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November 2010  RFF DP 10-59 

Are Energy Efficiency 

Standards Justified? 

Ian W.H. Parry, David Evans, and Wallace E. Oates 

DISCUSSION PAPER
© 2010 Resources for the Future. All rights reserved. No portion of this paper may be reproduced without 

permission of the authors. 

Discussion papers are research materials circulated by their authors for purposes of information and discussion. 

They have not necessarily undergone formal peer review. 

Are Energy Efficiency Standards Justified? 

Ian W.H. Parry, David Evans, and Wallace E. Oates 

Abstract 

This paper develops and parameterizes an overarching analytical framework to estimate the 

welfare effects of energy efficiency standards applied to automobiles and electricity-using durables. We 

also compare standards with sectoral and economywide pricing policies. The model captures a wide range 

of externalities and preexisting energy policies, and it allows for possible “misperceptions”—market 

failures that cause underinvestment in energy efficiency. 

Automobile fuel economy standards are not part of the first-best policy to reduce gasoline: fuel 

taxes are always superior because they reduce the externalities related to vehicle miles traveled. For the 

power sector, potential welfare gains from supplementing pricing instruments with efficiency standards 

are small at best. If pricing instruments are not feasible, a large misperceptions failure is required to 

justify efficiency standards, and even in this case the optimal reductions in fuel and electricity use are 

relatively modest. Reducing economywide carbon dioxide emissions through regulatory packages 

(combining efficiency and emissions standards) involves much higher costs than pricing instruments. 

Key Words: standards, energy taxes, market failure, climate, power sector, gasoline 

JEL Classification Numbers: Q48, Q58, H21, R48 
Contents 

1. Introduction ......................................................................................................................... 1

2. Analytical Framework ........................................................................................................ 4

A. Model Structure .............................................................................................................. 4

B. Welfare Formulas ........................................................................................................... 9

3. Parameter Values .............................................................................................................. 15

A. Basic Transportation Data ............................................................................................ 15

B. Basic Electricity Data ................................................................................................... 15

C. Externalities .................................................................................................................. 16

D. Misperceptions ............................................................................................................. 17

E. Other Parameters .......................................................................................................... 17

4. Quantitative Results.......................................................................................................... 18

A. Welfare Comparison of Efficiency Standards and Energy Taxes ................................ 18

B. Market Failures Required to Justify Different Levels of Efficiency Standards ........... 20

C. Role of Efficiency Standards in CO2 Mitigation .......................................................... 21

5. Conclusion ......................................................................................................................... 22

References .............................................................................................................................. 23

Appendix A. Analytical Derivations .................................................................................... 27

Appendix B. Additional Documentation for Parameters .................................................. 34
Resources for the Future Parry, Evans, and Oates 

1

Are Energy Efficiency Standards Justified? 

Ian W.H. Parry, David Evans, and Wallace E. Oates∗

1. Introduction 

In recent years, the political pendulum has swung back toward a far more interventionist 

role for government in U.S. energy markets. A major manifestation of this movement has been 

the proliferation of actual and proposed energy efficiency standards. Most prominent are the 

recent rulings on new light-duty vehicles requiring manufacturers to increase theirs fleets’ 

average fuel economy from 25 miles per gallon to about 35 mpg by 2016. In addition, household 

appliances are subject to numerous standards, incandescent light bulbs are being phased out, and 

federal energy efficiency standards are proposed for residential and commercial buildings. 

What kinds of market failure could justify energy efficiency standards? An obvious 

possibility is pollution externalities, especially emissions of carbon dioxide (CO2). Generally 

speaking, however, efficiency standards are an inferior instrument to energy or emissions taxes. 

Unlike the pricing approach, efficiency standards do not reduce the intensity of use of energy 

durables; in fact, they tend to increase their use through the “rebound effect” (Khazzoom 1980). 

Nor do they reduce pollution emissions per unit of energy or produce least-cost outcomes 

through equating marginal abatement costs across different sectors. 

A second class of potential market failures is associated with the so-called energy 

paradox, the observed reluctance of energy users to adopt apparently cost-effective, efficient 

technologies (Jaffe and Stavins 1994; Gillingham et al. 2009; Tietenberg 2009), even energysaving technologies with implicit rates of return in excess of 25 percent (Allcott and Wozny 

2009, Hausman 1979, Sanstad et al. 2006, Train 1985). Some analysts cite this as evidence that 

consumers misperceive energy efficiency benefits perhaps because of “missing” information. 

Others point out that there might be “hidden costs” not accounted for in these studies, such as 

product attributes (e.g., people may object to the quality of fluorescent lighting), various search 

 

∗ Ian W.H. Parry, corresponding author, Allen V. Kneese Senior Fellow at Resources for the Future (parry@rff.org); 

David Evans, U.S. Environmental Protection Agency; Wallace E. Oates, University of Maryland. We are grateful to 

Karen Palmer and seminar participants at Cornell University, the University of Maryland, the University of Arizona, 

the Society for Cost-Benefit Analysis, and the ASSA meetings for helpful comments and suggestions. Zachary 

Ficklin provided valuable research assistance. We also thank the U.S. Environmental Protection Agency (EPA) for 

financial support. This study has not been subject to EPA peer review, and the views expressed here should not be 

attributed to the EPA. 
Resources for the Future Parry, Evans, and Oates 

2

costs, high borrowing costs, and aversion toward “irreversible investments” with uncertain 

returns (Hassett and Metcalf 1993).1 At the same time, certain information programs (like the 

voluntary EPA Green Lights program and various EnergyStar programs) appear to have 

increased energy-efficiency investments (e.g., Howarth et al. 2000). 

The general issue is important because the source of the failures has direct policy 

implications. For externalities, the preferred approach is pricing measures, but for 

misperceptions, information programs can play an important role. In either case, efficiency 

standards appear to be a second-best measure whose justification presumably depends on 

practical constraints on the economically preferred policy. 

The actual and prospective adoption of energy efficiency standards raises a number of 

interrelated policy issues. First, what are their overall welfare effects and how do they compare 

with welfare effects from other policy options? Second, what combinations of market failures, 

particularly related to CO2 damages and the extent of misperceptions market failures, justify 

efficiency standards of given stringencies? And third, to what extent can efficiency standards, 

when combined with other regulatory approaches, achieve the cost-effectiveness of CO2 pricing 

instruments, should the latter prove difficult to implement in practice? 

Our paper provides an analytical framework for understanding these questions and for 

gaining some sense of the empirical magnitude of welfare effects, for policies affecting energy 

efficiency in the transport and power sectors. From the model we derive a series of empirically 

useful formulas that show how the welfare effects of regulatory and pricing policies depend on 

interactions with externalities, informational market failures, and preexisting energy policies. 

Although a richer framework than ours might incorporate capital dynamics, greater product 

disaggregation, and producer heterogeneity, a good deal may still be learned from the 

parsimonious model developed here, which captures the most important features for our 

purposes. 

In general, prior literature provides insight on some components of our welfare analysis 

but does not construct the overarching framework needed for the policy questions posed here. 

For example, previous studies have been largely sector specific and therefore do not compare the 

welfare effects of standards versus economywide pricing, nor do they compare and contrast 

 

1 Another possibility is that, rather than utility maximization, consumer behavior is based on simplified decision 

processes, like “rules of thumb,” because of cognitive constraints on processing information. There is, however, 

little empirical work relating these kinds of behavioral failures directly to decisionmaking on energy efficiency. 
Resources for the Future Parry, Evans, and Oates 

3

standards across the transport and power sectors, or explore to what extent regulatory packages 

can mimic emissions pricing. There has also been little attempt to simultaneously integrate 

externalities and informational market failures. And the conditions required to justify standards 

of different stringencies, with and without constraints on emissions pricing policies, have not 

been explicitly modeled.2 

One study that also, like ours, provides an overarching framework is Krupnick et al. 

(2010), who estimate the cost-effectiveness of a wide range of policies, including efficiency 

standards, to reduce CO2 and oil use. Their analysis is based on simulating a variant of the 

Department of Energy’s National Energy Modeling System (NEMS) out to 2030; ours is a much 

simpler, static analytical framework. Nonetheless, there is useful complementarity between the 

two studies, and some of our results on policy costs (gross of externalities) are broadly consistent 

with those from the far more sophisticated variation of NEMS. 

We summarize some of our findings as follows. 

First, efficiency standards are not part of the optimal policy (or policy combination) to 

address market failures for automobiles, even under our upper-bound case for misperceptions 

market failures. Fuel taxes have a much lower net cost because of (unpriced) externality benefits 

associated with reduced vehicle use (reduced traffic congestion and so on). The welfare gains 

forgone under efficiency standards from failing to exploit this response outweigh any potential 

advantage at targeting misperceptions failures more directly. Even if fuel taxes are fixed, 

efficiency standards are warranted only under stringent conditions. For example, using standards 

to cut fuel use by 5 percent under a standard value for CO2 damages is warranted only if 

consumers fail to internalize 44 percent of the savings from higher fuel economy. In fact, recent 

rulings that rapidly ramp up the corporate average fuel economy (CAFE) standards are not 

supported on welfare grounds, even under our bounding cases for market failures. 

Second, in the power sector, where there are no large ancillary externalities related to 

product use, even under our upper-bound case for misperceptions market failures, supplementing 

electricity taxation with an efficiency standard can increase welfare by only a relatively small 

amount. And if taxes are infeasible, the market failures needed to justify reducing economywide 

 

2 For a selection of prior literature, see Goldberg (1998), Austin and Dinan (2005), Kleit (2004), Fischer et al. 

(2007), Goulder et al. (2009), and Small (2009) on automobile fuel economy standards and Sanstad and 

Auffhammer (2010) on efficiency standards for the power sector. 
Resources for the Future Parry, Evans, and Oates 

4

electricity use under standards alone soon become implausibly large beyond reductions of a few 

percentage points. 

Third, regulatory approaches, even when used in least-cost combinations, are poor 

substitutes for pricing approaches. When large misperceptions failures justify a role for 

efficiency standards, and when these standards are used in combination with CO2 emissions 

standards for the power sector, the costs of this regulatory combination are around $15 billion 

more per year than for an emissions pricing policy (given the goals for reducing energy-related 

CO2 emissions envisioned in recent climate bills). 

