Estimating Pollution Abatement Costs:

A Comparison of “Stated” and “Revealed” Approaches

by

Rolf Färe*

Shawna Grosskopf**

and

Carl A. Pasurka, Jr.***

*Department of Economics and Department of Agriculture and Resource Economics

Oregon State UniversityCorvallis, OR

**Department of EconomicsOregon State University

Corvallis, OR

***U.S. Environmental Protection Agency (1809)Office of Policy, Economics, and Innovation

1200 Pennsylvania Ave., N.W.Washington, D.C. 20460

Phone: (202) 260-6197FAX: (202) 260-5732

E-Mail: PASURKA.CARL@EPA.GOV

C:\ELECTRIC\ELECT-12A.WPD

DRAFT -DO NOT QUOTE OR CITE WITHOUT PERMISSION OF THE AUTHORS

May 3, 2002

Earlier versions of this study was presented at the January 2001 AEA meetings in New Orleansand at the U.S. Environmental Protection Agency. Gale Boyd, Scott Farrow, and Anton Steurerprovided helpful comments on an earlier version of this study. We thank Curtis Carlson forproviding the capital stock and employment data, and Tom McMullen for providing the U.S.EPA emission estimates. Any errors, opinions, or conclusions are those of the authors andshould not be attributed to the U.S. Environmental Protection Agency.

Estimating Pollution Abatement Costs:

A Comparison of “Stated” and “Revealed” Approaches

Abstract

Surveys have been the principal method used to estimate costs associated with environmentalregulations in the United States. Although surveys have been widely used, there are concernsabout their accuracy. These concerns have been exacerbated by increased use of change-in-production process techniques to abate pollution. In order to investigate the accuracy of surveyestimates of pollution abatement costs, a joint production model is specified and data from powerplants in the United States for 1994 and 1995 are used to estimate pollution abatement costsincurred by power plants. These estimates of pollution abatement costs generated by the jointproduction model are then compared with survey estimates of pollution abatement costs incurredby power plants.

JEL Classification Code: Q28

I. Introduction

Surveys have been the principal method used to estimate the costs associated with

environmental regulations in the United States.1 The “Pollution Abatement Cost(s) and

Expenditures” (PACE) survey (U.S. Department of Commerce, 1996) estimated the pollution

abatement costs borne by U.S. manufacturing industries for 1973 through 1994 (excluding

1987). In addition to the PACE survey, the Form EIA-767 survey (“Steam-Electric Plant

Operation and Design Report”), which is administered by the Energy Information

Administration of the U.S. Department of Energy, includes questions concerning pollution

abatement expenditures. These survey estimates of pollution abatement costs, which were used

by the Bureau of Economic Analysis (BEA) in its discontinued annual report on pollution

abatement expenditures (see Vogan 1996), can be viewed as “stated costs.” For 1994, 64 percent

of BEA’s estimates of pollution abatement expenditures were from surveys and the remaining 36

percent of BEA’s estimates were derived from other sources (Vogan, 1996, p. 54) .

According to the System for Integrated Environmental and Economic Accounts, SEEA,

(United Nations 1993) current account expenditures for pollution abatement by business

establishments are classified as either external or internal pollution abatement activities. External

pollution abatement activities are undertaken by establishments for which the activity is its

primary or secondary activity (e.g., sewage treatment). Internal pollution abatement activities

(e.g., operating a scrubber) are those undertaken by establishments emitting the pollutant. While

surveys appear to be the appropriate method for estimating the extent of external pollution

abatement activities, they encounter difficulties when estimating the costs associated with

internal pollution abatement activities embodied in the technology used by a producer.

Although surveys of pollution abatement costs have been conducted for a more than

-2-

twenty-five years, there are concerns about their accuracy. One of the concerns is the difficulty

associated with estimating “change in production process” capital expenditures. The share of

manufacturing air pollution abatement capital expenditures represented by “change in production

process” techniques increased from 17.4 percent in 1973 to 48.3 percent in 1994 according to the

U.S. Department of Commerce (1976, p. 47 and 1996, p. 25). As the share of the pollution

abatement capital expenditures represented by “change in production process” techniques

increases, it becomes increasingly difficult to estimate the current account (i.e., operation and

maintenance) expenditures associated with pollution abatement activities. This measurement

difficulty arises because as an increasing percentage of abatement activities are imbedded in

production processes, it becomes increasingly difficult to determine which operating costs are

associated with pollution abatement activities.

Modeling pollution abatement activities is an alternative method of estimating the costs

associated with pollution abatement activities. There are two approaches to modeling pollution

abatement costs. One method assumes pollution abatement activities are separable from the

activities associated with producing the marketed output. Martin, Braden, and Carlson (1990)

and Bellas (1998) are examples of studies that estimate pollution abatement functions by

assuming pollution abatement activities are separable from electricity production.

The second method models the joint production of good and bad outputs, in the sense that

the bad outputs are byproducts of the production of good outputs.2 There are some advantages to

estimating pollution abatement costs by modeling the joint production of good and bad outputs.

First, it does not require information about pollution abatement technologies and their associated

costs. Instead, the cost of pollution abatement activities is measured by the reduced production of

-3-

the good output that results from reducing production of the bad output. The foregone

production of the good output occurs as a result of the reallocation of inputs from producing the

good output to their use in pollution abatement activities. Second, modeling the joint production

of good and bad outputs avoids the difficulties associated with survey efforts to estimate

pollution abatement costs associated with changes in the production process. Finally, synergies

among the abatement processes of two or more pollutants are automatically captured by the joint

output technology.

Joint production models have been used to measure the marginal abatement costs of

reducing emissions from electric utilities. Turner (1995) applied the methodology developed by

Färe et al. (1993) to power plant data from 1985 to 1987 in order to estimate the shadow price of

SO2 emissions. Coggins and Swinton (1996) also applied the Färe et al. (1993) methodology and

estimated the marginal abatement costs of reducing sulfur dioxide emissions by fourteen power

plants in Wisconsin using data from 1990 to 1992. The Coggins and Swinton study was

extended by Swinton (1998) whose sample included power plants in Wisconsin, Illinois, and

Minnesota for 1990 to 1992.

Kolstad and Turnovsky (1998) and Carlson et al. (2000) specified joint-production cost

functions to estimate the marginal abatement costs faced by electric utilities when reducing

sulfur dioxide emissions. While Färe et al. (1993) and Coggins and Swinton (1996) assume fuels

are homogeneous, Kolstad and Turnovsky (1998) and Carlson et al. (2000) incorporate

differences in fuel quality (i.e., differences in the sulfur content) when estimating their cost

functions. Kolstad and Turnovsky (1998) and Carlson et al. (2000) assume the restriction on

emissions faced by each plant is binding. Hence, a plant emits the maximum amount of the

-4-

pollutant permitted by the environmental regulation.

While several studies have specified joint production models to estimate marginal

abatement costs, there has been less interest in using joint production models to estimate the total

cost of pollution abatement activities.3 Thus the survey and joint production approaches have

not been directly compared, which is the purpose here.

This study estimates the cost of pollution abatement using the joint production approach

and derives the price of electricity that would prevail if that cost of abatement were equal to the

survey approach estimate, providing evidence concerning the consistency of the two approaches.

Our approach models the production of good outputs (i.e., marketed goods) and bad outputs

(i.e., emissions of air pollutants) within a data envelopment analysis (DEA) framework (see Färe

and Grosskopf, 1983). The original Färe and Grosskopf methodology measured the costs of

pollution abatement activities when the producer is restricted to maintaining its observed mix of

the good output and the bad output, which we modify in this paper. Färe, Grosskopf, and Pasurka

(1986) applied the Färe and Grosskopf (1983) framework to a cross section of data of 100 steam

power plants in the United States for 1975. They specified particulate matter, sulfur dioxide,

nitrogen oxides, heat discharge in water used by plant as the undesirable outputs and found a 1.3

percent reduction in the production of the desirable output as a result of the undesirable outputs

not being freely disposable. In their study, Färe, Grosskopf, and Pasurka (1986) did not control

for fuel quality (i.e., sulfur and ash content).

Färe et al. (1989) proposed an alternative methodology to measure the costs of pollution

abatement activities when the producer adopts a production process that allows an

equiproportional increase in the good output and decrease in the bad output relative to the

-5-

observed production levels.

When estimating pollution abatement costs with a joint production model, we distinguish

between two technologies. The free disposability or unregulated technology assumes the bad

output can be “thrown away” at no cost to the producer, whereas the weak disposability or

regulated technology allows for reductions in the production of the bad output via a proportional

decrease in the good output. Within this framework, pollution abatement costs are determined by

computing the difference between the maximum production of the good output under the

unregulated and regulated technologies. Since the unregulated and regulated production

possibilities frontiers are constructed from data that reflect the actual behavior of producers, the

cost estimates generated by the DEA framework can be viewed as the “revealed costs” (i.e., lost

revenue) of pollution abatement activities.

The electric utility industry represents a unique case in which plant-level data for inputs,

the good output, and the bad outputs are publically available. For each power plant included in

this study, its pollution abatement costs reported on the Form EIA-767 survey are compared with

its costs of pollution abatement activities estimated by modeling the joint production of the good

and bad outputs within the DEA framework. Linear programming (LP) problems are specified in

order to estimate the measurable pollution abatement costs for a panel data set of coal-fired

power plants from 1994 and 1995.

