What Does a Negawatt Really Cost?

by

Paul L. Joskow and Donald B. Marron

MIT-CEPR 91-016WP December 1991
M.I.T. LIBRARIES

JUL 2 9 1992; RHEcGtu I _
DISCUSSION PAPER

December 1991

WHAT DOES A NEGAWATT REALLY COST?

Paul L. Joskow

and

Donald B. Marroni

ABSTRACT

We use data from ten utility conservation programs to calculated the cost per Kwh

of electricity saved---the cost of a "negawatthour"----resulting from these

programs. We first compute the life-cycle cost per Kwh saved based on utility

experience and expectations associated with these conservation programs. The

resulting figures indicate that the cost of a negawatthour is substantially

higher than previously suggested by standard sources such as Lovins and EPRI

which are routinely cited by policymakers. The costs calculated for residential

programs in particular are much higher than conservation advocates have

suggested. We find substantial variation in costs for similar programs between

utilities as well as significant intra-utility variation in the cost per kWh

saved for specific sub-programs. Some of these programs appear to be

uneconomical even before correcting for biases in utility cost accounting and in

the measurement of actual electricity savings. The bulk of the expenditures and

savings from the utility conservation programs we reviewed are associated with

subsidies for commercial and industrial conservation investments rather than for

conservation investments made by residential customers. Furthermore, it is

likely that the values for the cost per kWh saved that we derive from utility

reports understate their true costs by a factor of two or more on average. The

actual costs per kWh saved are likely to be significantly higher, on average,

than those computed from utility reports because utilities frequently fail to

count important cost elements. They also frequently fail to base their estimates

of the electricity saved by the programs on ex Rost measurement of consumer

behavior, relying instead on notoriously inaccurate ex ante engineering

estimates. Better utility cost accounting procedures and the development and use

of sound sampling and statistical methods to measure the electricity savings

actually achieved by utility conservation efforts is essential to ensure that

only cost-effective conservation programs are pursued and to protect electricity

ratepayers from excessive costs.

1Department of Economics, MIT. We are grateful for financial support from

the MIT Center for Energy Policy Research (Joskow and Marron) and the National

Science Foundation (Marron). We are also grateful to the many people at the

utilities that we surveyed who provided us with the information that we relied

upon for this study. The analysis presented here is solely the responsibility

of the authors.

DISCUSSION PAPER

December 1991

WHAT DOES A NEGAWATT REALLY COST?

Paul L. Joskow

and

Donald B. Marron i

I. INTRODUCTION

It is widely believed that significant cost-effective opportunities

exist for consumers to use electricity more efficiently. In this context it

is often argued that there are many conservation opportunities for which the

life-cycle cost of investing in energy efficiency is significantly less than

the resulting savings in electricity costs. Amory Lovins of the Rocky

Mountain Institute, the most quoted, and quotable, proponent of this view,

estimates that end-use electricity efficiency in the United States can be

increased by as much as 70% at an average life-cycle cost of only 0.6 cents

per kWh saved. The Electric Power Research Institute (EPRI), a research

organization funded by the electric utility industry, estimates that end-use

electricity efficiency can by increased by almost 30% at an average life-cycle

cost of roughly 2.6 cents per kWh saved (both costs are from Fickett,

Gellings, and Lovins 1990). See Figure 1.

These numbers reflect the "gross" life-cycle costs of adopting more

efficient electric appliances and equipment -- what Lovins has dubbed the cost

SDepartment of Economics, MIT. We are grateful for financial support from

the MIT Center for Energy Policy Research (Joskow and Marron) and the National

Science Foundation (Marron). We are also grateful to the many industry

representatives who provided us with the information necessary for this study.

The views expressed here are those of the authors.
Figure 1: Potential Electricity Savings

a,C.)

ZC.3

C.3

uJco,.)

'J,

o0C,, 0

-2

Potential Electricity Savings (Percent of Total Electricity Consumption)

EPR1. Ind2. Re

3. Re

4. Co5. Co6. Co7. Co8. Co9. Ind10. R11. E12. R13. C

14. C15. C16. R17. RLov1. Lig

2. Lig3. Wa4. Dr5. El6. Co

7. In

8. El9. Re

10. S11. W

Reproduced from Alfred E. Kahn, "Rational Least-Cost Planning," The Electricity J

June 1991, page 14
of a "negawatt".2 That is, they represent the life-cycle cost of investing

in conservation before accounting for the resulting savings in electricity

costs. To calculate the net economic benefit of these conservation

expenditures, we must subtract from them the production costs of the

electricity they displace. While avoided electricity costs vary greatly

depending on customer utilization characteristics, the type of conservation

measures, and geographic location, a comparison of the Lovins and EPRI

estimates of the cost per kWh saved with average U.S. electricity prices is

instructive. In 1990, the average residential electricity price was about 8

cents per kWh and the average industrial price was about 5 cents per kWh

(Energy Information Administration (1991), p. 48). Prices vary between the

sectors because of differences in their demand characteristics.

Since these prices are expected to be roughly constant in real terms

over the next ten years (Energy Information Administration (1991), p. 48), it

is clear that the Lovins and EPRI estimates both suggest that large

opportunities exist for cost-effective (i.e. positive net benefits)

improvements in end-use electric efficiency.3 If consumers took advantage of

2 To be precise, these figures represent the cost of a "negawatthour".

Whereas watts, and hence "negawatts", refer to units of electric capacity,

watthours, and hence "negawatthours", refer to electric energy, the focus of our

analysis. In keeping with the standard lingo, however, we use the more common

term "negawatt" to refer to both capacity and energy.

3 Note that the electricity prices are cited only to provide an indication

of the magnitude of the difference between the projected costs and benefits of

conserving electricity. Actual savings from particular measures or programs

would, of course, vary, depending on utility characteristics as well as the

characteristics of the specific conservation options, particularly the relative

contributions on-peak (where capital and operating costs would be saved) and offpeak (where only operating costs would be saved). Average fuel and variable

operations and maintenance costs tend to be about 2 to 3 cents per kWh, thus

accounting for the bulk of off-peak electricity prices. A new base-loaded

natural gas-fired combined-cycle generating unit produces electricity at a cost

of about 5 to 6 cents per kWh.
3

these opportunities they would, in the long run, both save money and reduce

the rate of growth (or even absolute level) of electricity consumption. Such

a reduction in electricity demand would, in turn, reduce the future negative

impacts of electricity production on the environment.

The Lovins and EPRI estimates of abundant economical opportunities to

increase end-use electricity efficiency are having important effects on both

energy and environmental policy. The National Academy of Sciences relied

heavily on both the Lovins and EPRI energy efficiency "supply curves" in

developing recommendations for a set of "no regrets" policies to respond to

global warming concerns (National Academy of Sciences (1991)). More

importantly for our purposes, a growing number of state public utility

commissions have ordered utilities to develop programs that provide subsidies

to encourage consumers to make qualified electricity conservation investments.

In 1990 we estimate that electric utilities spent between $1.0 to $2.0 billion

on these electricity conservation programs, roughly 1% of revenues.*

However, expenditures on conservation are increasing rapidly. Consolidated

Edison Company of New York expects to spend over $4 billion of conservation

through 2008 and Pacific Gas and Electric expects to spend $1 billion to $2

billion over the next five years (Wall Street Journal, November 5, 1990, p.

Bl). Advocates of utility conservation programs envision as much as $165

billion of utility expenditures over the next ten to fifteen years (Hirst

(1991)). The costs of these programs are recovered by electric utilities from

their captive customers as higher electricity prices through the public

utility ratemaking process.

* We distinguish here between conservation programs designed to increase

end-use energy efficiency and so-called load management programs that shift

consumption from peak to off-peak periods.
Many economists, however, have expressed considerable skepticism

regarding the more extreme estimates of the cost of electricity conservation.

When electricity consumers invest in more efficient appliances and equipment

they reduce their electricity consumption and their electricity bills. At the

cost and price levels noted above, consumers should, in the long run, find it

to be in their self-interest to adopt many of the "untapped" conservation

opportunities identified by Lovins and EPRI. While it is not surprising that

all consumers are not operating on the efficiency frontier, the gap implied by

these numbers is surprisingly large. There is after all abundant econometric

evidence that the demand for electricity slopes downward, that appliance and

fuel choices are sensitive to energy prices, and that the energy efficiency of

U.S. industry has increased significantly over the last two decades.

On the other hand, advocates of aggressive utility-financed subsidy

programs for electricity conservation, as well as government mandated

appliance efficiency standards, tightened building codes, etc., argue that

consumers are not taking advantage of these economical conservation

opportunities because of a long list of market imperfections (for a discussion

of these imperfections see, for example, Fisher and Rothkopf 1989). They

argue further that the provision to consumers of information and, more

importantly, financial subsidies is required to overcome these barriers and

that electric utilities are in the best position to do both. Whether electric

utilities are in a good position to provide these services because they

actually have unique skills and capabilities or because, as regulated

monopolies, the associated costs can be easily passed on to customers (i.e.,

taxation by regulation) is a subject of some dispute.

It is not the purpose of this paper to try to resolve the disputes about
5 b

the significance of these market imperfections or the wisdom of requiring

utilities to respond to them by subsidizing conservation. Indeed, the authors

have somewhat different views on these issues. Rather, we have a more modest

goal in this paper. We seek to determine what we actually know about how much

it costs utilities and their customers to obtain improvements in end-use

electricity efficiency from the kinds of conservation options that are

typically identified as targets of opportunity by examining the available data

from actual utility program experience and expectations. In answering this

question, we also explore the strengths and weaknesses of the methods used by

utilities to estimate conservation costs and electricity savings attributable

to these programs. We do so by analyzing, within a standardized cost

accounting framework, the costs, savings, and measurement methods embodied in

conservation program experience and expectations for a sample of electric

utilities.5 Appendix A discusses how the utilities were chosen and the data

that were requested from them.

We believe that this analysis is valuable for several reasons. First,

the range of cost estimates for various conservation options is very wide. As

a result, reasonable people may have very different expectations about the

costs of improving energy efficiency and the effects of energy efficiency

improvements on the demand for electricity, new generating capacity needs, and

the environmental impacts of electricity supply. These differences, in turn,

have varying implications for broader energy and environmental policies. If

we are going to rely on utilities to capture electricity conservation

opportunities that would otherwise be squandered, we need to have a better

5 As we discuss, we have not attempted to examine all utility conservation

programs for reasons of both time and budget constraints. As a result, the

current paper should be viewed as a pilot study.
understanding of the costs and savings associated with actual utility

programs. The actual performance of these utility programs is thus the only

sensible basis for developing sound estimates of the costs of various

conservation options.

Second, the costs associated with energy conservation programs incurred

by utilities are passed through to customers in regulated electricity rates.

Public utility commissions have an obligation to ensure that these

expenditures are being made wisely and that the programs are being designed to

encourage customers to conserve at the lowest possible cost.' Designing and

implementing good conservation programs is much more difficult than first

meets the eye. These programs are most productively viewed as efforts by

utilities to alter consumer behavior.7 Viewed from this perspective,

responsible energy conservation programs must take account of the wide

diversity among customers in demand characteristics, the effects of natural

incentives customers have to conserve to save money, asymmetries of

information between customers and the utility, retail marketing problems, and

the profound difficulties that arise in measuring the actual savings achieved

by those customers who participate in utility programs. Programs that are not

designed properly to account for these considerations will be wasteful both

from the perspective of electricity consumers, who are effectively being taxed

'Industrial consumers have increasingly expressed concerns that the money

being spent by utilities on conservation programs is being wasted (Electric

Utility Week, November 18, 1991, p. 5). This is ironic since, as we shall see,

large commercial and industrial customers are receiving a disproportionate share

of the conservation subsidies provided by utilities.

7 Unfortunately, many regulatory commissions have urged utilities to

conceptualize conservation opportunities as equivalent to "supply-side"

resources. The conceptualization places regulators and utilities on a path that

is sure to lead to wasteful expenditures. For an elaboration of this point see,

e.g., Joskow (1988).
to pay for these programs, and society at large. Our examination of actual

utility programs provides an opportunity to identify the strengths and

weaknesses of utility program design, cost accounting, and electricity savings

measurement at an early stage in the evolution of these programs, so that

significant imperfections can be taken into account in future program design.