The rest of the paper is organized as follows. The next section develops the analytical 

model and derives key formulas. Section 3 discusses the baseline data. Section 4 presents the 

main quantitative findings. Section 5 summarizes and discusses model limitations. 

2. Analytical Framework 

We develop a static, general equilibrium model with CO2 emissions produced from 

vehicle fuel combustion, power generation, and other sectors. The model captures the possibility 

of suboptimal investment in energy efficiency for vehicles and electricity durables; broader 

externalities (which are especially important for automobiles); energy efficiency regulations that 

apply comprehensively to automobiles and partially to electricity durables (representing a 

combination of appliance standards, building codes, etc.); possible taxes on energy and CO2; and 

possible regulations on the power generation mix. Our analysis implicitly assumes that any 

regulations—efficiency or emissions standards—are “smart” in terms of providing credit-trading 

provisions that equate marginal compliance costs across different firms. 

A. Model Structure 

(i) Household utility 

At the start of the period, households purchase three durable goods for use over the 

period, indexed by i: an automobile (A); an electricity-intensive durable good (R) representing an 

aggregation of final and intermediate products that are, or could potentially be, subject to binding 

energy efficiency regulations (e.g., appliances, buildings, lighting); and an electricity-intensive 

durable good (N) representing an aggregation of all electricity-using goods that might be difficult 
Resources for the Future Parry, Evans, and Oates 

5

to regulate. Although R is an aggregate of many heterogeneous products, we indicate below how 

results would change if efficiency regulations were applied to narrower product categories.3 

The representative agent’s utility function is 

(1a) ( , , , , , , , ) A R N Y C Z A ZE u = u v v v I

(1b) ( , , ( )) i i i i i i v = v S m μ e

In (1b), vi(.) is subutility from use of durable good i, Si is purchases (at the start of the 

period), or the stock, of durable good i, and mi is the intensity with which this good is used—

miles driven per auto or hours of operation for electricity-using goods over the period.4 ei is 

energy consumption per unit of use of durable good i—that is, fuel use per mile of driving or 

electricity use per unit of time; the inverse of ei is thus energy efficiency. The role of ei in the 

utility function is to allow for possible hidden costs from reductions in energy intensity. For 

example, fuel-saving technologies have hidden costs if they imply reduced vehicle power, and 

fluorescent bulbs may have hidden costs if households prefer the brighter, instantaneous lighting 

from incandescent bulbs. Thus, μi represents a broad index of attributes from product i, where 

μi(.) is weakly concave with μi

′ ≤ 0. The ei may be determined by household choices (implicitly 

through their choice of models with and without advanced energy-saving technologies); 

alternatively, eA and eR may be set by the government, if energy intensity standards are binding.5 

In (1a), C is carbon dioxide emissions; ZA is an index of externalities related to 

automobile use, including local pollution, congestion, and accidents; ZE is local pollution from 

electricity generation. Here we have omitted externalities from oil dependence because they are 

difficult to define, let alone quantify—including them would be equivalent to attaching a higher 

value to CO2 reductions from the transport sector (see below). Finally, I is an aggregate of 

industrial consumer products, and Y is an aggregate of nonindustrial consumer goods and 

services that do not use electricity. The former sector captures CO2 emissions outside the power 

and (light-duty) transport sectors, and the latter represents “clean” consumption. 

 

3 Explicitly distinguishing the use of electricity durables at the industrial, commercial, and residential level would 

not affect our results. 

4 mi and Si enter vi separately, rather than as a product, to avoid the corner solution where only one good of type i is 

purchased in the entire economy. 

5 Historically, standards for autos have been defined in terms of fuel economy rather than fuel intensity. However, 

they are now integrated with CO2 (and hence energy) standards per mile. 
Resources for the Future Parry, Evans, and Oates 

6

All variables are economywide aggregates, expressed in per capita terms (variables are 

therefore continuous even though the quantity of durables is discrete at the individual level). A 

bar denotes a variable perceived as exogenous by individuals. u(.), overall utility, is increasing 

and quasi-concave in its first five arguments and declining in the last three; vi(.) is increasing and 

quasi-concave in its arguments. 

(ii) Perceived energy costs 

The (actual) lifetime energy cost for durable good i, denoted Li, is 

(2) i E E i i L = (q + t )m e ,i = R, N, A G G A A L = (q + t )m e

where discounting over the lifecycle is implicit. qE and qG are the producer prices of electricity 

and gasoline, respectively, and tE and tG are specific taxes on these goods (residential electricity 

use is currently taxed at the state level but gasoline is taxed at both federal and state levels). 

Lifetime costs equal product use, times energy consumption per unit of use, times the taxinclusive consumer price. 

The lifetime cost, as perceived by agents when the good is purchased, is (1−ρi)Li, where 

0 ≤ ρi ≤1 reflects the extent to which agents misperceive, or otherwise fail to internalize, future 

energy costs relative to actual costs. Such undervaluation could result from misperceptions over 

future energy efficiency, limitations on their cognitive ability to absorb and process information 

on energy efficiency, or systematic errors in forecasting energy prices or use of energy durables. 

Implicitly, government programs, such as required fuel economy stickers on salesroom cars or 

certified labeling of appliance efficiency through EnergyStar, imply a lower value of ρi. 

(iii) Externalities 

Externalities are defined by 

(3a)C z G z E z I

I

C

E

C

G = C + + , A AmAS A Z = z , Z E = zE E

(3b) AmAS A G = e , i i

miSi E = e , i ≠ G , E = ER + EN

G is gasoline consumption (the fuel consumption rate times miles per vehicle times the 

vehicle stock). Similarly, Ei is electricity use from product i, and E is total electricity use. 

In (3a), G

Cz , ECz and ICz denote the CO2 intensity of gasoline, electricity, and industrial 

production, respectively. G

Cz and ICz are given, but ECz is chosen by firms, implicitly through 

their chosen mix of power generation fuels. zA is an index of congestion, accident, and local 

pollution externalities per mile of automobile travel; local emissions vary with mileage rather 

than fuel use, given that all vehicles must satisfy the same emissions per mile standards 
Resources for the Future Parry, Evans, and Oates 

7

regardless of their fuel economy, which decouples emissions from fuel economy (Parry and 

Small 2005). zE is an index of local emissions, which vary in proportion to power generation 

(this potentially includes NOX, mercury, and SO2, depending on how these emissions are 

regulated). 

(iv) Production 

All firms are competitive and produce under constant returns; thus, producer prices equal 

unit production costs. For our purposes, this assumption seems reasonable for automobiles.6 For 

the power sector, the issue is more complex, and we return to it at the end of the paper. 

Product and energy prices are determined by 

(4a) ( ) i i i p = k e , pI = pI , pY = pY

(4b) G

G G C C q = q + t z

(4c) E

C C

E

E E C q = k (z ) + t z

(4d) C

E

E C k′ (z ) = t

where Ct is a uniform, economywide “price” on CO2 emissions, assumed not to exceed its 

Pigouvian level. For now, this represents an emissions tax, but later we also consider cap-andtrade systems. 

In (4a), pi is the consumer price and ki(.) the unit production cost for durable good i, 

respectively. ki(.) is increasing with respect to reductions in ei, reflecting the incorporation of 

(costly) energy-saving technologies into the product. pI and pY denote the (fixed) unit 

production costs of the industrial good and the clean good, respectively. In (4b), the producer 

price of gasoline equals the (fixed) unit production cost qG plus the pass-through of the CO2

price. In (4c), kE (.) is the unit production cost for power generation, which is increasing and 

convex with respect to reductions in E

Cz , reflecting costs of substituting coal with cleaner fuels 

(or possibly, down the road, installation of carbon capture and storage technologies). The 

producer price of electricity equals this cost plus the pass-through of the CO2 price. In (4d), we 

assume that power companies abate CO2 emissions from fuel switching until the incremental 

cost equals the (avoided) tax payment. 

 

6 Although in practice there are significant differences between the price of new autos and their unit production cost, 

this appears to make little quantitative difference to the overall efficiency costs of fuel economy regulations and fuel 

taxes, given their modest effect on new vehicle demand (e.g., Austin and Dinan 2005). 
Resources for the Future Parry, Evans, and Oates 

8

Firms play a passive role, meeting household demand for products, fuel, electricity, and 

energy efficiency (if it is not fixed by regulation). 

(v) Government 

The government sets maximum energy efficiency standards for automobiles, A e , and 

regulated electricity durables, R e . We also consider a policy combination involving these 

standards and an emissions standard imposed on the power sector, E

Cz . 

The government budget constraint, equating spending and revenue from gasoline, 

electricity, and emissions pricing, is 

(5)GOV = tGG +tEE +tCC

where GOV is a lump-sum government transfer to households to capture the recycling of revenue 

to the economy. 

(vi) Household optimization 

Households optimize in two steps. First, they make upfront choices over product 

purchases and energy efficiency, based on perceived lifecycle costs and planned use of durables 

and consumption goods, subject to a perceived, fixed-income budget constraint. Second, during 

the course of the period, they may reoptimize over product usage if energy costs differ from 

initial perceptions.7 As shown in Appendix A, this optimization implies that the private benefit 

from one additional durable good equals its price plus perceived lifetime energy cost; the private 

benefit from incremental usage of the durable good equals its (tax-inclusive) energy cost; and 

energy intensity is reduced until the resulting increase in cost of the durable equals the marginal 

saving in perceived lifetime energy costs less the value of any reduction in other product 

attributes. 

All demands are taken to be constant elasticity functions of the relevant own-price—

product prices plus perceived lifetime energy costs for durables, and energy prices for product 

usage and energy intensity (see Appendix A). In addition, we make the (reasonable) 

approximation that the demand for travel, industrial goods, and regulated and unregulated 

electricity durables are independent; this rules out, for example, the possibility that reduced 

emissions from sector-specific policies in the transport sector are partly offset by extra emissions 

 

7 This is reasonable because vehicle or appliance usage is an ongoing decision, unlike the one-off consumer durable 

purchase decision, which requires forecasting energy use and prices over a long period of time. 
Resources for the Future Parry, Evans, and Oates 

9

in the industrial and power sectors.8 From the demand functions we can decompose the 

following relations (Appendix A): 

(6) mG η G

SA +ηG Ae +η G =η G , + mi η E i S η E i e +η E Ei =η , i ≠ A

mA η G , SA η G and Ae η G are the elasticity of miles per vehicle, vehicle demand, and gasoline 

intensity with respect to gasoline prices, and the sum of these elasticities, η G , is the overall 

gasoline price elasticity. mi η E , i S η E and i e η E are the elasticity of usage of electricity durable i, the 

demand for that good, and its electricity intensity, with respect to electricity prices, and Ei η is the 

elasticity of electricity consumption for good i with respect to the electricity price. (All 

elasticities are negative.) 