This study represents the first attempt to compare estimates of pollution abatement costs

from a survey with pollution abatement costs estimated by a modeling approach and it allows us

to determine the extent of any divergence between the survey and modeling estimates and the

source(s) of any divergence. The remainder of this study is organized in the following manner.

-6-

In Section II, a review of surveys of pollution abatement expenditures by electric utilities is

presented. In Section III, the joint production model and the associated linear programming (LP)

programs are specified. In Section IV, the data and results are presented. Finally, Section V

summarizes this study, discusses future avenues of research, and examines the implications of

the empirical results of this study.4

II. Survey Estimates of Pollution Abatement Expenditures by Electric Utilities

The Federal Power Commission (FPC) Form 67 entitled “Steam-Electric Plant Air and

Water Quality Control Data” collected information about the operating costs associated with the

pollution abatement activities of power plants for 1969 through 1980.5 These data were

published in a series of annual reports by the U.S. Federal Power Commission for 1969 to 1973

and the Federal Energy Regulatory Commission for 1974 to 1976 (Appendix A lists these

reports). The EIA-767 survey “Steam-Electric Plant Operation and Design Report” is the

successor to FPC Form 67.6 Although Form EIA-767 was administered during 1981 to 1984, the

Energy Information Administration (EIA) does not consider these data to be as accurate as the

data starting in 1985.

In its annual report on pollution abatement expenditures, the Bureau of Economic

Analysis (see Vogan, 1996) used data collected by FPC Form 67 to estimate the costs associated

with the operation of air and water pollution abatement capital equipment of privately owned

electric utilities for the years from 1972 through 1980, and data from the EIA-767 survey were

used to estimate costs for the years from 1985 through 1994 (Farber and Rutledge 1989, pp. 12-

13 and 16 and Vogan 1996, p. 54). Changes in related series of data were used to generate

-7-

estimates for the years from 1981 to 1984.

This study investigates the relationship between the EIA-767 survey estimates of O&M

expenditures associated with abating particulate and sulfur emissions and modeling estimates of

pollution abatement costs.7 Throughout the remainder of this study, we refer to survey estimates

of pollution abatement expenditures as PACS and modeling estimates of pollution abatement

costs as PACM. In the next section, we introduce the theoretical model of the joint production

of good and bad outputs, which underpins our empirical work.

III. Modeling Pollution Abatement Costs

The opportunity cost of pollution abatement activities is the foregone production of the

good output resulting from the reallocation of inputs from producing the good output to pollution

abatement activities. In this section, a formal model of pollution abatement costs is developed

from a model of the joint production of good and bad outputs. In this study, the cost of pollution

abatement activities is the value of lost potential output due to regulation. This is the cost which

we will compare to the estimates of pollution abatement costs from the EIA-767 survey in order

to provide an indication of the accuracy of such surveys.

To derive pollution abatement costs and show that it can be interpreted as the value of

lost potential output we formulate two production models, one “regulated” and one

“unregulated.” In the regulated model, we explicitly recognize that good and bad outputs are

jointly produced and that the bad outputs cannot be disposed of freely. On the other hand, in the

unregulated model we allow bad (and good) outputs to be freely disposable.

In measuring the potential output loss, we differ from Färe, Grosskopf, and Pasurka

-8-

(1986) by not scaling all outputs including the bads, but rather scaling only on the good output.

Another difference is that we use an additive directional distance function rather than a

multiplicative Shephard distance function.

To model the abatement cost we introduce the required production model. Denoting

inputs by x = (x1, ... , xN) 0 úN+ and outputs by y = (y1, ... , yM) 0 ú +

M, the output sets are given by

(1) P(x) = {y: x can produce y}, x 0 úN+

We distinguish between good or desirable outputs yg = (y1g, ... , yG

g) and bad or undesirable

outputs

yb = (y1b, ... , yB

b), so that y = (yg, yb) 0 ú +M. Emissions of sulfur dioxide (SO2) and particulate

matter less than ten microns in diameter (PM-10), which are the bad outputs, are undesirable

byproducts of producing the good output - kilowatt-hours (kWh) - and therefore will be modeled

as such. In particular we say that the good and bad outputs are nulljoint or byproducts if

(2) (yg, yb) 0 P(x) and yb = 0 imply yg = 0.

Equation (2) means that no bad outputs are produced (yb=0) only if none of the good outputs are

produced (yg=0). Equivalently, if some good outputs are produced then some bad outputs must

also be produced.

We impose this assumption on our “regulated” model, and note that if good output is

produced, then some of the bad (byproducts) output is also produced. Moreover, in our

regulated model we assume that outputs (yg, yb) are weakly disposable, i.e.,

(3) y = (yg, yb) 0 P(x), 0 # θ # 1 imply (θyg, θyb) 0 P(x)

This assumption states that proportional reduction of good and bad outputs is feasible, but

reduction of bads alone may not be.

-9-

In addition to assumptions (2) and (3) we impose standard properties on P(x), including:

inputs and good outputs are freely disposable and P(x) is a compact, convex set (see Färe and

Primont, 1995, for details).

Prior to formally showing how to calculate the output loss due to regulation, we provide

some intuition based on a simple diagram. In Figure 1, the regulated output set, PR(x), is

bounded by the line segments 0abcd0. This output set has the properties that good and bad

outputs are weakly disposable and nulljoint. The unregulated output set, PU(x), is bounded by

0ebcd0, and includes the regulated technology in our example as a proper subset.

To measure the potential output loss, i.e., the difference in the two output sets, first an

observation (yg, yb) (point A in Figure 1) is projected to the boundary (point B in Figure 1) by

scaling good output. The distance AB represents the reduced production of the good output

resulting from technical inefficiency. Hence, this producer could increase production of its good

output without increasing production of its bad output.

The downward sloping segment of the frontier - bc - represents the possibility that a

producer can simultaneously increase production of the good output and reduce production of

the bad output. While not all frontiers have this downward sloping segment, there are two

possible explanations for why we might observe this counter-intuitive result. First, observation c

may represent an older technology than the other observations used to construct the frontier.

While the model assumes a frontier is constructed with observations with access to similar

technologies, this is not always the case. Second, observation c may represent an outlier due to

measurement error.

-10-

0

Figure 1. Measure of Potential Output Loss

y

b

Bb

c

d

C

A

y

g

e

a

-11-

In this study, costs associated with technical efficiency are not included in PACM. Here

we assume that technical inefficiency, which is represented by the distance between an

observation and the weak disposability frontier, occurs for reasons unrelated to pollution

abatement activities. Hence, this study defines PACM as the difference between the production

of the good output when the bad output is unregulated and the production of the good output

when the bad output is regulated. In our figure, the distance between the two output sets - here

BC - gives us the potential loss due to regulation. Again, we only expand the good output.

Assuming that we have k = 1, ... , K observations of inputs xk, fuel quality qk, and outputs

yk, we may formulate the output sets as an activity analysis or Data Envelopment Analysis

(DEA) model. The regulated model is

( ) ( ) { ( , ): ,...,

,...,

,...,

,...,

,...,

,..., }

4 1

1

1

1

1 1

0 1

1

1

1

1

1

P x y y z y y m G

z y y i B

z x x n N

z q q j J

z k K

z k K

R g bk km

gmg

k

K

k kib

ib

k

K

k kn nk

K

k kj jk

K

kk

K

k

= ≥ =

= =

≤ =

= =

= =

≥ =

=

=

=

=

=

∑

∑

∑

∑

∑

-12-

The intensity variables, zk, are the weights assigned to each observation when constructing the

production set (i.e., the production possibilities set). The inequality constraints in (4) on the

good outputs, ymg , m=1,...., G imply that these outputs are freely disposable.8 Together with the

equality constraints in (4) on the bad outputs (yib, i=1,..., B), good outputs and bad outputs are

weakly disposable, i.e., they can be scaled down jointly to zero and hence they satisfy (3). The

equality constraint on the undesirable qualities of the fuels consumed (qk) specifies that the

undesirable qualities of the fuel consumed by the reference technology must equal the

undesirable qualities of the fuels consumed by the observation.

This model satisfies the assumption of good and bad outputs being nulljoint provided

( ) ( ) ,...,5 0 11

a y i Bkib

k

K

> ==

∑

( ) ,...,b y k Kkib

i

B

> ==∑ 0 1

1

Condition (5a) states that every bad output is produced by some plant k, and (5b) states that

every plant k produces at least one bad output. To further illustrate null-jointness, assume that

each yib = 0 in the expression of the output set (4). Then, due to (5) each intensity variable zk

must be zero, implying that each good output ymg must be zero.

In addition, the output correspondence (4) models variable returns to scale since the

intensity variables sum to unity. That is, it allows for increasing, constant, and decreasing returns

to scale. The unregulated model is obtained from (4) by allowing for the free disposability of

bad outputs, i.e., by changing the i = 1,..., B equalities to inequalities.

