The Role of Industrial Structure Upgrading in Moderating the Impact of Environmental Regulation on Air Pollution: Evidence from China

Abstract

Air pollution is an important factor affecting human health and daily life. The Chinese government is making vigorous efforts to control air pollution. The upgrading of the industrial structure is a problem-solving tool in the environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of the industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderating effect model are used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The analysis of the results provides the following findings. Firstly, environmental regulations can significantly reduce emissions, but the deterioration of air quality does not have a significant impact on the improvement of environmental regulations. Secondly, industrial structure upgrading can reduce air pollution, but the worsening of the air quality will hinder the upgrading of industrial structures. Thirdly, environmental regulation can promote industrial structure upgrading. Lastly, industrial structure upgrading is a moderating variable and can positively moderate the impact of environmental regulations on air pollution.

Full text

Citation: Liu, Y.; Xie, C.-N.; Wang,
Z.-S.; Rebai, N.-E.H.; Lai, X.-M. The
Role of Industrial Structure
Upgrading in Moderating the Impact
of Environmental Regulation on Air
Pollution: Evidence from China.
Atmosphere 2023,14, 1537. https://
doi.org/10.3390/atmos14101537
Academic Editors: Daren Chen
and Haider A. Khwaja
Received: 7 July 2023
Revised: 10 September 2023
Accepted: 26 September 2023
Published: 8 October 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
atmosphere
Article
The Role of Industrial Structure Upgrading in Moderating the
Impact of Environmental Regulation on Air Pollution: Evidence
from China
Yu Liu 1, Chun-Ni Xie 1, Zi-Shuang Wang 1, Noure-El Houda Rebai 1and Xiao-Min Lai 2,*
1Wuhan Institute of Technology, College of Management, Wuhan 430205, China; lyu429@163.com (Y.L.);
xcn1028@163.com (C.-N.X.); ziwang1022@163.com (Z.-S.W.); rebai.noure@gmail.com (N.-E.H.R.)
2Wuhan Institute of Technology, Law and Business School, Wuhan 430205, China
*Correspondence: xiaomin.lai@foxmail.com; Tel.: +86-15827277086
Abstract:
Air pollution is an important factor affecting human health and daily life. The Chinese
government is making vigorous efforts to control air pollution. The upgrading of the industrial
structure is a problem-solving tool in the environment and economic growth cases. This paper aims to
explore the relationships among environmental regulation, the upgrading of the industrial structure
and air pollution. The PVAR (Panel Vector Auto Regression) model and moderating effect model
are used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to
2020. The analysis of the results provides the following findings. Firstly, environmental regulations
can significantly reduce emissions, but the deterioration of air quality does not have a significant
impact on the improvement of environmental regulations. Secondly, industrial structure upgrading
can reduce air pollution, but the worsening of the air quality will hinder the upgrading of industrial
structures. Thirdly, environmental regulation can promote industrial structure upgrading. Lastly,
industrial structure upgrading is a moderating variable and can positively moderate the impact of
environmental regulations on air pollution.
Keywords:
air pollution; environmental regulation; industrial structure; PVAR model; moderating effect
1. Introduction
According to the Global Environmental Performance Index (EPI) in 2020, the ranking
of China’s air quality is 137th among 180 countries. Since launching its open-door policy
and economic reform, China has experienced spectacular economic growth. However,
the conventional path of economic growth has caused unprecedented environmental
pollution and health risks [
1
,
2
]. The traditional economic growth model has caused resource
exhaustion and makes sustainable development difficult [
3
]. Frequent air pollution has
a significant impact on human health [
4
,
5
]. It has been shown that air pollution has
a serious impact on the general public and has become a major bottleneck for China’s
sustainable development [
6
]. In recent years, China’s government has attached great
importance to increasing environmental investment and promoting the upgrading of the
industrial structure. As the largest developing country, China’s environmental pollution
problem is universal and representative of the process of economic development and
construction. Recently, the discussion on the relationships among environmental regulation,
the upgrading of the industrial structure and air pollution in the academic field has been
getting heated.
There are three viewpoints concerning the impact of environmental regulation on air
pollution according to the previous literature. Firstly, environmental regulation is helpful
for reducing air pollution. Using the province-level [
7
] and prefecture-level [
8
] panel
data, some studies found that environmental regulation can suppress air pollution [9]. By
constructing difference-in-difference models, Zhang et al. found that the establishment
Atmosphere 2023,14, 1537. https://doi.org/10.3390/atmos14101537 https://www.mdpi.com/journal/atmosphere