B. Welfare Formulas 

We now discuss formulas (derived in Appendix A) for the marginal welfare effects of 

policy-induced reductions in gasoline, electricity, and CO2 emissions. We focus on marginal 

costs (denoted MC), defined net of welfare benefits from correcting market failures—thus, 

policies improve welfare up to the point where MC is zero. Marginal costs are obtained by totally 

differentiating the household’s indirect utility function with respect to a policy variable, 

accounting for changes in pollution, other externalities, and (balanced budget) government 

transfers, and dividing by the induced change in gasoline, electricity, or CO2. We also briefly 

discuss how results change when CO2 is fixed by a cap, when efficiency standards should 

complement pricing instruments, the relation between information dissemination programs and 

efficiency standards, and the welfare potential of policies combining efficiency standards with 

emissions rate standards. 

(i) Reducing gasoline use 

Gasoline tax. The marginal cost of a tax-induced reduction in gasoline ( tG MCG ), 

expressed per gallon, can be decomposed as follows: 

(7a) = − − −A

G C C C

t

G MC t EXT t z G ( )

G

S

G

m

G

A

A A A

e

EXT

η

η +η ⋅

G

S

G

e

G

A G G

A A

q t

η

η η ρ

+ − ⋅( + )⋅ 

 

8 For example, in Small (2009) the effects of transportation policies on the power sector are negligible. Down the 

road, the power and transportation sectors could become more integrated if there is substantial penetration of plug-in 

electric vehicles and greater competition between the two sectors for biomass-based fuels. For the most part, 

electricity durables that are unregulated (e.g., TV sets) are not close substitutes for regulated electricity durables 

(e.g., refrigerators, buildings). 
Resources for the Future Parry, Evans, and Oates 

10

where 

(7b) G G G G t q t G q G = + − Δ − 0 1/η )ˆ ( )(1 , 0

0

ˆ

G

G G G − Δ =

(7c) / λ C ZC EXT = −u , /λ A A ZA EXT = −z ⋅u

(7d) = 0 Ae η G if A e is binding 

ΔGˆ denotes the proportionate reduction in gasoline (and similarly for other variables 

with ^ below), and superscript 0 denotes an initial value prior to policy change. λ is the marginal 

utility of income. 

As indicated in (7c), EXTC is the (monetized) disutility, or external cost, per additional 

unit of CO2 emissions, and EXTA is the external cost from congestion, accidents, and local 

pollution, per extra auto mile. We assume EXTC and EXTA are constant (which is reasonable 

over the range of fuel reductions considered below). 

The first component of the marginal cost in (7a) is the prevailing fuel tax or wedge 

between forgone consumer benefits and savings in supply costs per gallon reduction in gasoline. 

In (7b) the tax rate rises with respect to the proportionate reduction in gasoline. Thus, this first 

component determines the slope of the marginal cost as well as contributing 0

Gt to its intercept, 

given preexisting gasoline taxes. All other components in (7a) serve to shift down the marginal 

cost curve and reduce its intercept. 

The second component nets out the marginal external benefit from reducing CO2

emissions per gallon, less any amount of this externality internalized through a CO2 tax. 

The third component nets out the marginal external benefit from reduced auto mileage. It 

equals EXTA , divided by eA to express externalities in costs per gallon, where eA falls with higher 

taxes as manufacturers incorporate fuel-saving technologies into vehicles. This component is 

multiplied by the fraction of the gasoline demand elasticity that is due to reduced overall mileage 

(through reduced intensity of vehicle use and reduced demand for vehicles) as opposed to 

improved fuel economy. The smaller the fraction of the marginal reduction in gasoline use that 

comes from reduced driving, the smaller the mileage-related externality benefits per gallon 

reduction in fuel use (Parry and Small 2005). 

The final component nets out the potential welfare gain from offsetting misperceptions 

market failures. It equals the noninternalized fraction of fuel economy benefits, times the 

consumer value per gallon of gasoline savings, times the fraction of the gasoline reduction that 

comes from improved fuel economy, and also reduced vehicle purchases (recall that vehicle 
Resources for the Future Parry, Evans, and Oates 

11

demand is excessive when ρ A < 1). Thus, different assumptions about the share of the 

incremental gasoline reduction that comes from improved fuel economy, as opposed to reduced 

mileage per vehicle, will alter the relative magnitude of the last two cost components in (7a), but 

in opposite directions. 

Finally, from (7d), preexisting and binding fuel economy standards alter the tax-induced 

welfare cost indirectly by eliminating the reduction in fuel intensity. This (greatly) reduces the 

magnitude of the last welfare component. However, it also implies that mileage now falls in 

proportion to gasoline use, which (greatly) increases mileage-related externality benefits per 

gallon reduction in gasoline. As discussed in Small (2009), Ae η G may still be nonzero, even with 

binding fuel economy standards (e.g., households with two vehicles may drive the more efficient 

one more intensively in response to higher fuel prices); hence our analysis with and without 

binding standards provides bounding cases. 

Energy efficiency standard. The marginal cost from tightening a (binding) fuel per mile 

standard ( Ae MCG ) is (Appendix A)

(8a) = − − +A

G C C C

e

G MC EXT t z A δ ( )

A

A

A

A

r

r

e

EXT

− ⋅1 A

A G G

r

q t − − ⋅ + ⋅

1

1

ρ ( ) 

(8b) G

r

G G G q t G q A

eA

G = + − Δ − (1− )

1

)ˆ ( )(1 η δ

(8c)

A

A A

A A

A

A de

d m S

m S

e

r ( ) = − ⋅ mA ≅ −ηG

rA denotes the rebound effect. As defined in (8c), this is the fraction of the initial fuel 

savings from an incremental reduction in fuel intensity (mAS A ) that is offset by the increase in 

mileage in response to lower fuel costs per mile ( A A A A − e ⋅ d(m S )/ de ). Here (following Small 

and Van Dender 2006), we approximate by assuming that changes in fuel economy do not affect 

vehicle demand. The rebound effect is therefore equivalent to mA −η G because proportionate 

reductions in fuel consumption and fuel prices have an equivalent effect on per mile fuel costs 

and vehicle use. 

δ G is the shadow price on gasoline. As shown in Appendix A, it corresponds to the gap 

between the increase in vehicle costs and possible loss of vehicle attributes, net of the actual 

savings in lifetime fuel costs, expressed per gallon of fuel savings. Its initial value (when 0 ˆG = ) 

is the preexisting fuel tax (which distorts equally all margins of behavior affecting fuel 

consumption). However, this shadow cost rises more rapidly with respect to Gˆ than the gasoline 

tax does in (7b) because the policy places almost the entire burden of fuel savings on improved 
Resources for the Future Parry, Evans, and Oates 

12

efficiency and does not exploit savings from reduced mileage per vehicle. In other words Ae MCG

is more steeply sloped than tG MCG . In fact, because of the rebound effect, an even larger 

improvement in fuel economy is required to achieve a given Gˆ (i.e., the rebound effect increases 

the rate at which δ G rises). 

The second component in (8a), reflecting net CO2 benefits per gallon, is the same as in 

(7a). However, the third component in (8a) is a positive (rather than negative) cost because 

mileage-related externalities increase with the rebound effect; thus, this component shifts up the 

marginal cost curve. On the other hand, to the extent that lifecycle costs are not internalized, the 

last component is a larger gain than under the tax. This is because all, rather than a fraction, of 

the gasoline reduction comes from improved fuel economy (or reduced vehicle demand). These 

gains are magnified (moderately) to the extent that the fuel economy increase must be higher, 

because of the rebound effect, to achieve a given reduction in fuel use. 

(ii) Reducing electricity use 

The marginal cost of reducing electricity through higher electricity taxes, expressed per 

kWh, is given by 

(9a) = − − − E

E C C C

t

E MC t EXT t z E ( ) EXTE

E

S

E

e

E

i R U

i E E

i i

q t

η

η η ρ

+ − ∑ ⋅ + ⋅ = ,

( ) 

where 

(9b) E E E E t q t E q E = + − Δ − 0 1/η )ˆ ( )(1

(9c) / λ E E ZE EXT = −z ⋅u

(9d) 0 Re η E = if R e is binding 

EXTE is the external cost from non-CO2 pollution, per extra kWh (assumed constant). 

The components of (9a) are essentially analogous to those for the gasoline tax in (7a), 

with two exceptions. One is that that non-CO2 externalities are proportional to all changes in 

electricity consumption (rather than just those from changes in product use). The other is that 

noninternalized lifecycle costs are weighted averages across the two electricity durables. 

The marginal cost of the efficiency standard ( Re MCE ) is similar to that for the auto 

efficiency standard in (8a and b) (see Appendix A for the formula). The big difference is that 

there is no exacerbation of non-CO2 externalities from the rebound effect. The final welfare 

component for the efficiency standard is analogous to that in (8a), with parameters applying only 

to the regulated durable. 
Resources for the Future Parry, Evans, and Oates 

13

(iii) Deriving iso-market failures curves required to justify efficiency standards 

For any given level of (prevailing or prospective) energy or emissions taxation, an 

efficiency standard that further reduces gasoline by amount Gˆ , or electricity by Eˆ , is fully 

efficient if the marginal cost curves, eG MCG and Re MCE , have negative intercepts and are exactly 

zero when evaluated at these reductions. From these conditions, it is straightforward to obtain 

“iso-market failure” curves that indicate combinations of CO2 (or energy security) externalities 

and misperceptions failures that are required to justify efficiency standards of given stringencies. 

(iv) Reducing (nationwide) CO2 emissions 

The marginal costs of energy taxes and efficiency standards are easily expressed in costs 

per ton of (economywide) CO2 reduced, by dividing the above expressions by CO2 per gallon or 

CO2 per kWh of electricity, as follows (see Appendix A): 

(10) A

C

t

t G

C

z

MC MC G

G = , A

C

e

e G

C

z

MC MC G

G = , E

C

t

t E

C

z

MC MC E

E = , E

C

e

e E

C

z

MC MC R

R =

The marginal cost per ton of reduced CO2 under the emissions tax, denoted Ct MCC , can be 

expressed thus (see Appendix A): 

(11a) Ct MCC

tG

C

G = α ⋅ MC Et

C

E +α ⋅ MC C

I

C E

z t EXT t E

+α ⋅( − ) +α ⋅

(11b) C C tC

η β ˆ = ⋅

Equation (11b) shows our assumed functional relation between economywide CO2

emissions and the CO2 tax, where β and η C are parameters. 

