. price. (A) Engineering-based estimated purchase price of US 17- to 

18-cubic-foot top-mounted refrigerator-freezers as a function of annual energy. Solid lines show the fit to 

the data using equation (2). (B) Evolution in the modeled price of the baseline, least efficient refrigerator 

200 400 600 800 1000 1200 1400

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

A

 1982

 1987

 1989

 1995

 2010

 Fit

Inflation-adjusted Purchase Price,

P

A (2008$)

Annual Energy Use, UEC (kWh/yr)
10

on the US market in each year (green points); a refrigerator with a fixed efficiency of a 1982-era baseline 

unit (blue points); and the incremental price associated with a fixed efficiency increase relative to the 

1982 baseline (red points). Solid lines show the exponential fit of (4) to the red and blue points. The 

incremental price for 1982 (hollow symbol) is excluded from the fit because 490 kilowatt-hours per year 

(kWh/yr) is well outside the estimated price-efficiency range for that year, so this point is derived from an 

extreme extrapolation. 

We mathematically characterize the price-efficiency relation as a baseline price ܲ௠௜௡ plus a power-law 

relation between unit energy consumption (UEC) and the additional price of achieving that efficiency 

level: 

0

( ) A min UEC

UEC

U

P UEC

C

P P

E



      

 (3) 

Here, Pmin is the minimum price of an appliance, PUEC is the additional price required to purchase an 

appliance whose energy consumption takes a reference value UEC0 (which we are free to choose), and 

 is the power law exponent, which, in economic terms, represents the elasticity of the energy-related 

price component with respect to efficiency. Figure 3(A) illustrates a reasonably good fit to the 

engineering-based price-efficiency estimates using this simple functional form, where we have fit the 

model in (3) to each of the five price-efficiency estimates separately, yielding five sets of fit parameters 

(see Table 2). 

Table 2. Results of fitting engineering-based price-efficiency estimates for refrigerators with a power-law 

model (3). 

Year Pmin PUEC UEC0  R2

1982 1260 38.1 1350 2.32 0.995 

1987 1270 20.7 925 6.47 0.991 

1989 961 37.6 955 4.07 0.995 

1995 796 67.9 700 2.65 0.993 

2010 717 36.8 482 5.90 0.991 

From these fits we can estimate the evolution of the price of a refrigerator with fixed efficiency and of the 

incremental price of a fixed increase in efficiency. For each of the five fits shown in figure 3(A), figure 

3(B) shows the actual baseline price, the modeled price of a 1982-era baseline unit, and the modeled price 

increase required to improve the 1982 baseline unit to a reference energy consumption of 490 kWh/yr, a 

value chosen since it lies in or near the range of UEC values covered by most of the engineering estimates 

after 1982. 

The evolution of our fit parameters over time demonstrates that the price of efficiency declines at a much 

faster rate than the price of the baseline appliance. We can quantify this by fitting our price measurements 

to a Moore’s-law type model in which price declines exponentially with time: 

0 ( )

0 X t t P Pe X X

        (4) 
11

where X represents either the 1982-baseline-efficiency unit or a unit with a UEC of 490 kWh/yr (see the 

SI for more detailed discussion of Moore’s-law type price trends). If the price of refrigerator efficiency 

mirrored the overall trend in refrigerator price, we might expect the incremental price of a fixed increase 

in efficiency to decrease at the same rate as the quality-adjusted price: 2.5% per year. This assumption has 

been used in recent appliance standards rulemakings that have incorporated experience curves [36]. 

Instead, the data presented here show that while the price of a fixed, low-efficiency refrigerator declines 

at that rate, 0.025  min  , the incremental price of improving that efficiency by a fixed amount drops 

much more quickly: 0.19  UEC  , or a rate of price decline of roughly 19% per year between 1987 and 

2010 (see figure 3(B)) 

This rapid drop in the price of efficiency may be what allowed the price of refrigerators to continue to 

decline even as efficiency has improved at 4% per year. The incremental price increase of a given 

efficiency improvement rapidly became negligible compared to the more slowly declining baseline price.

6. Price-efficiency trends for European Union refrigerators 

While the results for US refrigerators give some indication that the price of efficiency may have rapidly 

decreased over time, engineering curves are estimates, not observed prices. Aggregated European 

refrigerator sales data from ten European countries (Austria, Belgium, Great Britain, Italy, the 

Netherlands, Germany, Spain, and France, Sweden and Portugal) enable us to model the observed 

efficiency-categorized price over time directly.§

 Equivalent data are not available for the US because the 

US does not have labeling requirements with multiple EE categories. 

                                                           

§

 Europe uses a substantially different refrigerator test procedure than does the US; energy use figures are not 

directly comparable. 
12

Figure 4. Market-based price-efficiency estimates for European refrigerators illustrating the evolution 

over time. Points correspond to categorical averages over several years. Solid curves show the fit to these 

categorical averages using (5). 