Atmosphere 2023,14, 1537 2 of 15
of pilot zones for green finance reform and innovation (PZGRI) can reduce industrial
energy consumption and emissions [
10
]. Secondly, environmental regulation will cause
the deterioration of air pollution [
11
]. Hao et al. [
12
] used the first difference GMM
(generalized method of moments) method to explore the relationship and found that current
environmental regulation has not achieved the goal of controlling pollution. Thirdly, there
is a non-linear relationship between environmental regulation and air pollution [
13
]. In
addition, air pollution is an important consideration in the development of environmental
regulation. Baumol suggested that the predetermined environmental tax needs to be
adjusted in accordance with the pollution situation [
14
]. Theoretically, the optimal rate
of environmental tax on a particular activity is equal to the marginal social damage it
generates [15].
The literature on environmental regulation and industrial structure upgrading mainly
focuses on three aspects. To begin with, the “following costs” hypothesis posits that
environmental regulation can increase the additional costs of enterprises, squeeze out
profits and inhibit the upgrading of the industrial structure [
16
]. Then, Porter ’s hypothesis
finds that environmental regulation can stimulate the vitality of innovation and promote
the upgrading of the industrial structure. However, some of the literature holds that there
is a non-linear U-shaped relationship between them [17,18].
A number of research studies have been conducted on the connection between indus-
trial structure upgrading and air pollution. However, the influence mechanism of industrial
structure upgrading on air pollution is still unclear [
19
]. Through the construction of static
and dynamic spatial econometric models, Ma et al. [
20
] found that the optimization and
rationalization of industrial structures can significantly improve air quality. By constructing
a spatial econometric model, Yang et al. [
21
] concluded that industrial structure upgrading
can reduce carbon emissions by improving green total factor productivity. However, Feng
et al. [
22
] obtained the opposite conclusion. Feng tried to explore the effect of industrial
structure upgrading on carbon emissions in China, using the traditional OLS (Ordinary
Least Square) model and the dynamic SYS-GMM (System Generalized Method of Moments)
model. In addition, air pollution negatively impacts the fixed investment and innovation
activities of enterprises [
23
] and then affects the upgrading of the industrial structure [
24
].
According to the previous literature, there is considerable interest in the relationships
between environmental regulation, the upgrading of the industrial structure and air pol-
lution. However, few scholars systematically analyze the mechanism among them. This
study systematically explores the internal mechanisms underlying the relationship between
environmental regulation, industrial structure upgrading and air pollution by taking them
into the same analytical framework. The first major contribution is that the study uses the
PVAR model to explore the short-term and long-term interaction relationships between
environmental regulation, the upgrading of the industrial structure and air pollution. The
second major contribution is that this study uses the moderating model to explore the
possible moderating effect of industrial structure upgrading on the relationship between
environmental regulation and air pollution.
2. Theoretical Analysis and Research Hypothesis
This section explores the direct effect of environmental regulations on air pollution
and the moderating effect of industrial structure upgrading, as shown in Figure 1.
The negative externalities of environmental problems and unclear environmental
property rights lead to market failure in pollution control [
25
], which makes air pollution
control more dependent on the government. Environmental regulation is an effective
way for governments to control air pollution. Environmental regulation can directly
affect air pollution [
7
]. Environmental regulation will increase the costs of enterprises
and induce innovations, thus reducing air pollution [
26
,
27
]. Environmental regulation
can indirectly affect air pollution through foreign direct investment (FDI) [
7
]. Improving
environmental regulation can attract multinational companies with advanced technologies.

Atmosphere 2023,14, 1537 3 of 15
Foreign companies can introduce more clean technologies and abundant capital to the host
country, resulting in the “pollution halo effect”, thus reducing air pollution.
Atmosphere 2023, 14, x FOR PEER REVIEW 3 of 16
regulation can aract multinational companies with advanced technologies. Foreign com-
panies can introduce more clean technologies and abundant capital to the host country,
resulting in the “pollution halo eect”, thus reducing air pollution.
The rst hypothesis is proposed based on the above analysis.
H1: The improvement of environmental regulation can eectively promote the reduction in air
pollution.
Previous studies have explored the relationship between environmental regulation,
industrial structure upgrading and air pollution. When exploring the relationship be-
tween environmental regulation and air pollution, some studies consider industrial struc-
ture upgrading as a control variable and have found that it can reduce air pollution [28].
Du and Chen [29] take industrial concentration as a mediating variable and nd that en-
vironmental regulation can reduce the density of air pollution through promoting indus-
trial concentration. Industrial structure upgrading may act as a moderating variable and
can positively moderate the impact of environmental regulation on air pollution.
Industrial structure upgrading can enhance the impact of environmental regulation
on air pollution. Through promoting technological innovation [30], driving the transfor-
mation of production and promoting the upgrading of consumption [31], industrial struc-
ture upgrading can lead enterprises to develop in a green way.
Based on the above analysis, this study proposes the second research hypothesis:
H2: The upgrading of the industrial structure can positively moderate the eect of environmental
regulation on air pollution.
Figure 1. The impact paths of environmental regulation on air pollution.
3. Methods
3.1. Model Specications
The time-series vector autoregression (VAR) model was regarded as an alternative to
multivariate simultaneous equation models initially [32]. All variables in a VAR model are
treated as endogenous, which can eectively show the relationship among the variables.
Newey et al. introduced VAR in a panel-data seing and the panel VAR model has been
used in multiple applications across elds [33]. Further developed by Love and Zicchino
[34], the PVAR model has been widely used in the elds of economy, policy and industrial
structure. The PVAR model analyzes the dynamic relationships between variables
through generalized matrix estimation (GMM) and impulse response function (IRF).
Monte Carlo is a part of the impulse response function, which is used to generate the 5%
error bands.
The PVAR model is used to analyze the dynamic relationships between environmen-
tal regulation, industrial structure upgrading and air pollution from an independent per-
spective. This study uses the moderating eect model to explore the underlying
Figure 1. The impact paths of environmental regulation on air pollution.
The first hypothesis is proposed based on the above analysis.
H1:
The improvement of environmental regulation can effectively promote the reduction in air
pollution.
Previous studies have explored the relationship between environmental regulation,
industrial structure upgrading and air pollution. When exploring the relationship between
environmental regulation and air pollution, some studies consider industrial structure
upgrading as a control variable and have found that it can reduce air pollution [
28
]. Du
and Chen [
29
] take industrial concentration as a mediating variable and find that environ-
mental regulation can reduce the density of air pollution through promoting industrial
concentration. Industrial structure upgrading may act as a moderating variable and can
positively moderate the impact of environmental regulation on air pollution.
Industrial structure upgrading can enhance the impact of environmental regulation on
air pollution. Through promoting technological innovation [
30
], driving the transformation
of production and promoting the upgrading of consumption [
31
], industrial structure
upgrading can lead enterprises to develop in a green way.
Based on the above analysis, this study proposes the second research hypothesis:
H2:
The upgrading of the industrial structure can positively moderate the effect of environmental
regulation on air pollution.
3. Methods
3.1. Model Specifications
The time-series vector autoregression (VAR) model was regarded as an alternative to
multivariate simultaneous equation models initially [
32
]. All variables in a VAR model are
treated as endogenous, which can effectively show the relationship among the variables.
Newey et al. introduced VAR in a panel-data setting and the panel VAR model has
been used in multiple applications across fields [
33
]. Further developed by Love and
Zicchino [
34
], the PVAR model has been widely used in the fields of economy, policy
and industrial structure. The PVAR model analyzes the dynamic relationships between
variables through generalized matrix estimation (GMM) and impulse response function
(IRF). Monte Carlo is a part of the impulse response function, which is used to generate the
5% error bands.
The PVAR model is used to analyze the dynamic relationships between environ-
mental regulation, industrial structure upgrading and air pollution from an independent
perspective. This study uses the moderating effect model to explore the underlying mecha-
nisms of environmental regulation, industrial structure upgrading and air pollution from a
linkage perspective.
Model1 : Yit =α0+∑p
n=1βnYit−n+γi+σt+µit

