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RESEARCH ARTICLE
Bridging the gap between state–business interactions and air
pollution: The role of environment, social responsibility, and
corporate governance performance
Guanfei Meng | Jianglong Li | Xiuwang Yang
School of Economics and Finance, Xi'an
Jiaotong University, Xi'an, China
Correspondence
Jianglong Li, School of Economics and Finance,
Xi'an Jiaotong University, Xi'an, China.
Email: lijianglong@mail.xjtu.edu.cn
Funding information
Key Project of Social Science in Xi’an of China,
Grant/Award Number: 22JX82; Supporting
Plan for Innovation in Shaanxi Province of
China, Grant/Award Number: 2022KRM026;
National Natural Science Foundation of China,
Grant/Award Numbers: 71703120, 72173095
Abstract
Compared with the impact of listed corporates' environment, social responsibility,
and corporate governance (ESG) on financing and market value, considerably less
attention is paid to the role of ESG performance in improving environmental quality
in China. However, exploring the environmental motivation of listed corporates' ESG
engagement is vital for shareholder protection and corporate strategic development
under state–business interactions. Thus, we study the relationship between state–
business interactions and air pollution from the perspective of ESG using the dataset
of Chinese listed corporates. The results demonstrate that the state–business collu-
sion interactions increase air pollution by hindering the fulfillment of ESG. Mean-
while, this impact of heavy pollution corporates is higher than that of industrial
corporates. However, the deleterious relationship might be largely improved when
state–business interactions are “clean.”Finally, we observe that ESG mediates the
relationship between state–business interactions and pollutants.
KEYWORDS
air pollution, China, ESG, state–business interactions
1|INTRODUCTION
In recent years, environment, social responsibility, and corporate gov-
ernance (ESG) are driving revolutionary changes in corporates' strat-
egy and performance. The first reason is that the adverse reports of
environmental performance and irresponsible behaviors of a corpo-
rate cause enormous economic and financial losses for the corporate
(Capelle-Blancard & Petit, 2019). For example, the stock market
responds significantly to the negative news of environmental acci-
dents, and the loss is positively associated with the severe degree of
the disasters (Capelle-Blancard & Laguna., 2010). A second strand
considers that an excellent ESG implementation might improve air
quality, which could be a powerful tool to enhance their competitive
market advantages and environmental reputation (Jiménez-Parra
et al., 2018; Song et al., 2019). Jiménez-Parra et al. (2018) demon-
strate that the ESG reduces air pollution by the mediating effect of
eco-innovations. Yuan and Cao (2022) also corroborate that ESG per-
formance significantly promotes green innovation, while the green
innovation could effectively reduce hazardous air pollutant (Ma
et al., 2022). Meanwhile, the excellent ESG performance of corporates
might bring relatively stable market value and more resilient stock
prices (Broadstock et al., 2021; Lins et al., 2017). Accordingly, many
corporates have adopted ESG as a sustainable strategy when they have
a long strategic motivation facing rigorous environmental governance
(Arvidsson & Dumay, 2022; Babiak & Trendafilova, 2011;Jiménez-Parra
Abbreviations: AQI, air quality index; clean, state—business clean interactions; collusion,
state—business collusion interactions; CSMAR, China Stock Market & Accounting Research;
DDP, dividend as a percentage of distributable profits; DFR, dividend financing ratio; DYR,
dividend yield ratio; EPS, Earnings per share; ER, environmental regulations; ESG,
environment, social responsibility, and corporate governance; GDP, gross domestic product;
GI, government intervention; industry_SO
2
, industrial SO
2
emission; industry_soot, industrial
soot emission; light, nighttime light; MD, marketization degree; NADSRUC, National
Academy of Development and Strategy of Renmin University of China; OPE, Profit margin of
main business; PM
2.5
, particular matter 2.5; ROA, return on net assets; sbin, state—business
interaction; SO
2
, sulfur dioxide; Str, industrial structure; UEPS, Undistributed earnings per
share.
Received: 11 June 2022 Revised: 3 July 2022 Accepted: 2 August 2022
DOI: 10.1002/bse.3224
Bus Strat Env. 2022;1–13. wileyonlinelibrary.com/journal/bse © 2022 ERP Environment and John Wiley & Sons Ltd. 1
et al., 2018; Xu et al., 2018). However, the state–business collusion
interactions might hinder the fulfillment of ESG and increase the possi-
bility of irresponsible corporate behaviors (Chen et al., 2022;Zhou
et al., 2020).
1
For example, the “distorted”state–business interactions
such as corruption imply that local governments (officials) or corporates
do not follow their due roles, resulting in rent-seeking by corporates
and bribery by officials (Apriliyanti & Kristiansen, 2019; Brauers &
Oei, 2020). More importantly, the state–business collusion interactions
such as corruption deteriorate environmental governance outcomes
(Chen et al., 2022). Therefore, if we could evidence the significant
impact on the state–business relationship on ESG and air pollution, we
might reasonably infer that ESG has a mediating effect on the relation-
ship between state–business interactions and air pollution or environ-
mental governance.
