Heterogeneous Choice of Environmental Strategy for Heavily Polluting Firms Under Institutional Pressure in China

Heterogeneous Choice of Environmental Strategy for Heavily Polluting Firms Under Institutional Pressure in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Heterogeneous Choice of Environmental Strategy for Heavily Polluting Firms Under Institutional Pressure in China Sen Wang, Jianhua Yin, Xiaomei Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-253787/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract The impact of institutional pressure on the environmental strategy was analyzed and the heterogeneous choices available for corporate environmental strategy. A total of 597 publicly listed companies in heavily polluting industries were selected using multiple Logit models for empirical research. The results show that more companies choose environmental leadership strategies when the policy pressure is greatest; however, more companies choose pollution prevention strategies when the regulatory and public pressures are greatest; finally, organizations with more redundant resources and strong asset specificity are more inclined to choose environmental leadership strategies as institutional pressures increase. The findings provide a decision-making framework to promote environmental protection measures related to policy formulation, government supervision and public participation. Our study also provides empirical evidence to guide environmental strategic choices for heavily polluting enterprises. Environmental Engineering Environmental Policy environmental strategy policy pressure regulatory pressure public pressure organizational slack asset specificity Introduction In China today, a series of problems that include air pollution, water pollution and land desertification have become increasingly prominent. All sectors of society want to solve the environmental crisis and advocate green development (Marquis, Jackson, and Li 2015; D. Yang et al. 2019 ). In recent years, the number of environmental regulations and documents has increased dramatically (Fan, Shao, and Tang 2013), the media has paid more attention to environmental events (Y. Wang et al. 2017 ), and the number of public complaints about environmental protection has also increased (Kassinis and Vafeas 2006 ). In spite of this, China still faces serious environmental pollution. One metric for atmospheric conditions is the amount of fine inhalable particles with diameters typically 2.5 micrometers and smaller (PM 2.5 ). As an example, the annual average PM 2.5 concentration of 338 cities at and above the prefectural level is 39 g/m 3 , far exceeding the 10 g/m 3 standard set in the air quality guidelines of the World Health Organization. Heavily polluting enterprises are the “perpetrators” of most environmental problems. The implementation of advanced environmental management practices can effectively promote the green and high-quality development of China (Luo and Lai 2016 ). A survey of the social responsibility reports of listed companies shows that heavy polluters show great differences in environmental management practices. A small number of enterprises reduce the environmental load on the supply chain by adopting advanced environmental management technology. Enterprise source prevention and process control are also found, but many enterprises still adopt terminal management and passive environmental protection. The emerging “strategy-as-practice” holds that environmental management practice is the concrete expression of the enterprise environmental strategy (Kaplan 2011 ), and the difference of environmental management practice is reflected in the heterogeneity of the enterprise environmental strategy. Why do enterprises implement differentiated environmental strategies? Under what conditions does the enterprise adopt this kind of environmental strategy? In the published literature, research on environmental strategy selection mainly focuses on the institutional level, studies look at the institutional causes behind strategic choices. For example, formal and informal institutional pressure, such as environmental protection laws and regulations issued by the government (Clemens and Douglas 2006 ; Pope et al. 2020), regulatory flexibility (Winter and May 2001) and environmental protection organizations (Reid and Toffel 2009 ), has a significant impact on the environmental strategic choices of enterprises. Studies focusing on public pressure to analyze the environmental strategic responses of enterprises are rarely involved. However, the increasing attention and participation of the public on environmental issues often influences organizational behavior by putting pressure on the government (Zheng et al. 2013 ). The strategic choice of enterprises is analyzed from the perspective of the public, which appropriately complements the theory of environmental strategic choice. Institutional research can be traced back to the “Ceremony Conformity” view of the new institutionalist school, which emphasizes that enterprises conform to institutional rules in structure by means of compulsory, imitative and normative convergence (Dimaggio and Powell 1983) to obtain legitimacy (Meyer and Rowan 1977). Oliver ( 1991 ) introduced this institutional theory into the field of strategy, and pointed out that there is a differentiated response to organizational strategy under the constraints of institutional environment. A few scholars explored the heterogeneous response of enterprise environmental strategy under the pressure of similar systems from the perspectives of the awareness of managers and the enterprise life cycle (Yin, Wang, and Zhang 2019). However, research on the heterogeneous selection of enterprise environmental strategy based on resource characteristics is relatively deficient. Sharfman et al. (1988) pointed out that resources provide enterprises with choices to adapt to the external environment, which may affect the relationship between institutional pressure and environmental strategies of heavily polluting enterprises (Y. Yang, Wei, and Luo 2015). This paper attempts to answer the question of institutional pressure on the heterogeneous selection of environmental strategies in heavily polluting enterprises from the perspectives of organizational slack and asset specificity. Environmental investment arising due to institutional pressure places a serious demand on resources for enterprises with heavy pollution. Organizational slack to a certain extent not only compensates for the resource loss of environmental investment but also facilitates the integration of resources to make enterprises implement more positive environmental strategies. The higher the degree of asset specificity, the higher the default risk and cost of ignoring institutional pressure, which forces enterprises to implement more forward-looking environmental strategies. We integrate institutional theory and environmental strategic choice theory, analyze the influence of different institutional pressures on the choices of environmental strategy for heavily polluting enterprises, and look at organizational slack and asset specificity as heterogeneous environmental strategic responses to institutional pressures. A total of 597 listed companies were selected from the heavily polluting industries in China, and a number of Logit models were adopted for empirical research. The results show that companies choose environmental leadership strategies when the policy pressure is greatest; however, they choose pollution prevention strategies when the regulatory and public pressures are greatest; finally, organizations with more redundant resources and strong asset specificity are more inclined to choose environmental leadership strategies as institutional pressures increase. This study has several theoretical implications for institutional theory and the growing literature on environmental strategies. First, we extend the relationship between institutional pressure and environmental strategy. We find that increased regulatory or public pressure does not prompt enterprises to choose the environmental leadership strategy. Our results differ from earlier research that concludes that companies are more active in corporate environmental strategy as institutional pressure increases (Menguc, Auh, and Ozanne 2010). Second, this study also contributes to environmental strategies literature. Research on the heterogeneous selection of enterprise environmental strategy has based on enterprise life cycle and managers’ cognition (Yin, Wang, and Zhang 2019), resource characteristics is relatively deficient,we analyze the heterogeneous choices of environmental strategies for heavily polluting enterprises under the pressure of similar systems taking into consideration organizational slack and asset specificity. This expands, to a certain extent, the heterogeneity studies of environmental strategies. Third, this study also contributes to the growing literature on emerging economies. This research focuses on China, the country is facing serious threat of environmental pollution along with its rapid economic growth, and has underdeveloped legal systems of inefficient legal implementation due to the transition from a planned economy into a market-based economy. The findings will thus be of value for other emerging economies in pushing forward corporate environmental protection. The remaining sections of this study are arranged as follows: The second part is the theoretical basis and research hypothesis; the third part describes the research methods; the fourth part covers the analysis of the empirical results; and the fifth part is the conclusion. Theory And Research Hypothesis Institutional Pressure and Environmental Strategy Choice of Heavily Polluting Enterprises The classification of environmental strategy in academic circles has not been unified. Yin et al. (Yin, Wang, and Zhang 2019) used studies of the literature, investigations and interviews with heavy polluters to divide environmental strategy into reactive, pollution prevention and environmental protection leadership at low to high levels. Reactive environmental strategy focuses on terminal pollution control and passively responds to the environmental requirements of stakeholders; pollution prevention focuses on the prevention of pollution at the source of production, and adopts the methods of replacing raw materials and recycling to reduce and prevent waste generation; environmental protection leadership integrates external stakeholders into product procurement, design, production, sales and other areas, and coordinates with upstream- and downstream-related enterprises to reduce emissions to minimize the environmental burden in the product life cycle. The choice of environmental strategy for heavily polluting enterprises is limited by external institutional pressure, which includes policy pressure from strictly following government policies, laws and regulations, and regulatory pressure from accepting government environmental supervision (Freeman 2010 ). There are similarities between policy pressure and regulatory pressure in the environmental strategic choices of enterprises. The pollutant discharge standards of heavy polluters are usually higher when the government policies and regulations are stricter. Therefore, enterprises need to adopt diversified environmental protection practices such as source prevention or recycling to meet the policy requirements (Buysse and Verbeke 2003 ). Companies are more inclined to choose pollution-prevention or environmental-protection leader-type environmental strategies rather than reactive environmental strategies. In addition, the social costs paid by enterprises for their pollution violations, which include higher fines or closer environmental supervision, become higher as the policies and regulations become more stringent (Berrone et al. 2013). Such default risks force enterprises to implement more active environmental strategies. The difference between policy pressure and regulatory pressure on the selection of environmental strategy for enterprises