Atmosphere 2023,14, 1537 4 of 15
In Model 1,
Yit
is a three-variable vector
[Polltionit Regulationit TSit]
of section individ-
ual
i
at timepoint
t
.
Yit−n
is the n-order lag term of
Yit
.
Pollutionit
denotes the emissions
of
SO2
,
Regulationi t
denotes the tax of
SO2
, and
TSit
represents the upgrading of the in-
dustrial structure.
α0
is the intercept vector,
i
represents different provinces,
t
represents
different years,
p
is the lag order,
βn
is the coefficient matrix of the lagging variable,
γi
is
the individual effect, σtis the time effect, and µit is the random perturbation term.
Model2 : ln pollutionit =α0+β1ln controlit +β2ln regulationi t +εit
Model3 : ln pollutionit =α0+β1ln controlit +β2ln regulationi t +β3ln TSit +εit
Model4 : ln pollutionit =α0+β1ln controlit +β2ln regulationi t +β3ln TSit +β4ln regulationit ∗ln TSit +εit
In Models 2–4,
Pollutionit
is the dependent variable, denoting the emissions of
SO2
in
the
j
province of the
i
year.
Regulationi t
is the key explanatory variable, denoting the tax of
SO2
in the
j
province of the
i
year.
TSit
represents the upgrading of the industrial structure
in the
j
province of the
i
year.
Controlit
is a vector composed of the control variables [
7
,
8
,
35
],
and it mainly includes the control variables such as development, innovation, urban, open,
invest and energy.
α0
is the intercept term.
β
is the regression coefficient of the equations.
εit is the random error term.
3.2. Dependent Variables
In previous studies, scholars chose different indicators to measure air pollution, in-
cluding
PM2.5
[
36
,
37
],
CO2
[
38
], and
SO2
[
39
].
SO2
has a significantly negative effect on
human health, leading to various adverse health problems such as breathing difficulty,
pulmonary edema, eye irritation, asthma attacks, cardiopulmonary diseases and increased
mortality rates [
40
,
41
]. Additionally,
SO2
is a primary focus of environmental regulation.
Zhang et al. [
4
] utilized the Grossman Health Production Function to examine the impact
of
SO2
on public health and found that there was a positive correlation between them.
In consideration of data availability, this study selected
SO2
emissions as the dependent
variable to measure pollution. The data of
SO2
emissions came from the China Statistical
Yearbook, including 30 provinces in China from 2004 to 2020. These data represented
the emissions of industrial
SO2
. The data were calculated and reported by each province.
The methods for measuring
SO2
emissions in various provinces included detection data
methods, material measurement methods and emission coefficient methods, which are
different for different industries.
3.3. Independent Variables
Previous studies have selected indicators such as the number of environmental protec-
tion laws and the proportion of pollution control investment to the total industrial output
value and GDP [
42
] to measure the degree of environmental regulation. However, the
effectiveness of laws is contingent upon their enforcement. Additionally, the proportion of
investment in pollution treatment cannot reflect the regulation of every specific pollutant.
As one of the most important environmental regulations, environmental tax can overcome
the above shortcomings. This study selected the environmental tax of
SO2
as a key indicator
to measure environmental regulation.
3.4. Moderate Variables
According to theoretical mechanism analysis, air pollution is highly correlated with
environmental regulation. Industrial structure upgrading may play a moderating role in
this relationship. Industrial structure upgrading refers to the process of establishing and
achieving a more efficient industrial structure. According to Clark’s Law, some studies
employ the proportion of non-agricultural output value as a measure of industrial structure
upgrading. Since the 1970s, the information technology revolution has had a great impact
on industrial structures. It formed a trend of “economic service”. Especially after the

