Trading Equity for Efficiency in Environmental Protection? Environmental Justice Effects from the SO 2 Allowance Trading Program 1

Evan J. Ringquist School of Public and Environmental Affairs

Indiana University Bloomington, IN 47405 eringqui@indiana.edu

The research in this manuscript was funded in part by NSF Award xxxxxxxxxx

1 Direct all correspondence to Evan Ringquist, School of Public and Environmental Affairs, Indiana University.

1315 E. 10 th Street, Bloomington, IN 47404. 812.855.0732. eringqui@indiana.edu.1 Trading Equity for Efficiency in Environmental Protection? Environmental Justice Effects from the SO 2 Allowance Trading Program

Abstract

Objective: Arthur Okun made plain the fundamental conflict between efficiency and equity that arises in

the pursuit of policy goals (Okun 1974). While Okun was speaking about government tax and spending

polic y, the potential conflict between efficiency and equity is also manifest at the nexus of two of the most

far reaching changes in the environmental policy arena; the adoption of market

- like tools for pollution

control and the quest for environmental justice

. The paper assesses this potential tradeoff by determining

whether the pursuit of efficiency through the Clean Air Act Amendment’s (CAA) sulfur dioxide allowance

trading program (ATP) inadvertently transfers pollution into poor and minority communities.

Methods: I employ P robit , T obit , and GLS model s using data for all SO 2 trading activity between January

1995 and March 2009 to estimate whether allowance trading concentrates SO 2 emissions in poor

communities and communities of color. When using these models I control for sample selectivity and for

possible bias stemming from the modifiable areal unit problem.

Results: T he ATP does not concentrate sulfur dioxide emissions in black or Hispanic communities. To

the contrary, communities with high per centages of black and Hispanic residents experience fewer

imports of sulfur dioxide. A llowance trading does transfer SO 2 emissions into poorly educated

communities.

Conclusions: There is no inherent tradeoff between effic iency and equity when using market based

instruments for pollution control

. Policy makers, however, might make an effort to design and implement

future emissions trading programs in a manner that reduces the monitoring costs of tracking emissions

trading. By reducing monitoring costs, policy makers may prevent the concentration of emissions in

poorly educated communities while preserving the efficiency benefits of these instruments.2 1.0 Introduction

A generation ago Arthur Okun made plain the fundam ental conflict between efficiency and equity

that arises in the pursuit of policy goals (Okun 1974). While Okun was speaking about government tax

and spending policy, the potential conflict between efficiency and equity is also manifest at the nexus of

tw o of the most far reaching changes in the environmental policy arena; the adoption of market

- like tools

for pollution control and the quest for environmental justice. Environmental economists and

environmental justice advocates both offer legitimate criti ques of current and past practice in

environmental regulation. This paper focuses upon one prominent place where these two critiques

intersect , the sulfur dioxide allowance trading program (ATP) created by the 1990 Clean Air Act (CAA)

amendments. The primary concern of environmental justice advocates, and the focus of my

investigation, is whether by pursuing the efficient control of pollution the ATP transfers pollution into poor

and minority communities. By examining all SO 2 trading activity be tween January 1995 and March 2009,

I conclude that the ATP does not concentrate sulfur dioxide emissions in black or Hispanic communities,

but that trading does transfer significant SO 2 emissions into poorly educated communities.

The remainder of the paper proceeds in five stages. Section 2 discusses the potential tradeoff

between efficiency and equity in pollution control; i.e., the argument that market mechanisms for pollution

control might inadvertently concentrate pollution in poor and minority communi ties. This section also

reviews the limited empirical evidence on the topic. Section 3 describes the creation, content, and current

experience with sulfur dioxide allowance trading within the federal acid rain program. Section 4 describes

how I use the EPA’s allowance trading and compliance databases to create three measures of the

propensity and magnitude of SO 2 trading, and the effect that these trades have on the transfer of sulfur

dioxide emissions. Section 5 describes the modeling approaches employ ed and discusses the results of

several empirical models of SO 2 allowance trading behavior. Section 6 concludes.

2.0 Efficiency vs. Equity in Environmental Policy

Environmental protection policy is a quintessential example of the new social regulation that

became common at the federal level during the 1960s and the early 1970s (Eisner 1993). In environmental3

protection, policymakers identified an important market failure (negative externalities), and addressed this

failure using “ command and control ” regulations. These regulations were supposed to reduc e pollution

, while the associated improvements in environmental quality

-- a public good

-- were to benefit all citizens

more or less equally . Over the past t hirty years, policy analysts have questione d both of these aspects of

pollution control policy. These criticisms have crystallized into two of the most significant changes in the

environmental policy area. The first criticism took aim at the economic aspects of command and control

regulation. Be ginning in the mid

- 1970s, economists charge d that traditional environmental regulations were

inefficient and ineffective (see Tietenberg 1985 for a good review). These analysts argued that economic

incentive approaches to pollution cont rol would produce greater improvements in environmental quality at

far lower costs. A second, more recent criticism of environmental policy has been leveled by "environmental

justice" advocates who argue that e nvironmental regulations have not benefited a ll citizens equally. T hese

critics c ite evidence that poor and minority communities suffer disproportionate exposures to environmental

contaminants and levels of environmental risk. Concerns over environmental equity have drawn the

attention of political scientists and sociologists, much as concerns over efficiency in environmental

regulation drew the attention of economists over a decade earlier. Moreover, environmental justice

advocates seek to reorient federal environmental policy to emphasize social justice, much as economists

lobbied to reorient policy toward efficiency.

2.1 The Economic Critique of Environmental Regulation

From an economic perspective, the central drawback of command and control regulations is that

the y do not recognize unequal ma rginal cost s of pollution control across firms. F irm A may be able to

reduce pollution at half the cost of firm B, yet command and control regulations require both firms to meet

identical pollution reduction goals . A more efficient approach would recogni ze heterogeneous marginal

costs of control and allow firms for whom control comes more cheaply to reduce their pollution more, while

firms for whom reductions are costly can reduce their pollution less. In this way, we get the same reduction

in pollution at a lower cost.

Economic incentive approaches for environmental protection can b e grouped into three categories;

pollution taxes, subsidies

, and emissions trading. The most common of these is emissions trading. Under

emissions trading, th e government sets the desired level of environmental quality and allocates only as4

many pollution permits or credits as is compatible with th is predetermined level. No source can emit

pollution without a permit or credit. The government then allows firms (or other actors such as

environmental groups) to purchase these permits through market transactions. Firms with low costs of

control will reduce their pollution more than necessary so that they can sell their permits, and high control

cost firms w ill purchase permits rather than reduce their pollution. Emissions trading has several

advantages: (i) it requires firms to reveal their marginal costs of pollution control (i.e., the price they are

willing to pay for a pollution permit), (ii) it recogniz es differences across firms in the marginal cost of control,

and allows for varying levels of pollution reduction; (iii) because of (i) and (ii), emissions trading produces

the desired level of environmental quality at lower cost; and (iv) promotes technol ogical innovation in

pollution control as firms seek ways to reduce pollution so that they can sell their pollution credits.

