This unit has presented laws related to pollution prevention (P2) as well as the 1990 Pollution Prevention Act (PPA).For this assignment, locate a peer-reviewed article in the CSU Online Library pertaining to P2 and the PPA. Ensure that the components below are addressed in your review.

• Introduce the article, which would include a brief identification of the article’s premise and significant points, along with your evaluation of the article’s premise and supporting points.
• Using the article, in addition to the course textbook, explain various aspects of the PPA.
• In your explanation of the PPA, include a discussion of the federal clean water, air, and waste laws pertaining to P2.

Limit the number of direct quotations from references. The vast majority of your paper should contain paraphrased information from your sources and should include your own thoughts on the laws that you present. The

Understanding Peer-Reviewed Articles LibGuide

will aid you in this course as you research within the CSU Online Library. Your paper must be at least two full pages in length. A title page and references page must be included; however, these pages will not count toward meeting the minimum page requirement. You are required to utilize at least two sources, one of which must come from the CSU Online Library and one of which will be your textbook. Adhere to APA Style when constructing this assignment, including in-text citations and references for all sources that are used. Please note that no abstract is needed. 

RESEARCH ARTICLE

Can adoption of pollution prevention

techniques reduce pollution substitution?

Sangyoul Lee

☯

, Xiang BiID*

☯

Food and Resource Economics Department, University of Florida, Gainesville, Florida, United States of

America

☯ These authors contributed equally to this work.
* xiangbi@ufl.edu

Abstract

Pollution prevention (P2) has become an integral part of the U.S. environmental policy that

emphasizes the benefits of preventing pollution generation at the source over treatment or

recycling after the generation of wastes. This study extends the existing literature on the

effect of voluntary adoption of P2 in reducing toxic wastes by examining the extent to which

it reduces pollution substitution. We use facility panel data from the Toxics Release Inven-

tory from 1991 to 2011 to examine the effect of the adoption of P2 techniques on the ratios

of water releases to air releases, amounts of treatment to total releases, and amounts of

recycling to total releases while controlling for endogeneity of the adoption of P2 techniques

and facility fixed effects. We find that the adoption of P2 techniques reduces toxic air and

water releases equally, but it is associated with increases in treated and recycled wastes

over total releases to the environment.

1 Introduction

Pollution controls in the United States rely on command and control regulation that empha-

sizes end-of-pipe abatement. The problem with this method is that end-of-pipe abatement

often focuses on limiting direct releases to a single environmental medium. For example,

installing wet scrubbers in smokestacks often turns air pollutants to wastewater, thus solving

one environmental problem by creating another [1–3]. Additionally, pollution controls after

wastes generation hamper the awareness and ability of firms and workers to identify the root

causes of wastes [4]. Recognizing the limitations of end-of-pipe pollution controls, the 1990

Pollution Prevention Act (PPA) sought to shift the emphasis from end-of-pipe controls down-

stream to pollution prevention upstream by promoting the waste management hierarchy in

which pollution prevention (P2) at the source is preferred whenever feasible, followed by recy-

cling, treatment, and releases (including disposal) to the environment.

To provide the public a more comprehensive view of a facility’s waste management hierar-

chy, the PPA expanded the Toxic Release Inventory (TRI) to include additional reporting on

the adoption of P2 techniques and the amount of toxic wastes recycled or reused for energy

recovery (incineration) per each TRI chemical [5]. Between 1991 and 2012, a total of 370,000

adoptions of P2 techniques were reported by 21,550 TRI facilities [6].

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OPEN ACCESS

Citation: Lee S, Bi X (2019) Can adoption of

pollution prevention techniques reduce pollution

substitution? PLoS ONE 14(11): e0224868. https://

doi.org/10.1371/journal.pone.0224868

Editor: James D. Englehardt, University of Miami,

UNITED STATES

Received: May 3, 2019

Accepted: October 23, 2019

Published: November 7, 2019

Copyright: © 2019 Lee, Bi. This is an open access
article distributed under the terms of the Creative

Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in

any medium, provided the original author and

source are credited.

Data Availability Statement: Data used in the

study are publicly available through the website of

the U.S. Environmental Protection Agency (https://

www.epa.gov/toxics-release-inventory-tri-

program/tri-data-and-tools), with one exception.

The variable on facility’s employment is obtained

from national establishment time-series database

provided by Walls & Associates. Address: 1700

Trestle Glen Road Oakland, CA 94610-1846. Tel:

(510) 763-0641. Email: dwalls2@earthlink.com. To

protect the third-party proprietary information by

Walls, we have included anonymized dataset for

replication in the supplementary files.

http://orcid.org/0000-0001-8671-487X

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http://creativecommons.org/licenses/by/4.0/

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https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-and-tools

https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-and-tools

https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-and-tools

mailto:dwalls2@earthlink.com

Existing studies show that, even though many TRI chemicals are not directly regulated by

environmental regulations, public access to the TRI publication has motivated polluters to vol-

untarily reduce toxic releases due to pressures from local communities, investors, regulators,

and consumers [7–12]. Recent studies show that voluntary adoption of P2 techniques has con-

tributed to the reduction in toxic releases [6, 13]. However, amid declining toxic releases, total

production-related wastes (including amounts of wastes released, recycled, and treated)

increased by 40%, from 21.82 billion pounds to 30.57 billion pounds from 2010 to 2017 [5].

Such an increase is partly due to the economic recovery after the 2008 recession [5]. However,

given the policy emphasis of using P2 techniques to mitigate the limitation of end-of-pipe pol-

lution controls and to prevent wastes from entering the waste stream for treatment and recy-

cling, there is a need to empirically investigate whether the increase in total toxic wastes can be

mitigated by voluntary adoption of P2

techniques.

This examination is particularly important to quantify the overall environmental impact of

the voluntary adoption of P2 techniques in the presence of media-specific regulation (such as

the Clean Air Act). Media-specific regulation often causes pollution substitution by reducing

one type of pollution (e.g., air pollutants) at the expense of another type of pollution (e.g.,

water pollutants) [1–3,14]. Under pressure to comply with regulation, polluters may increase

the use of P2 techniques that are easier to implement with existing end-of-pipe pollution con-

trols (e.g., treatment and recycling), rather than making fundamental changes to prevent toxic

materials from entering the waste stream. This casts doubt on the full benefits of P2, for it is

bounded by media-specific regulation. Given that few environmental regulations in the United

States focus on multi-media emissions, if P2 techniques fail to deliver holistic reductions in

toxic wastes, any future U.S. pollution control policy may need to prioritize specific techniques

that minimize the “cradle to grave” life cycle of toxic materials.

However, it is difficult to discern substitution by observing declining releases and increas-

ing wastes separately without econometric analysis that controls the change in output. Even if

P2 techniques can reduce total production-related wastes, the relative proportions of treated

and recycled wastes may still increase while holding total releases constant. In other words, the

relative importance of treatment and recycling increases than what it would have otherwise

been. This paper empirically examines the effect of voluntary adoption of P2 techniques on

pollution substitution by examining the effect of P2 adoption on the ratios of toxic releases ver-

sus treatment or recycling of toxic wastes, and of toxic releases to water versus air. In contrast,

existing studies on pollution substitution focus on command-and-control regulation, rather

than voluntary adoption of P2 techniques. Studies on the adoption of P2 techniques focus on

its effect on total toxic releases to the environment [13] or individual type of production-

related wastes separately [6] but have not determined pollution substitution among different

waste management approaches, such as releases, treatment, and recycling. Additionally, this

paper examines the heterogeneous effects by type of P2 techniques in order to inform policy

makers on prioritizing P2 techniques that reduce environmental impacts to all environmental

media.

We use facility panel data from the Toxics Release Inventory (TRI) for the period 1991 to

2011. The comprehensive nature of TRI allows us to examine two decades of data on P2 tech-

nique adoption, toxic releases, and treated and recycled wastes, accounting for about 50% of

the U.S. manufacturing base from both public and private facilities [15]. We model the ratio of

emissions of one environmental medium to another environmental medium with respect to

the cumulative number of P2 techniques adopted, as well as facility specific fixed effects and

regulatory and public pressures that could influence a TRI facility’s toxic emissions. Instru-

mental variables are used to control for endogeneity of the decision to adopt P2 techniques.

Our results indicate that the adoption of P2 techniques did not significantly influence

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Funding: The authors received no specific funding

for this work.

Competing interests: The authors have declared

that no competing interests exist.

https://doi.org/10.1371/journal.pone.0224868

substitution between toxic air and water releases, indicating that P2 techniques control all

types of releases to the environment equally and mitigate substitution of releases. However, the

adoption of P2 techniques on process and equipment modifications is associated with

increases in waste treatment and recycling over total releases.

2 Background and related literature

The adoption and diffusion of P2 techniques rely on information provisions and voluntary

approaches rather than prescriptive technology standards. TRI is an information disclosure

tool to help local communities prepare for potential health and environmental hazards caused

by the storage, use, and release of toxic chemicals commonly used in manufacturing. Specifi-

cally, manufacturing facilities that have 10 or more full-time employees and that manufacture,

process, or use any TRI-listed chemicals in amounts exceeding 25,000 pounds per year for

manufacturing or processing, or 10,000 pounds per year for otherwise use, must submit annual

reports to the Environmental Protection Agency (EPA) on the amounts of toxic wastes

released and treated. As part of the voluntary (non-mandatory) policies to encourage P2, TRI

was expanded under the PPA to include additional reporting on the adoption of new P2 tech-

niques and the amount of toxic wastes recycled or used for energy recovery (incineration) per

each TRI chemical [5].

Earlier studies indicate that there are economic benefits associated with voluntary adoption

of P2 techniques. For example, P2 techniques and recycling that allow firms to significantly

reduce their waste management costs in compliance with government regulations [16] also

significantly contribute to their business profitability [17]. Adopting P2 techniques works well

with the total quality environmental management system in improving quality and efficiency

[18]. P2 techniques that substitute inputs and modify production processes provide firms a

faster payback period than end-of-pipe pollution controls that require fairly large capital

investment and management expertise [19, 20]. In addition, studies also indicate that pressures

from the public and regulators motivate polluters to voluntarily adopt P2 techniques. Firms

often use an environmental management system or P2 programs as a tactical channel to appeal

to their stakeholders [21] and to reduce future EPA enforcement actions [22].

Earlier studies find that the adoption of P2 techniques reduced toxic releases, although the

magnitude of the effects varied. Harrington, Deltas, and Khanna [13] show that adopting P2

techniques significantly reduced toxic releases by 35–50%, although the effect dissipates within

4~5 years after the first adoption, based on data from TRI facilities that belonged to S&P 500

firms for the period 1991–2001. Bui and Kapon [23] find that state-level P2 programs signifi-

cantly reduced annual TRI releases by 11–15% through panel analysis on TRI facilities over

the period 1988–2003. Bennear [24] shows that facilities subject to management-based regula-

tions achieved greater reductions in total toxic releases and adopted more P2 techniques (the

author infers that the reductions may have been mainly achieved through P2 techniques). Ran-

son et al. [6], examining the effects of P2 techniques on total releases, treated wastes, and recy-

cled wastes separately, find that between 1991 and 2012, a total of 370,000 adoptions of P2

techniques were reported by 21,550 TRI facilities and contributed to the reduction of direct

toxic releases by 9–16% for all TRI facilities.

Previous empirical studies examining pollution substitution focused on regulation, rather

than on voluntary P2 techniques. Gibson [2] shows that facilities regulated by the Clean Air

Act (CAA) transfer pollutants from air to water and to unregulated facilities within the same

parent company. Bi [1] shows that stricter air regulations induce coal-fired power plants to

shift air pollutants to waterways and landfills. In contrast, Sigman [25] finds little evidence of

pollution substitution between wastes disposal and air emissions that use chlorinated solvents,

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although higher costs of waste disposal caused air emissions to increase. Greenstone [3] mod-

els CAA-regulated toxic releases to air, water, and land in the iron and steel industry as a func-

tion of a county’s nonattainment designation and shows that a county’s nonattainment

designation decreased emissions across all media.

This study builds on the existing studies on pollution substitution and voluntary adoption

of P2 techniques but differs from the previous literature in three ways. First, the previous liter-

ature focuses on the effect of pollution substitution induced by environmental regulations, par-

ticularly the CAA (e.g., [1–3]), rather than voluntary adoption of P2 techniques. Second, the

existing studies focus on the effect of P2 techniques in reducing total toxic releases [6,13]

rather than on the ratios of pollutants across media pathways, and therefore are unable to

determine pollution substitution. Additionally, previous research finds that P2 techniques,

ranging from making improvements in equipment and raw materials to inventory controls,

have differential impacts on total toxic releases [6]. However, none has examined the effects of

various types of P2 techniques on pollution substitution. This study separates P2 techniques

into three categories and examines their effects on pollution substitution to identify P2 tech-

niques that minimize wastes.

3 Model

We model multiple pollutants (e.g., releases to air and to water) as inputs used by a TRI facility

for producing output, following the previous literature [2, 26, 27]. Modeling pollutants as

inputs follows the rationale that releasing pollutants entails explicit costs of violating regula-

tions or implicit costs of adverse publicity and that a profit maximizing facility will choose the

optimal ratio of polluting inputs to minimize these costs. For example, parts of the costs

include compliance to the existing regulations, due to the overlap between some of the TRI

chemicals and the existing regulations. Our study focusses on 296 TRI chemicals and 151 of

them are also classified as hazardous air pollutants or contributing to criteria air pollutants

subject to emission standards or national ambient air quality standards [28].

Following Gibson [2], we assume a TRI facility has two polluting inputs, R and W, and
another input, L (such as labor). The cost function can be expressed as C = f(Q)�C(PR,Pw,PL),
in which f(Q) represents the output quantity, and PR, Pw, and PL represent the cost of pollution
inputs R and W, and the cost of L, respectively. The facility operates in competitive input and
output markets, and the cost function is homogenous degrees of one in all prices. In other

words, if all prices double, the total costs will double, and the optimal allocation of inputs

derived from cost minimization does not change.

As a result of cost-minimization, the conditional input demand for one type of pollutant

can be obtained by differentiating the above equation with respect to its price, such that

W� ¼ f Qð Þ � @C
@PW

; R� ¼ f Qð Þ � @C
@PR

. The conditional input demand functions are homogenous

degrees of zero in prices. The ratio of the two optimal inputs demands can be represented with

ratios of the input prices:

W�

R�
¼

@C
@PW
@C
@PR

¼ hð

PR
PW

;
PL
PW

;
PL
PR
Þ ð1Þ

Estimating the ratio of optimal inputs with respect to the price proxies reveals the net sub-

stitution between the inputs and the estimation is independent of output quantity and does

not need to include the prices of all inputs, such as PL [2]. Net substitution with respect to
price change in pollutant R is expressed as the percentage change in pollutant W with respect
to price change in pollutant R minus the percentage change in pollutant R in response to price

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change in pollutant R, holding output level fixed [2]. It is more relevant than gross substitution
in policy analysis on pollution substitution because gross substitution also includes the input

demand response to change in output.

Following this theoretical insight, our empirical model proceeds by modeling the ratio of

pollutants as described in Eq (2) below:

lnðratioÞit ¼ ai þX
0

it� 1b1 þb2ppit� 1 þD
0
d

1
þðD0 � tÞd

2
þuit ð2Þ

The dependent variable, ln(ratio)it, is defined as facility i’s log ratio of two pollutants at year
t. ai represents unobserved facility specific fixed effects. ppit−1 is the variable of our interests,
and measures the intensity effect of P2 techniques on the implicit prices of the two pollutants

by affecting costs to comply with regulations or to appeal to the public.

Gibson [2] shows that the coefficient β2 can be expressed as a scalar function, β2 = νσ,
where σ is the elasticity of net substitution of the two pollutants with a scalar ν. A negative sign
of σ indicates the two pollutants are net complements and a positive sign indicates the two pol-
lutants are net substitutes (i.e., holding output fixed). ν is the percentage increase in relative
prices of two inputs (

PR
PW

) induced by one incremental adoption of P2 techniques. If P2 activities

successfully caused holistic reduction in all emissions, adopting P2 techniques will not incur

pollution substitution among the environmental media, thus estimates of β2 are expected to be
zero.

Additionally, X0it−1 represents a vector of facility i’s covariates affecting pollution substitu-
tion in the preceding year. D0 represents a vector of fixed effects that include state and industry
(defined by the 2-digit SIC codes) dummies. These dummy variables control for unobserved

factors common for a given state or a given industry that affect the pollution ratios. D0×t repre-
sents the interactions between state and industry dummies and the linear year trend to control

for state and industry specific year trends, such as industry’s technology change and state’s

environmentally friendly efforts, which could affect pollution substitution.

Although facility specific fixed effects can be controlled in estimating (2) by using fixed-

effects panel models, cumulative P2 may still be endogenous. For example, a facility or its par-
ent firm may have adopted environmental management systems that have been shown to

increase the adoption of P2 techniques [4,18]. Such systems may also improve chemical-use

efficiency and reduce pollution to all environmental media. Since these factors are unobserv-

able to us and are likely to be time-varying within the two decades of the sample period, fixed-

effects estimates on Eq (2) are likely to biased.

Thus we use 2SLS panel regression with instrumental variables and multiple levels of fixed

effects that takes into account the correlation of the error terms across time to estimate Eq (2)

[29]. The first-stage regression is shown in Eq (3) below, in which Z0it−2 represents the vector
of two time-varying instrumental variables that are lagged by two years.

ppit� 1 ¼ g0 þX
0

it� 1g1 þZ0it� 2g2 þD
0
g

3
þðD0 � tÞg

4
þvit� 1 ð3Þ

These two instrumental variables are excluded in the second stage of estimating Eq (2).

They are expected to be correlated with time-varying ppit−1 but are uncorrelated with time-
varying unobservables, such as adoption of the environmental management system in Eq (2),

except through their effects on ppit−1, conditional on other covariates. We discuss the choice of
the instrumental variables in details in section 3.2.

3.1 Variable construction

We estimate (2) for each one of the ratios: toxic releases to water versus releases to air, the

amount of toxic recycling wastes versus total toxic releases, and the amount of treated toxic

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wastes versus total toxic releases, respectively. This analysis focuses on a group of TRI toxic

chemicals (296) listed from 1988 onwards without any change in reporting requirements [28].

We refer to the 296 toxic chemicals as the “core chemicals” hereafter. These three log ratios are
generated using the inverse hyperbolic sine function that is similar to logarithm transforma-

tion and allows retaining zero-valued observations by transforming a variable (x) into
(lnðxþ

ffiffiffiffiffiffiffiffiffiffiffiffiffi
x2 þ 1
p

Þ [30]. To generate the three ratios, we aggregate the annual amount of total

toxic releases, recycled wastes, treated wastes, P2 techniques, water, and air releases for core
chemicals for each TRI facility. Specifically, the amount of total releases to water, air, and land
onsite, and the amount of toxics transferred to disposal offsite. We did not examine the ratio

for land to air releases separately, as 95% of the sample did not report direct onsite releases to

land.

We construct the variable ppit−1 to approximate the intensity of P2 techniques implemented
since a facility first became an adopter of P2 techniques because TRI reporting requirement

applies only for incremental (new) P2 techniques per TRI chemical in a given year. Specifically,

we first aggregate the cumulative number of P2 techniques reported by a facility for the core
chemicals up to one period prior, and then divide the cumulative number by the total number
of reported core chemicals by each facility up to one period prior. We refer to this new variable
as “cumulative P2” hereafter. Considering TRI provides annual data, we lag the cumulative P2
variable to avoid contemporaneous problems with the dependent variable. Nevertheless, a TRI

facility may discontinue any previously reported P2 techniques without having to report disa-

doption. We conduct robustness checks on the definition of the cumulative P2 variable in the
next section.

Following the previous literature, we use environmental inspections by state and federal

regulators on the facilities and each county’s attainment status as proxy variables for the regu-

latory pressure for a TRI facility. Hanna and Oliva [31] show that an actual inspection under

the CAA reduces a firm’s air emissions by 15% for the next five years using the event study

method. If CAA inspections increased the compliance cost to CAA, we would expect facilities

to reduce air releases more than other types of emissions, such as water releases and wastes for

treatment and recycling. We aggregate the annual number of inspections by federal and state

agencies at the facility level from EPA’s Aerometric Information Retrieval System (AIRS)

Facility Subsystem (AFS) database and use the unique facility level identifier to link the AFS

dataset with the TRI. We do not include inspections under the Clean Water Act (CWA) in our

empirical model because CWA focuses on conventional water pollutants instead of toxic water

pollutants.

We use a county’s nonattainment status as another variable for regulatory pressure. Under

the CAA, the National Ambient Air Quality Standards (NAAQS) for carbon monoxide, sulfur

dioxide, total suspended particle pollution (PM), ground-level ozone, nitrogen oxide, and lead

are set by the EPA, which is responsible for ensuring that ambient air quality in each county

meets the NAAQS [32]. Once a county exceeds any of the standards for a pollutant, the county

is designated a nonattainment area for the particular pollutant. In order for a nonattainment

county to meet the standard, the state government needs to develop a state implementation

plan. Once EPA approves the plan, facilities in the nonattainment county have to adopt pollu-

tion control methods to reach attainment status. Following the most recent literature, we

expect that facilities in nonattainment counties would reduce more air releases than other

types of toxic emissions.

We focus on attainment status for two types of pollutants, particulate matter and ground-

level ozone (most counties obtained attainment status for the rest of the criteria for air pollut-

ants by 1998). We define PM Nonattainment and Ozone Nonattainment as dummy variables

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that are equal to 1 if a county falls into nonattainment with PM or ozone, respectively, in a

given year, and 0 otherwise. Following previous studies, the change in a county’s attainment

status is considered exogenous to a facility’s toxic releases and the two variables are lagged by

one period in the estimations to allow time for counties and TRI facilities to respond to the

regulation [1–3]. Based on the location information of the TRI facilities, we merge the TRI

data with the county’s annual nonattainment status in compliance with the NAAQS from

EPA’s Green Book [33].

Natural log of thenumberof employees of the facility is included in the covariate vector to con-
trol for the size of the facility. TRI does not include information regarding the level of production

and the number of employees, so we use the number of employees obtained from the National

Establishment Time-Series [34] to control for the size of the facility. To take into account the

pressures from local communities to reduce toxic pollution, we use the annual unemployment

rate of the county in which a facility is located from the U.S. Bureau of Labor Statistics [35].

3.2 Instrumental variables

We apply two time-varying instrumental variables (IVs) based on findings from the previous

literature. They are the lagged proportion of facilities that have adopted P2 techniques in the

same industry (defined by the 2-digit SIC codes), excluding the facility itself, and the lagged

average number of federal inspections on other facilities, excluding the facility itself, in the

same state. Both IVs are lagged by two periods.

Both IVs are expected to be strongly correlated with the number of P2 techniques. Specifi-

cally, a facility is more likely to adopt P2 if others in its industry or its state peers have adopted

P2 techniques, either through information spillovers or through shared supply chains. For exam-

ple, Harrington [36] finds that a facility’s decision to adopt P2 techniques can be affected by the

trend of adopting P2 by other facilities in the same industry. Sam [22] finds positive correlation

between the number of P2 techniques adopted and the number of state-level environmental

inspections. Chang and Sam [37] examine whether greater numbers of P2 techniques adopted

led to greater numbers of environmental patents. They correct the endogeneity problem by

using the frequency of inspection by both federal and state authorities on each facility three years

prior as the IVs. We expect that the increasing regulatory pressure on peer facilities in the same

state is likely to increase the adoption of P2 techniques by peer facilities. This is likely to increase

a facility’s own adoption of P2 techniques through learning from peer facilities within a state.

The valid IVs have to satisfy the exclusion restriction. That is, conditional on the covariates,

the IVs should have no effect on pollution substitution in the current period except through a

facility’s own adoption of P2 up to the previous period. We believe these IVs satisfy the exclu-

sion restriction because they are based on the production circumstances of other facilities two

periods prior. Additionally, the number of inspections on other facilities for compliance with

the Clean Air regulation two periods prior depends on their violation history, state budget of

enforcements, and local complaints. While more inspections may motivate facilities from the

same state to adopt P2 techniques through deterrence, they are unlikely to influence a facility’s

pollution substitution after controlling for environmental inspections and other regulatory

pressures on the facility itself. Summary statistics of the explanatory and instrumental variables

are reported in Table 1.

4 Results

4.1 Main results

We report the estimated effects of cumulative P2 adoption on different types of pollution sub-

stitutions (Table 2), including total amount of wastes treated to total releases, total amount of

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water releases to total air releases, and total amount of wastes recycled to total releases. We also

report the first-stage regression results in column 1 and the test statistics on the validity of

instrumental variables (rows 10–11). The higher share of facilities that had adopted P2 tech-

niques in the same industry two periods prior is associated with the higher level of a facility’s

lagged cumulative P2 (column 1, row 1). Though the lagged average number of inspections on

other facilities in the same state alone did not significant influence a facility’s lagged cumula-

tive P2, both instruments are jointly strongly correlated with the endogenous variable as indi-

cated by the statistically significant F-statistics (F = 26.768) [29,38]. None of the Hansen’s J

statistics for orthogonality of the instruments is statistically significant at the 5% significance

level, indicating the instruments are orthogonal to the error terms (rows 10–11).

We find that the ratio of treated wastes versus total releases increased as facilities adopted

more P2 techniques (Table 2, column 2, row 3). The coefficient on the cumulative P2 tech-
niques was 0.083 and was statistically significant at the 5% significance level. Based on this

information, one additional P2 technique adopted per chemical increased the amount of treat-

ment in the current period by 8.3%, holding total releases and other covariates fixed. To put

this into context, the average total releases were 58,235 pounds, the average quantities of

treated wastes were 59,582 pounds, and the input ratio of treated wastes to total releases was

1.02 in 1991. Holding the covariates and total releases at their averages in 1991, adopting one

additional P2 technique per chemical increased the ratio of the two pollutants to 1.10. Such an

increase was equivalent to an increase of 4,945 pounds in the amounts of treated wastes. The

positive correlation between end-of-pipe treatment and adoption of P2 techniques is also

noted in Dutt and King [39]. They suggest that measuring wastes more precisely through treat-

ment allows workers and managers to better identify P2 opportunities. Similarly, our results

suggest that facilities use P2 techniques in combination with treatment to reduce total releases.

Similarly, we find that facilities that adopted greater cumulative P2 techniques had greater
substitution between recycling and total releases (Table 2, column 4). The adoption of one

Table 1. Summary statistics.

Variable Mean Std. Dev. Min Max

Dependent Variables

Treatment/Total release 0.85 2.03 0.00 15.79

Water/Air 0.09 0.60 0.00 11.13

Recycling/Total release 1.70 3.01 0.00 16.10

Offsite recycling/Total release 1.45 2.91 0.00 15.96

Explanatory Variables

Cumulative P2 (normalized by number of chemicals) 2.52 6.15 0.00 251.00

Total number of inspection (both federal & state) 0.28 0.63 0.00 96.00

PM nonattainment

(1 = nonattainment for PM, 0 = otherwise)

0.20 0.40 0.00 1.00

Ozone nonattainment

(1 = nonattainment for Ozone, 0 = otherwise)

0.41 0.49 0.00 1.00

Unemployment rate (%) in county 5.91 2.36 0.99 31.11

Number of employees (log) 4.79 1.30 0.69 10.20

Production ratio 1.02 0.298 0.005 2.995

Instrument Variables

Share of facilities adopting P2 in the same industry 0.53 0.17 0.00 1.00

The average number of inspections on other facilities in the same state 0.28 0.22 0.00 1.80

Note: Number of Observations = 190,395; Number of Unique Facilities = 21,650; Year = 1991 to 2011.

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additional P2 technique per chemical significantly increased the ratio of recycled toxics to total

releases by 17%, holding other variables constant. To put this coefficient into context in 1991,

the amounts of recycled toxics was 102,803 pounds, and its ratio to total releases was 1.77.

Based on the estimated coefficient on cumulative P2 in column 3 of Table 2, an additional
adoption of P2 techniques per chemical increased the ratio by 17%. This ratio changed to 2.07

following an adoption of a P2 technique, holding covariates and the toxic releases at their aver-

age levels in 1991, equivalent to an increase of 17,477 pounds of recycled toxic wastes. The

larger impact of adopting P2 techniques on recycled compared to treated wastes can be the

result of the joint use of recycling methods and P2 techniques. For example, Cagno, Trucco,

and Tardini [17] note that the adoption of P2 techniques is often accompanied by onsite recy-

cling that modifies the flow of the waste stream and recovers wastes to be used by other pro-

cesses within a facility. This saves a facility’s costs to process additional raw materials and to

dispose of hazardous wastes.

Table 2. Two–Stage fixed–effects estimation of effects of P2 on pollution substitutions.

Variable First Stage Second Stage

(1) (2) (3) (4)

Cumulative P2(t-1) Treatment/ Releases Water/Air Recycling/ Releases

Excluded IVs
Proportion of facility adoptions of P2 in same industry (t-2) 3.519���

(0.494) — — —

Avg. number of inspections on other facilities in same state (t-2) -0.140

(0.129) — — —

Covariates
Cumulative P2 (t-1) — 0.086�� 0.013 0.170���

(0.041) (0.017) (0.057)

Total inspections (t-1) 0.031 0.008 -0.001 -0.002

(0.019)

(0.012) (0.003) (0.008)

PM nonattainment (t-1) -0.031 0.043 0.010 0.040

(0.140) (0.031) (0.012) (0.054)

Ozone nonattainment (t-1) -0.093 0.038� -0.003 0.105���

(0.083)

(0.021) (0.008) (0.035)

Unemployment rate (%) (t-1) -0.005 0.002 -0.002 0.004

(0.012)

(0.003) (0.001) (0.005)

No. of employees (log) (t-1) 0.110�� -0.014 -0.009� -0.001

(0.048) (0.012) (0.005) (0.019)

Over-identification (Hansen’s J) — 0.187 0.037 1.182

P-value of Hansen’s J — 0.665 0.847 0.277

R-squared 0.706 0.768 0.595 0.747

Notes: N = 132,445, Number of facilities = 13,754.

��� p<0.01

�� p<0.05

� p<0.1.

Robust standard errors in parentheses (clustered by facility). All models control for facility specific fixed effects in addition to state and industry fixed effects and their

interactions with linear year trends. For all second–stage models, Hansen’s J statistics indicate the orthogonality of the instrumental variables cannot be rejected. The

analysis is from 1993 as a result of using two years’ lagged IVs. Weak instrument test statistics is 26.768, representing a statistically strong correlation between

endogenous variables and IVs, given the Wald F statistic based on the Kleibergen–Paap rk statistics in the presence of clustered

standard errors.

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We find no statistical evidence that adopting more P2 techniques is associated with greater

pollution substitution between water releases and air releases (Table 2, column 3). This implies

that adopting P2 techniques affects both types of direct releases to the environment equally,

which suggests that using voluntary P2 techniques can overcome the problem of direct releases

substitution across environmental media often induced by regulation.

The estimated impacts of other covariates are consistent with our expectations and previous

studies. The nonattainment designation of ground-level ozone in a county is associated with

an increase of 3.8% and 10.5% in the ratios of treatment to total releases and recycling to total

releases, respectively (Table 2, row 6). This positive effect of ground-level ozone nonattainment

status on pollution substitution is consistent with Bi [1]. However, a county’s attainment status

with respect to particulate matter does not significantly influence substitution between treat-

ment and total releases (Table 2, row 5). The different results between the two types of nonat-

tainment status may be due to the intrinsically different abatement approaches adopted by

facilities to abate particulate matter and ground-level ozone pollutants. For example, Bi [1]

finds that coal-fired power plants increased wastes transferred for recycling and treatment in

order to comply with the CAA for ground-level ozone pollutants by reuse and recover solu-

tions for pollution abatements. Compliance with PM emission standards can occur through

reducing particulate matter from air stacks by capturing flying ashes, which increases releases

to landfills or ash ponds. Furthermore, we do not find that regulatory pressures in terms of the

number of EPA inspections affect pollution substitution significantly (Table 2, row 4). This is

consistent with Bi [1] in which CAA inspections did not significant affect the amounts of toxic

wastes by coal-fired power plants.

4.2 Results by types of P2 techniques

The effects of P2 techniques on a facility’s pollution substitution may vary by types of tech-

niques. Currently, TRI facilities can report up to 43 types of P2 techniques, categorized into

eight types: (i) good operation practices (e.g., improving recordkeeping and production sched-

uling), (ii) inventory control, (iii) spill and leak prevention, (iv) raw material modification, (v)

process modification, (vi) cleansing and decreasing modification, (vii) surface and finishing

modification, and (viii) product modification. Among the eight types of P2 techniques, Ranson

et al. [3] find that raw material modification had the largest negative impact on toxic releases,

whereas changes in inventory control and operating practice did not significantly reduce toxic

releases. Sam [22] further categorizes the eight types into three groups: operational procedure

modification (e.g., good operation practice and inventory control), material modification (e.g.,

changing raw materials and product designs), and equipment modification (e.g., spill and leak

prevention and improving cleanup process). He finds that the effects of adopting P2 tech-

niques on subsequent environmental violations differ by types of P2 techniques. Only opera-

tional procedure modification (i.e., P2 types i and ii) reduced violations for all facilities.

To investigate the heterogeneous effects by types of P2 techniques on pollution substitu-

tions between treatment and releases and between recycling and releases, we separate the

cumulative P2 variable into three types of P2, following Sam [22], to examine their effects on
the three ratios, respectively (Table 3). Variable P2 PROC represents techniques related to
changes in operating procedure (P2 types i and ii); variable P2 EQUIP represents techniques
on the installation of environmentally friendlier equipment and processes (P2 types iii, v, vi,

and vii); and variable P2 MAT represents techniques involved in raw material and product
modification (P2 types iv and viii).

Because all three types of P2 techniques are potentially endogenous, we have recreated

three additional instruments based on the percentage of other facilities in the same industry

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(i.e., defined by the 2-digit SIC codes) that adopted the three specific types of P2 techniques

(i.e., procedure modification, material modification, and equipment modification) two years

prior. For each model reported in Table 3, the instrumental variables are percentages of facili-

ties that adopted P2 PROC, P2 EUIP, and P2MAT in the same industry two years prior, and
the average number of federal inspections on other facilities in the same state two years prior.

We find that P2 techniques related to equipment and process modification (P2 EQIP) sig-
nificantly increased the ratio of treated versus total releases by 10% (statistically significant at

10%) and recycled versus total releases by 21.6% (Table 3, columns 1 and 3, row 2). P2 tech-

niques related to procedure modification (P2 PROC) did not significantly affect any type of
pollution ratios. P2 techniques related to material modification (P2 MAT) only increased the
ratio of recycling versus total releases (statistically significant at 10%).

The results in Table 3 suggest that procedure modifications reduced all types of toxic wastes

equally, while P2 techniques involving modifying equipment (e.g., modifications in spill

Table 3. Second–Stage fixed–effects panel estimates on pollution substitution by types of P2 techniques.

Variable Dependent Variables

(1) (2) (3)

Treatment / Releases Water / Air Recycling / Releases

Cumulative P2 PROC (t-1) 0.028 0.003 0.057

(0.047) (0.014) (0.061)

Cumulative P2 EQUIP (t-1) 0.100� 0.017 0.216���

(0.051) (0.021) (0.071)

Cumulative P2 MAT (t-1) 0.105 0.010 0.172�

(0.072) (0.036) (0.104)

Total number of inspections (t-1) 0.008 -0.001 -0.002

(0.012) (0.003) (0.008)

PM nonattainment (t-1) 0.046 0.010 0.043

(0.031) (0.012) (0.053)

Ozone nonattainment (t-1) 0.036� -0.003 0.101���

(0.020) (0.008) (0.034)

Unemployment rate (%) (t-1) 0.002 -0.001 0.004

(0.003) (0.001) (0.005)

Number of employees (log) (t-1) -0.013 -0.008� 0.001

(0.012) (0.005) (0.018)

Weak instrument (Wald F-stat.) 15.671

Over-identification (Hansen’s J) 0.129 0.054 1.545

P-value of Hansen’s J 0.676 0.825 0.209

Adjusted R-squared 0.796 0.639 0.781

Notes: N = 132,445, Number of facilities = 13,754.
��� p<0.01 �� p<0.05 � p<0.1.

Robust standard errors in parentheses clustered by facility. All models control for facility specific fixed effects in addition to state and industry dummies and their

interactions with linear year trend. Hansen’s J statistics indicate the orthogonality of the instrumental variables (proportions of facilities adopting the three types of P2s

in the same industry and average number of inspections on other facilities in the state two years prior) cannot be rejected. The analysis is from 1993 as a result of using

two years lagged IVs. Weak instrument test presents a statistically strong enough correlation between endogenous variables and IVs, given the Wald F statistic based on

the Kleibergen–Paap rk statistics in the presence of clustered standard errors. But statistical significance for the first–stage F statistics is not determined because the

critical value of weak IV test for three endogenous variables corrected for size distortion is not available [37].

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prevention, cleaning, degreasing, and surface preparation) increased wastes to be treated or

recycled downstream relative to total releases. Ranson et al. [6] find that P2 techniques involv-

ing raw material and product modifications had the largest marginal impact on reducing toxic

releases. They argue that changing raw materials and products are more complex and resource

intensive, thus more effective in reducing actual pollution. Additionally, previous case studies

show that firms often fail to identify waste materials for treatment or reuse in the downstream

process unless they make changes in materials, procedures, and equipment [40,41]. Our results

suggest that changes in raw materials, product designs, and procedures should have priority

over changes in equipment and processes to mitigate pollution substitution induced by P2

techniques.

4.3 Robustness check

We conduct several robustness checks. First, we include the variable production ratio reported
in the TRI to control for changes in production scale at the facility level. This ratio of the cur-

rent year’s output versus last year’s output contributed by a TRI chemical is reported by TRI

facilities for each TRI chemical. This ratio is chemical-specific and should be reported as a dec-

imal ratio if reported correctly. Previous studies indicated some facilities reported negative

numbers or omitted decimals in reporting [39]. Following previous studies, we removed the

top and bottom 1% as outliers and took the median value of the reported production ratios for

all core chemicals for each TRI facility in a given year [42] and included it as an additional

covariate. The results are reported in Table 4. We find that the production ratio is positively

correlated with the amounts of treatment and recycling versus total releases. Nevertheless, the

main conclusions are robust to adding this additional variable.

Second, TRI reports only incremental (new) P2 techniques on an annual basis. A previous

study suggests that the effectiveness of P2 techniques in reducing toxic releases diminishes

after 5 years [12]. Therefore, to avoid over-estimating the effect of cumulative P2, in contrast

to accounting for P2 techniques since 1991 up to the preceding year, we only include the total

number of P2 techniques adopted in the most recent six years up to the preceding year as an

alternative definition for the cumulative P2 variable. The results are reported in Table 5. The
main conclusions are consistent with this alternative definition of cumulative P2 variable,
though the magnitude of the estimates on cumulative P2 is reduced. The effect on ratio of
treatment to releases is reduced from 8% to 4% and the effect on the ratio of recycling to

releases is reduced from 16% to 8%.

As another robustness check, we also include the estimated effects of cumulative P2 on the
levels of releases, amounts of wastes recycled, and amounts of wastes treated, separately

(Table 6). Consistent with existing literature, we find that adopting more P2 techniques signifi-

cantly reduces the average level of toxic releases and amounts of wastes treated. In addition, it

does not affect the amounts of wastes recycled. Our findings suggest that estimating the effect

of P2 on one pollutant at a time is unlikely to discover pollution substitution across categories.

Specifically, even though adoption of P2 techniques reduced both releases and treated wastes

on average, our results on the ratio of pollutants indicate that the reduction in releases is

greater proportionally to its reduction in treatment and recycling, resulting net substitution

between releases and other waste management methods.

Finally, we estimate Eq (2) using fixed-effects models without instrumental variables,

thereby treating cumulative P2 exogenous. The results are reported in Table 7. Without
addressing the endogeneity of cumulative P2, its effect is attenuated (Table 7, row 1). As
expected, the unobserved factors, such as adoption of environmental management systems,

are likely to be correlated positively with the adoption of P2 techniques and negatively with

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pollution substitution, which leads to downward bias in fixed effects estimation without the

use of IVs.

5 Conclusions and discussion

The objective of this study is to examine the extent to which voluntary adoption of P2 tech-

niques influences pollution substitution. Many studies have focused on the effect of regulatory

pressure on pollution substitution or the effect of P2 adoption on reduction in toxic releases.

This study contributes to the existing literature by providing the first empirical study on

whether adopting more P2 techniques can reduce the overall use of toxics in the manufactur-

ing industry.

Using data from 1991 to 2011, we set up three different types in pollution substitution as

our dependent variables: the ratio of wastes treated to total releases, water releases to air

releases, and wastes recycled to total releases. We use instrumental variables for the number of

P2 techniques adopted and find that adopting greater numbers of P2 techniques does not

influence the ratio between water releases and air releases. Previous studies find that adopting

P2 techniques voluntarily leads to decreases in total toxic releases to the environment. Our

Table 4. Second–Stage fixed–effects panel estimates on effects of P2 on pollution substitutions with production ratio.

Dependent Variables
(1) (2) (3)
Treatment / Releases Water / Air Recycling / Releases

Cumulative P2 (t-1) 0.066� 0.008 0.149���

(0.037) (0.015) (0.050)

Total number of inspections (t-1) 0.008 -0.002 -0.004

(0.011) (0.004) (0.008)

PM nonattainment (t-1) 0.058� 0.008 0.040

(0.031) (0.012) (0.053)

Ozone nonattainment (t-1) 0.033 -0.003 0.113���

(0.021) (0.008) (0.035)

Unemployment rate (%) (t-1) 0.000 -0.001 0.005

(0.003) (0.001) (0.004)

The number of employee (log) (t-1) -0.009 -0.007 0.002

(0.012) (0.005) (0.018)

Production ratio (t-1) 0.048��� 0.006 0.051��

(0.014) (0.005) (0.021)

Weak instrument (Wald F-stat.) 30.708���

Over-identification (Hansen’s J) 0.452 0.143 2.036

P-value of Hasen’s J 0.501 0.705 0.154

Adjusted R-squared 0.783 0.604 0.764

Notes: N = 122,033, Number of facilities = 13,186.

��� p<0.01 �� p<0.05 � p<0.1.

The number of observations was reduced to 122,033 since none all facilities reported valid production ratios. Robust standard errors in parentheses (clustered by

facility). All models control for facility–specific fixed effects, in addition to state and industry fixed effects and their interactions with linear year trends. For all models,

the Hansen’s J statistics indicate the orthogonality of the instrumental variables (proportion of facilities adopting P2 in same industry and average number of inspections

on other facilities in the state two years prior) cannot be rejected. Weak instrument test presents a statistically strong enough correlation between endogenous variable

and IVs, given the Wald F statistic that is based on the Kleibergen–Paap rk statistics in the presence of clustered standard errors.

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results indicate this voluntary approach reduces all types of direct releases to the environment

equally, thus curtailing pollution substitution often induced by regulations.

However, a voluntary policy to promote P2 is not a panacea to reduce total toxic wastes.

We find that adopting greater numbers of P2 techniques contributes to increases in wastes

emitted for treatment and recycling over total releases. Specifically, process and equipment

modifications have a greater effect than do raw material, product, and procedure modifica-

tions. These results suggest that the potential of P2 techniques in reducing or eliminating over-

all reliance on toxics in manufacturing may be limited, as facilities focus on reducing releases

to the environment through combining end-of-pipe and in-process waste management strate-

gies with particular types of P2 techniques that do not necessarily address the root causes of

toxic wastes. Thus, pollution control policy should emphasize waste minimization, considering

the life cycle of toxics, and prioritize the use of raw material and product modification. As

noted by Ranson et al. [6], raw material and product modifications are likely to be more

resource intensive, thus grants and technical assistance programs should target them.

Additionally, our results have implications for other countries that are considering appro-

priate policies to promote pollution prevention. Following the example of TRI, the Organisa-

tion for Economic Co-operation and Development (OECD) has recommended that member

countries establish reporting systems to track progress in pollution control and to facilitate

Table 5. Second–Stage fixed–effects panel estimates on effects of P2 on pollution substitutions using cumulative P2 techniques from recent five years.

Variable Dependent Variables
(1) (2) (3)
Treatment / Releases Water / Air Recycling / Releases

Recent 5 years’ Cumulative P2 (t-1) 0.037�� 0.006 0.079���

(0.018) (0.008) (0.023)

Total number of inspections (t-1) 0.011 -0.000 0.003

(0.012) (0.003) (0.007)

PM nonattainment (t-1) 0.039 0.010 0.032

(0.029) (0.012) (0.048)

Ozone nonattainment (t-1) 0.034� -0.003 0.097���

(0.019) (0.008) (0.031)

Unemployment rate (%) (t-1) 0.002 -0.001 0.005

(0.003) (0.001) (0.004)

The number of employee (log) (t-1) -0.006 -0.008� 0.013

(0.010) (0.005) (0.016)

Weak instrument (Wald-F test) 333.361���

Over-identification (Hansen’s J) 0.595 0.009 0.569

P-value 0.440 0.925 0.451

Adjusted R-squared 0.786 0.600 0.782

Notes: N = 132,445, Number of facilities = 13,754.
��� p<0.01 �� p<0.05 � p<0.1.

Robust standard errors in parentheses (clustered by facility). All models control for facility–specific fixed effects, in addition to state and industry fixed effects and their

interactions with linear year trends. For all models, the Hansen’s J statistics indicate the orthogonality of the instrumental variables (proportion of facilities adopting P2

in same industry and average number of inspections on other facilities in the state two years prior) cannot be rejected. Weak instrument test presents a statistically

strong enough correlation between endogenous variable and IVs, given the Wald F statistic that is based on the Kleibergen–Paap rk statistics in the presence of clustered

standard errors.

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exchanges of information on the best available techniques for P2 [43–45]. The establishment

of the European Integrated Pollution Prevention and Control directive also recognizes the lim-

itation of media-specific regulatory approaches and requires member countries to implement

a more systematic environmental management to address the “cradle-to-grave” life cycle of

toxic substances through the best available technologies while taking into account the hetero-

geneous environmental, technical, and economic conditions of the member countries. Our

results indicate such an information system can be further improved by highlighting the adop-

tion of waste minimization technologies by member countries.

There are several limitations associated with this study. First, our results apply to

manufacturing facilities subject to the TRI reporting requirements over the period 1991 to

2011. Since TRI reporting facilities tend to be larger polluters, this may limit the generality of

our findings on P2 programs implemented by other sectors, smaller businesses below the

reporting thresholds, and more recently implemented P2 techniques. Second, recycling and

treatment are still the preferred pollution control approaches over direct releases to the envi-

ronment. More importantly, treatment and recycling methods reduce the toxicity of wastes

Table 6. Second–Stage fixed–effects panel estimates on effects of P2 on levels of pollution.

Variable Dependent Variables
(1) (2) (3)

Log(treatment) Log(recycling) Log(total releases)

Cumulative P2 (t-1) -0.274��� -0.078 -0.133��

(0.094) (0.099) (0.058)

Total number of inspections (t-1) 0.0664��� 0.039� 0.014

(0.018) (0.022) (0.012)

PM nonattainment (t-1) 0.197�� 0.030 -0.082

(0.088) (0.093) (0.052)

Ozone nonattainment (t-1) 0.003 0.1744��� -0.073��

(0.058) (0.063) (0.035)

Unemployment rate (%) (t-1) 0.0254��� 0.0294��� -0.006

(0.007) (0.008) (0.005)

Number of employees (log) (t-1) 0.1704��� 0.1524��� 0.0934���

(0.034) (0.038) (0.019)

Production ratio (t-1) 0.1364��� 0.1684��� 0.2384���

(0.035) (0.037) (0.023)

Weak instrument (Wald F-stat.) 30.7084���

Over-identification (Hansen’s J) 1.126 1.936 0.0246

P-value of Hansen’s J 0.289 0.164 0.875

Observations 122,033 122,033 122,033

Number of Facilities 13,186 13,186 13,186

Adj. R-squared 0.773 0.771 0.783

Notes: Robust standard errors in parentheses (clustered by facility). All models control for facility specific fixed effects in addition to state and industry fixed effects and

their interactions with linear year trends.

��� p<0.01 �� p<0.05 � p<0.1.

For all models, the Hansen’s J statistics indicate the orthogonality of the instrumental variables (Proportion of facilities adopting P2 in same industry and average

number of inspections on other facilities in the state two years prior) cannot be rejected. Weak instrument test presents a statistically strong enough correlation between

endogenous variables and IVs, given the Wald F statistic based on the Kleibergen–Paap rk statistics in the presence of clustered standard errors.

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released to the environment through stabilization, destruction, and reuse. Due to data limita-

tion, it is difficult to incorporate chemical toxicity by calculating toxic-weighted emissions in

this analysis since chemical toxicity differs by environment medium and by specific treatment

and recycling method. Third, we focus on the average effect of P2 techniques without examin-

ing the effects of P2 techniques on pollution substitution by different industries due to data

limitation. Future studies can conduct industry specific analysis through surveys to develop

recommendations. For example, Gaona [46] shows that information on P2 techniques can be

used to track green chemistry practices in the United States. Future analyses can extend our

findings to chemical manufacturing to examine the effect of adopting P2 techniques related to

green chemistry on pollution substitution.

Supporting information

S1 File. Lee_Bi_PONE2019.

(ZIP)

Author Contributions

Conceptualization: Xiang Bi.

Data curation: Xiang Bi.

Formal analysis: Sangyoul Lee, Xiang Bi.

Funding acquisition: Xiang Bi.

Table 7. Fixed–effects panel estimation on effects of P2 on pollution ratios without IVs.

Varibles Dependent Variables

(1) (2) (3)

Log(treatment/

Release)

Log(Water/Air) Log(Recycling/

Releases)

Cumulative P2 (t-1) 0.001 0.000 -0.005�

(0.002) (0.001) (0.003)

Total number of inspections (t-1) 0.011 -0.001 0.000

(0.011) (0.003) (0.007)

PM nonattainment (t-1) 0.049 0.008 0.016

(0.028) (0.011) (0.046)

Ozone nonattainment (t-1) 0.022 -0.003 0.092���

(0.018) (0.007) (0.030)

Unemployment rate (%) (t-1) -0.002 -0.002�� -0.002

(0.003) (0.001) (0.004)

Number of employees (log) (t-1) -0.001 -0.007 0.023

(0.010) (0.004) (0.015)

Production ratio 0.047��� 0.003 0.024

(0.012) (0.004) (0.018)

Adj. R-squared 0.779 0.590 0.778

Notes: N = 130,133, Number of facilities = 13,925 Robust standard errors in parentheses (clustered by facility). All models control for facility–specific fixed effects, in

addition to state and industry fixed effects and their interactions with linear year trends.

��� p<0.01 �� p<0.05 � p<0.1.

https://doi.org/10.1371/journal.pone.0224868.t007

Pollution prevention

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http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0224868.s001

https://doi.org/10.1371/journal.pone.0224868.t007

https://doi.org/10.1371/journal.pone.0224868

Investigation: Sangyoul Lee, Xiang Bi.

Supervision: Xiang Bi.

Validation: Sangyoul Lee, Xiang Bi.

Writing – original draft: Sangyoul Lee, Xiang Bi.

Writing – review & editing: Sangyoul Lee, Xiang Bi.

References
1. Bi X. ‘Cleansing the air at the expense of waterways?’ Empirical evidence from the toxic releases of

coal-fired power plants in the United States. J Regul Econ. 2017; 51(1):18–40.

2. Gibson M. Regulation-induced pollution substitution. Rev Econ Stat. 2019;forthcoming. Available from:

https://doi.org/10.1162/rest_a_00797.

3. Greenstone M. Estimating regulation-induced substitution: The effect of the Clean Air Act on water and

ground pollution. Am Econ Rev. 2003; 93(2):442–48. Available from: https://doi.org/10.1257/

000282803321947498.

4. King AA, Lenox MJ. Lean and green? An empirical examination of the relationship between lean pro-

duction and environmental performance. Prod Oper Manage. 2001; 10(3):244–56

5. United States Environmental Protection Agency (EPA). Pollution prevention law and policies. United

States Environmental Protection Agency, Washington, DC; 2018a. Available from: https://www.epa.

gov/p2/pollution-prevention-law-and-policies#define.

6. Ranson M, Cox B, Keenan C, Teitelbaum D. The impact of pollution prevention on toxic environmental

releases from U.S. manufacturing facilities. Environ Sci Technol. 2015; 49(21):12951–57. https://doi.

org/10.1021/acs.est.5b02367 PMID: 26477531

7. Arora S, Gangopadhyay S. Toward a theoretical model of voluntary overcompliance. J Econ Behav

Organ. 1995; 28(3):289–309.

8. Delmas M, Montes-Sancho MJ, Shimshack JP. Information disclosure polices: Evidence from the elec-

tricity industry. Econ Inquiry. 2010; 48(2):483–98.

9. Hamilton JT. Pollution as news: Media and stock market reactions to the toxics release inventory data.

J Environ Econ Manage. 1995; 28(1):98–113.

10. Khanna M. Non-mandatory approaches to environmental protection. J Econ Surveys; 2001; 15(3):291–

324.

11. Khanna M, Anton WRQ. Corporate environmental management: Regulatory and market-based incen-

tives. Land Econ. 2002; 78(4):539–58.

12. Konar S, Cohen MA. Does the market value environmental performance? Rev Econ Stat. 2001; 83

(2):281–89.

13. Harrington DR, Deltas G, Khanna M. Does pollution prevention reduce toxic releases? A dynamic panel

data model. Land Econ. 2014; 90(2):199–221.

14. Alberini A. Environmental regulation and substitution between sources of pollution: An empirical analy-

sis of Florida’s storage tanks. J Regul Econ. 2001; 19(1):55–79.

15. Berchicci L. Towards an Open R&D System: Internal R&D Investment, External Knowledge Acquisition

and Innovative Performance. Res Policy. 2013; 42 (1): 117–127.

16. Freeman H, Harten T, Springer J, Randall P, Curran MA, Stone K. Industrial pollution prevention! A criti-

cal review. J Air Waste Manage Assoc. 1992; 42(5):618–56.

17. Cagno E, Trucco P, Tardini L. Cleaner production and profitability: Analysis of 134 industrial pollution

prevention (P2) project reports. J Cleaner Prod. 2005; 13(6):593–605.

18. Khanna M, Deltas G, Harrington DR. Adoption of pollution prevention techniques: The role of manage-

ment systems, demand-side factors, and complementary assets. Environ Res Econ. 2007; 44(1):85–106.

19. Dahab MF, Montag DL, Parr JM. Pollution prevention and waste minimization at a galvanizing and elec-

troplating facility. Water Sci Technol. 1994; 30(5):243–50.

20. Rajagopalan N, Boddu VM, Mishra S, Kraybill D. Pollution prevention in an aluminum grinding facility.

Metal Finishing. 1998; 96(11):18–24.

21. Florida R, Davison D. Gaining from green management: Environmental management systems inside

and outside the factory. California Manage Rev. 2001; 43(3):64–84.

Pollution prevention

PLOS ONE | https://doi.org/10.1371/journal.pone.0224868 November 7, 2019 17 / 18

https://doi.org/10.1162/rest_a_00797

https://doi.org/10.1257/000282803321947498

https://doi.org/10.1257/000282803321947498

https://www.epa.gov/p2/pollution-prevention-law-and-policies#define

https://www.epa.gov/p2/pollution-prevention-law-and-policies#define

https://doi.org/10.1021/acs.est.5b02367

https://doi.org/10.1021/acs.est.5b02367

http://www.ncbi.nlm.nih.gov/pubmed/26477531

https://doi.org/10.1371/journal.pone.0224868

22. Sam AG. Impact of government-sponsored pollution prevention practices on environmental compliance

and enforcement: Evidence from a sample of U.S. manufacturing facilities. J Regul Econ. 2010; 37

(3):266–86.

23. Bui LTM, Kapon S. The impact of voluntary programs on polluting behavior: Evidence from pollution

prevention programs and toxic releases. J Environ Econ Manage. 2012; 64(1):31–44.

24. Bennear LS. Are management-based regulations effective? Evidence from state pollution prevention

programs. J Policy Anal Manage. 2007; 26(2):327–48.

25. Sigman H. Cross-media pollution: Responses to restrictions on chlorinated solvent releases. Land

Econ. 1996; 72(3):298–312.

26. Fullerton D, Karney DH. Multiple pollutants, co-benefits, and suboptimal environmental policies. J Envi-

ron Econ Manage. 2018; 87:52–71.

27. Holland SP. Taxes and trading versus intensity standards: Second-best environmental policies with

incomplete regulation (leakage) or market power. National Bureau of Economic Research, Cambridge,

MA. 2009;NBER 15262. Available from: https://www.nber.org/papers/w15262

28. United States Environmental Protection Agency (EPA). Learn about the toxics release inventory.

United States Environmental Protection Agency, Washington, DC; 2018b. Available from: https://www.

epa.gov/toxics-release-inventory-tri-program/learn-about-toxics-release-inventory.

29. Correia S. A feasible estimator for linear models with multi-way fixed effects; 2016. Available from:

http://scorreia.com/research/hdfe .

30. Burbidge JB, Magee L, Robb AL. Alternative transformations to handle extreme values of the depen-

dent variable. J Am Stat Assoc. 1988; 83(401):123–27.

31. Hanna RN, Oliva P. The impact of inspections on plant-level air emissions. J Econ Anal Policy. 2010; 10

(1):Article-19.

32. United States Environmental Protection Agency (EPA). NAAQS table. United States Environmental

Protection Agency, Washington, DC; 2018c. Available from: https://www.epa.gov/criteria-air-

pollutants/naaqs-table.

33. United States Environmental Protection Agency (EPA). Nonattainment areas for criteria pollutants

(green book). United States Environmental Protection Agency, Washington, DC; 2018d. Available

from: https://www.epa.gov/green-book.

34. Walls & Associates. National establishment time-series (NETS) database: Database description; 2011.

35. United States Bureau of Labor Statistics (BLS). Local area unemployment statistics. United States

Bureau of Labor Statistics, Washington, DC; 2011. Available from: https://www.bls.gov/lau/#tables.

36. Harrington DR. Two-stage adoption of different types of pollution prevention (P2) activities. Res Energy

Econ. 2012; 34(3):349–73.

37. Chang C, Sam AG. Corporate environmentalism and environmental innovation. J Environ Manage.

2015; 153:84–92. https://doi.org/10.1016/j.jenvman.2015.01.010 PMID: 25687809

38. Baum CF, Stillman S, Schaffer ME. Enhanced routines for instrumental variables/GMM estimation and

testing. Stata J. 2007; 7(4):465–506.

39. Dutt N, King AA. The judgment of garbage: End-of-pipe treatment and waste reduction. Manage Sci.

2014; 60(7):1812–28.

40. King A. Retrieving and transferring embodied data: Implications for the management of interdepen-

dence within organizations. Manage Sci. 1999; 45(7):918–35.

41. Rothenberg S. Knowledge content and worker participation in environmental management at NUMMI. J

Manage Studies. 2003; 40(7):1783–1802.

42. Doshi AR, Dowell GW, Toffel MW. How firms respond to mandatory information disclosure. Strategic

Manage J. 2013; 34(10):1209–31.

43. Koutalakis C, Buzogany A, Börzel TA. When soft regulation is not enough: The integrated pollution pre-

vention and control directive of the European Union. Regul Gov. 2010; 4(3):329–44.

44. Organisation for Economic Co-operation and Development (OECD). Recommendation of the council

on integrated pollution prevention and control. Organisation for Economic Co-operation and Develop-

ment, Paris, France; 2018a;OECD/LEGAL/0256.

45. Organisation for Economic Co-operation and Development (OECD). Best available techniques for pre-

venting and controlling industrial pollution. Organisation for Economic Co-operation and Development,

Paris, France; 2018b.

46. Gaona SD. The utility of the toxic release inventory (TRI) in tracking implementation and environmental

impact of industrial green chemistry practices in the United States. Green Chemistry; 2018. Available

from: https://doi.org/10.5772/intechopen.70716

Pollution prevention

PLOS ONE | https://doi.org/10.1371/journal.pone.0224868 November 7, 2019 18 / 18

https://www.nber.org/papers/w15262

https://www.epa.gov/toxics-release-inventory-tri-program/learn-about-toxics-release-inventory

https://www.epa.gov/toxics-release-inventory-tri-program/learn-about-toxics-release-inventory

http://scorreia.com/research/hdfe

https://www.epa.gov/criteria-air-pollutants/naaqs-table

https://www.epa.gov/criteria-air-pollutants/naaqs-table

https://www.epa.gov/green-book

https://www.bls.gov/lau/#tables

https://doi.org/10.1016/j.jenvman.2015.01.010

http://www.ncbi.nlm.nih.gov/pubmed/25687809

https://doi.org/10.5772/intechopen.70716

https://doi.org/10.1371/journal.pone.0224868

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