Atmosphere 2023,14, 1537 5 of 15
reform and opening up, this trend has accelerated. The traditional indicator cannot reflect
the upgrading of the industrial structure in China. This study took the ratio of tertiary
industry output value to secondary industry output value to measure the upgrading of the
industrial structure (
TS
) [
43
]. The increase in this ratio indicates that industrial structures
have been upgraded.
3.5. Control Variables
A range of factors that can affect air pollution are controlled. The previous stud
ies [7,8,35]
have shown the level of economic (development), the level of innovation (innovation), the
level of urbanization (urban), the openness to trade (open), the investment scale (invest)
and energy efficiency (energy) are closely related to air pollution. Therefore, this study
chose the above variables as control variables.
3.6. Data Resource
Before 2018, China used pollutant discharge fees to control air pollution. China did not
implement environmental taxes, until Environmental Protection Tax Law of the People’s
Republic of China was officially implemented in 2018. There is no significant difference
in the object of collection, the scope of collection and the standard of collection between
pollutant discharge fees and environmental taxes. Therefore, this study uses the pollutant
discharge fees to measure environmental regulation before 2018. The environmental tax
burden can be measured through changes in the pollutant change fee [
44
]. As shown in
Figure 2, the overall level remains stable and only eight provinces have a significant change.
The tax rates for each pollutant are roughly the same as the former pollutant discharge
fees. The tax rate for each pollutant before 2018 can be replaced by pollutant discharge fees.
On 1 July 2003, the government promulgated the Regulations on the Administration of
the Charging and Use of Pollutant Discharge Fees. This had a huge impact on pollutant
discharge fees for a long time. Therefore, this study chose panel data from 2004. The
data of pollutant discharge fees from 2004 to 2018 were collected from the documents
of the Ministry of Finance and the Price Bureau. After 2018, the data of environmental
tax were collected from provincial tax bureaus. The data of other variables were mainly
collected from 2004 to 2020 of the China Statistical Yearbook on Environment and the China
Statistical Yearbook. The panel data consisted of 30 provinces in China from 2004 to 2020,
while Tibet, Taiwan, Hong Kong and Macau were not included due to data availability.
The data of fixed investment in 2020 were missing. This study used the moving average
method to supplement the missing individual data.
Atmosphere 2023, 14, x FOR PEER REVIEW 6 of 16
Figure 2. The changes between pollutant discharge fees and environmental taxes.
4. Results
4.1. Descriptive Analyses
For
2
SO
, this study collected data consisting of 30 provinces in China from 2004 to
2020, while Tibet, Taiwan, Hong Kong and Macau are not included due to data availabil-
ity. The data include
2
SO
emissions from industry sources, domestic sources and cen-
tralized pollution control facilities. The
2
SO
emissions of various industries are shown in
Figure 3. In 2020, the top ve provinces for
2
SO
emissions were Inner Mongolia, Liao-
ning, Shandong, Guizhou and Yunnan. The total emissions of the ve provinces are 1.027
million tons, accounting for 32.3% of the country’s
2
SO
emissions. Figure 4 shows the
sources of
2
SO
. The top three sources of
2
SO
emissions are the production of electricity
and heat power, smelting and pressing of metals, and manufacturing of non-metallic min-
eral products. The total
2
SO
emissions from the three sources are 2.07 million tons, ac-
counting for 79% of the
2
SO
emissions.
2
SO
mainly comes from the burning of fossil
fuels such as coal and crude oil [45], which is the energy source of the secondary industry.
The secondary industry refers to production and processing manufacturing, including the
production of electricity and heat power, smelting and pressing of metals and automobile
manufacturing, amongst others. As is shown in Figure 5, the proportion of the output
value of the secondary industry in the total output value shows a downward trend. As is
shown in Figure 6,
2
SO
also shows a downward trend. The tertiary industry, also known
as the service industry, mainly includes transportation, communications, commerce and
others. The tertiary industry is less dependent on fossil fuels than the secondary industry.
Promoting the development of the tertiary industry is conducive to reducing pollution.
As is shown in Figure 7, the ratio of the tertiary industry output value to secondary indus-
try shows an upward trend across the country. As is shown in Figure 8, the tax rate of
2
SO
showed an upward trend.
Figure 2. The changes between pollutant discharge fees and environmental taxes.

Atmosphere 2023,14, 1537 6 of 15
4. Results
4.1. Descriptive Analyses
For
SO2
, this study collected data consisting of 30 provinces in China from 2004 to
2020, while Tibet, Taiwan, Hong Kong and Macau are not included due to data availability.
The data include
SO2
emissions from industry sources, domestic sources and centralized
pollution control facilities. The SO2emissions of various industries are shown in Figure 3.
In 2020, the top five provinces for
SO2
emissions were Inner Mongolia, Liaoning, Shandong,
Guizhou and Yunnan. The total emissions of the five provinces are 1.027 million tons,
accounting for 32.3% of the country’s
SO2
emissions. Figure 4shows the sources of
SO2
.
The top three sources of
SO2
emissions are the production of electricity and heat power,
smelting and pressing of metals, and manufacturing of non-metallic mineral products. The
total
SO2
emissions from the three sources are 2.07 million tons, accounting for 79% of the
SO2
emissions.
SO2
mainly comes from the burning of fossil fuels such as coal and crude
oil [
45
], which is the energy source of the secondary industry. The secondary industry
refers to production and processing manufacturing, including the production of electricity
and heat power, smelting and pressing of metals and automobile manufacturing, amongst
others. As is shown in Figure 5, the proportion of the output value of the secondary industry
in the total output value shows a downward trend. As is shown in Figure 6,
SO2
also
shows a downward trend. The tertiary industry, also known as the service industry, mainly
includes transportation, communications, commerce and others. The tertiary industry is
less dependent on fossil fuels than the secondary industry. Promoting the development of
the tertiary industry is conducive to reducing pollution. As is shown in Figure 7, the ratio
of the tertiary industry output value to secondary industry shows an upward trend across
the country. As is shown in Figure 8, the tax rate of SO2showed an upward trend.
Atmosphere 2023, 14, x FOR PEER REVIEW 7 of 16
Figure 3. The emission of
2
SO
in 2020.
Figure 4. The emission of
2
SO
in 2020.
Figure 5. The trend of the proportion of output value of secondary industry.
Figure 3. The emission of SO2in 2020.
Atmosphere 2023, 14, x FOR PEER REVIEW 7 of 16
Figure 3. The emission of
2
SO
in 2020.
Figure 4. The emission of
2
SO
in 2020.
Figure 5. The trend of the proportion of output value of secondary industry.
Figure 4. The emission of SO2in 2020.