In (11a) G α , E α , Ez α , and I α are the share of the marginal, tax-induced reduction in 

nationwide CO2 emissions from reduced gasoline use, reduced electricity consumption, 

reductions in CO2 per unit of electricity, and reductions from the industrial sector, respectively 

(see Appendix A for definitions of these terms). The marginal cost of the CO2 tax is the envelope 

of the marginal costs of reducing emissions through these four behavioral responses. For 

industrial reductions, the marginal cost is simply the CO2 tax rate, since there are no broader 

market failures for this margin of behavior, but for reductions from reduced emissions intensity 

in the power sector, local pollution effects are netted out from the CO2 tax. 

(v) The distinction between cap-and-trade and emissions taxes 

We show, in Appendix A, that the marginal cost formulas t

G MCG , eG MCG , Et MCE , and 

Re MCE change in one regard when CO2 emissions are fixed by a binding cap-and-trade system 

rather than taxed. The term C C EXT − t in the second component of the marginal cost formulas is 
Resources for the Future Parry, Evans, and Oates 

14

replaced by Ct MCC . In this case, each unit reduction in CO2 due to the new policy is offset by a 

unit increase in emissions in the economy as a whole (via a reduction in the CO2 allowance 

price), resulting in a cost savings of Ct MCC . Thus, if the emissions price imposed by climate 

policy is greater than 50 percent of the external cost of CO2 emissions, there is a greater gain 

from reducing emissions through other policies when the carbon instrument is cap-and-trade 

rather than an emissions tax. 

(vi) Potential value of energy information programs 

As shown in Appendix A, the marginal cost of information programs, per unit reduction 

in gasoline, electricity, or CO2 emissions, is analogous to the marginal cost of the relevant energy 

efficiency standards (hence we do not illustrate separate results for information programs). 

Information programs increase energy efficiency, just like standards, though this is achieved 

indirectly through raising the perceived private benefits from higher efficiency. The effects of 

these programs may be limited if ρ is already close to zero. However, information programs 

avoid the risk of excessively increasing energy efficiency, in the sense of pushing the 

incremental costs of energy efficiency improvements beyond the point at which they are justified 

by discounted energy savings. 

(vii) Regulatory alternatives to CO2 pricing 

Although not a central focus of our paper, a CO2/kWh emissions standard can be a 

relatively low cost approach for exploiting the large potential for CO2 emissions reductions in the 

power sector. Our interest here lies in the extent to which combining this policy with efficiency 

standards can mimic the effects of a CO2 pricing policy, which is especially timely given the 

practical obstacles to pricing policies. 

As shown in Appendix A, the marginal cost of the emissions standard is approximately 

equivalent to the CO2 tax that would produce the same overall emissions reduction as the 

emissions standard, divided by the share of reductions under this CO2 tax that would come from 

reduced emissions intensity in the power sector. The emissions standard does also reduce 

electricity demand because abatement costs are passed forward into electricity prices. However, 

this effect is relatively small, given that the emissions standard does not create the equivalent of 

carbon tax payments, or allowances rents, that are reflected in higher electricity prices under 

market-based policies. 

All of the above formulas were simulated in a spreadsheet, using functional form 

assumptions noted above and parameter values described below. In addition, we also mention 
Resources for the Future Parry, Evans, and Oates 

15

some results for policy combinations that were obtained iteratively in the spreadsheet (rather 

than from explicit analytical formulas). 

3. Parameter Values 

Here we comment briefly on data assumptions for our simulations, as summarized in 

Table 1, which is representative of year 2008 or thereabouts. Parameters for the transportation 

sector are taken from prior literature; for the power sector we construct estimates by aggregating 

products within regulated and unregulated sectors. 

A. Basic Transportation Data 

We assume the average fuel economy of the (on-road) light-duty vehicle fleet is 23 miles 

per gallon, or alternatively, fuel intensity is 43.5 gallons per 1,000 miles (BTS 2009, Table 4.23). 

From Parry and Small (2005), we take the combined federal and (average) state gasoline tax to 

be $0.40/gallon And, based on the average price between 2003 and 2008, we assume a pretax 

gasoline price of $2.15/gallon.9 Initial fuel use is taken to be 130 billion gallons (BTS 2009, 

Table 4.5). 

Based on the widely cited study by Small and Van Dender (2006), we assume that the 

(long-run) own-price elasticity of gasoline is –0.4, with half of the response due to changes in 

fuel economy and half due to changes in vehicle miles. Thus, the gasoline demand elasticity is –

0.2 in the presence of fully binding fuel economy standards. We further assume that the mileage 

response is split equally between changes in the vehicle stock and miles per vehicle. This implies 

a rebound effect of 10 percent (for a given vehicle stock), which is approximately consistent with 

Small and Van Dender (2006). 

B. Basic Electricity Data 

Electricity consumption in the residential, industrial, and commercial sectors was 4,176 

billion kWh in 2007, according to the Energy Information Administration (EIA 2009). Based on 

classifying products, and aggregating over each product’s electricity use, in EIA surveys, we 

assume that 60 percent of this consumption is from durables that are, or are potentially, subject to 

efficiency standards. The main groups in the regulated category include most major appliances, 

lighting, buildings, and heating and cooling equipment, while the unregulated category includes 

 

9 See http://tonto.eia.doe.gov/dnav/pet/hist/mg_tco_usA.htm. 
Resources for the Future Parry, Evans, and Oates 

16

smaller appliances, audio and entertainment equipment, industrial processes such as assembly 

lines. We suspect that a large share of industrial electricity consumption is unlikely to be subject 

to efficiency standards given the heterogeneity of the electricity-consuming capital stock across 

industries and facilities. Furthermore, in the analysis below, we make the favorable assumption 

that the set of efficiency standards reduce consumption at least cost. That is, the marginal cost 

per kWh of consumption reduced of each individual product standard is the same. Energy 

intensity for the two electricity sectors is simply usage divided by 8760 hours per year. 

The average (pretax) electricity price over all end users was 8.2 cents/kWh in 2007, and 

state taxes averaged 0.9 cents/kWh (EIA 2010). 

We assume that the own-price elasticity of demand for electricity is –0.4 for both 

regulated and unregulated sectors, based on a long-run estimate by Myers et al. (2009), which is 

broadly consistent with other evidence (Sanstad and McMahon 2008). We further assume that 

the use per product, product demand, and electricity intensity elasticities are –0.13, –0.07, and –

0.2, respectively, for both sectors.10

C. Externalities 

As regards CO2 emissions intensities, a gallon of gasoline combustion produces 0.009 ton 

of CO2, and given the prevailing fuel mix, the average emissions intensity of power generation is 

0.0005 ton of CO2 per kWh (EIA 2009).11 We consider a benchmark value of $20/ton for CO2

damages and a range of up to $100/ton for sensitivity analysis (see Appendix B for the 

justification). The benchmark value implies CO2 damages of 18 cents/gallon and 1 cent/kWh. 

As regards oil dependence, Brown and Huntington (2009) put the external costs due to 

macroeconomic disruptions from world oil price volatility at equivalent to about 10 cents per 

gallon. Perhaps more important, dependence on an oil market influenced by hostile regimes 

might constrain U.S. foreign policy or threaten national security, though these geopolitical costs 

are not easily quantified. Therefore, we simply infer the implicit costs per gallon that would be 

required to justify automobile efficiency standards of different stringencies. 

 

10 The first figure is Myers et al. (2009)’s electricity demand elasticity over the very short run, when there is very 

limited scope for altering product demand or energy efficiency. 

11 The latter reflects an average rate over different fuels. In practice, natural gas is often the marginal fuel, but its 

emissions intensity of 0.00045 tons/kWh for state-of-the-art plants is close to the average (EIA 2009). 
Resources for the Future Parry, Evans, and Oates 

17

From Small and Verhoef (2007), we assume local pollution damages of $0.01/auto-mile. 

For marginal congestion costs, we update Parry and Small (2005)’s figure by 30 percent to 

$0.045/mile.12 For marginal accident externalities, we increase Parry and Small (2005)’s estimate 

to $0.035/mile, based on the value of a statistical life now assumed by the U.S. Department of 

Transportation, $5.8 million. Thus other externalities amount to $0.09/mile for autos. 

For electricity generation there are no externalities analogous to the energy security and 

product usage externalities applicable to autos.13 And local pollution benefits are modest, 0.13 

cents/kWh, because the bulk of high-damage emissions are capped under the sulfur trading 

program (see Appendix B). 

D. Misperceptions 

Appendix B describes empirical literature on implicit discount rates and possible reasons 

why they exceed market rates. We consider two bounding cases, one in which there is no 

misperceptions market failure (ρi = 0); that is, any differences between implicit and social 

discount rates are entirely explained by hidden costs. In the other, the entire difference is due to 

misperceptions over energy efficiency benefits. For electricity durables, we assume ρi (i = R, N) 

= 0.62 for this case, based on typical estimates of implicit discount rates; for automobiles, we 

adopt a bounding case of ρA = 0.65, based on a common assumption that consumers consider fuel 

savings from higher efficiency over only the first three years of a vehicle’s life (see Appendix 

B). 

E. Other Parameters 

We choose the αs in equation (11) to imply that for a 10 percent reduction in energyrelated, economywide CO2 emissions under a CO2 tax, 60 percent of these reductions would 

come from reduced emissions intensity in the power sector, 20 percent from reduced electricity 

demand, and 10 percent each from automobiles and from industrial and other sources. These 

assumptions are approximately consistent with U.S. EPA (2010), EIA (2008), and the relative 

responsiveness in the power and transport sectors implied by our own assumptions. Note that 

CO2 emissions from the power sector (which initially account for 40 percent of nationwide 

 

12 This accounts for recent growth in the value of travel time and congestion (based on data in CEA 2009, Table B 

47, and Schrank and Lomax 2009, Table 4). 