Then, how do we detect the causal relationship between state–
business interactions, air pollution, and ESG? An ideal dataset would
provide information on the ESG difference in various sectors when
the state–business interactions are randomly relocated to air pollu-
tion, given all else equal. Fortunately, China is a good setting for this
study because of its varied air pollution and different state–business
interactions. As depicted in Figure 1, we could find that the more seri-
ous the state–business collusion interactions are, the more severe air
pollution is. Moreover, China is the only country in the world with all
the industrial categories listed in the United Nations Industrial Classi-
fication
2
; thus, the comprehensive sample provides substantial varia-
tions that enable better understanding of corporates' ESG responses
to air pollution.
In addition, as a critical institutional arrangement in the officials'
evaluation systems, economic growth target management largely
determines officials' political careers under a top-down official gover-
nance system characterized by high centralization in China (Wu
et al., 2013). Thus, corporates and local officials have little incentive
to invest in environmental governance without effective regulations,
thereby easily forming the state–business collusion relationship. In
such a context, local governments may relax the regulatory standards
to allow corporates to discharge pollutants, causing severe pollution
outcomes. Many studies have evidenced that local governments in
China attempt to acquire business investments by lowering environ-
mental standards (Dong et al., 2021; Zhang & Fu, 2008). For example,
Candau and Dienesch (2017) corroborate that corrupt state–business
interactions in destination countries lower environmental standards to
attract polluting affiliates of the European corporates. More impor-
tantly, the law enforcement costs of local governments may increase
because jurisdictions with lower environmental regulations
(ER) attract pollution-intensive firms (Cao et al., 2019). However, sup-
pose that local politicians keep a state–business clean interactions
relationship with corporates without hidden agendas and private
intentions, they do not take advantage of their political resources for
private gains or financial interests. We expect that the state–business
clean interactions can effectively encourage corporates to implement
ESG performance and decrease pollution emissions.
An important policy implication of the abovementioned discus-
sion is that ESG could be regarded as a tool through which local gov-
ernments can strengthen existing environmental governance. A
corporate would adjust its business strategies related to ESG to react
to financial fluctuations because environmental liabilities could cause
financial losses. Policymakers also could utilize the corporate's incen-
tive to reduce pollution emissions and improve environmental quality.
Our contributions build on previous research in the following aspects.
1
The collusion relationship is a manifestation of the deterioration of the state–business
interactions, which decreases the performance of environmental governance deviated from
the expected set goal.
2
Data sources: http://www.gov.cn/xinwen/2019-09/20/content_5431714.htm, accessed by
June 27, 2022.
FIGURE 1 The average value of
state–business collusion interactions
and air pollution (SO
2
) in 2017–2019.
Note: The 101 minus state–business
health index measures the state–
business collusion interactions and
their average value in Figure 1is
calculated by arithmetic mean. The
unit for the SO
2
is μg/m
3
and the data
is collected from the Chinese Ministry
of Ecology and Environment.
2MENG ET AL.
First, instead of focusing on a single relationship such as state–
business interactions and environmental issues or ESG, this paper
explores the relationship among state–business interactions, environ-
mental issues, and ESG of listed corporates and detects the role of
ESG in the way that ESG performance improves air quality. Second,
the study might provide a new perspective for environmental
improvement in ways that enhance the coordinated collaboration
capacity of public policymakers with corporates. Third, we also
explore the mechanisms through which state–business clean interac-
tions improve air quality and promote the fulfillment of ESG, which
help us establish a better business environment and improve organiza-
tional green innovation and business strategies.
The remainder of this study is organized as follows. In Section 2,
we review the related literature and propose a research hypothesis.
Section 3provides the research design and data, including empirical
strategy, variable definitions, and data description. Empirical results
and further discussion are presented in Section 4. Section 5concludes
and discusses the policy implications.
2|LITERATURE REVIEW AND RESEARCH
HYPOTHESIS
2.1 |Literature review
Air pollution is a problem of widespread concern, on which an exten-
sive literature has investigated and found a wealth of conclusions.
Research on air pollution mainly includes two aspects: its causes and
consequences. For the former, various factors such as economic
growth (Pajooyan & Moradhasel, 2008), international trade (Kukla-
Gryz, 2009), industrialization (Bauer et al., 2019), urbanization (Wang
et al., 2020), ER (Ouyang et al., 2019), enterprise, and consumer
behavior (Khan et al., 2020; Lin et al., 2021) have a significant impact
on air pollution. For the latter, air pollution will lead to respiratory dis-
eases (Al-Kindi et al., 2020), the decline in quality of life
(Darçın, 2014), stock yield (Wu et al., 2018), and other psychological,
economic, and social effects (Lu, 2020).
2.1.1 | State–business interactions and air pollution
Focused on the perspective of corruption or anti-corruption, an
extensive literature has investigated the relationship between state–
business interactions and pollution emissions. Most of studies suggest
that state–business collusion interactions might cause more pollution
emissions. Ren et al. (2021) find that corruption leads to more carbon
emissions, especially the corruption in the public sector of developing
countries (Sinha et al., 2019). Hu and Shi (2021) present empirical evi-
dence of a strong correlation between collusion and pollution emis-
sions in China. In addition, literature such as Wang et al. (2018) infers
that control of corruption decreases pollution emissions in some
developing countries. Similarly, some studies suggest that anti-
corruption campaigns reduce air pollution (Liu & Dong, 2021; Zhou
et al., 2020). However, Arminen and Menegaki (2019) find that com-
pared to corruption, the climatic factor seems to significantly impact
carbon emissions for high- and upper-middle-income countries.
