Journal of Cleaner Production 222 (2019) 1009e1022
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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Public environmental appeal and innovation of heavy-polluting
enterprises
Yong Du a, *, Ziyi Li a, Jun Du b, Ning Li a, Bo Yan b
a
b
School of Economics and Management, Southwest University, No.2 Tiansheng Road, Beibei District, Chongqing, 400715, China
School of Economics and Management, Ocean University of Guangdong, Zhanjiang, 524088, China
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 30 August 2018
Received in revised form
7 February 2019
Accepted 4 March 2019
Available online 5 March 2019
In recent years, the rapid development of China’s economy has caused a certain degree of environment
damage, and the public environmental appeal has become increasingly strong, which has an impact on
the production, management and sewage sludge treatment of the heavy-polluting enterprises. Using the
search index of the network search engine as a proxy for public environmental appeal, this study examines how and why public environmental appeal affect innovation activities of them through multiple
regression analysis. The results show that public environmental appeal has positive effects on innovation
output, input and efficiency. Besides, mechanism analysis reveal that the increase of public environmental appeal can promote innovation level of heavy-polluting enterprises by promoting analyst
coverage and reputation mechanism. This study puts forward a unique perspective, which not only
extends the understanding of informal environmental regulations but also enlightens the sustainable
development of heavy-polluting enterprises in China.
© 2019 Elsevier Ltd. All rights reserved.
Keywords:
Public environmental appeal
Corporation innovation
Heavy-polluting enterprises
Analyst coverage
Reputation mechanism
1. Introduction
In the past few decades, the world has witnessed that fast-growing
emerging market economies (EMEs) have made great achievements
in economic development and resulted in severe environmental
pollution and ecological devastation, which has overwhelmingly
affected people’s normal lives (Li et al., 2017; Liao, 2018). As the
deterioration of the environment becomes one of the greatest challenges to the world, an increasing number of firms have started to take
the various innovation initiatives to achieve the goal of sustainability
of economic growth (Chen et al., 2018); (Wang et al., 2015). The heavypolluting enterprises have been criticized as the main source of
environment deterioration, for adding to environmental problems
such as climate change, depletion of natural resources, waste production, and lagging corporate environmental responsibility, and
have been required to accept greater responsibility and facilitate the
development of innovation (Zhou et al., 2016).
Innovation is an important source of sustainable competitive
advantage of enterprises and is certainly one of the economic
* Corresponding author.
E-mail addresses: dy772012@126.com (Y. Du), lzy972046@163.com (Z. Li),
dujun58@126.com (J. Du), swulining@swu.edu.cn (N. Li), 14960448@qq.com
(B. Yan).
https://doi.org/10.1016/j.jclepro.2019.03.035
0959-6526/© 2019 Elsevier Ltd. All rights reserved.
activities that investors focus on. Also, it is regarded as an effective
way to balance profitability and environmental responsibility while
achieving sustainable development at the same time (Li et al., 2017;
Liao, 2018). However, the effect of externalities could make enterprises invest little in innovation activities (Li et al., 2017) because
innovation is costly but comes with great financial pressure and
long-term return (Doran and Ryan, 2016). Previous researches have
studied the driving factors of innovation, dividing them into
external and internal factors. External factors highlight incentives
stemming from environmental policies (Liao, 2018), emission
trading (Borghesi et al., 2015), public attentions (Cheng and Liu,
2018), consumer green demand and competition pressure (Chen
et al., 2018). Internal factors emphasize situations and characteristics of enterprise, including human resources management
(Antonioli et al., 2013), corporate governance (Amore and
Bennedsen, 2016), technological trajectory (Saez-Martinez et al.,
2016), corporate profitability (Li et al., 2017), and manager characteristics (Wang et al., 2015). Among these driving factors, public
environmental appeal has been identified as a critical driver of
green innovation (Cheng and Liu, 2018(Cheng and Liu, 2018; Liao,
2018). As an informal environmental regulation, public environmental appeal, representing the public disclosure of pollution information, has been considered as the third wave of the regulation,
following after legal regulations and market-oriented regulations
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Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
(Tietenberg, 1998). When formal regulations on environmental
pollution do not exist or are not fully effective, the affected public is
often able to negotiate with factories through informal regulation
to reduce pollution (Pargal et al., 1997). The increasing public
attention to environmental pollution and the increasing disclosure
by media bring invisible pressure to the government and heavypolluting enterprises, which will form informal environmental
regulations. However, there is limited studies discussing the impacts of public environmental appeal, as an important informal
institution, on the green innovation of high-polluting enterprise.
In order to fill this research gap, our study explore the impact of
public environmental appeal on enterprise innovation and develop
the understanding. Considering the interaction between external
regulations and internal corporate governances, this research
further takes management myopia into account and explore how
and why it impacts the relationship between public environmental
appeal and innovation. In addition, in order to reveal the “black
box” of informal environmental regulations and enterprise innovation in China, analyst coverage mechanism and reputation
mechanism are introduced into the framework under the context of
information asymmetry, which provides an effective research
setting, as the deterioration of the environment has attracted a lot
of public attentions (Cheng and Liu, 2018).
The main empirical sections use descriptive statistics, multiple
regression analysis, Tobit model, and inter-group difference test.
Besides, instrumental variable estimation, Placebo test and so on
are used in other empirical sections. The samples are selected
Shanghai A-share listed enterprises in heavy-polluting industries
from 2011 to 2015.
2. Research background
As a country with the largest population in the world, China
enjoys high-speed economic growth at the cost of high consumption and high pollution for quite a long time. From 1998 to 2018,
China’s GDP grew from less than 0.32 trillion dollars to more than
13.6 trillion dollars. In just 30 years, China’s GDP grew by nearly
42.5 times, and the gap with the United States narrowed from
nearly 17 times to less than 2 times. Accompanied by a series of
ecological degradation problems and environmental pollution
events, “2018 global Environmental Performance Index”, which is
jointly released by Yale university, Columbia University, BBS and
other institutions of the world economy, shows that China and
India rank 120th and 177th among EMEs, indicating the environmental pressures brought by rapid economic growth. In terms of air
quality, China ranks fourth from the bottom due to the comprehensive assessment of PM2.5 and other aspects. This rank reflects
not only the severity of China’s environmental pollution, but also
the relatively weak environmental regulations. The elementary
contradiction between the productivity and production relations
determine that people will pay more attention to the physical and
mental health of future generations and environmental pollution
after meeting the basic needs for food and clothing. As the public
begin to realize the enterprise, especially the heavy-polluting ones,
should take the responsibilities of environment pollution which is
resulted from their operating activities, public environmental appeal is becoming an important informal regulation to promote the
government and the enterprises’ protection behavior. For example,
the “PM2.5 extraordinary” event in Beijing in 2011 officially aroused
the public’s concern on environmental pollution nationwide. In
2015, “the documentary under the dome” was broadcasted all over
the country, shot by former CCTV reporter Jing Chai, and made a
deep wave of criticizing the prior extensive economic growth mode
(investment that mainly relies on increasing production factors). At
the same time, it presented the contradiction between economic
development and public demands for the Chinese government’s
environment requirements. At the 19th national congress of the
communist party of China in October 2017, general secretary Xi
Jinping proposed “the new consensus is that beautiful scenery is as
valuable as gold and silver mines”, which means ecological construction and environmental protection in China has been taken to
a strategic perspective.
As a representative of developing countries, China has recognized that the coordinated development of environmental regulation and technological innovation through strengthening
environmental protection while transforming from the economic
development mode to intensive economic growth mode. In fact, the
“intensive economic growth mode” is contrast to extensive economic growth mode refers to increasing production by adopting
new technologies and new processes, improving machinery and
equipment, and increasing production scale based on scientific and
technological level. In this way, economic growth is achieved,
consumption is low, costs are low, product quality can be continuously improved, and economic benefits are high. Among this,
technological innovation is the decisive factor to realize the “winwin" goal of environmental protection and economic development.
Under the pressure of both informal and formal institutions, enterprises level especially heavy pollution industry will also have to
change its original rely on high consumption, high emission, high
pollution production pattern, by promoting environmentally
friendly and recyclable technological innovations, to achieve
“green”, “clean”, “energy saving”, “environmental protection” production purposes.
Although a large number of studies have been conducted on the
impact of formal regulations on enterprises’ innovation behaviors,
there is still no study directly expand research perspective to public
environmental appeal, as an informal regulation. China provides us
a good setting for exploring the association between informal
environmental institutions and innovation of firms, especially the
heavy-polluting enterprises. Thus, our research is of certain theoretical value and practical significance.