-13-

( ) ( ) { ( , ): ,...,

,...,

,...,

,...,

,...,

,..., }

6 1

1

1

1

1 1

0 1

1

1

1

1

1

P x y y z y y m G

z y y i B

z x x n N

z q q j J

z k K

z k K

U g bk km

gmg

k

K

k kib

ib

k

K

k kn nk

K

k kj jk

K

kk

K

k

= ≥ =

≥ =

≤ =

= =

= =

≥ =

=

=

=

=

=

∑

∑

∑

∑

∑

To measure the output loss due to regulation we apply a directional distance function, in

particular we choose a directional vector d 0 ú+G to be d= (1,...,1) then for some observation (xkN,

ykN) we compute

( ) ( , ; ) max{( , ) ( )}7 1 1rD y x y y P xR k k

kg

kb R k′ ′

′ ′′= + ⋅ ∈β

In our case with one good output the “efficient” output relative to the regulated technology is

( ) ( , ; )8 1y D y xkg R k k′

′ ′+r

-14-

corresponds to point A in Figure 1. corresponds to AB, and the sumykg′

rD y xR k k( , ; )′ ′ 1

of and corresponds to the production of the good output representedykg′

rD y xR k k( , ; )′ ′ 1

by point B.

The corresponding directional distance function of the unregulated technology is

( ) ( , ; ) max{( , ) ( )}9 1 1rD y x y y P xU k k

kg

kb U k′ ′

′ ′′= + ⋅ ∈β

and the efficient output relative to the unregulated technology is

( ) ( , ; )10 1y D y xkg U k k′

′ ′+r

where corresponds to AC, and the sum of and rD y xU k k( , ; )′ ′ 1 yk

g′

rD y xU k k( , ; )′ ′ 1

corresponds to the production of the good output represented by point C.

The revenue loss due to regulation is

( ) ( )( ) ( , ; ) ( , ; )11 1 1p y D y x p y D y xkkg U k k

kg R k k′

′′ ′

′′ ′+ − +

r r

or

[ ]( ) ( , ; ) ( , ; )12 1 1PACM p D y x D y xk U k k R k k= −′ ′ ′ ′ ′r r

-15-

where pkN is the observed price (i.e., revenue per kWh) of the good output for producer kN. The

difference inside the square brackets in (12) corresponds to the distance (BC) in Figure 1, which

is our estimate of the loss in output due to regulation.

We may compute the total loss of potential revenue by summing (12) over all kN:

( ) ( , ; ) ( , ; )13 1 1ΣPACM p D y x p D y xk U k k

k

k R k k

k= −′ ′ ′

′

′ ′ ′

′∑ ∑

r r

For feasible output vectors, the directional distance function is greater than or equal to

zero. It equals zero if and only if the observation vector (x kN, y kN) is on the production

possibilities frontier (i.e., the observation vector is technically efficient), while a point inside the

production frontier has a value greater than zero. Hence, the value of the directional distance

function represents the expansion of the good output required to project an observation (x kN, y kN)

from inside the production frontier to the frontier.

Next, we show how we use our estimate of lost revenue to provide a comparison to the

survey estimates of pollution abatement costs. We proceed by setting the lost revenue (equation

12) equal to the PACS incurred by producer kN. Then we can solve for the implied price per

kWh for kN

( ) $( , ; ) ( , ; )

141 1

pc

D y x D y xk

k

U k k R k k′

′

′ ′ ′ ′=−

r r

-16-

where ckN is the PACS for producer kN. The price estimates the revenue per kWh required$pk ′

for the value of the reduced production of the good output derived from the modeling method

(i.e., PACM) to equal PACS.

There are two ways to compute the mean of (14). We may compute the average of the

by summing (14) over all kN and dividing by the number of power plants or we can$pk ′

calculate the following:

( )( , ; ) ( , ; )

151 1

pc

D y x D y x

k

kU k k R k k

k k

=−

′

′′ ′ ′ ′

∑∑ ∑

′ ′

r r

The directional distance functions can be calculated as solutions to LP problems. In

order to determine PACM, two LP problems must be solved for each producer. When the bad

output is regulated, the LP problems impose weak disposability. As an example, we have for

observation kN:

-17-

( ) ( , ) max

. . ,...,

,...,

,...,

,...,

,...,

,...

16

1

1

1

1

1 1

0 1

1

1

1

1

1

rD x y

s t z y y m G

z y y i B

z x x n N

z q q j J

z k K

z k

R k k k

k kmg

k mg k

k

K

k kib

k ib

k

K

k kn k nk

K

k kj k jk

K

kk

K

k

′ ′ ′

′′

=

′=

′=

′=

=

=

≥ + =

= =

≤ =

= =

= =

≥ =

∑

∑

∑

∑

∑

β

β

, K

The weak disposability reference technology relative to which (x kN, y kN) is evaluated is

constructed from the observed production processes, i.e., the constraints are consistent with

PR(x) in (4). The solution to this LP problem gives the distance AB in Figure 1.

The value of the objective function represents the difference between the observed

production of the good output and the maximum potential production of the good output for a

given input vector and technology.

The first constraint of the LP problem represents the constraint imposed on the good

output. There is a separate constraint for each of the G good outputs of producer kN. The right-

-18-

hand side of the constraint represents the actual production of the good outputs for producer kN.

The left-hand side represents the production of the good output of the theoretical efficient

producer. The “greater than or equal to” sign imposes the restriction that the production of good

outputs by the theoretical producer must be greater than or equal to the observed production of

the good output of producer kN.

The second constraint of the LP problem represents the constraint imposed on the bad

output. There is a separate constraint for each of the B bad outputs produced by producer kN.

The equality sign associated with the constraint on the bad outputs imposes weak disposability

on the bad outputs. The right-hand side of the constraint represents the observed generation of

the bad outputs of producer kN. The left-hand side represents the level of the bad output

generated by the theoretical efficient producer. The difference between the LP problems for the

regulated and unregulated technologies are the constraints associated with bad outputs. The

“equal to” sign imposes the assumption of weak disposability on the bad outputs. For the

unregulated technology, the constraint is written as “less than or equal to.” Since βkN is excluded

from the constraints associated with the bad outputs, the decline in production of the good output

associated with environmental regulations assumes production of the bad output remains at its

observed level.

The third constraint of the LP problem represents the constraint imposed on input use.

There is a separate constraint for each of the N inputs employed by a producer. The right-hand

side of the constraint represents the observed input use of producer kN. The left-hand side

represents the inputs employed by the theoretical efficient producer. The inequality sign means

the theoretical producer cannot employ more inputs than producer kN.

-19-

The fourth constraint of the LP problem represents the constraint imposed on the

undesirable qualities of the fuels consumed by producer kN. There is a separate constraint for

each of the J undesirable attributes of the fuels. The undesirable qualities of the fuels are the

sulfur content of coal and oil and the ash content of coal. A higher sulfur or ash content of a fuel

represents more undesirable attributes of that fuel. The right-hand side of the constraint

represents the observed quality of the fuel consumed by producer kN. The left-hand side

represents the undesirable quality of the fuel consumed by the theoretical efficient producer.

The equality sign means the undesirable qualities of the fuel consumed by the theoretical

producer must equal those of the fuels consumed by producer kN.

A non-negativity constraint is imposed on the zk. The zk are the weights assigned to each

of the available production processes when constructing the production frontier. Since the

summation of the intensity parameters (i.e., the zk) is constrained to equal unity, variable returns

to scale is assumed for all of the LP problems.9

IV. Data and Results

The technology modeled in this study consists of one good output, “net electrical

generation” (kWh), and two bad outputs - emissions of sulfur dioxide (SO2) and particulate

matter less than ten microns in diameter (PM-10).10 The inputs consist of the capital stock, the

number of employees, and the heat content (in Btu) of the coal, oil, and natural gas consumed at

the plant. Undesirable fuel qualities consist of the ash content of coal and the sulfur content of

coal and oil. Carlson et al. (2000, pp. 1321-1322) discusses the derivation of the estimates of

the capital stock and number of employees. The Form EIA-767 survey is the source of

-20-

information about fuel consumption, fuel quality, and net generation of electricity. The U.S.

EPA is the source of emission estimates for PM-10 and SO2. In order to model a homogeneous

production technology, the sample consists of 237 power plants for 1994 and 232 power plants

for 1995. Although a power plant may consume coal, oil, or natural gas, coal must provide at

least 95 percent of the Btu of fuels consumed by it.11 Table 1 presents summary statistics of the

data and Appendix A contains a detailed discussion of the data.

The Form EIA- 861 survey provides information on sales of electricity and its associated

revenue from sales to ultimate consumers and sales for resale by each utility. In this study, the

revenue per kWh is identical for each power plant operated by a utility. When a power plant is

owned by more than one utility, it is assigned the revenue per kWh of its principal owner.

The EIA-767 survey requests information on operation and maintenance (O&M)

expenditures associated with both the collection and disposal of fly ash, bottom ash, and flue gas

desulfurization (FGD). Hence, six categories of expenditures in the EIA-767 survey are relevant

for this study. For the purposes of the PACS estimates used in this study, a nonresponse or a

response of “estimate not available” is treated as a zero. The instructions for the EIA-767 survey

(U.S. Department of Energy, 2001a, Plant Information -- Financial Information) state that

operation and maintenance (O&M) expenditures “... should exclude depreciation expense, cost

of electricity consumed, and fuel differential expense (i.e., extra costs of cleaner, thus more

expense fuel).”12 Appendix B contains a discussion of BEA’s use of the EIA-767 and how it

estimated the costs associated with consuming cleaner fuels. Collection activities can be viewed

as internal pollution abatement activities, while disposal activities can be viewed as external

pollution abatement activities. Only expenditures associated with collection activities are

-21-

included in the PACS-1 estimates reported in this study, whereas PACS-2 includes expenditures

associated with collection and disposal activities.