2.2 The Environmental Justice Critique of Environmental Regulation

Environmental justice advocates claim that traditionally disad vantaged communities are further

disadvantaged in that they must endure disproportionately high levels of exposure to environmental risks

. For some critics, the critical causal factor is race: higher levels of environmental risk in communities of color

are one more example of institutional racism in the U.S. (Bullard 1990). For other observers, race is less

important than class

-- traditionally disadvantaged lower classes are targeted to receive higher levels of

pollutants because they are poli tically unmobilized and pose less threat of opposition and delay in siting

environmentally hazardous facilities (Szasz 1994). The concerns of environmental justice advocates are

backed by significant empirical evidence (see Ringquist 2005 for a review)

. Federal p olicymakers responded quickly to the concerns of environmental justice advocates. The

Environmental Protection Agency (EPA) initiated its environmental justice efforts in 1990 with the creation of

the Environmental Equity Workgroup. Acting on the recommendations of the workgroup, the agency

creat ed the Office of Environmental Equity ( OEE, 1992), plac ed environmental justice coordinators in all

regional offices (1993), and expand ed the O EE into the O ffice of Environmental Justice (1994). Finally,

President Clinton signed Executive Order 12898 requiring all federal agencies to conduct environmental

justice evaluations of their programs , and develop plans to remedy any race and class biases in their

r egulatory decision making. EO 12898 is still in effect.5

2.3 The Potential Conflict between Efficiency and Equity in Environmental Protection

These two developments in environmental protection policy are related. Economic incentive

approaches emphasiz e efficiency , but p olicies that focus on efficiency may ignore equity (Okun 1974).

Emissions trading in particular may have significant distributional consequences. Under t rading

, some
facilities sell credits (i.e., reduc ing pollution more than required, making the surrounding area cleaner) while

other facilities purchase credits (allowing these facilities to avoid reducing pollution and perhaps even

increase emissions )

. Emissions trading programs embody the same goal as benefit

- cost analysis

– maximizing present net benefits at the firm and the societal level. W hile economic incentive approaches to

pollution control may reduce the overall marginal costs of pollution reduction, this gain in efficiency may

come at the cost of less obvious inequit ies in the distribution of pollution and environmental risk.

There are two links in the causal story connecting emissions trading to inequities in the distribution

of pollution. First, p ollution control costs are typically highest for older facilities, so newer facilities are likely

to sell emission credits while older facilities are likely to purchase the m. Therefore, pollution declines near
new facilities and remains high or increases near older facilities. Second, m any older facilities are located

near the former industrial heart of cities . Over the past several decades, these areas have experienced a

large influx of poor and minority residents as wealthier, white residents moved to the suburbs.

Consequently, if older facilities purchase emission credits, this may transfer pollution from outlying

communities into communities of the poor and people of color. In this manner, emissions trading m ay
exacerbate existing environmental inequities (for a more complete discussion of the potential conflicts

between emissions trading and environmental justice, see Chinn (1999) and Kaswan (2008) )

.

2.4. Previous Research

A handful of studies have examined the equity consequences of emissions trading. When

studying the distributional effects of the national sul fur dioxide allowance trading program, Ringquist

(1998) and Corburn (2002) find that the ATP does not transfer SO 2 into minority neighborhoods;

Shadbegian, Gray, and Morgan (2007) conclude that the monetized benefits from the ATP are distributed

equally ac ross racial and ethnic groups; and the US EPA (2005) concludes that the health benefits from

the ATP are distributed equally across racial, ethnic, and income categories. W hen studying California’s6

NOX trading system under the RECLAIM program, Fowlie, How land, and Mansur (2009) conclude that

changes in emissions do not vary significantly with neighborhood demographic characteristics.

If the extant literature is unanimous in concluding that there are no meaningful environmental

inequities stemming from emis sions trading programs, why do we need to study this question further?

T he results from the previous literature are suggestive but not conclusive. All previous studies of the

equity effects of the ATP examine a very small number of years early in the his tory of the emissions

trading program, and/or examine a small number of participating facilities. For example, Shadbegian et al

(2007) examine the first year, Ringquist (1998) examines the first two years, and Corburn (2002)

examines the first three years of trading behavior under the ATP

. Focusing on early implementation of the ATP has at least three consequences . First, all studies

examine the trading behavior of only a small number of firms . Phase I of the ATP, described in greater

detail below, ran from 1995 through 1999 and included only 110 electric generating facilities east of the

Mississippi River. By contrast, Phase II of the ATP (2000

- 2010) covered nearly 2000 polluting facilities

nationwide. By restricting their analysis to the first three years of ATP implementation, Ringquist (1998),

Corburn (2002), and Shadbegian et al. (2007) exclude the trading behavior of over 90% of the facilities

covered by the ATP. Second, the level of allowance trading was relatively low in 1995

- 1997 (see figure

1). Between 1995 and 2008, over 255 million allowances were traded under the ATP, but only 49 million

of these occurred before 1998. Previous research, then, excludes information from 80% of allowance s

trade d under the ATP. Third, during the early years of the ATP, more than 70% of allowance s were

traded between different generating units at the same physical location (i.e., different boilers at the same

power plant). These trades cannot transfer pollution spatially. By contrast, after 1998 fewer than 5 0% of

trades were intra

- facility transactions. Over 87 percent of allowance trades that had the effect of

transferring pollution spatially occurred outside of the temporal window considered in previous studies.

Therefore, c onclusions from these studies m ay not be representative of the ATP overall.

[Figure 1 About Here]

In addition, several studies examine equity effects at the regional level (Fowlie et al 2009) or

examine the long range transport of sulfates (Shadbegian et al. 2007; US EPA 2005), rather than the

localized concentrations of SO 2 emissions. By examining the local concentration of sulfur dioxide7

attributable to emissions trading between 1 995 and 200 9 , I hope to provide more conclusive evidence

regarding any equity effects from emissions tra ding.

3.0 The Sulfur Dioxide Allowance Trading Program

The Acid Rain Program (ARP) was established in Title IV of the 1990 C lean Air Act Amendments

. The goal of the ARP is to reduce sulfur dioxide emissions by 50 percent from 1980 levels by 2010 (i.e., by

about 8 million tons). The goal of a 50 percent emission reduction is to be attained in two phases. In Phase

I (1995

- 1999) , the 1990 CAA placed new emissions limit s on the 110 dirtiest coal fired utilities in 21 eastern

and Midwestern states. Beginn ing in 1995, each of these Phase I power plants had to meet increasingly

stringent emissions limitations. Electric power generators regulated under the ARP ha d several options for

meeting the new emission limits; they m ight install more effective pollutio n control equipment, switch to a

different electrical generation process, switch to a less polluting fuel, or purchase pollution allowances from

other sources. The last option, of course, has received the most attention from scholars and the media, and

is increasingly popular among utilities. An allowance is essentially a permit or a coupon, and each

allowance entitles the owner to emit one ton of sulfur dioxide. The EPA gave allowances equal to 5.7 million

tons of sulfur dioxide to Phase I power plants free of charge, allocating them based upon each unit’s

baseline fuel consumption and emission levels during 1985

- 87. Each year EPA reduce d the allowances

allocated to each Phase I power plant; thus, the plants either reduce d their emissions or purchase d

a dditional allowances from other sources. Phase I utilities c ould also meet these emissions targets by

reducing emissions at other “substitution” or “compensating” generating stations.