Atmosphere 2023,14, 1537 7 of 15
Atmosphere 2023, 14, x FOR PEER REVIEW 7 of 16
Figure 3. The emission of
2
SO
in 2020.
Figure 4. The emission of
2
SO
in 2020.
Figure 5. The trend of the proportion of output value of secondary industry.
Figure 5. The trend of the proportion of output value of secondary industry.
Atmosphere 2023, 14, x FOR PEER REVIEW 8 of 16
Figure 6. The trend of
2
SO
emissions.
Figure 7. The trend of the ratio of the tertiary industry output value to secondary industry.
Figure 8. The trend of the tax rates of
2
SO
.
Figure 6. The trend of SO2emissions.
Atmosphere 2023, 14, x FOR PEER REVIEW 8 of 16
Figure 6. The trend of
2
SO
emissions.
Figure 7. The trend of the ratio of the tertiary industry output value to secondary industry.
Figure 8. The trend of the tax rates of
2
SO
.
Figure 7. The trend of the ratio of the tertiary industry output value to secondary industry.

Atmosphere 2023,14, 1537 8 of 15
Atmosphere 2023, 14, x FOR PEER REVIEW 8 of 16
Figure 6. The trend of
2
SO
emissions.
Figure 7. The trend of the ratio of the tertiary industry output value to secondary industry.
Figure 8. The trend of the tax rates of
2
SO
.
Figure 8. The trend of the tax rates of SO2.
The average emissions of
SO2
are 506,600 tons per year. The average regional GDP
(gross domestic product) per capita is CNY 42,600 per year, and the average number of
patents granted in the region is 37,800 patents per year. The average level of urbanization
is 54.0% per year. For trade openness, the ratio of total imports and exports to total local
GDP can reach a maximum of 1.7. For the investment scale, the ratio of total fixed asset
investment output to local GDP reaches a maximum of 0.09. For energy efficiency, for every
CNY 10,000 increase in regional GDP, the mean consumption is 0.99 tons per year. The ratio
of tertiary industry output value to secondary output value reaches a maximum of 5.3. The
average tax charge per pollutant equivalent is CNY 1.36 (Table 1). The descriptive statistics
of the main variables are shown in Table 1.
4.2. PVAR Results
STATA software is used to run Model 1. GMM (Generalized Method of Moment)
and the impulse response function are performed to test the short-term and long-term
interaction between air pollution, industrial structure upgrading and environmental regu-
lation. The regression results are displayed in Table 2and Figure 2. In Figure 9, the area
between the first and third lines forms a 95% confidence interval. The second line represents
the impulse response value. All variables in the VAR model are treated as endogenous.
According to the literature review in the introduction, there may exist a bidirectional causal
relationship between environmental regulation and air pollution as well as between the
upgrading of the industrial structure and air pollution. In addition, there is a one-way
causal relationship between environmental regulation and industrial structure upgrading.
For
SO2
, the results of GMM are reported in column 2 of Table 2. In particular the
first lag of environmental regulation and the industrial structure upgrading negatively
determines the current level of
SO2
(p< 0.1). The first line of Figure 2reports the IRF of
SO2
.
The results show that the effect of one standard deviation shock of environmental regulation
and industrial structure upgrading on
SO2
is negative. This implies that environmental
regulation and industrial structure upgrading are beneficial for pollution reduction in the
short and long term, which is consistent with our hypothesis H1.
For the tax rate of
SO2
, the results of GMM are reported in column 3 of Table 2. The
coefficient of
SO2
emissions is
−
0.009 (p< 0.1), and the coefficient of the industrial structure
upgrading is insignificant. The second line of Figure 9reports the IRF of environmental
regulation. The effect of one standard deviation shock of
SO2
on environmental regulation is
negative. This shows that the deterioration of air quality does not have a significant impact
on the improvement of environmental regulation. For the upgrading of the industrial
structure, the results of GMM are reported in column 3 of Table 2. The coefficient of the
SO2