13 Power generation fuels are domestically supplied. And although there is sporadic congestion on the grid, the 

damage risk, averaged across annual, nationwide generation, is likely very small. 
Resources for the Future Parry, Evans, and Oates 

18

emissions) are approximately twice as large as those for autos (Table 1). Finally, based 

approximately on U.S. EPA (2010) and EIA (2008) we set η C = 1.2 (implying a moderately 

convex function), and we choose β such that the CO2 tax associated with an emissions reduction 

of 10 percent is $30 per ton. 

4. Quantitative Results 

This section compares the welfare effect of efficiency standards and energy taxes for the 

transport and power sectors and identifies conditions under which efficiency standards are 

warranted. The marginal costs of reducing economywide CO2 emissions under all policies are 

also compared. 

A. Welfare Comparison of Efficiency Standards and Energy Taxes 

(i) Reducing Gasoline 

Figure 1 shows the marginal cost of reducing gasoline under fuel taxes and efficiency 

standards with either no misperceptions failures (panel (a)) or our bounding case for the 

magnitude of misperceptions failures (panel (b)). These estimates incorporate preexisting fuel 

taxes but assume that under higher fuel taxes, efficiency standards are not binding. We note the 

following points. 

First, the marginal cost curves under the standards are more than twice as steep as those 

under the gasoline tax. This is because the fuel standard puts the entire burden of fuel reductions 

on improvements in fuel economy, whereas the gasoline tax also exploits fuel savings from 

reduced vehicle miles traveled. 

Second, with no misperceptions failures, the intercept of the marginal cost under the 

standard is well above that for the tax. For both policies, the preexisting gasoline tax contributes 

40 cents/gallon to the intercept, though this is partly offset by CO2 externality benefits of 18 

cents per gallon in the benchmark case. 

Under the gasoline tax there is an additional welfare gain of $1.04/gallon due to the 

reduction in congestion and other mileage-related externalities. Overall, the marginal cost curve 

under the gasoline tax in panel (a) has a negative intercept of 81 cents/gallon. The marginal cost 

is negative up to a fuel reduction of 11 percent, corresponding to the fuel reduction under the 

optimal gasoline tax, which is $1.26/gallon. Implementing this optimal tax would yield annual 

welfare gains of $5.8 billion. In contrast, mileage increases moderately under the fuel economy 

standard, and the resulting increase in congestion and other externalities further shifts up the 

marginal cost, so it has an overall intercept of 43 cents/gallon. In fact, in this scenario the 
Resources for the Future Parry, Evans, and Oates 

19

standard would impose potentially large (total) welfare losses—for example, $5.9 billion a year 

for a 5 percent fuel reduction. 

The third main point is that even with our bounding case for misperceptions failures, the 

marginal cost under the standard does not fall below that for the tax. Again, a fuel tax–efficiency 

standard combination is inferior to the fuel tax alone. This result is independent of climate and 

oil security externalities because they affect the intercept of both marginal cost curves by the 

same amount. 

The marginal cost under the gasoline tax is shifted down substantially further: now the 

intercept is –$1.64 per gallon, the optimal fuel tax rises to $3.08/gallon, and the fuel reduction 

under the optimized tax is 25 percent. The downward shift in the marginal cost curve is much 

larger under the standard because all, rather than a fraction, of the reduction in fuel use comes 

from improved fuel economy. However, even under this bounding case, the intercept of the 

marginal cost under the efficiency standard does not (quite) fall below that for the tax. 

If there are constraints on fuel taxes, fuel economy standards can still be warranted, but 

only with big misperceptions market failures. In our bounding case for these failures, a standard 

that cut fuel use by 8.9 percent would be optimal, though potential welfare gains are only about a 

third of those for the fuel tax. 

(ii) Reducing Electricity 

Figure 2 shows the marginal cost of reducing electricity under taxes and efficiency 

standards, again with no misperceptions failure (panel (a)) and our bounding case for the 

magnitude of the misperceptions failure (panel (b)). We note the following points. 

First, the marginal cost curve under the standard is much steeper than that under the 

electricity tax. This again reflects the failure of the standard to exploit reductions in product 

usage. However in addition, unlike the electricity tax, the standard fails to exploit any electricity 

savings in the unregulated sector (which accounts for 40 percent of use). 

Second, with no misperceptions failures, the electricity tax is welfare improving up to a 

reduction in electricity use of 5.5 percent. Implementing the optimal tax increase (1.4 cents per 

kWh) would generate modest welfare gains of $1.6 billion. Potential welfare gains under the 

efficiency standard are positive (unlike the corresponding case for autos) but very modest, at 

$0.3 billion (the optimized standard would reduce overall electricity use by just 1.3 percent). 

For these policies, local pollution benefits are approximately cancelled out by prior 

(state-level) electricity taxes. Both curves have negative intercepts of –1.3 cents/kWh, reflecting 

the carbon externality. 
Resources for the Future Parry, Evans, and Oates 

20

Third, accounting for the misperceptions failure, efficiency standards can be superior to 

electricity taxes, but only for modest reductions in electricity use, of 3 percent or less. The 

marginal cost in Figure 2(b) under the standard has a lower intercept (–8.4 cents/kWh) compared 

with the electricity tax (–5.2 cents/kWh). However, because of its much steeper slope, marginal 

costs under the standard quickly rise above those under the tax. If standards alone were 

optimized, total electricity use would be reduced by 5.5 percent (electricity use in the regulated 

sector would fall by 9.2 percent), but if the tax alone were optimized, electricity would be 

reduced by 24 percent. In principle, for any given reduction in electricity use, it would be 

optimal to supplement the electricity tax with an efficiency standard to skew reductions toward 

efficiency improvements (away from reductions in product use). But even in this upper-bound 

case for misperceptions, in general the potential welfare gains from this combination policy are 

only moderately larger than those from the electricity tax. For example, cutting electricity use by 

10 percent under the tax yields welfare gains of $19.8 billion, while achieving the same 

reduction under the combination yields welfare gains of $21.1 billion. 

B. Market Failures Required to Justify Different Levels of Efficiency Standards 

Figure 3 summarizes, in a different way, the results so far for the efficiency standards. It 

shows “iso-market failure” curves—that is, combinations of CO2 damages and misperceptions 

failures under which it is optimal to reduce fuel or electricity use by different amounts, given 

current energy taxes. 

For automobiles, there is still a high threshold for standards to improve welfare, even if 

current fuel taxes are fixed. For example, if there is no misperceptions market failure, even if 

CO2 damages were $100 per ton, cutting fuel use by 2.5 percent under standards is not 

warranted. Alternatively, reducing gasoline use by 5 percent or more by raising fuel economy 

standards is efficient only if consumers fail to internalize at least 44 percent of the value of fuel 

savings (under our benchmark value for CO2 damages). The recent tightening of CAFE standards 

actually represents a far more aggressive policy than this: new passenger vehicle fuel economy is 

set to rise from 25 mpg to about 35 mpg by 2016, implying a long-run gasoline reduction of 

about 25 percent (after allowing for the rebound effect). Most likely, there would be some 

improvement without regulation as new fuel-saving technologies are incorporated and fuel prices 

rise. Nonetheless, our analysis suggests that it is difficult to justify such a rapid ramp-up in the 

CAFE standards on welfare grounds (i.e., the iso-market failure cost curve for this scale of fuel 

reduction is outside our upper-bound cases for market failures). 

For the power sector, there is a lower threshold for efficiency standards to be optimal, but 

only for small (2.5 percent) reductions in energy use. For more substantial reductions, efficiency 
Resources for the Future Parry, Evans, and Oates 

21

standards for the power sector are even more difficult to justify, on market failure grounds, than 

for automobiles. In Figure 3, this is indicated by the higher position of the iso-market failure 

curve in panel b for the 5 percent electricity reduction compared with the corresponding curve in 

panel a, in part because only 60 percent of consumption is subject to these standards. And a 7.5 

percent reduction in electricity use under standards is warranted only under the most extreme of 

our scenarios for market failures. 

C. Role of Efficiency Standards in CO2 Mitigation 

Figure 4 shows marginal cost curves for reducing economywide CO2 emissions under 

alternative policies, where again panels a and b correspond to our two bounding cases for 

misperceptions failures. In contrast to Figures 1 and 2, we do not net out CO2 externality benefits 

from these costs. Again, these figures underscore our previous point that efficiency standards, 

and regulatory approaches more generally, are poor substitutes for pricing policies. 

With no misperceptions failures, the costs of either efficiency standard are huge, relative 

to those of the CO2 tax, for nonmarginal emissions reductions. An emissions standard for the 

power sector is dramatically more effective and cost-effective than efficiency standards. But 

even this regulatory policy does not perform that well relative to the CO2 tax. For example, for a 

CO2 reduction of 10 percent (which, roughly speaking, was projected for 2020 under recent 

federal cap-and-trade proposals), this policy costs $12.9 billion. In contrast, the CO2 tax costs 

only $2.3 billion for the same emissions reduction, given that it exploits more margins for 

abatement and it reduces driving-related externalities. In fact, to target these broader 

externalities, an optimal policy in this case would combine a fuel tax increase with a CO2 tax. 

With (our bounding case for) misperceptions failures, marginal costs under both 

efficiency standards and the gasoline and electricity taxes lie below marginal costs under the CO2

emissions tax for some range of reductions. However, for both the efficiency standards, marginal 

costs rise rapidly, becoming positive at economywide emissions reductions of 2 percent or less. 

In contrast, marginal costs under the electricity tax and CO2 tax are negative up to emissions 

reductions of about 8 and 15 percent, respectively. In fact, for the 10 percent reduction in CO2, 

total costs under the CO2 tax are now negative, –$10.8 billion. Even the least-cost regulatory 

approach involving both efficiency standards and the emissions standard has costs of plus $3.8 

billion (costs for this combination are not shown in Figure 4), or $14.6 billion more than the CO2

tax. 
Resources for the Future Parry, Evans, and Oates 

22

5. Conclusion 

Our analysis suggests that efficiency standards applied to the transport and power sectors 

are difficult to justify, certainly as a complement to pricing instruments, and even if pricing 

instruments are infeasible for the time being. Although there is much dispute about the 

magnitude of possible market failures associated with misperceptions over energy efficiency 

benefits, even under upper-bound assumptions for these market failures, only modest reductions 

in energy use or emissions are warranted under standards, and potential welfare gains are a minor 

or tiny fraction of those under pricing policies. 