Phase II of meeting the emissions reduction goal began in 2000 when all fossil fuel fired power

plants and other industrial electrical generating stations were brought in to the ARP. Beginning in 2000,

EPA began allocating pollution allowances to each of the more than 2000 facilities covered by Phase II.

Facilities that began operation after 1996 were ineligible to receive allowance allocations from EPA, and

instead had to purchase these allowances on the open market or from the EPA

. Allowances for all Phase II

facilities w ere reduced each year until the number of allowa nces equaled the sulfur dioxide emissions goal

of 8.95 million tons in 2010. Like Phase I facilities, after 2000 Phase II utilities had to either reduce their

emissions or purchase additional pollution allowances to meet these increasingly stringent emiss ion8

requirements (US EPA 1998). Finally, any utility, independent power producer, or industrial generating

facility that is not required to participate in the ATP may “opt

- in” to the program and receive sulfur dioxide

allowances (a unit may choose to opt in if it feels it can reduce its emissions cheaply and sell the extra

allowances at a profit). Each Phase I, Phase II, and opt

- in unit must obtain an acid rain permit from the EPA

and file a compliance plan outlining how it will meet emissions requirement s. Regulated facilities whose
emissions exceed their allowances are fined $2,000 per ton of excess emissions, adjusted for inflation

(currently, the fine is roughly $3 500 per ton). Regardless of how many allowances are held by the owners

of a particular facility , emissions from this facility may not exceed the standards established by Title I of the

1970 CAA or by relevant state regulations.

Electric utilities and other units regulated under the ARP can obtain additional allowances through

at least six methods. First, additional allowances may be purchased from the EPA at annual allowance

auctions. In Phase I, EPA auctioned 150,000 additional allowances each year from an allowance reserve

fund held by the agency

. In Phase II, EPA auctions 200,000 add itional allowances each year. 2 From 1995

- 2006 these auctions were managed by the Chicago Board of Trade, but they are now administered directly

by EPA. EPA allowance auctions are always over

- subscribed. Second, buyers may purchase additional

allowances placed for sale by others on the Chicago Board of Trade. Third, buyers may purchase additional

allowances from one of the many allowance brokers that purchase and hold allowances for resale. Fourth,

buyers may purchase allowances directly from other util ities and regulated units. Fifth, allowances can be

transferred between generating units within the same utility or utility holding company though intra

- utility

transfers (EPA stopped monitoring these intra

- facility transfers in 2006). Finally, if a util ity invests in energy

conservation or the production of energy from renewable sources, it may obtain allowances for a portion of

the sulfur dioxide emissions avoided through these investments from the EPA’s conservation and renewable

energy allowance reser ve. Allowance sales, however, are not limited to electrical generating units regulated

under the ARP. Nearly anyone can buy or sell emission allowances. Permits to emit sulfur dioxide have

been purchased by smelters, factories, environmental groups

-- e ven elementary school students.

According to officials at the EPA, "A guiding principle of the Acid Rain Program has been for EPA to

minimize its role in the allowance market" (Solomon 1994, 12). This means that EPA places no geographic,

2 A smaller number of allowances is available for direct purchase from the EPA. These allowances, however, are generally reserved for new independent power producers.9

temporal, or oth er restrictions on allowance trading. Moreover, unlike previous market incentive approaches

in air pollution control, the EPA plays no role in approving or disallowing allowance sales.

The EPA has two tools with which to monitor compliance with the AR P. First, the agency monitors

allowance trading activity through the Allowance Tracking System (ATS). The ATS is an automated system

that tracks the sale, purchase, trade, and retirement of pollution allowances. Each allowance has a unique

12 digit iden tification number to facilitate tracking transactions. In effect, the ATS acts much like a bank

where each regulated entity has a n account and where the EPA maintains several general accounts (e.g.,

the conservation and renewable energy reserve). Initial ly, reporting allowance transactions to the ATS was
voluntary, but reporting has been mandatory since 1998. 3 Second, under the ARP any electric generation

unit larger than 25 MW and all new generating units smaller than 25 MW that use high sulfur fuels mu st

continuously monitor their emissions of sulfur dioxide and other pollutants . These emissions data,

aggregated hourly, are reported to EPA on a quarterly basis and stored in the Emissions Tracking System,

EPA’s largest database. The EPA can easily comp are the number of allowances in facility ATS accounts

with the emissions record in the ETS to determine if emissions exceed allowances.

The acid rain program will be fully imple mented in 2010

. Moreover, the Clean Air Interstate Rule

(CAIR) embodies signi ficant changes in the ARP. Beginning in 2010, regulated utilities in the Eastern U.S.

will have to surrender two allowances for each ton of sulfur dioxide released to the atmosphere. In 20 15
,
these same utilities must surrender 2. 86 allowances for each t on of sulfur dioxide emissions. Given these

important changes, this is an especially opportune moment to study the effects of th e acid rain program.

4.0 Measuring SO 2 Transfers under the ATP

4.1 Measuring SO 2 Transfers

I obtained allowance trading reco rds for all facilities participating in the sulfur dioxide allowance

trading program between January 1995 and March 2009 (the end of the 2008 program year). When

measuring allowance transfers it is important to distinguish between vintage years and progra m years

.
A llowances are issued for vintage years and redeemed during program years. For example, under the

ARP Facility A may be entitled to 50,000 SO 2 allowances each year between 2000 and 2010. EPA will

3 EPA believes that the vast majority of early allowance transactions were reported to the ATS (US EPA 1998).10

allocate to Facility A 50,000 vintage year 2000 allowances, 50,000 vintage year 2001 allowances, etc.

Facilities can use allowances for the current and previous vintage years to settle emissions accounts in

the current program year, but cannot use allowances from future vintage years to settle accounts in the

current program year. That is, firms may bank or carry forward allowances from previous vintage years,

but they may not borrow allowances from future vintage years.

I calculate the net transfer of sulfur dioxide allowances from this data set usi ng the following

formula s

. [1] A itv = Allowances allocated to facility i for vintage year t.

[2] R it p = Allowances redeemed by facility i in program year t.

[3] B it p = A itv - R itp where v=p

Allowances banked by facility i in program year t

B it p >=0, becaus e if R it p > A itv , the remainder must come from purchased

allowances or previously banked allowances. Negative values of B it p cannot be carried over to new program years.

[4] T it p = A itv + ∑ v t=1 B itp - R itp

Net allowance transfers

Equation 4 shows that net allowance transfers for any facility are the sum of allocations for the

current vintage year and all banked allowances from previous vintage years, minus allowances redeemed

in the curre nt program year. An example might help clarify this calculation. Assume facility A receives an

initial allocation of 50,000 allowances for vintage years 1, 2, and 3 (i.e., A itv = 50,000 for tv=1, 2, and 3). If

facility A redeems 40,000 vintage year 1 al lowances in program year 1(R i1 =40,000), it can bank the

remaining allowances (B i1 = ∑ v t=1 B it = 10,000 = T i1 ). In program year 2, facility A redeems 45,000

allowances, banking the remaining 5,000 allowances, so that now B i2 =5000 and T i2 = ∑ v t=1 B it p = 15,000.