The paper proceeds in the following way. In Section II we present the

analytic framework within which we will examine the conservation costs

reported by utilities. In Section III we discuss the Lovins and EPRI energy

conservation "supply" curves within this framework. In Section IV we present

our analysis of the conservation costs reported by a sample of utilities. In

Sections V and VI we discuss a range of infirmities that afflict utility

conservation cost accounting and energy savings estimation, respectively. In

Section VII, we present a detailed discussion of a particularly important

issue: the treatment of free riders. In Section VIII we summarize our

conclusions and make recommendations for improving the planning, evaluating,

and accounting of utility conservation programs.

II. ANALYTIC APPROACH

Throughout this paper, our focus will be on the life-cycle cost of

energy conservation investments before taking into account the savings in

electricity costs resulting from conservation. That is, we examine the costs

of utility conservation programs in this paper, but not their benefits. We

take this approach for two reasons. First, this is the framework that has

been adopted by Lovins, EPRI, and many conservation advocates in order to

provide a simple method for comparing directly the cost of "supplying"

conservation with the cost of supplying electricity. Second, as a practical
8

matter, it is much easier for the outside analyst to calculate conservation

costs than it is to calculate net benefits; calculating the latter would

require significantly more information regarding program, customer and utility

characteristics.

Our focus throughout will be on utility conservation programs

exclusively, i.e., programs whose primary purpose is to reduce energy

consumption. We do not analyze load management programs, which are designed

primarily to shift loads in time, reduce peak loads, and, perhaps, increase

off-peak loads, or fuel substitution programs, which are typically intended to

substitute natural gas for electricity.

a. The Structure of Utility Conservation Programs

Before presenting a detailed discussion of the specific analytic

techniques we use to analyze electric conservation investments and estimate

their gross costs, it is useful to describe briefly the process by which

utilities attempt to promote improvements in electricity efficiency. The

typical utility conservation program is structured in the following way. The

utility identifies those end-uses in which it believes that significant

untapped cost-effective conservation opportunities exist. For each end-use,

it then identifies a set of conservation measures that could potentially tap

these opportunities. This set typically includes a wide range of measures for

residential, commercial, and industrial customers. Lighting measures are

typically included for all sectors. In addition, appliance and weatherization

measures are typically included in the residential sector. In the commercial

and industrial sectors, HVAC and efficient motor programs are generally

included. In the commercial and industrial area, in particular, packages of
measures must be tailored to the diverse characteristics of the customers who

fall in these groups. While utility programs vary widely in terms of the

specific conservation measures they promote and how they are packaged, many of

the measures identified by EPRI and Lovins are often included in these

programs.

To qualify for inclusion in a program a measure must typically pass some

type of "total resource cost" test. That is, the expected total cost of the

conservation measure (including both costs borne directly by the utility and

costs borne by the customer) must be less than or equal to the present

discounted value of the expected electricity supply costs that would otherwise

be incurred in order to serve that demand.8," The relevant cost is the

"incremental cost" associated with the most economical conservation investment

compared to the cost of the conservation decision consumers would have

otherwise made but for the effects of the utility's efforts.

Having identified the conservation measures that will be promoted, the

utility typically establishes an annual budget for each program, develops a

marketing and distribution program, and, increasingly, establishes a

monitoring and evaluation program that seeks to measure actual performance

(i.e. program cost and benefits) ex post. The programs generally provide a

mix of customer information and subsidies to customers who choose to

participate. Individual customers who seek to participate in a program will

* In some states, e.g. California, Massachusetts, and New York, regulators

require that the utility costs used in this test include additional, imputed

costs that are intended to represent the environmental externalities associated

with electric generation.

9 Utility programs also often include "low-income" and other redistributive

measures that are not always designed to be economical according to this

criterion.
10

frequently be audited for qualification given estimates of their individual

use characteristics and the likely effects of the measure on their electricity

use and the associated electricity costs saved.
b. A Framework for Calculating the Cost of Conserved Electricity

As we will discuss in Section IV, our initial calculations of the real

cost per kWh saved for different conservation measures and programs will be

based on data reported by utilities regarding utility and customer costs and

energy savings. These data are of uneven and often uncertain quality; in many

cases, they are also incomplete. In this section we present a framework for

computing the real cost per kWh saved that incorporates all of the relevant

cost and savings variables. Later sections will then discuss how various data

problems affect our ability to apply this framework to the available utility

data and the resulting implications for the interpretation of the numbers

reported by utilities.

The computational framework is quite simple. We seek to calculate the

real life-cycle cost per kWh saved (the cost of a "negawatthour") for utility

conservation programs. To do so we require information on the following

variables:

I - The incremental "total resource cost" of the utility conservation

program. This cost includes all direct and indirect costs

incurred by both the utility and participating customers in

connection with the conservation program. These costs include the

incremental costs of the conservation measures themselves, a

variety of administrative costs incurred by the utility and

transactions costs incurred by customers. The utility's

administrative costs include costs associated with promoting

conservation, delivering conservation measures, and monitoring and

evaluating the results of its efforts.

E - The annual kWh savings that result from customers participating in

the conservation program.

r - The real discount rate. 10

10 In what follows we use each utility's real after tax cost of capital as

the discount rate for its programs. This follows the typical practice of

utilities and their state utility commissions for evaluating the costs and

benefits of utility conservation programs. However, it is not at all obvious

(continued...)
12

L - The expected economic life of the conservation investments.

With this information in hand we can compute the real levelized cost of

the program (R), defined as the constant real stream of rental payments that,

over the life of the conservation measure, is equal to the incremental cost of

the conservation investment (I):

(1) I - PDV(R; r, L)

The real life-cycle cost per kWh saved (c), our ultimate objective, is then

given by:

(2) c - R/E - cost per kWh saved

As we will discuss in subsequent sections, accounting for all of the

relevant costs that should be included in I, estimating the energy savings

achieved by the conservation programs (E), and defining appropriate lives for

the conservation investments (L) can be quite difficult. Utilities handle

these difficulties in a variety of different ways.

c. An Introduction to The Free Rider Issue

In implementing the calculations described above, a number of important

issues arise in determining what costs and energy savings to include in the

analysis. One of the most important of these issues, the so-called "free

rider" problem, derives from the fact that at least some consumers

participating in a utility program would, in fact, adopt economical

conservation options without the utility's efforts. After all, if the cost of

1O(...continued)

that this is the correct discount rate to use for these types of projects. For

most utilities the real discount rate is in the 4% to 5% range. These values are

much lower than the real discount rates used by non-regulated firms or by most

residential customers (for estimates of the latter, see Hausman (1979) and

Hartman and Doane (1986)).
13

a negawatthour is anything like what Lovins and EPRI claim it is, many of the

conservation opportunities targeted by utilities should be economical for

customers even without any subsidies. As a result utilities are likely to be

spending some money to get customers to do what they would have done anyway

(now or in the future). These program participants are "free riders".

Energy savings and cost figures are often reported by utilities without

any adjustment for the impact of free riders. In evaluating the economics of

utility involvement in energy conservatior the net cost of conserved energy,

after appropriate adjustments for free rider effects, is the proper focus of

attention since it measures the actual change in resource use that results

from utility activities (in other words, it measures the marginal impact of

the conservation activities). Since most utilities do not provide enough

information to calculate the net cost of conserved energy properly adjusted

for free riders, we first report costs per kWh saved without free rider

adjustments. 11 It can be easily shown that this underestimates the true cost

of utility conservation programs. We offer a preliminary assessment of the

potential magnitude of this bias in Section VIII.

III. LOVINS AND EPRI ESTIMATES OF THE COST OF CONSERVED ENERGY

Before discussing the results of our analysis of utility conservation

programs, it is useful to discuss in greater detail the Lovins and EPRI

estimates of the cost per kWh saved. The energy conservation "supply" curves

(i.e., the curves relating the cost of conserved electricity to the quantity

saved) estimated by Lovins and EPRI are displayed in Figure 1. They reflect

11 In some cases, this calculation required that we "back out" free rider

adjustments already made by utilities. In these cases the free rider adjustments

were typically incorrect for our purposes anyway.
14

their respective estimates of the real incremental "societal" cost per kWh

saved associated with adopting more efficient energy conservation measures in

a wide variety of applications. If we draw a horizontal line in at the

average price of 7 cents per kWh, we can get a very rough sense for the

magnitude of the opportunities for cost-effective conservation implied by

these estimates.

Both sets of estimates embody explicit and implicit assumptions about

the relevant cost and operating characteristic variables discussed in the

previous section. We focus on only a few of these assumptions here. First,

the numbers reported by both Lovins and EPRI are best characterized as

estimates of "technical potential". Both sets of estimates reflect the

assumption that the entire stock of each of the appliance/equipment items

covered is comDletely replaced by the most efficient technology that is

"available". Both sets of estimates also ignore the opportunity cost of

scrapping existing appliances and equipment before they have reached the end

of their economic lives. Alternatively, they represent a sort of long run

equilibrium that could be thought of as emerging after the existing stock of

equipment is replaced; in this view, it must be emphasized that the curves

conceal a very important temporal dimension. They also apparently rely on

engineering estimates of equipment lives rather than estimates of economic

lives. The two may be very different, especially if there is rapid costreducing or quality-improving technical change associated with specific

conservation measures.

Second, Lovins' numbers reflect only his estimates of the incremental

costs of the relevant best practice technology; they do not include the

program costs a utility would incur in promoting conservation and monitoring
15

and evaluating the results. The latter, as we shall see, are often quite

substantial. Thus, Lovins' numbers must, at best, be treated as an optimistic

lower bound on the cost of utility-sponsored electricity conservation. In

contrast, the EPRI estimates build in a "retail mark-up" to reflect the fact

that utilities do incur costs beyond compensating customers for the purchase

of efficient appliances and equipment. This margin is somewhat arbitrary and

is based on limited utility experience. Neither set of estimates deals with

the impact of free riders.

In examining the Lovins and EPRI cost estimates depicted in Figure 1,

the most striking feature is the large disparity between the two curves. As

noted earlier, the Lovins figures imply savings of about 70% of U.S.

electricity demand at roughly 0.6 cents per kWh saved, while the EPRI figures

imply savings of almost 30% at roughly 2.6 cents per kWh saved (this figure

assumes that only the programs up through number 13 are implemented; the steep

part of the "supply" curve is omitted). A number of other features of the

graphs deserve comment as well, however.

In the graph of Lovins' estimates, it is noteworthy that the lighting

efficiency improvements are available at a negative gross cost. In other

words, the lighting improvements would save money for consumers even before

any electrical savings are taken into account. This is possible because

Lovins assumes that improved lighting systems, based on long-lived fluorescent

bulbs, will last sufficiently long as to avoid future maintenance and

replacement costs that more than exceed the incremental cost of the lighting

upgrade. If this is true, it indicates that consumers and firms are wasting

more than just electricity in their lighting design. Alternatively, it may be

taken as a signal of the great optimism implicit in the Lovins estimates. It
16

should be noted, however, that while Lovins has thus tried to account for one

effect that may reduce customer costs associated with electricity-using

equipment, he, like other analysts, still ignores other costs, in particular

transactions costs, that participants in conservation programs may also incur.

In the EPRI graph, the most striking feature is its essentially vertical

orientation after an efficiency improvement of about 30%. If this is true, it

implies that the amount of cost-effective electric conservation available in

the economy is essentially invariant for any cost of electricity above about 4

cents per kWh. We are skeptical that the actual cost of conservation exhibits

such an extreme degree of decreasing returns.
17

IV. WHAT DOES UTILITY DATA TELL US ABOUT THE COST OF A NEGAWATTHOUR?

It came as no surprise to us that it was not an easy task to transform

the information reported by utilities to their state regulatory commissions

into a set of comparable costs per kWh of electricity saved resulting from

these programs. Despite increasing utility involvement in conservation

activities, there does not yet exist any standardized method for reporting the

relevant data. Indeed, in most cases utilities do not report all of the

information necessary to determine the true economic costs of their

conservation programs. Much of what is reported is an mix of historical

experience and projections of future performance. Furthermore, the

assumptions and justifications underlying the information that is provided are

often lacking or of questionable validity. What does a negawatt really cost?