Of course, our analytical framework provides only a first-level assessment of welfare 

effects. Digging deeper, we see a number of caveats. 

 For example, our model assumes “smart” regulation that (implicitly) equates marginal 

compliance costs across producers within a sector. Moving forward, this may be a reasonable 

approximation for automobiles, given the recent extension of credit-trading provisions in the 

CAFE program. It is more questionable for the power sector, given the practical complexities of 

monitoring credit trading among heterogeneous producer groups (e.g., real estate developers and 

refrigerator manufacturers). At any rate, allowing the possibility of imperfect regulation would 

only strengthen our main findings. 

The same would apply to incorporating dynamics into our analysis. Distinguishing capital 

of different vintages would allow us to explore transitory inefficiencies from new product 

standards resulting from the uneven treatment of new and preexisting capital (though it would 

add considerable analytical complexity). 

We assume marginal-cost pricing. In practice, prices deviate from marginal costs because 

of cost-of-service regulation of power generation in many states and limited use of time-of-day 

pricing. However, it is difficult to make general statements about the economywide implications 

of these deviations (even with regard to their sign, let alone their magnitude), given that they are 

highly specific to region and time of day. 

Finally, our welfare analysis is incomplete in that it excludes interactions between energy 

and environmental policies and the distortions in the economy created by the broader fiscal 

system, particularly distortions in factor markets. Literature on this issue suggests that the tax 

revenue or allowance rent created by corrective taxes or cap-and-trade systems is potentially 

problematic (e.g., Goulder et al. 1999). This revenue or rent needs to be used in ways to increase 

economic efficiency, particularly by cutting other distortionary taxes, to offset efficiency losses 

in factor markets caused by higher energy prices and the consequently higher general price level 

and lower real factor returns. Standards, in contrast, have an advantage in this regard: they have a 
Resources for the Future Parry, Evans, and Oates 

23

weaker effect on the general price level (i.e., they do not involve the pass-through of tax 

revenues or allowance rents). 

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Ruderman, Henry, Mark D. Levine, and James E. McMahon. 1987. “The Behavior of the Market 

for Energy Efficiency in Residential Appliances including Heating and Cooling 

Equipment.” Energy Journal 8: 101–24.

Sallee, James, and Sarah West. 2010. “Testing for Consumer Myopia: The Effect of Gasoline 

Price Changes on the Demand for Fuel Economy in Used Vehicles.” Working aper, 

Harris School, University of Chicago. 
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Sanstad, Alan H., and Maximilian Auffhammer. 2010. “Energy Efficiency in the Residential and 

Commercial Sectors.” Agricultural and Resource Economics Department, University of 

California, Berkeley. 

Sanstad, Alan H., and James E. McMahon. 2008. “Aspects of Consumers’ and Firms’ Energy 

Decision-Making: A Review and Recommendations for the National Energy Modeling 

System (NEMS).” Papers from the Workshop on Energy Market Decisionmaking for the 

New NEM. Energy Information Administration: Washington, DC. 

Sanstad, Alan H., W. Michael Hanemann, and Maximillian Auffhammer. 2006. “End-use Energy 

Efficiency in a ’Post-Carbon’ California Economy: Policy Issues and Research 

Frontiers.” California Climate Change Center at UC-Berkeley, Berkeley, CA. 

Schrank, David, and Timothy Lomax. 2009. The 2009 Urban Mobility Report. Texas 

Transportation Institute, Texas A&M University, College Station. 

Small, Kenneth A. 2009. “Energy Policies for Passenger Transportation: A Comparison of Costs 

and Effectiveness.” Working paper, University of California, Irvine. 

Small, Kenneth A., and Kurt Van Dender. 2006. “Fuel Efficiency and Motor Vehicle Travel: The 

Declining Rebound Effect.” Energy Journal 28: 25–52. 

Small, Kenneth A., and Erik Verhoef. 2007. The Economics of Urban Transportation.

Routledge, New York. 

Stern, Nicholas. 2007. The Economics of Climate Change. Cambridge University Press, 

Cambridge. 

Tietenberg, Tom. 2009. “Reflections—Energy Efficiency Policy: Pipe Dream or Pipeline into the 

Future?” Review of Environmental Economics and Policy 3: 304–20. 

Train, Kenneth. 1985. “Discount Rates in Consumers’ Energy-Related Decisions: A Review of 

the Literature.” Energy 10: 243–53. 

U.S. Environmental Protection Agency (EPA). 2010. EPA Analysis of the American Power Act 

in the 111th Congress. Washington, DC. 

U.S. Interagency Working Group on Social Cost of Carbon (IAWG). 2010. Technical Support 

Document: Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 

12866. U.S. Government, Washington, DC. 

Weitzman, Martin L. 2009. “On Modeling and Interpreting the Economics of Catastrophic 

Climate Change.” Review of Economics and Statistics 91: 1–19. 
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Appendix A. Analytical Derivations 

Household first-order conditions 

Households solve a two-step optimization procedure using equations (1), (2), and (4). First, they 

choose the quantity of durable goods Si, energy consumption rates ei, planned use of durables mi, and 

planned consumption of I and Y, to maximize utility as follows: 

(A1)φ =

( , , ( )), , , , , } N N N N N Y C Z A ZE v S m μ e I

i

i A

INC GOV ∑ ki ei i qE tE miei S ≠+ λ{ + − ( ( ) + (1− ρ )( + ) )

(k (e ) (1 )(q t )m e )S p I p Y} − A A + − ρ A G + G A A A − I − Y

where λ is the marginal utility of income and φ(.) is the (perceived) indirect utility function. The budget 

constraint here equates disposable income, which includes fixed private income of INC, with purchases of 

durable goods, expected lifetime energy spending, and planned consumption of other goods. In the second 

stage, during the course of the period, agents may reoptimize over product usage (and by implication, 

consumption of Y) if energy costs differ from initial perceptions. 

This optimization yields 

(A2a) S i i Li u p i /λ = + (1− ρ )

(A2b) m A G G A u S q t e A ( / λ ) = ( + ) , m i E E i u S q t e i ( / λ ) = ( + ) , i ≠ A

(A2c) i i i i ue Si k L e i − ′ = (1− ρ )⋅ ∂ / ∂ − (1/ λ) /

(A2d) Y Y u / λ = p , I I u / λ = p

If, through regulation, the energy intensity for product i is reduced below the level that would be 

implied by equation (A2c), then this condition becomes 

(A2e) δ (1 ρ ) / μ μ / λ ~ i G i i i i i − k′ − = − ⋅ ∂L ∂e − u ′

where δ G

~ is the shadow value of the constraint, expressed per unit of gasoline intensity. The 

interpretation for equations (A2a–A2e) is provided in the main text. Note that the use of energy durables 

N { ( , , ( )), ( , , ( )),

, , , , A A A A A R R R R R

i i i

u v S m e v S m e

S m e I Y

Max μ μ
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28

in (A2b) may differ from planned use, if agents misperceive future energy prices. This discrepancy is 

taken into account in our choice of values for ρi. 

Demand functions and equation (6) 

From the household’s first-order conditions and budget constraint, we obtain demand functions 

for products, product usage, and energy efficiency. All demands can be expressed as functions of energy 

prices. Even though product demands in (A2a) depend on product prices, these depend on energy 

efficiency, which in turn is a function of energy prices. Demand functions are assumed to have constant 

elasticities with respect to their own prices. And as discussed in the text, we approximate by assuming 

that demands for the three energy durables are taken as being decoupled, and so, for example, higher 

gasoline prices do not have a (significant) effect on the demand for electricity durables like household 

appliances. This implies 

(B1a)

SA

G

G G

G G

A

A

q t

q t

S

S η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 ,

mA G

G G

G G

A

A

q t

q t

m

m

η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 ,

eA

G

G G

G G

A

A

q t

q t

e

e

η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0

(B1b)

i S

E

E E

E E

i

i

q t

q t

S

S η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 ,

mi

E

E E

E E

i

i

q t

q t

m

m

η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 ,

i e

E

E E

E E

i

i

q t

q t

e

e

η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 ,i ≠ A

In these equations, superscript 0 denotes an initial value, and parameters SA η G and so on are 

elasticities (hidden costs are implicit in the expressions below). Using these equations and (3a), we can 

also obtain 

(B2a)

G

G G

G G

q t

q t

G

G η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0

(B2b)

Ei

E E

E E

i

i

q t

q t

E

E η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0 , i ≠ A,

E

E E

E E

q t

q t

E

E η

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = 0 0

where η G = mG η G

SA +ηG Ae +η G is the gasoline demand elasticity, = Ei η + mi η E i S η E i e +η E , i ≠ A is the 

demand elasticity for electricity used by durable good i, and 

Ei E

i

E Ei η = ∑η /

 is the demand elasticity 

for total electricity use. 

Elasticities are defined by 
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29

(B3a)

A

G G

G

S A

G S

q t

t

S A +

∂

∂

η = ,

A

G G

G

m A

G

m

q t

t

A m +

∂

∂

η = ,

A

G G

G

e A

G

e

q t

t

e A +

∂

∂

η = , G

q t

t

G G G

G

G

+

∂

∂

η =

(B3b)

i

E E

E

S i

E S

q t

t

S i +

∂

∂

η = ,

i

E E

E

m i

E

m

q t

t

i m +

∂

∂

η = ,

i

E E

E

e i

E

e

q t

t

e i +

∂

∂

η = ,

i

E E

E

i

E E

q t

t

E

i

+

∂

∂

η =

and so on. 

From differentiating equation (3b) with respect to gasoline and electricity prices, and using (B1) 

and (B3), we can obtain the decomposition of elasticities in (6). 

Equation (7a): Marginal welfare effect of gasoline tax 

The actual (as opposed to perceived) indirect utility is defined by the household’s utility 

maximization problem with future costs correctly measured (deviations from this optimization, due to 

consumer misperceptions, will therefore reduce actual utility). Totally differentiating (A1) with respect to 

tG, with ρi =1 and electricity and industrial variables constant (because they are assumed independent of 

transportation prices) gives, after substituting for gasoline and lifetime gasoline costs, and using (4a) to 

replace the vehicle price pA, 

(C1) { }

G

A

S A A

G dt

dS

u p L

dt

d

A = − λ( + ) φ + { − + } +

G

A

m G G A A dt

dm

u q t e S A λ( )

{ }

G

A

e G G A A A A dt

de

u q t m S k S A − λ( + ) − λ ′ { }

G

Y Y dt

dY

+ u − λp

G G

A

Z

G

C dt

dGOV

dt

dZ

u

dt

dC

u A + + + λ − λG

Note that deA / dtG = 0 if the fuel economy standard is binding. 

