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Environmental Benefit-Cost Analysis and the National Accounts.

Preliminary Draft: Do Not Quote.

March, 2015.

Nicholas Z. Muller Associate Professor of Economics Middlebury College and NBER

Middlebury, VT 05753 (802) 443 5918

nmuller@middlebury.edu

Abstract

This paper proposes a novel integration of environmental benefit cost analysis (BCA)

and the national income and product accounts (NIPAs). Compliance costs are likely in

the NIPAs while non-market benefits are not. Integrating BCA and the NIPAs relies on

computing an augmented measure of output: value-added less environmental damage.

Policy evaluations in this framework assess changes in levels and rates of growth in

augmented output, with and without policy in question. The empirical application

evaluates the adoption of flue gas desulfurization (FGD) technology by power plants in

the U.S. between 2005 and 2011. The empirical BCA finds that augmented output in

2008 was nearly $14 billion higher due to scrubber installation between 2005 and 2008.

In 2011, output was almost $20 billion higher due to FGD adoption between 2008 and

2011. FGD adoption in both time periods increased growth by about 0.06%. State

growth effects were heterogeneous. West Virginia’s output expanded by 1.2% more due

to FGD use.

Keywords: Benefit-cost analysis, pollution control costs, environmental accounting, air

pollution.

JEL Codes: Q51, Q53, Q56, Q58, Q52.

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1. Introduction

During macroeconomic shocks the aggregate measurement of performance is important

for policymakers and the general public. After all, the first coordinated estimates of

national income in the United States were produced in the 1930s during the single most

significant downturn of the 20th or the 21st century. Further, World War II, and the need

for planning of wartime production, motivated annual estimation of national product

(USBEA, 2006). Such measurement tends to focus on “conventional” or market-oriented

indicators such as unemployment, gross output, as well as indexed measures of prices

and consumer confidence. Comprehensive mensuration of performance should also

encompass evaluation of public policy. Such assessment of laws, rules, and statutes

often assumes the form of benefit-cost analysis (BCA). The importance of BCA

intensifies during downturns as public resources are stretched thin and sensitivity to

policy-induced distortions is magnified as resilience declines1.

For large-scale policies it is commonplace for analysts to characterize impacts in terms

of changes in Gross Domestic Product (GDP) or GDP growth (CBO, 2013). Whether or

not policy impacts are captured by conventional macroeconomic aggregates such as

GDP depends on the nature of the policy in question. Certain programs may have

effects largely within the market boundary. In such contexts, market-oriented indices

capture the ramifications of policy. However, for environmental BCA it seems likely (if

not probable) that some outcomes manifest outside the market boundary2. Relying on

GDP to measure the consequences of environmental policy is problematic because GDP

does not, as currently defined, encompass extra-market consequences.

The problem, however, is worse than simply obtaining an incomplete picture of policy

outcomes. Reliance on the extant national accounts for environmental policy assessment

yields not only a partial assessment of policy but a potentially biased appraisal because

the “C” of BCA are in market indicators, while the “B” are not. This argument is

especially important during recessions because, during such adverse shocks, the

drumbeat in the popular press and among some political circles against environmental

1 Evidence of BCA’s significance during such times is found in three executive orders issued during or

immediately following three recent recessions. Executive Order (EO) 12291 was promulgated by the

Reagan Administration in 1981. EO 12866 was set forth by the Clinton Administration in 1993. The

Obama Administration issued EO 13563 in 2011. Each stated the importance of BCA.

2 United States Environmental Protect ion Agency (1999; 2010) reports that the vast majority of monetary benefits from the Clean Air Act are due to reduced mortality risk.

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policy rises to a fever pitch3. Broadly, that environmental policy harms growth at a time

when the economic system is under stress lies at the core of this position. An evaluation

of this claim reliant on the conventional National Income and Product Accounts (NIPAs)

may be problematic for the reasons stated above.

So, if not GDP what should the policy community employ to succinctly and accurately

convey policy consequences? To answer this, the current paper demonstrates a new

connection between BCA and the NIPAs. The article computes an augmented measure

of GDP, Environmentally-Adjusted Value Added (EVA), (Muller 2014a; 2014b), which

is defined as GDP less environmental pollution damage. Environmental policy BCA is

incorporated directly into the EVA in two ways. In a particular time period, damages

from pollution emissions are deducted from market GDP in a standard with-and-

without policy comparison4. Second, secular changes in damages, output (GDP), and

correspondingly, in the EVA are employed to estimate augmented rates of growth.

Comparison to a no-policy counterfactual then yields the effect of the policy on the

augmented measure of EVA growth.

Admittedly, this approach is justified only for policies and programs that have the

potential to yield large enough effects as to be detectable in the NIPAs and the regional

accounts. Prior research has demonstrated that the consequences of the Clean Air Act

and air pollution, generally, in the U.S. are large relative to GDP (USEPA, 1999; 2010).

Further, the National Academies of Science’s National Research Council (NAS NRC)

alluded to the potential importance of an augmented accounting system specifically

focusing on air pollution damage during periods of rapid emission reductions (NAS

NRC, 1999, p. 147). This latter reference is especially relevant to the 2008 to 2011 time

period as macroeconomic conditions and air pollution policy both conspired to reduce

emissions. Thus, data and modeling capabilities as well as the magnitude of the Act

bolster the focus on air pollution as a test case for the synthesis of BCA and the NIPAs.

To explore the potential synthesis of BCA with the NIPAs, this article asks: how did

adoption of abatement technology for sulfur dioxide (SO2) affect an augmented

measure of output that deducts environmental cost from GDP5? Since the costs of

3 Of course, the present paper is not equipped to weigh in on this particular debate. For scholarly work on the employment-environmental policy relationship see: Greenstone, 2012; Morgenstern et al., 2002. For writings in the popular media see: New York Times, 2011; Bloomberg, 2014; U.S. Chamber of Commerce, 2012; Forbes, 2012. 4 In this framework, reductions in damage due to policy are benefits. 5 SO2 is a common air pollutant regulated as a criteria air pollutant by the Clean Air Act. SO2 plays an important role

in the formation of fine particles which are associated with adverse effects on human health. Further, prior

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purchasing, installing, and operating abatement technology are already in GDP, the

monetary benefits of pollution control are the critical augmentation in this context. The

use of abatement devices specifically for SO2 is a costly action undertaken by firms

because of regulation. For example, the capital cost for an FGD unit installed on a new

500MW generator is reported to range from $110 million to $150 million (Sargent and

Lundy, 2007). Investments of this magnitude (that do not yield private or internal

benefits to the firm) must be induced. In particular, scrubbing technology adoption is

due to incentives or mandates established by the Clean Air Act which may manifest

directly through federal policy or indirectly through state implementation of the Act. In

either case, the adoption of scrubbing technology is due to a policy intervention. Thus,

application of some form of BCA is appropriate.

The empirical analysis relies on exploiting detailed, facility-level data regarding the use

of particular abatement technologies. The United States Environmental Protection

Agency (USEPA) and the U.S. Department of Energy (USDOE) report the type and

timing of adoption of flue-gas desulfurization (FGD) units, also known as scrubbers,

among the power generation fleet in the U.S. In addition, both agencies report annual

consumption of fuel by plants, net generation of electricity, and emissions of SO2.

Capitalizing on these data, the analysis employs reported emissions and an integrated

assessment model to estimate “observed” damages. The paper then constructs a

counterfactual scenario in which the documented adoption of abatement devices did not

occur. Rather, emissions rates are assumed to have persisted at their pre-abatement

intensities. Counterfactual emissions are estimated as the product of the pre-abatement

emissions rates (expressed in tons/million btu, mmbtu, of fuel consumed) times post-

abatement generation (mmbtu). The resulting tonnage is processed through the

integrated assessment model. In both the observed and counterfactual cases, damages

are tabulated in accord with the principles established in the NIPAs and in previous

research (Nordhaus, 2006; Muller, Mendelsohn, Nordhaus, 2011; Muller 2014a; 2014b);

gross external damage (GED) is computed as the product of plant-specific marginal

damage and reported or counterfactual emissions for each case. Then, EVA (GDP –

GED) is estimated with and without the use of abatement technology. In a particular

time period, this facilitates a test of whether the use of abatement technology has a

detectable effect on EVA levels. (See figure 1 for a schematic representation of the BCA

design which is an application of the method to the state of West Virginia.)

research has demonstrated that SO2 contributes a significant share to national GED (Muller and Mendelsohn, 2007;

Muller, Mendelsohn, Nordhaus, 2011).

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This simulation design is implemented in three time periods: 2005, 2008, and 2011. The

years of 2005, 2008, and 2011 are selected for study because, first, USEPA reports

national emission inventories in three-year increments6. Second, the power generation

fleet in the U.S. made a significant investment in scrubbing technology between 2005

and 2011 (USDOE, 2014). (See figure 2.) It is therefore conceivable that the benefits and

costs due to adoption of FGD units could be detected in the EVA, and thus, the

demonstration of synthesizing BCA and the NIPAs is perhaps appropriate during this

time period.

With emission observations for 2005 and 2008, the first set of simulations identifies FGD

units adopted between 2005 and 2008. Then, observed and counterfactual emission

estimates for 2008 are used to estimate observed and counterfactual pollution damage

and EVA. EVA growth between 2005 and 2008 is computed with and without scrubbing

technology in 2008. The second set of simulations repeats this design for the 2008 and

2011 time periods. Observed and counterfactual emissions for 2011 are used to calculate

pollution damage with and without FGD units installed between 2008 and 2011. EVA is

computed for both cases and EVA growth between 2008 and 2011 (straddling the Great

Recession) is computed with and without scrubbing technology activated between 2008

and 2011. This last comparison is targeted to explore how FGD adoption and use during

the Great Recession affected augmented growth rates.

1.a. State and Regional Accounts: Are EVA growth rates illusory?

The BCA exercise conducted in this paper tracks FGD installation at particular power

generation facilities. At the most aggregated level, the effects of FGD use are related to

EVA across all states that installed scrubbing units within the time periods under

examination. To connect the adoption of pollution abatement technology to a measure

of state economic performance, the USBEA state accounts are employed to compute

state EVA. Then, in accord with the simulation design described above, EVA levels and

growth are estimated for each state that put FGD units in place.

Computing state GED raises difficult accounting issues; in particular how one interprets

differences in EVA and GDP growth, which are related to those initially discussed by

Nordhaus (2008). By reporting GED and EVA regionally, the appearance is that the

beneficiaries of EVA growth due to GED reduction lie within that region. This is, after

all, a common interpretation of regional GDP growth. (If one reads that California’s

state GDP increased by 5 percent in a prior year, one tends to think that Californians are

somehow better off.) However, in the current context, this may not be the case. For

6 For example, national emission inventory data is available for: 1999, 2002, 2005, 2008, and 2011.

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example, the GED produced from, say, a large power plant is associated with, or due to,

emissions that may travel across state or regional boundaries. Correcting the state or

regional accounts to include GED reduces to attributing an external cost borne in one

region back to production conducted in another zone. This point is especially important

in the context of the BCA conducted in the empirical section of the paper. Reductions in

external costs due to the adoption of FGD units would manifest, in the augmented

accounting system, within the region of emission. All else equal, this causes EVA to rise

in the emitting region. Yet, the population reaping the real physical health benefits may

not be within the same region.

This raises a cautionary note regarding interpretation of the spatial accounts, especially

in the context of BCA. Although EVA growth in the emitting region increases if GED

drops, it is important to note that (1) the real beneficiaries may not be within the same

region and (2) the only way in which EVA growth of this sort would actually manifest in

the emitting region is if the polluter faces the costs of waste disposal. The GED are

external costs and if there is no means by which polluters realize these costs, whether

they increase or decrease only matters to the non-local exposed population. The

argument also says nothing about how the costs would enter the polluter’s balance

sheet. For example, this could occur through corrective taxation, tradable permits, or

through payments to something like Nordhaus’ (2008) abatement industry. Finally, and

this is point (3), the paper also says nothing about whether these costs, once paid, are

redistributed to victims nor what an optimal level of GED would be.