The honest answer is that neither we nor anyone else really knows with any

precision! We do know that it costs more than is suggested by Lovins and

EPRI, has a higher variance than implied by their analyses, and is understated

in the reports issued by most of the utilities whose programs we have

examined.

Given these data limitations, the approach we have taken is to do the

best that we can with the data that are available, relying as much as possible

on the assumptions made and data reported by the utilities. 12 After

computing and discussing the apparent cost per kWh of the electricity that

utilities claim has been saved by their programs, we will then go on to

discuss missing data and underlying assumptions to better understand what, if

12 In some cases, we have made some minor adjustments to the reported

utility figures. For example, one utility operated similar programs in

consecutive years, but reported customer costs only in the second year. These

costs have been used to impute customer costs to the programs in the previous

year.
18

any, biases are contained in the results and to provide guidance for better

data reporting, monitoring, and evaluation methods.

Before proceeding to the presentation of our initial computations of the

cost per kWh saved revealed by utility program experience and expectations, we

ask the reader to keep a number of caveats in mind. First, many utilities do

not report all of the utility costs and direct customer costs that should be

included in the incremental cost of the affected conservation measures ((I) in

the earlier section). Second, energy savings (E) are frequently based

primarily on engineering estimates rather than adjusted by ex post measurement

of actual changes in consumer behavior. Third, utilities use a wide range of

assumptions for the lives of what would appear to be roughly identical

conservation investments. Finally, the data reported contain a mix of

information based on historical experience and projections of expected future

performance.

Table 1 reports the results of our efforts to use the information

reported by the utilities to compute the total societal cost per kWh saved

resulting from their conservation programs. For the reasons discussed

earlier, the reported costs per kWh saved have, to the extent possible,

excluded adjustments for free riders and, as a result, are biased downward.

(As we shall see, the values are biased downward for other reasons as well.)

The results are reported separately for residential programs and

commercial/industrial programs along with the weighted average cost per kWh

saved for each utility's entire program. In each case the costs reported

represent the weighted average of the individual program and measure costs in
DISCUSSION DRAFT TABLE 1

COST PER KWH SAVED: SUMMARY OF RESULTS

(1991 CoeatIKwh)

UTIIuTY WEIGHTEDPRO AM RSIDNTIAL COMMRCIUINDUSTRIAL AVERAOB COST ACCOUNTING MEASUR

Average RBas _ Averagi p

1 7.6 3.4 -21.6 6.7 3

2 4.9 2.0 - 22.3 3.5

.1

Rueae All Utility? All Direct Customer?0-10.3 6.9 Most No No0- 17.1 3.8 No No Some

3 10.4 3.0 - 15.4 1.9 0.8 - 8.0 2.8 Yes Yes Some

4 4.7 0.6 - 33.3 N/A 1.7 - 5.9 N/A No Yes No

SA 22.1 5.2 - 181.4 3.1 2.2 - 9.6 3.3 No No Yes

SB 6.8 3.9 - 9.2 4.4 3.7 - 4.6 4.8 No No No

5BI 7.6 3.9 - 20.2 6.4 3.1 - 7.3 6.6 No No Yes

6 12.4 2.9 - 14.1 1.5 0.2 - 4.7 1.9 No Yes No7 4.8 0.4 - 68.8 1.9 13 - 4.9 2.2 No No No

8A 7.2 3.2 - 22.1 2.4 1.4 - 18.1 3.0 No No No8B 4.4 2.1 - 12.6 3.6 2.6 - 53 3.7 No Yes No

9 8.0 4.7 - 163.9 3.5 1.8 - 18.8 4.5 Most Yes No

10 3.5 0.3 - 5.5 2.0 1.9 - 2.4 3.0 Yes No No1 1 ------ -- Imdequte Ilformatiom To Compute Cost/Kwh Saved-------12 ------ l-- dadequate lmformatiom to Compute Cost/Kwb Saved-------13 ----------- No Resposse To Repeated Requests------------

HPRI 3.0 0.9 - 3.6 2.3 0.7 - 3.9 2.5 No Yes No

LOVINS < 2.0 -1.8 - 4.0 <0.6 -1.8 - 1.0 0.6 No Yes No

NES 2.9 2.0 - 3.9 N/A N/A N/A No Yes No

RETAIL PRICB

1990 8.1 5.1 6.9
these groups. 13 We also report the range of costs experienced in the subprograms that make up each category. In the two cases for which such data

were made available, we report information for two separate years.

For comparison purposes we also report, where available, comparable

numbers reported by EPRI and Lovins, as well as figures reported for a set of

residential applications considered in the Bush Administration's National

Energy Strategy (NES) (Energy Information Administration 1990).

Unfortunately, Lovins' figures are not broken down conveniently between

residential and commercial/industrial applications, so we only have rough

upper bounds to rely on plus the average for all of the applications that he

identifies. Average U.S. retail electricity prices for residential and large

commercial/industrial customers are also included for comparison.

Finally, Table 1 also provides information about whether or not a

utility has accounted for all relevant utility and direct customer costs that

are properly included in the total societal cost of the conservation

investments and whether or not the electricity savings estimates for the

programs are based on ex post measurement of consumer behavior.

The values reported in Table 1 imply that the estimates of the costs per

kWh saved by utility conservation programs reported by EPRI, Lovins, and the

NES are significantly lower than what utilities have achieved or expect to

achieve in practice. The difference is most striking for residential

programs. Every single residential program has an average cost per kWh saved

13 As noted previously, our concern is with the net present value cost of

these programs. Because of the non-linearities involved in discounting, it turns

out that the correct weights to use in calculating weighted average program costs

are the net present values of the kWh saved in each program. While the idea of

discounting kWh, r ^I(

+' an dollars, may at first appear odd, it is, in fact, a standard tool in

calculating the costs of utility conservation programs.
20

that exceeds the values reported by EPRI, Lovins, and the NES. In some cases

the difference is extremely large. Furthermore, there is significant intrautility variation in the cost per kWh saved across individual sub-programs.

Almost every utility has stumbled into at least one conservation effort that

costs many times more than predicted by EPRI, Lovins, and the NES.

The results for the commercial and industrial programs are closer to the

estimates offered by EPRI, although half of the utility programs cost 50% more

than projected by EPRI. Lovins' numbers are too low by a factor of two to ten

reflecting, in part, their failure to recognize that, in addition to measure

costs, utilities also incur costs to implement and evaluate conservation

programs. Again, several utilities have stumbled into commercial/industrial

programs that costs substantially more than the high end of the range reported

by EPRI.

The figures for the commercial and industrial programs should also be

compared to the results of a study by Nadel (1990a; see also 1990b) that

surveyed conservation and load management programs at 58 utilities, primarily

as of year-end 1988. Nadel found that most commercial and industrial

conservation programs had costs of less than 4 cents per kWh, and that a

significant number were around 1 cent per kWh. While there are significant

differences between his approach and that employed here (he does not include

customer costs and he uses his own lifetime estimates rather than those of the

utilities, among other differences), our results are clearly in the same

ballpark. Some utilities do indeed report program costs in the 1 cent per kWh

range, while many report them in the 3 to 4 cent range.

While many of these programs thus appear to be economical, it is clear

that the Lovins and EPRI numbers significantly underestimate what it actually
21

costs utilities to "buy negawatts". As we shall see, since many utilities

have not accounted for all relevant costs, the actual difference between the

costs per kWh saved resulting from utility programs and the numbers reported

by EPRI, Lovins, and the NES is likely to be even larger than Table 1

indicates. Indeed, the cost per kWh saved may be understated by a factor of

two or more on average.

Another striking feature of the figures in Table 1 is the consistent and

significant disparity in the cost per kWh saved between the residential

programs and the commercial/industrial programs. Without exception, the

utilities report that their residential programs cost significantly more, per

kWh saved, than their commercial and industrial programs. This result is

consistent with the EPRI estimates, in which residential programs are

estimated to cost almost twice as much as commercial/industrial programs.

While this finding indicates that commercial/industrial programs may

have a significant edge over their residential counterparts in terms of cost

per kWh saved, it is also true that commercial/industrial programs are

expected to have a much larger aggregate impact on electricity consumption.

As shown in Table 2, for the utilities we surveyed, residential conservation

programs make up only about 20Z of projected energy savings (and only about

30% of net present value expenditures);"1 the remaining 80% of energy (and

70% of costs) derive from the commercial/industrial programs. This finding

differs sharply from the EPRI analysis, in which some 32Z of energy savings

(and 381 of costs) derive from residential conservation.

14 These figures would be significantly lower, on the order of 131 of energy

from the residential sector, were it not for the one outlier, Utility 10, at

which residential energy is 68% of the total. Utility 10 is the only utility in

our sample at which expected residential energy savings exceed those of the

commercial/industrial sector.
DISCUSSION DRAFT

TABLE 2

SECTORAL DISTRIBUTION OF COSTS AND ENERGY SAVINGS

NPV COSTS NPV ENERGY SAVE

Utility Program Residential C&I Residential C&I

1 21% 79% 19% 81%

2 28% 74% 20% 80%3 36% 64% 10% 90%4 NIA N/A N/A N/A

5A 5% 95% 1% 99%5B 22% 78% 15% 85%

5B1 22% 78% 19% 81%

6 25% 75% 4% 96%7 22% 79% 10% 90%

8A 32% 69% 13% 87%8B 19% 81% 16% 84%

9 43% 58% 24% 76%

10 79% 21% 68% 32%

WeightedAverage I 30% 70% 20% 80%

EPRI 38% 62% 32% 68%
22

Given these findings with regard to both cost per kWh saved and total

kWh saved, the utility data appear to indicate that the potential for large

economic and environmental benefits from energy conservation lies not in the

residential sector, but in the commercial and industrial sectors.

It is also clear from the results in Table 1 that there is significant

variability in costs per kWh saved among the various utilities. These

differences can be traced to several factors. First, and of great importance,

are the significant differences in cost accounting and energy savings

estimation that exist among the utilities. As Table 1 indicates, only two of

the utilities account for all of the relevant utility and direct customer

costs in their reports. 15 Cost accounting and energy savings estimation

issues will be examined more closely in Sections V and VI, respectively.

Second, there are, perhaps, systematic differences among the energy markets

within which the utilities operate. For example Utility 10 has much lower

electric rates than does Utility 1 and a much greater penetration of electric

space heating. Third, the values reported in Table 1 are based on measure

lives and energy savings estimates reported by the utilities. As we shall see

presently, there is sometimes significant variation among utilities in assumed

measure lives for what are roughly the same pieces of equipment. These

differences are difficult to understand, but they can have very large

implications for the cost per kWh saved. Only three of the utilities base

their savings estimates on ex nost measurement techniques. Finally, there are

real differences between the program selection, marketing, and delivery

15 We are only in a position to determine whether or not the utilities made

an effort to account for all of the relevant costs by including an appropriate

entry in their reports. We cannot determine whether they have actually measured

the relevant costs accurately.
techniques used by the utilities.

In addition to examining the sectoral and aggregate costs reported by

utilities, we have also examined the costs incurred at a more disaggregate

level for a set of specific conservation initiatives that are common to many

of the utilities. Table 3 reports the results for six types of programs that

are commonly pursued by utilities: Residential Appliances (Refrigerators and

Freezers), Residential Lighting, New Residential Construction, Commercial and

Industrial Lighting, Commercial and Industrial Motors, and Commercial and

Industrial New Construction.