Largely in response to the air pollution on corruption, effects of eco-
nomic and social have been received considerable attention in recent
literature, including ER (Zhou et al., 2020), energy consumption (Sinha
et al., 2019), information, and communication technologies (Liu
et al., 2021). Besides, corruption decreases the transparency of envi-
ronmental decentralization, which leads to deterioration of the air pol-
lution (Hao et al., 2021).
2.1.2 | State–business interactions and ESG
With the enhancement of environmental protection, the disclosure of
ESG information might be a mandatory requirement, especially for
some listed corporates of the heavy pollution sector. But, corporates
with higher political connections rely less on ESG strengths, and the
extent of corruption on ESG is larger than other social norms or regu-
lations (Hossain & Kryzanowski, 2021). Moreover, from the corruption
perspective, a few literatures have found evidence that anti-
corruption improves ESG performance (Kong et al., 2021). Focused on
the impact of local corruption on ESG, Ucar and Staer (2020) also sup-
port the view that high corruption rates reduce ESG scores.
2.1.3 | ESG to mitigate air pollution
As the vital pollution emissions unit, corporates directly affect air
quality by choosing different production patterns and intensity of pol-
lutant discharge. Jiménez-Parra et al. (2018) survey the relation
between ESG and air pollution, and the results reveal a positive
impact of ESG on reducing air pollution. Although ESG may not
directly impact environmental performance in manufacturing corpo-
rates, it has a significant positive effect on environmental strategy and
green innovation, improving environmental performance (Kraus
et al., 2020). Evidence from China's A-share listed corporates indicates
that ESG has increased enterprise technological innovation invest-
ment (Zhou et al., 2019). Ahmad et al. (2021) indicate the positive
association between ESG and environmental performance in
Pakistan's manufacturing and service sectors. In the micro-level view,
employees' pro-environmental behaviors play an important role in
reinforcing the mediating effects of ESG on enterprise environmental
performance (Afsar & Umrani, 2020; Ahmad et al., 2021).
2.2 |Theoretical analysis and hypothesis
development
The state–business collusion interactions may affect air quality
through two main channels. On the one hand, it is difficult for the
MENG ET AL.3
government to achieve the trade-off between economic growth and
environmental management because the promotion of government
officials depends on economic progress rather than environmental
management (Li & Zhou, 2005). Therefore, the local government,
especially in transition economies, prefers to reinforce economic
development and ignores environmental issues by attracting more
corporates (especially capital-intensive or labor-intensive corporate)
for investment, providing more preferential policies (such as tax incen-
tives, low price in land, and energy usage), which can be regarded as a
kind of collusion between governments and corporates. Government
releases a signal of lax ER, and the corporates are tacitly approved not
to fulfill ESG performance, which are likely to worsen air quality. On
the other hand, the government strictly attempts to enforce environ-
mental protection regulations. However, corporates with intensive
pollution or high emissions are more inclined to obtain production
licenses by concealing the real ESG practices (Moratis & van
Egmond, 2018) and even try bribing government officials directly
(Chen et al., 2018). Apparently, it is a kind of state–business collusion
interactions and is harmful to environmental protection. Therefore,
we propose two hypotheses as follows:
H1. State–business collusion interactions worsen air
quality.
H2. State–business collusion interactions hinder the
fulfillment of ESG.
In terms of the ESG among the relations between state–
business collusion interactions and air quality, it can be also
divided into two aspects. For the one side, ER are regarded as
“hard”constraints for corporates to disclose ESG practices regu-
larly. But, when state–business collusion interactions are taken
into consideration, the government's environmental supervision
will be weakened, and corporates' pollution discharge will be shel-
tered, or corporates can selectively report the ESG performance.
For the other side, with the “soft”constraints that is the enhance-
ment of consumers and investors' knowledge in environmental
protection and green-growth, corporates take the initiative to pro-
vide green or eco-friendly products and services in respond to
meet consumers expectations, as well as to increase corporates'
profits and stock prices, or to deal with competition (Kowalczyk &
Kucharska, 2020; Hoque et al., 2018). In this regard, taking advan-
tage of state–business collusion interactions, the wise entrepre-
neurs prefer to disclose ESG practices and accept the supervision
from the government. As a result, whether passively obeying envi-
ronmental protection regulations or taking the initiative to disclose
ESG performance, it is uncertain for corporates to reduce or aggra-
vate air pollution when state–business collusion interactions are
considered. The followinghypothesisisposited:
H3. ESG might have a mediating relationship between
state–business interactions and air pollution.
3|RESEARCH DESIGN AND DATA
3.1 |Model design
To test the impact of state–business interaction on air pollution, we
specify the following model:
pollutionit ¼α0þα1sbinit þXControlsit þYear þProvince þεit ð1Þ
where pollution
it
represents pollutant concentrations such as air qual-
ity index (AQI), particulate matter 2.5 (PM
2.5
), and sulfur dioxide (SO
2
)
and industrial pollutant emissions such as industrial SO
2
emissions
and industrial soot emissions. sbin
it
is state–business interaction the
of the icity in tyear, including the state–business collusion interac-
tions and state–business clean interactions. Controls are a set of city-
level control variables covering economic development, industrial
structure, marketization degree (MD), governmental intervention
(GI) and ER. With pooled data of only 3 years, it might lose much
degree of freedom if we choose city fixed effect; thus, we set the
province fixed effect in this study. Meanwhile, we control the year
fixed effect. Standard errors are clustered at the city level. Similarly,
the impact of state–business interaction on ESG is estimated as
Equation (2):
ESGijt ¼β0þβ1sbinit þXControlsijt þYear þIndustry þξijt ð2Þ
where ESG
ijt
represents the jcorporate's ESG in icity and tyear. Con-
trols are a set of corporate-level variables covering return on net
assets (ROA), dividend financing ratio (DFR), dividend yield ratio
(DYR), dividend as a percentage of distributable profits (DDP), profit
margin of main business (PMB), earnings per share (EPS), and undis-
tributed earnings per share (UEPS). We additionally supplement year
and sectoral fixed effects. Standard errors are clustered at the city
and sectoral level.