In order to characterize the spatial distribution of benefits from FGD use, the integrated

assessment model is employed to estimate the share of benefits that manifest within the

state where the FGD unit(s) were installed. This tack is intended to explore imbalances

between physical benefits of abatement and the reduction in costs of waste disposal

(levied on the emitter, in the state containing the facility). There is an element of

environmental federalism here (see similar work by Banzhaf, Chupp, 2012) in the sense

that if a state regulator were charged with administering policy that levied taxes

proportional to the GED, it is conceivable that such a policymaker would do so at a rate

that reflects in-state damage. The natural question spawned by this spatial accounting

exercise is to ask: what is the difference in EVA if only in-state GED were used to

augment GDP rather than total (including inter-state) GED? This is explored

empirically using the integrated assessment model which is discussed briefly below and

more completely in section 3.

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1.b. Empirical Framework: Integrated Assessment Model.

GED for air pollution is computed using AP2, an integrated assessment model, which is

a new version of a model that has been used in prior publications (Muller and

Mendelsohn, 2007; 2009; NAS NRC, 2010; Michalek et al., 2010; Muller, Mendelsohn,

Nordhaus, 2011; Muller, 2011; 2012; 2013; 2014a; 2014b). AP2 connects emissions of five

common air pollutants to estimates of ambient concentrations, exposures, physical

impacts, and monetary damage. Monetization, while contentious in some contexts,

facilitates direct inclusion of damage into the national accounts. The air pollutants

covered by the model are: nitrogen oxides (NOx), sulfur dioxide (SO2), fine particulate

matter (PM2.5), ammonia (NH3), and volatile organic compounds (VOC). While the

particular focus of this paper is on FGD use and therefore, SO2, all of the pollutants

listed above are used by the model to estimate baseline concentrations by county. The

policy simulations are then conducted relative to a baseline that reflects all emissions as

reported by the USEPA.

Using emissions data reported by the USEPA for five data years, (USEPA, 2002; 2005;

2008; 2011; 2014), an air quality model, that has been statistically tested against available

monitoring data (Muller, 2011), links emissions to annual average concentrations.

Exposures are tabulated by tracking populations of sensitive receptors (human

populations and crops, for example). A unique feature of AP2 is its spatial resolution.

The model tracks exposure at the county level by using detailed population, crop,

timber and physical capital inventories. In this paper changes in the inventories across

time are captured through the use of year-specific data. For example, the U.S. Census

Bureau reports county-level population estimates that change annually. Physical

impacts, like reduced crop yields, and increased rates of illness, are estimated through

the use of concentration-response functions that have been published in peer-reviewed

publications. Valuation uses either reported market prices (for crops) or standard non-

market valuation techniques (the Value of a Statistical Life, or VSL, approach is used to

value mortality risks –see Viscusi and Aldy, 2003).

In this paper AP2 is first-and-foremost used to compute marginal damages for SO2

emitted from power plants in the U.S. The marginal damages are estimated using the

algorithm developed by Muller and Mendelsohn, (2009). This entails estimating

baseline damage in a given year. Then, one ton of SO2 is added to one source. The

subsequent change in concentrations, exposures, physical effects and monetary damage

across all counties is tabulated. Because the only change is the additional ton, the

change in damage is strictly attributable to source subject to the emission change.

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1.c. Related Literature.

There are many papers that focus on environmental accounting, and even more

specifically, on inclusion of pollution damage in the national accounts. These include

very early papers such as: (Ayres and Kneese, 1969; Leontief, 1970; Nordhaus and Tobin,

1972). More recent empirical research includes: (Peskin, 1989; Bartelmus, 2008; Muller,

Mendelsohn, Nordhaus, 2011).

This paper is most closely related to Muller (2013; 2014a). While the earlier papers

provide evidence that changes in the GED affect changes in aggregate measures of

growth, the current analysis extends the work conducted in Muller (2013; 2014a) in two

ways. First, the present analysis includes emissions and market accounting aggregates

from 2011. This is an important addition because 2011 provides an observation on both

emissions and economic activity “after” the Great Recession. The analysis reports the

first GED estimates for this important data year. The current work is also, to the

author’s knowledge, the first to attempt to synthesize environmental BCA and

augmented national income and product accounts.

In addition to the papers referenced above, more recent contributions to the empirical

environmental accounting literature that specifically focus on air pollution damage

include: Bartelmus, 2009; Muller, Mendelsohn, Nordhaus, 2011. Further, many prior

authors have estimated air pollution damage (Mendelsohn, 1980; Burtraw et al., 1998;

Banzhaf et al., 2006; Fann et al., 2009; Levy, Baxter, Schwartz, 2009). The paper also

relates to, and is informed by, work in conceptual environmental accounting that

frames the treatment of air pollution damage (NAS NRC, 1999; Abraham and Mackie,

2006; Nordhaus, 2006).

1.d. Snapshot of Results.

The empirical results for FGD adoption between 2005 and 2008 indicate that EVA was

approximately $14 billion higher than it would have been without new scrubbing

technology. Total abatement cost associated with installation of new units in this time

period range from $275 million to $750 million, depending on assumptions. The benefit-

cost ratio across the 20 states that adopted new FGD units during this time period

ranges between 20:1 and 56:1. EVA grew 0.07% more quickly with FGD units that

without between 2005 and 2008 in these 20 states. EVA growth in six states was at least

0.10% more rapid because of the installation of scrubbers. This result indicates that

adoption of pollution control devices increased augmented state output over the time

period 2005 to 2008. This finding manifests because the paper employs a measure of

economic performance that is inclusive of air pollution damage. Note that if growth

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were measured using market GDP, benefits as reported in this paper would not be

counted and the only measured effect of policy over this time period would have been

the roughly $275 to $750 million in abatement control costs. Hence, adopting the

augmented EVA index allows for inclusion of non-market monetary benefits. And this

extension fundamentally changes the nature of the effect of the installation of FGD units

on growth.

Turning to FGD installation between 2008 and 2011, in total, power generation facilities

in 30 states put new scrubbers in place during this time period. The magnitude of

benefits was almost $20 billion in 2011. Abatement costs also increased. Total

investment in scrubbers was estimated to be $345 million using lower cost assumption

and $900 million using higher cost assumptions. The benefit-cost ratio ranged between

22:1 and 57:1. From 2008 to 2011, EVA growth in these states was, on average, 0.06%

more rapid due to FGD adoption. Seven states displayed EVA growth that was at least

0.10% greater due to FGD installation. West Virginia EVA growth was estimated to be

1.2% greater because of new FGD units put in place between 2008 and 2011. In accord

with the findings for the period 2005 to 2008, adoption of scrubbers between 2008 and

2011 enhanced growth in the EVA index. Investment in air pollution control reduces the

risk of premature death and chronic illness. Provided that performance is measured in a

way that is inclusive of these non-market impacts, reducing emissions of SO2 through

FGD technology accelerates growth relative to the no-scrub counterfactual.

For a small number of states scrubber adoption changes the sign of the difference

between GDP growth and EVA growth. For example, market GDP in North Dakota

increased by 5.5% from 2008 to 2011. With scrubbing EVA expanded by 5.7%. Without

the new FGD units, EVA growth would have been 5.4%. Thus, in the particular case of

North Dakota, EVA based on observed emissions augments growth relative to market

GDP. Counterfactual EVA without new scrubbers attenuates growth relative to GDP.

Installation of scrubbers, therefore, changes the orientation of EVA relative to GDP. The

state of Missouri exhibits similar results.

In summary, evidence reported herein suggests that adoption of FGD technology: (1)

has an appreciable and positive effect on augmented measures of output and growth; (2)

for a few states (West Virginia, for example) has a dramatic effect on EVA growth; (3)

may change the orientation between market growth and augmented growth.

The remainder of the paper is organized as follows. Section 2. presents the conceptual

and empirical models, and section 3. presents the empirical results. Section 4. concludes

and raises topics for future research.

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2. Methods

This analysis computes Gross External Damage (GED) which is a price-times-quantity-

metric of pollution damage (Muller, Mendelsohn, Nordhaus, 2011). Nominal GED is

tabulated for sector (i) at time (t) by multiplying the pollution marginal (per-ton )

damage for pollutants (s) emitted by source (j) times reported emissions as shown in (1).

����� = ∑ ∑ (��� × ����)������� (1)

In order to convert nominal GED to real GED, this analysis employs Fisher price

deflators, which are pollutant-and-year-specific, that are estimated and reported in

Muller (2013). All values in this paper are real (both GED and market VA). However, to

simplify notation, in the following the price index notation is subsumed7.

While the empirics begin by reporting the GED as a national aggregate, regional and

state GED are also reported. The spatial decomposition for region (r) is shown in (2).

������ = ∑ ∑ (���� × �����)��������� (2)

where: Jri = the number of sources in sector (i) in region (r).

Following prior authors, environmental damage (GED) is subtracted from the

conventional NIPA aggregate (VA, or in the case of total economy measures, GDP) to

yield environmentally-adjusted value added (EVA), (Nordhaus, Tobin, 1972; Peskin,

1989; Bartelmus, 2008)

����� = (���� − �����) (3)

Combining the regional and state GED with regional and state measures of output

facilitates the computation of regional and state EVA.

As stated above the paper focuses on changes in the EVA across five time periods.

Annualized real rates of growth for sector (i) in VA, denoted δvi, GED, denoted δgi, and

EVA, shown as δei, are defined in (4a), (4b), and (4c), respectively: (t) denotes number of

periods forward from base period (0).

��� = ���������� � − 1 (4a)

7 ������"#$ = ∑ ∑ (%&��'���� × ����)������� this expression shows that the marginal damages for

pollutant (s) emitted from source (j) at time (t) are divided by the estimated Fisher pollution price deflator (Pfts) in order to estimate real GED. The Fisher pollution price deflators use 2005 as the benchmark year and use the method developed in Muller (2013).

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��( = �)*+��)*+��� � − 1 (4b)

��" = �*����*����� � − 1 (4c)

Of particular interest is a comparison of conventional and augmented rates of growth:

���, ��"In addition, one would like to be able to characterize the conditions under which

market growth is greater than, equal to, or less than augmented growth. Although the

identities shown in (5) are straightforward, they are useful as a guide or point of

reference used to interpret the empirical results that follow later in the paper.

��� = ��" ./: ��� = ��(��� < ��" ./: ��� > ��(��� > ��" ./: ��� < ��(

(5)

2.a Policy Counterfactual Analyses.

The empirical benefit-cost analyses conducted in this paper explores whether and how

the inclusion of abatement costs and monetary benefit estimates associated with

adoption of FGD units affect the EVA augmented indicator.

2.a.1 Benefit estimation.

The simulation design is quite simple. Power plants that installed, or retro-fitted, FGD

units between 2005 and 2008 are identified using the USDOE Form 923 databases

(USDOE, 2014). For 2005, and therefore prior to scrubber installation, the emission-

intensity of these particular units is computed in terms of tonnage SO2 emitted per heat

input of fuel consumed (mmbtu).

34556,� = 7*8��9,�:8��9,�; (6)

Where: E2005,i = SO2 emissions reported in 2005 for facility (i)

N2005,i = heat input facility (i), in 2005.

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Emissions and heat input are provided by USEPA (2014d). Next, emissions without the

installed FGD units are estimated by multiplying pre-installation I2005,i times reported

heat input in 2008:

�<455=,� = >34556,�? × >@455=,�? (7)

Next the GED for plant (i) is estimated in 2005, and for 2008, with and without the FGD

unit. Thus, GED in 2005, as shown above is:

���4556,� = �4556,� × �4556,� (8)

The GED with the FGD for unit (i) in 2008 is: ���455=,� = �455=,� × �455=,�. Finally,

estimated GED under the no-FGD counterfactual relies on estimated emissions:

���A455=,� = �455=,� × �<455=,� (9)

Then, with the estimated GED values for 2005, and for 2008 with and without the FGD

units, EVA in both periods is computed by subtracting GED from VA as shown in (3).

Annual EVA growth between 2005 and 2008 is tabulated for the FGD (observed) and no

FGD counterfactual cases using the formula in (4c). This simulation design is then

repeated for the period between 2008 and 2011.