It is important to understand that utilities generally package various

measures together so that they can conserve on auditing and marketing costs

and capture all of the significant electricity conservation opportunities

located in an establishment. As a result, most conservation measures are

delivered as part of conservation packages, rather than as single elements

(this is particularly true of some of the New Construction programs which

target all electricity-using equipment in a new building). This makes direct

comparison of the utility costs with the EPRI and Lovins estimates somewhat

tricky, since these latter are estimated at the level of specific devices or

end-uses. This is even true for the categories, such as appliances, lighting,

and motors, that correspond fairly well with measure categories identified by

EPRI and Lovins as potential sources of large savings. The same caveat also

applies, although to a lesser extent, to comparing the costs of particular

program types across utilities. Utilities structure their programs

differently, and thus programs in the same end-use category may have

significantly different costs and performance.

The figures reported in Table 3 parallel those reported in Table 1. For
DISCUSSION DRAFT TABLE 3

COST PER KWH SAVED: SELECTED PROGRAMS

(1991 cents/kWh)

UTIITY REFRIG/ RBSID RESID NEW EFFICIENT COMMER COMMER NEW

PROGRAM ELBBBR IJOHUnNG CONSTRUCTION MOTORS ~ CSTRUCTION

1 10.5 6.2 21.6 N/A N/A 10.3

2 WA 2.7 22.3 1.V17.1 1.8 2.7

3 WfA NWA NA 1.3 0.8 N/A

4 4.8 5.0 3.6/6.6 1.7/2.0 5.1/5.9 2.0

5A 181.5 20.8 N/A N/A 2.3/10.0 2.25B 4.4 8.6/9.2 5.3 N/A 4.5 3.75C 4.4 9.2/20.2 5.3 N/A 7.1 3.1

6 8.1 2.9 2.9 1.2/2.8 1.5 N/A7 68.8 16.7 15.1 N/A 1.4 3.3

8A N/A 19.4 N/A N/A 1.4 18.08B N/A 5.5 12.5 N/A 3.7 2.6

9 3.9/11.0 4.9 163.9 3.5/4.1 2.5 18.8

10 N/A 0.8 1.6 - 3.5 N/A 2.2 2.4

BPRI 3.3 0.9 N/A 2.7 1.4 N/A

LOVINS mNA <0.0 NIA <1.0 <0.0 N/A

NES 3.9 N/A N/A N/A N/A N/A
24

residential applications, the EPRI, NES, and Lovins numbers significantly

underestimate the actual costs reported by utilities. For commercial and

industrial programs the EPRI numbers are closer to and fall within the range

of values computed for utility programs. Lovins' numbers, on the other hand,

are low by a very significant margin. We should recall as well, that in most

cases the values reported in Table 3 do not include all relevant utility and

direct customer costs and, as a result, tend to be underestimates. It should

be clear, however, that despite the fact that these programs do not live up to

Lovins' expectations, in many cases utilities are successful in promoting

efficient motors, efficient commercial lighting, and the construction of new

energy efficient buildings at what appear to be very low costs relative to the

price and cost of the electricity that is saved even if we allow for

significant under reporting of costs.

The residential programs are clearly more problematical. Several of

these programs do not appear to be economical or, at best, are marginally

economical. Indeed, some of the values reported are astronomical. The

figures for utility refrigerator and freezer conservation programs are

noteworthy both for their variability, and, in two cases, for their sheer

magnitude.16 Further, the reported costs are underestimates of the

appropriate cost per kWh saved since customer costs are not included.

One reason why residential programs fare so poorly is that some

components of these programs are designed to achieve distributional goals

rather than cost-effective energy efficiency goals. It has become common for

"These two cases are both appliance labeling programs. The results show

that such programs are simply not appropriate for most utilities. They should

be implemented over a much larger geographic area to take advantage of the

economies of scale inherent in such programs.
25

utilities to be required to implement programs that are specifically designed

to improve the electric efficiency of low-income households. From an

efficiency perspective, it is often argued that such households are

particularly likely to suffer from market imperfections and thus may provide

one source of substantial conservation possibilities. In addition, it is also

argued that improving the electric efficiency of low-income households is an

appropriate method for achieving distributional goals.

In a number of cases, utilities plan and evaluate their low-income

programs on a different basis from that used to analyze programs designed to

be cost-effective. In particular, a number of utilities do not use a strict

cost-benefit test to determine whether low-income programs should be pursued.

As a result, such programs are likely to produce improvements in electricity

efficiency at a higher cost than would programs that are designed to meet a

strict cost-effectiveness test. As can be seen from Table 3A, there is some

tendency for low-income programs to be more expensive than other residential

programs. However, this is far from a universal result. In any case, it is

clear that the low-income programs are by no means the sole reason that

residential programs are so costly.

In Table 3 we also continue to observe substantial variation in the cost

per kWh saved even for very similar sub-programs. Thus, the variations we

observed in Table 1 are likely to be primarily a consequence of the factors

identified above (cost accounting, measure lives, savings measurement, program

implementation), rather than a result of differences in the composition of

individual utility programs.
DISCUSSION DRAFT

TABLE 3A

LOW-INCOME RESIDENTIAL PROGRAMS

(1991 cents/kWh)

Utility Low-Income Other Residential Residential

Program Programs Programs Total

1 6.3 8.8 7.6

2 9.5 4.4 4.9

3 N/A 10.4 10.4

4 5.4 4.5 4.7

5A 20.8 30.4 22.1

5B 8.6 6.5 6.8

5B1 20.2 6.4 7.6

6 2.9 12.7 12.4

7 46.0 3.6 4.8

8A 22.1 7.0 7.2

8B 4.2 4.4 4.4

9 11.6 5.7 8.0

10 1 1.6 3.6 3.6
V. UTILITY COST ACCOUNTING

The results reported in Table 1 and Table 3 represent the real cost of a

kWh expected to be saved by utility conservation programs based on the

information made available by utilities to their state regulatory agencies.

In reviewing the utility information in the course of calculating the numbers

reported in these tables, it became clear to us that all of the cost

information necessary to calculate properly the total resource cost of these

programs was frequently not reported, or was reported in ways that were not

useful for our purposes. Further, utility cost and energy accounting

procedures differ significantly from one another. As a result, some of the

variation in the values reported in Tables 1 and 3 reflects the fact that some

utilities neglect to include more of the relevant costs than do others. In

order to understand some of the infirmities in utility cost accounting

procedures, it is necessary to explore in more detail exactly what costs are

relevant for computing properly the cost per kWh saved by utility conservation

programs.

As we discussed earlier, the costs reported in Table 1 depend on four

key variables: the aggregate utility and customer costs incurred to achieve

the conservation savings (I), the economic lives of the associated investments

(L), the energy saved by these investments (E), and the real discount rate

(r).'7 In this section we will explore the appropriate components of the

aggregate utility and customer costs (I).

The figures used by utilities for (I) should properly include the

following elements:

17 The real discount rates used by utilities are within a sufficiently

narrow range that we will not discuss discount rate issues in this paper.
Kd - The total direct installed cost of a specified conservation

measure. This cost will be shared in some way between the

utility, the consumer, and, in some cases, third parties'8 .

Ka - Total direct costs incurred by the utility to implement specific

conservation subprograms over and above direct measure costs.

These costs include advertising, marketing, and other promotion,

as well as monitoring and evaluation costs. Since the latter such

costs often occur a number of years after installation, this

figure should be interpreted as including the net present value of

the relevant stream of such costs.

Ko - An appropriate share of those administrative "overhead" costs

allocable to a utility's conservation activities. These costs

include office space and administrative support, conservation

planning, executive and legal costs, and some portion of

regulatory preparation costs.19

Ad - The total direct installed cost of the energy-using equipment that

a customer would otherwise install in the absence of the utility

program.

0 - The net present value change in other costs resulting from the

adoption of the more efficient device.20

The relevant incremental resource cost (I) of a utility conservation program

is then given by:

(3) I- Kd + Ka + K + o - Ad

The foregoing definitions assume that the electricity customer faces a

"I A typical third-party bearing some of the costs would be a public housing

authority.

"gThese are administrative costs that cannot easily to assigned to specific

subprograms within the portfolio that makes up the utility's overall conservation

program.

20 Typical examples include incidental savings of natural gas and heating

oil that occur in houses that receive building shell improvements and the reduced

maintenance costs that may sometimes be associated with more energy efficient

appliances and equipment. Any change in the end-use satisfaction (e.g., reduced

lighting) could also be included here.
28

choice between purchasing a standard device and a more efficient device having

the same expected economic life. In fact, in a number of cases, in particular

retrofit applications, the real economic decision is between operating an

existing device (e.g., an old motor), or scrapping it in favor of a new

efficient one. In such cases, it is necessary to impute a remaining scrappage

value to the retired equipment. This value would add to the cost of the

program. While such costs should be included in practice, they are

particularly difficult to estimate and will be ignored in this paper.2 1 For

the remainder of the paper, we will also ignore the other cost category (0),

as this makes up a very small portion of costs reported by utilities (of

course, like scrappage costs, this category might turn out to be significant

if we could track it carefully).

Table 4 summarizes some of the significant attributes of the information

regarding costs and energy savings that are reflected in the data that we used

to compute the cost per kWh saved figures reported in Table 1. Of particular

interest to us in this section are the categories of significant costs that

are not reported by various utilities; in Section VI we will focus on the

methods used to estimate and verify energy savings.

a. Missing Utility Costs

It is evident from Table 4 that utilities frequently do not report all

of the costs that they have, or will, incur as a result of their conservation

programs. The costs associated with direct marketing and installation of

21 Of the utilities in our survey, only one appears to have adjusted its

cost figures for this issue. In its motor rebate program evaluation, Utility 6

analyzed cases in which customers replace a failed motor or an operating one.

Replacement of an operating motor was assumed to cost significantly more.
cA
29

program measures, including customer incentive payments and some degree of

auditing, are generally reported by the utilities. However, many types of

administrative costs, including the future measurement and evaluation of

conservation savings, are not universally tracked and reported by the

utilities. The exclusion of such costs can lead to significant underestimates

of the cost per kwh saved resulting from conservation investments.

Two cost items tend repeatedly to be ignored by utilities. These items

are measurement/evaluation costs and various administrative and overhead costs

that would not be incurred in the absence of the utility conservation

programs. Given the difficulty of accurately measuring the electricity

savings resulting from utility conservation programs (see Section VI),

measurement and evaluation activities are likely to become significant cost

elements in utility conservation programs. As yet, not all utilities have

recognized these costs in their planning. Administrative and overhead costs

include everything from the opportunity cost of office space used by the

conservation staff and associated building services to planning, legal, and

executive resources used to design programs, monitor their implementation, and

defend them in regulatory proceedings to the extent that these costs would not

be incurred in the absence of one or more conservation programs. With only

three clear exceptions, these administrative and overhead costs were not

clearly allocated by utilities to their conservation program costs. Because

of these exclusions, it is likely that the utility portion of the costs used

in developing Tables 1 and 3 are understated for some utilities. Thus, the

actual costs of conservation are likely to be higher than is reported in these

tables.

In order to get a better feeling for the potential magnitude of the
30

missing costs it is useful to divide costs reported by utilities into two

categories: direct expenditures on conservation measures by both the utility

and customers (Kd - Ad) and implementation, monitoring, measurement, and

administrative costs (K, + Ko), which we will refer to simply as

administrative costs for simplicity in what follows. In Table 5, we report

estimates of the ratio of administrative costs to measure costs as reported by

seven utilities in our sample, as well as for EPRI ([K, + Ko]/[Kd - Ad]). The

costs reported by other utilities could not be disaggregated into direct

measure costs and administrative cost components.

The figures display many of the same characteristics as the cost data

reported in Table 1. In particular, they display a significant degree of

variability between utilities, especially for residential programs. In

addition, the administrative cost fractions in the residential sector are with

only one exception higher than the corresponding fractions in the

commercial/industrial sector, in contrast to EPRI's assumptions.

In interpreting these figures, two caveats should be kept in mind.

First, some utilities, as will be discussed in greater detail below, do not

report the full customer portion of direct measure costs. To the extent that

this occurs, the figures in Table 5 will overstate administrative costs as a

fraction of direct measure costs. On the other hand, as just discussed, not

all utilities report all the administrative costs that ought to be allocated

to conservation programs. To the extent that this is true, the figures

reported in Table 5 will be underestimates of the administrative to direct

cost ratio.