is reflected in that government policy provides financial subsidy or differentiated policy support for environmental protection enterprises to encourage enterprises to choose a more positive environmental strategy (D. Williamson, Lynch-Wood, and Ramsay 2006). However, the regulatory pressure focuses on the warning and punishment of environmental violations by heavily polluting enterprises, and no effective incentive mechanism has been put in place (Vannoorenberghe 2012 ). Enterprises are unwilling to invest more resources to implement an environmental protection leadership strategy when they can avoid the risk of environmental violation by implementing a prevention strategy for environmental pollution. Informal institutional pressure comes from the behaviors and standards established for enterprises by professional organizations and social actors (Hu, Song, and Wang 2017 ), which are mainly manifested as the need for enterprises to keep in line with social norms (Clarkson et al. 2008 ). A large number of studies have shown that although social norms are not formal institutions, they play a decisive role in corporate strategic decision-making (Lounsbury, Ventresca, and Hirsch 2003). As a part of informal organizations, the public can make a collective voice through newspapers, the Internet and other news media, directly pressuring enterprises (King 2008 ). The public can also indirectly transmit complaints to enterprises about pollution and advocate environmental protection to the government, which forces enterprises to choose a positive environmental strategy. In a way that is similar to regulatory pressure, social organizations and the public focus on whether enterprises comply with environmental protection regulations, which makes it difficult to effectively motivate enterprises to comply and choose more advanced environmental strategies. H1a, H1b and H1c are proposed based on the aforementioned analysis. H1a: Heavy polluters will more likely choose environmentally friendly leader-oriented environmental strategies as policy pressure increases; H1b: Heavy polluters will more likely choose a pollution-prevention environmental strategy as regulatory pressure increases; H1c: Heavy polluters will more likely choose pollution-prevention environmental strategies as public pressure increases. The Moderating Effect of Resource Characteristics on Institutional Pressure and Environmental Strategic Choice of Heavily Polluting Enterprises Enterprises in the same industry and region, which are constrained by a set of environmental regulations, government regulations and public supervision, may face similar institutional pressures. Enterprises should adopt a homogeneous environmental strategy as described in Meyer’s “protocol consistency” viewpoint (Meyer and Rowan 1977). In practice, the strategic response of enterprises to institutional pressures is heterogeneous. Why do enterprises choose heterogeneous strategy? This paper attempts to give an explanation from the perspective of resource characteristics. Both the institutional theory and the resource-based view fully affirm the importance of resources in the process of strategic selection of enterprises. Institutional theory holds that enterprises can obtain scarce resource input through establishing relations with stakeholders to meet their legitimacy (Baum and Oliver 1992 ). Heavy polluters can build a good environmental image and improve their corporate reputation by creating and maintaining stakeholders concerned with environmental responsibility, environmental litigation and corporate environmental protection practices (Huang and Chen 2015 ). The resource-based view points out that irreplaceable, valuable and hard-to-imitate resources are an important consideration for the strategic decisions of enterprises (Barney, Wright, and Ketchen Jr 2001). The differences in resources lead different enterprises to choose the differentiation strategy most suitable for external requirements (Carnes et al. 2019). Specifically, the influence of institutional pressure on the strategic choice of enterprise environment may depend on two internal resource differences: organizational slack and asset specificity. Organizational slack is the stock of idle resources that an organization can transfer or redeploy to achieve organizational goals (C. Chen, Wan, and Zhu 2019). Bradley et al. ( 2011 ) believe that organizational slack enhances organizational adaptability, enables it to adapt to changes in the external environment, and is capable of adopting diversified strategies and even triggering strategic changes (Zhao, Zhang, and Chen 2014 ). As external institutional pressures increase, for example when the government raises pollutant discharge standards and imposes stricter pollution penalties, resource-rich enterprises are better able to ensure the necessary resources and talents, promote active environmental management practices and implement more advanced environmental strategies. In contrast, when the organizational slack is relatively small, enterprises often make use of scarce resources to meet their most urgent needs, focusing on the business efficiency of enterprises, ignoring environmental requirements or responding to external pressure in a greenwash way, and environmental strategy is more passive. In addition, organizational slack can effectively buffer the uncertain risks generated by enterprise investment and research and development (Xie and Wei 2016 ). Heavily polluting enterprises are more inclined to choose more positive environmental strategies as the external institutional pressure increases, which is followed by the introduction of more environmental protection equipment and resources for clean production and green innovation. Enterprises face the risk of loss of fixed assets caused by the renewal of new environmental protection equipment, as well as the risk of failure of green research and development. Enterprises with more organizational slack are more conducive to buffering these risks and promoting the implementation of advanced environmental strategies. The following hypotheses are proposed based on the aforementioned analysis: H2a: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as policy pressure increases; H2b: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as regulatory pressure increases; H2c: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as pressure from public opinion increases. Asset specificity refers to the extent to which an asset can be redeployed and utilized by users without sacrificing its production value (O.E. Williamson 1984 ). It also reflects to some extent the submerged characteristics of asset specificity (Pang and Zhuang 2017 ). The value loss of organizations with a high degree of asset specificity in the case of default is much higher than that of enterprises with a low degree of asset specificity. Therefore, with the strengthening of external institutional pressure, changes to the existing environmental management practices are difficult to meet the environmental needs of stakeholders. Failure to adopt a more active environmental strategy will lead to the loss of more environmentally sensitive investors and customers or result in higher legal costs and government sanctions (Kassinis and Vafeas 2009). Conversely, if an enterprise avoids default and redeploys proprietary assets away from existing uses, its productive value will be lost (Yao, Tang, and Pan 2009 ). Environmentally concerned corporate stakeholders will force enterprises to allocate resources to further advanced environmental practices as government environmental policies become more stringent or regulation becomes more frequent. The value of these proprietary assets will be greatly reduced if they are allocated and tilted towards the environmental protection. Therefore, enterprises with strong proprietary assets will be more inclined to seek more positive environmental strategies to cope with these pressures as institutional pressure increases. The following hypotheses are proposed based on the aforementioned analysis. H3a: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as policy pressure increases; H3b: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as regulatory pressure increases; H3c: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as public pressure increases. Research Methods Sample Selection and Data Collection We used the definition and classification of heavily polluting industries described in the Guidelines for Environmental Information Disclosure of Listed Companies (Draft for Comments) issued by the Ministry of Environmental Protection. In this paper, 640 enterprises in heavily polluting industries were selected according to industry classification from the Guotai’an database. Problems existing in the data, for example outliers and absence of data, were dealt with before the model was built as follows. (1) Companies marked as ST (stocks that have lost money for two consecutive years) in 2015 were manually deleted; (2) Observation values that were missing or zero values for core indexes, such as enterprise fixed assets, return on assets and listing time in the database, were supplemented using information obtained from the annual reports of enterprises. Finally, 597 enterprises were selected for our analysis. Measurement of Variables Here we describe the explained, explanatory and moderator variables used in our study. Explained variables. Environmental strategy (EnvStr), which refers to the environmental strategy measurement method proposed by Lin ( 2012 ), analyzes and codes relevant environmental protection practices in the social responsibility reports of listed companies as follows. When words such as “waste of energy,” “sewage treatment” and “environmental clean-up” appear in the report, it means that the enterprise implements reactive environmental strategy (ReaStr), and the environmental strategy is encoded as 1; when the words “reuse,” “recycle” and “source control” appear in the report, it indicates that the enterprise adopts the pollution prevention strategy (PreStr), and the code number is 2; when the report mentions words such as “product life cycle,” “supply chain participation” and “green products,” companies implement green leadership strategy (LeaStr), which is coded 3; when keywords representing different types of environmental strategies appear in the sample of enterprises, it is considered that the enterprise has implemented a relatively higher level of environmental strategy. In addition, some enterprises have not released their social responsibility reports. In these cases, we searched their official websites and screened out relevant environmental protection information for coding using the aforementioned principles. Finally, in order to reduce subjective bias in the process of artificial coding, this study involved a double-blind coding method. The degree of matching for the final two codes is as high as 92.8%, which indicates relatively high reliability. Explanatory variables. Policy pressure (Pol) refers to the way in which different scholars use different measures to measure policy pressure. This paper adopts a number of environmental administrative regulations issued by local governments based on the research of Wang and Xu (2015). Regulatory pressure (Reg) is defined as follows in our study. Berrone et al. (2013) used the number of inspections by regulated entities to measure regulatory pressure in their research. This is based on the premise that companies in provinces with more inspections by regulated entities face greater regulatory pressure than those in provinces with less inspections by regulated entities. We use the number of administrative punishment cases of local governments as a proxy variable of regulatory pressure by referring to the measurement method of Berrone et al. (2013). Public pressure (Pub) is defined as follows in our study. Dasgupta & Wheeler ( 1997 ) used letters of public complaints on local problems to represent the attention of the public to environmental protection, and believed that enterprises in regions with many telephone