While Yaisawarng and Klein (1994) interpret the sulfur content of fuels as an “bad”

input, we view the sulfur content as a quality of the fuel accounted for by the model. Accounting

for the sulfur and ash content of the fuel allows us to model the fuels as a homogeneous inputs.

By assuming no change in the sulfur and ash content of the coal and oil consumed and no change

in the ash content of the coal consumed by the power plant, we exclude the costs associated with

switching to fuels with fewer undesirable qualities (e.g., coal with a lower sulfur level). Since

the estimates of pollution abatement costs reported in the EIA-767 survey exclude the costs

associated with fuel switching, the constraint on the ash and sulfur content of the fuels forces the

reference technology to consume the same quality of fuel as the observation. This allows us to

focus solely on comparing the estimates from our model with the “stated” costs of environmental

protection activities reported in the EIA-767 survey.13

Separate LP problems are solved for each coal-fired power plants in 1994 and 1995.

Table 2 presents results for each power plant in 1995 and Appendix C reports the results for

1994. Column (1) lists the reduced production of electricity (in kWh) which is the following

component of equation (12): Column (2) lists the pkN[ ]r rD y x D y xU k k R k k( , ; ) ( , ; ) .′ ′ ′ ′−1 1

observed for each power plant. Column (3), which is calculated using equation (12), is the

product of columns (1) and (2). Column (4) is the ratio of the reduced production of electricity,

which is reported in column (1), to the observed production of electricity. Column (5) reports

PACS-1, which is estimate of PACS for producer kN - ckN - which includes only collection

-22-

expenditures. Column (6), which is estimated using equation 14, lists the estimated price $pk ′

associated with column (5). Column (7) reports PACS-2, ckN, which includes collection and

disposal expenditures. Column (8), which is estimated using equation 14, lists the estimated

price associated with column (7). $pk ′

The results in Table 2 show the total ΣPACM estimates exceed the ΣPACS estimates.14

For 1995, ΣPACM is $2,364 million, while ΣPACS-1 is $524 million and ΣPACS-2 is $697

million. In 1994, ΣPACM is $2,538 million, while ΣPACS-1 is $360 million and ΣPACS-2 is

$518 million. If the 10 power plants with the highest ΣPACM (i.e., lost output in excess of $75

million) in 1995 are excluded, ΣPACM declines to $983 million. If the 10 power plants with the

highest ΣPACM (i.e., lost output in excess of $100 million) in 1994 are excluded, ΣPACM

declines to $774 million. Finally, it is worth noting that the reduced production of electricity (in

KWh) associated due to environmental regulations is 4.22 percent in 1994 and 3.87 percent in

1995 of the observed electric generation of all power plants in our sample.

The finding that ΣPACM exceeds ΣPACS is surprising for several reasons. Five factors

lead to the expectation that the PACS estimates would exceed the PACM estimates. First,

respondents might have an incentive to overstate the costs associated with pollution abatement

activities.15 Second, respondent to the EIA-767 survey may perceive environmental regulations

as more binding than the joint production model used to generate the PACM estimates.

Third, the technology specified in this study is assumed to be noncumulative (i.e., the

technology available to a producer consists solely of the processes used in that year). Since

pollution abatement activities have been undertaken by power plants for several decades (see

-23-

U.S. Department of Commerce, 1982), the unregulated technology based solely on data from

1994 or 1995 is unlikely to represent the true unregulated technology. If a process (i.e.,

observation) from an earlier period allows a power plant to produce more electricity than can be

produced with the same input vector in period t, then the true unregulated technology is not

accurately modeled. Instead of an unregulated technology, it is more accurate to depict it as the

least regulated technology available in the current year. The consequence of the failure to depict

the true unregulated technology is a downward bias in the “revealed” estimates of measurable

pollution abatement costs generated by the data used in this study.

Fourth, if a power plant operates a pollution abatement device (e.g., a scrubber) and the

plant produces more of the desirable output with a given input vector than any other plant, the

DEA model will determine there are no pollution abatement costs - PACM - even though PACS

reports expenditures associated with the operation of the pollution abatement device. Since

some of the O&M disposal expenditures in the EIA-767 survey may represent external pollution

abatement activities and expenditures for materials not included as inputs in the production

technology modeled in this study, the PACS estimates may exceed the PACM estimates.16

However, there are several explanations for the finding that PACM is greater than PACS.

One explanation is associated with the expenditure categories in the EIA-767 survey. The

PACM estimates may capture opportunity costs of pollution abatement activities excluded from

the PACS estimates (e.g., paperwork costs associated with environmental regulations).

A second explanation is the PACM estimates include the costs of electricity consumption

associated with pollution abatement activities, while the EIA-767 survey excludes the cost of

electricity associated with pollution abatement activities.17 Since pollution abatement activities

-24-

are one of the uses of the electricity consumed at the plant, some of the fuels consumed and the

labor employed by the plant are used to generate the electricity consumed for pollution

abatement activities. As a result, PACM estimates include the costs of electricity consumed for

pollution abatement activities.

A third explanation is respondents to the EIA-767 survey may perceive environmental

regulations as less binding constraints than the DEA model used to generate the PACM

estimates. The specification of the regulated and unregulated technologies reflect assumptions

about how to determine the costs associated with pollution abatement activities. When

answering the EIA-767 survey, the respondents may perceive a different baseline technology

than the unregulated technology specified by the DEA methodology used to derive the PACM

estimates. Alternatively, lower PACS estimates may reflect the perception of respondents that

the options available to electric utilities in an unregulated world are more limited than assumed

by economic models.

A fourth explanation for the discrepancy is the treatment of nonreponses to questions

regarding O&M expenditures for pollution abatement activities associated with reducing sulfur

dioxide and PM-10 emissions. Do respondents perceive no O&M expenditures or are these

instances of respondents failing to report O&M expenditures when in fact there are pollution

abatement activities? The electronic files containing the results of the EIA-767 survey do not

indicate whether the zeros represent nonresponses or zeros on the actual survey form. Those

cases in which nonreponses mask pollution abatement expenditures provide a downward bias to

the estimates from the EIA-767 survey.18

A fifth explanation for why PACM estimates exceed the PACS estimates is the PACM

-25-

estimates may be influenced by outliers in the sample which creates an upward bias in the

PACM estimates. There are two ways to address this concern. A simple approach is to eliminate

a certain percentage of the outliers. Although there is no statistical theory justifying such a

procedure, it provides insights into the effect of outliers on the results. A more sophisticated

approach is using a bootstrap technique, which tests the sensitivity of the results to outliers in the

data, to add a stochastic element to the analysis.

Finally, the regulated technology specified in this study is valid if producers are engaged

in pollution abatement activities. If the free disposability is the correct technology, then the

observations used to construct the regulated technology are simply inefficient producers relative

to the unregulated production frontier. In this case observations used to construct the regulated

frontier are in fact inefficient, and the PACM estimates are biased in an upward direction.

The accuracy of the results of the modeling approach can be validated in two ways. First,

the data can be used to estimate the marginal abatement cost of reducing a ton of SO2 emissions.

Since previous modeling efforts have yielded reasonable estimates of the marginal abatement

costs of reducing SO2 emissions, these calculations would indicate if the data and model used in

this study yield atypical results. Since the EIA-767 survey provides data on the sulfur content of

the coal and oil, it is possible to implement a materials balance analysis of sulfur in order to

determine the average cost of abating a ton of sulfur emissions as a second method of validating

the results of this study. This calculation would provide insights into whether the data and

model yield reasonable estimates of the average cost of abating SO2 emissions.

-26-

V. Conclusions

This study investigated the relationship between “stated” cost estimates of pollution

abatement activities and the costs of pollution abatement activities “revealed” by the actual

behavior of the regulated entities through a comparison of PACS and PACM estimates for U.S.

coal-fired power plants. The latter views the costs of pollution abatement activities as the value

of the reduced production of the good output due to environmental regulations. This alternative

method is based on a DEA model, which allows us to model joint production with and without

regulations and estimate pollution abatement costs as the difference in production in the two

models. We compare these estimates with the survey estimates of the pollution abatement costs

borne by power plants in 1994 and 1995.

In estimating pollution abatement costs using our DEA approach, we model the

unregulated and regulated technologies using notions of free and weak disposability,

respectively. Hence, the joint production model represents an example of the advantage of

establishing the link between pollution abatement costs and production technologies. This study

illustrates the potential of using a joint production model to assess the costs of reducing air

pollutants emitted into the atmosphere.

This model could be estimated parametrically-- either a parametric cost or distance

function can be specified and estimated as a frontier model (see for example Färe et al., 1993).

This involves estimating one regulated and one unregulated function for all observations.