In program year 3, facility A increases capacity and/or output and redeems 70 ,000 allowances (R i3 ). With

an initial allocation of only 50,000 allowances for vintage year 3 (A i3 ), facility A must redeem an additional

20,000 allowances; 15,000 of these will be drawn from allowances banked from previous vintage years

(∑ v t=1 B it p , or T i2 ), with the remaining 5,000 being purchased from EPA, brokers, or other facilities. In

program year 3, then, T i3 = - 5,000, and ∑ v t=1 B it p =0 going in to program year 4. Note that T it p is negative if11

a facility imports pollution to its location. Our dis cussion will be easier to follow if net imports of pollution

receive positive values, so our measure of net pollution transfers employs the following transformation:

[5] SO 2 Imports = (T it p ) * (

- 1)

With this transformation, negative values for SO 2 import s represent allocated allowances that a facility

has not redeemed. These allowances may be banked or sold by the facility, but we do not distinguish

between these two potential outcomes. In this paper I only measure the difference between levels of

pollu tion that a facility could have released given its allocation of allowances, and the pollution that was
released as measured by the redemption of allowances.

Figure 2 shows that the distribution of SO 2 imports is centered around 0 (most facilities in mo st
years redeem almost all of their allocated allowances), with a strong negative skew. Far more facilities

bank or sell allowances than import them. This distribution is consistent with the general conclusion that

the sulfur dioxide allowance trading pr ogram has produced significant reductions in overall SO 2 emissions

(Ellerman et al. 2000)

. [Figure 2 about here]

4.2 Three Dependent Variables

I use the measure of SO 2 imports from equation [5] to craft three dependent variables. First, I

simply examine whether a facility is a net importer of sulfur dioxide. To answer this question, I

dichotomize SO 2 imports so that facilities that import and redeem allowances in program year t receive a

value of 1, and facilities that do not receive a value of 0. This variable takes on a value of 1 in roughly 37

percent of facility

- years.

T he central question is whether facilities in poor and/or minority communities redeem SO 2

allowances in excess of their allocation s

. The second dependent variable, then, simply measu res the

magnitude of SO 2 imported by a facility. This variable takes on a value of zero for all facilities where

imports are less than or equal to zero, and takes on the value from equation [5] where imports are greater

than zero. We observe 3402 facilit y
- years in which net SO 2 imports are positive. I mports are positively

skewed. The mean number of allowances imported in a particular year is 2177, with a median of 1012

allowances and a maximum value of 255313 allowances. We observe 1373 facility

- years wh ere imports

exceed 1000 allowances.

Finally, we may be interested in net SO 2 imports arising from the allowance trading program, not

simply which facilities import pollution. That is, we might be interested in the difference between actual

and potential S O 2 emissions for all facilities in all years in our sample

, regardless of whether this

difference is positive or negative ( i.e., the entire range of values on the variable calculated in equation

[5] ) . When using this variable, however, we need to take car e when interpreting negative values.

Negative values arise when allocations exceed redemptions during any particular period. The remaining

allowances can be banked or sold, but we do not know which. Therefore, it is important to view these

negative valu es as a measure of unrealized pollution potential, rather than as a measure of allowances

banked or sold. We observe some level of SO 2 trading in 6202 facility

- years, or in roughly 63 percent of

the 9565 facility

- years where trading might take place. Sum mary statistics for this variable reflect the

predominance of banking/sales behavior over allowance importation : the mean value for this variable is

-
31629 allowances, the median is

- 2 315, and the minimum value is

- 1,923,077 allowances. The maximum

value for this variable is 255313, the same maximum value from the second dependent variable.

Appendix 1, included for reviewers , illustrates that allowance trading under the ATP is a

nationwide phenomenon. Virtually all states contain facilities that import an d bank or export substantial

volumes of SO 2 allowances.

5.0 Modeling SO 2 Imports under the ATP

5.1 Model Construction

5.1.1. Defining “Affected Community” in Environmental Justice Research

. The central question

for this study is whether the ATP has th e effect of importing SO 2 emissions in to poor and minority

communities. There is significant debate among environmental justice scholars regarding the appropriate

spatial definition of “ affected community.” Commonly used levels of spatial aggregation inc lude counties,

ZIP codes, census tracts, and circular areal units defined using facility latitude

- longitude coordinates and

GIS software. Early research encouraged the use of smaller spatial units in an effort to avoid aggregation

bias (e.g., Anderton et al. 1994). A meta

- analysis of the environmental justice literature, however,13

showed no systematic differences in measures of inequity attributable to spatial aggregation (Ringquist

2005)

. I define affected community in two ways. First, consistent with much of the previo us work on

environmental justice, I define a spatial community using five

- digit residential ZIP codes. Second, I define

a spatial community using 1 and 3 mil e radii centered upon facilities engaging in emissions trading under

the ATP. I employ this second definition of affected community because the most important lesson from

the work of Mohai and Saha (2007) is that using any administrative unit

-- county, ZIPcode, census tract,

etc.

– to define affected community runs the risk of introducing bias into estimates of environmental

equity. I use the areal apportionment method to measure the characteristics of the population residing

within one a nd three miles of facilities engaging in pollution trading.

5.1. 2 . Selection Issues in Estimating Equity Effects of the ATP. With th ese two definitions of

community, we can refine the central question as whether facilities in ZIP codes /1 mile radius circ les/3

mile radius circles with large percentages of black, Hispanic, and/or poor residents are more likely to

import SO 2 allowances . One method of answering this question is to treat the polluting facility as the unit

of analysis, employ a measure of net SO 2 allowance trading as the dependent variable in a general linear

model, and predict this dependent variable using the demographic and economic characteristics of

communiti es surrounding these facilities. The few studies examining this question have approached it in

just this manner ( Ringquist 1998; Corburn 2002; US EPA 2005).

The a llowance trading program cannot concentrate SO 2 emissions in a community i f the

community d oes not have a facility eligible to particip ate in the ATP

. A drawback of the empirical

approach described in the previous paragraph is that it assumes that facilities eligible to participate in the

ATP are distributed inde pendently of the racial and clas s characteristics of communities . If polluting

facilities are distributed in part based upon the characteristics of the surrounding community

– that is, if

polluting facilities are concentrated in poor or minority communities

– the approach employed in pr evious

work will produce biased and inconsistent estimates of the equity effects of allowance trading (e.g., Moffitt

1991)

. The assumption that facility location is independent of the demographic and economic

characteristics of neighborhoods is at odds w ith m ost of the research in environmental justice (e.g.,14

Ringquist 2005; Mohai and Saha 2007)

. Therefore

, we should model the effect s of allowance trading as

conditional upon the location of polluting facilities; i.e., they are jointly determined.