3.2 |Variable definitions
3.2.1 | Dependent variable: Pollution emissions
This paper attempts to use the AQI, PM
2.5
, and SO
2
(unit of PM
2.5
and SO
2
:μg/m
3
) as the dependent variable. Additionally, we also
adopt industrial SO
2
emissions and industrial soot emissions (Unit: t)
as robustness tests. The two industrial pollutants are chosen based
on the following reasons. First, industrial SO
2
emissions are one of
China's biggest air pollution problems due to rapid urbanization and
rising energy consumption. And the 70% of energy in China is sup-
plied by coal combustion, whose by-product is mostly industrial SO
2
emissions (Chen et al., 2018). Second, industrial SO
2
emissions and
industrial soot emissions are the focus of many ER (Chen
et al., 2018).
4MENG ET AL.
3.2.2 | Independent variable: State–business
interactions
Two indicators express the state–business interactions, that is, the
state–business collusion interactions and state–business clean
interactions. The state–business collusion interactions are differ-
ent from corruption, which is a broader concept than corruption.
Actually, collusion is that collaborative parties reach a side contract
to make them benefit from the collaboration. However, the state–
business collusion interactions usually suppress the normal market
competition through illegal forms such as deceiving, misleading,
and monopolization (Tsai et al., 2021). Thus, local governments
manipulate resource allocation to support the collusive corporates
in exchange for the local economic development (Nie & Li, 2013).
Meanwhile, these collusive corporates relying on political
resources might achieve business profits, whereas these circum-
stances might cause entrepreneurs to prefer to foster the collusive
relationship with local officials rather than spend much effort on
fulfilling ESG. During anti-corruption campaign of China since
2013, the Chinese president Xi Jinping stated a “clean”new type
of state–business interactions and defined state–business clean
interactions, that is, “local politicians need to be away from businesses
to certain extents, work by the laws, and don't collude with them to go
beyond public-private boundaries.”
3
Following the Research Report on the Relationship between Govern-
ment and Business in Chinese Cities (Research Report) issued by the
National Academy of Development and Strategy of Renmin University
of China (NADSRUC), we design the state–business collusion interac-
tions by 101 minus state–business health index, and it is expressed in
natural logarithm form at the end. The state–business health index is
consisted of five aspects: the government's care for the corporates
(10%), the government's services for the corporates (40%), the corpo-
rates' tax burden (10%), the government's integrity (10%), and the
government's transparency (30%). Among, the state–business clean
interaction is consisted of the government's integrity and the govern-
ment's transparency. Additionally, the detailed construction process
and data sources of the state–business health index and the state–
business clean interactions might learn from the document.
4
TABLE 1 Descriptive statistics of main variables
Variable N Mean S.D. Definition
Panel A: city-level
collusion 810 67.34 16.47 101-state–business health index (logarithm form)
clean 810 53.54 19.99 Research Report of NADSRUC (logarithm form)
AQI 810 70.38 18.82 Natural logarithm of AQI
PM
2.5
810 41.78 13.30 Natural logarithm of PM
2.5
SO
2
810 14.81 8.95 Natural logarithm of SO
2
in air pollution
industry_SO
2
763 14,673.35 17,193.97 Natural logarithm of industrial SO
2
emissions
industry_soot 762 15,975.43 20,486.14 Natural logarithm of industrial soot emissions
light 810 141,303.40 107,274.00 Natural logarithm of nighttime light
Str 810 41.83 13.95 Industrial structure (%)
GI 810 0.22 0.11 Governmental intervention
ER 810 0.84 0.15 Environmental regulation
MD 810 1.26 0.79 Marketization degree
Panel B: corporate-level
ESG 10,560 19.94 8.97 Hexun ESG rating score
ROA 10,560 5.61 339.03 Return on net assets
DFR 10,560 1.67 2.51 Dividend financing ratio
DYR 10,560 3.45 367.17 Dividend yield ratio
DDP 10,560 2.57 4.41 Dividend as a percentage of distributable profits
OPE 10,560 31.79 1703.69 Profit margin of main business
EPS 10,560 0.41 3.97 Earnings per share
UEPS 10,560 1.61 5.98 Undistributed earnings per share
Note: The N and S.D. indicate observations and standard deviation in the research sample.
3
Data sources: http://guoqing.china.com.cn/keywords/2018-12/18/content_74284511.htm,
accessed by June 27, 2022.
4
Data sources: http://nads.ruc.edu.cn/upfile/file/20191231162604_414000_57466.pdf,
accessed by June 27, 2022.