and Internet complaints faced a relatively high level of public pressure. Clarkson et al. ( 2008 ), when investigating the impact of supervision on public opinion for corporate environmental behavior, used relevant environmental reports by the media as proxy variables for the effect of supervision on public opinion. We believe that media reports focus more on the pressure of public opinion faced by enterprises, and most of the attitudes and opinions of the public on environmental pollution and other issues are not reflected through media channels. The number of public complaints about environmental issues was therefore used to represent the public pressure by referring to the research of Dasgupta & Wheeler ( 1997 ). Moderator variables. The ratio between working capital and sales was selected as the proxy variable of organizational slack (Slack) according to the existing literature (Fleming and Bromiley 2003 ). Asset specificity (Speci) takes into account that the investment of enterprises in production plants and machinery equipment is not easy to be redeployed. The logarithm of the ratio of fixed assets to the number of employees of the company was adopted as the proxy variable of asset proprietary taking Berrone ( 2013 ) as a reference. In addition, we controlled environmental strategy influencing factors from the perspectives of enterprises and executives to eliminate the influence of other factors on the regression model and data analysis according to previous research literature (2013). Factors at the enterprise level include enterprise size(Size), industry type (Indu), time to market(Time), and financial performance (ROA); factors at the executive level include age of the chairman(Age) and education (Educ). Table 1 describes the relevant variables. Table 1 Description of major variable measures Variable categories Variable name Variable symbol Variable measure Explained variable Environmental strategies EnvStr Code of the relevant environmental protection practices in the social responsibility reports of listed companies Explanatory variables Policy pressure Pol Number of environmental administrative regulations issued by local governments Regulatory pressure Reg Number of administrative punishments imposed by local governments Public pressure Pub Number of public complaints on environmental issues Regulating variables Organizational slack Slack Ratio of working capital to sales Asset specificity Speci Ratio of fixed assets of a company to the number of employees Analysis Of Research Results Descriptive Statistics and Correlation Analysis All continuous variables were treated with WinSOR1% to remove the influence of outliers on the regression results. Descriptive statistics and correlation analysis results, excluding major variables outside the industry, are reported in Tables 2 and 3 , respectively. Table 2 Descriptive statistics of key variables variable The mean The standard deviation The maximum The minimum EnvStr 1.63 0.60 3 1 Poli 1.52 1.64 9.33 0 Reg 9.05 5.55 27.02 0.991 Peo 54.75 92.89 443.27 2.287 Slack 0.038 0.705 8.58 -10.32 Speci 11.32 3.13 16.57 0 Size 22.42 1.36 27.04 18.19 ROA 0 .03 0. 09 0.392 -0.884 Time 17.54 4.64 25 4 Age 53.51 6.84 75 29 Educ 3.50 0.94 6 1 Table 3 Pearson correlation coefficient between major variables variable 1 2 3 4 5 6 EnvStr 1.00 Poli 0.22*** 1.00 Reg 0.043 0.2*** 1.00 Peo 0.019 -0.12*** -0.30 1.00 Slack 0.021 -0.014 0.024 0.065 1.00 Speci 0.15*** -0.014 0.032 -0.008 0.027 1.00 * P < 0.1, ** P < 0.05, *** P < 0.01. The correlation coefficient between policy pressure and environmental strategy was 0.22, and there was a positive correlation (Table 3 ). The correlation between regulatory pressure, public pressure and environmental strategy was not significant, which preliminarily indicated the rationality of hypothesis H1. The correlation coefficients of all explanatory variables were less than 0.4, and there was no serious multicollinearity. Regression Analysis of the Relationship Between Institutional Pressure and Environmental Strategy of Heavily Polluting Enterprises The environmental strategy-type of explained variable belongs to ordered multi-categorical variables, which are estimated by a multi-logit model. The regression results of the relationship between institutional pressure and environmental strategy are shown in Table 4 . Models M1 to M4 successively added explanatory variables for policy, regulatory and public pressure. As can be seen from the estimation results of model M4, the influence of policy pressure on enterprises to choose a responsive and environmentally friendly leadership environmental strategy was negative ( β =-0.288, P < 0.01) and positive ( β = 0.221, P < 0.05), respectively. The more heavily polluting enterprises reject the reactive strategy and were more likely to choose an environmental leadership strategy as policy pressure increased. H1a was verified. The influence of regulatory pressure on the choice of a reactive environmental strategy was significantly negative ( β =-0.0438, P < 0.05), and the influence on the choice of an environmental leadership strategy by an enterprise was not significant. This indicated that enterprises tend to choose pollution prevention strategies as the government strengthens regulatory pressure. The results verified hypothesis H1b. The influence of public pressure on the choice of a reactive environmental strategy by an enterprise was significantly negative ( β =-0.007, P < 0.01), and the influence on the choice of an environmental leadership strategy by an enterprise was not significant. This indicated that enterprises tend to choose pollution prevention strategies as the government strengthens public pressure.The empirical results confirmed hypothesis H1c. Table 4 Regression results of the relationship between institutional pressure and corporate environmental strategy Explanatory variables Explained variable M1 M2 M3 M4 ReaStr LeaStr ReaStr LeaStr ReaStr LeaStr ReaStr LeaStr Poli -0.28*** (-3.78) 0.21** (2.19) -0.26*** (-3.48) 0.214** (2.19) -0.288*** (-3.69) 0.221** (2.25) Reg -0.017 (-0.90) 0.007 (0.22) -0.0438** (-2.11) 0.0113 (0.30) Peo -0.007*** (-3.55) 0.001 (0.47) Size -0.136* (-1.78) -0.0849 (-0.53) -0.16** (-2.05) -0.074 (-0.46) -0.16** (-2.05) -0.074 (-0.46) -0.140 (-1.73) -0.102 (-0.65) ROA 0.872 (0.72) 2.706 (0.98) 0.525 (0.42) 3.102 (1.15) 0.468 (0.37) 3.158 (1.16) -0.0641 (-0.28) 0.0479 (1.08) Time 0.00278 (0.15) 0.0784**(2.03) -0.001 (0.05) 0.08** (1.96) -0.002 (-0.01) 0.078** (1.97) 0.00614 (0.39) 0.0192 (0.59) Age 0.00264 (0.39) 0.0209 (0.66) 0.002 (0.01) 0.029 (0.87) 0.001 (0.06) 0.0274 (0.82) -0.154 (-1.31) 0.554** (2.16) Educ -0.181 (-1.61) 0.517** (2.03) -0.194* (-1.69) 0.56** (2.13) -1.96* (-1.70) 0.557** (2.13) -0.153*** (-4.36) -0.106 (-1.63) Indu -0.136*** (-4.08) -0.102 (-1.60) -0.14*** (-4.17) -0.10 (-1.54) -0.14*** (-4.21) -0.10 (-1.53) 5.319** (2.42) -3.784 (-0.90) Cons 4.134** (2.04) -3.703 (-0.87) 5.301** (2.52) -4.962 (-1.2) 5.44*** (2.58) -5.01 (-1.16) -0.288*** (-3.69) 0.221** (2.25) observations 597 597 597 597 LR chi2 37.14 62.21 64.19 37.89 Prob > chi2 0.0002 0.0000 0.0000 0.0040 Pseudo R2 0.1011 0.1424 0.1610 0.1050 The baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p < 0.1**, P < 0.05, *** P < 0.01 Analysis of the Moderating Effect of Organizational Slack The regression results of multiple logit models for the adjustment effect of organizational slack are shown in Table 5 . Models M5, M6 and M7 use multiple logit models in turn to challenge the consistency of “etiquette,” which verified that organizational slack is a heterogeneous choice for the environmental strategy of heavily polluting companies under policy, regulatory and public pressures. The estimation result of model M5 showed that the coefficients of the interaction terms between redundant resources and policy pressure were β =-0.00725 ( P < 0.1) and β = 0.0676 ( P < 0.1), which indicated that the more redundant organizations of heavily polluting enterprises and the increase in policy pressure make enterprises more inclined to choose environmentally protection-led environmental strategies. Hypothesis H3a was verified. In model M6, the coefficients of the interaction terms between redundant resources and regulatory pressure were β =-0.065 ( P < 0.05) and β = 0.17 ( P < 0.1), respectively. This indicated that enterprises with abundant organizational slack reject reactive strategies and prefer environmental leadership strategies as regulatory pressure increases. Hypothesis H3b was verified. Model M7 showed that the interaction term coefficient between redundant resources and public pressure was not significant, and the empirical results did not adequately verify H3c. Table 5 Regression results of organizational slack on heterogeneous response to environmental strategy of heavily polluting enterprises under institutional pressure Explained variable Explanatory variables M5 M6 M7 ReaStr LeaStr ReaStr LeaStr ReaStr LeaStr Poli -0.00167 (-0.21) 0.0153 (0.92) -0.6*** (-3.48) 0.24** (2.19) -0.2*** (-3.69) 0.221** (2.25) Reg -0.085*** (-3.47) 0.0247 (0.74) -0.017 (-0.90) 0.007 (0.22) -0.04** (-2.11) 0.0113 (0.30) Peo -0.004* (-2.46) -0.00057 (-0.29) -0.007*** (-3.56) 0.00121* (2.59) -0.07*** (-3.55) 0.001 (0.47) Slack -0.0881 (-0.52) 1.001 (1.50) -0.05 (-1.43) 1.062 (1.50) -0.15 (-1.83) 1.117 (1.32) Poli*Slack -0.00725* (-2.44) 0.0676* (2.55) Reg*Slack -0.065** (-2.94) 0.17* (2.12) Peo*Slack -0.00003 (-0.05) 0.00151 (1.36) Size -0.171* (-2.01) -0.05 (-0.29) -0.16** (-2.05) -0.074 (-0.46) -0.140 (-1.73) -0.102 (-0.65) ROA 0.801 (0.53) 1.527 (0.49) 0.468 (0.37) 3.158 (1.16) -0.0641 (-0.28) 0.0479 (1.08) Time 0.0792 (0.26) 0.08** (1.96) -0.002 (-0.01) 0.078* (1.97) 0.00614 (0.39) 0.0192 (0.59) Age 0.0130 (0.81) 0.00663 (0.21) 0.001 (0.06) 0.0274 (0.82) -0.154 (-1.31) 0.554** (2.16) Educ -0.191 (-1.62) 0.555* (2.16) -1.96* (-1.70) 0.557** (2.13) -0.13*** (-4.36) -0.106 (-1.63) Indu -0.141*** (-4.06) 0.116* (1.79) -0.4*** (-4.21) -0.10 (-1.53) 5.319** (2.42) -3.784 (-0.90) Cons 4.942* (2.31) -2.824 (-0.66) 5.44*** (2.58) -5.01 (-1.16) -0.8*** (-3.69) 0.221** (2.25) observations 597 597 597 LR chi2 62.21 64.19 92.63 Prob > chi2 0.0000 0.0000 0.0040 Pseudo R2 0.1149 0.1230 0.1472 The baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p < 0.1, ** P < 0.05, *** P < 0.01 Analysis of the Regulatory Effect of Asset Specificity The regression results of multiple logit models for the proprietary regulatory effect of assets of heavily polluting enterprises are shown in Table 6 . Models M8, M9 and M10 successively adopt a number of logit models, which challenges the idea of “etiquette” consistency from the perspective of asset ownership. This verified the regulating effect of asset ownership on the relationship between policy pressure, regulatory pressure, public pressure and environmental strategy for heavily polluting enterprises. In model M8, the coefficients of the interaction terms between asset specificity and policy pressure were β =-0.0244 ( P < 0.05) and β = 0.0653 ( P < 0.1), respectively. This result showed that enterprises tend to choose a more positive environmental strategy when the policy pressure increases and the asset specificity is higher. Hypothesis H4a was verified. In model M9, the coefficients of the interaction terms between asset specificity and regulatory pressure were β =-0.0154 ( P < 0.1) and β = 0.00358 ( P < 0.1), respectively. This indicates that enterprises tend to choose an environmental leadership strategy as the regulatory pressure increases. Hypothesis H4b was verified. In model M10, the coefficients of the interaction terms between asset specificity and public pressure were β =-0.0004 ( P < 0. 