The standard solution for this problem is to employ a two

- equation model where the first equation

predicts whether a community contains a facility eligible to participate in the ATP (the selection model)

and the second equation predicts the outcome or effect of allowance trading (the structural model )

(Heckman 1979; Puhani 2000)

. Controlling for sample selection in this manner requires a unit of analysis

that is de fined prior to the assignment of units to treatment and control groups. Moreover, a ll

observations must be able to be assigned to either the treatment group or the control group. For

example, if one wants to examine the effect of school vouchers on educational attainment, one first has to

predict which students receive a voucher and whi ch do not, and then condition estimates of the effect of

vouchers on the probability that a student receives a voucher. We define the unit of analysis

– the

student

– before assigning students to treatment and control groups using the receipt of vouchers

, and

the sample is fully defined by students that receive and do not receive vouchers . By the same token, if

we want to examine whether SO 2 trading concentrates pollution in poor and minority communities, we

need to predict which communities will contain a n ATP eligible facility, and condition estimates of the

effects of facility trading on the probability that a community contains such a facility. In this example, we

define the unit of analysis

– e.g., ZIP code s

– before assigning unit s to “treatment” and “control” groups

based upon whether they contain a n ATP eli gible facility , and the sample is fully defined by ZIP codes

that contain and do not contain these facilities . When defining affected communities using the GIS areal

apporti onment method , however, the unit of analysis is defined simultaneously with the treatment group

. While treatment group communities are defined as residents living within 1 or 3 miles of a n ATP eligible

facility , there is either (a) no corresponding control g roup (i.e., no pre

- determined set of areas that do not

contain facilities) , or (b) an infinitely large control group made up of all possible 1 and 3 mile circles in the

US that do not contain eligible facilities. In either case, it is impossible to estimate a selection equation. 4

Therefore, I use two modeling strat egies. To control for potential bias arising from the

nonrand om distribution of polluting facilities I estimate two e quation sample sel ection models where

4 Mohai and Saha (2007) establish control groups by identifying a number of 1, 2, and 3 mile radii circles not containing facilities equal to the number of facilities defining treatment groups. This approach arbitrarily excludes a n infinite number of potential control group communities and large area s of the United States, with unknown effects on model results.15

affected communities are defined using 5 digit residential ZIP codes. To control for pot ential bias arising

from the unit

- hazard coincidence problem (i.e., from using ZIP codes) I est imate structural models where

affected communities are defined using GIS areal apportionment

.

5.1. 3 . The Structural Model. To estimate the effect that allowance trading has on S O 2
emissions in communities of color, I use variables measuring the percentage of black households and the

percentage of non

- white Hispanic households in each community . To estimate any class biases in the

effects from allowance trading I use a variable measuring the percentage of households in a community

with incomes below the poverty line. If allowance trading concentrates sulfur dioxide emissions in poor

communities and communities of color, parameter estimates for these variables will be positive

. Note that

these hypotheses are not causal

– we do not expect the race, ethnici ty, or economic resources of area

residents to influence facility trading behavior. Any significant correlations should be interpreted as

unintended equity effects from allowance trading that is motivated by efficiency.

Allowance trading behavior on the p art of firms may be influenced by the ability of area residents

to monitor and mobilize against the large scale importation of pollution (e.g., Hamilton 1995) . Therefore,

in addition to the race, ethnicity, and poverty variables, the str uctural model includes two variables

measuring the capacity to engage in monitoring and political action: the percentage of adult residents that

do not have a high school diploma, and the percentage of dwellings that are owner

- occupied. Poorly

educated re sidents have a lower capacity to monitor firm trading behavior, while both education and

home ownership are strongly related to political activity (Rohe, van Zandt, and McCarthy 2002). If firm

trading behavior is affected in the manner hypothesized here

, SO 2 imports should be positively related to

the proportion of residents without a high school diploma (where residents have lower monitoring

capability) and negatively related to the proportion of owner occupied homes (where residents are more

politically active)

. Finally, the structural model includes four control variables. First, firms may seek to limit public

opposition to pollution emissions and/or minimize pollution exposure to the surrounding population. To

account for this possibility, I include a variable measuring the population density of affected communities

. Second, the supply of allowances (A itv ) may affect the propensity of a firm to engage in allowance trading

and the volume of these trades . Third, the propensity to import pollution and the level of imports should16

be positively related to the pollution intensity of the facility. I measure pollution intensity as the pounds of

sulfur generated each year per megawatt of electricity generating capacity at the facility. 5 Ceteris paribus,

fac ilities with a larger supply of allowances will be more likely to sell them, while pollution intensive

facilities will be more likely to purchase allowances, thereby importing pollution. Finally, figure 1 shows

that allowance trading has increased ov er time. To control for the maturation of the SO 2 allowance

market, I include a time counter variable.

5.1. 4 . The Selection Model. Not all ZIP codes contain polluting facilities, so the dependent

variable in the selection model takes on a value of 1 fo r ZIP codes containing these facilities and 0 for ZIP

codes without the m . Ringquist (2008) proposes that environmental justice research ought to consider

multiple explanations for the location of noxious facilities. F ollow ing this advice, the selection m odel

predicts the distribution of pollut ing facilities using variables represent ing market forces (property values,

measured by the median housing value); the potential political power of area residents ( measured by the

percentage of adult residents withou t a high school diploma, median household income, and median

household income squared); and the racial and ethnic characteristics of communities ( measured as the

percentage of black households and non

- white Hispanic households). These are standard predict ors of

facility location in the environmental justice literature. Note that the median household income and

median housing value variables are unique to the selection model, which substantially improves the

predictions from the structural model ( Wooldridg e 2002 ).

5.2 Modeling the Propensity to Import SO 2

I model the propensity of a facility to import sulfur dioxide in any year utilizing the structural model

described in section 5.1. 3 and the dichotomous dependent variable described in section 4.2. When

defining affected community using residential ZIP codes I also utilize the selection model described in

section 5.1. 4 , and parameter estimates are obtained using a bivari ate Probit model with sample selection

estimated using full information maximum likelihood (Heckman 1979; Pulani 2000)

. When affected

communities are defined using GIS areal apportionment, p arameter estimates are obtained using a

standard Probit model. Results are reported in table 1

. [Tab le 1 about here]

5 These data were ob tained from FERC form 826.17 The selection model demonstrates that the location of facilities eligible to participate in the ATP

– mostly electric generating plants and large industrial facilities

-- is strongly related to the racial and ethnic

character of communi ties . S pecifically, the probability that a ZIP code contains a facility engaging in

allowance trading increases substantially with the percentage of black and Hispanic households. Market

forces also matter, as ZIP codes with high property values are sign ificantly less likely to contain these

facilities. Finally, the potential political power of area residents is significantly related to the distribution of

polluting facilities, as these facilities are more likely to be located where large percentages of residents do

not have high school diplomas. T he selection equation also shows the curvilinear relationship between

income and facility location that is so common in environmental justice research: the probability that a ZIP

code contains a polluting facil ity first increases and then decreases with income (i.e., polluting facilities

tend to be located in working class communities, but not in exceptionally poor or wealthy communities).

The ZIP code structural model presents no evidence that the pro bability a facility will import SO 2 is positively related to the racial or ethnic characteristics of the community. Parameter esti mates for the

black and Hispanic household variables are negative, with the Hispanic parameter significantly different

from zero. If there is an equity effect from allowance trading in this model, it works to the advantage of

communities of color; e.g., a ten percent increase in the percentage of Hispanic households in a ZIP code

is associated with a 4 .1 percent decrease in th e probability that a facility in that ZIP code will import SO 2

allowances (see figure 3) . On the other hand, the probability that a facility will import sulfur dioxide

allowances is negatively related to the education level of area residents ; a 10 percent increase in the

percentage of adult residents without a high school diploma is associated with a 5 .6 percent increase in

the probability that a facility will import pollution. Somewhat unexpectedly, there is also a positive

relationship between the perce ntage of homeowners in a ZIP code and the probability that a facility will

import sulfur dioxide allowances ; a ten percent increase in the former is associated with a 4.5 percent

increase in the latter

. The control variables generally display coefficient estimates in the expected

directions; facilities in densely populated areas are less likely to import pollution, facilities with large

supplies of allowances are less likely to import them; and the probability of SO 2 imports increases over

time. Pollution intensity is independent of the propensity to import SO 2 in specification 1.