3. Literature review and hypotheses development
3.1. Driving factors research of enterprise innovation
In general, driving factors of enterprise innovation can be
divided into two kinds of drivers: external institutional drivers and
internal governance drivers. Firstly, the research on external drivers
primarily adopts the resource-based and institutional theories as
its theoretical foundations (Porter, 1991; Porter and Vanderlinde,
1995) and environmental regulations including mandatory laws,
rules and policies implemented by governments remain dominating driving force compared to other factors (Hojnik et al., 2016),
such as placing requirements on the technologies used by enterprises and for the concentration of pollutant discharge (Bergek and
Berggren, 2014), limiting production to the non-qualified production lines (Liao, 2018), incorporating environmental taxes and
pollution control subsidies (Xie et al., 2017) and so on. According to
Porter hypothesis, appropriately strict environmental regulations
and flexible environmental policy tools can promote innovation
(Porter and Vanderlinde, 1995; Ambec and Barla, 2002). Enterprises
especially the heavy-polluting ones have to adopt a series of
innovation activities to eliminate backwards production capacity,
improve production processes to reduce energy consumption and
pollution emissions, avoid punishment, reduce costs and meet the
government’s requirements (Lui and Leamon, 2014; Ghisetti and
Pontoni, 2015; Hojnik et al., 2016; Zhao and Sun, 2016; Liu and
Wang, 2017; Liao, 2018). In addition, based on Porter hypothesis,
previous studies that attempt to clarify the relationship between
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
formal regulation and innovation activities have provided various
results. For instance, Yuan and Xiang (2018) indicated that the
stringency of environmental regulations and rules contribute to an
increase of innovation expenditures of a focal firm. Furthermore,
there are more and more scholars focusing on the informal regulation drivers which are information-based and are not mandatory
enforcement requirements (Lindeneg, 1992; Desrochers and
Haight, 2014; Song et al., 2017; Cheng and Liu, 2018; Liao, 2018;
Li et al., 2018). In fact, most informal regulation can improve public’s initiative in enterprises environmental participating and promote environmental innovation through market-oriented
mechanisms (Liao, 2018). Previous researches have studied public
attitudes toward high pollution and high emission projects (Liu
et al., 2017), analyst coverage (He and Tian, 2013), policy uncertainty (Bhattacharya, 2014), stock liquidity (Fang et al., 2014) and so
on. Secondly, previous research on internal governance driving
factors of enterprise innovation have pointed out that the awareness of enterprises’ senior managers, internal resources and
knowledge. The need for external capital (Vidaver-Cohen and
Brønn, 2015; Hojnik et al., 2016; Marzucchi and Montresor, 2017;
Acharya and Xu, 2017) could also affect the enterprises’ environmental innovation. In fact, internal drivers influence enterprise
innovation primary through inducing the managers to evaluate the
involved benefits, costs and risks in the adoption of environmental
innovation (He and Liu, 2018) and achieving current or future
revenue maximization.
In spite of the relatively large empirical evidence on the drivers
of enterprise innovation, some other critical issues are still largely
unexplored, especially the area of the mediating and moderating
mechanisms between these drivers is still not thoroughly explored
in innovation studies.
3.2. Consequence research of public environmental appeal
There is no doubt that following news media trends, public
appeal is huge and can have a direct impact on behaviors of both
governments and companies as an important informal institution
(Quesnel and Ajami, 2017; Cheng and Liu, 2018). Meanwhile, the
proxy variable, such as search trends, Wikipedia article edits are
widely used to measure the public appeal (Bennett et al., 2018). The
prior research on public environmental appeal basically focus on its
consequence, that is, public environmental appeal how to effect
environmental protection behaviors as an informal institution,
either from government perspective or enterprise perspective. For
example, Marciano et al. (2014) studied public attitudes in Maine
(USA) mill towns toward forest-based bio refineries, Bird et al.
(2014) studied public attention regarding nuclear power in
Australia, Liu et al. (2017) studied public attitudes toward modern
coal-fired power plant projects in China. All above researches prove
that the public demand for environmental protection can prompt
the government to take corresponding actions and push the government to make greater. The influence of public appeal on enterprise environmental protection behavior has also been widely
discussed. For example, prior studies argue that the enterprise
especially the heavy-polluting ones have begun to use more environmentally friendly and recyclable materials for production,
develop technological innovations (Almeida et al., 2018), adopt
processes that optimize efficiency for cleaner production (Fan et al.,
2018), use advanced management methods to improve corporate
environmental performance (Clarkson et al., 2011). In addition,
some literature studied the impact of public environmental appeal
on enterprise operation and management as well as the governance
mechanism (Franzen and Meyer, 2010; He and Liu, 2018). As
Kathuria (2007) has pointed out the limitations of formal regulations to stem pollution in developing countries, and as a result,
1011
there is a growing interest in the potential of informal regulations
to achieve environmental goals. However, research on public appeal for environmental protection in China started late, and due to
the difficulty in obtaining and collecting enterprise environmental
behaviors data, the existing research results are relatively limited
and some other consequences and their moderating mechanisms
especially the indirect ones have not been conducted.
3.3. Public environmental appeal and enterprises innovation
As mentioned above, innovation, especially the heavy-polluting
enterprise innovation, is worth studying due to its uniqueness.
However, Chinese government’s intervention in enterprises mainly
involves market regulations and functional supervision, which has
a great impact on investment and trade, but the direct impact on
innovation activities is relatively slight (Xie et al., 2017). Meanwhile, many researches about public appeal effects on enterprise
environmental protection behaviors have been conducted, while
most of them are only limited to benchmarks like “greenhouse gas
emissions”, “waste production” and “water consumption”, whether
or not disclosed in the annual reports (Braam et al., 2016). As far as
we know, there is no literature directly use public environmental
appeal as the direct driving factor to study how it affects the heavypolluting enterprise innovation activities. Due to the mechanism of
informal regulation is much more complicated, there is particularly
lack of empirical testing of mechanisms. The research gap that
whether public environmental appeal has significant influence on
innovation behavior appears. To fill this research gap, we try to do
the following analysis and develop related research hypothesis
(Fig. 1): it has been proposed that enterprises, especially heavypolluting ones, tend to seek technology innovation when they
have more passion and responsibility on environmental governance (Blackman, 2010). However, according to information
asymmetry theory (Jensen and Meckling, 1976), there is a serious
information asymmetry between outside stakeholders and internal
managers (Fang et al., 2014). Especially, innovation activities are
commonly characterized by high risk and high investment, long
periodicity, high uncertainty and high probability of failure (He and
Tian, 2013), Thus, it is easier for outside stakeholders to require
financing costs higher and underestimate enterprise in the short
run. In other words, information asymmetry would make the enterprises engage in innovation activities which will probably cause
a decline in profits and dissatisfaction with the performance in
financial statements form investors. As Cheng and Liu (2018) have
pointed out, in a modern democratic society, the power of public
attention is great and can have a direct impact on the behavior of
both the government and companies.
In this case, on the one hand, the public environmental appeal
can relieve the information asymmetry between outside
Fig. 1. Hypothesized conceptual model.
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Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
stakeholders and internal managers and promote the environmental innovation of enterprises, through reflecting the positive
awareness of public participation in environmental governance (Li
et al., 2017) and communicating their true demands for environmentally friendly products to the market (Bird, 2014). Meanwhile,
under the public pressure, the government will provide fiscal
subsidy for the heavy-polluting enterprises and help them to carry
out environmental innovation activities (Liao, 2018). From this
perspective, the enterprises will promote self-discipline,
strengthen governance of environmental pollution, and ensure
implementation of environmental innovation through getting a
new higher market valuation and lower financing costs.
Considering the above reviews, this study proposes the
following hypothesis:
H1. With the increase of public environmental appeal, the innovation level of heavy-polluting enterprises will improve
significantly.
On the other hand, public environmental appeal may also have a
negative impact on the innovation of heavily polluting enterprises.
The stronger public environmental appeal is, the more likely the
public will reflect their appeal through news reports and parades
(Quesnel and Ajami, 2017). The attention of the public to environmental problems, and the exposure of pollution information by
newspaper, television, network and other media have brought
enormous pressure of public opinion to heavy-polluting enterprises, which have affected the reputation and social image of enterprises and may have a further impact on the stock price of
enterprises (Li et al., 2017). As (Chen et al., 2013) have pointed out
that, when the stock market is pricing, it cannot correctly evaluate
the value of enterprise’s current innovation activities according to
the enterprises’ past innovation behavior. Specifically, the greater
the public pressure on heavy-polluting enterprises is, the higher
returns investors will require as compensation, resulting in higher
cost of equity capital. Similarly, capital from debt will decrease, as
creditors demand higher return. As a result, investors will
conversely tend to underestimate the value of those enterprises
that actively carry out innovation activities, and reduce their investment in such enterprises, which leads to the shortage of
innovation funds in these enterprises and the decline of innovation
level (Li and Shen, 2011; Liu, 2016).
Meanwhile, to prevent public resentment and maintain social
stability, the government have to exert strict administrative pressure, imposing huge fine to force them to complete the environmental control objectives (Liao, 2018). At this time, brought by
formal environmental regulation, enterprises have to invest funds
to deal with pollution. Due to the limited capital of enterprises, the
increase of pollution treatment cost forms the crowding-out effect
of innovative funds and finally leads to a decline in innovation
levels.
Based on the above analysis, this study proposes the following
hypothesis:
H2. With the increase of public environmental appeal, the innovation level of heavy-polluting enterprises will drop significantly.
4. Research method
4.1. Variables and data sources
4.1.1. Variables
Explained variable: The number of patent applications (Patenti,tþ1). It is used to measure the innovation level of the enterprise,
referring to (Fang et al., 2014). Considering the time lapse between
the research and development (R&D) and gaining the patent
output, the number of patent applications in the next year plus 1’s
natural logarithm is used to measure the innovation behavior. The
existing literature mainly measure the innovative behavior of enterprises by investment in R&D or the number of patents. The
reasons for this paper choosing the number of patents are as follows: Firstly, the investment in R&D, which only represents the
innovation resource inputted by the enterprise, cannot reflect the
output result directly. By contrast, the number of patent application
can directly reflect the achievement and performance of the enterprise’s innovation activity. Secondly, the chances are that the
information is lost because the investment in R&D is disclosed by
enterprises voluntarily. None investment in R&D disclosed does not
necessarily mean that innovative activities have not been carried
out. However, the information of the number of patents is always
complete, which can reflect the innovation level of the enterprise
accurately.
Explanatory variable: Natural logarithm of annual average of
pm2.5 Baidu index in each province (Appeali,t). Baidu index is based
on the search volume of Internet users in Baidu, taking keywords as
the statistical object, scientifically analyzing and calculating the
weighted sum of search frequency of each keyword in Baidu web
search. Zheng et al., (2012) used Google Search to construct the
“green” degree index of Beijing housing project. This paper follows
the research method of Zheng et al. (2012). Since Google withdrew
from the mainland China in April 2010, the public rarely used
Google. This paper uses the search index obtained from Baidu, the
largest search engine in China, as an indicator of public environmental appeal. Specifically, we take “PM2.500 as the keyword, search
by province and year, and construct the variable Appeal using the
annual average of Baidu index in each province. We assume that the
keyword search index of “PM2.5” is positively related to public
environmental appeal, and the stronger the public environmental
appeal are, the more frequently the environmental pollution information is searched on the Internet. Therefore, the PM2.5 Baidu
index gained from the above method can reflect the public environmental appeal in the concerning region.