As it turns out, two of the utilities with the best - i.e., most

complete - customer cost data (utilities 3 and 9) have the two highest overall
DISCUSSION DRAFT

TABLE 5

REPORTED ADMINISTRATIVE COSTS

AS A FRACTION OF

DIRECT MEASURE COSTS

Utility Program Residential C&I TOTAL

1 48% 42% 44%

2 12% 5% 7%

3 261% 31% 70%

4 30% 30% 30%

5A - - -

5B - - -

5B1 - - -

6 11% 11% 11%

7 -

8A

8B - - -

9 480% 40% 107%

10 29% 48% 33%

EPRI 20% 27% 25%

LOVINS 0 0 0

NES 0 N/A N/A
31

ratios of administrative to direct measure costs. The low ratios of utilities

2 and 6, on the other hand, may well be due to incomplete tracking of utility

administrative costs. The EPRI figures and those reported for utility 4 are

merely projections and do not reflect actual program activity. Both are based

on a study by Berry (1989) in which administrative costs for commercial and

residential programs were estimated to be in the 20% to 30% range, based on a

review of the experience of three utilities. Based on our results, a 30%

administrative cost fraction appears to be at the low end of a reasonable

range for the commercial/industrial programs. While the data are far from

conclusive, it would appear that such a figure would understate, perhaps by a

great deal, the administrative cost burden of residential programs,

however.22

Taken together, these facts indicate that administrative to direct cost

ratios are likely significantly higher than assumed by EPRI in its cost

projections. In the commercial/industrial sector there is ample evidence of

administrative to direct measure cost ratios of 30% to 50%, while in the

residential sector, ratios are likely to be even higher. Moreover, it should

be emphasized that for certain subprograms, administrative costs may make up

an extremely large share of total costs. For such programs, the exclusion of

administrative costs in cost analysis would be particularly misleading.

22In his study, Nadel (1990a) found that, for 46 utilities reporting such

costs, "indirect" costs (what we have called administrative costs here) averaged

36% of utility direct measure costs for commercial/industrial programs. Since

this study excluded consideration of customer costs, the implied value for the

administrative to direct cost ratio is some lower value. Our results indicate

that a ratio of the same magnitude (30% to 40% or more) is more likely to be at

the low end of the appropriate range when applied to total direct measure costs.
b. Missing Customer Costs

Customer costs fall into three general categories. The first includes

the direct incremental costs associated with purchasing and installing the

various conservation measures. In some cases the utility pays the full

incremental cost of the measures, but in other cases the customers, or a

third-party, pay a share of these costs. The second category includes the

transactions costs borne by customers. The latter costs include time spent

shopping, with energy auditors, and dealing with those who install the

measures (e.g. waiting at home for the electrician to show up and keeping your

eye on him to make sure he doesn't steal the silver). They also include the

value of lost business and inconvenience incurred while the measures are being

installed. Transactions costs are real costs that should in principle be

taken into account when calculating the cost of conservation measures. In an

ordinary market context, of course, it would be the individual consumer that

would take these transactions costs into account when making purchasing and

investment decisions. The third category, closely related to the first,

includes the value of any existing devices that may need to be scrapped in

order to participate in the utility program. These may include the usage

value of a second refrigerator in an appliance turn-in program or the

undepreciated value of an operating motor that is scrapped in order to take

advantage of a utility rebate program. If these costs are not reported by

utilities then the costs per kWh reported in Tables 1 and 3 will be

understated. We examine the accounting treatment of each category of costs in

turn.

In a typical program, utility incentive payments made to customers for

the installation of efficient devices do not cover all of the direct
33

incremental costs (I) of the conservation measures. Thus, the utility and the

customer share in the cost of improved efficiency. As a result customer costs

must be added to utility costs in order to determine the total resource cost

of the conservation programs.23 Unfortunately, few utilities perform

adequate tracking of the costs incurred by participants in their programs. In

our sample of utilities, only four appeared to track customer costs in a

rigorous manner. Even in these cases, moreover, the utility was sometimes

unable to track costs in certain programs.

When a utility is subsidizing a conservation investment in order to

alter customer behavior, it is important to know whether the subsidies are

serving partially to compensate consumers for bearing transactions costs. If

there are no customer transactions costs then utility subsidies can simply be

treated as a transfer payment which merely determines who pays what fraction

of Kd - Ad. However, if the subsidies are compensating consumers for bearing

transactions costs they would not have otherwise borne, then the portion of

the subsidy that is compensation should be treated as an additional cost. We

do not make this calculation here, but merely notes its relevance.

None of the programs attempt to measure customer transactions costs.

This is not surprising since such costs would be very difficult, if not

impossible, to measure. Yet customer transactions costs are very real

economic costs that should in principle be accounted for in evaluating the

societal cost of utility conservation programs (as anyone who has stayed home

all day waiting for the electrician to show up for a 9 AM appointment will

understand).

23 There is no double counting here. If the customer makes the conservation

investment and is then provided with a subsidy by the utility, the customer cost

is computed by subtracting the incentive payment from the total measure cost.
34

With the one exception noted previously, none of the programs that we

reviewed take account of the remaining life of existing appliances and

equipment. For retrofit applications this failure could lead to a significant

underestimate of the incremental cost of conservation since it assumes that

the customer would have purchased a new less efficient piece of equipment

immediately rather than, say, five years from now. While the failure to take

customer replacement decisions into account tends to bias the estimates of the

cost of conserved energy downward, this is not as serious a failure as first

meets the eye. When utilities compute the benefits associated with the

conservation investment they also assume that the customer would have replaced

his existing equipment with a new model immediately. To the extent that the

model the customer is assumed to acquire absent utility subsidies is more

energy efficient than the existing equipment, the failure to take remaining

lives into account will also overestimate the energy saved by the adoption of

more energy efficient conservation measures.

c. Summary of Cost Bias Results

All of the cost biases in utility accounting for the costs of

conservation appear to point one way. As a result, the costs per kWh reported

in Tables 1 and 3 understate the true costs of these programs, perhaps very

substantially. Without seeking to diminish the importance of this result,

which appears unassailable, we should note one (partially) mitigating factor.

Many (though by no means all) of the energy conservation programs are still in

their early years. There are undoubtedly start-up costs and inefficiencies in

implementation, associated with the creation of such programs; these will tend

to show up in our cost estimates, but may not continue as programs mature and
35

benefit from learning by doing.
VI. UTILITY ESTIMATES OF ENERGY SAVED

The real costs per kWh saved reported in Tables 1 and 3 are obviously

quite sensitive to the underlying estimates of energy savings. Most serious

analysts recognize that it is quite difficult to measure accurately the energy

savings resulting from utility conservation efforts. These difficulties arise

because of the diversity in customer utilization patterns, changes in these

utilization patterns over time, the limited information a utility has about

both the base level of and changes in the utilization of individual

participants, differences in characteristics between participants and the

population upon which "typical customer" utilization data are based, changes

in behavior induced by conservation, etc.

In some cases it is possible to obtain good savings estimates by using

statistical methods to compare utilization patterns of participating customers

with those of similar non-participating customers. Such an approach requires,

however, the careful identification of appropriate control groups, collection

of data on all relevant customer characteristics, and careful monitoring of

consumption and changes in customer characteristics for the treatment and

control groups over a sufficient period of time to capture all relevant

behavioral changes. In other applications, especially when there are

significant idiosyncratic customer specific characteristics, it may be very

difficult to make accurate measurements of savings. What is clear is that

measurement of savings requires careful thought, extensive data collection,

careful analysis, time, and (probably) a lot of money.24

24 For a much more detailed discussion of the issues that arise in

conservation program evaluation, the reader is referred to a recent pair of

reports from EPRI. EPRI (1991a) provides an overview of conservation evaluation

techniques, including relevant statistical and econometric methods; the companion

(continued...)
37

A related difficulty involves the use of engineering models to forecast

the energy savings of conservation programs. It has not been uncommon for ex

Rost evaluations to reveal that these ex ante engineering estimates were

significantly overstated. Studies done in the mid 1980s, for example, found

that residential weatherization programs in the Pacific Northwest produced

only 50% to 80% of the aggregate savings estimated by engineering models

(National Economic Research Associates (1987), pp. 38-64, and references

therein). Monitored programs for two utilities in New England have revealed

savings of only 27% to 33% of engineering estimates (Anderson (1991) and

Northeast Power Report (1991)). We have come across no studies where

engineering estimates yielded significantly smaller energy savings than were

realized based on ex post measurement techniques.

Despite the obvious problems and difficulties associated with relying on

engineering estimates of energy savings, most of the utilities in our sample

continue to base their projections of costs and benefits on such estimates.

Of the few that use some types of ex post evaluations to measure actual

savings, only utility 5 provided enough data to allow a systematic comparison

of the results with the ex ante engineering forecasts. This utility, which

has undertaken a serious effort to measure the actual savings achieved by its

programs, found that actual savings in 1990 were only 68% of the savings

predicted by engineering estimates.

Most utilities acknowledge that techniques to monitor and measure actual

savings have not advanced very far and that more work is needed to do so. An

24(...continued)

volume (EPRI 1991b) discusses some case studies in which various techniques were

used. The reports have extensive lists of references, and thus are useful guides

to the literature.
38

increasing number are expanding their measurement and monitoring efforts.

However, the failure to integrate sample selection, data collection, and

analysis into the design of the conservation programs themselves makes it more

difficult to come up with sound estimates of actual savings. At this point,

we must conclude that, at best, the savings estimates reported by many of the

utilities in our sample are unsupported by sound measurement of the actual

responses to their programs. In addition, based on existing ex post

measurement efforts it would not be surprising if energy savings reported

significantly overestimate actual energy savings. This in turn implies that

the costs per kWh saved reported in Tables 1 and 3 are likely to be

underestimated as well. If the 68% ratio of measured savings to engineering

estimates experienced by one utility is representative, it would imply that

costs per kWh for estimates based on engineering models alone would be about

50% higher than what is reported in these tables.

b. Conservation Measure Lives

The cost of a conservation option can be very sensitive to the assumed

life of the measure. The appropriate way to measure the life of a

conservation option is to measure its economic life, i.e., the number of years

that a customer will actually continue using the appliance or equipment in its

intended use. Economic lives are likely to be less than the physical

engineering life of a piece of equipment. This is particularly likely to be

true when cost-reducing or quality-improving technological change is rapid or

where customers can easily substitute another piece of equipment (e.g.
DISCUSSION DRAFT

TABLE 6A

ASSUMED LIVES FOR COMMON MEASURES

Residential Programs

(Years)

Efficient New Efficient Water Heatei

Utility Progrm Lighting Appliance Construction Blitz Water Heater Wra

1 14 14 15 12 - -

2 6 30 - - 6

3 - - - 15 - -

4 3 15 20 - 15 -

5 4 15 - 3.7 - 5

6 15 22 - 10

7 7 15 26 - - 8

8 7 15 26 - - 8

9 7 20 25 - - 10

10 2 - 70 - - -

EPRI 8 20 20 - 10 -

NES 19 -- 13 -
TABLE 6B

ASSUMED LIVES FOR COMMON MEASURES

C&I Programs

(Years)

UtilitPrram Uht Motors New Cons HVAC

1 15 15 15 -

2 7 15 12-15 -

3 10 20 - 17

4 10 10 20

5 14 30 20

6 17-20 20 -

7 16 10 18 -

8 16 10 18 -

9 5-20 12-17 25 -

10 20 - 37.5 -

EPRI 10 20 15 -

DISCUSSION DRAFT
residential lighting).25,26

The sources of the lives used by utilities in selecting conservation

measures and designing programs are typically not well documented. By and

large, however, the lives appear to be engineering lives based on standard

manuals of physical lives for some "typical" population of consumers. In some

cases these lives appear to have been adjusted based on information drawn from

actual customer behavior. The use of engineering estimates of measure lives

is troublesome. Engineering lives are rarely equal to the actual economic

lives of appliances and equipment. Nor is there any reason to believe that

program participants have the same characteristics as the average consumer for

whom an engineering estimate of measure life is computed.