Atmosphere 2023,14, 1537 9 of 15
tax rate is 0.083 in lag 1 (p< 0.1) and the coefficient of
SO2
is
−
0.001 (p< 0.1). The third line of
Figure 9reports the IRF of industrial structure upgrading. The upgrading of the industrial
structure responds positively to the regulation, which indicates that the improvement of
environmental regulation can promote the upgrading of the industrial structure.
Table 1. Descriptive statistics.
Variable Measure Unit Mean Standard
Deviation Min Max
Dependent
variable Pollution SO2emissions
(ten thousand tons) Ten thousand tons 50.06 39.68 0.09 171.50
Independent
variable Regulation The tax of SO2Yuan/kg 1.360 1.740 0.420 12.00
Control
variable
Development
GDP per capita
Ten thousand yuan
4.265 2.840 0.422 16.48
Innovation The number of
patents granted
Ten thousand piece
3.782 7.274 0.007 70.97
Urban
The proportion of urban
resident population in the
total permanent
resident population
% 54.00 14.00 25.00 98.00
Open
The ratio of the total value
of imports and exports to
local GDP
% 30.0. 36.00 1.00 170.00
Invest The ratio of total fixed asset
investment to local GDP - 0.0100 0.0100 0 0.0900
Energy The ratio of actual energy
use to local GDP
Tons of standard
coal per ten
thousand yuan
0.990 0.650 0.190 4.190
Moderating
variable TS
The ratio of tertiary industry
output value to secondary
industry output value
- 1.150 0.600 0.530 5.300
Table 2.
Short-term interaction among environmental regulation, the upgrading of the industrial
structure and air pollution.
Variables Pollution Regulation TS
Coefficient 95% CI p> |z| Coefficient 95% CI p> |z| Coefficient 95% CI p> |z|
L.h_pollution 1.067 (0.944, 1.190) 0.000 −0.009 (−0.020, 0.001) 0.100 −0.001 (−0.003, 0.000) 0.078
L2.h_pollution −0.129 (−0.253, −0.006) 0.039 0.005 (−0.007, 0.018) 0.389 0.001 (−0.000, 0.002) 0.143
L.h_regulation −0.498 (−3.193, 2.196) 0.076 1.126 (0.408, 1.844) 0.002 0.083 (−0.004, 0.171) 0.062
L2.h_regulation 1.721 (−0.177, 3.619) 0.060 0.151 (−0.063, 0.366) 0.168 −0.009 (−0.040, 0.022) 0.571
L.h_TS −32.527 (−58.82, −6.226) 0.015 −1.725 (−4.757, 1.306) 0.265 0.778 (0.236, 1.319) 0.005
L2.h_TS 25.345 (11.63, 39.05) 0.000 −0.147 (−1.185, 0.890) 0.781 −0.190 (−0.312, −0.068) 0.002
Observations 420 420 420
4.3. Moderating Results
The STATA software is used to operate Models 2–4, and the regression results are
displayed in Table 3. In Model 2, the coefficient of air pollution is
−
0.520 (p< 0.1), indicating
that environmental regulation can significantly reduce air pollution. This further confirms
our H1 hypothesis. The upgrading of the industrial structure is added in Model 3. The
coefficient of industrial structure upgrading is
−
0.363, indicating that the upgrading of the
industrial structure can reduce air pollution. The cross term of environmental regulation
and industrial structure upgrading (C_regulation*C_TS) is added in Model 4. The coeffi-
cient of the cross term is
−
0.349 (p< 0.1), indicating that the moderating effect is significant.
The upgrading of the industrial structure can positively strengthen the reduction effect of
environmental regulation on air pollution, which is consistent with H2. To visualize the
moderating effect, an interaction diagram of the moderating effect is presented in Figure 10.

Atmosphere 2023,14, 1537 10 of 15
Atmosphere 2023, 14, x FOR PEER REVIEW 11 of 16
.
Figure 9. Long-term interaction among environmental regulation, the upgrading of the industrial
structure and air pollution.
4.3. Moderating Results
The STATA software is used to operate Models 2–4, and the regression results are
displayed in Table 3. In Model 2, the coecient of air pollution is −0.520 (p < 0.1), indicating
that environmental regulation can signicantly reduce air pollution. This further conrms
our H1 hypothesis. The upgrading of the industrial structure is added in Model 3. The
coecient of industrial structure upgrading is −0.363, indicating that the upgrading of the
industrial structure can reduce air pollution. The cross term of environmental regulation
and industrial structure upgrading (C_regulation*C_TS) is added in Model 4. The coe-
cient of the cross term is −0.349 (p < 0.1), indicating that the moderating eect is signicant.
The upgrading of the industrial structure can positively strengthen the reduction eect of
environmental regulation on air pollution, which is consistent with H2. To visualize the
moderating eect, an interaction diagram of the moderating eect is presented in Figure
10.
Table 3. The moderating eect test results.
Model 2
Model 3
Model 4
Dependent
Variable
Pollu-
tion
95% CI
p Value
Pollution
95% CI
p Value
Pollution
95% CI
p Value
Independ-
ent
variable
regulation
−0.520
−0.733,
−0.308
0.000
−0.479
−0.683,
−0.275
0.000
−0.246
−0.440,
−0.051
0.013
Control
variable
develop-
ment
−0.058
−0.293,
0.175
0.612
−0.041
−0.274,
0.190
0.715
0.027
−0.119,
0.173
0.718
Innovation
−0.040
−0.097,
0.016
0.156
−0.038
−0.096
0.018
0.179
−0.005
−0.048,
0.037
0.803
Urban
0.369
−0.542,
1.281
0.414
0.319
−0.095,
2.911
0.476
−0.039
−0.440,
0.650
0.706
open
−0.035
−0.114,
0.044
0.371
−0.028
−0.110,
−0.053
0.488
−0.039
−0.108,
−0.028
0.257
Figure 9.
Long-term interaction among environmental regulation, the upgrading of the industrial
structure and air pollution.
Table 3. The moderating effect test results.
Model 2 Model 3 Model 4
Dependent
Variable Pollution 95% CI pValue Pollution 95% CI pValue Pollution 95% CI pValue
Independent
variable regulation −0.520 −0.733,
−0.308 0.000 −0.479 −0.683,
−0.275 0.000 −0.246 −0.440,
−0.051 0.013
Control
variable
development −0.058 −0.293,
0.175 0.612 −0.041 −0.274,
0.190 0.715 0.027 −0.119,
0.173 0.718
Innovation −0.040 −0.097,
0.016 0.156 −0.038 −0.096
0.018 0.179 −0.005 −0.048,
0.037 0.803
Urban 0.369 −0.542,
1.281 0.414 0.319 −0.095,
2.911 0.476 −0.039 −0.440,
0.650 0.706
open −0.035 −0.114,
0.044 0.371 −0.028 −0.110,
−0.053 0.488 −0.039 −0.108,
−0.028 0.257
invest 0.127 0.012,
0.243 0.032 0.123 0.006,
0.239 0.039 0.158 0.028,
0.287 0.372
energy 0.285 −0.180,
0.752 0.221 0.325 0.179,
0.830 0.198 0.297 −0.093,
0.688 0.136
Moderating
variable
TS −0.363 −0.826,
0.100 0.120 −
0.4680
−0.929,
0.007 0.142
C_regulation*C_TS
−0.349 −0.493,
−0.205 0.000
_cons 14.204 4.789,
16.098 0.001 10.693 4.827,
16.558 0.001 9.508 −3.303,
−1.264 0.000
N510 510 510
r2_a 0.907 0.907 0.913
Prob > F 0.0 0.0 0.0

