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 University Corvallis, OR **Department of Economics Oregon 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D.C. 20460 Phone: (202) 260-6197 FAX: (202) 260-5732 E-Mail: [email protected]C:\ELECTRIC\ELECT-12A.WPD DRAFT - DO NOT QUOTE OR CITE WITHOUT PERMISSION OF THE AUTHORS May 3, 2002 Earlier versions of this study was presented at the January 2001 AEA meetings in New Orleans and at the U.S. Environmental Protection Agency. Gale Boyd, Scott Farrow, and Anton Steurer provided helpful comments on an earlier version of this study. We thank Curtis Carlson for providing 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 and should not be attributed to the U.S. Environmental Protection Agency.
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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
DRAFT -DO NOT QUOTE OR CITE WITHOUT PERMISSION OF THE AUTHORS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
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
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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.
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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.
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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.
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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 )
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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).
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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.
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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
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
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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.