Our concerns about the reliance on engineering estimates of measure

lives, indeed about the reliability of the measure lives assumed by the

utilities in our sample, is reinforced by the substantial variation across

utilities in the measure lives that are assumed (and imbedded in the cost

calculations reported in Tables 1 and 3) for similar measures. Table 6

displays the measure lives assumed by several of the utilities in our sample

for several common conservation measures. The degree of variability in

2s The fact that a light bulb may have a physical life of 8 years is not

relevant for computing costs if a significant fraction of the participants remove

the bulbs completely or place them in closets where they are used only a few

hours a year.

26 As a technical note, we should also point out that the common use of mean

measure lives in conservation cost analyses systematically overstates energy

savings, and thus systematically understates the cost of conservation. This

happens because the measure of energy savings used in the cost analysis is

actually the net present value of electricity saved. It can be easily shown that

the net present value of a stream of energy savings resulting from measures of

varying lifetimes is less than the net present value of the stream of energy

savings that results when all measures are assumed to last exactly the mean

lifetime. Based on some numerical simulations, this effect appears small (on the

order of 1%), however, and is ignored here.
40

assumed lifetimes is sometimes quite striking. It is hard to imagine that an

efficient lightbulb last seven times longer in residences in Utility l's

service area than it does in Utility 10's service area, or that an efficient

lightbulb lasts three times longer in commercial establishments in Utility 2's

service area than it does in Utility 10's service area.

Some of this variation can be explained by legitimate differences in the

measures installed. For example, commercial lighting packages typically

include a mix of fluorescent bulbs, efficient ballasts, and other devices such

as reflectors, occupancy switches, etc. To a certain extent, the differences

in assumed measure lives may reflect different utility assumptions about the

mix of long-lived ballasts (on the order of 15-20 years) with short-lived

bulbs (6-14 years); of course, we are then left with the question of why the

utilities have such a range of assumed mixes. Some of the variation may also

represent actual differences in operating usage. It seems entirely reasonable

to suppose, for example, that the expected lifetime of motors varies

significantly with the type of local industry. However, we believe that it is

unlikely that the large variation in assumed measure lives that we observe can

be explained primarily by such factors.

We have no way to know what the correct operating lifetimes are for the

various measures in utility conservation programs. However, we believe that a

significant portion of the variability in utility assumptions regarding

operating lifetimes indicates that they don't know either. In order to get a

sense for the potential impact of changing operating lifetime assumptions on

the cost per kWh saved reported in Tables 1 and 3, we have done the following

rough calculation. For each of the programs reported in Table 3 we have

recalculated the levelized costs of conservation using the longest and
DISCUSSION DRAFT

TABLE 7A

COST PER KWH SAVED: SELECTED RESIDENTIAL PROGRAMS

Sensitivity To Variations In Measure Lives

(centl/kWh)

UTILlTY REFRIG/ RESID RESID NEW

PROGRAM PPEBZER LUGTING CONSTRUCTION

Reported High Low Reported High Low Reported High Low

1 10.5 10.5 8.6 6.2 30.3 4.9 21.6 21.6 13.8

2 WNA WA N/A 2.7 7.1 1.2 22.3 30.5 19.5

3 NA N/A N/A N/A N/A N/A N/A N/A N/A

4 4.9 5.1 4.1 5.0 7.3 1.1 3.6 4.3 2.4

SA 181.4 190.4 150.6 20.8 37.1 5.2 N/A N/A N/A

SB 4.4 4.6 3.6 8.6 28.1 3.9 5.3 8.4 4.4

SC 4.4 4.6 3.6 20.2 66.1 9.2 5.3 8.4 4.4

6 8.1 8.5 6.8 2.9 19.9 2.9 2.9 3.6 2.0

7 68.8 71.9 59.7 16.7 50.5 8.5 15.1 19.4 12.8

8A WA WA N/A 19.4 58.5 9.8 N/A N/A N/A

8B NA NA N/A 5.5 N/A N/A 12.6 N/A N/A

9 11.0 13.6 11.0 4.9 14.8 2.3 163.9 209.4 126.5

10 NA N/A WNA 0.8 0.8 0.1 3.5 6.5 3.5

EPRI 3.3 4.2 3.3 0.9 3.1 0.4 N/A N/A N/A

LOVINS WA WNA N/A <0.0 N/A N/A N/A N/A N/A

NBES 3.9 4.7 3.8 N/A N/A WA N/A N/A N/A

Note 1: High value reflects short life and low value reflects long life.
DISCUSSION DRAFT

TABLE 7B

COST PER KWH SAVED: SELECTED COMMERCIAL PROGRAMS

Sensitivity To Variations In Measure Lives

(cests/kWh)

tIlT IY BPPICIBNT COMMIER COMMER NEW

PROGRAd gNO= ]LIGOHTING CONSTRUCTON

Reported High Lro Reported High Low Reported High Low

I WA WA N/A N/A N/A N/A 10.3 11.6 7.1

20 17.1 22.2 14.7 1.9 1.9 1.0 2.7 2.7 1.6

3 1.3 2.1 1.3 0.8 1.1 0.5 NIA N/A N/A

4 2.0 2.0 1.3 5.1 6.8 3.2 2.0 2.8 1.5

SA WA WA NfA 10.0 17.2 7.9 2.2 3.0 1.6

5A WA N•A WA 4.3 6.5 3.0 3.7 4.6 2.4

SC WA WA WA 6.9 10.6 4.9 3.1 3.9 2.1

6 2.8 4.4 2.8 1.4 3.0 1.4 N/A N/A N/A

7 WA N/A WA 1.4 2.5 1.3 3.3 4.1 2.6

8A NA N/A NWA 1.4 2.5 1.3 18.1 23.2 14.6

8B NA NWA NfA 3.7 N/A N/A 2.6 NA NfA

9 4.1 4.9 3.1 2.6 4.9 2.4 18.8 19.6 11.5

10 NWA NA NWA 22 4.7 2.2 2.4 4.4 2.4

EPRI 2.7 3.7 2.3 1.4 1.9 0.9 N/A NIA NIA

LOVINS <1.0 N/A N/A <0.0 N/A N/A N/A N/A N/A

NES NWA N/A NIA N/A N/A N/A N/A N/A N/A

Note 1: High value reflects short life and low value reflects long life.

*Utility #2 also has a motor program in the 1 cent/kWh range.
41

shortest lifetimes reported by any utility for that program type. As should

be clear, the long lifetimes result in low costs of conservation, while the

short lifetimes result in high costs. These results are reported in Tables 7A

and 7B.

In interpreting the figures in these tables, it is important to keep in

mind the non-linear relationship that exists between estimated costs and

operating lifetimes. While halving the estimated operating life of a measure

reduces the estimated physical kWh of energy saved by 50%, it reduces the net

present value of the lifetime stream of energy savings by significantly less.

As one would expect, this affect is more pronounced the longer the lifetimes

in question. Thus, a halving of estimated operating lifetimes leads to less

than a doubling of estimated costs per kWh.

Table 7A reports the results for the three residential programs for

which we have the data necessary to do the calculations. For

refrigerator/freezer programs the effects of varying lifetime assumptions

within the high/low range is fairly small at about 25%. For residential

lighting programs the difference is much larger, more than a factor of six.

For residential new construction programs the variation in costs is in the

middle, roughly 75%, reflecting the fact that, while there is substantial

variation among the assumed measure lives, even the shortest life is quite

long.

Table 7B reports similar results for three commercial/industrial

programs. For efficient motor programs the low lifetime assumption increases

the estimated cost per kWh saved by roughly 60% over the high lifetime

assumption. For commercial lighting the difference is closer to 100% and for

commercial new construction the difference is about 75%, essentially the same
42

as that for residential new construction.

For five of the program types, variations in assumed lifetimes lead to

variations in the realized cost per kWh that range from about 25% to about

100%; for residential lighting programs, however, the variation is so great,

and the shortest life so short, that the highest costs are more than six times

the lowest. Clearly, credible estimates of the costs of utility conservation

programs require much more credible information on measure lives.

VII. FREE RIDERS

The existence of free riders in utility conservation programs creates

special difficulties both for measuring savings and for cost accounting. When

utilities spend money to get participation from customers who would have

adopted the conservation measures on their own, the utility is effectively

spending money and receiving no social benefit from these expenditures. To

the extent that the utility spends real resources to attract, monitor, and

evaluate free riders (as opposed to merely making transfer payments) there is

a net social loss. The proper way to compute the social cost per kWh saved

from utility conservation programs requires that energy savings attributable

to free riders not be credited to the conservation program and that measure

costs (Kd - Ad) incurred by free riders and the utility not be counted as a

cost of the program. Thus, developing the costs per kWh saved that properly

make adjustments for free rider effects requires estimating the degree of free

ridership in a program and adjusting cost and energy figures to reflect the

estimated free ridership.

a. Utility Estimates of Free Riders
43

While most of the utilities in our sample recognize, at least verbally,

that they ought to account for free riders in their program evaluations,27

only six actually include free rider estimates for significant components of

their programs in their cost-benefit analyses. Table 8 displays these assumed

free rider fractions for a set of conservation measures.28

The most striking feature of Table 8 is the fact that there are not many

entries in it. While free rider issues have received a great deal of

discussion by utilities and their regulators, little progress appears to have

been made in estimating empirically the fraction of participants in utility

conservation programs who are free riders. Another striking feature of Table

8 is that some programs have very high free rider fractions. In these cases,

it appears that an active market for conservation may already exist; if so, it

is questionable whether these market segments are appropriate targets for

utility subsidies.

While the six utilities reporting free riders differ somewhat in how

they incorporate them in cost-benefit analyses, the five of them that have

undertaken evaluations do share a common approach to estimating free riders:

27 A number of utilities also emphasize the potential existence of so-called

"free drivers", consumers who choose to adopt efficient technologies because of

a utility program but do not actually participate in the program. While the

existence of such consumers is, of course, plausible, we have seen little

evidence that they are a significant side benefit of utility programs. In

particular, the claim made by some utilities that free drivers likely offset free

riders seems both arbitrary and extremely unlikely. For some evidence regarding

free drivers, the reader is referred to EPRI (1991b, in particular p. 2-16) and

references therein.

28 It must be emphasized that the level of aggregation used affects the

reported free rider fractions significantly. For example, at the micro level

some "programs", e.g., the promotion of particular HVAC types, may have free

rider fractions approaching 100%. At the macro level, e.g.,

commercial/industrial retrofits, however, the same free riders may appear much

smaller. The figures in Table 8 are intended to represent a middle ground,

neither too micro nor too macro.
DISCUSSION DRAFT

TABLE 8

FREERIDER ASSUMPTIONS

Residectial Programs CommercialIndustrial Programs

Utility

mrm ti AL N Wtt A&2ir CQ. _ "libtins Motoo New Cons Otbef rV

1 - - - - - - - - 2 - - - 25-40% - 21% 55% - - 3 - - - - 17% 27% 8% - - 4 - - - - - - - - - 

5A - - - - - 4-21% 5-62% - - 

B5BB1 - - - - - 4-21% 5-62% - - 

6 - - - - - - - - -

8A - - - - - 34-409 - - 4-8% 8B - - - - - 34-40% - - 4-8% 

7 - - - - - 23-25% - - 4-8% 9 - 30% (some 2 - - 30% 30% 2% 30% 

10 - - - - - - - - - 

EPRI -

NES - - - - - - - - - 

LOVINS - - - - - - - - - 
44

the use of surveys designed to determine what prompted participation in a

program. Free riders are typically assumed to be those participants who

report that they would have undertaken the conservation investment even in the

absence of the utility program.29 The fraction of participants thus

categorized as free riders is then typically used to adjust proportionately

the energy savings of the program and, in some cases, certain of the program

costs.