Atmosphere 2023,14, 1537 11 of 15
Atmosphere 2023, 14, x FOR PEER REVIEW 12 of 16
invest
0.127
0.012,
0.243
0.032
0.123
0.006,
0.239
0.039
0.158
0.028,
0.287
0.372
energy
0.285
−0.180,
0.752
0.221
0.325
0.179,
0.830
0.198
0.297
−0.093,
0.688
0.136
Moderating
variable
TS
−0.363
−0.826,
0.100
0.120
−0.4680
−0.929,
0.007
0.142
C_regula-
tion*C_TS
−0.349
−0.493,
−0.205
0.000
_cons
14.204
4.789,
16.098
0.001
10.693
4.827,
16.558
0.001
9.508
−3.303,
−1.264
0.000
N
510
510
510
r2_a
0.907
0.907
0.913
Prob > F
0.0
0.0
0.0
Figure 10. The eect of moderating.
5. Discussion
This study aims to explore the relationship between environmental regulation, the
upgrading of the industrial structure and air pollution and examine whether the upgrad-
ing of the industrial structure can positively moderate the impact of the environmental
regulation on air pollution.
Environmental regulation can signicantly reduce emissions, but the deterioration of
air quality does not have a signicant impact on the improvement of environmental reg-
ulation. This study nds that environmental regulations can reduce emissions, which is
consistent with previous research [3–5]. Vikas et al. observed a signicant decrease in
2
SO
emission in India from 2010 to 2020, aributing this improvement to the implemen-
tation of stringent environmental regulations [46]. Teng Wang et al. [28] also found that
environmental regulation had a signicant negative eect on air pollution and the coe-
cient was −0.123 based on panel data of 248 Chinese cities from 2003 to 2016. The coe-
cient in our study is −0.52. It is proved that the impact of environmental regulation on air
pollution has been strengthened in the last 5 years. Zhang et al. also found that seasonal
environmental regulation policies can signicantly improve air quality in the short term
[9]. Firstly, environmental regulation could increase the control costs of enterprises
[26,27], thus reducing energy consumption and curbing environmental pollution [16]. Sec-
ondly, improving environmental regulation can aract more foreign direct investment.
Foreign companies often bring advanced clean technologies [47,48] and abundant capital
to the host country, resulting in the “pollution halo eect”, which aids in reducing air
pollution [49]. This study also nds that the worsening of air quality does not have a sig-
nicant impact on the improvement of environmental regulation. Environmental issues
Figure 10. The effect of moderating.
5. Discussion
This study aims to explore the relationship between environmental regulation, the up-
grading of the industrial structure and air pollution and examine whether the upgrading of
the industrial structure can positively moderate the impact of the environmental regulation
on air pollution.
Environmental regulation can significantly reduce emissions, but the deterioration
of air quality does not have a significant impact on the improvement of environmental
regulation. This study finds that environmental regulations can reduce emissions, which is
consistent with previous research [
3
–
5
]. Vikas et al. observed a significant decrease in
SO2
emission in India from 2010 to 2020, attributing this improvement to the implementation
of stringent environmental regulations [
46
]. Teng Wang et al. [
28
] also found that envi-
ronmental regulation had a significant negative effect on air pollution and the coefficient
was
−
0.123 based on panel data of 248 Chinese cities from 2003 to 2016. The coefficient
in our study is
−
0.52. It is proved that the impact of environmental regulation on air
pollution has been strengthened in the last 5 years. Zhang et al. also found that seasonal
environmental regulation policies can significantly improve air quality in the short term [
9
].
Firstly, environmental regulation could increase the control costs of enterprises [
26
,
27
],
thus reducing energy consumption and curbing environmental pollution [
16
]. Secondly,
improving environmental regulation can attract more foreign direct investment. Foreign
companies often bring advanced clean technologies [
47
,
48
] and abundant capital to the host
country, resulting in the “pollution halo effect”, which aids in reducing air pollution [
49
].
This study also finds that the worsening of air quality does not have a significant impact on
the improvement of environmental regulation. Environmental issues have a highly signifi-
cant role in economic development. It is a huge challenge for governments to coordinate
high-quality economic development and environmental protection. Baumol suggested that
the environmental tax should be adjusted to the pollution situation [
14
]. Presley K pointed
out that the setting of pollution tax should be at an economically appropriate level [
50
]. In
order to relieve the financial burden, the current environmental tax is set lower than the
cost of governance and the optimal tax rate [
51
]. Therefore, an inferior air quality does not
have a significant impact on the improvement of environmental regulations in China.
The industrial structure upgrading can reduce air pollution, but the worsening of
the air quality will hinder the upgrading of the industrial structure. By constructing a
spatial econometric model, Yang et al. [
20
] concluded that industrial structure upgrading
could reduce carbon emissions by improving green total factor productivity, which is
consistent with our results. There are some reasons to explain the results. The industrial
structure upgrading can promote innovations [
52
], improve resource allocation efficiency