2.a.2. Abatement cost estimation.

Abatement costs are computed using estimates for FGD unit costs reported in the

literature. Investments by firms in FGD technology consist of both capital costs and

operations and maintenance costs. Ellerman et al., (2000) report detailed FGD cost

estimates associated with power plants regulated under Phase I of the Acid Rain

Program. The reported scrubbing costs span a range from approximately $200/ton SO2

removed to $800/ton, with the latter considered an outlier (Ellerman et al., 2000). These

costs are levelized estimates of both capital and operations and maintenance expenses.

Further, the USEPA (2014b) estimates FGD technology costs that vary by the nameplate

capacity of the generating unit. For larger units with capacity of greater than 400MW,

costs, on the low end are reported to be $200/ton SO2 removed and on the higher end

are $500/ton. For smaller units (< 400MW) the low end of costs per ton SO2 removed is

$500/ton and at the upper end $5,000/ton. Anecdotally, most of the units that installed

scrubbers between 2005 and 2011 (and therefore of direct relevance to this study) are

larger than 400MW in nameplate capacity. Using nameplate capacity data for each plant

provided by USEPA (USEPA, 2014c), the USEPA’s levelized cost estimates are

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attributed to each plant in two scenarios: one corresponding to a high cost realization

and another for a low cost realization.

Armed with these estimates abatement costs are computed as the product of the

levelized ($/ton SO2 removed) costs times the estimated tonnage abated by each facility

that installed a scrubber in the time period under examination. Hence, the difference

between observed emissions in, say, 2008, for facility (i), and the no scrub counterfactual

is: ∆�455=,� = �<455=,� − �455=,�. Costs are given by: CD455=,� = ∆�455=,� × ED455=,�, where TC

represents the total cost estimate and LC is levelized ($/ton) cost.

Finally, in the no-scrub counterfactual case, such costs would not have been incurred by

the firm. As such, total costs are added to reported GDP under the no-scrub

counterfactual. Several important caveats are noted. First, the cost estimate, such as it is,

is at best a partial reflection of costs in the sense that it does not reflect impacts on the

labor market or in terms of returns on other productive investments that regulated

firms may have made. Second, the rudimentary calculation does not reflect convexity in

the marginal cost of abatement curve. Third, heterogeneity in actual abatement costs

based on age of the plant, sulfur content of coal used, and disposal of wastes are not

captured and these may be important sources of variation in costs incurred by each

plant.

2.b Estimation of Benefits.

AP2 is an integrated assessment model that has been used in prior analyses (Muller

2011; 2013; 2014a, 2014b). AP2 is an updated version of the APEEP model (Muller and

Mendelsohn, 2007; 2009; NAS NRC, 2009; Muller, Mendelsohn, and Nordhaus, 2011).

AP2, like APEEP, is a standard integrated assessment model (IAM) in that it connects

emissions to monetary damages through six modules: emissions, air quality modeling,

concentrations, exposures, physical effects, and valuation. (For a complete description

of the AP2 model see Muller, 2011.) The distinguishing feature of AP2 is its spatial

detail and that it is calibrated to compute marginal damages. In the current analysis,

AP2 is used to compute marginal damages for five pollutants over five data years at

nearly 10,000 individual and grouped sources in the contiguous U.S.

AP2 uses pollutant-specific source-receptor matrices to connect emissions to

concentrations. Cell (j,r) in the matrices characterizes the effect on ambient

concentrations in a receptor region (r) due to a one ton emission from source (j). AP2

defines receptor regions at the county resolution. In each county receptor zone, AP2

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catalogs human populations among 19 age groups, crop and timber yields, and an

inventory of physical capital. These inventories change through time from 1999 to 2011.

Annual, pollutant-specific emissions vectors are processed through the corresponding

source-receptor matrices to yield annual ambient concentration estimates. Exposures

are then tabulated as the product of concentrations and populations. Physical effects

due to exposure are estimated by using concentration-response functions. Paramount

among these are the functions that govern the exposure-mortality relationship. In

keeping with prior papers that have measured air pollution damage, AP2 is set to use

the PM2.5-adult mortality dose-response function reported in Pope et al., (2002) and the

O3-mortality dose-response function reported by Bell et al., (2004). The final link in the

modeling chain affixes a value to the estimated physical effects. For goods traded in

markets (crops, for example), AP2 uses market prices. In order to value changes in

mortality risks, the current analysis uses the Value of a Statistical Life (VSL) approach

(Viscusi, Aldy, 2003). AP2 is calibrated to use a VSL of $6 million which is fairly

standard in air pollution damage analyses in the U.S. (USEPA, 2010; Fann et al., 2009).

The AP2 model uses the following algorithm to estimate marginal damages. For a given

year, emissions of each pollutant are attributed to one of 10,000 sources according to the

USEPA’s National Emissions Inventory (USEPA, 2002; 2005; 2008; 2011; 2014). Next, the

model is run through to estimate damages at baseline emission levels. Then for a

particular source, one ton of a particular pollutant is added to baseline emissions. The

model is then run again; AP2 computes concentrations, exposures, physical effects, and

damages following the additional ton. The difference between baseline damages and

damages under the add-one-ton case comprise the monetary damage of the additional

ton.

This algorithm consists of the spatial sum (across receptor counties) of the difference in

damages as shown in expression (10):

��� = ∑ (����# − ����F )G��� (10)

where: D = monetary damage in receptor county (r), time (t), species (s) a = add-one-ton emission scenario b = baseline emission scenario

The algorithm is repeated for all five pollutants, for the 10,000 sources, and the five data

years for a total of 250,000 marginal damage estimates. The marginal damages are then

matched to emissions data by source, pollutant, and year to compute GED as shown in

(1).

15

It is of interest to characterize or estimate the share of damage that occurs within the

state of emission. To accomplish this, a minor change to (10) is made that restricts the

set of receptors over which (10) aggregates. Let Rk denote receptors with state (k). Then

the in-state marginal damage is shown in (11).

����H = ∑ (����# − ����F )GI��� (11)

Then, GED for sector (i) within state (k) due to emissions of (s) from source (j) are given

by (12).

������H = ����H × ���� (12)

The in-state share of damage is: ������H

������J

2.b.1 Sensitivity Analysis and Distributional Effects.

Prior research has demonstrated that damages from air pollution are especially

sensitive to modeling assumptions focusing on human health effects (USEPA, 1999;

Muller and Mendelsohn, 2007). In a sensitivity analysis, the adult mortality dose-

response function reported in a recent meta-analysis (Roman et al., 2008), which

estimates a degree of sensitivity of mortality rates to PM2.5 exposure that is about twice

as large as that in the default case (Pope et al., 2002) is used. The sensitivity analysis also

uses a different VSL. Rather than the $6 million default value, the paper employs the $2

million VSL estimated by Mrozek and Taylor (2000) in their meta-analysis of revealed

preference studies. Under each of these alternatives, benefit-cost ratios and EVA growth

rates are computed and compared to the default case.

The analysis explores the incidence of benefits from scrubber adoption according to

county-level race and income data (U.S. Census, 2014). Specifically, the paper reports

the change in PM2.5 concentrations for counties that have greater than the national

average share of white, African American, Asian, and Hispanic populations. Changes

are reported both in percentages and levels. The empirical section also computes the

change in monetary benefits by these race-based categories. In addition, the change in

benefits and PM2.5 concentrations due to scrubber adoption for the two time periods

discussed above are related to quantiles in the income distribution.

3. Results

16

The results section is decomposed into three parts. Section 3.a. focuses on the benefit-

cost analysis of the implementation of FGD units between 2005 and 2011. Tables

included in this sub-section report: levels of benefits and costs, levels of the EVA index,

and growth rates of EVA. Section 3.b focuses on the incidence of benefits according to

county-level demographic characteristics. Section 3.c consists of a sensitivity analysis.

3.a Benefit Cost Analysis: Flue Gas Desulfurization.

Table 1 reports the abatement costs and benefits that accrued in 2008, resulting from the

adoption of FGD technology between 2005 and 2008. In total, for the 20 states that

installed FGD units between 2005 and 2008, the benefit-cost ratio was calculated to be

52:1 and 20:1 in the low cost and high cost cases, respectively. Total abatement costs

ranged between $275 million and $750 million, while benefits were estimated at $13.8

billion.

Ten of the twenty states that installed FGD technology are covered in table 1. The states

shown display the largest benefit-cost ratios. Beginning with the state of Virginia, GED

with installed FGD technology was $608 million. Without new FGD units put in place

between 2005 and 2008, GED would have been $1.2 billion. Thus, new scrubbers

reduced GED by one-half. Abatement cost estimates range between $7 and $17 million.

The benefit-cost ratios were estimated to be 32:1 using the high abatement cost estimate

and 81:1 when employing the low abatement cost approach.

In West Virginia, GED with FGD technology was $73 million. Without scrubbing

technology that was installed between 2005 and 2008, GED was estimated to be $1.8

billion. Thus, benefits amounted to $1.7 billion. Two different estimates of costs are

provided. At the lower bound, abatement costs incurred by facilities in West Virginia

were $25 million. Using the high cost estimate, abatement costs increase to $62 million.

Putting costs and benefits together, in West Virginia, the benefit cost ratio ranges from

70:1 down to 28:1.

The next three states in table 1 are Pennsylvania, Ohio, and Indiana, in order of

descending benefit-cost ratios. FGD units installed in Indiana and Ohio each generated

benefits estimated at over $2 billion. In Ohio, abatement due to scrubbing “removed”

about 75% of state GED. The percentage reduction (relative to the counterfactual) in

Indiana was on the order of 50%. The benefit-cost ratios for scrubber installation in each

of these three states were about 60:1 using the low abatement cost approach and 25:1

corresponding to the high cost scenario.

17

FGD units put in place at facilities in Kentucky, the next state in table 1, generated

benefits of $1.1 billion. Abatement costs were less than $50 million. Using the low cost

assumption, the benefit-cost ratio was about 60:1. Under the high cost assumption, the

ratio is approximately 25:1. Further, the share of GED abated through the installation of

new scrubbing technology between 2005 and 2008 in these states was about 40% to 50%.

Figure 3 shows the change in SO2 emissions in 2008 due to adoption of scrubbers

between 2005 and 2008. Large changes were concentrated at plants along the Ohio River

and in North Carolina. The spatial pattern of abatement in the map supports the central

findings in table 1 in that the benefits of technology adoption manifest where, or nearby

to, large tonnage of abatement occurred.

Figure 4 further concretizes the benefits reported in table 1 by mapping the change in

ambient PM2.5 in 2008 estimated to result from FGD units installed in between 2005 and

2008. The map focuses on the eastern two-thirds of the U.S. because no FGD units were

installed in the west. The greatest reduction in PM2.5 concentrations occurred along the

Ohio River near the tri-state convergence of Ohio, West Virginia, and Pennsylvania. In

this small zone, PM2.5 annual average concentrations fell by between 0.75 to 0.95 ug/m3.

The connection to table 1 is clear in that Ohio, West Virginia, Pennsylvania, and

Kentucky (also adjacent to the zone with large PM2.5 reductions) were all among the

states that experienced large benefits from SO2 reductions. Further, Ohio incurred the

second largest abatement costs (computed as a direct scaling of tons abated). Figure 4

also shows an area of considerable PM2.5 reductions (between 0.5 ug/m3 and 0.75

ug/m3) extending eastward through southern Pennsylvania, Maryland, and Virginia.

Further, some of the large eastern metropolitan areas lie within a zone of PM2.5

reductions in excess of 0.5 ug/m3.

Figure 5 completes the modeling exercises by mapping the change in monetary damage,

the GED, due to the PM2.5 reductions shown in figure 4. Because the GED is dominated

by human health effects, large cities within the zones of PM2.5 change shown in figure 4

exhibit the greatest impacts of scrubbing. Figure 5 indicates that the large cities along

the eastern seaboard incurred significant reductions in damage due to FGD units

installed between 2005 and 2008. Counties within each of these major metropolitan

areas experience benefits in excess of $100 million. Moving westward, cities in the

industrial Midwest, in closer proximity to plants with large emission reductions, also

show significant benefits. These include Pittsburgh, Cleveland, Columbus, and Detroit.