Substituting the household’s first-order conditions (A2) into (C1), the second and fourth terms 

cancel, and the sum of the first and third terms is 

(C2)

⎭

⎬

⎫

⎩

⎨

⎧ − + +

G

A

A A

G

A

A G G A A dt

de

m S

dt

dS λρ (q t ) e m

From differentiating the first expression in (3a) with respect to tG, holding E and I fixed gives 

(C3)

G

G

C

G dt

dG

z

dt

dC =

Differentiating the second expression in (3a) with respect to tG gives 
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30

(C4)

G

A A

A

G

A

dt

d m S

z

dt

dZ ( ) =

And using the definition of elasticities in (B3a), 

(C5) =

⎭

⎬

⎫

⎩

⎨

⎧

+

G

A

A A

G

A

A A

G dt

de

m S

dt

dS

e m

dG / dt

1

G

S

G

e

G

A A

η

η +η ,

A G

S

G

m

G

G

A A G

dG dt e

d m S dt A A

η

η +η = /

( )/

Finally, from totally differentiating the government budget constraint (5) with respect to tG, we 

obtain 

(C6)

G

C

G

G

G dt

dC

t

dt

dG G t

dt

dGOV = + +

Dividing (C1) by dG dtG − / to express the utility effect per gallon reduction in gasoline, and by 

λ to express utility in monetary units, and substituting (C2)-(C6), along with (7c), gives the welfare 

decomposition in equation (7a). 

Deriving equation (7b): Relation between tax rate and gasoline reduction 

This is easily obtained from the gasoline demand function in (B2a). First invert, and then 

substitute for 0 G / G using 0 1 / ΔGˆ = − G G . 

Deriving equation (8a): Marginal welfare effect of fuel economy standard 

Setting G G e = e (i.e., the fuel economy standard is binding) and totally differentiating (A1) with 

respect to Ge , with ρi =1, electricity and industrial variables constant, and dS A / deG = 0 (see text), 

gives 

(D1) =

G de

dφ { } − + +

G

A

m G G A A de

dm

u q t e S A λ( ) {ue qG tG mASA kASA} A − λ( + ) − λ ′ { }

G

Y Y de

dY

+ u − λp

G G

A

Z

G

C de

dGOV

de

dZ

u

de

dC

u A + + + λ

Following the derivation of (C2) above, using (A2e) instead of (A2d), the first three terms 

amount to 

(D2) A G G mASA − λρ (q + t ) δ G

~ −

And analogous to (C3) and (C4) above, 
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31

(D3)

G

G

C

G de

dG

z

de

dC =

(D4)

G

A A

A

G

A

de

d m S

z

de

dZ ( ) =

Using the definition of the rebound effect in (8c), 

(D5)

G

A A

A A A

G de

d m S

m S e

de

dG ( ) = + (1 ) A A A = m S − r ,

A

A

A A

A A A

r

r

dG de e

d m S de

− = − 1

1

/

( )/

And from differentiating the government budget constraint, 

 (D6)

G

C

G

G

G de

dC

t

de

dG

t

de

dGOV = + 0

where 0

Gt is the preexisting gasoline tax. Substituting (D2)-(D6) into (D1), and using the definition of 

external costs in (7c), we obtain (8a), where /( / ) 0 ~ G G G G δ = t + δ dG de . 

Deriving equation (8b): relation shadow price and fuel reduction 

δ G can be viewed as a virtual tax that reduces gasoline use by improving fuel economy, adjusted 

for the offsetting fuel increase from the rebound effect. That is, the relevant elasticity for this shadow 

price is (1 ) A

e

G r A η − rather than η G for an increase in the gasoline tax (affecting all margins for fuel 

reduction opportunities). Thus, under the fuel economy standard, the gasoline demand function in (B2a) 

can be written, 

(E1)

(1 )

0 0

eA rA G

G G

G G

q t

q

G

G

−

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ =

η

δ

Inverting using 0 1 / ΔGˆ = − G G gives (8b). 

Deriving equation (9a): Marginal welfare effect of electricity tax 

Following the analogous derivation to derivations (C1)-(C6), but this time for raising the 

electricity tax, yields 

(F1) ∑{ } ≠

= − +

i A E

E

S i i

E dt

dS

u p L

dt

d i

i λ( ) φ +∑{ − + } +

i≠A E

i

m E E i i dt

dm

u q t e S i λ( )
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32

∑{ } − + − ′

i E

i

e E E i i i i dt

de

u q t m S k S i λ( ) λ { }

E

Y Y dt

dY

+ u − λp

E E

E

Z

E

C dt

dGOV

dt

dZ

u

dt

dC

u E + + + λ − λE

(F2)

⎭

⎬

⎫

⎩

⎨

⎧ − ∑ + + ≠ E

i

i i

E

i

i i

i A

i E E dt

de

m S

dt

dS λ ρ (q t ) e m

(F3) = ∑

i E

E i

C

E dt

dE

z

dt

dC

(F4) ∑≠

= i A E

i i

E

E

E

dt

d m S

z

dt

dZ ( )

(F5) =

⎭

⎬

⎫

⎩

⎨

⎧

+

E

i

i i

E

i

i i

i E dt

de

m S

dt

dS

e m

dE / dt

1

i

i i

E

S

E

e

E

η

η +η ,

i

i i

i E

S

E

m

E

i E

i i E

dE dt e

d m S dt

η

η +η = /

( )/

(F6)

E

C

i E

i

E

E dt

dC

t

dt

dE E t

dt

dGOV = + ∑ +

Dividing (F1) by −∑

i

dEi dtE / to express the utility effect per unit reduction in electricity, and 

by λ to express utility in monetary units, and substituting (F2)-(F6), along with (9c), gives the welfare 

decomposition in equation (9a). 

Deriving equation (9b): Relation between tax rate and electricity reduction 

Again, this is easily obtained from the electricity demand function in (B2b) after inverting and 

substituting 0 E / E with 0 1 / ΔEˆ = − E E . 

Deriving the marginal welfare effect of efficiency standard for the power sector 

Following the analogous derivation to derivations (D1)-(D6), but this time for the efficiency 

standard (and ignoring cross-price effects among energy durables), we obtain 

(G1) =

R de

dφ + { } − + +

R

R

m E E R R de

dm

u q t e S R λ( ) {ue qE tE mRSR kRSR} A − λ( + ) − λ ′

{ }

R

Y Y de

dY

+ u − λp

R R

E

Z

R

C de

dGOV

de

dZ

u

de

dC

u E + + + λ

(G2) R E E R R − λρ (q + t )m S δ R

~ −
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33

(G3)

R

E R

C

R de

dE

z

de

dC =

(G4)

R

R R

E

R

E

de

d m S

z

de

dZ ( ) =

(G5)

R

R R

R R R

R

R

de

d m S

m S e

de

dE ( ) = + (1 ) R R R = m S − r ,

R

R

R R R

R R R

r

r

dE de e

d m S de

− = − 1

1

/

( )/

(G6)

R

C

R

R

E

R de

dC

t

de

dE

t

de

dGOV = + 0

Substituting (G2)-(G6) into (G1), we obtain 

(G7) = − − − E

R C C C

e

E MC EXT t z R δ ( ) EXTE

E

E E E

r

q t − − ⋅ + ⋅

1

1

ρ ( ) 

where /( / ) 0 ~ R E R R R δ = t +δ dE de . 

Deriving equation (11a): Marginal welfare effect of a CO2 tax 

The marginal welfare cost of an increase in the CO2 tax can be decomposed as follows: 

(H1)

1

1

/

1 / −

⎭

⎬

⎫

⎩

⎨

⎧

⋅

⎭

⎬

⎫

⎩

⎨

⎧

∂

∂

+

∂

∂

+

∂

∂

+

∂

∂ =

C C

I

C I

E

C

E

C C

E

C E

G

C G

C

dt

dZ

dt

dp

dt p

dz

dt z

dt

dt t

dt

dZ dt t

d dt φ φ φ φ

λ

φ

λ

The first component is the gasoline tax equivalent of the increase in the CO2 tax. That is, the CO2

tax effectively raises the gasoline tax per gallon by G

G C C dt / dt = z . And an incremental increase in the 

gasoline tax has a welfare cost per gallon of (( / )/ )/( / ) G G

t MCG t dG dt G = ∂φ ∂ λ . Making these 

substitutions in (H1), and using (10), gives the first component of equation (11a), where 

(H2)

C

G G C

A

G C

dZ dt

z dG dt dt dt

/

( / )( / ) α =

This expression is the share of the reduction in economywide CO2 reductions that comes from 

reduced gasoline use. 

The second component in (H1) is the electricity tax equivalent of the increase in the CO2 tax. 

That is, the CO2 tax has the equivalent effect on electricity output as an increase in the electricity tax of 

E

E C C dt / dt = z . And an incremental increase in the electricity tax has a welfare cost per kWh of 
Resources for the Future Parry, Evans, and Oates 

34

(( / )/ )/( / ) E E

t

E MC t dE dt E = ∂φ ∂ λ . Making these substitutions in (H1), and using (10), gives the second 

component of equation (11a), where 

(H3)

C

E E C

E

E C

dZ dt

z dE dt dt dt

/

( / )( / ) α =

This expression is the share of the reduction in economywide CO2 reductions that comes from 

reduced electricity use. 

The third component in (H1) is the equivalent effect of the CO2 tax in terms of reducing 

emissions per unit of electricity, holding electricity output constant. From (4d) the added abatement cost 

per unit reduction in the emissions rate is E C k′ = t , though there is a gain from reducing local pollution of 

EXTE per unit reduction in emissions. Thus, the welfare cost per unit of emissions reduction is the third 

component in equation (11a), where 

(H4)

C

C

E

z C

dZ dt

E E dz dt

/

⋅ /

α =

Finally, aside from CO2, there are no market failures associated with production and use of the 

industrial good, and therefore the welfare cost per unit of emissions reduction from this sector is simply 

tC. (This can be verified by differentiating the utility function with respect to the tax-induced increase in 

the unit price of this good, I

Cz , taking account of the increase in government transfers ( / ) C

I

zC I + dI dt ). 