4.1.1.1. Control variables. Referring to the existing literature, the
control variables included in the model are as follows:
(1) Profitability (Roai,t): return on assets, net income divided by
total assets.
(2) Tobin Q value (TQi,t): market value divided by total assets.
(3) Cash flow (CFOi,t): net cash flows from operating activities
divided by total assets.
(4) Board size (Boardi,t): natural logarithm of number of boards.
(5) Proportion of independent directors (Indepi,t): number of
independent directors divided by number of boards.
(6) Management stock holding (Mshi,t): number of executive
stock holdings divided by total number of shares.
(7) Equity concentration (Firstsharei,t): proportion of the largest
shareholder’s stock holding.
(8) Proportion of fixed assets (Tangiblei,t): net fixed assets
divided by total assets.
(9) Debt to assets ratio (Leveragei,t): total liabilities divided by
total assets at end of period.
(10) Enterprise age (Agei,t): the number of years that the enterprise has existed.
(11) Enterprise size (Sizei,t): natural logarithm of total assets.
(12) Enterprise ownership (Soei,t): an indicator variable taking the
value of one if the enterprise is a state-owned enterprise,
zero otherwise.
(13) Duality (Duali,t): an indicator variable taking the value of one
if the CEO is also the chairman of the board, zero otherwise.
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Table 1
Symbols of the variables.
Type of the Variables
Variables
Symbols
Explained variable
Explanatory variable
Control variables
Patent applications
Baidu search index of PM2.5
Profitability
Tobin Q value
Cash flow
Board size
Proportion of independent directors
Management stock holding
Equity concentration
Proportion of fixed assets
Debt to assets ratio
Enterprise age
Enterprise size
Enterprise ownership
Duality
GDP index
Patent
Appeal
Roa
TQ
CFO
Board
Indep
Msh
Firstshare
Tangible
Leverage
Age
Size
Soe
Dual
GDP_GRO
(14) GDP index (GDP_GROi,t): GDP index based on the previous
year.
All the symbols are listed in Table 1.
4.1.1.1. Empirical methods. Firstly, we use multiple regression
analysis to test the impact of public environmental appeal on
innovation behavior of heavy-polluting enterprises, it allows us to
control for many other factors that simultaneously affect the
dependent variable.
Secondly, the distribution of dependent variable piles up at zero,
which is roughly and continuously distributed over positive values,
so the Tobit model could be suitable for this condition.
In robustness tests, we also employ Zero-inflated negative
binomial regression where the dependent variable is the number of
patent applications without logarithmic. When nonnegative
dependent variable is a count variable, a linear model might not
provide the best fit over all values of the explanatory variables.
Furthermore, our results may be biased because of omitted
variables, so we use Instrumental variables estimation to solve the
problem of endogeneity.
Then, a model was built to test the effects of public environmental appeals on innovation of heavy-polluting enterprises:
1013
from 2011 to 2015 as the original samples. 2011, when the “PM2.5
burst table” event occurred, is the beginning of our sample period.
This is when the public began to pay more attention to the air
pollution caused by haze than before, with substantially increase of
the public environmental appeal. The selection of heavy-polluting
enterprises is based on the research by Liu (2015). According to
the “announcement on the implementation of special emission
limits for atmospheric pollutants” issued by Ministry of Environmental Protection of People’s Republic of China and the “listed
companies Industry Classification guidelines” issued by the Securities Regulatory Commission (2012), we select enterprises of 11
industries serve as the sample of heavy-polluting enterprises,
including petroleum and natural gas mining industry (B07), ferrous
metal mining and separation industry (B08), non-ferrous metal
mining and separation industry (B09), petroleum processing,
coking and nuclear fuel processing industry (C25), chemical raw
materials and chemical products manufacturing industry (C26),
chemical fiber manufacturing industry (C28), rubber and plastic
products industry (C29), non-metallic mineral products industry
(C30), ferrous metal smelting and calendering industry (C31), nonferrous metal smelting and calendering industry (C32) and electricity, thermal production and supply industry (D44). Furthermore, we deal with the samples according to the following
principles: (1) removing the special treatment enterprises; (2)
removing the samples which miss relevant data; (3) all continuous
variables are winsorized at 1% and 99%. Finally, 1881 effective
company-year observations are obtained. In some regression
models, the sample size changes due to the new variables and
replacement variables.
We obtained the number and type of patent applications,
corporate finance and governance data from CSMAR database.
Baidu search index data are derived from the keyword search index
on the Baidu website.
4.2. Results
4.2.1. Descriptive statistics and inter-group difference test
Table 2 presents the descriptive statistical analysis of the
relevant variables. The total number of observations of heavypolluting enterprises is 1,881. The mean value of the explained
variable Patent is 2.430, while the maximum and minimum
values are 6.531 and 0 respectively, which shows that the innovation performance of each enterprise is quite different. As for
Patenti;tþ1 ¼ b0 þ b1 Appeali;t þ b2 Roai;t þ b3 TQi;t þ b4 CFOi;t þ b5 Boardi;t þ b6 Indepi;t þ b7 Mshi;t
þb8 Firstsharei;t þ b9 Tangiblei;t þ b10 Leveragei;t þ b11 Agei;t þ b12 Sizei;t þ b13 Soei;t
X
X
þb14 Duali;t þ b15 GDP GROi;t þ bi
Year þ bj
Industry þ εi;t
In this model, i and t were, respectively, enterprise and year, εi;t
was the disturbance. The fixed effects of industry and year were
also controlled. Besides, the industry virtual variables were taken as
the first-class code according to the “listed companies Industry
Classification guidelines” (2012) issued by the Securities Regulatory
Commission.
4.1.2. Data sources
To construct our sample, we select the A-share listed heavypolluting enterprises in Shanghai and Shenzhen Stock Exchange
(1)
the appeal among different province, the value of PM2.5 Baidu
search index (Appeal) is 5.422 range from 8.132 to 2.565,
reflecting that there is a significant difference in public environmental appeals among different provinces. In terms of the
control variables, the mean value of Firstshare is 37.3%, which
indicate that the equity of heavily polluting enterprises is
concentrated. The average asset-liability ratio of heavily polluting
enterprises is 46.1%. The mean value of variable Soe is 0.471,
which indicates that about 47% of the enterprises in the sample
are state-owned enterprises. Likewise, the statistics of the
remaining variables are reasonable.
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Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Table 2
Descriptive statistics.
Variable
Obs
Mean
S.D.
Min
Median
Max
1.Patent
2.Appeal
3.Roa
4.TQ
5.CFO
6.Board
7.Indep
8.Msh
9.Firstshare
10.Tangible
11.Leverage
12.Age
13.Size
14.Soe
15.Dual
16.GDP_GRO
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
1,881
2.430
5.414
0.031
2.119
0.048
2.180
0.367
0.122
0.373
0.340
0.461
14.376
22.258
0.471
0.214
0.093
1.481
1.161
0.049
1.368
0.064
0.190
0.047
0.204
0.158
0.169
0.220
5.102
1.362
0.499
0.410
0.020
0.000
2.565
0.148
0.878
0.130
1.609
0.313
0.000
0.075
0.054
0.049
3.000
19.991
0.000
0.000
0.031
2.485
5.561
0.029
1.708
0.046
2.197
0.333
0.000
0.362
0.320
0.467
14.000
21.991
0.000
0.000
0.089
6.531
8.099
0.155
9.187
0.221
2.708
0.533
0.666
0.821
0.797
0.925
26.000
26.272
1.000
1.000
0.150
Table 3 shows the test results of differences between regions
with high and low appeal. We present the result of mean difference
test in Panel A. For areas with high public environmental appeal,
the mean value of Patent for heavily polluting enterprises is 2.544,
and the mean value of Patent in areas with low public environmental appeal is 2.324. The mean difference is 0.220, significant at
the level of 1%, which indicates that the innovation level of heavy-
polluting enterprises in areas with high appeal is higher than that
of areas with low appeal. A similar conclusion can be reached by the
median test. Both of these results support the hypothesis of this
paper to some extent.
To make the two samples more similar, we employ the Propensity Score Matching method. Specifically, this research generates a dummy variable Appeal_dummy. If the public environmental
appeal index is greater than the third quartile of the sample (the
third quartile mainly to expand the selection of the control group),
then the Appeal_dummy equals to 1, representing the treatment
group. Otherwise, the Appeal_dummy equals to 0, representing the
control group. Using Appeal_dummy as the explanatory variable,
Size, Roa, TQ, CFO and Soe as the characteristic variables to carry out
the logit regression, the propensity score of each sample is calculated. Furthermore, the samples from the control group according
to the matching method that is not put back at 1:1. More precisely,
every sample in the treatment group with a sample in the control
group which has the closest propensity score to it. Ultimately, there
are 460 pairs of samples matched in this way. Fig. 2 shows the
density function graphs before and after matching. As can be seen,
the samples in treatment group and control group are much more
similar after matching. The test results of differences between regions with high and low appeal after matching are shown in
Table 4. The mean difference and median difference are significant
at the level of 5% and 10%, which confirms the previous conclusion.
Table 3
Test of differences between regions with high and low appeal.