18

Further to the west, Chicago also falls into the top benefit category. In the southern

states, no counties incur changes in damages in excess of $100 million8.

Table 2 reports abatement costs and benefits for 2011. There were roughly 30 states that

contained facilities which installed FGD units between 2008 and 2011. The bottom row

of table 2 reports the aggregate cost and benefit results across all 30 states. Abatement

costs on the low end were estimated to be $344 million. Using the high cost assumption,

costs were calculated at almost $900 million. Benefits of scrubbing adoption were

computed to be $19.8 billion. Hence, the benefit-cost ratio across all states was 57:1

using lower costs and 22:1 using higher costs.

Table 2 displays the ten states in which adoption of FGD technology generated the

largest benefit-cost ratios. Note that the three states with the highest benefit-cost ratios

conducted relatively little abatement (one can infer this from the table given that

abatement costs are scaled from emission reductions). Further, these states either are in

the urban northeast (Delaware and New Jersey) or in close proximity to the large

metropolitan areas in the northeast (Virginia). As such, the benefits per ton of

abatement for facilities in these states are higher than more rural states. In New Jersey,

the installation of FGD units reduced GED by 80%. In Delaware, scrubber installation

decreased state GED by a much smaller share. In Virginia, GED fell by about one-half

due to FGD use. The benefit-cost ratios, using the low cost assumption, in each of these

three states exceeded 80:1. Employing the high cost settings, the ratios in these states

were greater than 30:1.

A review of the remaining seven states in table 2 indicates that the benefits generated by

abatement conducted in Pennsylvania due to adoption of scrubbing technology

between 2008 and 2011 were much larger than those in other states. Specifically, FGD

installation in Pennsylvania produced $5.3 billion in benefits. Abatement costs incurred

ranged between $80 million and $200 million. Thus, the benefit-cost ratio for FGD unit

installation in Pennsylvania ranged from 68:1 (low cost case) to 27:1 (high cost

assumption). GED in Pennsylvania fell by over 80% due to FGD use.

Scrubbers put in place in West Virginia reduced GED from an estimated level of $2.4

billion to $166 million. This amounts to a reduction of 93%. The results for Kentucky are

similar to those for West Virginia. Without new scrubbers, state GED would have been

8 Note that a few counties in the western states show small changes in damages. This occurs because concentrations in some of the western states are projected to change by very small amounts and human populations in the counties showing a change in damage are large. For example, the Denver, Albuquerque, and Salt Lake City metropolitan areas show up on the map.

19

$1.5 billion. Adoption of scrubbers reduced GED to $295 million. FGD units put in place

between 2008 and 2011 dropped state GED by 80%. Benefits due to new scrubbers

adopted between 2008 and 2011 of greater than $500 million also manifest in Ohio,

North Carolina, and Michigan

Figure 6 displays the estimated change in SO2 emissions in 2011 due to scrubber

installation that occurred between 2008 and 2011. Large tonnage changes manifested in

Western Pennsylvania, West Virginia, Maryland, and Georgia. Figure 7 maps ambient

PM2.5 concentrations in 2011 due to the installation of FGD units between 2008 and 2011.

Broadly, the pattern in figure 7 is quite similar to that shown in figure 4. Differences in

concentrations between the observed emission case and the no-scrub counterfactual are

generally increasing from west-to-east, with the greatest change in concentrations in the

Mid-Atlantic states. Two aspects of figure 7 are worth noting. First, the maximum

change in PM2.5 is larger than in 2008 (shown in figure 4). There are a few counties in

and near large cities with changes in PM2.5 levels over 1 ug/m3. Second, the counties

that show differences in PM2.5 concentrations of between 0.75 and 1 ug/m3 lies further

to the east, and, therefore, in closer proximity to the large eastern metropolitan areas.

Figure 8 displays the change in GED. Relative to the change in damage in 2008, figure 8

indicates that most of the counties in and near large eastern cities incur changes in

damage in excess of $100 million. In addition to having large exposed populations,

recall from figures 6 and 7 that plants in or nearby to these areas adopted scrubbers,

and that many of these counties also experience significant changes in PM2.5. Some of

the same metropolitan areas in the Midwest as shown in figure 5 also display large

damages including Pittsburgh, Cleveland, Detroit and Chicago. And in the southern

states, metropolitan areas in North Carolina and Georgia incurred changes in damage

in excess of $75 million.

3.a.1. Integration of Benefit-Cost Analysis with Augmented National Accounts.

Table 3 reports, for all sectors, state GDP, GED, and EVA in 2005, and 20089. The ten

states shown in table 3 have the largest difference (measured between the observed case

with scrubbing and the no-scrub counterfactual) in EVA growth rates from 2005 to 2008.

Growth rates are reported subsequently in table 5.

In West Virginia, GDP expanded from $42.4 billion in 2005 to $44 billion in 2008, while

GED concurrently fell from $9.5 billion to $6.6 billion. EVA increased from $33 billion in

9 The results in tables 1 and 2 show GED among plants that installed scrubbers. Tables throughout the remainder

of the analysis report total state GED inclusive of all sectors of the state economy.

20

2005 to $37.4 billion in 2008. In the counterfactual without scrubbing installations

between 2005 and 2008, EVA is lower (because the GED is higher) at $35.8 billion. West

Virginia’s state output is considerably smaller in magnitude than the remaining nine

states in table 3. Yet, the reduction in EVA in the no-scrub counterfactual (about $1.6

billion) is comparable, in absolute terms to that reported for other states. Indiana’s state

GDP is on the order of $200 billion (five-times larger than West Virginia) and the

difference between observed and counterfactual EVA is $2.2 billion. As such, one can

infer at this stage that the effect of scrubber adoption on state EVA growth is much

larger in West Virginia than in other states.

The largest absolute difference in state EVA occurred in Ohio. State output decreased

from $396 billion to $380 billion between 2005 and 2008. GED fell from $35 billion to $24

billion. The difference in EVA for Ohio is estimated to be $3.0 billion. For the 20 states

that adopted FGD units between 2005 and 2008, GDP increased from about $4.7 trillion

to $4.9 trillion from 2005 to 2008. GED fell by almost one-third. The difference between

EVA (with FGD adoption) and counterfactual EVA is estimated to have been $13 billion.

Although table 3 does not report EVA growth, one can infer that scrubbing, ceteris

peribus, increased EVA growth through the reduction in GED. That is, EVA was $4.7

trillion in 2005. With scrubbing it increased to $4.9 trillion in 2008. In the counterfactual,

EVA was estimated to be $4.75 trillion in 2008. The rate of increase is larger with

observed FGD units in place. Hence, scrubbing enhanced or augmented EVA growth

relative to the no-scrub counterfactual.

Table 4 repeats the analysis featured in table 3 for the case of 2011. The ten states shown

in table 4 have the largest difference (measured between the observed case with

scrubbing and the no-scrub counterfactual) in EVA growth rates from 2008 to 2011. First,

West Virginia has the largest difference in EVA growth rates between the observed and

counterfactual cases between 2008 and 2011, just as this state did between 2005 and 2008.

State GED fell even further from $6.6 billion in 2008 to $3.2 billion in 2011. The gap

between state EVA with and without scrubbing technology installed between 2008 and

2011 was $2.1 billion.

Pennsylvania had the largest reported real GDP in both 2008 and 2011 of the states in

table 4. GED dropped by about 33 percent. The gulf between EVA in the observed and

counterfactual cases was $5 billion. For Pennsylvania, EVA was $421 billion in 2008.

With new FGD technology EVA grew to $467 billion in 2011. Without new scrubbers

EVA would have been less than $462 billion. EVA expanded more rapidly with FGD

technology put in place between 2008 and 2011.

21

North Dakota, akin to West Virginia, displays relatively small state output: GDP was

$25 billion in 2008 and $32 billion in 2011. North Dakota GED increased by about 20%

from 2008 to 2011; the disparity between EVA with and without FGD units was about

$200 million. This was the only state in table 4 to show an increase in GED from 2008 to

2011.

For the 30 states that added scrubbing technology between 2008 and 2011, GDP

increased from $7.9 trillion in 2008 to $8.1 trillion in 2011. GED fell from $264 billion to

$210 billion. The gross difference between EVA with observed emissions and EVA

under the counterfactual was about $18 billion.

Table 5 reports, for all sectors, state GDP, GED, and EVA growth rates between 2005

and 2008. The states are ranked in descending order of the spread between EVA growth

rates, with and without FGD technology adopted between 2005 and 2008. As suggested

by the results in table 3, West Virginia is an outlier; the difference in augmented growth

rates is 1.17%. This is nearly four-times larger than the difference in EVA growth

estimated in Indiana, Kentucky, and Ohio (the states with the next largest effects on

scrubbing on EVA growth). With FGD technology, West Virginia EVA expanded at an

annual rate of 3.23% between 2005 and 2008. Without scrubbers, state EVA would have

grown by 2.07%. Note that abatement costs are added back to market GDP under the no-

scrub counterfactual. This assumption will all else equal, diminish the difference

between EVA growth in the observed and counterfactual cases.

The difference in EVA growth stems from the significantly slower reduction in GED:

damages dropped by 8.7% annually with FGD technology and 3.2% without the

installed scrubbers. Further, with FGD technology, EVA growth outpaced market GDP

growth by over 2 percentage point (see – Muller, 2014a). Without new FGD installation,

EVA growth exceeded GDP growth by just over 1 percentage point.

Indiana, Kentucky, and Ohio, three spatially contiguous states each bordering the Ohio

River – and each with more than one facility that adopted new scrubbers during this

time period - show similar differences in state EVA growth between the observed and

counterfactual cases. The differences were between one-fifth and one-third of a

percentage point. With installed scrubbers, GED in these states dropped by between 8

and 9 percent, annually. Without FGD technology, GED is estimated to have fallen by

between 5 and 6 percent. Notice that GDP growth in both Indiana and Ohio was

negative between 2005 and 2008. These manufacturing-based economies were

exhibiting effects of the Great Recession. For Indiana, EVA increased between 2005 and

2008. Thus, the augmentation changes the sign of growth. Further, the difference in

22

growth rates (market GDP relative to EVA) was about 0.8% with scrubbing and one-

half of a percentage point without new FGD units. In Ohio, augmentation of GDP does

not change the sign of growth. However, EVA suggests growth that was “less negative”

than market GDP. Specifically, GDP contracted by 1%, annually, while EVA fell by 0.3%.

Without new scrubbers EVA decreased by over one-half of a percentage point.

North Carolina and Alabama exhibit differences in EVA growth with and without

scrubbers of between 0.1% and 0.2%. The remaining states in table 8 have small

differences (less than 0.10%) between state EVA growth in the observed and

counterfactual scenarios.

For the 20 states that put FGD units in place between 2005 and 2008, the difference in

EVA growth with and without scrubbing was 0.07%. GED fell by 8% annually with

scrubbing, and by 6% in the no-scrub counterfactual. Importantly, adoption of FGD

units is estimated to have enhanced state growth between 2005 and 2008, provided

output is measured in a manner that is inclusive of both the benefits and costs of scrubber

adoption. Although the difference was modest in aggregate across all states, significant

heterogeneity manifests in the degree to which FGD installation boosts augmented

output.

Table 6 repeats the analysis in table 5 for the 2008 to 2011 time periods. The results for

West Virginia again stand out relative to the other states in the table. The gap between

state EVA growth rates with and without scrubbing technology installed between 2008

and 2011 is over 1 full percentage point. With new scrubbers, state GED fell by nearly 17

percent annually. Importantly, without new scrubbers, state GED would have

decreased by about 5 percent per annum.

In Pennsylvania, GED fell by nearly 14% per year. Without new FGD units, GED would

have fallen by 8%, annually. The difference in EVA growth, with and without scrubbers

is estimated to have been 0.28%.