A potentially serious problem with this approach is that it fails to

recognize that free riders may well have utilization characteristics

significantly different from the average participant. That is, free riders

are not just a random draw from the population of customers for whom the

conservation option is cost effective. In particular, free riders are likely

to be customers for whom the economics of choosing the "appropriate"

conservation option are most favorable (e.g. long hours of use, long expected

life, short life of existing equipment, relatively low installation cost,

etc.). All else equal, this would imply that free riders, on average, save

more per participant than non-free riders. If this is true, it implies that

using the fraction of participants identified as free riders to adjust costs

and energy savings will bias downward the estimates of the net cost per kWh

saved.

Unfortunately, the data tracked by the utilities have provided little

basis for testing these propositions. One utility did collect data that

2' It is beyond the scope of this paper to enter into a discussion of the

problems associated with designing good surveys and the use of survey results to

calculate free rider rates. Moreover, utilities appear generally aware of the

difficulties in designing and implementing good survey instruments. Thus, we

ignore the general issue of using surveys to estimate free ridership. Instead,

we will concentrate on the specific question of how the survey results are used

to develop free ridership estimates.
45

provide some insight into this issue, however. In evaluating two of its

programs, Utility 2 used the proportion of energy savings attributable to free

riders, rather than the proportion of participants identified as free riders,

as the basis for adjusting reported costs per kWh. Since the utility also

tracked the proportion of participants identified as free riders,30 it is

possible to compare these two methods of estimating free ridership for these

two programs.

As part of its evaluation of its motor rebate program, the utility

conducted a survey that allowed for participants designated as free riders to

be cross-tabulated with the size of motor purchased and its expected hours of

use per year. The utility found that 42% of the participants were free

riders. However, the free riders accounted for 551 of the energy saved by all

participants. In this program, then, the fraction of energy savings

attributable to free riders is significantly more (13 percentage points) than

the fraction of participants identified as free riders.

The opposite result occurred in a second evaluation, this time of a

commercial lighting rebate program. In this case, the utility performed a

survey, cross-tabulated with energy use projections, to estimate that 27% of

participants, but only 21%,of energy savings, were attributable to free

riders. In this case, the use of a participant free rider fraction rather

than an energy free rider fraction would overstate the impact of free

ridership. These two cases suggest that the assumption that free riders have

identical characteristics to the average participant may be a poor one and

that efforts to collect free rider information should include both the

30 These fractions were not reported in the evaluation reports, but were

kindly provided to us by a representative of the utility.
46

identification of free riders and their utilization characteristics.

b. Free Riders and Diffusion Patterns

To this point we have focused on free riding as essentially a static

problem. In fact, free riding is properly conceptualized not as a simple

static decision "but for" utility subsidies, but in terms of shifts in the

diffusion curve for the relevant pieces of equipment and appliances. Thus, in

principle, free riding should capture both the effects of utility programs on

the timing of adoption of efficiency measures (earlier rather than later) and

on the equilibrium penetration levels. Figure 2 displays hypothetical

diffusion curves for efficient commercial lighting with and without a utility

program. The curve C1 depicts the fraction of existing commercial buildings

that would install efficient lighting over time in the absence of the utility

conservation program. After ten years 70% of the buildings are assumed to

have installed these devices. The curve C2 is a hypothetical diffusion curve

for efficient lighting after taking account of the utility's conservation

program. The conservation program does two things. It speeds up diffusion,

and it increases the maximum penetration rate achieved after ten years.

From this perspective, free riding is not an all or nothing phenomenon.

It is not just a question of whether some of this year's participants would

have adopted a conservation measure absent the utility's program, but when

they would have adopted the measure. Assume for example that a utility survey

indicates that none of the participants would have adopted a measure with a

ten year life this year and, therefore, ass ~as that the free rider effect is

zero. However, if all of the participants would have installed the measure

two years from now in the absence of the program, the static approach will
Figure 2

Hypothetical Conservation Diffusion 

1

Proportion

Adopting

ConservatiorMeasure

0 5 10 15 

Time
47

significantly overestimate the actual savings achieved by the program this

year. Rather than achieving ten years of energy savings for each participant

the program actually only yields two years of energy savings. In general, the

failure to account for dynamic diffusion effects appears to lead to

overestimates of energy savings and underestimates of the cost per kWh saved.

The proper measurement of free riders raises many of the same sample

design and statistical analysis issues as are created by the challenge of

measuring energy savings more generally. Utility efforts to measure free

riders are just beginning to evolve and are still quite crude. No effort has

been made even to conceptualize the free rider measurement problem in the

dynamic diffusion context depicted in Figure 2. Ignoring these effects biases

downward the computed values of the cost per kWh saved reported in Tables 1

and 3.

c. The use of free rider estimates in calculating net costs

Once they have estimated free ridership, utilities typically proceed to

adjust their cost and energy savings estimates to account for these free

riders. Such adjustments are typically accomplished by multiplying estimated

energy savings by one minus the free rider fraction and then using this "net

energy saved" figure to compute the benefits of the program. In our notation,

this is equivalent to replacing E in the denominator of equation (2) by E

times one minus the free rider fraction (FR). Note that statistical

techniques that compare participant usage with that of non-participants may

perform a similar adjustment, even if the effect of free riders is not

estimated separately. In some cases, utilities also subtract the free riding

customers' share of the expenditures on conservation measures from I in
DISCUS~IUN UKAP I

INCF

TABLE 9

REASE IN COST/KWH SAVED DUE TO FREERIDER ADJUSTMEN

rrr LIAI

Administrative Costs as a

Fraction of Direct Measure Costs

FreeRiders 25% 50 75% 1000 125% 150% 175% 200% All

0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

10% 2% 4% 5% 6% 6% 7% 7% 7% 11%

20% 5% 8% 11% 13% 14% 15% 16% 17% 25%

30% 9% 14% 18% 21% 24% 26% 27% 29% 43%40% 13% 22% 29% 33% 37% 40% 42% 44% 67%50% 20% 33% 43% 50% 56% 60% 64% 67% 100%60% 30% 50% 64% 75% 83% 90% 95% 100% 1500/070% 47% 78% 100% 117% 130% 140% 148% 156% 233%

80% 80% 133%0 171% 200%/ 222% 240%0 255% 267% 400%/90% 1800/ 3000% 386% 450%0 500% 540% 573% 600% 900%

6ý
equation (3).

This approach does not properly account for the conservation costs

attributable to free riders. When free riders are removed from the analysis

both the energy savings attributable to them and all incremental measure costs

(both customer and utility) should be subtracted as well. In this way we

remove from the analysis all utility costs that are transfer payments and any

measure costs incurred by the free riders, since these would have been

incurred anyway. The traditional approach has the effect of biasing the cost

per kWh saved upward. While this problem does not affect the numbers in

Tables 1 and 3, since these do not include free rider adjustments, the point

should be kept in mind when analyzing other reports of conservation costs.

To illustrate the potential effect that appropriate adjustments for free

riders may have on the costs per kWh saved reported in Tables 1 and 3, we have

performed a set of simulations whose results are reported in Table 9. The

simulations assume that we can divide the incremental costs of conservation

(I) into two components: measure costs (Kd - Ad) and other "administrative"

costs incurred by the utility (K, + KO). We then compute the appropriate net

(of free riders) cost per kWh saved by making several adjustments to the

energy savings and incremental cost values in equations (1), (2), and (3).

First, we multiply the estimated gross energy savings (E) by (1-FR) to get the

net energy savings (E'). Next, we compute the associated incremental societal

cost as I' - I - FR*(Kd - Ad) (i.e., we eliminate the incremental measure

costs attributable to free riders). Substituting these values in equations

(1) and (2), we then obtain the net cost per kWh saved (c').

Table 9 displays the ratio of the adjusted cost per kWh saved (c') to

the unadjusted cost per kWh saved (c) for various assumptions about the free

0
DISCUSSION DRAFT

TABLE 10

SELECTED ESTIMATES OF RATIO OF ADJUSTED TO

UNADJUSTED COST PER KWH SAVED

Adjusted/

Admin Cost/ Free Unadjusted

Utility Program Measure Cost Riders Cost/kWh Saved

2 Water Heater Wrap 22% 25% 1.06

Retrofit Motors 173% 55% 1.77

Retorfit Lighting 10% 21% 1.02

3 Central AC Rebate 82% 17% 1.09

Commerc Audits 22% 4% 1.01

Lighting Rebate 52% 27% 1.13

HVAC Rebate 38% 43% 1.21

Motor Rebate 241% 8% 1.06

10 Appliance Rebates 90% 18% 1.10

Various Comm Programs: 10% 30% 1.04

50% 30% 1.14

100% 30% 1.21

180% 30% 1.28
49

rider fraction and the ratio of administrative costs to measure costs. The

"All" column refers to the case when the only costs of conservation are

administrative. In addition, they represent the results of using the common

technique of dividing total program costs (including costs associated with

free riders) by energy savings that do not include free riders.

As is clear from the table, the larger is the free rider fraction, the

more the unadjusted costs need to be increased to reflect the true social

costs per kWh saved. It is only when all costs are administrative, however,

that the necessary adjustment is as large as that implied by the "divide total

costs by energy net of free riders" approach. Similarly, the larger is the

ratio of "administrative" costs to measure costs, the higher is the cost per

kWh saved, since these real cost outlays by the utility (rather than transfer

payments) must be recovered over a smaller number of kWh. In evaluating the

efficiency of a program it is thus not enough to know the free rider fraction

alone. We need to know both the free rider rate and the administrative cost

component of the utility's program.

From the table, we see that free riders are clearly not much of a

problem when both the free rider fraction and the administrative cost ratio

are relatively small. As both increase the failure to adjust for free rider

effects leads to much more significant biases in estimates of the cost per kWh

saved. The question, of course, is where utility programs actually tend to

be. Only three utilities provided enough information to estimate the bias

from failing to adjust for free riders. Table 10 reports estimates of the

free rider fraction, the ratio of administrative costs to direct measure

costs, and the associated ratio of adjusted costs to unadjusted for the

programs for which we have sufficient information. It is evident that, for
this sample, the unadjusted costs per kWh could be understated by as little as

1% or as much as 77%.

It should be noted that the fundamental problems associated with

measuring energy savings in general, and free ridership in particular, are by

no means peculiar to the area of electricity conservation; indeed, they are

likely to arise in a number of important policy areas. A similar problem

arises whenever economic agents are to be paid an incentive to do what they

might have otherwise done anyway. This problem is inherent, e.g., in

proposals that emitters of CO2 be allowed to offset those emissions (if they

are regulated in the future) by reforestation projects. The cheapest source

of reforestation, from the polluter's point of view, will be from timber

companies that would have planted the trees anyway. An offset system will

thus need some mechanism for distinguishing a "true" incremental tree from a

"free rider". Similarly, proposals to reduce pressure on over-harvested

fishing stocks sometimes include the idea of paying incentives to boat owners

to retire from the business. Such incentives will clearly be most attractive

to the marginal producers, i.e., those that would have stopped producing

anyway.

The basic point, then, that should be taken from this analysis is that

the design of incentive regulations systems, particularly in the environmental

area, must take care to understand the informational structure within which

agents will respond to the incentives. If agents have a significant amount of

private information, they may be able to take advantage of the system without

providing the benefits it was designed to produce.
IX. CONCLUSIONS

The goal of this paper was to answer the question "What does a negawatt

really cost?" Despite all of the rhetoric suggesting that electricity savings

from conservation expenditures are "too cheap to meter", the available

evidence suggests that we don't really know with a great deal of precision, at

least in part because the savings are not being "metered" properly. What we

can say is the following:

1. Computations of the cost per kWh saved from utility conservation programs,

derived entirely from the data on costs and energy saved reported by

utilities, suggest that the cost of a "negawatt" is substantially higher than

is generally thought based on standard references such as Lovins and EPRI.

This is especially true for residential programs where computed costs are much

higher than proponents of utility conservation programs have suggested. Most

utilities have pursued at least some subprograms in both the residential and

the commercial sectors where the cost per kWh saved is very high and the

associated expenditures are clearly uneconomical.