The use of joint production models to estimate the costs associated with pollution

abatement activities follows in the tradition of using economic models to estimate the costs of

regulations. The costs now depend on the specification of the production technology (i.e., the

-27-

functional form and the associated elasticities of substitution) which is comparable to efforts that

estimate the costs of other types of economic policies. In fact, the joint production of good and

bad outputs has been specified in CGE models such as Jensen and Rasmussen (2000) to estimate

the costs associated with proposed reductions in CO2 emissions.

We believe production models provide a useful complement to survey methods used to

identify pollution abatement costs. If internal pollution abatement activities consist primarily of

end-of-pipe technologies, then surveys should provide an adequate means of estimating the costs

of these activities. However, as an increasing share of the internal activities associated with

abating air pollutants involve integrated technologies, surveys become an exercise in “stated”

costs. In that case, economic models, which are more closely tied to production theory,

represent a means of estimating the costs associated with pollution abatement activities.

Since the EIA-767 survey excludes expenditures associated with fuel switching, the

expenditures reported in the EIA-767 survey are associated with end-of-pipe pollution abatement

activities. Survey estimates of the costs of these activities are likely to be more accurate than

cost estimates associated with change in production process abatement techniques. Hence, the

divergence between the “stated” and “revealed” costs estimates reported in this study should be

smaller than a study comparing model estimates of pollution abatement costs with survey

estimates of the costs associated with change in process abatement technologies.

Future investigations using the joint production model specified in this study might

include additional bad outputs, incorporate the revenue from the sale of byproducts, and expand

the sample to include observations from earlier years in order to obtain a more accurate estimate

of the unregulated technology.

-28-

Although this study is concerned with the costs of pollution abatement activities, it is

possible to speculate on whether the results of this study are relevant to the discussion about the

“stated” vs. “revealed” methods used to estimate the benefits of environmental controls. It

seems reasonable to assume the individuals responsible for completing the EIA-767 survey are

more familiar with the costs of pollution abatement activities than the typical respondent to a

contingent valuation survey. Hence, the divergence between the “state” and “revealed” costs of

this study is likely to be less than the divergence found by a comparable study of benefits.

-29-

References

Bellas, Allen S. (1998), “Empirical Evidence of Advances in Scrubber Technology,” Resourceand Energy Economics, 20, No. 4 (December), 327-343.

Brännland, Runar, Rolf Färe, and Shawna Grosskopf (1995), “Environmental Regulation andProfitability: An Application to Swedish Pulp and Paper Mills, Environmental andResource Economics, 6, No. 1, 23-36.

Carlson, Curtis, and Dallas Burtraw, Maureen Cropper, and Karen Palmer (2000), “SulfurDioxide Control by Electric Utilities: What are the Gains from Trade?” Journal ofPolitical Economy, 108, No. 6 (December), 1292-1326.

Coggins, Jay and John Swinton (1996), “The Price of Pollution: A Dual Approach to ValuingSO2 Allowances,” Journal of Environmental Economics and Management, 30, No.1(January), 58-72.

Farber, Kitt and Gary Rutledge (1989), “Pollution Abatement and Control Expenditures:Methods and Sources for Current-Dollar Estimates,” mimeo.

Färe, Rolf and Shawna Grosskopf (1983), “Measuring Output Efficiency,” European Journal ofOperational Research, 13, 173-179.

Färe, Rolf, Shawna Grosskopf, C.A. Knox Lovell and Carl Pasurka (1989), “MultilateralProductivity Comparisons When Some Outputs are Undesirable: A NonparametricApproach,” Review of Economics and Statistics, LXXI, No. 1 (February), 90-98.

Färe, Rolf, Shawna Grosskopf, C.A. Knox Lovell, and Suthathip Yaisawarng (1993),“Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach,”Review of Economics and Statistics, 75, No. 2 (May), 374-380.

Färe, Rolf, Shawna Grosskopf, and Carl Pasurka (1986), “Effects on Relative Efficiency inElectric Power Generation Due to Environmental Controls,” Resources and Energy, 8,No. 2 (June), 167-184.

Färe, Rolf and Daniel Primont (1995), Multi-Output and Duality: Theory and Application,Boston: Kluwer-Nijhoff Publishing.

Gollop, Frank M. and Mark J. Roberts (1983), “Environmental Regulations and ProductivityGrowth: The Case of Fossil-fueled Electric Power Generation,” Journal of PoliticalEconomy, 91, No. 4, 654-674.

-30-

Jensen, Jesper and Tobias N. Rasmussen (2000), “Allocation of CO2 Emissions Permits: AGeneral Equilibrium Analysis of Policy Instruments,” Journal of EnvironmentalEconomics and Management, 40, No. 2 (September), 111-136.

Kolstad, Charles D. and Michelle H.L. Turnovsky (1998), “Cost Functions and NonlinearProcess: Estimating a Technology with Quality-Differentiated Inputs,” Review ofEconomics and Statistics, 80, No. 3 (August) 444-453.

Martin, David W., John B. Braden, and J. Lon Carlson (1990), “Estimation of Process Changefor Industrial Pollution Abatement,” Journal of the Air and Waste ManagementAssociation, 40, 211-216.

Streitwieser, Mary L. (1996), “Evaluation and Use of the Pollution Abatement Costs andExpenditures Survey Micro Data,” Center for Economic Studies, CES 96-1,(http://www.ces.census.gov/ces.php/papers#1996)

Streitwieser, Mary L. (1997), “Using the Pollution Abatement Costs and Expenditures MicroData for Descriptive and Analytic Research,” Journal of Economic and SocialMeasurement, 23, No. 1, 1-25.

Swinton, John B. (1998), “At What Cost Do We Reduce Pollution? Shadow Prices of SO2Emissions,” Energy Journal, 19, No. 4, 63-83.

Tran, Ngoc-Bich and V. Kerry Smith (1983), “The Role of Air and Water Residuals for SteamElectric Power Generation,” Journal of Environmental Economics and Management, 10,No. 1 (March), 18-34.

Turner, Judi A. (1995), “Measuring the Cost of Pollution Abatement in the U.S. Electric UtilityIndustry: A Production Frontier Approach,” Ph.D. Dissertation, University of NorthCarolina, Chapel Hill, NC.

United Nations (1993), Handbook of National Accounting, Studies in Methods, Series F, No. 61,Integrated Environmental and Economic Accounting, Interim Version, United Nations:New York. (ST/ESA/STAT/SER.F/61, UN publication, Sales No. E.93.XVII.12).

U.S. Department of Commerce, Bureau of Economic Analysis (1982), “Stock of Plant andEquipment for Air and Water Pollution Abatement in the United States, 1960-81,” Surveyof Current Business, 62, No. 11 (November), 18-25.

U.S. Department of Commerce, Bureau of Economic Analysis (1994), “Integrated andEnvironmental Satellite Accounts,” Survey of Current Business, 74, No. 4 (April), 33-49.(see “NIPA Related Articles” for PDF and HTML versions of the article at:http://www.bea.doc.gov/bea/an1.htm )

-31-

U.S. Department of Commerce, Bureau of the Census (1976), Pollution Abatement Costs andExpenditures: 1973, Current Industrial Reports, MA200, Washington, D.C.: U.S.Government Printing Office.

U.S. Department of Commerce, Bureau of the Census (1996), Pollution Abatement Costs andExpenditures: 1994, Current Industrial Reports, MA200, Washington, D.C.: U.S.Government Printing Office. (http://www.census.gov/prod/2/manmin/ma200x94.pdf).

U.S. Department of Energy, Energy Informational Administration (2001), FORM EIA-767“Steam-Electric Plant Operation and Design Report,” and its accompanying instructionsare located at: (http://www.eia.doe.gov/cneaf/electricity/page/forms.html).

U.S. Department of Energy, Energy Informational Administration (2001), FORM EIA-861“Annual Electric Utility Report,” and its accompanying instructions are locatedat:(http://www.eia.doe.gov/cneaf/electricity/page/forms.html).

U.S. Department of Energy, Energy Informational Administration (1999), information collectedby FORM EIA-861 “Annual Report of Public Utilities” is located at:http://www.eia.doe.gov/cneaf/electricity/page/eia861.html

U.S. Department of Energy, Energy Informational Administration (1999), Electric PowerAnnual - 1998 (Volume II).(http://www.eia.doe.gov/cneaf/electricity/epav2/epav2_sum.html).

Vogan, Christine (1996), “Pollution Abatement and Control Expenditures, 1972-94,” Survey ofCurrent Business, 76, No. 9 (September), 48-67. (See “NIPA Related Articles” at:http://www.bea.doc.gov/bea/an1.htm for PDF and HTML versions of the article)

Yaisawarng, Suthathip and J. Douglass Klein (1994), “The Effects of Sulfur Dioxide Controls onProductivity Change in the U.S. Electric Power Industry,” Review of Economics andStatistics, 76, 447-460.