Panel A Mean difference
Variable
Patent
High appeal(N ¼ 908)
2.544
Low appeal(N ¼ 973)
2.324
Meandiff(p-value)
0.220*** (0.001)
High appeal(N ¼ 908)
2.565
Low appeal(N ¼ 973)
2.485
Meandiff(p-value)
0.080*** (0.007)
Panel B Median difference
Variable
Patent
*p < 0.1; **p < 0.05; ***p < 0.01.
Fig. 2. Density function graphs before and after matching.
Table 4
Test of differences between regions with high and low appeal after matching.
Panel A Mean difference
Variable
Patent
High appeal(N ¼ 460)
2.643
Low appeal(N ¼ 460)
2.431
Meandiff(p-value)
0.212** (0.031)
High appeal(N ¼ 460)
2.639
Low appeal(N ¼ 460)
2.565
Meandiff
0.074a(0.068)
Panel B Median difference
Variable
Patent
a
p < 0.1; **p < 0.05; ***p < 0.01.
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
1015
Table 5
Results of regression.
VARIABLES
Fig. 3. Public environmental appeal and patent of high-polluting firms.
4.2.2. Results and analysis
Fig. 3 shows the visual relationship between public environmental appeal and patents of high-polluting firms and indicates
that innovation of heavy-polluting enterprise is positively related
to the public environmental appeal. To test the impact of public
environmental appeal on innovation behavior of heavy-polluting
enterprises, we use multiple regression analysis that allows us to
control for many other factors that simultaneously affect the
dependent variable. Furthermore, considering the distribution of
dependent variable piles up at zero but is roughly continuously
distributed over positive values, Tobit model could be quite suitable
for this condition. Fig. 4 shows that the distribution of the explained
variable (Patent) is a mixed distribution composed of a discrete
point (zero) and a continuous distribution. Therefore, it is more
appropriate to adopt the Tobit model.
Table 5 represents the results of estimating Equation (1). The
result of OLS regression is listed in the column (1). The estimation
coefficient of Appeal is 0.341, and it is significant at the 1% level,
which means that the increase of public environmental appeal
makes the number of patent applications the next year increase by
about 34.1% on average. Considering that there are many sample
enterprises with no patent application, this paper reports the result
of Tobit regression in column (2). The estimated coefficient of
(1) OLS
(2) Tobit
Patent
Patent
***
Appeal
Roa
TQ
CFO
Board
Indep
Msh
Firstshare
Tangible
Leverage
Age
Size
Soe
Dual
GDP_GRO
Constant
Industry FE
Year FE
0.350 (6.40)
2.220***(2.68)
0.025(-0.81)
0.479(0.88)
0.575***(2.87)
1.108(1.50)
0.113(0.60)
0.157(0.75)
0.867***(-3.76)
0.381*(-1.86)
0.021***(-3.00)
0.496***(13.45)
0.057(0.69)
0.103(-1.32)
6.938***(3.06)
12.274***(-12.40)
Yes
Yes
0.384***(6.14)
2.634***(2.77)
0.043(-1.22)
0.442(0.72)
0.692***(3.04)
1.171(1.39)
0.200(0.93)
0.161(0.67)
1.001***(-3.79)
0.468**(-1.99)
0.024***(-2.97)
0.547***(12.97)
0.069(0.73)
0.101(-1.14)
7.181***(2.77)
13.960***(-12.34)
Yes
Yes
Adj R2
F Value
Pseudo R2
LR chi2
Observations
0.231
27.933***
e
e
1,881
e
e
0.072
501.39***
1,881
*p < 0.1; **p < 0.05; ***p < 0.01.
Appeal is 0.373, and it also passes the statistical test at the level of
1%. The above results show that the increase in public environmental appeal has significantly increased the innovative output of
heavily polluting enterprises, which supports the hypothesis H1.
That is, public environmental appeal can relieve the information
asymmetry, the enterprises will promote self-discipline, strengthen
governance of environmental pollution, and ensure implementation of environmental innovation through getting a new higher
market valuation and lower financing costs. In terms of control
variables, enterprise size, board size and return on assets have
significant positive impacts on the patent output of the enterprise,
indicating that the larger and the more profitable the enterprises
are, the more innovative output they will have, which is due to the
high risk and high investment of innovation activities. Only enterprises with large scale and strong profitability have enough funds
and technology to carry out innovative activities. The impacts of
proportion of fixed assets and asset-liability ratio on the patent
output of enterprises are significantly negative, indicating that the
innovation output of enterprises with more fixed assets and more
debt financing is less than the others. Likewise, the estimated coefficient of firm age is also significantly negative, indicating that the
innovation output of enterprises in growth period is relatively more
than that in other period, and the innovation output of mature
enterprises will then experience a decline. The estimated coefficient of GDP index is significantly positive, indicating that the
higher the level of economic development, the better the innovation performance of enterprises in these areas. In conclusion, the
regression results of the above control variables are basically
consistent with the theoretical expectations and the results of
available literature.
5. Robustness tests
5.1. Endogeneity treatment
Fig. 4. Patent distribution of high-polluting firms.
In order to deal with endogeneity effects, two tests are introduced. Overall, in areas with strong public environmental appeal,
1016
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Table 6
Instrumental variable estimation.
VARIABLES
(1)
(2)
Appeal
Patent
lnNET
Appeal
Control variables
Constant
Industry FE
Year FE
0.561***(33.97)
Yes
0.125(0.36)
Yes
Yes
0.236***(2.68)
Yes
11.679***(-11.13)
Yes
Yes
Adj R2
F Value
Observations
0.861
556.162***
1,881
0.229
26.259***
1,881
*p < 0.1; **p < 0.05; ***p < 0.01.
the level of economic development is usually high. When people
live a better life, they are willing to pay more attention to environmental protection, and the channels of conveying public
opinion appeal, such as network, are also more complete. Moreover, in areas where the regulations are conducive to enterprise
innovation, enterprises are likely to attract innovative technicians.
The public environmental appeal and the innovation behavior of
the heavy-polluting enterprises may be affected by some common
factors. Thus, instrumental variable estimation and Placebo test are
introduced to deal with them.
5.1.1. Instrumental variable estimation
In this paper, the natural logarithmic of the annual Internet
broadband access port data in each province (lnNET) is used as the
instrumental variable of PM2.5 Baidu search index. The data of the
Internet broadband access port comes from the disclosure of the
official website of the National Bureau of Statistics. The more
Internet broadband access ports, the more Internet users, so the
Baidu search index of PM2.5 will be higher too, but the Internet
broadband access ports has no direct impact on the patent output
of enterprises. We use two-stage least square method (2SLS) to
carry out instrumental variable estimation. Table 5 shows the
estimated results. The results of the first stage estimation are
shown in Table 6, column (1). The estimated coefficient of the
instrumental variable lnNET is 0.563, which is significant at the
level of 1%, in accordance with the theoretical expectation. F value
is far greater than 10, which significantly excludes the problem of
“weak instrumental variable”, indicating that the choice of instrumental variable is reasonable. The second stage estimation results
are listed in Table 5, column (2). The estimated coefficient of Appeal
is 0.235, which is also significant at the level of 1%, which is
consistent with the former results, indicating that public environmental appeal can significantly promote innovation in heavily
polluting enterprises, which is still valid after considering the
endogenous problem.
5.1.2. Placebo test
It is possible that the increase in the innovative output of the
heavily polluting enterprises as the public environmental appeal
increase is only a coincidence. Therefore, referring to Chen et al.
(2017), Placebo test was used to verify the research conclusion.
Specifically, the PM2.5 Baidu search index is randomly transformed
among provinces, to generate simulated explanatory variables, and
the regression of model (1) is repeated 100 times and 500 times. If
the factors that affect innovation output are not public environmental appeal but other unobserved factors, the estimated coefficients of the simulated explanatory variables will still be
significantly positive. On the contrary, if the public environmental
appeal really influences the innovation output of heavy-polluting
enterprises, the estimated coefficient of the simulated explanatory variables will not be significant. In Table 7 Panel A and B show
the descriptive statistics of the estimated coefficients and t values
of Appeal when repeating for 100 times and 500 times respectively.
The results show that the estimated coefficients of Appeal are not
significantly different from 0. According to Fig. 5, it is also clear that
Table 7
Placebo test.
Panel A Random simulation for 100 times
Estimation coefficient
t value
Frequency
Mean
S.D.
p5
p25
p50
p75
p95
100
100
0.0015
0.0320
0.0479
1.0349
0.0834
1.8023
0.0326
0.7025
0.0034
0.0740
0.0259
0.5592
0.0798
1.7238
Frequency
Mean
S.D.
p5
p25
p50
p75
p95
500
500
0.0021
0.0453
0.0484
1.0464
0.0822
1.7743
0.0328
0.7063
0.0005
0.0099
0.0294
0.6350
0.0755
1.6338
Panel B Random simulation for 500 times
Estimation coefficient
t value
Fig. 5. Placebo test results of 100 (500) simulated times.
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
the proportion whose coefficients are significantly positive or
significantly negative is very small. In addition, the true value of the
estimated coefficients is also on the right side of the distribution of
the simulated coefficients. This is consistent with the basic
conclusion– the increase of innovation output is due to the increase
of public environmental appeal, not other unobserved factors.
The chances are that the increase in the innovative output of the
heavily polluting enterprises with the increase of public environmental appeal is only a coincidence. Specifically, the PM2.5 Baidu
search index is randomly transformed among provinces to generate
simulated explanatory variables, and the regression of model (1) is
repeated 100 times and 500 times. If the factors that affect innovation output are not public environmental appeal but other unobserved factors, the estimated coefficients of the simulated
explanatory variables will still be significantly positive. On the
contrary, if the public environmental appeal does influence the
innovation output of heavy-polluting enterprises, the estimated
coefficient of the simulated explanatory variables will not be
significant.