Production in North Dakota between 2008 and 2011 resulted in rapid growth in market

output and significant increases in state GED. With new FGD units state GED grew by

3.4%. In the no-scrub counterfactual, GED would have grown by over 6%. Further,

North Dakota’s EVA growth rate between 2008 and 2011 was 5.6% with FGD installed

and 5.4% in the no-scrub counterfactual. GDP growth in North Dakota was reported to

be 5.5%. Counter-factual EVA growth was lower than market growth.

The results for North Dakota provide the opportunity to make a broad point about the

relationship between GDP, GED, and EVA growth. Recall from section 2 the rules

23

regarding GDP, GED, and EVA growth. If GDP growth is less than GED growth, EVA

expansion will be lower than that for GDP; from expression (5): ��� > ��" ./: ��� < ��(.

Under the counterfactual, because state GDP expanded more slowly than GED, 5.5%

versus 6.2%, market growth was greater than EVA growth (5.5% compared to 5.4%).

Note that with FGD installation in North Dakota, GED increased at a rate of 3.4%.

Importantly, because this rate of expansion was lower than that for market GDP (5.5%),

with new scrubbers, EVA growth outpaced GDP in North Dakota. This is an important

result. It implies that installation of FGD units effectively changed the sign of the

difference between EVA and GDP growth in North Dakota. Without new scrubbers, an

augmented performance index applied in North Dakota reveals lower growth than the

market index. In marked contrast, with new FGD units, the EVA index suggests more

rapid growth than does GDP.

This result also holds for Missouri. State GDP expanded by 0.87%. EVA accelerated by

0.94%. However, without FGD units put in place between 2008 and 2011, EVA growth

would have been 0.86%. As with North Dakota, installation of FGD units in Missouri

effectively changed the sign of the difference between EVA and GDP growth.

Beyond the ten states shown in table 6, there were seven states (of the 30 that adopted

new FGD units between 2008 and 2011) that had EVA growth rates that were lower

than market rates of growth under both the observed and counterfactual cases. In each of

these seven states, installation of FGD units reduced the difference between GDP

growth and EVA growth. (The spread became less negative.)

Table 7 restricts the analysis conducted in table 6 to benefits that manifest within the

state where FGD units were installed. Figure 9 displays the key parameter driving the

results in table 7 – the share of benefits that manifest in-state. The map clearly indicates

that Florida and Texas retain the largest share of benefits in-state. Broadly, more

populous states tend to reap a larger share of benefits than more rural states. As such,

Pennsylvania, Maryland, Virginia, and Ohio (among others) retain between 10% and 25%

of benefits within state borders. Indiana and Michigan retain smaller shares despite

having large cities within their borders. These smaller shares stem from the fact that

FGD units were installed on plants that are near their borders with downwind states.

More westerly states (Wyoming, North Dakota, for example) show very small shares of

in-state benefits.

The bottom row of table 7 indicates that, when the analysis is limited to just “in-state”

benefits, the difference in EVA growth with and without FGD installation falls (from

24

0.06%) to 0.01%. Hence, most of the EVA-growth boosting effect of FGD installation

manifests in benefits across state boundaries. This reduction is especially prominent in

the ten states listed in table 7. Nearly all of the difference in EVA growth rates is

eliminated when examining with-state effects. For example, including interstate benefits,

FGD units installed in West Virginia increased EVA growth by 1.2%. In-state benefits

yield an increase of just 0.01%. This result stems from the relatively large export share

for West Virginia (see figure 9). For Pennsylvania, which retains 23% of benefits in-state,

the EVA growth effect of scrubbing is just 0.06%, relative to 0.28% including interstate

benefits.

Again North Dakota and Missouri are interesting cases. For North Dakota, the gap

between EVA growth in the observed and counter-factual cases was 0.24%. Restricting

benefits (reduced GED) to that which occurred in North Dakota changes the sign of the

difference in EVA between the observed and counterfactual contexts. Thus, EVA would

have expanded more rapidly without scrubbing in North Dakota. This results manifests

because nearly all of the benefits materialize outside of North Dakota. However, the full

abatement costs of scrubbers are attributed back to the North Dakota economy. Hence,

the boost to EVA due to FGD installation (the benefits) does not happen inside the state

borders of North Dakota. In the restricted in-state accounting experiment, there is no, or

very little, boost. All that is left in the augmentation are abatement costs, which under

the counterfactual are added back to state GDP. As a result, the no new FGD

counterfactual yields higher growth than the observed case. A similar phenomenon

manifests in Missouri.

To summarize table 7, focusing just on in-state benefits drastically reduces the

difference in EVA growth rates between the observed case and the no-scrub

counterfactual. From the perspective of a state regulator, the results in table 7 are those

that matter most directly. Recall the discussion from section 1 regarding the

interpretation of state and regional accounts. GED is deducted from GDP to estimate

EVA. Abatement is reflected in higher EVA growth because (unpriced) costs, the

remaining GED, fall. Here’s the cautionary note from section 1. Although EVA growth

increases due to abatement in a particular (emitting) region, the physical benefits

manifest in other regions. This is a function of how the air pollutants, SO2 in particular,

move through the atmosphere and it is also related to the location of specific plants

within states. The result is that a state regulator with a more limited objective function

(only in-state benefits matter), will consider GED reductions from abatement that are

much smaller in magnitude than the (interstate) total benefits.

25

This finding has important implications with respect to the design of an augmented

accounting regime such as the EVA. Clearly a federal regulatory body is the appropriate

institution to design and implement such a system. The interstate flow of air pollution

suggests that a considerable share of benefits from installing abatement technology

manifest across state lines. A myopic state regulator would have no incentive to build

such effects into a state-based EVA. And, as shown in table 7, the growth effects of

scrubber installation would be miscalculated to a considerable degree.

3.b Incidence of Benefits by Income and Demographics.

Table 8 reports changes in ambient pollution concentrations and monetary benefits by

the racial characteristics of exposed populations. In all counties, PM2.5 concentrations

were estimated to be 4.5 percent higher under the counterfactual. This amounts to a

difference of about 0.35 ug/m3. Counties with a larger share of African Americans that

the average share over all counties experienced the largest absolute and percentage

reduction in PM2.5. Ambient PM2.5 levels were estimated to be 5.5 percent lower due to

FGD adoption. Counties with large Asian populations also incurred relatively large

reductions in PM2.5. Among populations with greater than average shares of white and

Hispanic persons, the reductions in PM2.5 were lower. Intuitively, the same pattern

holds for monetary benefits of abatement due to FGD unit installation. The largest

percentage change in monetary damages manifests in counties with greater than

average shares of black populations.

The final column in table 8 reports the benefits on a per-capita basis. Across all counties,

the benefits per-capita were estimated to be $125. Among counties with large than

average population shares of African Americans, benefits per-capita were over $158. All

three of the remaining race categories show benefits per capita that were lower than the

all-county results. In particular, in counties with a larger than average share of Hispanic

persons, the benefits per capita were calculated to be just $67. Figure 10 displays the

benefits(or the change in damage) per capita, by county in 2011. Many areas in the

states of Pennsylvania, Maryland, and West Virginia incur benefits in excess of

$200/person. Counties with the largest per capita benefits tend to be those in rural

locations in these three states.

Table 9 displays the benefits of FGD adoption according to income quantiles, by county.

The greatest percentage changes in PM2.5 concentrations occurred in counties toward

the bottom end of the income distribution. For example, in counties below the 5th

percentile, the reduction in PM2.5 was about 4.9 percent. The change across all counties

was estimated to be 4.4 percent. In contrast, counties at or above the median income

26

level experienced reductions in PM2.5 below the 4.4 percent all county average. The one

exception to this result is for counties with income levels above the 99th percentile in

which PM2.5 reductions were calculated at 4.8 percent.

3.c. Sensitivity Analysis

Table 10 displays the first set of results from the sensitivity analysis. In particular the

table reports the benefit cost ratios (using the high abatement cost approach) for

scrubbers installed between 2005 and 2008, and between 2008 and 2011. Beginning with

the left panel of table 10, the benefit cost ratio for scrubbers installed on facilities in

Virginia between 2005 and 2008 generated a benefit cost ratio of 32:1 using the default

modeling approach. However, this ratio increased to 51:1 when using the PM2.5-

mortality dose response function published in Roman et al. (2008). Note that the

mortality coefficient reported in Roman et al., (2008) is about 60% larger than that found

by Pope et al., (2002) which is employed in the baseline case. The ratio was estimated to

be 13:1 conditional on a $2 million VSL (which is one-third of the magnitude of the

default $6 million VSL). A review of the remaining nine states in the left-hand panel of

table 10 reveals that the benefit-cost ratios range between 44:1 and 27:1 when the

alternative dose-response function is employed. Similarly, the ratios extend from 11:1

down to 7:1 when the VSL parameter is set to $2 million VSL. The right-hand panel of

table 10 shows a very similar pattern in that the change in the benefit-cost ratios is

proportional to the difference in the underlying model parameters.

Table 11 reports the effect that modifying the dose-response and VSL parameters has on

EVA growth rates. In particular, the table displays the difference in EVA growth with

and without scrubbers installed between 2005 and 2008 (in the left-panel) and from 2008

to 2011 (in the right-panel). Beginning with the left-hand panel, under the default

assumptions, state EVA increased by 1.17 percentage points more rapidly due to

scrubber installation in West Virginia. This spread increased to 1.89 percent using the

alternative dose-response function (Roman et al., (2008)). The difference in EVA fell to

0.45 conditional on the $2 million VSL. The responsiveness to the different modeling

assumptions of the spread between EVA growth with and without new scrubbers

across the remaining nine states in table 11 follows from the difference in the magnitude

of the alternative parameters. That is, for the $2 million VSL, the difference in growth

rates is roughly one-third that estimated using the default $6 million VSL. And, using

the larger Roman et al., (2008) dose-response parameter, the gap between EVA growth

rates is greater by roughly one-half than under the default assumptions. These patterns

generally hold for 2011.

27

4. Conclusions.

This paper demonstrates a new synthesis between empirical BCA and the NIPAs. This

conflation relies on the estimation of EVA, an augmented measure of output, defined as

market output less external environmental damage. The motivation for the exercise

stems from three areas. First, measurement of economic performance during

macroeconomic shocks is especially important. Second, comprehensive measurement

should encompass private sector output and the consequences of public sector

interventions both within and beyond the market boundary in the NIPAs. Third,

evaluation of public policy, especially in terms of consequences on growth, must rely on

a metric of growth that is inclusive of both benefits and costs. This latter point is

paramount in the context of environmental policy since a significant share of benefits

may manifest outside, or external to, the market boundary. Thus, relying on a market

index would, or could, yield a potentially biased picture of policy outcomes. The BCA

in this paper zeros in on adoption of FGD technology at power plants in the U.S.

between 2005 and 2011. This time period was characterized by rapid accumulation of

abatement capital because of anticipated changes in the Clean Air Act relevant to fossil

fuel-fired power plants.

At the most aggregated level, the empirical results show that GED has fallen from 1999

to 2011. While prior work (Muller, 2014a) demonstrated this trend from 1999 to 2008,

the current findings indicate that this secular trend has continued through 2011. Further,

growth in the augmented EVA index continues to outpace market GDP.

The empirical BCA results for the period from 2005 to 2008 indicate that EVA was

approximately $14 billion higher than it would have been without new scrubbing

technology. Total abatement cost associated with installation of new units in this time

period range from $275 million to $750 million, depending on assumptions. The benefit-

cost ratio across the 20 states that adopted new FGD units during this time period

ranges between 20:1 and 60:1. Further, EVA grew 0.07% more quickly with FGD units

that without between 2005 and 2008 in these 20 states. Between 2008 and 2011, in total,

power generation facilities in 30 states put new scrubbers in place during this time

period. Benefits of FGD installation amounted to almost $20 billion in 2011.

Concurrently, abatement costs also increased. Total investment in scrubbers was

estimated to range between $345 million and $900 million. Thus, the corresponding

benefit-cost ratio was from 20:1 to 60:1. From 2008 to 2011, EVA growth in states that

adopted scrubbers was 0.06% more rapid due to FGD adoption.