-32-

Table 1Summary Statistics

(232 coal-fired power plants, 1995)

Units Mean Sample Std. Dev. Maximum MinimumElectricity kWh 4,876,519,321.05 4,388,002,481.93 20,222,352,000.00 43,132,000.00PM10 short tons 646.50 890.51 5,886.58 2.95SO2 short tons 35,249.83 38,474.21 265,995.43 455.00Capital stock dollars 390,367,237.70 395,943,999.06 2,869,737,691.00 29,177,515.00Employees workers 193.71 131.42 895.00 32.00Heat content of coal Btu 48,916,883,060,344.80 43,240,874,632,070.90 193,574,141,400,000.00 726,537,600,000.00Heat content of oil Btu 89,323,787,790.52 130,103,019,337.72 1,168,644,552,600.00 0.00Heat content of gas Btu 99,732,542,241.38 312,380,167,220.96 2,678,259,900,000.00 0.00Sulfur content of coal short tons 25,715.47 32,423.30 186,213.12 230.40Sulfur content of oil short tons 6.77 10.53 61.99 0.00Ash content of coal short tons 223,532.99 268,196.09 1,840,282.65 2,442.50

-33-

Table 21995 Results

PLANT NAME ReducedOutput (kWh)

Price($/ KWH)

PACM($1,000)

ReducedOutput /ObservedOutput

PACS-1 ($1,000)

EstimatedPrice forPACS-1

PACS-2 ($1,000)

EstimatedPrice forPACS-2

BARRY 0 0.0504 0 0.0000 2,560 undefined 2,570 undefinedGADSDEN 89,500,000 0.0504 4,510 0.2328 183 0.0020 183 0.0020GORGAS 0 0.0504 0 0.0000 2,457 undefined 2,457 undefinedGREENE COUNTY 2,600,000 0.0504 131 0.0008 138 0.0531 427 0.1642E.C. GASTON 507,600,000 0.0504 25,579 0.0603 2,986 0.0059 4,858 0.0096DOLET HILLS 0 0.0525 0 0.0000 0 undefined 0 undefinedCHOLLA 0 0.0776 0 0.0000 6,813 undefined 6,813 undefinedLIMESTONE 0 0.0578 0 0.0000 10,012 undefined 18,277 undefinedARAPAHOE 33,300,000 0.0588 1,958 0.0349 0 0.0000 1,243 0.0373CHEROKEE 88,200,000 0.0588 5,186 0.0249 0 0.0000 7,657 0.0868COMANCHE 0 0.0588 0 0.0000 0 undefined 259 undefinedVALMONT 0 0.0588 0 0.0000 0 undefined 284 undefinedINDIAN RIVER 0 0.0626 0 0.0000 312 undefined 2,899 undefinedBRANDON SHORES 134,700,000 0.0608 8,192 0.0148 837 0.0062 7,032 0.0522CRIST 309,800,000 0.0559 17,331 0.0858 416 0.0013 2,846 0.0092LANSING SMITH 0 0.0559 0 0.0000 62 undefined 216 undefinedBIG BEND 0 0.0640 0 0.0000 6,570 undefined 6,570 undefinedF.J. GANNON 0 0.0640 0 0.0000 1,049 undefined 1,049 undefinedARKWRIGHT 0 0.0601 0 0.0000 0 undefined 0 undefinedBOWEN 0 0.0601 0 0.0000 613 undefined 613 undefined

-34-

HAMMOND 0 0.0601 0 0.0000 83 undefined 136 undefinedHARLIEE BRANCH 497,400,000 0.0601 29,869 0.0572 932 0.0019 932 0.0019JACK MCDONOUGH 14,500,000 0.0601 871 0.0049 0 0.0000 0 0.0000MITCHELL 35,300,000 0.0601 2,120 0.1050 0 0.0000 0 0.0000YATES 11,300,000 0.0601 679 0.0042 2,142 0.1896 3,061 0.2709E.D. EDWARDS 0 0.0547 0 0.0000 601 undefined 1,043 undefinedCOFFEEN 656,800,000 0.0497 32,630 0.2125 1,839 0.0028 2,920 0.0044GRAND TOWER 0 0.0497 0 0.0000 319 undefined 687 undefinedHUTSONVILLE 0 0.0497 0 0.0000 178 undefined 313 undefinedMEREDOSIA 0 0.0497 0 0.0000 201 undefined 321 undefinedCRAWFORD 3,200,000 0.0749 240 0.0019 1,175 0.3672 1,718 0.5369JOLIET 0 0.0749 0 0.0000 943 undefined 1,621 undefinedKINKAID 444,600,000 0.0749 33,299 0.1892 1,401 0.0032 2,071 0.0047POWERTON 703,200,000 0.0749 52,668 0.1472 2,335 0.0033 3,548 0.0050WAUKEGAN 78,700,000 0.0749 5,894 0.0283 3,173 0.0403 4,330 0.0550WILL COUNTY 0 0.0749 0 0.0000 2,819 undefined 4,421 undefinedJOPPA 32,900,000 0.0187 616 0.0042 244 0.0074 244 0.0074BALDWIN 0 0.0615 0 0.0000 1,018 undefined 1,018 undefinedHAVANA 124,100,000 0.0615 7,632 0.0815 61 0.0005 61 0.0005HENNEPIN 0 0.0615 0 0.0000 341 undefined 341 undefinedWOOD RIVER 0 0.0615 0 0.0000 193 undefined 193 undefinedSTATE LINE 0 0.0576 0 0.0000 701 undefined 1,064 undefinedCLIFTY CREEK 0 0.0159 0 0.0000 2,909 undefined 4,542 undefinedTANNERS CREEK 2,614,900,000 0.0407 106,533 0.6388 672 0.0003 1,086 0.0004E.W. STOUT 0 0.0493 0 0.0000 375 undefined 375 undefinedH.T. PRITCHARD 0 0.0493 0 0.0000 240 undefined 240 undefined

-35-

PETERSBURG 429,000,000 0.0493 21,169 0.0408 10,631 0.0248 10,631 0.0248BAILLY 545,800,000 0.0595 32,481 0.1865 690 0.0013 1,467 0.0027CAYUGA 1,316,900,000 0.0404 53,235 0.2168 169 0.0001 169 0.0001EDWARDSPORT 0 0.0404 0 0.0000 557 undefined 557 undefinedGALLAGHER 0 0.0404 0 0.0000 237 undefined 237 undefinedWABASH RIVER 0 0.0404 0 0.0000 367 undefined 367 undefinedF.B. CULLEY 0 0.0441 0 0.0000 2,165 undefined 2,938 undefinedLANSING 653,900,000 0.0497 32,487 0.8431 190 0.0003 299 0.0005MILTON KAPP 0 0.0497 0 0.0000 174 undefined 1,106 undefinedPRAIRIE CREEK 0 0.0524 0 0.0000 99 undefined 496 undefinedRIVERSIDE 200,000 0.0524 10 0.0005 526 2.6300 526 2.6300COUNCIL BLUFFS 0 0.0522 0 0.0000 699 undefined 699 undefinedNEAL NORTH 6,800,000 0.0522 355 0.0013 1,530 0.2250 1,530 0.2250BURLINGTON 0 0.0524 0 0.0000 2 undefined 2 undefinedRIVERTON 0 0.0490 0 0.0000 41 undefined 41 undefinedLA CYNGE 3,554,500,000 0.0544 193,464 0.4788 5,434 0.0015 5,434 0.0015LAWRENCE 0 0.0465 0 0.0000 1,707 undefined 2,088 undefinedBIG SANDY STREAM 0 0.0313 0 0.0000 1,049 undefined 1,511 undefinedE.W. BROWN 0 0.0391 0 0.0000 387 undefined 387 undefinedGHENT 1,407,100,000 0.0391 54,998 0.1193 2,856 0.0020 2,856 0.0020GREEN RIVER 0 0.0391 0 0.0000 660 undefined 660 undefinedCANE RUN 30,700,000 0.0452 1,389 0.0127 6,684 0.2177 6,684 0.2177MILL CREEK 547,100,000 0.0452 24,754 0.0743 18,947 0.0346 19,132 0.0350C.P. CRANE 3,926,700,000 0.0608 238,803 2.4064 898 0.0002 1,262 0.0003RP SMITH 0 0.0477 0 0.0000 219 undefined 271 undefinedDICKERSON 200,000 0.0674 13 0.0001 1,677 8.3850 2,591 12.9550

-36-

MORGANTOWN 0 0.0674 0 0.0000 1,938 undefined 3,068 undefinedMT TOM 0 0.0287 0 0.0000 60 undefined 697 undefinedB.C. COBB 18,700,000 0.0633 1,184 0.0094 3,663 0.1959 4,771 0.2551J.H. CAMPBELL 0 0.0633 0 0.0000 771 undefined 771 undefinedJ.C. WEADOCK 130,600,000 0.0633 8,269 0.0660 590 0.0045 590 0.0045J.R. WHITTING 0 0.0633 0 0.0000 239 undefined 721 undefinedMARYSVILLE 0 0.0732 0 0.0000 0 undefined 186 undefinedMONROE 0 0.0732 0 0.0000 0 undefined 1,957 undefinedRIVER ROUGE 0 0.0732 0 0.0000 0 undefined 955 undefinedST CLAIR 0 0.0732 0 0.0000 0 undefined 1,752 undefinedTRENTON CHANNEL 147,900,000 0.0732 10,833 0.0396 0 0.0000 1,845 0.0125PRESQUE ISLE 0 0.0523 0 0.0000 1,792 undefined 1,792 undefinedSYL LASKIN 0 0.0373 0 0.0000 20 undefined 20 undefinedCLAY BOSWELL 665,000,000 0.0373 24,833 0.1026 2,911 0.0044 2,911 0.0044BLACK DOG 0 0.0507 0 0.0000 381 undefined 665 undefinedHIGH BRIDGE 0 0.0507 0 0.0000 218 undefined 383 undefinedALLEN S KING 0 0.0507 0 0.0000 1,066 undefined 1,762 undefinedRIVERSIDE 1,282,600,000 0.0507 65,053 0.6544 990 0.0008 990 0.0008HOOT LAKE 0 0.0448 0 0.0000 176 undefined 216 undefinedASBURY 0 0.0490 0 0.0000 129 undefined 129 undefinedMONTROSE 0 0.0544 0 0.0000 526 undefined 526 undefinedLABADIE 0 0.0543 0 0.0000 1,539 undefined 1,539 undefinedSIOUX 3,348,000,000 0.0543 181,841 0.9994 479 0.0001 479 0.0001J E CORETTE 0 0.0423 0 0.0000 251 undefined 306 undefinedREID GARDNER 0 0.0612 0 0.0000 0 undefined 0 undefinedMOHAVE 0 0.1033 0 0.0000 1,943 undefined 3,211 undefined