MENG ET AL.5
3.2.3 | Mediating variable: ESG
Hexun.com provides a corporate-level ESG score of Chinese
listed firms with a maximum of 100. A higher score represents a
better corporate’ESG performance. We adopt the comprehensive
ESG score to proxy for corporate’ESG performance.
Additionally, we winsorize the data at the 1% and 99% levels to
decrease the impact of extreme data. And we delete some
corporate-level observations owing to missing many financial values.
As such, we obtain a sample with 10,560 observations.
3.2.4 | Control variables
Based on previous literature (Deng et al., 2020; Feng et al., 2020;He
et al., 2022; Hua et al., 2018; Jiménez-Parra et al., 2018; Liu
et al., 2021; Qi et al., 2020), the control variables mainly contain two
kinds of influencing factors in the regression, including corporate level
and city level, in order to reduce bias from omitted variables, which is
shown in Table 1. At the corporate level, the higher probability of the
corporate, the better the firm's current status and growth prospects
are. Then, the corporates have the strong incentives of ESG in busi-
ness operations and acquire market reputation. Thus, most studies
use ROA, return on equity, and Tobin's Q as the control variables.
Taking into account growth ability, profitability, profit quality, and risk
control, this paper adopts various and vital financial indicators to con-
trol the impact of overall financial status of corporates on ESG. There-
fore, we use various and vital financial indicators to control for firm
profitability, including ROA, DFR, DYR, DDP, PMB, EPS, and UEPS.At
the city level, we employ nighttime light to proxy for the economic
development owing to the possible gross domestic product (GDP)
manipulation (Xu et al., 2015). We use the ratio of industry employees
to the total employees of all sectors in a city to proxy for the change of
industrial structure. And we add the possible factors influencing pollu-
tion emissions, including MD, GI, and ER. The MD and GI are measured
by the ratio of private-sector employees to the total employees and the
fiscal spending as a percentage of GDP, respectively. Following the
study of Li and Zou (2018), the ER is based on the three indicators of
urban domestic sewage treatment rate, domestic waste harmless treat-
ment rate, and comprehensive utilization rate of industrial solid waste.
After standardized treatment, the entropy weight method is used to
synthesize the ER indicators of various cities.
3.3 |Data collection and sample description
These listed corporate-level data are mainly from the China Stock
Market & Accounting Research (CSMAR) database in 2017–2019,
while ESG data are scraped from Hexun.com.
5
The ESG
evaluation system from Hexun.com covers 13 secondary indicators
and 37 tertiary indicators along several aspects, including
shareholder responsibility, employee responsibility, supplier, customer
and consumer responsibility, environmental responsibility, and social
responsibility, which can reflect the environmental, social responsibil-
ity, and corporate governance. It has been widely adopted in ESG-
related literatures in China in recent years (He et al., 2022).
The data period 2017–2019 is selected because the state–
business interactions index from the Research Report was issued firstly
by the National Academy of Development and Strategy of the Renmin
University of China in 2018. The recent research report was issued in
2021. Generally speaking, the data year of various domestic and for-
eign statistical data and rankings is usually 1 year later than the
release year. Thus, we compile state–business interactions index data
over 2017–2019 and merge the corresponding other data.
The city-level data on air pollution are from the Chinese Ministry
of Ecology and Environment in 2017–2019, while data on control var-
iables (such as GI, ER, and MD) are from the Chinese City Statistical
Yearbook. The state–business collusion interactions are measured by
the state–business interaction index from the Research Report issued
by the NADSRUC.
6
The data of state–business clean interactions are
directly obtained from Research Report issued by NADSRUC. Earth
Observation Group produces the nighttime light data with the opera-
tional linescan sensor onboard defense meteorological satellite pro-
gram satellites, whose elaborate procession is followed by the study
of Shi et al. (2014).
Moreover, we pay much attention to the industry and heavy
pollution sectors. The industry sector includes the mining sector and
TABLE 2 Sample distribution
Sectors 2017 2018 2019 Total Percent
Agriculture 38 36 34 108 1.02%
Industry 2,397 2,368 2,327 7,092 67.16%
High polluting 916 901 882 2,699 25.56%
Services 953 928 904 2,785 26.37%
Transportation 99 93 90 282 2.67%
Construction 99 96 98 293 2.77%
Total 3586 3521 3453 10,560 Not applicable
Percentage 33.96% 33.34% 32.70% Not applicable 100%
Note: Due to repeated additions, the total sample does not include the high-polluting sectors.
5
Data sources: http://stockdata.stock.hexun.com/zrbg/Plate.aspx?date=2019-12-31,
accessed by June 27, 2022.
6
Data sources: http://nads.ruc.edu.cn/zkcg/ndyjbg/index.htm, accessed by June 27, 2022.
6MENG ET AL.
manufacturing sectors. This is because that 67.16% of the sample
corporates are from the industry sector, and many heavy pollution
corporates are also from industry sector (Table 2). According to
Guidelines for Environmental Information Disclosure of Listed Compa-
nies of China and the 2012 version of the industry classification stan-
dard of the China Securities Regulatory Commission as well as
following the study of Zhong et al. (2022) and Ma et al. (2021), the
heavy pollution sector refers to the mining, textile, paper and paper
products, petroleum, chemical industry, chemical fiber, black (non-
ferrous) metal smelting and processing, rubber and plastic, pharma-
ceutical, fur products, and coal-dominated electricity and heating
provisions. The detailed classification of the heavy pollution sector
is presented in Table A1.