1) and β = 0.1676 ( P < 0.05), respectively. This indicated that the probability of enterprises choosing reactive, pollution-defense and environmental leadership strategies increased successively as the pressure exerted by the public on enterprises increased. The empirical results confirmed H4c. Table 6 Regression results of asset specificity in heterogeneous response to environmental strategy of heavily polluting enterprises under institutional pressure Explained variable Explanatory variables M8 M9 M10 ReaStr LeaStr ReaStr LeaStr ReaStr LeaStr Poli -0.292*** (-3.52) 0.252* (2.48) -0.302*** (-3.69) 0.233* (2.33) -0.319*** (-3.76) 0.238* (2.26) Reg -0.0444* (-2.10) 0.0091 (0.24) -0.0557* (-2.43) 0.0123 (0.32) -0.0605* (-2.39) 0.0122 (0.36) Peo -0.007*** (-3.62) 0.00084 (0.40) -0.007*** (-3.56) 0.00121 (0.59) -0.00525** (-2.94) -0.00226 (-0.6) Speci -0.0781 (-1.62) 1.001 (1.50) -0.05 (-1.43) 1.062 (1.50) -0.0781 (-1.62) 1.101* (1.99) Poli* Speci -0.0244** (-2.62) 0.0653* (2.23) Reg* Speci -0.0154* (-2.41) 0.00358* (2.22) Peo* Speci -0.0004* (-2.14) 0.1676** (2.85) Size -0.126 (-1.46) 0.0125 (0.07) -0.135 (-1.56) 0.00924 (0.05) -0.147 (-1.70) -0.0211 (-0.11) ROA 0.387 (0.25) 0.662 (0.19) 0.426 (0.28) 1.001 (1.16) 0.651 (0.43) 1.306 (0.37) Time 0.0848 (0.29) -0.627 (-0.93) -0.0203 (-1.02) 0.0858* (2.08) -0.016 (-0.80) 0.0165 (0.59) Age 0.00768 (0.48) 0.0194 (0.54) 0.00927 (0.57) 0.0289 (0.83) 0.0104 (0.64) 0.0245 (0.71) Educ -0.135 (-1.13) 0.560* (2.05) -0.113 (-0.94) 0.557** (2.13) -0.152 (-1.27) 0.498 (1.87) Indu -0.156*** (-4.35) -0.095 (-1.44) -0.158*** (-4.41) -0.0980 (-1.48) -0.148*** (-4.17) -0.0814 (-1.24) Cons 4.961* (2.24) -6.201 (-1.32) 5.165* (2.32) -6.586 (-1.43) 5.034* (2.28) -5.325 (-1.15) Observations 597 597 597 LR chi2 90.31 90.93 91.59 Prob > chi2 0.0000 0.0000 0.0040 Pseudo R2 0.1612 0.1638 0.1701 The baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p < 0.1, ** P < 0.05, *** P < 0.01 Robustness Test A robustness test was carried out to investigate the robustness of the main effect. First, the proxy variable of regulatory pressure was replaced by the number of staff in regional environmental protection agencies to verify whether the above conclusions were still valid. Second, multiple Probit regression was used for verification since the dependent variable belongs to the ordered multiple categorical variables (Q. Chen 2014 ). The substitution of regulatory pressure proxy variables and the adoption of multiple Probit model regression showed that the empirical results and conclusions were unchanged (Table 7 ). The empirical results were stable and robust. Table 7 Regression results of robustness test Explanatory variables Explained variable Sample Total Logit of alternative regulatory pressures(M13) Multinomial Probit Regression of sample population (M14) ReaStr LeaStr ReaStr LeaStr Poli -0.299*** (-3.68) 0.210** (2.07) -0.226*** (-3.81) 0.126* (1.80) Reg -0.0592** (-2.38) 0.00465 (0.14) -0.0401** (-2.31) 0.004 (0.16) Peo -0.00517*** (-3.06) 0.00089 (0.47) -0.00547*** (-3.87) 0.000151 (0.10) Size -0.152* (-1.87) -0.0866 (-0.54) -0.119 (-1.75) -0.0839 (-0.79) ROA 0.376 (0.28) 3.062 (1.16) 0.320 (0.29) 2.174 (1.20) Time -0.00659 (-0.34) 0.0777* (1.98) -0.00661 (-0.40) 0.0538** (2.06) Age 0.00744 (0.47) 0.0280 (0.83) 0.00504 (0.37) 0.0162 (0.72) Educ -0.172 (-1.45) 0.559* (2.14) -0.122 (-1.23) 0.329 (1.94) Indu -0.149*** (-4.25) -0.0974 (-1.52) -0.133*** (-4.52) -0.0773* (-1.80) Constant term 5.340** (2.46) -4.770 (-1.11) 4.512** (2.48) -2.309 (-0. 38) Observations 597 597 LR chi2 86.54 N Wald chi2 N 72.55 Prob > chi2 0.0000 0.0000 The baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p < 0.1, ** P < 0.05, *** P < 0.01. Research Conclusions And Implications Main Conclusions This paper focuses on how heavily polluting enterprises choose heterogeneous environmental strategies, analyzes the relationship between institutional pressures and environmental strategic choices for such enterprises, and explores the heterogeneous choices of enterprise environmental strategy from the perspective of resources available. The main conclusions are as follows. The influence of different institutional pressures on the choice of environmental strategy differs. Firstly, enterprises were more likely to adopt an environmental leadership strategy if the policy pressure was greater. Secondly, enterprises increasingly rejected the reactive environmental strategy if the regulatory pressure or public pressure increased, but there was no significant impact on whether the company made a choice for an environmental leadership strategy under these conditions. Our results differ from earlier research that concludes that companies are more active in corporate environmental strategy as institutional pressure increases (Menguc, Auh, and Ozanne 2010). Our study subdivides the types of environmental strategies. It finds that increased regulatory or public pressure does not prompt enterprises to choose the environmental leadership strategy because the two kinds of pressure do not form an effective incentive mechanism for enterprises. Organizational slack makes the response of corporate environmental strategy induced by institutional pressure heterogeneous. Organizational slack positively regulated the relationship between policy pressure and environmental strategy selection for heavily polluted enterprises. Similarly, enterprises were more inclined to choose advanced environmental strategies, the higher the organizational slack for heavily polluting enterprises, and the greater the regulatory pressure on these companies. This paper verifies that redundant resources can effectively buffer enterprise resource shortages and make enterprises better adapt to the external institutional environment, and is consistent with most of the research results on organizational slack (Kuusela, Keil, and Maula 2017). However, previous studies mostly focused on the role of organizational slack in the field of enterprise capability and innovation (Geiger and Makri 2006 ; Iyer and Miller 2008). Our paper extended organizational slack to the level of enterprise strategy and found that redundant resources promoted enterprises to implement more advanced environmental strategies. The relationship between institutional pressure of asset specificity promotion and environmental strategic choice was investigated. For enterprises with strong proprietary assets, the greater the public and regulatory pressure or public pressure, the more inclined they were to choose the environmental leadership strategy. Research on asset specificity in transaction economic theory mostly uses the degree of asset specificity to weigh whether an organization implements vertical integration strategy (H. Wang, Zhao, and Chen 2017). Our paper takes asset specificity as an important strategic resource for heavily polluting enterprises who want to cope with external institutional pressure and implement active environmental protection practices. It thus extends the research to include asset specificity. In addition, the heterogeneity rather than the homogeneity of the responses to institutional pressure on corporate environmental strategy was studied from the perspective of organizational slack and asset specificity. This has enriched research on the heterogeneous selection of environmental strategies by heavily polluting enterprises under the institutional isomorphism. Policy Proposal Our paper proposes the following policy recommendations for government and relevant private sector enterprises based on our research findings. At the government level, the first step is to actively promote the implementation of environmental governance policies. The central government has improved the top-level design of environmental governance policies. Local governments, which have focused on the central policies and guidelines and the current situation for regional pollution, have formulated policies and measures suitable for local green development. These measures include pollution control, ultra-low emission subsidies, carbon emission trading and others. At the same time, relevant government departments should increase the frequency and availability of supervision, and correctly guide and advocate the participation of all people in pollution measures. Central and local environmental protection supervision should act alternately and complement each other. This will help to reduce the “fluke mentality” of enterprises and the opportunistic behavior to engage in activities with illegal emissions. Also, the Internet should be used as the channel to increase the exposure of inspection results and force enterprises to pay attention to the treatment of pollutants at source. In general, institutional incentives and pressure should be combined to jointly promote the sustainable development of heavily polluting enterprises. At the enterprise level, it is suggested that enterprises with abundant organizational slack tilt and allocate resources to environmental governance. In the face of increasingly strict requirements for environmental protection systems, enterprises respond in different ways to institutional pressure. Enterprises that have more redundancy within their organization can realize upgrades to environmental strategy by allocating redundant resources to environmental protection. This alleviates the impact of external pressure on enterprises. If an enterprise has a high degree of proprietary assets, it should implement a strategy to avoid environmentally sensitive external stakeholders taking measures such as divestment, which will cause losses to the enterprise and threaten its survival. Declarations Compliance with Ethical Standards The authors have no relevant competing interests to disclose. Human Participants and Animals are not involved in the research. Consent to Participate and Publish The authors approve the version to be participated and published. Author contributions Sen WANG and Jianhua YIN conceived the study and were responsible for the design and development of the data analysis. Xiaomei ZHU was responsible for data collection and analysis. Funding This study is supported by the National Natural Science Foundation of China (No.72002013, No.71874029), Project of Beijing Municipal Education Commission (No. SM202111417005),Academic Research Projects of Beijing Union University (No. SK80202001, No. XP202010). Disclosure statement The authors have no relevant financial or non-financial interests to disclose. The datasets used in the study are available from the corresponding author upon request. References Barney, Jay, Wright, Mike, and Ketchen Jr, David J. 2001. "The resource-based view of the firm: Ten years after 1991." Journal of management , 27 (6): 625-641. https://doi.org/10.1002/smj.4250160303 . Baum, Joel AC, and Oliver, Christine. 1992. "Institutional embeddedness and the dynamics of organizational populations." American sociological review : 540-559. https://doi.org/10.2307/2096100 . Berrone, Pascual, Fosfuri, Andrea, Gelabert, Liliana, and Gomez ‐Mejia, Luis R. 2013. "Necessity as the mother of ‘green’inventions: Institutional pressures and environmental innovations." Strategic Management Journal , 34 (8): 891-909. https://doi.org/10.1002/smj.2041 . Bradley, Steven W, Shepherd, Dean A, and Wiklund, Johan. 2011. "The importance of slack for new organizations facing ‘tough’environments." Journal of Management Studies , 48 (5): 1071-1097. https://doi.org/10.1111/j.1467-6486.2009.00906.x . Buysse, Kristel, and Verbeke, Alain. 2003. "Proactive environmental strategies: A stakeholder management perspective." Strategic management journal , 24 (5): 453-470. https://doi.org/10.1002/smj.299 . Carnes, Christina Matz, Xu, Kai, Sirmon, David G, and Karadag, Reha. 2019. "How Competitive Action Mediates the Resource Slack–Performance Relationship: A Meta‐Analytic Approach." Journal of Management Studies , 56 (1): 57-90. https://doi.org/10.1111/joms.12391 . Chen, Cheng, Wan, Shan, and Zhu, Le. 2019. "Executive Compensation of state-owned enterprises and corporate Social responsibility—Organizational slack and the moderating effect of marketization process." China Soft Science , 6: 129-137. Chen, Qiang . 2014. Advanced Econometrics and Stata Applications (2nd Edition) . Higher Education Press. Clarkson, Peter M, Li, Yue, Richardson, Gordon D, and Vasvari, Florin P. 2008. "Revisiting the relation between environmental performance and environmental disclosure: An empirical analysis." Accounting, organizations and society , 33 (4-5): 303-327. https://doi.org/10.1016/j.aos.2007.05.003 . Clemens, Bruce, and Douglas, Thomas J. 2006. "Does coercion drive firms to adopt ‘voluntary’green initiatives? Relationships among coercion, superior firm resources, and voluntary green initiatives." Journal of business research , 59 (4): 483-491. https://doi.org/10.1016/j.jbusres.2005.09.016 . Dasgupta, Susmita, and Wheeler, David. 