[Figure 3 About Here]18 Surprisingly, the error terms of the selecti on and structural models in table 1 are only weakly

correlated

, suggesting that there are no unobserved factors causing the location of pollution facilities and

the decision to import SO 2 allowances to be jointly determined. In this situation, there is little risk of bias

in the Probit parameters from the GIS areal a pportionment models . The results from these models are

consistent with those from the ZIP code model. Most importantly, th ere is no evidence that facilities in

poor or minority communities are more likely to import SO 2 . Parameters for the black and Hisp anic

household variables are negative, as is the parameter for the percentage of households in poverty

. T he

parameter estimate for Hispanic households is significantly different from zero when defining affected

communities using 3 mile and 1 mile radii, while the parameter for black households i s significantly

different from zero only when defining affected communities using a 1 mile radius. The size of these

effects is small : a 10 percent increase in the proportion of black or Hispanic households within 1 mi le of a

facility is associated with a 0.8 percent and 3.2 percent reduction in the probability that the facility will

import SO 2 allowances, respectively

. In the GIS models we again find that the probability that a facility

will import SO 2 allowances increases with the percentage of poorly educated residents. For example, a

10 percent increase in the proportion of residents without a high school diploma within 3 miles of a facility

is associated with a n 8.9 percent increase in the likelihood that the facility imports SO 2 . 6

5.3 Modeling the Magnitude of SO 2 Imports

The second analysis predicts the importation of SO 2 , measured in tons, by any facility during any

year. T he structural model described in section 5.1. 3 is used to predict the positive definite (left

censored) dependent variable described in section 4.2. When defining affected commun ities using

residential ZIP codes, I also use the selection model described in section 5.1. 4. Parameter estimates are

obtained using Tobit for the structural equation s and Probit for the selection equation ( Wooldridge 2002).

Results are reported in table 2.

6 Standard practice in environmental equity research is to obtain separate estimates of equity effects for black, Hispanic, and poor residents. Independent effects are interesting in their own right, and are most relevant for po licy makers since race and ethnicity define protected classes eligible to seek remedy for discriminatory practice s while income status does not. Nevertheless, these factors are often highly correlated, so estimating their effects independently may underes timate overall inequities attributable to race, ethnicity, and income. Therefore, I combined the black, Hispanic, and poverty variables into a single standardized sum med index and entered this variable into the structural equations in table 1. The parame ter estimate for this variable was uniformly negative and significantly different from zero , indicating no meaningful aggregate inequities stemming from allowance trading.19 [Table 2 About Here}

Parameter estimates for the selection equation in table 2 differ from those in table 1, but they tell

a similar story. ZIP codes with large percentages of black and Hispanic residents are more likely to

contain polluting facilities. ZIP codes with high property values are significantly less likely to contain

these facilities, and the income of area residents has a parabolic relationship with the probability of facility

location. One substantial difference is that the percentage of area residents w ithout a high school

diploma is not an important predictor of facility location in table 2. A second difference is the larger and

significant correlation between the error terms in the selection and structural equations

, validating the

choice to estimat e the equity effects of allowance trading as conditional upon the location of polluting

facilities.

[Figure 4 About Here]

The results for the structural equation using ZIP codes are also similar to those from table 1. T he

percentage of black and Hispanic resident s is inversely related to the magnitude of sulfur dioxide imports

, and both parameter estimates are significantly different from zero. A ten percent increase in black

households is associated with a 410 ton reduction in pollution imports, while a ten percent i ncrease in

Hispanic households is associated with a n 816 ton reduction (see figure 4) . A ten percent increase in the

percentage of residen ts without a high school diploma, however , increases SO 2 allowance imports by

1 07 2 tons. Home ownership con tinues to display a counter

- intuitive relationship; a 10 percent increase in

the number of homeowners in a ZIP code is associated with a 35 1 to n increase in SO 2 imports. P overty

levels are independent of pollution imports. T he contr ol variables in table 2 have a different effect on the

magnitude of sulfur dioxide imports than they do on the propensity to import pollution. As expected, the

magnitude of SO 2 imports is positively associated with pollution intensity, and sulfur dioxide imports

increase over time. On the other hand, population density is independent of SO 2 imports , a nd contrary to

expectations, facilities with large allowance allocations import more allowances.

The statistically significant correlation between the errors from the selection and structural

equations means that we ought to take care when interpreting the results from the two structural models

defining affected communities using the GIS areal apportionment method . Since the magnitude of this

correlation is small, howev er (0.26), any bias in the parameters from the GIS models is likely to be small20

as well. When we define affected communities using residents within 1 or 3 mile s of polluting facilities,

the results are consistent with those from the ZIP code model. Most importantly, the GIS models in table

2 provide no evidence that imports of SO 2 allowances ar e larger for facilities surrounded by large

percentages of black or Hispanic residents. Once again, the parame ter estimates for the percentage of

black and Hispanic households is negative and significant, suggesting that facilities in minority

communities imp ort less SO 2 . Moreover, the magnitude of this negative relationship is larger than we find

in the ZIP code model: e.g., a 10 percent increase in black and Hispanic residents living within 3 miles of

a polluting facility is associated with 703 and 215 1 ton reductions in SO 2 import s, respectively. 7 While

poverty also has a negative relationship with SO 2 imports, t his parameter is statistically different from zero

only in the 3 mile radius model. Finally, the GIS models show a positive relationship between SO 2

allowance imports and the percentage of poorly educated residents. For example, a 10 percent increase

in the proportion of residents without a high school diploma within 3 miles of a facility is associated with a

6628 ton increase in allowance imports

. 8

5.4 Modeling Net SO 2 Imports

The final analysis models net SO 2 allowances imports , measured in tons, by any facility during

any year. I model net SO 2 imports utilizing the structural model described in sec tion 5.1. 3 to predict the

continuous, uncensored dependent variable described in section 4.2 . When defining affected

communities using ZIP codes I also use t he selection model described in section 5.1. 4 . Parameter

estimates are obtained using O LS for th e structural equation s and Probit for the selection equation

( Heckman 1978) . Results are reported in table 3

. [Table 3 About Here]

Parameter estimates for the selection equation in table 3 tell a story identical to that from table 2

,
so the discussion here will focus on the results from the structural model. The percentage of black and

Hispanic resident s in a ZIP code is inversely related to net SO 2 imports, and both parameter estimates

7 The larger coe fficients may be an artifact of uncontrolled selection bias

.