4|EMPIRICAL RESULTS
4.1 |Baseline results
Table 3shows the baseline findings of Equation (1). Columns (1)–(3) show
that the coefficients of the state–business collusion interactions are
positive and statistically significant at the 5% and 1% levels after control-
ling the provincial- and year-fixed effects. This supports H1.Ahigher
level of state–business collusion interactions is positively associated with
air pollution. Specifically, a 10% increase in collusion implies increases in
AQI concentration by 0.382%, increases in PM
2.5
concentration by
0.457%, and increases in SO
2
concentration by 1.116%. Our result is con-
sistent with prior literature (Dincer & Fredriksson, 2018; Lisciandra &
Migliardo, 2017; Sulemana & Kpienbaareh, 2020), showing the state–
business collusion interactions such as corruption will raise air pollution.
Moreover, the point estimates in SO
2
rise significantly relative to AQI
and PM
2.5
. This is because industrial sectors mostly produce SO
2
emis-
sions, and China's industrial SO
2
emissions account for 30% of the total
exhaust gas emissions (Yuan et al., 2020). Meanwhile, the state–business
collusion interactions mainly influence industrial sectors; for example,
industrial sectors such as coal, oil, electricity, and other manufacturing
fields have become the hardest hit area for corruption since the anti-
corruption campaign in China.
7
7
The executives in the energy field have been sacked one after another, which becomes the
hardest hit by corruption, http://fanfu.people.com.cn/n/2015/0428/c64371-26919263.
html, accessed by June 8, 2022
TABLE 4 The impact of state–
business collusion interactions on ESG Dependent variable: ESG
(1)
Full sample
(2)
Industry
(3)
Heavy pollution
State–business collusion interactions 0.3260**
(0.1608)
0.2348*
(0.1318)
0.2880***
(0.0894)
Controls YES YES YES
Industry/year YES YES YES
Adj-R
2
0.3712 0.4466 0.5468
N 10,560 7092 2699
Note: The state–business collusion interactions in natural logarithm. The standard errors are reported in
parentheses, which are clustered at the city and sector level for all regressions. Industry and year
represent sectoral fixed effect and year fixed effect. The controls are ROA, DFR, DYR, DDP, PMB, EPS,
and UEPS.
***p< .01. **p< .05. *p< .1.
TABLE 3 The impact of state–
business collusion interactions on air
pollution
Dependent variable: air pollution
(1)
AQI
(2)
PM
2.5
(3)
SO
2
State–business collusion interactions 0.0382**
(0.0157)
0.0457**
(0.0219)
0.1116***
(0.0335)
Controls YES YES YES
Province/year YES YES YES
Adj-R
2
0.7810 0.6962 0.5807
N 810 810 810
Note: The state–business collusion interactions and air pollution in natural logarithm. The standard errors
are reported in parentheses, which are clustered at the city level for all regressions. Province and year
represent provincial fixed effect and year fixed effect. The controls include the light, Str, MD, GI, and ER.
***p< .01. **p< .05. *p< .1.
MENG ET AL.7
Table 4presents the baseline findings of Equation (2), showing
the impacts of the state–business collusion interactions on ESG. Col-
umn (1) of Table 4suggests that the state–business collusion interac-
tions are also associated with a reduction in the ESG performance of
corporates. It supports H2; that is, the state–business collusion inter-
actions increase air pollution by decreasing the ESG. The results of
Columns (2) and (3) indicate that the state–business collusion interac-
tions also significantly decrease the ESG of the industry and heavy
pollution sectors, and the coefficient of heavy pollution sectors is
higher than that of industry sectors. As mentioned above, the state–
business collusion interactions might provide these corporates with
the means to avoid burdensome environmental taxes and fines and
loosen ER, thereby reducing the incentive for ESG fulfillment.
To clearly present the role of ESG in reducing pollutants, we
depict Figure 2. First, the results indicate that ESG is negatively
related to pollutants in the industry sector and heavy pollution sector.
The findings are strongly related with the results of Tables 3and 4.
And together with Hypotheses 1and 2, we propose that ESG medi-
ates the relationship between state–business interactions and air pol-
lution, supporting H3.
4.2 |Robustness checks
4.2.1 | Various pollutants
We replace air pollution with industrial SO
2
emissions and industrial
soot emissions in Equation (1). As shown in Table 5, the results are
similar to those of Table 3. Thus, our baseline findings do not change
by employing alternative pollutants. Moreover, we also attempt to
exclude the time confounding factors of AQI, PM
2.5
, and SO
2,
such as
the economic cycle and macroeconomic policies over time; thus, we
add year fixed effect in regression. As depicted in Figure 3, the rela-
tionship between ESG and pollutants is still negative.
4.2.2 | Alternative ESG measurement
As a proxy for ESG performance, we employ aggregate and individual
ESG evaluation rating scores. However, ESG rating scores are also
classified into five grades (A, B, C, D, and E). Thus, we divide the score
index ESG into five categories as a robustness test according to the
existing literatures, for example, Bae et al. (2021), Feng et al. (2022),
and Do and Kim (2020). Specifically, we assign 5 scores for A, 4 scores
for B, 3 scores for C, 2 scores for D, and 1 for E. The higher the
scores, the better the ESG performance. The results of Table 6pre-
sent that the impact of the state–business collusion interactions on
ESG degree is significantly negative except for the heavy pollution
sectors, but its impact is still negative, indicating that the collusion still
significantly inhibits ESG implementation. These findings again sup-
port the abovementioned results and verify the mediating role of ESG
between state–business interactions and pollutants.