1997. Citizen complaints as environmental indicators: evidence from China . The World Bank. Dimaggio, Paul J, and Powell, Walter W. 1983. "The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields." American sociological review : 147-160. https://doi.org/10.2307/2095101 . Fan, Qunlin, Shao, Yunfei, and Tang, Xiaowo. 2013. "The impact of environmental policy, technological progress, and market structure on environmental technology innovation." Science Research Management , 6. Fleming, Lee, and Bromiley, Philip. 2003. A prospect theory model of R&D allocation and invention. Harvard Business School Working Paper Series. Freeman, R Edward. 2010. Strategic management: A stakeholder approach . Cambridge university press. Geiger, Scott W, and Makri, Marianna. 2006. "Exploration and exploitation innovation processes: The role of organizational slack in R & D intensive firms." The Journal of High Technology Management Research , 17 (1): 97-108. https://doi.org/10.1016/j.hitech.2006.05.007 . Hu, Jun, Song, Xianzhong, and Wang, Hhongjian. 2017. "Informal institution, hometown identity and corporate environmental governance." Management World , 3: 76-94. Huang, Rongbing, and Chen, Danping. 2015. "Does environmental information disclosure benefit waste discharge reduction? Evidence from China." Journal of Business Ethics , 129 (3): 535-552. https://doi.org/10.1007/s10551-014-2173-0 . Iyer, Dinesh N, and Miller, Kent D. 2008. "Performance feedback, slack, and the timing of acquisitions." Academy of Management Journal , 51 (4): 808-822. https://doi.org/10.5465/amr.2008.33666024 . Kaplan, Sarah. 2011. "Research in cognition and strategy: Reflections on two decades of progress and a look to the future." Journal of Management Studies , 48 (3): 665-695. https://doi.org/10.1111/j.1467-6486.2010.00983.x . Kassinis, George, and Vafeas, Nikos. 2006. "Stakeholder pressures and environmental performance." Academy of Management Journal , 49 (1): 145-159. https://doi.org/doi.org/10.5465/amj.2006.20785799 . George, Kassinis, and Nikos, Vafeas 2009. "Environmental performance and plant closure." Journal of Business Research , 62 (4): 484-494. https://doi.org/10.1016/j.jbusres.2008.01.037 . King, Brayden G. 2008. "A political mediation model of corporate response to social movement activism." Administrative Science Quarterly , 53 (3): 395-421. https://doi.org/10.2189/asqu.53.3.395 . Kuusela, Pasi, Keil, Thomas, and Maula, Markku. 2017. "Driven by aspirations, but in what direction? Performance shortfalls, slack resources, and resource‐consuming vs. resource‐freeing organizational change." Strategic management journal , 38 (5): 1101-1120. https://doi.org/10.1002/smj.2544 . Lin, Haiying. 2012. "Cross-sector alliances for corporate social responsibility partner heterogeneity moderates environmental strategy outcomes." Journal of Business Ethics , 110 (2): 219-229. https://doi.org/10.1007/s10551-012-1423-2 . Lounsbury, Michael, Ventresca, Marc, and Hirsch, Paul M. 2003. "Social movements, field frames and industry emergence: a cultural–political perspective on US recycling." Socio-economic review , 1 (1): 71-104. Luo, Danglun, and Lai, Zaihong. 2016. "Investment of heavily polluting enterprises and promotion of local officials–based on the practical investigation of data for prefectural cities during 1999-2010." Accounting Research , 4: 42-48. Marquis, Chris, Jackson, Susan E, and Li, Yuan. 2015. "Building sustainable organizations in China." Management and Organization Review , 11 (3): 427-440. Menguc, Bulent, Auh, Seigyoung, and Ozanne, Lucie. 2010. "The interactive effect of internal and external factors on a proactive environmental strategy and its influence on a firm's performance." Journal of Business Ethics , 94 (2): 279-298. https://doi.org/10.1007/s10551-009-0264-0 . Meyer, John W, and Rowan, Brian. 1977. "Institutionalized organizations: Formal structure as myth and ceremony." American journal of sociology , 83 (2): 340-363. https://doi.org/10.1086/226550 . Oliver, Christine. 1991. "Strategic responses to institutional processes." Academy of management review , 16 (1): 145-179. https://doi.org/10.5465/amr.1991.4279002 . Pang, Fanglan, and Zhuang, Guijun. 2017. "Commitment to asymmetric trading of proprietary assets and inter-firm trust." Industrial Engineering and Management , 22 (3): 128-134. Pope, Holly C, Draper, Carrie, Younginer, Nicholas, Whitt, Olivia, and Paget, Christopher. 2020. "Use of decision cases for building SNAP-Ed implementers’ capacities to realize policy, systems, and environmental strategies." Journal of nutrition education and behavior , 52 (5): 512-521. https://doi.org/10.1016/j.jneb.2019.09.020 . Reid, Erin M, and Toffel, Michael W. 2009. "Responding to public and private politics: Corporate disclosure of climate change strategies." Strategic Management Journal , 30 (11): 1157-1178. https://doi.org/10.1002/smj.796 . Sharfman, Mark P, Wolf, Gerrit, Chase, Richard B, and Tansik, David A. 1988. "Antecedents of organizational slack." Academy of Management review , 13 (4): 601-614. https://doi.org/10.5465/amr.1988.4307484 . Vannoorenberghe, Gonzague. 2012. "Firm-level volatility and exports." Journal of International Economics , 86 (1): 57-67. https://doi.org/10.1016/j.jinteco.2011.08.013 . Wang, Heli, Zhao, Shan, and Chen, Guoli. 2017. "Firm‐specific knowledge assets and employment arrangements: Evidence from CEO compensation design and CEO dismissal." Strategic Management Journal , 38 (9): 1875-1894. https://doi.org/10.1002/smj.2604 . Wang, Shubin, and Xu, Yingzhi. 2015. "Environmental regulation and haze pollution decoupling effect: based on the perspective of enterprise investment preferences." China Industrial Economics , 4: 18-30. Wang, Yun, Li, Yanxi, Ma, Zhuang, and Song, Jinbo. 2017. "Media coverage, environmental regulation and corporate environment behavior." Nankai Business Review , 12: 42-54. Williamson, David, Lynch-Wood, Gary, and Ramsay, John. 2006. "Drivers of environmental behaviour in manufacturing SMEs and the implications for CSR." Journal of business ethics , 67 (3): 317-330. https://doi.org/10.1007/s10551-006-9187-1 . Williamson, Oliver E. 1984. "The economics of governance: framework and implications." Zeitschrift für die gesamte Staatswissenschaft/Journal of Institutional and Theoretical Economics (H. 1): 195-223. Winter, Søren C, and May, Peter J. 2001. "Motivation for compliance with environmental regulations." Journal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management , 20 (4): 675-698. https://doi.org/10.1002/pam.1023 . Xie, Weimin, and Wei, Huaqian 2016. "Market competition organizational slack and enterprise R & D investment." China Soft Science , 8: 102-111. Yang, Defeng, Wang, Aric Xu, Zhou, Kevin Zheng, and Jiang, Wei. 2019. "Environmental strategy, institutional force, and innovation capability: A managerial cognition perspective." Journal of Business Ethics , 159 (4): 1147-1161. https://doi.org/10.1007/s10551-018-3830-5 . Yang, Yang, Wei, Jiang, and Luo, Laijun. 2015. "Who is using government subsidies to innovate? —Joint adjustment effect of ownership and distortion of factor market." Management World , 1: 75-86. Yao, X., Tang, X. W., and Pan, J. M. . 2009. "Research on supply chain Partnership and Alliance Profit Model based on asset specificity." China Soft Science , 23 (1): 118-122. Yin, Jianhua, Wang, Sen, and Zhang, Lingling. 2019. "Heterogeneous Response of Corporate Environmental Strategy under Institutional Isomorphism." Journal of Beijing Institute of Technology (Social Sciences Edition) , 21 (4): 47-55. Zhao, Yapu, Zhang, Wenhong, and Chen, Silei. 2014. "Impact of organizational slack on firm exploration in a dynamic environment." Science Research Management , 35 (2): 10-16. Zheng, S. Q., Wan, G. H., Sun, W. Z., and Luo, D. L. 2013. "Public demands and urban environmental governance." Management World , 6: 72-84. Cite Share Download PDF Status: Published Journal Publication published 03 Sep, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 11 Apr, 2021 Reviewers invited by journal 12 Mar, 2021 Reviews received at journal 12 Mar, 2021 Editor invited by journal 10 Mar, 2021 Editor assigned by journal 24 Feb, 2021 First submitted to journal 17 Feb, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-253787","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":16439398,"identity":"530e09ec-dd5c-4cb0-ac96-e7f60c685c7c","order_by":0,"name":"Sen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdjiL+RiEPkBICzMzjMWWRrIWHjPitMg78x+T+Lmj1p5fuufbY942Bjm+GwmMnwvwaDE8zMwm2XvmeOLMOWe3GwO1GEveSGCWnoFPSzMz2w3etmMJBjdyt0kDtSRuuJHAxsxDQMvNv23H7O1v5DwDaaknqEWemZntNm9bDeMGiRw2kBagdQS0GDAzm/+WbTuQOONGmrnhnHMShjPPPGyWxmtLe+Njw7dtdfb8M5KfPXhTZiPPdzz54Ge8thwAU4dhfAkgZmzAowFoC0S6Dq+iUTAKRsEoGOEAAChASDhbEWDlAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9853-4072","institution":"Beijing Union University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Wang","suffix":""},{"id":16439399,"identity":"ebfa6c75-6c62-4c3f-9a43-05706a545340","order_by":1,"name":"Jianhua Yin","email":"","orcid":"","institution":"University of International Business and Economics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Yin","suffix":""},{"id":16439400,"identity":"38239449-3ec2-4a57-a5ba-8483a8e98cd7","order_by":2,"name":"Xiaomei Zhu","email":"","orcid":"","institution":"Beijing Union University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2021-02-18 04:57:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-253787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-253787/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-021-16090-9","type":"published","date":"2021-09-03T09:34:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":17372151,"identity":"6af0af75-5b73-4a18-bf46-971bf85a2ee7","added_by":"auto","created_at":"2022-01-17 09:34:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-253787/v1/2ec6733d-0e23-4a78-ade4-f218e610ee17.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHeterogeneous Choice of Environmental Strategy for Heavily Polluting Firms Under Institutional Pressure in China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eIn China today, a series of problems that include air pollution, water pollution and land desertification have become increasingly prominent. All sectors of society want to solve the environmental crisis and advocate green development (Marquis, Jackson, and Li 2015; D. Yang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In recent years, the number of environmental regulations and documents has increased dramatically (Fan, Shao, and Tang 2013), the media has paid more attention to environmental events (Y. Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the number of public complaints about environmental protection has also increased (Kassinis and Vafeas \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In spite of this, China still faces serious environmental pollution. One metric for atmospheric conditions is the amount of fine inhalable particles with diameters typically 2.5 micrometers and smaller (PM\u003csub\u003e2.5\u003c/sub\u003e). As an example, the annual average PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 338 cities at and above the prefectural level is 39 g/m\u003csup\u003e3\u003c/sup\u003e, far exceeding the 10 g/m\u003csup\u003e3\u003c/sup\u003e standard set in the air quality guidelines of the World Health Organization.