8 I also combined the black, Hispanic, and poverty variables into a single standardized summed index and entered this vari able into the structural equations in table 2 . The parameter estimate for this variable was uniformly negative and significant

, indicating no meaningful aggregate inequities stemming from allowance trading.21

are significantly different from zero. A ten percent increase in black households is associated with a

10,010 ton reduction in net imports, while a ten percent increase in Hispanic households is associated

with a 15,196 ton reduction (see figure 5 ). Consistent with what we saw in tables 1 and 2, rather than

concentrati ng SO 2 in areas with high percentages of minority residents, allowance trading appears to

work to the advantage of these areas . On the other hand, we see again that allowance trading has the

effect of concentrating SO 2 emissions in poorly educated communi ties. A 10 percent increase in the

percentage of adult residents is associated with a 3833 ton increase in net SO 2 imports. In addition, table

3 reports the first evidence that allowance trading may concentrate pollution in poorer communities; a 10

perce nt increase in the percentage of households below the poverty line is associated with a 325 ton

increase in net allowance imports. We should be careful when interpreting this parameter estimate as

evidence of environmental inequity. Recall that the mean value for the net SO 2 import variable is

- 31629

tons. Holding all other independent variables at their mean values, then, a 10 percent increase in the

percentage of households in poverty does not increase pollution in an absolute sense

– it is simply

asso ciated with smaller reductions in emissions. Home ownership is unrelated to net sulfur dioxide

imports in table 3 , and the control variables again largely conform to expectations. As expected, facilities

with large allowance allocations import fewer allo wances, pollution intensive facilities are more likely to

import SO 2 allowances, and net SO 2 imports have increased over time . Somewhat unexpectedly,

population density is positively related to net SO 2 imports 3.

[Figure 5 About Here]

T he correlation between the selection and structural models in table 3 is very large (

- 0.91 ) and

statistically significa nt. The correlation indicates that unmeasured factors i ncreas ing the probability a ZIP

code wil l contain a polluting facility also decrease net SO 2 allowances traded. A large a negative

correlation between the selection and structural models suggests that estimating the structural model

alon e is likely to produce badly biased parameters that are often of the wrong sign. This is exa ctly the

situation we observe in table 3. When controlling for selection bias in the ZIP code models, we estimate a

negative relationsh ip between the race and ethnicity o f area residents and net imports of S O 2 . In the GIS

models that do not control for selection bias, however, we estimate a positive relationship between the

p ercentage of black residents and net SO 2 imports. In fact, most of the parameters from the GIS models22

are signed opposite to those from the ZIP code models. To be clear, this is not a result of using different

spatial definitions of affe cted community . E stimates from the ZIP code and GIS models in tables 1 and 2

– where the selection and structural equations are only weakly correlated

-- a re fully comparable

. Moreover, if we estimate only the structural ZIP code model in table 3, the results are biased in the same

way as those from the GIS models ( e.g., the parameter for the percentage of black residents is 251. 76).

Differences between the ZIP code and GIS results in table 3 are driven wholly by the inability t o control

for selection bias using GIS areal apportionment. T he results from the GIS models in table 3 serve as an

examp le of the potential consequences of failing to control for sample select ion where it exists. 9 10

6.0 Discussion and Conclusions

Several observe r s have raised concerns about the equity effects of market based environmental

policy instruments in general and emissions trading systems in particular ( e.g. Chinn 1999). A handful of

previous studies have investigated this question and found that emission trading has not created pollution

“hot spots” within poor and minority comm unities. While these studies offer some reassurance that there

is not a n inevitable tradeoff between efficiency and equity in environmental protection, their conclusions

should be viewed as provisional because they focus on very short periods of time immedi ately following

the inception of pollution markets (e.g., Ringquist 1998, Corburn 2002; Shadbegian et al. 2007), on small

scale pollution markets (e.g., Fowlie, et al. 2009), study the trading behavior of a small number of

polluting facilities (e.g., Corbu rn 2002; US EPA 2005), or examine equity effects at a regional rather than

a local level (US EPA 2005).

By contrast, this analysis focuses on the largest emissions trading market in the United States

(the ATP), examines the trading behavior of nearly tw o thousand polluting facilities over a 14 year period,

and investigates potential environmental inequities at the local level. From this analysis I conclude that

the sulfur dioxide allowance trading program does not produce the unintended consequence of

9 I also combined the black, Hispanic, and poverty variables in to a single standardized summed index and entered this variable into the structural equations in table 3 . The parameter estimate for this variable was uniformly negative, indicating no meaningful aggregate inequities stemming from allowance trading.
10 Net allowance trades are highly skewed, so the results i n tables 1

- 3 may be driven by a few facilities importing large numbers

o f allowances. To test for this, I re

- estimated all m odels using only those observations below the 90 th percentile of allowance imports. All statistical results remain unchanged.23

c oncentrating SO 2 emissions in black and Hispanic neighborhoods; i.e., emissions trading does not

produce racial or ethnic environmental inequities. In fact, there is a negative relationship between the

percentage of black and Hispanic households in a comm unity and the probability that polluting facilities

will import SO 2 and the magnitude of these imports. The ATP p ollution market, it seems, m ay play a role

in remedying existing environmental inequities. These results are robust across different dependen t

variables , different definitions of affected community, and different model specifications. There is also

scant evidence that markets for sulfur dioxide concentrate pollution in poor communities.

T he SO 2 allowance market is the oldest and largest pollution mar ket in the US, but it is not the

only one. Similar national markets exist fo r oxides of nitrogen ( NOx )

, and regional markets exist for a

number of pollutant s including volatile organic compounds (in California ) and carbon dioxide ( CO 2 ) (in

New England) . Moreover, markets have been proposed as mechanisms to control domestic mercuty

pollution, and most observers believe that an international market for CO 2 trading is likely to be a central

part of any agreement to address climate change. A natural question, then, is whether the results

regarding the equity consequences of SO 2 trading apply to other allowance trading programs

. There is a

growing literature in environmen tal economics that assesses what types of environmental problems are

best suited to emissions tr ading (e.g., Richards 2000), but further research is required to determine

whet her such trading may generate inequities in the distribution of these pollutants.

Finally, the analysis uncovers one p otentially problematic result; allowance trading appears to

c oncentrate SO 2 emissions in communities having large percentages of adults without a high school

diploma. Th is effect is generally smaller than the positive effects associated with race and ethnicity , and

this result may be less relevant for public policy s ince the poorly educated are not a protected class under

the 1964 Civil Rights Act and its associated regulations. If additional research examining other emissions

trading programs establishes the generalizability of these results, policy makers might mak e an effort to

design and implement future emissions trading programs with an eye toward minimizing the costs of

monitoring pollution trading on the part of area residents. By reducing monitoring costs, policy makers

may provide safeguards against concentrati ng emiss ions in poorly educated communities while

preserving the efficiency benefits of these instruments.24

Figure 1: SO 2 Allowance Trading Activity 1994

- 2008

Source: http://www.epa.gov/airmarkets/progress/ARP_4.html25

Figure 2: Net Imports of SO 2 Allowances by ZIP Code26

Figure 3: Effect of Race, Ethnicity, and Education on the Propensity to Import SO 2