4.3 |Additional analysis
We have realized that the state–business collusion interactions
increase pollution concentration through the abovementioned
FIGURE 2 The relationship between ESG and pollutants in the industry sector and heavy pollution sector in 2017–2019. Note: Figures (a), (c),
and (e) depict the relationship between corporate's ESG and AQI, PM
2.5
, and SO
2
in the industry sector, respectively; Figures (b), (d), and (f) depict
the relationship between corporate's ESG and AQI, PM
2.5
, and SO
2
in heavy pollution sector, respectively. Air pollution is expressed in the natural
logarithm. And we exclude the sectoral confounding factor of ESG by adding the sectoral fixed effect in regression.
TABLE 5 Alternative measurement of pollutants
(1)
industry_SO
2
(2)
industry soot
State–business collusion
interactions
0.5797***
(0.1230)
0.5935**
(0.2645)
Controls YES YES
Province/year YES YES
Adj-R
2
0.4859 0.3592
N 763 762
Note: The state–business collusion interactions and pollutants emissions in
natural logarithm. The standard errors are reported in parentheses, which
are clustered at the city level for all regressions. The controls and fixed
effect are the same with the Table 3.
***p< .01. **p< .05. *p< .1.
8MENG ET AL.
FIGURE 3 The relationship between ESG and pollutants in the industry sector and heavy pollution sector in 2017–2019 excluding time
confounding factors. Note: Figures (a), (c), and (e) depict the relationship between corporate's ESG and AQI, PM
2.5
, and SO
2
in industry sector,
respectively; Figures (b), (d), and (f) depict the relationship between corporate's ESG and AQI, PM
2.5
, and SO
2
in heavy pollution sector,
respectively. Air pollution is expressed in the natural logarithm. The other setting is the same as Figure 2.
TABLE 6 Alternative measures for
ESG Dependent variable: ESG degree
(1)
Full sample
(3)
Industry
(4)
Heavy pollution
State–business collusion interactions 0.0056***
(0.0019)
0.0067**
(0.0012)
0.0044
(0.0031)
Controls YES YES YES
Ind/year YES YES YES
Adj-R
2
0.0937 0.1369 0.2276
N 10,560 7092 2699
Note: The state–business collusion interactions in natural logarithm. The standard errors are reported in
parentheses, which are clustered at the city and sector level for all regressions. The controls and fixed
effect are the same with the Table 4.
***p< .01. **p< .05. *p< .1.
TABLE 7 The impact of the state–business clean interactions on
pollutants emissions
Dependent variable: air pollution
(1)
industry_SO
2
(2)
industry soot
State–business clean interactions 0.1262*
(0.0707)
0.1311*
(0.0780)
Controls YES YES
Province/year YES YES
Adj-R
2
0.4605 0.3327
N 763 762
Note: The state–business clean interactions and pollutants emissions in
natural logarithm. The standard errors are reported in parentheses
clustered at the city level for all regressions. The controls and fixed effect
are the same with the Table 3.
***p< .01. **p< .05. *p< .1.
TABLE 8 The impact of the state–business clean interactions on
ESG
Dependent variable: ESG
(1)
Industry
(2)
Heavy pollution
State–business clean interactions 0.8867***
(0.1964)
0.5933*
(0.2815)
Controls YES YES
Ind/year YES YES
Adj-R
2
0.4469 0.5461
N 7092 2699
Note: The state–business clean interactions in natural logarithm. The
standard errors are reported in parentheses clustered at the city and
sector level for all regressions. The controls and fixed effect are the same
with the Table 4.
***p< .01. **p< .05. *p< .1.
MENG ET AL.9
analysis. And it is harmful to the ESG fulfillment, in turn, further
worsens environmental quality. In this section, we further discuss
which state–business interactions are helpful for the improvement of
environmental quality and ESG fulfillment and focus mainly on the
industry sector and heavy pollution sector. This is because the two
sectors play a crucial role in pollution emissions. Meanwhile, we still
set the dependent variable as the industrial SO
2
emissions and indus-
trial soot emissions because they are major pollution emissions for the
two sectors. We find that the coefficients of the state–business clean
interactions on pollutants and ESG are significantly negative and posi-
tive, respectively (Tables 7and 8). It indicates that conditional on all
else equal, the state–business clean interactions are more likely to
decrease pollution emissions and encourage corporates to engage in
ESG. Moreover, different from the effect of the state–business collu-
sion interactions, the impact of the state–business clean interactions on
ESG in industry sector corporates is larger than that of the heavy pollu-
tion sector corporates, which might have two reasons. First, the amount
and categories of industry sector corporates are much higher than that
of the heavy pollution sector corporates; for example, the industry sec-
tor covers 35 two-digit industries while the heavy pollution sector only
contains 19 two-digit industries. Thus, it might cause stronger connec-
tions between the state–business collusion interactions and ESG in
industrial corporates. Second, corruption is worst in manufacturing
except for the heavy pollution sector such as coal, oil, and electricity
fields.
8
Thus, the state–business clean interactions might exert a more
significant effect on the industrial corporates than the heavy pollution
corporates.