\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e Heavily polluting enterprises\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e are the \u0026ldquo;perpetrators\u0026rdquo; of most environmental problems. The implementation of advanced environmental management practices can effectively promote the green and high-quality development of China (Luo and Lai \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A survey of the social responsibility reports of listed companies shows that heavy polluters show great differences in environmental management practices. A small number of enterprises reduce the environmental load on the supply chain by adopting advanced environmental management technology. Enterprise source prevention and process control are also found, but many enterprises still adopt terminal management and passive environmental protection.\u003c/p\u003e \u003cp\u003eThe emerging \u0026ldquo;strategy-as-practice\u0026rdquo; holds that environmental management practice is the concrete expression of the enterprise environmental strategy (Kaplan \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the difference of environmental management practice is reflected in the heterogeneity of the enterprise environmental strategy. Why do enterprises implement differentiated environmental strategies? Under what conditions does the enterprise adopt this kind of environmental strategy? In the published literature, research on environmental strategy selection mainly focuses on the institutional level, studies look at the institutional causes behind strategic choices. For example, formal and informal institutional pressure, such as environmental protection laws and regulations issued by the government (Clemens and Douglas \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pope et al. 2020), regulatory flexibility (Winter and May 2001) and environmental protection organizations (Reid and Toffel \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), has a significant impact on the environmental strategic choices of enterprises. Studies focusing on public pressure to analyze the environmental strategic responses of enterprises are rarely involved. However, the increasing attention and participation of the public on environmental issues often influences organizational behavior by putting pressure on the government (Zheng et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The strategic choice of enterprises is analyzed from the perspective of the public, which appropriately complements the theory of environmental strategic choice.\u003c/p\u003e \u003cp\u003eInstitutional research can be traced back to the \u0026ldquo;Ceremony Conformity\u0026rdquo; view of the new institutionalist school, which emphasizes that enterprises conform to institutional rules in structure by means of compulsory, imitative and normative convergence (Dimaggio and Powell 1983) to obtain legitimacy (Meyer and Rowan 1977). Oliver (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) introduced this institutional theory into the field of strategy, and pointed out that there is a differentiated response to organizational strategy under the constraints of institutional environment. A few scholars explored the heterogeneous response of enterprise environmental strategy under the pressure of similar systems from the perspectives of the awareness of managers and the enterprise life cycle (Yin, Wang, and Zhang 2019). However, research on the heterogeneous selection of enterprise environmental strategy based on resource characteristics is relatively deficient. Sharfman et al. (1988) pointed out that resources provide enterprises with choices to adapt to the external environment, which may affect the relationship between institutional pressure and environmental strategies of heavily polluting enterprises (Y. Yang, Wei, and Luo 2015). This paper attempts to answer the question of institutional pressure on the heterogeneous selection of environmental strategies in heavily polluting enterprises from the perspectives of organizational slack and asset specificity. Environmental investment arising due to institutional pressure places a serious demand on resources for enterprises with heavy pollution. Organizational slack to a certain extent not only compensates for the resource loss of environmental investment but also facilitates the integration of resources to make enterprises implement more positive environmental strategies. The higher the degree of asset specificity, the higher the default risk and cost of ignoring institutional pressure, which forces enterprises to implement more forward-looking environmental strategies.\u003c/p\u003e \u003cp\u003eWe integrate institutional theory and environmental strategic choice theory, analyze the influence of different institutional pressures on the choices of environmental strategy for heavily polluting enterprises, and look at organizational slack and asset specificity as heterogeneous environmental strategic responses to institutional pressures. A total of 597 listed companies were selected from the heavily polluting industries in China, and a number of Logit models were adopted for empirical research. The results show that companies choose environmental leadership strategies when the policy pressure is greatest; however, they choose pollution prevention strategies when the regulatory and public pressures are greatest; finally, organizations with more redundant resources and strong asset specificity are more inclined to choose environmental leadership strategies as institutional pressures increase.\u003c/p\u003e \u003cp\u003eThis study has several theoretical implications for institutional theory and the growing literature on environmental strategies. First, we extend the relationship between institutional pressure and environmental strategy. We find that increased regulatory or public pressure does not prompt enterprises to choose the environmental leadership strategy. Our results differ from earlier research that concludes that companies are more active in corporate environmental strategy as institutional pressure increases (Menguc, Auh, and Ozanne 2010). Second, this study also contributes to environmental strategies literature. Research on the heterogeneous selection of enterprise environmental strategy has based on enterprise life cycle and managers\u0026rsquo; cognition (Yin, Wang, and Zhang 2019), resource characteristics is relatively deficient,we analyze the heterogeneous choices of environmental strategies for heavily polluting enterprises under the pressure of similar systems taking into consideration organizational slack and asset specificity. This expands, to a certain extent, the heterogeneity studies of environmental strategies. Third, this study also contributes to the growing literature on emerging economies. This research focuses on China, the country is facing serious threat of environmental pollution along with its rapid economic growth, and has underdeveloped legal systems of inefficient legal implementation due to the transition from a planned economy into a market-based economy. The findings will thus be of value for other emerging economies in pushing forward corporate environmental protection.\u003c/p\u003e \u003cp\u003eThe remaining sections of this study are arranged as follows: The second part is the theoretical basis and research hypothesis; the third part describes the research methods; the fourth part covers the analysis of the empirical results; and the fifth part is the conclusion.\u003c/p\u003e "},{"header":"Theory And Research Hypothesis","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Pressure and Environmental Strategy Choice of Heavily Polluting Enterprises\u003c/h2\u003e \u003cp\u003eThe classification of environmental strategy in academic circles has not been unified. Yin et al. (Yin, Wang, and Zhang 2019) used studies of the literature, investigations and interviews with heavy polluters to divide environmental strategy into reactive, pollution prevention and environmental protection leadership at low to high levels. Reactive environmental strategy focuses on terminal pollution control and passively responds to the environmental requirements of stakeholders; pollution prevention focuses on the prevention of pollution at the source of production, and adopts the methods of replacing raw materials and recycling to reduce and prevent waste generation; environmental protection leadership integrates external stakeholders into product procurement, design, production, sales and other areas, and coordinates with upstream- and downstream-related enterprises to reduce emissions to minimize the environmental burden in the product life cycle.\u003c/p\u003e \u003cp\u003eThe choice of environmental strategy for heavily polluting enterprises is limited by external institutional pressure, which includes policy pressure from strictly following government policies, laws and regulations, and regulatory pressure from accepting government environmental supervision (Freeman \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There are similarities between policy pressure and regulatory pressure in the environmental strategic choices of enterprises. The pollutant discharge standards of heavy polluters are usually higher when the government policies and regulations are stricter. Therefore, enterprises need to adopt diversified environmental protection practices such as source prevention or recycling to meet the policy requirements (Buysse and Verbeke \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Companies are more inclined to choose pollution-prevention or environmental-protection leader-type environmental strategies rather than reactive environmental strategies. In addition, the social costs paid by enterprises for their pollution violations, which include higher fines or closer environmental supervision, become higher as the policies and regulations become more stringent (Berrone et al. 2013). Such default risks force enterprises to implement more active environmental strategies. The difference between policy pressure and regulatory pressure on the selection of environmental strategy for enterprises is reflected in that government policy provides financial subsidy or differentiated policy support for environmental protection enterprises to encourage enterprises to choose a more positive environmental strategy (D. Williamson, Lynch-Wood, and Ramsay 2006). However, the regulatory pressure focuses on the warning and punishment of environmental violations by heavily polluting enterprises, and no effective incentive mechanism has been put in place (Vannoorenberghe \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Enterprises are unwilling to invest more resources to implement an environmental protection leadership strategy when they can avoid the risk of environmental violation by implementing a prevention strategy for environmental pollution.\u003c/p\u003e \u003cp\u003eInformal institutional pressure comes from the behaviors and standards established for enterprises by professional organizations and social actors (Hu, Song, and Wang \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which are mainly manifested as the need for enterprises to keep in line with social norms (Clarkson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A large number of studies have shown that although social norms are not formal institutions, they play a decisive role in corporate strategic decision-making (Lounsbury, Ventresca, and Hirsch 2003). As a part of informal organizations, the public can make a collective voice through newspapers, the Internet and other news media, directly pressuring enterprises (King \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The public can also indirectly transmit complaints to enterprises about pollution and advocate environmental protection to the government, which forces enterprises to choose a positive environmental strategy. In a way that is similar to regulatory pressure, social organizations and the public focus on whether enterprises comply with environmental protection regulations, which makes it difficult to effectively motivate enterprises to comply and choose more advanced environmental strategies.\u003c/p\u003e \u003cp\u003eH1a, H1b and H1c are proposed based on the aforementioned analysis.