As with the Netherlands data, we use sales-weighted averages of retail prices (not quality-adjusted) 

obtained from The GfK Group. We adjusted prices for both inflation and consumer purchasing power 

differences between countries [37] and converted all prices into 2008 Austrian purchasing power parity 

(PPP) Euros. We obtained comparative price indices for purchasing power parity adjustments between 

countries from table 1357 of the 2012 US Statistical Abstract [38]. 

We obtained refrigerator price data binned according to EE grade, which range from A++ to G for each 

country in the sample. The grades correspond to an energy efficiency index (EEI), which is defined as a 

unit’s energy consumption divided by the consumption of an average model (based on all models on the 

market from 1990–1992) for units of the same adjusted volume. The European Commission adopted the 

grades in 1992 (92/75/EC). The grades were implemented into national ordinances between 1994 and 

1998. The A+ and A++ grades were introduced in 2003 (2003/66/EC) and began being implemented in 

2004. To scale the EEI to the energy use of a refrigerator, we set an EEI of 100% at 463 kWh/yr, the 

value the European Commission cited in its COLD II report [39]. Refrigerator volume was approximated 

as constant during 1995–2009. 

Similar to the Netherlands refrigerator analysis, European electricity prices are estimated using data from 

Eurostat [40] and UK Department of Energy and Climate Change [41]. We use the same lifetime and 

discount rate assumptions as those for US refrigerators: 17.1 years and 5.0% per year, respectively. 

We perform a simplified fit to the European data using (5) and assuming an exponential dependence of 

price on time (i.e. a form of Moore’s law: see SI for details): 

120 150 180 210 240 270 300 330 360 390 420

300

400

500

600

700

800

C

Inflation-adjusted Price, P

A (2008 Euro)

Energy Consumption, UEC (kWh/yr)

 1995-1998

 1999-2002

 2003-2005

 2006-2010
13

0 ( )

0 0 ( ,) ( / ) t t P UEC t P e UEC UEC UEC UEC

     (5) 

where is a reference price at a particular reference annual energy use UEC0 and reference time t0. 

Figure 4 shows the fit to the European market data, which describes the data reasonably well (see SI for 

additional details). Within a category corresponding to an EE grade, the average inflation-adjusted price 

declined by 4.5% per year between 1995 and 2009, while the average shipment-weighted inflationadjusted price computed across all efficiency categories declined by approximately 1.3% per year. The 

market average estimated UEC simultaneously decreased by 4.2% per year. As in the US, European 

refrigerator prices have declined even as efficiency policies have increased the average efficiency on the 

market. 

7. Life-cycle cost optimization 

By analyzing price trends for baseline prices and for the incremental price of efficiency, it may be 

possible to generate more accurate price-efficiency forecasts and thereby devise more optimally beneficial 

policies. We can combine (2) and (3) above to obtain an equation for LCC as a function of UEC. 

Minimizing this equation with respect to UEC then yields the following optimization condition (see the SI 

for a detailed derivation): 

LCC P P min A UEC    .   (6)

Inserting the Moore’s-Law model for the time evolution of price from (4) gives an equation for the time 

evolution of the minimum LCC: 

0 0

1

0 ( )/(1 )

0

0

( ) ( ) ( ) min UEC tt tt

A UEC

U C

L

E

PVOC UEC LCC t P e P e

P





   



       

     

,   (7)

where 0 ( ) PVOC UEC L is the present value of the operating costs for an appliance with lifetime L whose 

UEC is equal to UEC0 . Equations similar to this one could be used to derive more accurate forecasts of 

the LCC-minimizing UEC value, thus enabling EE policies that more precisely maximize the economic 

benefit to consumers. 

Figure 5 illustrates using these equations to forecast the UEC that minimizes refrigerator LCC in 2020, 

based on the 2010 price-efficiency relation shown in figure 3(A). If the price of all refrigerators is 

predicted to decline by 2.5% per year regardless of efficiency level, the LCC-minimizing UEC is 

expected to decline from 410 kWh/yr in 2010 to 400 kWh/yr in 2020. This is the approach to price trends 

that is currently used in DOE EE standards analysis, and which was first utilized in support of the 

refrigerator standard issued in 2011 [36, 42]. If, instead, the incremental price of efficiency is forecast to 

decline at 19% per year (while Pmin continues to decline at 2.5% per year), the LCC minimum point is 

expected to decrease an additional 17.5% to 330 kWh/yr, yielding an additional LCC reduction of $97 for 

each of the estimated 13 million refrigerators sold in the US in 2020 [42]. If all the refrigerators 

purchased in 2020 saved the additional 70 kWh/yr enabled by the faster incremental price decline, they 

UEC0 P
14

would save a cumulative 15 TWh of electricity over their 17-year lifetime, which corresponds to 

approximately 9 million metric tons/yr of avoided CO2 emissions with present-day electricity generation. 