5|CONCLUSION AND POLICY
IMPLICATIONS
This paper aims to explore the impact of the state–business interactions
on air pollution from the role of ESG. Our findings demonstrate that the
state–business collusion interactions worsen air quality and hinder the
fulfillment of ESG. However, the state–business clean interactions
could decrease pollutants emissions and promote ESG practices that
enable the corporates acquire competitive advantages and achieve sus-
tainable development. Importantly, better ESG practices might result in
improvements to air quality. This result also implies that the promotion
effects of state–business clean interactions on environmental gover-
nance can be reinforced through better ESG implementation.
Based on the stakeholder theory, the conclusions emphasize that
when involving a specific issue such as environmental governance, the
impacts of the various parties involved might need to be considered
comprehensively. This is because that both the corporates and local
governments have an incentive to improve air quality in state–
business clean interactions; thus, a joint effort by all stakeholders is
necessary. Thus, it usually might be advisable to cover micro-level var-
iables reflecting the impacts of various stakeholders in model design
when intending to explain the causal relationship characterized by
state–business intervention.
This study discusses that ESG mediates the relationship between
state–business interactions and air pollution. Previous literature has evi-
denced that state–business interactions and ESG are important drivers
of environmental governance. However, few studies propose that an
ESG strategy might be exploited to mediate the relationship between
state–business interactions and environmental governance. This study
found that ESG is negatively related to pollutants emissions, and the
state–business clean interactions could reinforce ESG practices, in turn,
further decreasing pollutant emissions. Our findings help researchers
understand the mediating mechanisms of ESG between state–business
interactions and environmental governance and help corporates realize
the importance of ESG in enhancing their green reputation.
In addition, this study also brings some policy implications for corpo-
rate managers and local governments. First, the mediating role of ESG
reminds managers to attach more importance to furthering green con-
sciousness, enhancing environmental adaptability, and using an appropri-
ate approach to adjust their strategic business to meet the requirements
for ER. In turn, these efforts also would improve organizational social rep-
utation, help corporates obtain more resources for strategic development,
and acquire corporate competitive advantages. Especially, the industrial
and heavy pollution listed corporates that are not ESG-sensitive should
realize that one of the principal ways available for decreasing pollutants
emissions is by exercising better ESG practices.
Second, the Chinese government has tried transforming a low-
carbon and environmentally friendly society in recent years. However,
the governments might realize the critical role played by the corpo-
rates' ESG, especially for reducing pollutants emissions. The policy-
makers not only need to create a state–business clean interactions
but also design incentives, for example, tax breaks or a pre-
requirement to bid for public tenders for corporates to encourage
them to bear more social and environmental responsibility. This is
because if more corporates in the certain industry fulfill ESG, the
norms, values, and beliefs prevalent in that industry will be generated.
It might promote non-ESG-sensitive corporates to participate in ESG
to follow their socially responsible peers in order to maintain their
social legitimacy and ensure their long-term operation.
ACKNOWLEDGMENTS
This study is supported by National Natural Science Foundation of
China (Nos. 72173095 and 71703120), the Supporting Plan for Inno-
vation in Shaanxi Province of China (No. 2022KRM026), and the Key
Project of Social Science in Xi'an of China (No. 22JX82).
CONFLICT OF INTEREST
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
AUTHOR CONTRIBUTIONS
Guanfei Meng performed the research design, methodology, visualiza-
tion, writing—original draft, and writing—review & editing. Jianglong Li
8
Corruption in China: What Companies Need to Know, http://www.charneyresearch.com/
wp-content/uploads/2015/01/White-Paper-Corruption-in-China-FINAL-v3.pdf, accessed by
June 29, 2022.
10 MENG ET AL.
performed the conceptualization, investigation, funding acquisition,
project administration, supervision, methodology, writing—original
draft, and writing—review & editing. Xiuwang Yang performed the
writing—original draft and writing—review & editing.
ORCID
Jianglong Li https://orcid.org/0000-0001-5388-5859
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How to cite this article: Meng, G., Li, J., & Yang, X. (2022).
Bridging the gap between state–business interactions and air
pollution: The role of environment, social responsibility, and
corporate governance performance. Business Strategy and the
Environment,1–13. https://doi.org/10.1002/bse.3224
12 MENG ET AL.
APPENDIX A
TABLE A1 The specific industries of heavy-polluting industries
Code Two-digit Industry (GB/TY 4754–2011) Code Two-digit Industry (GB/TY 4754–2011)
B06 Coal mining and dressing industry C22 Papermaking and paper product industry
B07 Oil and natural gas exploitation industry C25 Industries of petroleum processing, coking, and nuclear
fuel processing
B08 Ferrous metal ore mining and dressing industry C26 Manufacturing of chemical raw materials and chemical
products
B09 Non-ferrous metal ore mining and dressing industry C27 Pharmaceutical industry
B10 Nonmetallic mining and dressing industry C28 Chemical fiber manufacturing
B11 Mining ancillary activities C29 Rubber and plastic products industry
B12 Other mining industry C31 Industry of ferrous metal smelting and rolling
processing
C17 Textile industry C32 Industry of non-ferrous metal smelting and rolling
processing
C18 Textile and apparel industry D44 Industry of electric power and heat production and
supply
C19 Leathers, furs, feathers and related products and
footwear industry
MENG ET AL.13