In Table 7 Panel A and B, we present the descriptive statistics of
the estimated coefficients and t values of Appeal when repeating
for 100 times and 500 times respectively. The results show that the
estimated coefficients of Appeal are not significantly different from
0. According to Fig. 5, it is also clear that the proportion whose
coefficients are significantly positive or significantly negative is
very small. In addition, the true value of the estimated coefficients
is also on the right side of the distribution of the simulated coefficients. This is consistent with the basic conclusion of this paper
– the increase of heavy-polluting enterprises’ innovation output is
due to the increase of public environmental appeal, not other unobserved factors.
5.2. Controlling the impact of formal environmental regulation
In order to test whether the positive effect of public environmental appeal on the innovation of heavy-polluting enterprises is
affected by the government’s formal environmental regulations,
this paper controls the influence of the government’s formal
environmental regulation in model (1). The regression result is
shown in Table 8. We use the natural logarithm of the number of
laws and regulations related to “environmental pollution” issued by
each province in each year, which is searched on the website of
“Beida magic weapon” (lnlaw), as a control variable, and the
regression result is presented in Table 8, column (1). We use the
natural logarithm of the completed investment amount of the industrial pollution control in each province published by the “Statistical Yearbook of China” as government environmental
Table 8
Controlling the impact of formal Environmental Regulation.
VARIABLES
(1)
(2)
(3)
Patent
Patent
Patent
Appeal
lnlaw
lnPCI
lnlaw_lnPCI
Control variables
Constant
Industry FE
Year FE
0.364***(6.53)
0.083(-1.29)
0.371***(6.73)
Yes
11.818***(-11.24)
Yes
Yes
Yes
10.923***(-9.90)
Yes
Yes
0.403***(7.03)
2.229**(2.45)
0.651**(2.07)
0.184**(-2.45)
Yes
20.117***(-5.15)
Yes
Yes
Adj R2
F Value
Observations
0.232
26.748***
1,881
0.234
27.105***
1,881
0.236
25.150***
1,881
*p < 0.1; **p < 0.05; ***p < 0.01.
0.106***(-2.76)
1017
regulation agency index (lnPCI), and the regression result is presented in Table 8, column (2). We put lnlaw, lnPCI and the interaction between the two variables into the model (1) to regress, and
the result is listed in Table 8, column (3). It can be noticed that the
estimated coefficients of Appeal in the three regressions are 0.364,
0.371 and 0.403 respectively, all of which are significant at the level
of 1%, indicating that after controlling the influence of the government’s formal environmental regulation, public environmental
appeal still has a significant positive impact on the innovation
output of heavy-polluting enterprises. Therefore, in addition to the
government’s formal environmental regulations, informal public
environmental appeal will also promote enterprise innovation to a
certain extent.
5.3. Zero-inflated negative binomial regression
We employ zero-inflated Negative Binomial regression where
the dependent variable Patent_Num is the number of patent applications in the next year, without logarithmic processing. The
reasons why we choose zero-inflated Negative Binomial regression
are as follows.
Firstly, the limitation of Poisson regression is that the expectation and variance of Poisson distribution must be equal, which is
called equidispersion. However, the variance of Patent_Num is
obviously larger than its expectation, which is called overdispersion. Besides, according to the LR test after Negative Binomial
regression, the overdispersion parameter alpha is significant at the
level of 1%, which indicates that the dependent variable is overdispersion. Therefore, we employ Negative Binomial regression
rather than Poisson regression. Secondly, given the high number of
zero counts in the number of patent applications, we employ zeroinflated Negative Binomial regression. Furthermore, according to
the Vuong test, the statistic Vuong is 3.44 (positive), which indicates
that zero-inflated Negative Binomial regression is better than
standard Negative Binomial regression.
The results of Negative Binomial regression and zero-inflated
Negative Binomial regression are presented respectively in
Table 9, column (1) and column (2). The estimated coefficients of
Appeal in the two regressions are 0.321 and 0.300 respectively,
both of which are significant at the level of 1%, indicating that our
conclusion is still robust.
6. Further research
6.1. Considering the types of innovation output
In China context, patents are divided into three types: invention,
utility model, and appearance design. To test the different impact of
the public environmental appeal on the three types of patent applications of heavily polluting enterprises, we further use the
number of patent applications for invention (Patent1), the number
of patent applications for utility models (Patent2) and the number
of patent applications for design (Patent3) as explained variables
separately, and respectively carry out the regression of model (1).
The regression results are shown in Table 10. The estimated
coefficients of PM2.5 Baidu search index (Appeal) in each column
are significantly positive at the level of 1%, indicating that the public
environmental appeal has a significant positive effect on the three
types of patent applications of heavy-polluting enterprises. The
estimated coefficients of PM2.5 Baidu search index for the number
of invention patents, utility model patents, and design patents are
0.408, 0.155 and 0.091 respectively, suggesting that public environmental appeal has the greatest positive effect on the production
of invention patents of heavy-polluting enterprises, and the effect
on the other two kinds of patents is weaker. This may be because
1018
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Table 9
Zero-inflated Negative Binomial regression.
VARIABLES
Table 11
Considering the impact on innovation investment and innovation efficiency.
(1)NBR
(2)ZINB
Patent_Num
Patent_Num
Appeal
Control variables
Constant
Industry FE
Year FE
0.321***(5.43)
Yes
13.654***(-13.17)
Yes
Yes
0.300***(5.21)
Yes
12.557***(-12.33)
Yes
Yes
Adj R2
Chi2
Observations
0.044
707.492***
1,881
e
714.241***
1,881
VARIABLES
(1)
(2)
Rdsales_ratio
Innoefficiency
Appeal
Control variables
Constant
Industry FE
Year FE
0.439***(5.93)
Yes
7.829***(5.53)
Yes
Yes
0.012***(3.68)
Yes
0.441***(-6.96)
Yes
Yes
Adj R2
F Value
Observations
0.329
33.858***
1408
0.138
11.708***
1408
*p < 0.1; **p < 0.05; ***p < 0.01.
*p < 0.1; **p < 0.05; ***p < 0.01.
Table 10
Considering the types of innovation output.
the estimated coefficient of Appeal for Innoefficiency is 0.012, which
is significantly positive at the level of 1%, indicating that the increase in public environmental appeal not only significantly increase the innovation input and output of heavy-polluting
enterprises, but also improve the innovation efficiency of enterprises (the patent applications converted from unit R&D input are
more).
VARIABLES
(1)
(2)
(3)
Patent1
Patent2
Patent3
Appeal
Control variables
Constant
Industry FE
Year FE
0.416***(8.41)
Yes
12.390***(-13.83)
Yes
Yes
0.160***(2.97)
Yes
10.558***(-10.79)
Yes
Yes
0.093***(3.46)
Yes
0.246 (0.51)
Yes
Yes
Adj R2
F Value
Observations
0.239
29.064***
1,881
0.202
23.636***
1,881
0.030
3.757***
1,881
*p < 0.1; **p < 0.05; ***p < 0.01. t statistics in parentheses.
the main purpose of innovation in heavily polluting enterprises is to
realize transformation and upgrading, transforming from low efficiency, high energy consumption and high emission development
mode to high efficiency, low energy consumption and low emission
development mode. In order to reduce the production cost and
achieve greater economic benefits, heavy-polluting enterprises
need to carry out invention innovations actively to improve production processes, enhance pollution treatment technologies, save
energy and reduce emission. Consequently, the more indentation
patent applications of heavy-polluting enterprises are, the higher
the quality of innovation output are. This also supports that public
environmental appeal has substantial effect on the innovation of
heavy-polluting enterprises, which plays an important role in
solving the pollution problems caused by heavy-polluting
enterprises.
6.2. Considering the impact on innovation investment and
innovation efficiency
To further study the impact of public environmental appeal on
innovation investment and innovation efficiency of heavy-polluting
enterprises, we refer to the study of Yu et al. (2018). In this paper,
innovation investment intensity (Rdsales_ratio) is measured by the
ratio of R&D investment to operating income, and innovation efficiency (Innoefficiency) is measured by the ratio of natural logarithm
of patent applications to natural logarithm of R&D investment. We
use Rdsales_ratio and Innoefficiency as explained variables respectively to do the regression of model (1), and the regression results
are shown in Table 11. As can be seen, column (1), the estimated
coefficient of Appeal for Rdsales_ratio is 0.439, which is significantly positive at 1% level. It shows that there is a significant positive correlation between public environmental appeal and R&D
investment intensity of heavy-polluting enterprises. In column (2),
6.3. Public environment appeal, management myopia and
innovation of heavy-polluting enterprises
As a strategic choice of enterprises, innovation is a long-term
activity with high investment and high risk, which brings mostly
long-term benefits to the development of enterprises and is not
only affected by the above internal and external environmental
factors, but also restricted by the limited rationality and cognitive
model of senior managers (Hambrick and Mason, 1984; Walsh and
Anderson, 1995). As mentioned above in 3.3 section, the power of
public environmental appeal is huge, and have a direct impact on
the behavior of both the government and companies. In the context
of information asymmetry, the huge pressure would make investors are not satisfied with the performance in financial statements and the profit-seeking nature of the capital market will
cause excessive pressure on the management of enterprises,
especially listed companies, and cause “short-sighted" behaviors,
that is, management myopia. In this case, enterprise management
often worry about that stakeholders outside tend to attribute the
decline of short-term performance to the incompetence of them,
and trigger their career concerns (Fang et al., 2014). Besides that,
from the perspective of principal-agent theory, management
myopia also induces by occupational anxiety and lazy behaviors
caused by hedonism. To sum up, whether from the information
asymmetry theory or the principal-agent theory, short-termism
could be detrimental to long-term innovation (Fang et al., 2014),
and to improve performance and beautify financial statements,
“short-sighted" managers will reduce the positive relationship
between public environmental appeal and innovation level of
enterprises.