28

For a small number of states scrubber adoption changed the sign of the difference

between market and augmented growth. To demonstrate, market GDP in North Dakota

increased by 5.5% from 2008 to 2011. With scrubbing EVA expanded by 5.7%. Without

the FGD units installed between 2008 and 2011, EVA growth would have been 5.4%. For

North Dakota, EVA based on observed emissions augmented growth relative to market

GDP, while counterfactual EVA attenuated growth relative to GDP.

The sample integration of environmental BCA with the NIPAs provides evidence that

environmental policy may have an appreciable, positive effect on augmented measures

of output and growth. This finding stems from the inclusion of large monetary benefits

directly into the EVA metric. Second, the particular context explored herein is

characterized by significant spatial heterogeneity; for a few states (West Virginia, for

example) integrating BCA with state output has a dramatic effect on augmented state

growth. Finally, in a few cases, evidence is reported here that environmental policy may

change the orientation between market growth and augmented growth.

A few important caveats to the results presented above are worth pointing out. First,

the treatment of costs is admittedly rudimentary in the sense that only direct abatement

or compliance costs associated with FGD installation are computed and then included

as part of the BCA. General equilibrium effects in factor markets and in particular the

labor market are beyond the scope of the present study. Further, impacts of electricity

prices on measures of welfare such as consumer surplus, while potentially important,

are left to future inquiry. Second, benefits are sensitive to assumptions in the integrated

assessment model. While the paper explores a few alternative approaches to benefit

measurement there are two potentially important issues note directly addressed. One of

these is statistical uncertainty in the benefits estimates. That is, parameters such as the

VSL and the dose-response function are uncertain. As such, a Monte Carlo analysis is

the proper tool for exploration of statistical uncertainty in the benefit estimates. An

exercise of this type is a possible extension of work conducted herein. And finally,

model uncertainty is not treated. That is, repeating the exercise with a different

integrated assessment model would provide a sense of whether the key results reported

herein are in some way a function of the approach to benefit measurement used in this

paper.

29

Tables.

Table 1: Costs and Benefits of Abatement from FGD Adoption between 2005 and 2008:

Ten States with Largest Benefit-Cost Ratio.

State Low Cost Case

High Cost Case GED

GED No Scrub

Total Benefit

B/C (1)

B/C (2)

VA 7 17 608 1,150 541 81 32 WV 25 62 73 1,800 1,720 70 28 PA 15 36 1,260 2,180 920 63 25 OH 51 126 1,250 4,400 3,160 62 25 IN 39 97 1,870 4,130 2,260 58 23 KY 18 47 1,170 2,250 1,070 59 23 MA 0 0 313 318 4 52 21 GA 17 43 2,380 3,260 880 51 20 MN 5 18 22 324 301 55 17 IA 4 11 117 301 184 43 17 Total (20 States)

274 751 10,420 24,260 13,840 52 20

A = All values expressed in millions of real $2000.

(1) = Benefit cost ratio computed with low cost case.

(2) = Benefit cost ratio computed with high cost case.

30

Table 2: Costs and Benefits of Abatement from FGD Adoption between 2008 and 2011:

Ten States with Largest Absolute Benefit.

State Low Cost Case

High Cost Case GED

GED No Scrub

Total Benefit

B/C (1)

B/C (2)

NJ 1A 3 24 131 107 98 39 DE 0 0 154 161 7 84 33 VA 3 7 257 484 227 81 32 OH 10 24 2,080 2,830 742 76 31 NC 12 30 132 1,030 903 75 30 IN 2 4 42 163 121 70 28 PA 77 192 1,090 6,350 5,260 68 27 MI 11 28 661 1,420 763 67 27 WV 33 83 166 2,400 2,240 67 27 KY 18 44 295 1,480 1,190 67 27

Total (30 States)

344 892 8,700 28,470 19,770 57 22

A = All values expressed in millions of real $2000.

(1) = Benefit cost ratio computed with low cost case.

(2) = Benefit cost ratio computed with high cost case.

31

Table 3: Sulfur Dioxide Abatement Technology and GDP, GED, EVA Levels in 2005 and

2008.

2005 2008

State GDP GED EVA GDP GED EVA

West Virginia 42.4A,C 9.5 33.0 44.0 6.6 37.4 (35.8)B

Indiana 216.1 23.2 192.9 216.0 16.7 199.3 (197.1)

Kentucky 118.2 12.5 105.7 119.0 8.6 110.4 (109.4)

Ohio 395.6 35.4 360.2 380.0 24.2 355.8 (352.8)

North Carolina 308.5 16.7 291.8 322.0 9.6 312.4 (310.5)

Alabama

127.6 9.3 118.2 131.0 7.2 123.8 (123.3)

Georgia 316.0 14.9 301.1 322.0 13.4 308.6 (307.8)

Pennsylvania 434.2 34.8 399.4 452.0 30.8 421.2 (420.3)

Virginia 294.3 11.0 283.3 299.0 7.9 291.1 (290.6)

Iowa 106.6 5.8 100.7 109.0 3.5 105.5 (105.3)

Total (20 States)

4,704 244 4,461 4,930 174 4,753 (4,740)

A = All values expressed in billions real ($ 2000)

B = EVA computed using “no-scrub” counterfactual. Abatement costs calculated at high

cost case and then added to GDP.

C = GDP, GED, and EVA reflect all sector output and emissions for each state economy.

32

Table 4: Sulfur Dioxide Abatement Technology and GDP, GED, EVA Levels in 2008 and

2011.

2008 2011 State GDP GED EVA GDP GED EVA

West Virginia 44.0A,C 6.6 37.4 52.0 3.2 48.8 (46.7)B

Pennsylvania 452.0 30.8 421.2 483.9 17.0 466.9 (461.8)

North Dakota 25.5 2.1 23.4 31.6 2.5 29.1 (28.9)

Kentucky 119.0 8.6 110.4 129.8 7.0 122.8 (121.6)

Maryland 213.0 8.2 204.8 228.4 4.7 223.6 (221.8)

Alabama 131.0 7.2 123.8 133.1 5.3 127.8 (126.8)

Georgia 322.0 13.4 308.6 322.4 8.6 313.8 (311.9)

Missouri 195.0 7.3 187.7 201.9 7.0 194.9 (194.2)

Michigan 298.0 11.2 286.8 313.7 8.4 305.3 (304.6)

Ohio 380.0 24.2 355.8 405.9 20.9 385.0 (384.3)

Total (30 States)

7,920 264 7,660 8,060 210 8,054 (8,036)

A = All values expressed in billions real ($ 2000)

B = EVA computed using “no-scrub” counterfactual. Abatement costs calculated at high

cost case and then added to GDP.

C = GDP, GED, and EVA reflect all sector output and emissions for each state economy.

33

Table 5: Sulfur Dioxide Abatement Technology, GED, EVA, and GDP Growth between

2005 and 2008.

State

GDP GED GED

No Scrub EVA

EVA – EVA

No Scrub West Virginia 0.92A,C -8.71 -3.23 3.23

(2.07)B

1.17

Indiana -0.01 -7.93 -4.96 0.83 (0.55)

0.27

Kentucky 0.17 -8.93 -6.20 1.09 (0.86)

0.24

Ohio -1.00 -9.07 -6.25 -0.31 (-0.52)

0.21

North Carolina 1.08 -13.03 -8.62 1.72 (1.57)

0.16

Alabama 0.67 -6.26 -4.54 1.16 (1.05)

0.10

Georgia 0.47 -2.59 -1.03 0.62 (0.55)

0.07

Pennsylvania 1.01 -3.00 -2.28 1.34 (1.29)

0.05

Virginia 0.40 -7.79 -6.26 0.68 (0.63)

0.05

Iowa 0.57 -12.11 -10.97 1.17 (1.13)

0.04

Total (20 States)

1.16 -8.11 -6.33 1.60 (1.53)

0.07

A = All values annualized rates of growth (%) computed using formulae in 4a 4b, 4c.

B = EVA computed using “no-scrub” counterfactual.

C = GDP, GED, and EVA reflect all sector output and emissions for each state economy.

34

Table 6: Sulfur Dioxide Abatement Technology, GED, EVA, and GDP Growth between

2008 and 2011.

State

GDP GED GED

No Scrub EVA

EVA – EVA

No Scrub

West Virginia 4.24A,C -16.72 -4.81 6.86 (5.65)B

1.20

Pennsylvania 1.72 -13.81 -7.75 2.61 (2.33)

0.28

North Dakota 5.48 3.44 6.24 5.66 (5.42)

0.24

Kentucky 2.19 -4.91 -1.11 2.68 (2.44)

0.24

Maryland 1.76 -12.84 -4.99 2.22 (2.01)

0.21

Alabama 0.40 -7.52 -3.23 0.80 (0.61)

0.19

Georgia 0.03 -10.52 -5.76 0.42 (0.27)

0.15

Missouri 0.87 -1.07 1.22 0.94 (0.86)

0.08

Michigan 1.29 -7.02 -4.97 1.58 (1.52)

0.06

Ohio 1.66 -3.60 -2.75 1.99 (1.95)

0.05

National 1.08 -5.78 -3.58 1.27 (1.21)

0.06

A = All values annualized rates of growth (%) computed using formulae in 4a 4b, 4c.

B = Values in parenthesis report growth of EVA computed using “no-scrub”

counterfactual.

C = GDP, GED, and EVA reflect all sector output and emissions for each state economy.

35

Table 7: EVA Growth between 2008 and 2011 with In-State Benefits.

Total In-State State

EVA

EVA – EVA

No Scrub

EVA

EVA – EVA

No Scrub

Fraction Benefits In-State

West Virginia 6.86A,C

(5.65)B

1.20 6.86 (6.84)

0.01 0.05

Pennsylvania 2.61 (2.33)

0.28 2.61 (2.55)

0.06 0.23

North Dakota 5.66 (5.42)

0.24 5.66 (5.67)

-0.01 0.01

Kentucky 2.68 (2.44)

0.24 2.68 (2.67)

0.01 0.07

Maryland 2.22 (2.01)

0.21 2.22 (2.21)

0.01 0.10

Alabama 0.80 (0.61)

0.19 0.80 (0.79)

0.01 0.13

Georgia 0.42 (0.27)

0.15 0.42 (0.40)

0.02 0.18

Missouri 0.94 (0.86)

0.08 0.95 (0.95)

-0.00 0.07

Michigan 1.58 (1.52)

0.06 1.58 (1.57)

0.01 0.09

Ohio 1.99 (1.95)

0.05 1.99 (1.99)

0.01 0.17

National 1.27 (1.21)

0.06 1.27 (1.26)

0.01 0.12

A = All values annualized rates of growth (%) computed using formulae in 4a 4b, 4c.

B = Values in parenthesis report growth of EVA computed using “no-scrub”

counterfactual.

C = EVA reflect all sector output and emissions for each state economy.

36

Table 8: Benefit Incidence and County Demographics: 2011

PM2.5 Benefit Benefit/ Capita Race (I)A (II)B (I)A (II)C

All Counties 4.467 (4.153)

0.349 (0.330)

4.182 (3.883)

11.7 (35.0)

125.598 (122.375)

WhiteD 4.223 (4.245)

0.317 (0.332)

3.966 (3.977)

7.2 (20.9)

119.033 (127.595)

Black 5.496 (3.715)

0.469 (0.293)

5.114 (3.463)

22.7 (53.2)

158.736 (104.957)

Asian 4.752 (4.675)

0.393 (0.379)

4.410 (4.345)

34.3 (65.8)

116.782 (118.265)

Hispanic 2.813 (3.430)

0.214 (0.296)

2.629 (3.176)

16.9 (51.1)

66.738 (90.148)

A = Columns (I) show percentage change. B = Column (II) for PM2.5 shows change in ambient concentration (ug/m3). C = Column (II) for benefits show change in damage, or benefit in ($ millions). D = Distinctions by race show results for counties with fraction of each ethnicity above the national mean fraction for that ethnic group.