-37-

MERRIMACK 0 0.0849 0 0.0000 307 undefined 857 undefinedFOUR COURNERS 0 0.0776 0 0.0000 17,426 undefined 24,668 undefinedGOUDEY 0 0.0809 0 0.0000 84 undefined 240 undefinedGREENIDGE 0 0.0809 0 0.0000 104 undefined 186 undefinedMILLIKEN 1,836,000,000 0.0809 148,530 0.9237 2,584 0.0014 2,703 0.0015C R HUNTLEY 0 0.0850 0 0.0000 0 undefined 430 undefinedDUNKIRK 0 0.0850 0 0.0000 477 undefined 1,432 undefinedROCHESTER 0 0.0873 0 0.0000 0 undefined 637 undefinedASHEVILLE 1,638,500,000 0.0593 97,213 0.6280 113 0.0001 194 0.0001CAPE FEAR 50,900,000 0.0593 3,020 0.0327 28 0.0006 108 0.0021LEE, H.F. 92,900,000 0.0593 5,512 0.1077 144 0.0016 144 0.0016ROXBORO 969,400,000 0.0593 57,515 0.0755 571 0.0006 1,418 0.0015L V SUTTON 0 0.0593 0 0.0000 286 undefined 286 undefinedW H WEATHERSPOON 77,000,000 0.0593 4,568 0.3054 58 0.0008 58 0.0008G G ALLEN 0 0.0558 0 0.0000 1,240 undefined 1,911 undefinedBUCK 0 0.0558 0 0.0000 666 undefined 752 undefinedCLIFFSIDE 1,913,700,000 0.0558 106,832 0.7961 918 0.0005 1,386 0.0007DAN RIVER 0 0.0558 0 0.0000 382 undefined 460 undefinedMARSHALL 0 0.0558 0 0.0000 1,250 undefined 3,524 undefinedRIVERBEND 534,200,000 0.0558 29,822 0.6661 718 0.0013 885 0.0017R M HESKETT 0 0.0545 0 0.0000 143 undefined 160 undefinedBECKJORD 500,000 0.0540 27 0.0001 2,997 5.9940 4,971 9.9420MIAMI FORT 0 0.0540 0 0.0000 2,565 undefined 2,565 undefinedASHTABULA 0 0.0815 0 0.0000 185 undefined 577 undefinedAVON LAKE 0 0.0815 0 0.0000 1,508 undefined 4,252 undefinedEAST LAKE 0 0.0815 0 0.0000 1,150 undefined 4,137 undefined

-38-

CONESVILLE 0 0.0593 0 0.0000 10,369 undefined 12,180 undefinedO H HUTCHINGS 0 0.0607 0 0.0000 319 undefined 368 undefinedJ.M. STUART 0 0.0607 0 0.0000 2,667 undefined 3,392 undefinedNILES 0 0.0728 0 0.0000 397 undefined 744 undefinedR E BURGER 0 0.0728 0 0.0000 468 undefined 888 undefinedW H SAMMIS 0 0.0728 0 0.0000 2,830 undefined 7,542 undefinedMUSKINGUM RIVER 0 0.0391 0 0.0000 922 undefined 1,928 undefinedKYGER CREEK 0 0.0173 0 0.0000 2,223 undefined 2,882 undefinedBAY SHORE 25,900,000 0.0765 1,981 0.0082 568 0.0219 774 0.0299MUSKOGEE 0 0.0508 0 0.0000 883 undefined 2,837 undefinedELRAMA 0 0.0756 0 0.0000 6,290 undefined 10,345 undefinedPORTLAND 0 0.0691 0 0.0000 604 undefined 604 undefinedTITUS 1,000,000 0.0691 69 0.0010 322 0.3220 322 0.3220CONEMAUGH 156,400,000 0.0601 9,395 0.0133 11,011 0.0704 11,011 0.0704HOMMER CITY 0 0.0601 0 0.0000 1,949 undefined 1,949 undefinedSEWARD 0 0.0601 0 0.0000 872 undefined 872 undefinedSHAWVILLE 0 0.0601 0 0.0000 1,574 undefined 1,574 undefinedKEYSTONE 0 0.0601 0 0.0000 4,237 undefined 4,237 undefinedNEW CASTLE 344,000,000 0.0625 21,504 0.2290 423 0.0012 606 0.0018BRUNNER ISLAND 67,800,000 0.0631 4,275 0.0087 1,418 0.0209 2,278 0.0336MONTOUR 0 0.0631 0 0.0000 1,698 undefined 4,120 undefinedARMSTRONG 0 0.0484 0 0.0000 999 undefined 1,285 undefinedHATFIELD'S FERRY 0 0.0484 0 0.0000 3,353 undefined 3,938 undefinedMITCHELL 0 0.0484 0 0.0000 3,500 undefined 5,279 undefinedH B ROBINSON 0 0.0593 0 0.0000 6 undefined 17 undefinedLEE, W.S. 82,700,000 0.0558 4,617 0.1666 664 0.0080 769 0.0093

-39-

MCMEEKIN, SILAS 1,503,800,000 0.0561 84,356 0.9904 59 0.0000 731 0.0005WATEREE 24,100,000 0.0561 1,352 0.0058 190 0.0079 1,048 0.0435BIG BROWNT 0 0.0632 0 0.0000 3,430 undefined 5,338 undefinedCARBON 0 0.0427 0 0.0000 433 undefined 583 undefinedCLINCH RIVER 296,900,000 0.0430 12,757 0.0728 1,009 0.0034 1,799 0.0061GLEN LYN 0 0.0430 0 0.0000 72 undefined 1,441 undefinedPOTOMAC RIVER 66,000,000 0.0674 4,450 0.0335 1,494 0.0226 2,169 0.0329BREMO 549,200,000 0.0622 34,161 0.4138 0 0.0000 0 0.0000CHESTERFIELD 85,500,000 0.0622 5,318 0.0120 383 0.0045 383 0.0045CENTRALIA 0 0.0427 0 0.0000 1,268 undefined 1,930 undefinedAMOS 0 0.0430 0 0.0000 1,825 undefined 8,173 undefinedKANAWHA 5,300,000 0.0430 228 0.0044 631 0.1191 1,043 0.1968ALBRIGHT 0 0.0479 0 0.0000 740 undefined 962 undefinedFORT MARTIN 0 0.0479 0 0.0000 1,148 undefined 1,855 undefinedHARRISON 0 0.0479 0 0.0000 24,235 undefined 28,257 undefinedRIVESVILLE 0 0.0479 0 0.0000 195 undefined 351 undefinedWILLOW ISLAND 0 0.0479 0 0.0000 762 undefined 866 undefinedKAMMER 0 0.0391 0 0.0000 531 undefined 1,239 undefinedMITCHELL 8,500,000 0.0391 332 0.0010 1,000 0.1176 2,121 0.2495MT STORM 1,000,000 0.0622 62 0.0001 2,228 2.2280 2,228 2.2280PORT WASHINGTON 21,400,000 0.0523 1,119 0.0268 1,332 0.0622 1,332 0.0622SOUTH OAK CREEK 0 0.0523 0 0.0000 4,444 undefined 4,444 undefinedNELSON DEWEY 0 0.0459 0 0.0000 270 undefined 325 undefinedPULLIAM 0 0.0442 0 0.0000 683 undefined 1,299 undefinedWESTON 0 0.0442 0 0.0000 1,049 undefined 1,157 undefinedALMA 0 0.0345 0 0.0000 86 undefined 315 undefined