\u003c/p\u003e \u003cp\u003eH1a: Heavy polluters will more likely choose environmentally friendly leader-oriented environmental strategies as policy pressure increases;\u003c/p\u003e \u003cp\u003eH1b: Heavy polluters will more likely choose a pollution-prevention environmental strategy as regulatory pressure increases;\u003c/p\u003e \u003cp\u003eH1c: Heavy polluters will more likely choose pollution-prevention environmental strategies as public pressure increases.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eThe Moderating Effect of Resource Characteristics on Institutional Pressure and Environmental Strategic Choice of Heavily Polluting Enterprises\u003c/span\u003e \u003c/p\u003e \u003cp\u003eEnterprises in the same industry and region, which are constrained by a set of environmental regulations, government regulations and public supervision, may face similar institutional pressures. Enterprises should adopt a homogeneous environmental strategy as described in Meyer\u0026rsquo;s \u0026ldquo;protocol consistency\u0026rdquo; viewpoint (Meyer and Rowan 1977). In practice, the strategic response of enterprises to institutional pressures is heterogeneous. Why do enterprises choose heterogeneous strategy? This paper attempts to give an explanation from the perspective of resource characteristics.\u003c/p\u003e \u003cp\u003eBoth the institutional theory and the resource-based view fully affirm the importance of resources in the process of strategic selection of enterprises. Institutional theory holds that enterprises can obtain scarce resource input through establishing relations with stakeholders to meet their legitimacy (Baum and Oliver \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Heavy polluters can build a good environmental image and improve their corporate reputation by creating and maintaining stakeholders concerned with environmental responsibility, environmental litigation and corporate environmental protection practices (Huang and Chen \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The resource-based view points out that irreplaceable, valuable and hard-to-imitate resources are an important consideration for the strategic decisions of enterprises (Barney, Wright, and Ketchen Jr 2001). The differences in resources lead different enterprises to choose the differentiation strategy most suitable for external requirements (Carnes et al. 2019). Specifically, the influence of institutional pressure on the strategic choice of enterprise environment may depend on two internal resource differences: organizational slack and asset specificity.\u003c/p\u003e \u003cp\u003eOrganizational slack is the stock of idle resources that an organization can transfer or redeploy to achieve organizational goals (C. Chen, Wan, and Zhu 2019). Bradley et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) believe that organizational slack enhances organizational adaptability, enables it to adapt to changes in the external environment, and is capable of adopting diversified strategies and even triggering strategic changes (Zhao, Zhang, and Chen \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As external institutional pressures increase, for example when the government raises pollutant discharge standards and imposes stricter pollution penalties, resource-rich enterprises are better able to ensure the necessary resources and talents, promote active environmental management practices and implement more advanced environmental strategies. In contrast, when the organizational slack is relatively small, enterprises often make use of scarce resources to meet their most urgent needs, focusing on the business efficiency of enterprises, ignoring environmental requirements or responding to external pressure in a greenwash way, and environmental strategy is more passive. In addition, organizational slack can effectively buffer the uncertain risks generated by enterprise investment and research and development (Xie and Wei \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heavily polluting enterprises are more inclined to choose more positive environmental strategies as the external institutional pressure increases, which is followed by the introduction of more environmental protection equipment and resources for clean production and green innovation. Enterprises face the risk of loss of fixed assets caused by the renewal of new environmental protection equipment, as well as the risk of failure of green research and development. Enterprises with more organizational slack are more conducive to buffering these risks and promoting the implementation of advanced environmental strategies. The following hypotheses are proposed based on the aforementioned analysis:\u003c/p\u003e \u003cp\u003eH2a: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as policy pressure increases;\u003c/p\u003e \u003cp\u003eH2b: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as regulatory pressure increases;\u003c/p\u003e \u003cp\u003eH2c: Heavy polluters that are more redundant organizations will be more inclined to choose environmental leadership strategies as pressure from public opinion increases.\u003c/p\u003e \u003cp\u003eAsset specificity refers to the extent to which an asset can be redeployed and utilized by users without sacrificing its production value (O.E. Williamson \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). It also reflects to some extent the submerged characteristics of asset specificity (Pang and Zhuang \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The value loss of organizations with a high degree of asset specificity in the case of default is much higher than that of enterprises with a low degree of asset specificity. Therefore, with the strengthening of external institutional pressure, changes to the existing environmental management practices are difficult to meet the environmental needs of stakeholders. Failure to adopt a more active environmental strategy will lead to the loss of more environmentally sensitive investors and customers or result in higher legal costs and government sanctions (Kassinis and Vafeas 2009). Conversely, if an enterprise avoids default and redeploys proprietary assets away from existing uses, its productive value will be lost (Yao, Tang, and Pan \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Environmentally concerned corporate stakeholders will force enterprises to allocate resources to further advanced environmental practices as government environmental policies become more stringent or regulation becomes more frequent. The value of these proprietary assets will be greatly reduced if they are allocated and tilted towards the environmental protection. Therefore, enterprises with strong proprietary assets will be more inclined to seek more positive environmental strategies to cope with these pressures as institutional pressure increases. The following hypotheses are proposed based on the aforementioned analysis.\u003c/p\u003e \u003cp\u003eH3a: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as policy pressure increases;\u003c/p\u003e \u003cp\u003eH3b: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as regulatory pressure increases;\u003c/p\u003e \u003cp\u003eH3c: Heavy polluters that have a higher degree of asset specificity will be more inclined to choose an environmental leadership strategy as public pressure increases.\u003c/p\u003e \u003c/div\u003e "},{"header":"Research Methods","content":" \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample Selection and Data Collection\u003c/h2\u003e \u003cp\u003eWe used the definition and classification of heavily polluting industries described in the Guidelines for Environmental Information Disclosure of Listed Companies (Draft for Comments) issued by the Ministry of Environmental Protection. In this paper, 640 enterprises in heavily polluting industries were selected according to industry classification from the Guotai\u0026rsquo;an database.\u003c/p\u003e \u003cp\u003eProblems existing in the data, for example outliers and absence of data, were dealt with before the model was built as follows. (1) Companies marked as ST (stocks that have lost money for two consecutive years) in 2015 were manually deleted; (2) Observation values that were missing or zero values for core indexes, such as enterprise fixed assets, return on assets and listing time in the database, were supplemented using information obtained from the annual reports of enterprises. Finally, 597 enterprises were selected for our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of Variables\u003c/h2\u003e \u003cp\u003eHere we describe the explained, explanatory and moderator variables used in our study.\u003c/p\u003e \u003cp\u003eExplained variables. Environmental strategy (EnvStr), which refers to the environmental strategy measurement method proposed by Lin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), analyzes and codes relevant environmental protection practices in the social responsibility reports of listed companies as follows. When words such as \u0026ldquo;waste of energy,\u0026rdquo; \u0026ldquo;sewage treatment\u0026rdquo; and \u0026ldquo;environmental clean-up\u0026rdquo; appear in the report, it means that the enterprise implements reactive environmental strategy (ReaStr), and the environmental strategy is encoded as 1; when the words \u0026ldquo;reuse,\u0026rdquo; \u0026ldquo;recycle\u0026rdquo; and \u0026ldquo;source control\u0026rdquo; appear in the report, it indicates that the enterprise adopts the pollution prevention strategy (PreStr), and the code number is 2; when the report mentions words such as \u0026ldquo;product life cycle,\u0026rdquo; \u0026ldquo;supply chain participation\u0026rdquo; and \u0026ldquo;green products,\u0026rdquo; companies implement green leadership strategy (LeaStr), which is coded 3; when keywords representing different types of environmental strategies appear in the sample of enterprises, it is considered that the enterprise has implemented a relatively higher level of environmental strategy. In addition, some enterprises have not released their social responsibility reports. In these cases, we searched their official websites and screened out relevant environmental protection information for coding using the aforementioned principles. Finally, in order to reduce subjective bias in the process of artificial coding, this study involved a double-blind coding method. The degree of matching for the final two codes is as high as 92.8%, which indicates relatively high reliability.\u003c/p\u003e \u003cp\u003eExplanatory variables. Policy pressure (Pol) refers to the way in which different scholars use different measures to measure policy pressure. This paper adopts a number of environmental administrative regulations issued by local governments based on the research of Wang and Xu (2015).\u003c/p\u003e \u003cp\u003eRegulatory pressure (Reg) is defined as follows in our study. Berrone et al. (2013) used the number of inspections by regulated entities to measure regulatory pressure in their research. This is based on the premise that companies in provinces with more inspections by regulated entities face greater regulatory pressure than those in provinces with less inspections by regulated entities. We use the number of administrative punishment cases of local governments as a proxy variable of regulatory pressure by referring to the measurement method of Berrone et al. (2013).