37

Table 9: Benefit Incidence and County Per Capita Personal Income: 2011

PM2.5 Benefit Benefit/ Capita Race (I)A (II)B (I)A (II)C

All Counties 4.373 (4.098)

0.341 (0.325)

4.096 (3.832)

11.6 (34.9)

122.98 (120.78)

< 5th 4.881 (3.314)

0.394 (0.289)

4.567 (3.084)

2.4 (2.4)

143.52 (115.29)

5th – 25th 4.576 (3.100)

0.375 (0.268)

4.286 (2.895)

4.0 (4.6)

141.37 (106.29)

25th – 50th 4.601 (4.296)

0.366 (0.330)

4.310 (4.036)

7.6 (13.4)

135.50 (129.58)

50th – 75th 4.255 (4.413)

0.331 (0.340)

3.983 (4.130)

11.8 (28.4)

117.02 (125.00)

75th – 95th 3.974 (4.397)

0.284 (0.338)

3.728 (4.102)

20.0 (54.0)

97.63 (115.39)

95th – 99th 3.942 (4.055)

0.282 (0.350)

3.700 (3.749)

32.0 (73.1)

94.76 (108.87)

> 99th 4.775 (5.206)

0.380 (0.444)

4.430 (4.836)

57.0 (103)

108.79 (122.75)

A = Columns (I) show percentage change. B = Column (II) for PM2.5 shows change in ambient concentration (ug/m3). C = Column (II) for benefits show change in damage, or benefit in ($ millions). D = Distinctions by race show results for counties with fraction of each ethnicity above the national mean fraction for that ethnic group.

38

Table 10: Sensitivity Analysis: Benefit Cost Ratio for FGD Units.

2008 2011

State

Default Roman $2M VSL

State

Default Roman $2M VSL

VA 32 51 13 NJ 39 62 16 WV 28 44 11 DE 33 53 13 PA 25 40 10 VA 32 51 13 OH 25 39 10 OH 31 49 12 IN 23 37 9 NC 30 47 12 KY 23 36 9 IN 28 39 10 MA 21 33 8 PA 27 43 11 GA 20 32 8 MI 27 43 11 MN 17 27 7 WV 27 43 11 IA 17 27 7 KY 27 43 11

39

Table 11: Sensitivity Analysis: EVA growth with and without new FGD units.

2008 2011

State

Default Roman $2M VSL

State

Default Roman $2M VSL

WV 1.17 1.89 0.45 WV 1.20 1.94 0.44 IN 0.27 0.44 0.10 PA 0.28 0.45 0.11 KY 0.24 0.38 0.09 ND 0.24 0.51 0.12 OH 0.21 0.34 0.08 KY 0.24 0.39 0.09 NC 0.16 0.26 0.06 MD 0.41 0.34 0.08 AL 0.10 0.17 0.04 AL 0.19 0.32 0.07 GA 0.07 0.11 0.03 GA 0.15 0.24 0.06 PA 0.05 0.09 0.02 MO 0.08 0.13 0.03 VA 0.05 0.07 0.02 MI 0.06 0.10 0.02 IA 0.04 0.07 0.02 OH 0.05 0.08 0.02 Total (20 States)

0.07 0.11 0.03 Total (30 States)

0.06 0.09 0.02

40

Figures

Figure 1: Schematic Demonstration of BCA: West Virginia.

0

10

20

30

40

50

60

2005 2008 2011

Bil

lio

ns

($) State GDP

EVA No Scrub

EVA

GED No Scrub

GED

41

Figure 2: Use of FGD by Facilities in the Acid Rain Program: 1995 to 2011.

0

200

400

600

800

1000

1200

1400

0

100

200

300

400

500

600

Units with FGD

Units in ARP

42

Figure 3: Difference in SO2 emissions by plant between observed and no-scrub

counterfactual: 2008.

Gulf of Mexico

AtlanticOcean

PacificOcean

Canada

Mexico

Difference SO2 (tons)0

1 - 5,000

5,001 - 10,000

10,001 - 25,000

25,001 - 50,000

50,001 - 75,000

75,001 - 100,000

100,001 - 163,292

43

Figure 4: Difference in PM2.5 Concentrations between no-scrub counterfactual and

observed emissions of SO2 for 2008.

Gulf of Mexico

AtlanticOcean

Canada

Mexico

Change PM2.5 (ug/m^3)0.00 - 0.05

0.06 - 0.10

0.11 - 0.25

0.26 - 0.50

0.51 - 0.75

0.76 - 0.96

44

Figure 5: Difference in monetary damage between no-scrub counterfactual and

observed emissions of SO2 for 2008.

Gulf of Mexico

AtlanticOcean

Canada

Mexico

Change in Damage ($million)0 - 1

2 - 10

11 - 25

26 - 50

51 - 75

76 - 100

101 - 279

45

Figure 6: Difference in SO2 emissions by plant between observed and no-scrub

counterfactual: 2011

Gulf of Mexico

AtlanticOcean

PacificOcean

Canada

Mexico

Difference SO2 (tons)0

1 - 5,000

5,001 - 10,000

10,001 - 25,000

25,001 - 50,000

50,001 - 75,000

75,001 - 100,000

100,001 - 163,292

46

Figure 7: Difference in PM2.5 concentrations between no-scrub counterfactual and

observed emissions of SO2 for 2011.

Gulf of Mexico

AtlanticOcean

Canada

Mexico

Change PM2.5 (ug.m^3)0.00 - 0.05

0.06 - 0.10

0.11 - 0.25

0.26 - 0.50

0.51 - 0.72

0.73 - 1.00

1.01 - 1.10

47

Figure 8: Difference in monetary damage between no-scrub counterfactual and

observed emissions of SO2 for 2011.

Gulf of Mexico

AtlanticOcean

Canada

Mexico

Change in Damage ($million)0 - 1

2 - 10

11 - 25

26 - 50

51 - 75

76 - 100

101 - 333

48

Figure 9: Share of monetary benefits from FGD installation accruing within state: 2008 –

2011.

Gulf of Mexico

AtlanticOcean

PacificOcean

Canada

Mexico

Fraction of Benefits In-state.0.00

0.01 - 0.05

0.06 - 0.10

0.11 - 0.25

0.26 - 0.43

49

Figure 10: Benefits of FGD Installation (per capita): 2011

Gulf of Mexico

AtlanticOcean

Canada

Mexico

Damage/Capita ($)1 - 10

11 - 50

51 - 100

101 - 150

151 - 200

201 - 300

301 - 469

50

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55

Appendix.

Results reported in the appendix encompass national GED/VA by sector for each of the

five years 1999, 2002, 2005, 2008, and 2011. Next, regional and state accounts are

reported. Finally, GED for the utility sector, by region are displayed.

A.1. Augmented National Accounts.

Table 1 reports aggregate pollution damage (GED) relative to reported VA for each of

the five data years covered by the analysis. For the total economy, GED began in 1999 at

about 6% of GDP. Relative to total economy GDP, air pollution damage has fallen by 1%

over each of the three year periods. Thus, in 2011 GED amounts to 2% of GDP. Among

the approximately 20 sectors of the U.S. economy, there is considerable heterogeneity in

the GED/VA ratios in any given year, and in the secular trends in the pollution

intensity ratios. For example, the agriculture and forestry sector generated GED that

exceeded VA in 1999: the GED/VA ratio was 1.19. However, GED produced by this

sector has fallen so precipitously, relative to VA, that the GED/VA in 2011 was just 0.49.

Displaying a similar pattern, the utility sector GED/VA ratio in 1999 was 0.86 (air

pollution damage amounted to 86% of VA in 1999) and this pollution intensity measure

dropped to 0.28 in 2011. Utility GED fell especially rapidly between 2005 and 2008 and

2008 and 2011. In a subsequent empirical analysis reported in this paper, the role of air

pollution policy on the GED-intensity of output from the utility sector is explored.

Drawing on results reported later in the paper, much of the reduction in GED from this

sector stems from compliance with air pollution policy.

Both the manufacturing and transportation sectors also display similar trends in the

GED/VA ratios. The manufacturing GED/VA ratio drops from 0.07 in 1999 to 0.02 in

2011. Beginning in 1999, the transportation sector showed a GED/VA ratio of nearly

0.20. This ratio fell nearly by one-half between 2005 and 2008 as new pollution fuel

content regulations took effect in 2006 (see Muller, 2014b). The waste management

sector generated GED that totaled nearly 20% of VA in 1999. GED produced from this

sector fell by one-half (relative to VA) in 2002, and then by one-half again in 2011. As

reported in prior research most of the GED from this sector stems from emissions at

sewage treatment facilities and from incineration of municipal waste. Many sectors in

the U.S. economy produce no measureable GED when expressed relative to VA. For

example, the finance and insurance sector, real estate, management, and technical

services display GED/VA ratios in all periods that are essentially zero.

56

Table 2 reports the difference in the annualized growth rates in EVA (VA – GED) and

VA10. For the total economy, between 1999 and 2002, EVA outpaced VA by 0.57%. Then,

between 2002 and 2005 EVA increased by 0.25% faster than market VA. Heading into

the Great Recession the spread between EVA growth and VA increased to 0.33%. This

gap was effectively maintained between 2008 and 2011; EVA increased over the time

period spanning the Great Recession by 0.28% more than market VA.

As in table 1, there is heterogeneity across sectors in the spread between EVA and VA

growth rates. At one extreme is the agriculture and forestry sector. From 1999 to 2002,

EVA switches sign from negative in 1999 to positive in 2002. In this case, annual growth

rates have no meaning. From 2002 to 2005, the EVA for this sector expanded by over 25%

points faster than market VA. This startling difference derives from EVA in 2002 being

quite close to zero and from the rapid reduction in GED relative to VA. Recall that

formula (4c) employs the base period EVA in the denominator. So, as EVA approaches

zero, EVA growth increases without bound. The difference between EVA and VA

growth rates for this sector falls to 9% between 2005 and 2008, and less than 1% between

2008 and 2011.

At the other end of the spectrum, many of the sectors that do not yield significant

amounts of air pollution damage do not show any measureable difference between the

EVA and VA growth rates. This supports the findings of zero GED/VA ratios reported

in table 1; these sectors include finance and insurance, real estate, technical services, and

management.

Between these two extremes lie most sectors in the economy. The utility sector EVA

expanded by between 10% and 20% more rapidly than market VA. Note that the spread

of 20% points between EVA and VA growth from 2005 to 2008 coincides with the

reduction of the utility GED/VA ratio from 0.70 to 0.49. Augmented growth in the

manufacturing sector outpaced reported growth by less than 1%. Both wholesale and

retail trade also have augmented growth rates that exceed VA growth by less than 1%.

Note that table 2 indicates several sectors during particular time periods showed

augmented growth that was lower than market VA growth rates. For example, the

construction sector , during the 2005 to 2008 period, had an augmented growth rate that

was lower than that for VA. During this time period, construction GED increased which

decreases EVA. Transportation displayed a similar result between 1999 and 2002.

Accordingly, table 1 reports that transportation GED/VA increased from 1999 to 2002.

10

The results for the total economy between 1999 and 2008 were reported in Muller (2014a).

57

A2. Regional and State Accounts.

Table 3 reports the regional decomposition of growth rates of the GED, VA, and the

EVA between 1999 and 2011. The GED/VA ratio is also reported11. The national

average annual growth rate of GED was -6.3 percent. New England experienced a

reduction in GED of over 9 percent, annually. Nationally, the economy grew at an

annual rate of 2.0 percent between 1999 and 2011. In New England, VA grew at an

annual rate of 1.6 percent. EVA grew by nearly 1.8 percent, which is smaller than the

national average EVA growth rate of 2.3 percent. For the region, EVA outpaced VA by

0.15 percent. In the Mideast region, GED dropped by 8.1 percent, EVA increased by 2.3

percent, and VA climbed by 2.0 percent, annually. The difference between EVA and VA

growth rates in this region was about 0.4 percent.

In the Southeast region, GED dropped by 7.6 percent, while VA grew by 1.8 percent.