Referring to the study of Wang and Zeng (2013), we use the ratio
of current short-term investment to total assets at the beginning of
the period (Shortinv) to measure the myopia tendency of the enterprise management, and the sum of the three accounts – “trading
financial assets”, “available-for-sale financial assets” and “held-tomaturity investment” as a measure of short-term investment of
enterprises. Also, we put the management myopia variable and the
interaction between management myopia and PM2.5 Baidu search
index into model (1) to construct the following model:
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Patenti;tþ1 ¼ b0 þ b1 Appeali;t þ b2 Shortinvi;t þ b3 Appeal Shortinvi;t þ bi
X
X
þbj
Year þ bk
Industry þ εi;t
The regression results are shown in Table 12, column (2). It can
be seen that the coefficient of interaction between management
myopia and PM2.5 Baidu search index is significantly negative at
the level of 5%, meaning management myopia has significantly
reduced the positive impact of public environmental appeal on the
innovation of heavy-polluting enterprises, and it is even turned into
a negative effect. It means that when the public appeal for the
environment increase, the public pressure on heavy-polluting enterprises increases, and if the managers of the heavy-polluting
enterprises are short-sighted, when they are uncertain about the
future development prospects of the enterprises, they tend to cut
down innovation activities to make sure that short-term benefits
are realized as far as possible.
7. Mechanism analysis
The prior sections obtained the empirical evidence of public
environmental appeal promoting the innovation of heavy-polluting
enterprises, and the conclusion is robust. This section will try to
further explore and analyze the specific mechanism in which public
environmental appeal promote the innovation of heavy-polluting
enterprises.
7.1. Analyst coverage analysis
As the public appeal for the environment become stronger, the
heavy-polluting enterprises with large pollutant emissions are paid
more and more attention by the public, the media, the government
and so on. In the context of information asymmetry, when the
negative information goes public, outside stakeholders will ask for
a higher risk premium (Sengupta, 1998), which makes the enterprise external financing costs increase (Handa and Linn, 1993),
especially to listed companies with negative news of polluting. The
increase of the cost of financing will greatly influence the innovation investment. Under this circumstance, the future development
strategy of heavy-polluting enterprises has become a widespread
concern. At this time, analyst coverage can effectively reduce the
information asymmetry between enterprises and the public as an
Table 12
Public environment appeal, management myopia and innovation of heavy-polluting
enterprises.
VARIABLES
(1)
(2)
Patent
Patent
Appeal
Shortinv
Appeal_Shortinv
Control variables
Constant
Industry FE
Year FE
0.350***(6.40)
Yes
12.274***(-12.40)
Yes
Yes
0.375***(6.54)
5.082(1.63)
1.141**(-2.02)
Yes
12.712***(-12.43)
Yes
Yes
Adj R2
F Value
Observations
0.231
27.933***
1,881
0.237
25.228***
1,794
*p < 0.1; **p < 0.05; ***p < 0.01.
X
CVS
1019
(2)
effective medium access to information. It means that the more
analysts tracking the companies have, the more collected valuable
information can be provided to the public, finally reducing the
external financing costs (Deboskey and Accounting, 2013).
Furthermore, the company stock trading cost is lower and more
liquid (Lang and Lundholm, 2010; He and Tian, 2013; Chen et al.,
2017). Thus, we assume that public environmental appeal will
have a greater impact on the innovation output of the enterprises
that receive more attention from analysts by alleviating the information asymmetry and agency problems in their innovation
activities.
In order to test this assumed mechanism, we adopt the number
of the research reports that the enterprise tracked and analyzed
within a year as the degree of analyst concern of the heavypolluting enterprise. Furthermore, we carry on the grouping
regression. Specifically, enterprises with more research reports
than the year-industry median are considered to have a high degree
of analyst concern, while the rest are those with a relatively low
analyst concern.
The regression results is shown in Table 11, column (1) and (2). It
is clear that in the high analyst concern sample group, the estimated coefficient of Appeal is 0.447, which is significant at the level
of 1%, and in the sample group with low analyst concern, the
estimated coefficient is 0.190, which is significant at the level of 1%.
The chi2 value of the inter-group difference test is 5.18, significant
at the level of 5%, which shows that there is a significant difference
between the two groups, that is, compared with the heavypolluting enterprises with low analyst concern, public environmental appeal has a greater positive impact on the innovative
output of heavy-polluting enterprises with high analyst coverage.
7.2. Reputation analysis
Besides the analyst coverage, as an effective medium access to
information between enterprises and the public, can effectively
reduce the information asymmetry, there may be another different
mechanism on these issues, and that is the role of reputation
mechanism.
As public environmental appeal is increasingly strong, heavypolluting enterprises are likely to be the target of public criticism,
and their reputation will be great affected. The consumers motivated by environmental protection consciousness may reduce or
even stop the purchase of their products, which has a great impact
on these enterprises’ production and operation. In addition, reputation is the “quality” signal for managers themselves, too. In the
human resource market, the reputation of the manager is an
important evidence of the management ability of the operator to
develop and innovate, and the result of the successful operation of
the enterprise lasts for a long period.
In this sense, the choice of high-cost innovation activities by
heavy-polluting enterprise is essentially a “credit war” that takes
financial strength as guarantee, hoping to maintain the reputation
of enterprises, and win back the profit eventually. In fact, the nature
of the corporate reputation effect is to send a signal to the market
that the patent holder has the motivation to maintain his own
patent and reputation, which is also known as the publicity effect.
1020
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
Table 13
Mechanism analysis.
VARIABLES
(1)
(2)
(3)
(4)
Patent
Patent
Patent
Patent
High analyst concern
Low analyst concern
High reputation importance
Low reputation importance
Appeal
Control variables
Constant
Industry FE
Year FE
0.447***(5.05)
Yes
12.825***(-8.14)
Yes
Yes
0.190***(2.65)
Yes
7.647***(-4.94)
Yes
Yes
0.493***(6.31)
Yes
10.393***(-7.12)
Yes
Yes
0.162**(2.12)
Yes
12.201***(-7.99)
Yes
Yes
Adj R2
F Value
Observations
0.220
12.941***
888
0.151
9.384***
993
0.229
14.241***
937
0.159
9.492***
943
*p < 0.1; **p < 0.05; ***p < 0.01.
Thus, we assume that heavy-polluting enterprise which attaches
high importance to its reputation will more actively turns public
environmental appeal into the internal driving force of enterprise
innovation and the level of their innovation output will be higher.
Furthermore, we assume that the heavy-polluting enterprises
whose products are directly oriented to consumers have higher
sales expenses and attach more attention to their reputation.
To test the assumed reputation mechanism, we use sales expenses to measure the degree of heavy-polluting enterprises
attaching importance to their reputation and carry on the grouping
regression. Specifically, if the sales expenses are larger than the
year-industry median, it is regarded as the enterprise attaching
high importance to its reputation valuing and the others attaching
low importance to their reputation. The results of the test are reported in Table 13, column (3) and (4). It can be found that the
estimated coefficient for the sample group with high reputational
importance is 0.476, which is significant at the level of 1%, but in the
sample group with low reputation importance, the estimated coefficient is 0.159, which is significant at the level of 5%. The chi2
value of the inter-group difference test is 9.42, significant at the
level of 1%, which shows that the difference between the two
groups is significant, that is, compared with the heavy-polluting
enterprises with low reputation importance, the public environmental appeal has a greater positive effect on the innovation output
of heavy-polluting enterprises with high reputation importance.
8. Discussion
8.1. Research findings and implications
First of all, by empirical testing the distinct effects of public
environmental appeal on heavy-polluting enterprise innovation,
we found that public environmental appeal would play a more
positive role in relieving information asymmetry between outside
stakeholder and the enterprise, comparing to the crowding-out
effect of innovative funds by increase of finance difficulties (Jiang
et al., 2013). That is, due to public environmental appeal, the enterprises environmental behaviors, such as promoting selfdiscipline and strengthening pollution governance will send out
positive signals to the market, and therefore get new higher market
value and lower financing cost.
Second, this research divides enterprise innovation proxy–
patent into three types, including invention, utility model and
appearance design. According to the findings of our research, public
environmental appeal affects greatest positive on invention patents
and weakest positive on appearance design, according to the
empirical results, heavy-polluting enterprises have more strong
motivation to develop technologies (Liao, 2018). To make up the
limitation that existing researches only focus on innovation patent,
we further replace innovation proxy from patents to investment
and efficiency. Positive relations still hold, meaning public environmental appeal influence not only innovation input and output
but also innovation efficiency (Johnstone et al., 2017). Furthermore,
to examine the underlying relation between external informal
regulation and internal corporate governance, we introduce management myopia as an intervening variable (Bernstein, 2015), and
results reveal that when public pressure on heavy-polluting enterprises increases with public environmental appeal, shortsighted managers prefer realizing short-term benefits to engaging
in innovative activities (Fang et al., 2014).
Another area of importance that is yet to be thoroughly explored
in innovation studies is the moderating mechanisms. Our study
explores analyst coverage as an effective medium access to information and reputation as a guarantee signal to the market in the
context of information asymmetry. According to the findings, they
both have positive reinforcement on the relation between public
environmental appeal and innovative output, which means heavypolluting enterprises alleviate information asymmetry more when
they are paid more attention by analysts. They are likely to turn
public environmental appeal into internal motivation more actively
when reputation is more precious.
8.2. Policy recommendations
In heavy-polluting industries, enterprises are not only exposed
to higher intensity of industrial environmental regulation (Jiang
et al., 2018) and greater cost for environmental governance but
also attracted more public attention and undervalued market
values more (Cheng and Liu, 2018). Meanwhile, they still hope to
have more funds for innovation activities (Manderson and Kneller,
2012), and higher economic benefits through technology and
equipment improvements (Porter and Vanderlinde, 1995). Thus, for
heavy-polluting enterprise, the most important thing is to balance
the input costs of innovation and future earnings. However, the
government cannot provide them with excessive environmental
subsidies, which would result in their dependence (Jiang et al.,
2018), so it is necessary for them to reduce the extra expenditures of public pressure, especially the fallen stock price and the
raised financing costs.