Atmosphere 2023,14, 1537 12 of 15
and optimize energy consumption structure [
43
], thereby further reducing emissions. The
study also shows that the worsening of the air quality will hinder the upgrading of the
industrial structure. The improvement of enterprise total factor productivity (ETFP) has a
significant impact on the industrial structure upgrading [
24
]. However, the deterioration of
the air will add extra treatment costs for companies and reduce the ETFP [
53
]. Specifically,
air pollution has a negative impact on the inflow of talent [
54
], thus hampering innovation.
Meanwhile, air pollution has a “capital crowding-out effect”, reduces regional fixed assets
investment and hinders economic development. Innovation and fixed investment are
t
wo im
portant factors of ETFP, and the negative impact of air pollution on them will inhibit
the development of enterprises and prevent the upgrading of the industrial structure [
24
].
Environmental regulation can promote industrial structure upgrading, which consists
of the previous study [
54
].The underling mechanisms are that innovation is one of the
driving forces of industrial structure upgrading. Environmental regulations can enhance
the ability of innovation [52], thus promoting industrial structure upgrading.
The most important finding of this study is that industrial structure upgrading is a
moderating variable and can positively moderate the impact of environmental regulation
on air pollution. Yang Song finds the environmental regulation has a negative effect on
air pollution, the coefficient is
−
0.339. After adding the variable of the upgrading of
the industrial structure, the coefficient is
−
3.53 [
28
]. The coefficient of the cross term
of environmental regulation and industrial structure upgrading in this study is
−
0.349.
All of these demonstrate how the upgrading of the industrial structure can amplify the
reduction effects of environmental regulation on air pollution. The underlying mechanisms
remain unclear, but could be twofold. The improvement of environmental regulation can
promote the industrial structure upgrading [
54
]. The industrial structure will promote
innovations [
52
], thus further reducing air pollution. Additionally, as environmental
regulations have improved, consumer environmental awareness has gradually grown [
55
].
The consumer demand can lead enterprises to change the product production structure [
7
].
The upgrading of the industrial structure can improve resource allocation efficiency and
optimize energy consumption structure, thereby further reducing emission [43].
6. Conclusions and Implication
Since launching its open-door policy and economic reform, China has experienced
spectacular economic growth. Meanwhile, China has caused unprecedented environmental
pollution [
1
,
2
]. Although China has enacted numerous measures to protect the air, the
effects of environmental regulation are not universally agreed upon. To objectively evaluate
the effect of these policies and provide empirical evidence for the government, benchmark
analysis is performed. Four key conclusions are obtained. Firstly, environmental regulation
can significantly reduce emissions, but the deterioration of air quality does not have a
significant impact on the improvement of environmental regulation. Secondly, industrial
structure upgrading can reduce air pollution, but an inferior air quality will hinder in-
dustrial structure upgrading. Thirdly, environmental regulations can promote industrial
structure upgrading. Lastly, industrial structure upgrading is a moderating variable and
can positively moderate the impact of environmental regulation on air pollution.
The main policy implications of this study are summarized as follows. Firstly, current
environmental regulation does not exert its optimal effect. The reason for this may be
that due to economic growth, the environmental tax has been set at a very low level
for a long time. In order to make full use of the environmental tax, the government
should reform the tax rate and ensure that it adapts to the actual pollution situation and
economic development. Secondly, industrial structure upgrading can reduce air pollution,
according to the experimental results. Therefore, the government should enact a more
rational industrial policy to improve resource allocation efficiency and optimize energy
consumption structure. Thirdly, from the linkage perspective, environmental regulation
can reduce air pollution by means of industrial structure upgrading. The government

Atmosphere 2023,14, 1537 13 of 15
should promote industrial policy as well as environmental regulation and regard industrial
policy as an important supplement to environmental regulation.
7. Limitation
This study still has some limitations. This study only uses the emissions of
SO2
to
measure the pollution. In future research, we will add nitrogen oxide data from 30 provinces
in China, excluding Tibet, to enhance the validity of the experimental results. For the impact
of environmental regulation on the industrial structure, we provide reasonable explanations
where possible, but the underlying mechanism remains unclear.
Author Contributions:
Y.L. conceptualized the paper and designed the methodology; Z.-S.W. investi-
gated the date and analyzed the date; N.-E.H.R. performed the supervision; C.-N.X. edited the paper;
X.-M.L. reviewed and revised the original draft. All authors have read and agreed to the published
version of the manuscript.
Funding:
This research was funded by the Major Project of Philosophy and Social Science Research
in Hubei Colleges and Universities (21ZD063), the Major Project of Philosophy and Social Science
Research in Hubei Colleges and Universities (22D055), Human Social Science Foundation Projects
of Wuhan Institute of Technology (R202102), 15th Graduate Education Innovation Fund of Wuhan
Institute of Technology (CX2022291).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Restrictions apply to the availability of these data. Data were obtained
from the China National Bureau of Statistics and are available at http://www.stats.gov.cn/ (accessed
on 13 September 2022).
Conflicts of Interest: The authors declare no conflict of interest.
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