EVA grew by over 2.3 percent which is 0.5 percentage points faster than VA. The Great

Lakes region also shows a difference between EVA and VA growth rates that was

considerably larger than the national average. In all of the remaining four regions EVA

growth was greater than VA by a margin smaller than the national average. Notably,

the far west region and New England had the smallest difference between EVA and VA

growth rates; the difference in New England was about 0.15 and in the far west states,

the difference was just 0.18.

Table 3 also reports GED/VA ratios for all regions in 1999 and 2008. For the entire

country, the GED/VA ratio dropped from 0.063 to 0.023. Output became roughly one-

third as pollution-intensive. In 2011, output produced in the Great Lakes region was the

most pollution-intensive (GED/VA = 0.04), while production in New England

generated the least pollution damage per dollar output (GED/VA = 0.006).

Table 4 further decomposes the GED from region to state. The table, in order to

conserve space, ranks states according to the difference in EVA and VA growth rates

and displays the ten states with the largest difference. West Virginia experienced a

decline in GED of over 11 percent annually. VA grew by 2.3 percent and EVA expanded

by 5.7 percent. The difference between EVA and VA growth is, therefore, over 3 full

percentage points, as was reported in Muller (2014a) for the 1999 to 2008 period. The

GED/VA ratio for West Virginia was 0.36 in 1999. The air pollution damage generated

by production in West Virginia comprised over one-third of the total value of state

output. In 2008, the GED/VA ratio for this West Virginia dropped to 0.06.

11 These regional divisions are made by the USBEA in reporting regional measures of output. For an explanation of the states that belong to each region see http://www.bea.gov/regional/docs/regions.cfm.

58

In North Dakota, GED fell by over 5 percent per annum between 1999 and 2011. VA

expanded by over 5 percent. EVA was estimated to have increased by 7.7 percent for a

difference of 2.2 percentage points relative to VA. The GED/VA ratio in North Dakota

was 0.28 in 1999. However, this ratio dropped to under 0.08 in 2011. Hence, air

pollution intensity of output in this state fell by over two-thirds.

Wyoming and Kentucky, the next states in table 4 show rates of EVA growth that are

greater than VA growth by just about 1 full percentage point. In addition, for Wyoming

and Kentucky output became roughly one-third as pollution-intensive in 2011 relative

to 1999. For Kentucky, the GED/VA ratio dropped by about one-half. In the remaining

six states (Indiana, Alabama, Illinois, Pennsylvania, Ohio, and Tennessee) EVA growth

in these states exceeded reported VA growth by between 0.88 and 0.54 percentage

points. Notice that these states are in the Mideast, Southeast, and Great Lakes USBEA

regions. Each of these regions, as reported in table 3, displayed EVA growth that

outpaced VA growth by more than the national economy.

A3. The Utility Sector.

Table 5 shows the regional decomposition of utility GED. Beginning at the national

level, utility GED fell at an annual rate of -5.8 percent between 1999 and 2011. VA was

effectively flat, and EVA expanded at an annual rate of 16 percent. Thus EVA outpaced

VA growth by approximately 15 percentage points. First, two regions show GED/VA

ratios greater than unity in 1999. As such percentage changes in EVA are not presented.

For these regions, the changes in the GED/VA ratios provide a sense of how much GED

has changed. For the Southeast region utility GED intensity dropped from 1.14 to 0.33.

In the Great Lakes region GED-intensity decreased from 1.45 to 0.65.

Except for the far west region, the difference between market and the augmented rates

of growth are much larger in this sector than for the entire economy. Correcting for the

air pollution externality has a greater impact on measures of growth in this sector. In

the New England and Southwest regions the difference between EVA and VA rates is

over 2 percentage points. In the Rocky Mountain region, EVA growth is 1 percentage

point larger than VA. In the Mideast region the difference is over 7 percent. And in the

Plains region EVA grew by over 8 points faster than VA.

Table 5 indicates that the GED in the utility sector fell precipitously from 1999 to 2011.

Further, the rates of decline were spatially varied from over 10 percent annual rates of

reduction in three eastern regions to less than 3 percent declines in the Rocky Mountain

region. Federal air pollution policy that directly affects the utility sector, especially the

power generation industry, changed in significant ways between 1999 and 2011. In 2000,

59

Phase Two of the Acid Rain Program, promulgated under Title IV of the 1990

Amendments to the Clean Air Act, commenced. This event brought many additional

electric generating units under regulatory control. Further, in 2005 the USEPA

announced the Clean Air Interstate Rule (CAIR) as a replacement for the Acid Rain

Program. The aggregate limits on SO2 emissions stipulated by CAIR were on the order

of 90 percent more stringent than existing limits. (Although CAIR was not subsequently

enacted, the credible threat of such stringent SO2 caps inspired investment in pollution

control technology – see Chan et al., 2014). At issue is that federal air pollution policy is

likely to have played an important role in the dramatic differences between augmented

(EVA) and market (VA) growth rates for the utility sector. To explore the connection

between policy and growth in a more targeted fashion, the next section of the paper

reports estimates of the benefits and abatement costs associated with FGD units

installed between 2005 and 2008, and those put in place subsequent to 2008.

60

Supplementary Tables:

Table 1: Air Pollution Damage Intensity: GED/VA.

Sector 1999 2002 2005 2008 2011

Agriculture/Forestry 1.19A 0.81 0.62 0.51 0.49 Mining 0.08 0.03 0.08 0.05 0.03 Utilities 0.86 0.80 0.70 0.49 0.28 Construction 0.06 0.06 0.04 0.05 0.05 Manufacturing 0.07 0.05 0.05 0.03 0.02 Wholesale Trade 0.00 0.00 0.00 0.00 0.00 Retail Trade 0.01 0.01 0.01 0.00 0.00 Transportation and Warehousing 0.19 0.21 0.18 0.10 0.08 Information 0.00 0.00 0.00 0.00 0.00 Finance and Insurance 0.00 0.00 0.00 0.00 0.00 Real Estate and Rental and Leasing 0.00 0.00 0.00 0.00 0.00 Professional, Scientific, and Technical Services 0.00 0.00 0.00 0.00 0.00 Management of Companies and Enterprises 0.00 0.00 0.00 0.00 0.00 Administrative, Waste Management and Remediation Services

0.19 0.09 0.05 0.08 0.05

Educational Services 0.01 0.01 0.01 0.01 0.00 Health Care and Social Assistance 0.00 0.00 0.00 0.00 0.00 Arts, Entertainment, and Recreation 0.06 0.05 0.06 0.05 0.04 Accommodation and Food Services 0.02 0.03 0.03 0.04 0.02 Other Services (except Public Administration) 0.01 0.01 0.01 0.00 0.00 Total Economy 0.06 0.05 0.04 0.03 0.02 Total Economy with Greenhouse Gases 0.08B 0.06 0.06 0.05 0.04 Total Economy with Greenhouse Gases (95th) 0.09C 0.08 0.08 0.07 0.06

A = All values expressed as the ratio of real GED to real VA.

B = Values include damages from CO2 produced from energy production and

consumption using social cost of carbon value of ($28/tCO2 in 2011).

C = Values include damages from CO2 produced from energy production and

consumption using 95th percentile of the distribution of social cost of carbon ($78/tCO2

in 2011).

Source = VA from US BEA (37). GED author’s calculations.

61

Table 2: Difference in Annual Rates of Growth: EVA Growth less VA Growth.

Sector 1999-2002

2002-2005

2005-2008

2008-2011

Agriculture/Forestry A 27.65% 9.32% 0.82% Mining 1.84% -1.46% 1.11% 0.87% Utilities 11.61% 15.32% 20.18% 11.78% Construction 0.23% 0.72% -0.48% 0.12% Manufacturing 0.79% 0.21% 0.64% 0.25% Wholesale Trade -0.13% 0.09% 0.04% 0.00% Retail Trade 0.16% 0.00% 0.03% 0.00% Transportation and Warehousing -0.83% 1.40% 2.98% 0.81% Information 0.00% 0.00% 0.00% 0.00% Finance and Insurance 0.00% 0.00% -0.01% 0.01% Real Estate and Rental and Leasing 0.00% 0.00% 0.00% 0.00% Professional, Scientific, and Technical Services 0.02% 0.00% 0.00% 0.00% Management of Companies and Enterprises 0.00% 0.00% 0.00% 0.00% Administrative and Support and Waste Management and Remediation Services 4.21% 1.48% -0.91% 1.15% Educational Services 0.14% -0.06% 0.01% 0.13% Health Care and Social Assistance -0.04% 0.06% 0.01% 0.00% Arts, Entertainment, and Recreation 0.12% -0.09% 0.29% 0.42% Accommodation and Food Services -0.60% 0.12% -0.20% 0.35% Other Services (except Public Administration) 0.06% 0.02% 0.11% 0.11% Total Economy

0.57%

0.25%

0.33%

0.28%

A = All changes expressed in annualized rates (%) using the formulas in 4a, 4b, and 4c.

A = EVA switches sign from 1999 to 2002 and as such the percentage change is

meaningless.

62

Table 3: Regional Annual Rates of Growth and Pollution Intensity: All sectors.

US BEA 1999 - 2011 GED/VA Region GED VA EVA EVA-VA 1999 2011

New England

-9.35 1.63 1.79 0.15 0.024 0.006

Mideast -8.08 1.96 2.34 0.38 0.060 0.017

Southeast

-7.61 1.82 2.32 0.50 0.081 0.025

Great Lakes -6.54 0.79 1.32 0.53 0.098 0.040 Plains

-3.80 2.08 2.41 0.32 0.070 0.034

Rocky Mountains

-4.12 2.96 3.19 0.22 0.041 0.017

Southwest

-3.49 3.52 3.73 0.21 0.044 0.019

Far West

-4.45 2.07 2.25 0.18 0.038 0.017

National -6.33 1.96 2.32 0.36 0.063 0.023

A = All changes expressed in annualized rates (%) using the formulas in 4a, 4b, and 4c.

63

Table 4: State Annual Rates of Growth and Pollution Intensity: All Sectors.

US BEA Growth rates: 1999 - 2008 GED/VA Region GED EVA VA EVA-VA 1999 2008

West Virginia -11.87A 5.68 2.30 3.38 0.364 0.061

North Dakota -5.17 7.66 5.48 2.18 0.278 0.078

Wyoming -4.29 7.91 6.89 1.03 0.142 0.038 Kentucky -7.22 2.10 1.17 0.93 0.152 0.054 Indiana -6.35 2.43 1.55 0.88 0.149 0.056 Alabama -6.98 2.40 1.70 0.70 0.115 0.040 Illinois -7.56 1.82 1.18 0.64 0.106 0.036 Pennsylvania -6.98 2.34 1.72 0.62 0.103 0.035 Ohio -6.14 1.29 0.67 0.62 0.119 0.051 Tennessee -8.59 1.90 1.36 0.54 0.084 0.024 National -6.33 2.32 1.96 0.36 0.064 0.023

A = All changes expressed in annualized rates (%) using the formulas in 4a, 4b, and 4c.

64

Table 5: Regional Annual Rates of Growth and

Pollution Intensity: Utility sector.

US BEA 1999 - 2011 GED/VA Region GED EVA VA EVA-VA 1999 2011

New England

-11.49A 1.40 -0.70 2.10 0.276 0.070

Mideast -10.11 7.04 -0.56 7.60 0.669 0.199

Southeast

-10.20 B -0.31 B 1.140 0.325

Great Lakes

-7.83 B -1.41 B 1.448 0.646

Plains

-5.22 8.79 0.10 8.69 0.781 0.405

Rocky Mountains

-2.97 0.99 -0.03 1.02 0.302 0.212

Southwest

-5.07 3.60 0.83 2.77 0.427 0.207

Far West

-5.70 0.68 0.45 0.23 0.051 0.024

National -5.75 16.06 0.42 15.64 0.774 0.284

A = All changes expressed in annualized rates(%) using the formulas in 4a, 4b, and 4c.

B = EVA percent change not reported because of sign change.

65

Supplementary Figures:

Figure 1A: VA, EVA and GED for the Utility Sector

All values in real ($2000)

0

50

100

150

200

250

300

1999 2002 2005 2008 2011

($ b

illi

on

s, r

ea

l 2

00

0)

VA

EVA

GED

66