-40-

GENOA 0 0.0345 0 0.0000 148 undefined 871 undefinedDAVE JOHNSTON 0 0.0427 0 0.0000 2,650 undefined 3,736 undefinedNAUGHTON 0 0.0427 0 0.0000 2,505 undefined 2,508 undefinedMADGETT 566,000,000 0.0345 19,542 0.3656 396 0.0007 711 0.0013NAVAJO 0 0.0602 0 0.0000 3,723 undefined 4,069 undefinedMILLER, JAMES 0 0.0504 0 0.0000 2,569 undefined 2,771 undefinedPLEASANTS 0 0.0479 0 0.0000 18,161 undefined 20,287 undefinedWHITE BLUFF 133,200,000 0.0533 7,102 0.0138 0 0.0000 0 0.0000DUCK CREEK 0 0.0547 0 0.0000 3,996 undefined 4,649 undefinedNEWTON 1,209,600,000 0.0497 60,094 0.2076 9,493 0.0078 10,441 0.0086EAST BEND 2,096,000,000 0.0540 113,100 0.4869 6,340 0.0030 6,857 0.0033ZIMMER, W.H. 0 0.0540 0 0.0000 19,637 undefined 24,828 undefinedKILLEN 0 0.0607 0 0.0000 58 undefined 288 undefinedBELL RIVER 62,400,000 0.0732 4,571 0.0070 0 0.0000 1,799 0.0288WANSLEY 35,000,000 0.0601 2,102 0.0042 533 0.0152 553 0.0158IATAN 297,000,000 0.0544 16,165 0.0636 542 0.0018 589 0.0020JEFFERY ENERGY CNTR 0 0.0465 0 0.0000 2,225 undefined 2,486 undefinedTRIMBLE COUNTY 0 0.0452 0 0.0000 1,854 undefined 1,854 undefinedVICTOR DANIEL 0 0.0469 0 0.0000 690 undefined 1,586 undefinedCOLSTRIP 0 0.0423 0 0.0000 15,316 undefined 16,518 undefinedR.M. SCHAHFER 207,900,000 0.0595 12,372 0.0277 13,881 0.0668 14,507 0.0698SHERBURNE COUNTY 0 0.0507 0 0.0000 11,232 undefined 11,232 undefinedSOONER 0 0.0508 0 0.0000 697 undefined 933 undefinedBIG STONE 2,462,200,000 0.0448 110,370 0.9825 222 0.0001 438 0.0002WYODAK 837,700,000 0.0427 35,806 0.3345 2,587 0.0031 3,037 0.0036BOARDMAN 0 0.0475 0 0.0000 0 undefined 95 undefined

-41-

GIBSON 0 0.0404 0 0.0000 10,158 undefined 12,833 undefinedMcINTOSH 0 0.0621 0 0.0000 50 undefined 72 undefinedA B BROWN 0 0.0441 0 0.0000 8,606 undefined 10,230 undefinedFLINT CREEK 0 0.0414 0 0.0000 154 undefined 171 undefinedWELSH 178,800,000 0.0414 7,399 0.0240 817 0.0046 817 0.0046MARTIN LAKE 0 0.0632 0 0.0000 25,097 undefined 32,142 undefinedMONTICELLO 0 0.0632 0 0.0000 8,960 undefined 12,780 undefinedRUSH ISL 0 0.0543 0 0.0000 790 undefined 790 undefinedHUNTER 0 0.0427 0 0.0000 7,602 undefined 8,907 undefinedROCKPORT 0 0.0407 0 0.0000 1,525 undefined 2,354 undefinedPLEASANT PRAIRIE 0 0.0523 0 0.0000 2,408 undefined 2,408 undefinedCOLETO CREEK 74,700,000 0.0573 4,283 0.0187 153 0.0020 349 0.0047HARRINGTON 0 0.0416 0 0.0000 876 undefined 1,460 undefinedTOLK STATION 0 0.0416 0 0.0000 457 undefined 761 undefinedPAWNEE 0 0.0588 0 0.0000 0 undefined 708 undefinedMAYO 180,300,000 0.0593 10,697 0.0444 96 0.0005 191 0.0011OTTUMWA 0 0.0524 0 0.0000 125 undefined 125 undefinedMOUNTAINEER 0 0.0430 0 0.0000 1,858 undefined 2,905 undefinedSANDOW 0 0.0632 0 0.0000 4,817 undefined 6,775 undefinedLOUISA 4,200,000 0.0524 220 0.0014 438 0.1043 438 0.1043NEAL SOUTH 0 0.0522 0 0.0000 615 undefined 615 undefinedPIRKEY 0 0.0414 0 0.0000 3,422 undefined 3,544 undefinedCOLUMBIA 56,000,000 0.0459 2,569 0.0080 1,306 0.0233 1,787 0.0319BELEWS CREEK 0 0.0558 0 0.0000 1,437 undefined 3,640 undefinedJIM BRIDGER 0 0.0427 0 0.0000 10,971 undefined 11,267 undefinedHUNTINGTON 0 0.0427 0 0.0000 2,297 undefined 3,068 undefined

-42-

GAVIN, JAMES M. 0 0.0391 0 0.0000 21,302 undefined 21,302 undefinedCOYOTE 686,000,000 0.0545 37,414 0.2350 2,778 0.0041 3,231 0.0047VALMY 0 0.0672 0 0.0000 1,471 undefined 1,590 undefinedCHESWICK 0 0.0756 0 0.0000 135 undefined 135 undefined

Totals 43,833,900,000 2,363,936 524,323 696,831

-43-

1. Throughout this study, “costs” and “expenditures” are used interchangeably. Sincedepreciation costs are not included, the model actually estimates the current account expenditures associated with pollution abatement activities.

2. Two perspectives on incorporating emissions into production models have emerged in theliterature. One view holds emissions are inputs, while the other view maintains emissions arebad outputs. We model emissions as bad outputs.

3. Brännland, Färe, and Grosskopf (1995) specified a joint production model in order to estimateand unregulated short-run profit functions of the Swedish pulp and paper mills. The ratio ofthese two profit functions constitutes the cost of the environmental regulations.

4. All appendices, data, and GAMS programs are available from Carl Pasurka on request.

5. Gollop and Roberts (1983), Tran and Smith (1983), and Färe, Grosskopf, and Pasurka (1986)are among the studies using data from the FPC Form 67.

6. Bellas (1998) used annual flue gas desulfurization (FGD) costs from the EIA-767 survey forthe years from 1985 through 1991, excluding 1988, in his study investigating the existence oftechnical progress in the pollution abatement activities of electric utilities.

7. The O&M expenditures exclude revenue from the sale of by-products. In its annual report onpollution abatement expenditures, the Bureau of Economic Analysis used data collected by theFPC Form 67 to estimate the by-product sales revenues associated with sulfur and flyashrecovered from air pollution abatement activities and bottom ash from solid waste collection anddisposal for 1972 through 1980, and data from the EIA-767 survey were used to estimate the by-product sales revenue for 1985 through 1987 (Farber and Rutledge 1989, pp. 16-17). Changes inrelated series of data were used to generate estimates for 1981 to 1984.

8. Free disposability means the good output can be disposed of without the use of any inputs. This can be stated formally as (yg, yb) 0 P(x) and ygN # yg imply (ygN, yb) 0 P(x).

9. If no constraint is imposed on the summation of the intensity parameters (i.e., the zk),constant returns to scale is assumed.

10. Although some plants abate emissions of nitrogen oxide (NOx), there are no estimates of theassociated O&M costs. Hence, this study does not model NOx emissions as a regulatedpollutant.

11. Several plants are excluded due to their consumption of petroleum coke and other types offuel (i.e., blast furnace gas, coal-oil mixture, fuel oil #2, methanol, propane, wood and woodwaste, and refuse, bagasse and other nonwood waste). Although a number of plants consumefuels other than coal, petroleum, and natural gas, these other fuels represent very smallpercentages of total fuel consumption (in Btu). For the purposes of the technologies modeled in

ENDNOTES

-44-

this study, it was decided to exclude those plants whose consumption of these other fuelsrepresented more than 0.0001 percent of its total consumption of fuel (in Btu). The consumptionof other fuels by those plants whose consumption represents less than 0.0001 percent of its fuelconsumption is ignored when modeling the production technology.

12. The Pollution Abatement Costs and Expenditures survey (U.S. Department of Commerce1996) of manufacturing plants included the costs of electricity used for pollution abatementactivities.

13. The constraint imposed on the “bad” inputs by Yaisawarng and Klein (1994) specifies thatthe reference technology can use fuels of equal or lower quality than the observation whoseefficiency is being estimated. Hence, the bad input is modeled as being freely disposable. In thiscase the observation is able to switch to a higher quality fuel (e.g., lower sulfur coal). Using thatspecification in this study would result in PACM including the cost of fuel switching.

14. ΣPACS refers to the sum of the survey estimates of pollution abatement costs for all powerplants in the sample.

15. The first page of “General Information” about the EIA-767 form contains a paragraphdescribing the possible sanctions the government can bring against those utilities failing torespond to the survey.

16. The EIA-767 estimates include “... all contract and self-service pollution abatement O&Mexpenditures...” (U.S. DOE, “General Information” for Form EIA-767, 2001, “Plant Information-- Financial Information,” Schedule I, Section C, Item 1).

17. According to the instructions for “Generator Information” (Schedule IV, Item 4) of the EIA-767 survey, “net electrical generation” consists of the total amount of electrical energy generatedminus electricity consumed at the plant.

18. Blanks in the PACE survey are treated as zeros for the purpose of generating the publishedstatistics and in estimating standard errors (see Streitwieser,1996, p. 23; 1997, p. 12). Unpublished data from the BEA suggest it treated nonresponses from the EIA-767 survey aszeros. Appendix C contains a more detailed discussion of this issue.