### 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Probability of Pollution Import



### Percentage Change from Mean Black Hispanic No Diploma27

Figure 4: Effect of Race, Ethnicity, and Education on Magnitude of SO 2 Imports



### - 1000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Pollution Imports



### Percentage Change from Mean Black Hispanic No Diploma28

Figu re 5: Effect of Race, Ethnicity, Class, and Education on Net SO 2 Imports

- 50000 - 45000 - 40000 - 35000 - 30000 - 25000 - 20000 - 15000 - 10000 - 5000 0

- 100 0 100 200 Net SO 2 Allowance Trades



## Percentage Change From Mean Black
Hispanic
Poverty
No Diploma29

TABLE 1. Predicting the Decision to Import SO 2 Allowance s

____________________________________________________________________________________ Independent ZIP Code 3 Mi le GIS 1 Mile GIS Variables Model a Model b Model c Structural Model %African American

- 0.001 7

- 0.0014

- 0.0022 * * (0.0016 ) ( 0.0013 ) ( 0.0011 ) % Hispanic

- 0.01 23 ***

- 0.0095 ***

- 0.0094 *** (0.0021 ) ( 0.0014 ) ( 0.0013 ) % Poverty 0.0037

- 0.0092**

- 0.0012 (0.00 31 ) ( 0.0036 ) ( 0.0030 ) % No Diploma 0.01 66 *** 0.0389 *** 0.0258* ** (0.00 20 ) ( 0.0040 ) ( 0.0034 ) % Home Ownership 0.01 34 *** 0.0154 *** 0.0106*** (0.00 14 ) ( 0.0015 ) ( 0.0011 ) Populat ion Density

- 0.0147*** 0.0 069** 0. 0099 (0.0049) (0.0 0 27 ) (0.0 070 ) Allowance Allocation

- 0.0000***

- 0.0000 ***

- 0. 0000* ** (0.0000) (0.00 00 ) (0. 0000 ) Pollution Intensity

- 0.0004 0.0011 0.0015 * (0.0008) ( 0.0008 ) ( 0.0008 ) Year 0.0914*** 0. 0952* ** 0.0955*** (0.0034) (0. 0034 ) ( 0.0033 ) Constant

- 1 84.57 ***

- 192.61 ***

- 192.67*** ( 6.83 ) ( 6.75 ) ( 6.70 )

Selection Model %African American 0.0106*** (0.000 4 ) % Hispanic 0.0134 *** (0.0007) Medi an Housing Value

- 0.0013 *** (0.0002) M edian Household Income 0.0602 *** (0.0032 ) Median Household Income 2

- 0.0000*** (0.0000) % No Diploma 0.0048 *** (0.0008 ) Constant

- 2. 2989 *** (0.0 829 )

Wald χ 2 1129.49 *** 1261.41*** 1201.57*** Psuedo

- R 2 na 0.14 0.14 rho 0.07 na na Number of Cases 40 470 9956 9956

a figures are coefficients from bivariate P robit model with sample selection, robust standard er rors in parentheses b figures are coefficients from Probit model, robust standard errors in parentheses

c figures are coefficients from Probit model, robust standard errors in parentheses *p<.10; **p<.05 ; ***p<.01, one

- tailed tests30

TABLE 2 . Predicting Imports of SO 2 Allowance s

____________________________________________________________________________________ Independent ZIP Code 3 Mile GIS 1 Mile GIS Variables Model a Model b Model c Structural Model %Afri can American

- 41.02***

- 70.26***

- 52.76*** ( 10.09 ) (23.40) (20.68) % Hispanic

- 81.56***

- 215.07***

- 187.02*** ( 15.07 ) (28.07) (24.97) % Poverty

- 9.57

- 109.53**

- 41.86 ( 12.21 ) (63.57) (53.46) % No Diploma 107.20 *** 662.79*** 475.40*** ( 8.62 ) (67.79) (57.88) % Home Ownership 35.07 *** 194.90*** 151.18 ( 7.85 ) (26.12) (19.74) Population Density 0.0230 0.2946* 0.1636 (0.0 233 ) (0.1578) (0.1363) Allowance All ocation 0.0628 ***

- 0.0709***

- 0.0681*** (0.00 23 ) (0.0144) (0.0144) Pollution Intensity 2.99** 116.14*** 121.95*** ( 1.36 ) (13.18) (13.15) Year 0. 49 ** 1430.16*** 1443.46*** (0. 24 ) (68.18) (68.20) Constant

- 6608.62 ***

- 2901614***

- 2922827*** ( 880.37 ) (136787) (136814)

Selection Model %African American 0.0024*** (0.0004) % Hispanic 0.0036*** (0.000 7) Median Housing Value

- 0.0009*** (0.0001) Median Household Income 0.0398*** (0.0018) Median Household Income 2

- 0.0000*** (0.0000) % No Diploma

- 0.0004 (0.0007) Constant

- 1.5007*** (0.0528)

Wald χ 2 or F 55.00*** 928.41*** 877.71*** rho 0. 26 ** na na Number of Cases 41703 9956 9953

a figures are coefficients from Tobit model with Probit sample selection model , robust standard errors in parentheses b figures are coefficients fro m T obit model, robust standard errors in parentheses

c figures are coefficients from Tobit model, robust standard errors in parentheses *p<.10; **p<.05 ; ***p<.01, one

- tailed tests31

TABLE 3 . Predicting Net SO 2 Allowance Trading

_____________ _______________________________________________________________________ Independent ZIP Code 3 Mile GIS 1 Mile GIS Variables Model a Model b Model c Structural Model %African American

- 1009.88*** 306.08*** 235.73*** ( 71.95) ( 60.90) ( 46.64) % Hispanic

- 1519.60***

- 31.41

- 39.07 ( 98.55) (52.66) (42.70) % Poverty 324.82**

- 960 .33***

- 778.87*** ( 136.16) (244.13) (200.61) % No Diploma 383.33** 610.65*** 579.74*** ( 137.14) (212.19) (184.34) % Home Ownership 57.73 287.56*** 141.21*** ( 38.90 ) ( 48.19) ( 33.83) Population Density 0.1715***

- 0.1623

- 0.0122 (0.0445) (0.4626) (0. 3766) Allowance Allocation

- 0.3822**

- 2.27***

- 2.27*** ( 0.1303) ( 0.11) ( 0.11) Pollution Intensity 217.63***

- 499.85***

- 496.48*** ( 63.95) (114.82) (115.01) Year 207.09

- 3333.28***

- 3327.85*** (110.39) (274.14) (275.40) Constant

- 265215 6663411*** 6662386*** (221869) ( 550476) (553451)

Selection Model %African American 0.0061*** (0.0004) % Hispanic 0.0085*** (0.0005) Median Housing Value

- 0.0038*** (0.0006) Medi an Household Income 0.0107*** (0.0017) Median Household Income 2

- 0.0000*** (0.0000) % No Diploma 0.0014 (0.0007) Constant

- 1.1571*** (0.0517)

Wald χ 2 444.22*** na n a rho

- 0.91*** na na R 2 na 0. 27 0. 27 Number of Cases 40470 9956 9953

a figures are coefficients from OLS model with P ro bit sample selection model , robust standard errors in parentheses b figures are coefficients from OLS model, robust standard errors in parentheses

c figures are coefficients from OLS model , robust standard errors in parentheses *p<.10; **p<.05 ; ***p<.01, one

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Appendix 1: Distribution of Net Allowance Trading Under the ATP

Red triangles represent facilities that import SO 2 allowances, with darker shades of red representing greater import s. Blue circles represent facilities that export or bank SO 2 allowances, with darker blue representing greater exports or banking.