The share of the emissions reductions from the industrial sector is 

(H5)

C

C

I

I C

dZ dt

I dz dt

/

⋅ /

α =

Appendix B. Additional Documentation for Parameters 

CO2 Damages 

Some studies (e.g., Nordhaus 2008) value CO2 damages at about $10/ton, while others value it at 

about $80/ton (e.g., Stern 2007). One reason for the different estimates is that because of long 

atmospheric residence times and the gradual adjustment of the climate system, today’s emissions have 

very long range consequences, and their discounted damages are therefore highly sensitive to assumed 

discount rates. Some analysts (e.g., Heal 2009) argue against discounting the utility of future generations 

on ethical grounds, to avoid discriminating against people just because they are born in the future; others 
Resources for the Future Parry, Evans, and Oates 

35

(e.g., Nordhaus 2007) view market discounting as essential for meaningful policy analysis and to avoid 

perverse policy implications in other contexts. 

The second reason for different CO2 damage assessments has to do with the treatment of extreme 

catastrophic risks, such as the possibility that an unstable feedback mechanism in the climate system 

might lead to a truly catastrophic warming. In particular, it is possible that the marginal damages from 

CO2 emissions are arbitrarily large if the probability distribution over future climate damages has “fat 

tails”—that is, the probability of increasingly catastrophic outcomes falls more slowly than marginal 

utility rises (with diminished consumption) in those outcomes (Weitzman 2009). Others (e.g., Nordhaus 

2009) have critiqued the fat-tails hypothesis on the grounds that we can head off a future catastrophic 

outcome by radical mitigation measures, and possibly geoengineering, in response to future learning 

about the seriousness of climate change. 

A recent Inter-Agency Working Group (U.S. IAWG 2010) recommended using a value of $21.4 

per ton of CO2 for 2010, with additional values of $4.70, $35.10, and $64.90 per ton for sensitivity 

analysis. We begin with a benchmark value of $20 per ton and use a range of $10 to $100 for sensitivity 

analysis. 

Local pollution from electricity generation 

In our analysis, local pollutants consist of sulfur dioxide, nitrogen oxides, fine particulate matter 

(with a diameter less than 2.5 micrograms), and coarse particulate matter (with a diameter between 2.5 

and 10 micrograms) from natural gas and coal combustion. We use the generation-weighted national 

estimates of damages per kWh reported in a study by the National Research Council (NRC 2009). We 

then weight these per kWh damages by the share of total 2007 production as reported in EIA (2009), 

assuming that other methods of generation are not a significant source of local air pollutants (oil-fired and 

waste-fired generation are less than 2.1 percent of total generation). Our estimate of the average local 

pollution damage is 1.7 cents per kWh. However, baseload coal-fired generation accounts for a 

disproportionately large share of these damages, but the marginal fuel (typically natural gas) accounts for 

a disproportionately large share of reductions in electricity use. To make some adjustment for this, we 

assume a local pollution benefit from electricity reductions of 1.3 cents per kWh. 
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36

Energy efficiency misperceptions 

There is evidence, from studies examining consumer purchases of a wide range of domestic 

appliances and other energy-related investments, that consumers have very high implicit discount rates for 

energy efficiency.14 However, whether these high implicit discount rates reflect market failures or hidden 

costs is very unsettled. 

Market failure explanations may reflect the basic problem of the costly acquisition and processing 

of information. Many consumers may not know what the relevant cost savings are. Furthermore, 

consumers may believe that the prospective cost savings are exaggerated.15 On the other hand, high 

implicit discount rates may instead reflect hidden costs, like reductions in other product attributes as a 

result of higher energy efficiency. Moreover, rational consumers may hold back on what may be largely 

“irreversible investments” with uncertain returns (Hassett and Metcalf 1993) because of risk aversion or 

liquidity constraints. 

If high implicit discount rates reflect real information problems, consumers are largely unaware 

of the potential costs savings from energy-efficient technologies, and this corresponds to a positive value 

for ρi. In contrast, if these high rates reflect hidden or other costs, this corresponds to a positive value for 

i i uμ μ′ , which does not enter the welfare formulas because it is an internalized cost. We therefore 

consider two bounding cases meant to span the range of possibilities: one where the difference between 

implicit and social discount rates reflects a misperceptions market failure (i.e., ρi > 0) and another where it 

is explained entirely by hidden costs (i.e., ρi = 0). 

For electricity durables, we use a bounding case of ρi (i = R, N) = 0.62. This is equivalent to 

discounting electricity savings at 25 percent, compared with a social discount rate of 5 percent, for a 

product with a 20-year life, where excessive discounting is due entirely to market failures. 

 

14 Hausman (1979), examining household purchases of room air-conditioners, found implicit discount rates of 

around 20 percent. Later studies found comparable, and in some instances, much higher rates. Dubin and McFadden 

(1984), for example, in a study of space-heating and water-heating investments, also found implicit discount rates of 

around 20 percent, but Gately (1980) found much higher discount rates, 45 to 300 percent, in his study of consumer 

purchases of refrigerators. Likewise, Ruderman et al. (1987) calculated implicit discount rates ranging all the way 

from 20 to 800 percent on residential purchases of heating and cooling equipment and appliances. Little (1984) and 

Berkovec et al. (1983) report implicit discount rates of 32 percent for thermal shell investments and 25 percent for 

space-heating systems. 

15 For example, Hassett and Metcalf (1995) found, after accounting for all the costs, that the realized return to attic 

insulation (9.7 percent) was well below that promised by engineers and manufacturers. 
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37

For automobiles, empirical literature on implicit discount rates is more mixed.16 We follow one 

scenario explored by NRC (2002) in which consumers value fuel economy improvements by considering 

(undiscounted) fuel savings over the first three years of a vehicle life. Using NRC’s assumptions for 

vehicle life and declining vehicle usage with age, with a social discount rate of 5 percent, this implies ρA = 

0.65 if there are no hidden costs. 

 

16 One study, by Dreyfus and Viscusi (1995), finds a discount rate of 11 to 17 percent, though the average new car 

loan interest rate was 12.6 percent in their sample, suggesting that car buyers may have been constrained by liquidity 

rather than myopic. Another study, by Allcott and Wozny (2009), however, suggest consumers may consider fuel 

savings from efficiency improvements over only three years. This finding is consistent with EIA’s National Energy 

Modeling System, in which new auto buyers are assumed to use payback periods of three years. On the other hand, 

Sallee and West (2010) estimate implicit discount rates close to market rates, based on a study of the used-auto 

market. For more discussion of the literature, see Greene (2010). 
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Table 1. Baseline Data, 2008 

Autos Regulated Non‐regulated

Basic data for energy‐using products

Energy intensity, gal./1000 miles or kWh/hour 43.5 0.73 0.48

Initial energy tax, $/gal., $/kWh 0.4 0.009 0.009

Initial pre‐tax energy price, $/gal., $/kWh 2.15 0.082 0.082

Initial quantity of fuel/energy, bn gallons,  billion kWh 130 2,506 1,670

Price elasticities (standards non‐binding)

elasticity with repsect to own price of energy ‐0.40 ‐0.40 ‐0.40

fraction of elasticity from:

reduced usage per product 0.25 0.33 0.33

reduced demand for product 0.25 0.17 0.17

reduced energy intensity per unit of use 0.50 0.51 0.51

External costs

CO2 intensity, tons/gal., tons/kWh 0.009 0.0005 0.0005

CO2 damages, $/gal., $/kWh 0.18 0.01 0.01

Other external costs, $/mile, $/kWh 0.09 0.01 0.01

0.65 0.62 0.62

Source. See text and Appendix B.

Table 1. Baseline Data

Electricity Durables

(for 2008)

Non‐internalized fraction of lifecycle energy costs in

misperceptions scenario
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Figure 1. Marginal Cost of Policies to Reduce Gasoline 

a. No Misperceptions Failure

‐2.0‐1.5‐1.0‐0.5

0.00.51.01.52.0

0 5 10 15 20 25 30

% reduction in gasoline

Marginal cost, $/gallon

Gasoline tax Efficiency s tanda rd

b. Misperceptions Failu

‐2.0‐1.5‐1.0‐0.5

0.00.51.01.52.0

0 5 10 15 20 

% reduction in gasoli

Marginal cost, $/gallon

Gasoline tax Efficiency s tan
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Figure 2. Marginal Cost of Policies to Reduce Electricity 

a. No Misperceptions Failure

‐0.10‐0.08‐0.06‐0.04‐0.02

0.000.020.040.060.080.10

0 10 20 30

% reduction in electricity

Marginal cost, $/kWh

Electricity tax Efficiency standard

b. Misperceptions Failu

‐0.10‐0.08‐0.06‐0.04‐0.02

0.000.020.040.060.080.10

0 10 20 

% reduction in elect

Marginal cost, $/kWh

Electricity tax Efficien
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Figure 3. Market Failures Required to Justify Efficiency Standards of Different Stringen

Note. For the power sector, reducing electricity use by 7.5 percent is optimal only for extreme scenarios for market failures.

a. Fuel Intensity Standard

0.00.10.20.30.40.50.60.7

0 20 40 60 80 100

CO2 damage, $/ton

Misperceptions fraction, ρ

opt. fuel reduction, 2.5% opt. fuel reduction 5%opt. fuel reduction 7.5%

b. Energy Efficiency Stand

0.00.10.20.30.40.50.60.7

0 20 40 60 

CO

2 damage, $/ton

Misperceptionsfraction, ρ

opt. elec. reduction 2.5% opt. elecopt. elec. reduction 7.5%
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Figure 4. Marginal Cost of Policies to Reduce CO2 Emissions

Note. Costs are defined gross of CO2 externality benefits. 

‐100

‐80‐60‐40‐20

0

20406080

100

0 5 10 15

Marginal cost, $/ton of CO2

% reduction in economywide CO2

a. No Misperceptions Failure

Gasoline taxStandard‐‐autoselectricity taxStandard‐‐electricityCO2taxEmissionsstandard

‐100

‐80‐60‐40‐20

0

20406080

100

0 5 

Marginal cost, $/ton of CO2

% reduction in econ

b. Misperceptions F

Gasoline taxStandard‐‐autoelectricity taxStandard‐‐elecCO2taxEmissionsstan