2. There is very wide inter-utility variation in the cost per kWh saved.

This variation exists even when we narrow our focus to subprograms with very

similar characteristics. While this variation may reflect differences in

economical conservation opportunities and the effectiveness of individual

programs, it is unlikely that these explain more than a small part of the

variation. A large part of the variation probably results from differences

across utilities in the costs that they measure and report, differences in

assumed measure lives, and differences in the way energy savings are measured.
3. The costs per kWh saved that we reported in Tables 1 and 3 are likely

significant underestimates of the true cost per kWh saved. This downward bias

is due to the failure to report all relevant costs, the reliance on

engineering estimates of savings rather than ex post measurement of actual

savings, and the failure to make adjustments for free rider effects. Further

biases may result from adopting measure lives that are too long. It is

difficult to know exactly how large this downward bias is, but we would not be

surprised if the reported costs per kWh saved understate the actual costs per

kWh saved by a factor of two or more on average.

4. It is unlikely that conservation is nearly as large a "free lunch" as many

proponents of electricity conservation programs have suggested. Public policy

enthusiasm for utility conservation programs will have to be tempered by the

reality that, when all relevant costs are acknowledged and we actually "meter"

it, electricity conservation is not nearly as cheap as is often advertised.

5. Utility sponsored conservation programs are more costly than proponents

have argued in part because utilities are intermediaries between customers and

measure suppliers and must spend significant sums of money over and above the

direct costs of conservation measures to deal with fundamental problems of

asymmetric information and adverse selection. Many of the costs a utility

must incur would not have to be incurred by an individual consumer motivated

by self-interest to invest in cost-effective conservation. Thus utility

conservation programs should not be viewed as a substitute to stimulating more

conventional private market responses to conservation opportunities.
53

6. There do appear to be significant conservation opportunities that can be

achieved economically by utilities, especially in the commercial and

industrial sector. However, in order to better refine program designs, costs,

and savings, it is quite clear that utilities still have a lot of progress to

make with regard to measurement and accounting for all relevant costs,

accurate measurement of the electricity savings that their programs are

achieving, and measurement of free rider effects.

7. Since utility expenditures on conservation are not necessarily "too cheap

to meter," and the costs incurred by utilities are rolled into the (higher)

rates we all pay as a consequence of these programs, utilities and their

regulators must pay more attention to proper cost accounting, the development

of better techniques to measure electricity savings achieved by the programs,

and measurement and accounting for free rider effects.

8. Free riders do matter, though not so much as some standard free rider

adjustments would lead one to believe. Both energy savings and costs should

be adjusted to reflect free ridership. Program evaluations that ignore free

riders will understate the true costs of energy conservation, while

evaluations that adjust only energy savings, but leave costs unchanged, will

overstate the true costs of conservation. Given the heterogeneity in usage

characteristics that may exist among participants, free rider estimates based

only on the proportion of participants that are free riders may be misleading.

10. Utility data indicate that the potential for large-scale, cost-effective

electric conservation is dominated by the commercial and industrial sectors.
54

Insofar as we find it easier to believe that residential consumers, rather

than corporate ones, are likely to face a variety of barriers to making

investments in energy efficiency, we find this result surprising. Indeed, we

believe that many people visualize conservation programs as being designed to

help residential consumers. As the corporate orientation of conservation

programs becomes more widely known, we would not be surprised to see some sort

of backlash against them among consumer advocates and residential ratepayer

groups.

We believe that there are a number of steps that utilities and their

regulators can take to develop better, more credible and verifiable

conservation programs. These steps include:

1. We agree with Hirst (1989) that it is important to develop and

disseminate a standardized conservation cost and energy savings reporting

system, analogous to the existing FERC system for reporting financial data and

supply related information. However, because conservation programs have

characteristics that make them quite different from supply side resources, the

development of a comprehensive cost measurement and accounting system must be

developed in conjunction with the definition of all of the elements required

to compute the costs and benefits of utility conservation programs. We have

identified the kinds of cost information that should be collected. The proper

measurement of each of these cost elements requires further work, however.

Since a few utilities have managed to account for all of the relevant cost

items, there is no reason why all utilities could not quickly agree on which

utility and customer cost data are relevant.
55

2. The reliance on ex ante engineering estimates of energy savings achieved

by conservation programs is simply unacceptable. There is abundant evidence

that there can be significant variations between ex ante predicted savings and

ex post measurement of actual savings. Utilities have experience measured

savings that are as low as 30% of ex ante savings estimates. If significant

sums are to be spent by utilities on conservation programs, a great deal of

progress needs to be made in the development and use of sampling techniques

and statistical analysis to estimate what impacts conservation programs

actually have had on electricity utilization. The measurement problem may be

difficult to solve in many cases, but it must be addressed more aggressively.

There is significant virtue in moving forward with collective efforts to

develop measurement techniques and the necessary data rather than relying on

each utility to develop its own measurement methods and its own data. The

recent EPRI reports (1991a, 1991b) are an good start in this direction.

3. It is unacceptable to ignore free rider effects. Consumers have powerful

incentives to invest in conservation when the cost of conserving is

significantly less than the bill savings that result from having to pay for

less electricity. When utilities spend money to convince customers to do what

they would have done anyway without these efforts, they are wasting scarce

resources. While there is no doubt that some market imperfections keep

conservation from achieving its full potential as rapidly as would be

desirable in an ideal world, market mechanisms can and do stimulate

conservation by consumers. Efforts to understand better the dynamics of

consumer behavior and to measure free rider effects should be an important

part of all utility conservation programs.
56

4. Good conservation programs, and appropriate accounting and measurement

methods, will only evolve if they are designed and implemented using an

appropriate conceptual framework. Many of the problems with existing programs

are, in part, a consequence of the erroneous conceptualization of conservation

investments as "supply sources" that has been promoted by many conservation

advocates and accepted by many state regulatory commissions. Conservation

investments are a "supply source" only in the trivial sense that if there is

less demand then less supply will be required to balance supply and demand.

Thinking about conservation in this way is the road to ruin for good program

design and implementation.

The proper rationale for utility conservation programs is that they are

mechanisms to ameliorate information, financial and other market imperfections

that lead consumers to fail to make cost-reducing investments. It must be

recognized that good programs should be designed to break down these market

barriers and market imperfections. They should seek to alter consumer

behavior by improving customer information, increasing customer incentives,

and stimulating the evolution of markets that do not yet exist. They should

not seek to create costly utility and regulatory bureaucracies that

permanently replace these market mechanisms. Ideally, as these market

barriers are removed the role of the utility should recede as it is replaced

by better functioning market mechanisms that rely on the self-interest of

individual consumers to reduce their electric bills. Utility programs should

be designed with these "make the market work" goals in mind.
REFERENCES

Anderson, Kent. 1991. Key Issues in Least Cost Planning. NERA Working Paper

#10, National Economic Research Associates, White Plains, March.

Berry, Linda. 1989. The Administrative Costs of Energy Conservation Programs,

ORNL/CON-294. Oak Ridge, Tennessee: Oak Ridge National Laboratory, November.

Electric Power Research Institute. 1991a. Impact Evaluation of Demand-Side

Management Programs, Volume 1: A Guide to Current Practice, EPRI CU-7179,

prepared by RCG/Hagler-Bailly, Inc. Boulder, CO, February.

Electric Power Research Institute, 1991b. Impact Evaluation of Demand-Side

Management Programs, Volume 2: Case Studies and Applications, EPRI CU-7179,

prepared by RCG/Hagler-Bailly, Inc. Boulder, CO, September.

Energy Information Administration. 1990. Energy Consumption and Conservation

Potential: Supporting Analysis for the National Energy Strategy, SR/NES/90-

02. Washington, D.C.: U.S. Department of Energy, December 12.

Energy Information Administration. 1991. Annual Energy Outlook, DOE/EIA-0383

(91), Washington, D.C.: U.S. Department of Energy, March.

Fickett, Arnold P., Gellings, Clark W., and Lovins, Amory. 1990. "Efficient

Use of Electricity", Scientific American 263 (September): 64-74.

Fisher, Anthony and Rothkopf, Michael. 1989. "Market Failure and Energy

Policy", Energy Policy 17(4): 397-406.

Hartman, Raymond S., Doane, Michael J. 1986. "Household Discount Rates

Revisited", The Energy Journal 7(1): 139-148.

Hausman, Jerry A. 1979. "Individual Discount Rates and the Purchase and

Utilization of Energy-Using Durables", The Bell Journal of Economics 10(1):

33-54.

Hirst, Eric. 1989. Comparison of EIA Data Collections: Electricity Supply

and Demand. Oak Ridge, Tennessee: Oak Ridge National Laboratory, October.

Hirst, Eric. 1991. "Fulfilling the Demand-side Promise," Public Utilities

Fortnightly, July 1.

Joskow, Paul L. 1988. "Testimony on Electricity Policy Issues Before the

Subcommittee on Energy and Power, House Committee on Energy and Commerce",

March 31, reprinted in Competition in Electricity: New Markets and New

Structures. J. L. Plummer and S. Troppmann, eds. Public Utility Reports,

1990.

Nadel, Steven. 1990a. Lessons Learned: A Review of Utility Experience with

Conservation and Load Management Programs for Commercial and Industrial
Customers. Albany, N.Y.: New York State Energy and Research Development

Authority.

Nadel, Steven. 1990b. "Electric Utility Conservation Programs: A Review of

the Lessons Taught by a Decade of Program Experience", in Proceedings of the

ACEEE 1990 Summer Study in Energy Efficiency in Buildings", American Council

for an Energy-Efficient Economy.

National Academy of Sciences. 1991. Policy Implications of Greenhouse

Warming, Washington, D. C.: National Academy Press.

National Economic Research Associates (NERA). 1987. "Assessment of U.S.

Electric Utility-Sponsored Conservation Programmes", report prepared for the

Central Electricity Generating Board, December 18.

Northeast Power Report. 1991. "CLF: Put ComElectric C&LM Program in

Receivership for Mismanagement", October 4, page 1.

nr.;: ·$
12/05/91

Appendix A: Selection of Utilities

We wrote letters to a dozen utilities requesting information about the

characteristics, costs, analytical assumptions, and measurement techniques

associated with their conservation programs. In order to avoid confidentiality

problems we asked that information be provided which was publicly available and

that had been or would be provided to state regulatory agencies. These

particular utilities were chosen because they were identified as having "good"

conservation programs by experts with whom we spoke. Information from this set

of utilities was supplemented by similar information obtained earlier from two

additional utilities which we had relied on to formulate our survey questions.

The utilities in our survey universe plan to spend over $700 million on

conservation programs in 1991.

All but one of the utilities responded to our requests for information.

Two of the responding utilities did not have or would not provide us with all of

the information required to calculate the life-cycle cost per kWh saved

associated with their programs. Several responding utilities provided us with

additional follow-up information beyond our initial requests and were extremely

helpful in assisting us in understanding their programs. In two cases we

obtained conservation program information for two utilities that were part of the

same holding company. In one of these cases the programs for the two affiliates

were so similar that we analyzed only one of them. In the other case the

programs were sufficiently different that we analyzed the results separately.

In two cases (utility 5 and utility 8) we had information for two time periods

and have reported separate computations for each (5A, 5B and 8A, 8B) for each

time period. In one case we were able to adjust projected values for the most

recent program period (5B) with information on measured energy savings that had

not been available when the utility put its program data together (5B1).

The data provided to us from utilities is an uneven mix of historical

experience, projections adjusted for the utility's historical experience, and

simple projections made by the utility based on engineering data and experience

of other utilities. As a result, the numbers that we are able to compute are a

mix of actual utility experience and projections of what utilities think the

conservation programs will cost and achieve.

Although we did not promise confidentiality, and no utility requested

confidentiality, we have decided, for now, not to identify the results of our

analysis with the individual utilities from whom we obtained information. The

details of utility conservation programs have increasingly become contentious

regulatory issues. The purpose of our project was not to point any fingers at

particular utilities and we do not want our work to be used in this way.

Further, we are making this Working Paper available for comment to the utilities

that provided us with information and revisions may be required in light of their

comments. We do feel that is appropriate, however, to list the utilities in our

original survey universe, including those that did not provide adequate

information to us. Those utilities are: