Wang2020

Atmospheric Environment 231 (2020) 117522
Available online 21 April 2020
1352-2310/© 2020 Elsevier Ltd. All rights reserved.
The impact of environmental policy stringency on air quality
Ke Wang a
, Mingyi Yan a
, Yiwei Wang a
, Chun-Ping Chang b,*
a
School of Economics and Finance, Xi’an Jiao Tong University, Xi’an, Shaanxi, China
b
Shih Chien University, Kaohsiung, Taiwan
H I G H L I G H T S
� We analyze the impact of environmental policy strictness on air quality by using multi-national and multi-year data.
� The robustness of the model is tested from many methods, which ensures the reliability of the conclusion.
� We analyzed the reasons why EPS had no significant effect on PM2.5 emission, and enriched the relevant literature.
� We put forward two channels of EPS affecting emissions, and give the improvement direction of EPS in the future.
A R T I C L E I N F O
JEL classification:
Q53
Q58
Keywords:
Environmental policy stringency
Air quality
GMM
A B S T R A C T
Based on panel data of 23 OECD countries from 1990 to 2015, this research empirically analyzes the impact of
environmental policy strictness on air quality (proxied by PM2.5, CO2, NOx, and SOx emissions) and compares it
to PM2.5 exposure using the method of System Generalized Moments (SYS-GMM). We further test the robustness
of the empirical results by the methods of restricted samples period, cross-sectional regression, and Least Squares
Dummy Variable Corrected (LSDVC) model. Overall, we find that environmental policy stringency (EPS) has a
negative impact on CO2, NOx, and SOx emissions, while EPS presents a weak impact on both PM2.5 emissions and
PM2.5 exposure, due to two potential reasons: first, the causes of PM2.5 are complex, and environmental policies
are difficult to succeed; second, during the process of formulating the EPS composite index, there is no emphasis
on the policy of PM2.5 restrictions. Overall, our empirical results confirm the role of environmental policy
stringency and point out some shortcomings.
1. Introduction
Environmental pollution is actually a negative externality. To
internalize this externality and decrease pollutant emissions from citi
zens, enterprises, and governments, it is undoubtedly inevitable for
authorities to formulate appropriate environmental policies and laws
(Zheng et al., 2014). In fact, no matter how people, businesses, and
organizations go about limiting pollutant discharge, developing
renewable energy technology, or protecting clean water sources and
arable land, such measures to improve the environment cannot be
separated from actual participation by the government (Kalkuhl et al.,
2013; Bose, 2014; Feng et al., 2019). An environmental policy is one of
the main ways for any government to participate in environmental
governance (Chen et al., 2019; Chang et al., 2019).
Studies that evaluate environmental policies related to pollutant
emissions and air quality are often referred to as air pollution account
ability literature. The research has two basic frameworks: the classic
chain of accountability and the direct chain of accountability. The
classic chain mainly studies the impact of air quality regulations on
pollutant emissions and air quality after its promulgation and uses
health results as the final direction of research (Samet, 2014; Zigler
et al., 2016). Some studies have also extended the impact onto the
economic environment (Buehn and Farzanegan, 2013; Jin et al., 2016).
The main goal of accountability research is to explain the impact of
every link in the accountability chain from regulations to health through
the analysis of causality (if it exists) and ultimately to achieve the pur
pose of effective health improvement through the formulation of future
environmental regulations (Nagpure et al., 2014; Henneman et al.,
2019). However, during the process of accountability chain trans
mission, some confounding factors are encountered, resulting in them
and the impact of regulatory actions upstream of the accountability
chain to be not completely independent. For example, pollutant emis
sions are not only affected by relevant regulations, but also by other
factors such as the economic environment, forest coverage, and
* Corresponding author.
E-mail address: cpchang@g2.usc.edu.tw (C.-P. Chang).
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: http://www.elsevier.com/locate/atmosenv
https://doi.org/10.1016/j.atmosenv.2020.117522
Received 10 December 2019; Received in revised form 20 March 2020; Accepted 14 April 2020

2
renewable energy use (Pope et al., 2017; Borhan et al., 2018; Zheng
et al., 2019); while air quality is affected by some measurable major
sources of emissions (emissions from the energy sector) and is also
affected by other unpredictable factors such as car exhaust and chemical
reactions between pollutants (Shang et al., 2014; Tessum et al., 2014).
This makes it difficult to attribute changes in all aspects of the
accountability chain to changes brought about by environmental
regulations.
It can be imagined that the determination of causality between any
two links in the classic chain of accountability is full of challenges, let
alone trying to evaluate each link. Considering these issues, Zigler and
Dominici (2014) proposed that replacing the classic accountability
chain with a direct accountability chain has become a more operational
research framework. Direct accountability is a method to determine the
impact of regulatory behavior through statistical methods. It removes
unnecessary links in the classic accountability chain and directly ex
amines whether environmental regulations have changed air quality or
health, rather than focusing too much on the middle factors of the classic
chain. Although, like the classic chain, the direct chain is also disturbed
by various confounding factors, research tends to treat these factors as
control variables. Unmeasured or other confounding factors with less
influence are attributed to the model’s error term (Martenies et al.,
2015; Guan et al., 2016). The object of these studies is usually a single
region (Lin et al., 2013; Masona et al., 2019), and the scope of research is
usually the short-term impact of environmental policies (Estrella et al.,
2018; Wu et al., 2020). Few articles analyze the effects of environmental
policies from multiple countries in a region for many years. Our research
uses the direct accountability chain as the research framework and
through the EPS index system (Botta and Ko�
zluk, 2014) promulgated by
the OECD successfully estimates the impact of multiple years of regu
latory actions on pollution emissions in European and North American
countries, which have more stringent environmental policy standards.
Our hypothesis is that stringent environmental policies inhibit
pollutant emissions and positively impact the environment. Appendix 1
lists part of the instruments used in the EPS composite index, including
the energy sector and the broader economy. Based on these instruments,
we believe that the impact of stringent environmental policies on air
quality may come from two channels. The first one is the direct cost
channel, which is very simple for instruments like tax and emission
permits. Higher prices per unit of pollutants mean higher stringency,
because the incentive to reduce emissions increases as emission costs
rise. A similar explanation exists for lower (more stringent) emission
limits. In addition, instruments such as emission limits and maximum
allowable sulphur content in diesel fuel are also direct costs. The higher
the emission limits are, the lower is the policy strictness. Such a limit is
an artificial market constraint. Once they exceed the limitations, en
terprises will be fined, which greatly increase the opportunity cost of
excess emissions, so that enterprises’ emissions will be maintained at a
reasonable level. The second is the indirect cost channel, which corre
sponds to various subsidies, such as government spending on renewable
energy research and development, wind power tariff, solar power tariff,
and so on. Higher subsidies mean stricter environmental policies,
because they increase the opportunity cost of pollution and encourage
innovation in clean technologies.
Our research contributes to the fields of the environment and envi
ronmental policy in five aspects. First, we reveal the impacts of envi
ronmental policy stringency (EPS) on air quality, by estimating CO2,
NOx, SOx, PM2.5 emissions, and PM2.5 exposure, thus enriching the
literature on air quality and expanding it towards environmental policy.
Second, our research is based on the changes of environmental policy
strictness and pollutant emissions in 23 OECD member countries span
ning 26 years (1990–2015), which makes our conclusions more
persuasive and widely applicable. The approach we adopt is the System
Generalized Method of Moments (SYS-GMM) model.1
Third, our finding
is that EPS has a negative impact on CO2, NOx, and SOx emissions, while
it has a weak impact on both PM2.5 emissions and PM2.5 exposure in
these sample countries. We provide a representative case for countries
that encounter environmental problems and the effectiveness of their
environmental policies. Fourth, we conduct three robustness estima
tions: a restricted sample period from 1998 to 2015, using a new model
of Least Squares Dummy Variable Corrected (LSDVC), and dividing
countries through cross-sectional regression. All these estimations show
that the GMM model we use is robust. Through these tests, we partially
solve the endogenous problem, which shows that the model is robust
and the conclusion has statistical significance. Finally, we find that EPS
has a clear sustained impact on the environment, indicating that EPS is
still beneficial to air quality even in the long run.
The rest of the paper runs as follows. Section 2 introduces the data,
variables, and models. Specifically, we first present the data sources and
the basis of variable selection, next provide descriptive statistics for all
variables, and finally describe the model. Section 3 shows the empirical
results and the robustness test. Here, we put forward our main view that
EPS has a negative impact on pollutant emissions. The last section
summarizes the conclusions and puts forward suggestions for the
improvement of EPS.
2. Data and methodology
2.1. Data
2.1.1. Data source
The main purpose of this study is to find the impacts of environ
mental policy stringency (EPS) on environmental quality. Specifically,
we want to show whether EPS has a positive or negative effect on PM2.5,
CO2, NOx, and SOx emissions.2
Table 1 lists the definitions of all variables, including dependent
variables (PM2.5, CO2, NOx, and SOx emissions), independent variable
(EPS), and control variables (GDP, Forest Area, Renewable Energy, FDI,
Population Density, and Patent Applications), and indicates the data
sources. The data format used in this paper is panel data. Panel data have
advantages in solving the problem of missing variables, providing dy
namic information of observed objects, greatly increasing sample size,
and improving estimation accuracy (Hassan et al., 2011).
2.1.2. Variables
(1) Dependent variables
From the viewpoint of energy use and development, EPS mainly
considers policy instruments that can affect air quality, but does not care
about other forms of pollution, such as water pollution and solid waste
pollution. Therefore, we use several major pollutants, including PM2.5,
CO2, NOx, and SOx emissions, as the criteria for measuring air quality.
1
The sample countries herein include Austria, Belgium, Canada, Czech Re
public, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy,
Netherlands, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden,
Switzerland, Turkey, United Kingdom, and United States.
2
EPS (Botta and Ko�
zluk, 2014) is a composite index approach that measures
environmental policy stringency (EPS) in OECD countries. EPS has two indices:
the energy sector and the broader economy. The inherent logic of the definition
of environmental policy strictness is that if one environmental policy makes the
cost of polluting the environment (whether explicit cost or opportunity cost)
greater than another, then the strictness of this policy is considered to be
stronger.
K. Wang et al.

3
The main reasons for choosing these variables are as follows. First, they
cover most of the sources of air pollution (Kampa and Castanas, 2008;
Apte et al., 2015; Lelieveld et al., 2015), and thus the emissions of these
polluting gases can help us to accurately measure air quality. Second,
the instruments (CO2, NOx, SOx tax, CO2 emission permit, industrial
diesel tax, etc.) used to constitute the EPS composite index directly or
indirectly affect the dependent variables we use. For example, when a
country imposes a tax on CO2 emissions, the cost of CO2 emissions will
increase and CO2 emissions will decrease. Therefore, it is appropriate to
select these variables as dependent variables.
PM2.5 refers to particles of matter with a diameter of less than 2.5
μm. The concept used in this article includes only human-induced
emissions. Other PM2.5 produced by complex chemical changes are
difficult to accurately observe and are not in the scope of research. In
addition, except for the four countries of Czech Republic, Greece, Nor
way, and United States, which provided data to the OECD in response to
the questionnaire and comments from member states in 2019 (or sub
mitted reports to UNFCCC, CRF tables), the remaining 19 sample
countries’ emission data were officially submitted by Parties to the
Secretariat of the Convention on Long-Range Transboundary Air
Pollution (LRTAP Convention) of the UNECE-EMEP emissions database.
PM2.5 exposure used in this paper refers to population weighted
concentrations of fine particulates and to the population exposed to
concentration levels above WHO guideline values. The specific calcu
lation method is to weight the PM2.5 annual average concentration of
different regions (usually divided by urban and rural areas) by popula
tion and finally add them at the national level. The estimates are taken
from the Global Burden of Disease (GBD) 2017 project.
(2) Explanatory variables
In order to find the possible impact of EPS on air quality, this paper
uses EPS as the explanatory variable.
According to Brunel and Levinson (2016), there are three challenges
to measure the strictness of environmental policies. One is that it is
difficult to balance the multidimensionality of environmental regula
tions; the other is that there are problems in the identification (or
implementation) of strictness; and the third is the problem of missing
data. Concernning about the multidimensional nature of environmental
regulations, EPS uses a comprehensive index to measure the stringency
of environmental policies in OECD countries. The indicators used
include both economic incentives (fees, tariffs, permit, and caps/limits)
and necessary command and control regulations (for example, limit the
emissions of particulate matter, SOx, and NOx from large coal-fired
power plants). This makes EPS highly integrated.
The identification of strictness can be defined as the difficulty in
properly assessing the effect achieved (e.g. the reduction of emissions by
the sector or the improvement of air quality) and to what extent it can be
attributed to the strictness of environmental policies. These results may
be influenced by other confounding factors, such as the operations of
labor and financial markets. At the same time, due to the differences of
productivity, technology, and ecological environment in different re
gions, the strictness of policies may be affected by environmental
quality. For example, the terrain in Los Angeles is difficult to drain
pollutants quickly, while in some windy areas, it is easy to do so, which
means that more stringent policies are needed to ensure the same effect
in Los Angeles. In addition, we need to realize that there is a certain gap
between the strictness of the law and actual strictness. During the pro
cess of the policy changing into government behavior, it may vary by
time and by region, and the actual strength of tracking or punishing any
violation of the control regulations will change.
These issues make the construction of a reasonable comprehensive
index very challenging. The first is that the environmental policy chosen
needs to be widely representative. EPS mainly focuses on the power
sector, which makes important contributions to greenhouse gases and
pollutants. In OECD countries, this function is often performed by
electricity-generating firms, which often take combined heat and power
generation into account, and their emissions are rarely affected by other
activities apart from related environmental policies. At the same time,
most samples used herein are from OECD countries in Europe. Countries
exhibit high homogeneity in the sector’s environmental regulations in
order to distinguish the technical level according to the size of the fuel
and scale, and so it has regional comparability.
The last problem is missing data. Due to standards and technical
constraints, this problem is often prevalent in developing countries. The
countries and sectors included in EPS have avoided data loss much
better.
There are also six control variables used herein, which we present
below.
2.1.3. GDP
Borhan et al. (2018) discussed the impact of GDP on pollution, using
the data of ASEAN G8 from 1965 to 2010, and found that with the in
crease of GDP, the pollutant emissions will rise first, but when GDP
reaches a certain height, the pollutant emissions will start to decline
again. We can see that the economic level of a country does have a
certain impact on pollutant emissions. This paper uses the logarithmic
form of per capita GDP to express this impact.
2.1.4. Forest area
Vegetation offers the functions of adsorbing polluted gases and
particulates in air, purifying radioactive substances, disinfecting, and
sterilizing. Zheng et al. (2019) studied the relationship between PM2.5
exposure and forest coverage in 12 cities of Heilongjiang Province,
China and found that forest coverage has a negative impact on PM2.5
exposure - that is, the higher the forest coverage is, the poorer is the
PM2.5 exposure. Irga et al. (2015) noted that high forest coverage
provides better air quality, which is mainly reflected in the reduction of
particulate matter content in air leaks. In order to make the data com
parable, our paper uses forest area (% of land area) to express vegetation
coverage in different countries.
2.1.5. Renewable energy
Ever since the time of industrialization, primary energy such as coal,
oil, and natural gas is one of the main causes of air pollution (Pope et al.,
2017). Shafiei and Salim (2014) also empirically proved that using
renewable energy reduces CO2 emissions. Thus, we take the rate of
renewable energy consumption to clearly summarize the impact of energy
use on air quality.
Table 1
Variable definitions and data sources.
Variable Definition Source
PM2.5 Emissions in kilograms per capita/year OECD
PM2.5
Exposure
Exposure to PM2.5 (micrograms per m3
) OECD
CO2 Emissions in metric tons per capita/year OECD
NOx Emissions in kilograms per capita/year OECD
SOx Emissions in kilograms per capita/year OECD
EPS Environmental policy stringency in OECD
countries (a composite index approach)
OECD
GDP GDP per capita constant at 2010 US dollars World Bank.
WDI
Forest Area Forest area (% of land area) FRA
Renewable
Energy
Renewable energy consumption (% of total final
energy consumption)
IEA
FDI Foreign direct investment, net inflows (% of
GDP)
World Bank.
PPIPD
Population
Density
Population density (people per sq. km of land
area)
World Bank.
WDI
Patent
Applications
Patent applications, residents World Bank.
WDI
K. Wang et al.

4
2.1.6. FDI
According to Liu et al. (2018), we recognize that foreign direct in
vestment will bring pressure on the invested country’s wastewater and
CO2 emissions by increasing the number of factories, emissions of
transport vehicles, and fuel consumption. Thus, we introduce FDI (% of
GDP) as one of the control variables.
2.1.7. Population density
The relationship between population and environment has been
widely targeted by scholars. McMichael (2002) held that worldwide
population growth has caused great damage to the environment. Rah
man (2017) believed that the air quality of 11 Asian populous countries
was damaged by their growing population from 1960 to 2014, noting
that the long-term impact from an increase in population on CO2
emissions is unfavorable. In order to be comparable, this paper uses
population density to describe population.
2.1.8. Patent applications
Technological levels are often ignored in the analysis of environ
mental problems, but in fact, energy utilization rate, renewable energy
utilization rate, pollutant purification rate, wastewater recovery rate,
and other factors closely related to pollution discharge have deep-seated
relationships with such levels. It can be said that countries and regions
with more technology have stronger control over the environment
(Larkin et al., 2005). Our paper thus employs the logarithmic form of
patent applications to describe the technical level of a country. Tech
nological level impacts the environment through the energy utilization
rate, pollutant purification rate, wastewater recovery rate, etc. (Larkin
et al., 2005). Our paper employs the logarithmic form of Patent appli
cations to describe the technical level of a country.
2.2. Data description statistics
This paper uses two kinds of PM2.5, which are located in the first and
second rows of the table. According to the emission data of several kinds
of polluting gases, there are wide differences among countries, espe
cially in the standard deviations of NOx and SOx at 17.547 and 24.177,
respectively. The standard deviation of EPS is 0.916, and its highest
score is nearly 20 times greater than the lowest score, thus denoting that
the strictness of environmental policies in the countries also varies a lot.
In addition, we use the logarithmic form for both GDP and Patent Ap
plications. Table 2 provides more details about the variable presentation
of statistical results, which are not covered here.
2.3. Empirical method
The main aim of our study is to find the possible impact from EPS to
air quality, employing the panel data method over the period
1990–2015. Therefore, we set up the following panel data model:
EMi;t ¼ α0 þ α1EPSi;t þ γZi;t þ μi þ υi þ εi;t: (1)
Here, EM represents emissions of PM2.5, CO2, NOx, and SOx; EPS is
the main independent variable; and Z includes all control variables that
affect the environment. In this paper, Z specifically refers to GDP, Forest
Area, Renewable Energy, FDI, Population Density, and Patent Applica
tions; μi refers to the time fixed effect variable, υi refers to the regional
fixed effect variable; and εi;t refers to other influencing factors that are
not reflected in the model - that is, the error term.
The standard panel fixed effects model does not consider the po
tential endogeneity of some independent variables and the dynamic
specification of dependent variables, raising the potential that the esti
mation results may be inconsistent. To solve the problems of the fixed
effects model, Arellano and Bond (1991) proposed the difference GMM
estimator. However, the difference eliminates the non-observed cross-
section of individual effect and other variables that do not change with
time, and sometimes the lag order of variables is not an ideal tool var
iable, which results in the problem of being a weak tool variable
(Blundell and Bond, 1998; Bond et al., 2001). In order to solve the
problem of weak instruments, Arellano and Bover (1995) and Blundell
and Bond (1998) developed another GMM estimator, called the system
GMM. In the dynamic panel data model estimation process, the system
estimation method can overcome many disappointing features in the
general estimation method.
In this paper we use the two-step GMM estimation method and add
dynamic variables to better analyze the impact of EPS on air quality. The
reason why the two-step GMM is used is that the Sargan test of the one-
step GMM estimation does not consider the problem of hetero
scedasticity, and so the estimated value may be biased (Bond et al.,
2001). The system GMM estimate is:
EMi;t ¼ α0 þ α1EMi;t 1 þ α2EPSi;t þ γZi;t þ εi;t: (2)
In equation (2), EMi;t 1 represents the lagged value of the dependent
variable. The other variables have the same meaning as above.
3. Empirical results
3.1. Estimated results
Because of the condition of using the model, we need to check the
stability of the data before any estimation. We use three different
methods to test the unit root in Table 3, IPS, ADF, and PP, whereby most
variables are significant at least at 10% no matter which method is used.
The main reason for using these three methods is that the panel data we
use have missing values and are unbalanced. Based on the results, we
believe that the panel data used have no unit root and meet the re
quirements of the model.
Under the dotted line of Table 4 and Table 5 appear the results of the
Table 2
Descriptive statistics.
Variable Observations Mean Std Dev Min Max
PM2.5
(emissions)
545 7.645 11.396 0.75 71.092
PM2.5 (exposure) 250 14.342 5.744 6.086 30.489
CO2 598 8.484 3.541 2.3 20.3
NOx 598 31.190 17.547 4.727 95.146
SOx 598 23.717 24.177 0.769 169.687
EPS 550 1.811 0.916 0.208 4.133
GDP 595 10.332 0.622 8.614 11.425
Forest Area 588 31.429 15.762 6.749 73.689
Renewable
Energy
598 15.086 13.958 0.608 61.378
FDI 578 4.308 8.088 15.989 87.442
Population
Density
588 128.067 107.552 3.045 502.817
Patent
Applications
580 7.704 1.662 4.234 12.571
Table 3
Panel unit root tests.
Variable IPS ADF PP
PM2.5 8.339*** 4.483*** 3.832***
CO2 1.350* 7.245*** 4.150***
NOx 1.261 5.830*** 4.329***
SOx 8.830*** 1.438* 6.512***
EPS 1.980** 7.431*** 4.332***
GDP 6.412*** 3.316*** 1.627*
Forest Area 2.317** 4.874*** 2.728**
Renewable Energy 2.370*** 1.644* 4.935***
FDI 7.068*** 1.656** 14.446***
Population Density 2.623*** 7.690*** 1.880**
Patent Applications 1.635* 3.160*** 6.241***
Note: *p < 0.1, **p < 0.05, ***p < 0.01.
K. Wang et al.

5
Arellano–Bond test of first-order autocorrelation (AR1), Arellano–Bond
test of second-order autocorrelation (AR2) and Sargan tests. The original
assumption of AR(1) is that there is no autocorrelation. We see that all
variables in Table 4 or Table 5 are significant at least at the 10% level.
This denotes a rejection of the original assumption, the first-order dif
ference of error terms is correlated, and it is necessary to consider the
dynamics of dependent variables. The original assumption of AR(2) is
that there is no autocorrelation. We see that all variables are insignifi
cant whether in Table 4 or Table 5, implying that the higher-order dif
ference of error terms is not correlated with the original assumption, and
so the results are consistent. Finally, the results of the Sargan test in
Tables 5 and 6 are insignificant in most cases, indicating that there is no
over-recognition of tool variables. These tests further illustrate the
reliability of the results.
The dependent variable we use is emissions of PM 2.5, CO2, NOx, and
SOx respectively, and the main explanatory variable is EPS. We also
show the results of PM2.5 exposure in Table 5. Due to limitation of data
availability on PM2.5 exposure, different sample sizes are used in
Table 5.
Starting from Table 4, the coefficient of EPS shown in column (1) is
not significant, which is different from our assumption, meaning that
PM2.5 emissions are not affected by EPS. This result runs contrary to the
literature, in which PM2.5 emissions are influenced by rigid environ
mental policies, although environmental policies vary for different re
gions (Fann et al., 2012; Chen et al., 2014; Lurmann et al., 2015). The
second, third, and fourth columns report the estimated findings of CO2,
NOx, and SOx emissions, respectively. The results show that the co
efficients for EPS are significantly negative at least at the 1% level. This
means that EPS has a negative impact on CO2, NOx, and SOx emissions
and confirms that a stringent environmental policy improves air quality
in our sample countries (Castellanos and Boersma, 2012; Boyce and
Paster, 2013; Liu et al., 2018).
We are interested in the reasons why environmental policy strin
gency presents weak effects on PM2.5. According to Bell et al. (2007)
and Xing et al. (2016), we find that PM2.5 refers to particles in the air
smaller than or equal to 2.5 μm. It is a general term for a class of sub
stances, but not a single substance. It includes sulfate, nitrate, ammo
nium salt, organic compound, elemental carbon, etc. Unlike CO2, NOx,
and SOx, PM2.5 comes from not only natural or human pollution sour
ces, but is also produced by complex physical and chemical reactions of
gaseous substances (like NOx, SOx, and VOCs) in the atmosphere.3
According to Wang et al. (2008), the main sources of PM2.5 in China
are industry, vehicle pollution, dust pollution, biomass burning, and
others, covering most areas of production and life, and the main pollu
tion sources vary among different regions. We find that this conclusion is
consistent with the results of Saliba et al. (2010) in the Mediterranean
region, who showed that the causes of PM2.5 are very complex, and the
effects of the various causes are relatively average. Therefore, it is
difficult to find a policy for PM2.5 emissions that covers a wide range of
applications, inducing restricted policy strictness.
According to the definitions of PM 2.5 emissions and PM2.5 expo
sure, we believe the former mainly measures PM2.5 produced by direct
emissions, while the latter includes not only PM2.5 directly produced,
but also PM2.5 indirectly formed by complex physical and chemical
reactions in the atmosphere. The PM2.5 generated by direct emissions in
Table 4 is not yet affected by EPS, while PM2.5 exposure with more
complex causes is clearly not affected by EPS. The results of the first
column of PM2.5 exposure in Table 5 confirm our conjecture that the
EPS coefficient is not significant, indicating that the current indicators of
EPS in OECD countries are not binding on PM2.5. The second, third, and
fourth columns of Table 5 show the results when the dependent variable
is CO2, NOx, and SOx, respectively, in which the coefficients of EPS are
significantly negative at least at the 10% level. It means that EPS has a
negative impact on CO2, NOx, and SOx emissions, thus confirming once
again the view that the stricter environmental policies are, the less
emissions there will be from CO2, NOx, and SOx.
Fig. 1 shows the spatial distribution of EPS and various emissions in
Table 4
Results of SYS-GMM (PM2.5 emissions).
Variable (1) (2) (3) (4)
PM2.5 CO2 NOx SOx
L.PM2.5 0.972***
(116.600)
L.CO2 0.950***
(48.942)
L.NOx 0.881***
(56.494)
L.SOx 0.901***
(65.163)
EPS 0.122 0.163*** 1.585*** 1.067***
(1.356) (-3.626) (-6.446) (-3.276)
GDP 0.128 0.314** 2.863*** 0.381
(0.438) (2.507) (4.647) (0.572)
Forest Area 0.017 0.005 0.018 0.004
(-1.305) (0.995) (-0.959) (-0.139)
Renewable Energy 0.017 0.017** 0.029 0.191***
(1.351) (-2.502) (1.182) (4.764)
FDI 0.045*** 0.016** 0.073*** 0.002
(2.896) (-2.028) (-2.764) (-0.050)
Population Density 0.002 0.001 0.008*** 0.027***
(-0.952) (-1.327) (-2.616) (-4.416)
Patent Applications 0.007 0.036 0.043 0.251
(-0.097) (-0.752) (-0.341) (-1.211)
AR(1) (P-value) 0.000 0.000 0.000 0.000
AR(2) (P-value) 0.404 0.360 0.249 0.782
Sargan (P-value) 0.000 0.731 0.121 0.000
N 455 500 500 500
Notes: t statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.
Table 5
Results of SYS-GMM (PM2.5 exposure).
Variable (1) (2) (3) (4)
PM2.5 CO2 NOx SOx
L.PM2.5 0.737***
(17.729)
L.CO2 0.821***
(13.253)
L.NOx 0.530***
(13.344)
L.SOx 0.536***
(11.797)
EPS 0.322*** 0.134* 1.922*** 2.956***
(3.867) (-1.849) (-4.427) (-2.722)
GDP 0.642*** 0.134 10.005*** 3.733***
(4.437) (1.413) (11.393) (3.415)
Forest Area 0.079*** 0.058** 0.968*** 1.751***
(3.256) (2.298) (-6.478) (-5.686)
Renewable Energy 0.157*** 0.065*** 0.285** 0.211
(-6.528) (-3.205) (-2.347) (0.717)
FDI 0.010 0.008 0.001 0.093
(0.846) (0.775) (0.026) (-0.841)
Population Density 0.002 0.003 0.090*** 0.048*
(0.724) (-1.388) (-5.289) (-1.723)
Patent Applications 0.534*** 0.011 4.974*** 3.833***
(-3.334) (0.082) (-8.665) (3.074)
AR(1) (P-value) 0.003 0.015 0.006 0.090
AR(2) (P-value) 0.711 0.178 0.311 0.560
Sargan (P-value) 0116 0.961 0.934 0.939
N 172 172 172 172
Notes: t statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Due to
incomplete data of PM2.5 exposure, there is a smaller sample size than Table 4.
3
VOCs are not a single pollutant, but a general term of some pollutants.
There are many definitions of VOCs. WHO defines VOCs as various organic
compounds with a boiling point of 50 �
C–260 �
C at room temperature.
K. Wang et al.

6
Fig. 1. Distribution of EPS and emissions.
K. Wang et al.

7
the form of averages (excluding the two non-European countries of the
United States and Canada in the samples) in order to observe the rela
tionship more intuitively. We note that the main pollution problems
faced by the countries are different. There are countries with plural
pollution sources (Finland and Czech Republic) and also countries with
single pollution sources (Turkey).
From the distribution of geographical location, we can see that the
countries with higher EPS in P1 are mainly in the west and south of
Europe, such as Germany, France, Switzerland, Italy, and United
Kingdom, while the EPS values of eastern and northern European
countries are lower. The distribution of CO2 and NOx in P3 and P4 shows
that the main emissions are concentrated in the north of Europe (Finland
and Norway). P5 presents that SOx is mainly distributed in Poland,
Czech Republic, Turkey, Greece, and other countries in eastern Europe.
Combined with our empirical results, we confirm once again that EPS
has a certain impact on CO2, NOx, and SOx and plays a certain role in
controlling both compound-source countries and single-source coun
tries. The distribution of PM2.5 in P2 shows that there are countries with
high PM2.5 emissions in the north (Norway), middle (Czech Republic),
east (Slovak Republic), and west (Portugal) of Europe. Therefore, the
relationship between EPS and PM2.5 can be simply verified by observing
the distribution map of PM2.5 emissions. There is no special relationship
between EPS and PM2.5.
3.2. Robustness
In order to prove more evidence to support our findings, this paper
uses three different methods in Table 6 to test the robustness: restricted
samples period (1998–2015), a new method (LSDVC-BB), and sub-
samples obtained by cross-sectional regression.4
In Panel A, we take sub-sample data of a period of time (1998–2015)
for a robustness test. There are two reasons for choosing this period.
First, the Kyoto Protocol was formally initiated at the end of 1997, which
was the first time that countries globally agreed to legally restrict
greenhouse gas emissions. We deem that the environmental policies of
countries that have signed the Kyoto Protocol must be stricter and more
standardized than those before it (Kelemen, 2010; Almer and Winkler,
2017). Choosing this period can also help us to better observe the
effectiveness of EPS. Second, due to data availability, the variables of
PM2.5 and Patent Applications in some countries have missing observa
tions during 1990–1997. In order to obtain more balanced panel data,
we have reason to estimate the data after 1998. After removing the part
from 1990 to 1997, we are able to present more complete data. We find
that, except for PM2.5, the coefficients of EPS are significantly negative
at least at the 5% level, which is consistent with the results of the whole
sample. This indicates that EPS has inhibitory effects on CO2, NOx, and
SOx during the period 1998–2015.
In panel B, we use the LSDVC model to estimate the results in the case
of the full samples. The dynamic GMM estimators we employ are
asymptotically consistent, but have a relatively large variance in finite
samples compared with the standard LSDVC estimator. In the case of a
small sample size, the SYS-GMM estimate may cause a weak instru
mental variable problem, resulting in a biased estimation result (Soto,
2009). Thus, we estimate the models of LSDVC (BB), which represent the
bias corrected estimates initialized by Blundell and Bond (1998). The
results again show that except for PM2.5, the coefficients of EPS are
significantly negative at least at the 5% level, which meets our expec
tation, and this is consistent with the results of the whole samples in
Table 4. At the same time, the estimation of a long-run relationship is
also basically consistent with Table 4.
In panel C, the robustness estimation which we use is cross-sectional
regression. We first calculate the cross-sectional regression coefficients
of all samples and choose Austria, Canada, Greece, Norway, Portugal,
Spain, and Turkey according to the symbol of the CO2 coefficient (the
CO2 coefficients of the selected countries are all positive, which help
avoid the estimation deviation caused by different observation objects).
Overall, panel C shows the final results, which are similar to those of
Panel A and Panel B. The robustness of the results is thus supported. We
conclude that EPS has a negative impact on CO2, NOx, and SOx emis
sions, but not for both PM2.5 and its exposure.
According to Appendix 1, we observe that in the process of EPS ac
counting, there are renewable energy indicators related to wind energy
and solar energy. As an incentive, a government can subsidize the price
of electricity generated by wind or solar energy. Therefore, in order to
avoid the amplification or weakening of the estimated results of the
model by Renewable Energy in the control variables, the robustness test in
panel D eliminates it. The final results are basically the same as the other
three robustness tests, indicating that EPS has a restraining effect on
pollutant emissions.
In the transportation microenvironment, personal PM2.5 exposure is
higher than the normal level (Adams et al., 2001). Therefore, when
considering the impact of EPS on PM2.5 exposure, we need to select
samples with consistent transportation conditions for comparison and
eliminate the differences caused by transportation conditions. We
collect inland passenger transport data of 21 sample countries (including
road transport and railway transport, passenger kilometers, Millions),
excluding seven countries with significantly higher transport volume
than the other samples, and conduct a robustness test on the remaining
samples. The results show that even by considering the transportation
environment, EPS still has no significant impact on PM2.5, which in
dicates that the structure of EPS does not cover the policy of restricting
PM2.5 well, and further improvement is needed in the future.5
Finally, in order to make the article more credible, we use the
Environmental Performance Index (EPI) as another explanatory variable
to compare the estimate of EPS (because of the problem of data avail
ability, we use EPI data from 2001 to 2015). The results show at least at
the 1% level that the growth of environmental performance has a
negative impact on CO2, NOx, and SOx emissions, but the effect on PM2.5
emissions is still not significant. This consistent with the results of the
EPS estimation, which indicates that the practice of environmental
policy (whether EPS or EPI) has an inhibitory effect on pollutant emis
sions. As for the reason why these two indices have no significant impact
on PM2.5, we speculate that there are two possibilities. First, the gen
eration source of PM2.5 is complex. At present, the data monitored in a
large area are basically PM2.5 of human emissions. There are no effec
tive statistics for the generated part of the combination, resulting in data
distortion; second, the policy included in the index has an insufficient
binding force on PM2.5, which will be another direction to further
improve the index.6
4
The LSDVC estimator is a small sample deviation method similar to the
LSDV estimator. The principle is that when the fixed effect estimator is used to
estimate the coefficients of the dynamic panel model, the deviation tends to
zero with a decrease of T. Therefore, the LSDV estimator can be used to estimate
the dynamic panel model first, and then the existing deviation can be sub
tracted to obtain the corrected estimator. Bun and Kiviet (2003) and Bruno
(2005) believed that LSDVC is more stable and effective than GMM. LSDVC is
currently used in energy research, employment, and other fields (Chang and
Berdiev, 2011; Bogliacino et al., 2012).
5
Due to the lack of Austria and Ireland data, we only find inland passenger
transport data of 21 sample countries. The seven countries excluded are Can
ada, France, Germany, Italy, Spain, United Kingdom, and United States.
6
EPI is a project supported by the McCall MacBain foundation and carried
out by Yale University Center for Environmental Law & Policy in cooperation
with the World Economic Forum. The purpose of EPI is to use quantitative
indicators to explain the government’s performance in a series of pollution
control and natural resource management challenges, so as to identify the
success or failure of environmental policies. This is similar to the functions and
objectives of EPS.
K. Wang et al.

8
Table 6
Robustness analysis.
Panel A: Restricted samples period (1998–2015)
Variable (1) (2) (3) (4)
PM2.5 CO2 NOx SOx
L.PM2.5 0.919***
(58.313)
L.CO2 0.684***
(18.709)
L.NOx 0.952***
(40.046)
L.SOx 0.865***
(31.803)
EPS 0.269*** 0.126** 0.651*** 1.125***
(3.186) (-2.524) (-3.058) (-3.050)
N 325 333 333 333
Panel B: LSDCV-BB
L.PM2.5 0.907***
(56.001)
L.CO2 0.718***
(23.819)
L.NOx 0.946***
(63.64)
L.SOx 0.915***
(75.986)
EPS 0.017 0.109** 0.526*** 0.478
(-0.242) (-2.181) (-2.653) (-1.358)
Long-run effect 0.183 0.385** 9.760** 5.670
(-0.251) (-2.080) (-2.348) (-1.362)
N 428 473 473 473
Panel C: Cross-sectional Regressions
L. PM2.5 0.919***
(31.447)
L.CO2 0.718***
(13.516)
L.NOx 0.890***
(32.706)
L.SOx 0.827***
(22.363)
EPS 0.151 0.075* 1.416*** 3.440***
(0.961) (-1.783) (-5.305) (-5.139)
N 129 159 159 159
Panel D: Without Renewable Energy
Variable (1)
PM2.5
(2)
CO2
(3)
NOx
(4)
SOx
L. PM2.5 0.979***
(96.916)
L.CO2 0.947***
(36.341)
L.NOx 0.941***
(56.987)
L.SOx 0.912***
(58.033)
EPS 0.248*** 0.158*** 0.584*** 0.796**
(3.054) (-3.425) (-3.163) (-2.127)
N 455 500 500 500
Panel E: Countries Without a High Level of Inland Passengers
L. PM2.5 0.764***
(14.495)
L.CO2 0.853***
(18.086)
L.NOx 0.831***
(22.637)
L.SOx 0.750***
(19.911)
EPS 0.016 0.019 0.745** 1.410**
(0.131) (0.122) (-1.995) (-2.076)
N 165 175 175 175
Panel F: Environmental Performance Index (EPI)
L. PM2.5 0.995***
(119.283)
(continued on next page)
K. Wang et al.

9
4. Conclusions and policy implications
The aim of our research is to find the possible internal relationship
between EPS and air quality. We use panel data from 23 OECD countries
from 1990 to 2015 to estimate the SYS-GMM model with PM2.5 expo
sure and PM2.5, CO2, NOx, and SOx emissions as dependent variables, as
well as the EPS composite index as an explanatory variable. The results
show that EPS has a negative impact on CO2, NOx, and SOx in the 23
sample countries. The stricter the environmental policy is, the lower the
emissions are.
We conversely find that EPS has a weak impact on both PM2.5
emissions and PM2.5 exposure in order to see why EPS fails to affect
PM2.5 emissions. First, the causes of PM2.5 are complex, the main
sources of pollution vary greatly for different places, and environmental
policies are difficult to succeed. Second, in the process of formulating the
EPS composite index, there is no emphasis on the policy of PM2.5 re
striction, and only particulate matter emission limits of newly built coal-
fired power plants are included in the index system, which cannot meet
the demand for PM2.5 suppression. Considering the obvious effect of
EPS on CO2, NOx, and SOx, it is suggested that the construction idea of
EPS is actually correct.
Due to the lack of policy indicators related to PM2.5 in EPS, and
transportation is one of the main sources of PM2.5, it is suggested to
improve EPS through the following two ways: first, increase the strict
ness indicators of policies related to oil supply, fuel vehicle emissions,
etc.; and second, reduce emissions through subsidies, such as those for
more environmentally friendly gas vehicles or electric vehicle
infrastructure.
We propose herein two channels about how EPS affects air quality:
direct cost channel and indirect cost channel. Based on these two
channels, we believe that a country can utilize different ways to improve
the environment. The first is the direct cost channel. In addition to
levying taxes on various emissions and selling emission permits, a spe
cial regulatory authority can be set up to impose fines on enterprises
with excess emissions and to show the strictness of the policy according
to the proportion of fines. Second, for the indirect cost channel, gov
ernments can subsidize the emissions of pollutants or the production of
renewable energy and further encourage businesses to increase con
sumption of pollution purification and renewable energy production,
thus taking a more active role in improving the environment.
We arrive at the conclusion that stricter policies are more conducive
to air quality, but there are still some limitations. First of all, we do not
use positive environmental indicators such as negative oxygen ion
content, because many of the sample countries’ data are missing from
these indicators. In future research, we will pay special attention to this
aspect. Secondly, while only the current policy strictness index has no
significant impact on PM2.5, we have failed to provide detailed rec
ommendations on the improvement of EPS, which can be improved in
future research. Finally, most of the samples used are OECD countries in
Europe and North America, and therefore there is a lack of discussion on
Asian and other developing countries where environmental issues are
more prominent. The cause of this problem is mainly due to the data
availability in Asian countries. For example, China began to use TPACE-
P emission inventories in 2003, employs large-scale statistics of SO2,
NOx, CO, CH4, and other indicators, and PM, VOCs, and Hg emissions
statistics have only appeared in the past 10 years (in 2015, the revised
Air Pollution Control Law included particulate matter, volatile organic
compounds, nitrogen oxides, and greenhouse gases into the scope of air
pollution supervision and management). Nonetheless, the findings of
this paper still have certain applicability in these countries, because the
control channels and principles of pollutant policies in different coun
tries’ environmental policies are the same. Although the standards for
the definition of pollution emissions are not uniform in different coun
tries, the principles of their environmental policies are the same.
Through the way of expanding tax revenue, the cost of enterprise
emissions will increase, and some command regulations will also in
crease the risk of illegal emission enterprises being reported. In addition,
subsidies for clean energy will also stimulate the process of energy
substitution. All these measures will curb the emission of pollutants.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
CRediT authorship contribution statement
Ke Wang: Data curation, Formal analysis, Writing - original draft.
Mingyi Yan: Investigation,Conceptualization. Yiwei Wang: Formal
analysis, Investigation, Methodology. Chun-Ping Chang: Writing - re
view & editing, Supervision, Validation, Visualization.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosenv.2020.117522.
Table 6 (continued)
Panel A: Restricted samples period (1998–2015)
Variable (1) (2) (3) (4)
PM2.5 CO2 NOx SOx
L.CO2 0.883***
(21.326)
L.NOx 0.904***
(41.933)
L.SOx 0.909***
(40.813)
EPI 0.013 0.298*** 0.109*** 0.134***
(1.181) (-3.334) (-4.448) (-3.685)
N 316 316 316 316
Notes: The control variables are not reported, but are available upon request. The values in parentheses denote the t statistics. *p < 0.1, **p < 0.05, ***p < 0.01.
K. Wang et al.

10
Appendix 1. Instruments Used in the EPS Composite Index
Instruments included in the energy sector indicator
(1)
Instrument
(2)
Definition
(3)
Direct or indirect
Renewable Energy Certificates Trading Scheme % of renewable electricity that has to be procured annually ◇
Energy Certificate Emission Trading Scheme % of electricity saving that has to be delivered annually ◇
CO2 Tax Tax rate in EUR/ton ◇
NOx Tax Tax rate in EUR/ton ◇
SOx Tax Tax rate in EUR/ton ◇
Feed-In Tariff for Wind EUR/kWh ○
Feed-In Premium for Wind EUR/kWh ○
Feed-In Tariff for Solar EUR/kWh ○
Feed-In Premium for Solar EUR/kWh ○
Particulate Matter Emission Limit Value Value of Emission Limit in mg/m3
◇
SOx Emission Limit Value Value of Emission Limit in mg/m3
◇
NOx Emission Limit Value Value of Emission Limit in mg/m3
◇
Government R&D Expenditures for Renewable Energy Technologies Expressed as % of GDP ◇
Broader economy sector indicator
Tax on diesel for industry Total tax for a liter of diesel used in transport for industry ◇
Maximum content of sulphur allowed in diesel Value dictated by the standard ◇
Notes: “◇” in column (3) indicates that the corresponding instrument in column (1) has a direct impact on air quality. “○” indicates that the impact is indirect.
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