Under this circumstance, it is suggested that enterprises maintain their innovation activities transparently and ensure implementation of innovation on information asymmetry through
allowing more analysts to trace or more actively disclose asymmetric information about new product development, R&D investment and innovation efficiency, to satisfy public environmental
appeal and stakeholder outside corresponding interest requirements. In addition, the government are supposed to treat
public environmental appeal as a new stimulus for environmental
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
improvement, energetically advocating and encouraging public
participation in public environmental affairs and provide various
efficient platforms and channels for the public to report and
monitor environmental destruction.
8.3. Limitations and directions for future research
The implications of our research should be considered within
the confines of the study’s limitations. The following limitations
should be considered.
First limitation is the indicators chosen to measure innovation
need to be improved. Although patent data can reflect the innovation output in a certain, but it fails to explain the improved
innovation and process innovation in environmental protection
cases (Liao, 2018). Future studies can use manual collection to get
cleaner green innovation data from the dimensions of industry and
region.
Secondly, the theoretical framework needs to be expanded. This
study only explores the role of public environmental appeal as an
informal regulation in the context of information asymmetry and
neglects the effect of mandatory environmental regulation (Wu
et al., 2018; Yuan and Xiang, 2018). In view of this, future
research can introduce this kind of mandatory environmental
regulation into the framework and explore their combined effects
and mechanisms.
Finally, this study does not consider the other EMEs’ special
circumstances, and only uses China’s domestic listed companies as
a sample. Although our results confirm the positive relation exits
between public environmental appeal and heavy-polluting innovation, perhaps it is related to the choice of enterprises formed
under the traditional Chinese culture, so the applicability of the
research conclusion needs to be verified. Therefore, it would be
interesting to analyze in a future line of research the relation of
public environmental appeal in local enterprises innovation with
EMEs’ national conditions and data.
9. Conclusion
Public environmental appeal as an in formal regulation is
becoming a hot topic in academic, and this research extends its
consequences to enterprise innovation. Based on the Porter hypothesis, information asymmetry theory and principal-agent theory, this research shows that public environmental appeal has
positive effects on innovation output (patent application), innovation input (R&D investment) and innovation efficiency (the ratio of
natural logarithm of patent applications to natural logarithm of
R&D investment). In addition, distinguishing types of innovation
output, we found public environmental appeal have the greatest
positive effect on production of invention patents and the weakest
positive effect on appearance design. The results of further research
show that management myopia weakens the relationship between
public environmental appeal and innovation. The mechanism
analysis shows that the increase of public environmental appeal
can promote innovation level of heavy-polluting enterprises by
promoting analyst coverage and reputation mechanism. This study
puts forward a unique perspective, which not only extends the
understanding of informal environmental regulations but also enlightens the sustainable development of heavy-polluting enterprises in China. Building on these above insights and findings, we
expect that future research will extend to other EMEs’ nations and
advance our understanding of the influences of informal environmental regulations on innovation.
1021
Acknowledge
This study is funded by the National Natural Science Foundation
of China (No. 71572153),the Fundamental Research Funds for the
Central Universities (No. XDJK2019C006, No. SWU1709201), the
Chongqing Federation of Social Science Circles Program (No.
2017YBGL159, No. 2018PY61), and the Humanities and Social Sciences Research Major Cultivation Project in Southwest University
(No. 15XDSKZD005).
References
Acharya, V., Xu, Z., 2017. Financial dependence and innovation: the case of public
versus private firms. J. Financ. Econ. 124 (2), 223e243. https://doi.org/10.1016/
j.jfineco.2016.02.010.
Ambec, S., Barla, P.J.E.L., 2002. A Theoretical Foundation to the Porter Hypothesis.
Amore, M.D., Bennedsen, M., 2016. Corporate governance and green innovation.
J. Environ. Econ. Manag. 75, 54e72. https://doi.org/10.1016/j.jeem.2015.11.003.
Antonioli, D., Mancinelli, S., Mazzanti, M.J.R.P., 2013. Is environmental innovation
embedded within high-performance organisational changes? The role of human resource management and complementarity in green business strategies
42 (4), 975e988.
Almeida,, C., Sevegnani, F., Agostinho, F., Liu, G.Y., Yang, Z.F., Coscieme, L.,
Giannetti, B.F., 2018. Accounting for the benefits of technology change:
Replacing a zinc-coating process by a water-based organo-metallic coating
process. J. Clean. Prod. 174, 170e176.
Bennett, W.L., Segerberg, A., Yang, Y., 2018. The strength of peripheral networks:
negotiating attention and meaning in complex media ecologies. J. Commun. 68
(4), 659e684. https://doi.org/10.1093/joc/jqy032.
Bergek, A., Berggren, C.J.E.E., 2014. The impact of environmental policy instruments.
Innovation: A review of energy and automotive industry studies 106 (1),
112e123.
Bernstein, S., 2015. Does going public affect innovation? J. Financ. 70 (4),
1364e1403. https://doi.org/10.1111/jofi.12275.
Bhattacharya, S., 2014. Institutional review board and international field research in
conflict zones. PS Political Sci. Polit. 47 (4), 840e844. https://doi.org/10.1017/
s1049096514001140.
Bird, D.K., Haynes, K., Honert, R.V.D., Mcaneney, J., Poortinga, W.J.E.P., 2014. Nuclear
power in Australia. A comparative analysis of public opinion regarding climate
change and the Fukushima disaster 65 (2), 644e653.
Bird, J., 2014. Game changers for irrigated agriculture-do the right incentives exist?
Irrig. Drain. 63 (2), 146e153. https://doi.org/10.1002/ird.1838.
Blackman, A., 2010. Alternative pollution control policies in developing countries.
Rev. Environ. Econ. Pol. 4 (2), 234e253. https://doi.org/10.1093/reep/req005.
Borghesi, S., Cainelli, G., Mazzanti, M., 2015. Linking emission trading to environmental innovation: evidence from the Italian manufacturing industry. Res. Pol.
44 (3), 669e683. https://doi.org/10.1016/j.respol.2014.10.014.
Braam, G.J.M., de Weerd, L.U., Hauck, M., Huijbregts, M.A.J., 2016. Determinants of
corporate environmental reporting: the importance of environmental performance and assurance. J. Clean. Prod. 129, 724e734. https://doi.org/10.1016/
j.jclepro.2016.03.039.
Chen, Q.Y., Ma, L.J., Yi, Z.H., 2017. Analyst coverage and corporate’s innovation
performance: the logic of China. J. Nankai Business Review. 3, 15e27.
Chen, X., Yi, N., Zhang, L., Li, D., 2018. Does institutional pressure foster corporate
green innovation? Evidence from China’s top 100 companies. J. Clean. Prod. 188,
304e311. https://doi.org/10.1016/j.jclepro.2018.03.257.
Chen, Y.-S., Shih, C.-Y., Chang, C.-H., 2013. Patents and market value in the US
pharmaceutical industry: new evidence from panel threshold regression. Scientometrics 97 (2), 161e176. https://doi.org/10.1007/s11192-013-0999-3.
Cheng, J., Liu, Y., 2018. The effects of public attention on the environmental performance of high-polluting firms: based on big data from web search in China.
J. Clean. Prod. 186, 335e341. https://doi.org/10.1016/j.jclepro.2018.03.146.
Clarkson, P.M., Overell, M.B., Chapple, L., 2011. Environmental reporting and its
relation to corporate environmental performance. Abacus-a Journal of Accounting Finance and Business Studies 47 (1), 27e60. https://doi.org/10.1111/
j.1467-6281.2011.00330.x.
Deboskey, D.G.J.R., Accounting, o.Q.F., 2013. The Impact of Multi-Dimensional
Corporate Transparency on Us Firms’ Credit Ratings and Cost of Capital, vol.
40, pp. 101e134, 1.
Desrochers, P., Haight, C.E., 2014. Squandered profit opportunities? Some historical
perspective on industrial waste and the Porter Hypothesis. Resour. Conserv.
Recycl. 92, 179e189. https://doi.org/10.1016/j.resconrec.2014.07.001.
Doran, J., Ryan, G., 2016. The importance of the diverse drivers and types of environmental innovation for firm performance. Bus. Strateg. Environ. 25 (2),
102e119. https://doi.org/10.1002/bse.1860.
Fan, Y.V., Varbanov, P.S., Klemes, J.J., Nemet, A., 2018. Process efficiency optimisation
and integration for cleaner production. J. Clean. Prod. 174, 177e183. https://
doi.org/10.1016/j.jclepro.2017.10.325.
Fang, V.W., Tian, X., Tice, S., 2014. Does stock liquidity enhance or impede firm
innovation? J. Financ. 69 (5), 2085e2125. https://doi.org/10.1111/jofi.12187.
Franzen, A., Meyer, R., 2010. Environmental attitudes in cross-national perspective:
1022
Y. Du et al. / Journal of Cleaner Production 222 (2019) 1009e1022
a multilevel analysis of the ISSP 1993 and 2000. Eur. Sociol. Rev. 26 (2),
219e234. https://doi.org/10.1093/esr/jcp018.
Ghisetti, C., Pontoni, F.J.E.E., 2015. Investigating policy and R&D effects on environmental innovation. A meta-analysis 118 (5), 57e66.
Hambrick, D.C., Mason, P.A., 1984. Upper echelons - the organization as a reflection
of its top managers. Acad. Manag. Rev. 9 (2), 193e206. https://doi.org/10.2307/
258434.
Handa, P., Linn, S.C., 1993. Arbitrage pricing with estimation risk. J. Financ. Quant.