Determining price trends at the required level of granularity for such a forecast is challenging, and it may 

not always be possible to produce forecasts with sufficient certainty for use in a regulatory analysis. In 

this case, given the potentially rapid price evolution of efficient technologies, regular regulatory updates 

are especially important to maintaining EE policies that are close to the social optimum. 

Figure 5. Dependence of LCC on baseline and incremental price declines. The blue curve shows the 

dependence of refrigerator LCC on UEC in 2010 according to the model in figure 3(A) (the LCC rises at 

high UEC because increased energy costs outweigh decreased first cost). The solid portion of the line 

indicates the range of efficiencies considered in the refrigerator rule published in 2011. The green curve 

shows how the LCC may change in 2020 if both the baseline and incremental price of efficiency decrease 

at 2.5% per year. The magenta curve illustrates how the LCC in 2020 may change more rapidly if the 

incremental price decreases at 19% per year instead. The arrows with corresponding color indicate the 

LCC minima. The black arrow indicates the minimum efficiency performance standard that was 

determined in 2011 and will go into effect in 2014. 

8. Conclusion 

By examining the 35-year history of appliance standards, we observe an accelerated decline in appliance 

LCC post-standards in all cases studied and an accelerated decline in appliance price in most cases. We 

200 300 400 500 600

1000

1100

1200

1300

1400

1500

1600

1700

1800

 2010

 2020, Uniform Price Decline

 2020, Faster Incremental Price Decline

Life-cycle Cost, LCC (2008$)

Unit Energy Consumption, UEC (kWh/yr)
15

also find that the incremental price of efficiency generally decreases faster than the baseline price of 

appliances, which may help explain the sustained long-term price decline under standards. 

Since 1978, the US Department of Energy has analyzed energy-efficiency standards for a growing 

number of residential appliances and devices using a standard-setting process that is governed by a 

number of procedural and technical requirements. The regulatory analysis of each standard includes a 

“bottom-up” engineering estimate for the incremental cost of achieving higher efficiency. Prior to 2011, 

declines in cost described by learning curve trends were not included in this analysis. Since 2011, 

learning-curve trends have been used in the determination of US appliance efficiency standards. The 

regulatory analysis currently uses only the learning trends of the base price and assumes no additional 

innovation induced by EE standards. The present analysis suggests that this approach is likely to be 

conservative. 

Our analysis indicates that the rate of price decline for the incremental price of efficiency may be as much 

as an order of magnitude faster than for the baseline price. If this observation is correct, better modeling 

of the potentially much faster price decline for efficient technologies may produce more accurate 

forecasts of cost-effective energy savings. Future research could help determine how to model dynamic 

market reactions to regulation and the potentially complex time dependence of market distributions for 

regulated products. 

Some of the possibilities for future research may be indicated by some of the new models of price 

dynamics in regulated appliance markets have been explored by very recent work undertaken by Spurlock 

[13] and Van Buskirk [11]. 

Spurlock evaluated a model of second-degree price discrimination in a quality-differentiated market for 

household durables based on the classic work by Mussa and Rosen [43] for US clothes washer standards 

in 2004 and 2007. Spurlock found that “along with a level drop in prices at the time the new standard 

went into effect, price trends broke downward, particularly following the 2007 standard change. The 

within-model downward trend in prices was particularly pronounced in the higher efficiency categories.” 

In a similar exploration of a differentiated market, Van Buskirk explored a different style of model for the 

dynamics of the European refrigerator market where learning-by-doing occurred at different rates for 

different efficiency categories of refrigerators. Because cumulative experience scaled up more rapidly on 

a per-category basis as higher efficiency refrigerators were adopted by the market, this model reproduced 

the observation of a rapidly changing incremental cost of efficiency that is noted by the present study. 

Alternatively, some of the observations of the accelerated decline of appliance purchase price under 

standards may also be explained as a spillover effect [44] of improved efficiency technology. That is, 

learning -curve-driven productivity improvements in the manufacture of high-efficiency products could 

spillover and potentially yield productivity improvements for baseline products. 

Substantial research into multiple potential modeling approaches will likely be required to settle on new 

modeling techniques that can reliably produce forecasts consistent with the empirical observations of this 

study. But if such new models can be developed, they may improve the dynamic forecasting of the 

interplay between innovation and regulatory impacts. Such improved forecast methods are likely to aid in 
16

the development of more optimal energy-efficiency policies that could provide additional economic 

benefits for consumers and climate mitigation efforts. 

Acknowledgements 

This research is supported, in part, by the US Department of Energy, Office of Energy Efficiency and 

Renewable Energy, under contract No. DE-AC02-05CH11231. Gabrielle Dreyfus assisted with review 

and preparation of manuscript for submission. The corresponding author acknowledges funding support 

from the Collaborative Labeling and Appliance Standards Program (www.clasponline.org). 
i

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