Anal. 28 (1), 81e100. https://doi.org/10.2307/2331152.
He, J., Tian, X., 2013. The dark side of analyst coverage: the case of innovation.
J. Financ. Econ. 109 (3), 856e878. https://doi.org/10.1016/j.jfineco.2013.04.001.
He, X., Liu, Y., 2018. The public environmental awareness and the air pollution effect
in Chinese stock market. J. Clean. Prod. 185, 446e454. https://doi.org/10.1016/
j.jclepro.2018.02.294.
Hojnik, J., Ruzzier, M.J.E.I., Transitions, S., 2016. What drives eco-innovation?
A review of an emerging literature 19, 31e41.
Jensen, M.C., Meckling, W.H., 1976. Theory of firm - managerial behavior, agency
costs and ownership structure. J. Financ. Econ. 3 (4), 305e360. https://doi.org/
10.1016/0304-405x(76)90026-x.
Jiang, F.X., Wang, Z.J., Bai, J.H., 2013. The dual effect of environmental regulations’
impact on innovationddan empirical study based on dynamic panel data of
Jiangsu manufacturing. J. China Industrial Economics. 7, 44e55.
Jiang, Z., Wang, Z., Li, Z., 2018. The effect of mandatory environmental regulation on
innovation performance: evidence from China. J. Clean. Prod. 203, 482e491.
https://doi.org/10.1016/j.jclepro.2018.08.078.
Johnstone, N., Managi, S., Rodriguez, M.C., Hascic, I., Fujii, H., Souchier, M., 2017.
Environmental policy design, innovation and efficiency gains in electricity
generation.
Energy
Econ.
63,
106e115.
https://doi.org/10.1016/
j.eneco.2017.01.014.
Kathuria, V., 2007. Informal regulation of pollution in a developing country: evidence from India. Ecol. Econ. 63 (2e3), 403e417. https://doi.org/10.1016/
j.ecolecon.2006.11.013.
Lang, M., Lundholm, R.J.C.A.R., 2010. The relation between security returns, firm
earnings. and Industry Earnings 13 (2), 607e629.
Li, D., Zheng, M., Cao, C., Chen, X., Ren, S., Huang, M., 2017. The impact of legitimacy
pressure and corporate profitability on green innovation: evidence from China
top 100. J. Clean. Prod. 141, 41e49. https://doi.org/10.1016/j.jclepro.2016.08.123.
Li, G., He, Q., Shao, S., Cao, J., 2018. Environmental non-governmental organizations
and urban environmental governance: evidence from China. J. Environ. Manag.
206, 1296e1307. https://doi.org/10.1016/j.jenvman.2017.09.076.
Li, P.G., Shen, Y.F., 2011. Social norms, capital markets and environmental governance: empirical evidence from the perspective of institutional investors. J. The
Journal of World Economy 6, 126e146.
Li, Q., Liu, G., Cai, B., Leamon, G., Liu, L.-C., Chen, Z.-A., Li, X., 2017. Public awareness
of the environmental impact and management of carbon dioxide capture, utilization and storage technology: the views of educated people in China. Clean
Technol. Environ. Policy 19 (8), 2041e2056. https://doi.org/10.1007/s10098017-1387-0.
Liao, Z., 2018. Environmental policy instruments, environmental innovation and the
reputation of enterprises. J. Clean. Prod. 171, 1111e1117. https://doi.org/10.1016/
j.jclepro.2017.10.126.
Lindeneg, K., 1992. Instruments in environmental-policy - different approaches.
Waste Manag. Res. 10 (3), 281e287.
Liu, F., Lyu, T., Pan, L., Wang, F., 2017. Influencing factors of public support for
modern coal-fired power plant projects: an empirical study from China. Energy
Policy 105, 398e406. https://doi.org/10.1016/j.enpol.2017.03.017.
Liu, W., Wang, Z., 2017. The effects of climate policy on corporate technological
upgrading in energy intensive industries: evidence from China. J. Clean. Prod.
142, 3748e3758. https://doi.org/10.1016/j.jclepro.2016.10.090.
Liu, X.H., 2016. Public pressure, the nature of property right and corporate financing
behavior – based on the "PM2.5 explosion" event. J. Economic Science. 2,
67e80.
Liu, Y.G., Liu, M.N., 2015. Have smog affected earnings management of heavypolluting enterprises?ddbased on the political-cost hypothesis. J. Account.
Res. 3, 26e33.
Lui, L.C., Leamon, G.J.E.P., 2014. Developments towards Environmental Regulation of
CCUS Projects in China, vol. 63, pp. 6903e6911.
Manderson, E., Kneller, R., 2012. Environmental regulations, Outward FDI and
heterogeneous firms: are countries used as pollution havens? Environ. Resour.
Econ. 51 (3), 317e352. https://doi.org/10.1007/s10640-011-9500-z.
Marciano, J.A., Lilieholm, R.J., Teisl, M.F., Leahy, J.E., Neupane, B., 2014. Factors
affecting public support for forest-based biorefineries: a comparison of mill
towns and the general public in Maine, USA. Energy Policy 75, 301e311. https://
doi.org/10.1016/j.enpol.2014.08.016.
Marzucchi, A., Montresor, S.J.E.E., 2017. Forms of knowledge and eco-innovation
modes. Evidence from Spanish manufacturing firms 131, 208e221.
Pargal, S., Hettige, H., Singh, M., Wheeler, D., 1997. Formal and informal regulation of
industrial pollution: comparative evidence from Indonesia and the United
States. World Bank Econ. Rev. 11 (3), 433e450. https://doi.org/10.1093/wber/
11.3.433.
Porter, M.E., 1991. America green strategy. Sci. Am. 264 (4) https://doi.org/10.1038/
scientificamerican0491-168, 168-168.
Porter, M.E., Vanderlinde, C., 1995. Toward a new conception of the environmentcompetitiveness relationship. J. Econ. Perspect. 9 (4), 97e118. https://doi.org/
10.1257/jep.9.4.97.
Quesnel, K.J., Ajami, N.K., 2017. Changes in water consumption linked to heavy news
media coverage of extreme climatic events. Science Advances 3 (10). https://
doi.org/10.1126/sciadv.1700784.
Saez-Martinez, F.J., Diaz-Garcia, C., Gonzalez-Moreno, A., 2016. Firm technological
trajectory as a driver of eco-innovation in young small and medium-sized enterprises.
J.
Clean.
Prod.
138,
28e37.
https://doi.org/10.1016/
j.jclepro.2016.04.108.
Sengupta, J.K., 1998. New efficiency theory: extensions and new applications of data
envelopment analysis. Int. J. Syst. Sci. 29 (3), 255e265. https://doi.org/10.1080/
00207729808929519.
Song, H., Zhao, C., Zeng, J., 2017. Can environmental management improve financial
performance: an empirical study of A-shares listed companies in China. J. Clean.
Prod. 141, 1051e1056. https://doi.org/10.1016/j.jclepro.2016.09.105.
Tietenberg, T., 1998. Disclosure strategies for pollution control. Environ. Resour.
Econ. 11 (3e4), 587e602. https://doi.org/10.1023/a:1008291411492.
Vidaver-Cohen, D., Brønn, P.S., 2015. Reputation, responsibility, and stakeholder
support in scandinavian firms. A Comparative Analysis 127 (1), 49e64.
Walsh, J.S., Anderson, P.H., 1995. Owner-manager adaption-innovation preference
and employment performance - a comparison of founders and non-founders in
the irish small firm sector. J. Small Bus. Manag. 33 (3), 1e8.
Wang, F., Cheng, Z., Keung, C., Reisner, A., 2015. Impact of manager characteristics
on corporate environmental behavior at heavy-polluting firms in Shaanxi,
China.
J.
Clean.
Prod.
108,
707e715.
https://doi.org/10.1016/
j.jclepro.2015.09.059.
Wang, H.M., Zeng, D.M., 2013. An empirical study on the effect of managerial
myopia on the corporate investment behavior: from the perspective of shareholders’ short term profit pressure. J. The Theory and Practice of Finance and
Economics 1, 34e38.
Wu, J., Xu, M., Zhang, P., 2018. The impacts of governmental performance assessment policy and citizen participation on improving environmental performance
across Chinese provinces. J. Clean. Prod. 184, 227e238. https://doi.org/10.1016/
j.jclepro.2018.02.056.
Xie, R.-h., Yuan, Y.-j., Huang, J.-j., 2017. Different types of environmental regulations
and heterogeneous influence on "green" productivity: evidence from China.
Ecol. Econ. 132, 104e112. https://doi.org/10.1016/j.ecolecon.2016.10.019.
Yu, Y.H., Zhao, Q.F., Ju, X.S., 2018. Inventor executives and innovation. J. China Industrial Economics. 3, 136e154.
Yuan, B., Xiang, Q., 2018. Environmental regulation, industrial innovation and green
development of Chinese manufacturing: based on an extended CDM model.
J. Clean. Prod. 176, 895e908. https://doi.org/10.1016/j.jclepro.2017.12.034.
Zhao, X., Sun, B., 2016. The influence of Chinese environmental regulation on corporation innovation and competitiveness. J. Clean. Prod. 112, 1528e1536.
https://doi.org/10.1016/j.jclepro.2015.05.029.
Zheng, S., Wu, J., Kahn, M.E., Deng, Y., 2012. The nascent market for "green" real
estate in Beijing. Eur. Econ. Rev. 56 (5), 974e984. https://doi.org/10.1016/
j.euroecorev.2012.02.012.
Zhou, Z., Xiao, T., Chen, X., Wang, C., 2016. A carbon risk prediction model for
Chinese heavy-polluting industrial enterprises based on support vector machine. Chaos, Solit. Fractals 89, 304e315. https://doi.org/10.1016/
j.chaos.2015.12.001.