\u003c/p\u003e \u003cp\u003ePublic pressure (Pub) is defined as follows in our study. Dasgupta \u0026amp; Wheeler (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) used letters of public complaints on local problems to represent the attention of the public to environmental protection, and believed that enterprises in regions with many telephone and Internet complaints faced a relatively high level of public pressure. Clarkson et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), when investigating the impact of supervision on public opinion for corporate environmental behavior, used relevant environmental reports by the media as proxy variables for the effect of supervision on public opinion. We believe that media reports focus more on the pressure of public opinion faced by enterprises, and most of the attitudes and opinions of the public on environmental pollution and other issues are not reflected through media channels. The number of public complaints about environmental issues was therefore used to represent the public pressure by referring to the research of Dasgupta \u0026amp; Wheeler (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModerator variables. The ratio between working capital and sales was selected as the proxy variable of organizational slack (Slack) according to the existing literature (Fleming and Bromiley \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Asset specificity (Speci) takes into account that the investment of enterprises in production plants and machinery equipment is not easy to be redeployed. The logarithm of the ratio of fixed assets to the number of employees of the company was adopted as the proxy variable of asset proprietary taking Berrone (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) as a reference.\u003c/p\u003e \u003cp\u003eIn addition, we controlled environmental strategy influencing factors from the perspectives of enterprises and executives to eliminate the influence of other factors on the regression model and data analysis according to previous research literature (2013). Factors at the enterprise level include enterprise size(Size), industry type (Indu), time to market(Time), and financial performance (ROA); factors at the executive level include age of the chairman(Age) and education (Educ). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the relevant variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of major variable measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable measure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplained variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCode of the relevant environmental protection practices in the social responsibility reports of listed companies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of environmental administrative regulations issued by local governments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of administrative punishments imposed by local governments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of public complaints on environmental issues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegulating variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizational slack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio of working capital to sales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsset specificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpeci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio of fixed assets of a company to the number of employees\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Analysis Of Research Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and Correlation Analysis\u003c/h2\u003e \u003cp\u003eAll continuous variables were treated with WinSOR1% to remove the influence of outliers on the regression results. Descriptive statistics and correlation analysis results, excluding major variables outside the industry, are reported in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of key variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe standard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe maximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe minimum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 .03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0. 09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEduc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation coefficient between major variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* P\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation coefficient between policy pressure and environmental strategy was 0.22, and there was a positive correlation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The correlation between regulatory pressure, public pressure and environmental strategy was not significant, which preliminarily indicated the rationality of hypothesis H1. The correlation coefficients of all explanatory variables were less than 0.4, and there was no serious multicollinearity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis of the Relationship Between Institutional Pressure and Environmental Strategy of Heavily Polluting Enterprises\u003c/h2\u003e \u003cp\u003eThe environmental strategy-type of explained variable belongs to ordered multi-categorical variables, which are estimated by a multi-logit model. The regression results of the relationship between institutional pressure and environmental strategy are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Models M1 to M4 successively added explanatory variables for policy, regulatory and public pressure. As can be seen from the estimation results of model M4, the influence of policy pressure on enterprises to choose a responsive and environmentally friendly leadership environmental strategy was negative (\u003cem\u003eβ\u003c/em\u003e=-0.288, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.221, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. The more heavily polluting enterprises reject the reactive strategy and were more likely to choose an environmental leadership strategy as policy pressure increased. H1a was verified. The influence of regulatory pressure on the choice of a reactive environmental strategy was significantly negative (\u003cem\u003eβ\u003c/em\u003e=-0.0438, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the influence on the choice of an environmental leadership strategy by an enterprise was not significant. This indicated that enterprises tend to choose pollution prevention strategies as the government strengthens regulatory pressure. The results verified hypothesis H1b. The influence of public pressure on the choice of a reactive environmental strategy by an enterprise was significantly negative (\u003cem\u003eβ\u003c/em\u003e=-0.007, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the influence on the choice of an environmental leadership strategy by an enterprise was not significant. This indicated that enterprises tend to choose pollution prevention strategies as the government strengthens public pressure.The empirical results confirmed hypothesis H1c.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of the relationship between institutional pressure and corporate environmental strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eExplained variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28***\u003c/p\u003e \u003cp\u003e(-3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003cp\u003e(2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26***\u003c/p\u003e \u003cp\u003e(-3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.214**\u003c/p\u003e \u003cp\u003e(2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.288***\u003c/p\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.221**\u003c/p\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003cp\u003e(-0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0438**\u003c/p\u003e \u003cp\u003e(-2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003cp\u003e(0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003cp\u003e(-3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.136*\u003c/p\u003e \u003cp\u003e(-1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0849\u003c/p\u003e \u003cp\u003e(-0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16**\u003c/p\u003e \u003cp\u003e(-2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003cp\u003e(-0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.16**\u003c/p\u003e \u003cp\u003e(-2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003cp\u003e(-0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003cp\u003e(-1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003cp\u003e(-0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.872 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.706\u003c/p\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003cp\u003e(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.102\u003c/p\u003e \u003cp\u003e(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.158\u003c/p\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0641\u003c/p\u003e \u003cp\u003e(-0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00278 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0784**(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003cp\u003e(1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.078**\u003c/p\u003e \u003cp\u003e(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00614 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00264 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0209\u003c/p\u003e \u003cp\u003e(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0274\u003c/p\u003e \u003cp\u003e(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003cp\u003e(-1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.554**\u003c/p\u003e \u003cp\u003e(2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEduc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003cp\u003e(-1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517**\u003c/p\u003e \u003cp\u003e(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.194*\u003c/p\u003e \u003cp\u003e(-1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.96*\u003c/p\u003e \u003cp\u003e(-1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.557**\u003c/p\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.153***\u003c/p\u003e \u003cp\u003e(-4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003cp\u003e(-1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.136***\u003c/p\u003e \u003cp\u003e(-4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003cp\u003e(-1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14***\u003c/p\u003e \u003cp\u003e(-4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14***\u003c/p\u003e \u003cp\u003e(-4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.319** (2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.784\u003c/p\u003e \u003cp\u003e(-0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.134** (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.703\u003c/p\u003e \u003cp\u003e(-0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.301** (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.962\u003c/p\u003e \u003cp\u003e(-1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44***\u003c/p\u003e \u003cp\u003e(2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003cp\u003e(-1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.288***\u003c/p\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.221**\u003c/p\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e37.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e62.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e64.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e37.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.1050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1**, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the Moderating Effect of Organizational Slack\u003c/h2\u003e \u003cp\u003eThe regression results of multiple logit models for the adjustment effect of organizational slack are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Models M5, M6 and M7 use multiple logit models in turn to challenge the consistency of \u0026ldquo;etiquette,\u0026rdquo; which verified that organizational slack is a heterogeneous choice for the environmental strategy of heavily polluting companies under policy, regulatory and public pressures. The estimation result of model M5 showed that the coefficients of the interaction terms between redundant resources and policy pressure were \u003cem\u003eβ\u003c/em\u003e=-0.00725 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0676 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), which indicated that the more redundant organizations of heavily polluting enterprises and the increase in policy pressure make enterprises more inclined to choose environmentally protection-led environmental strategies. Hypothesis H3a was verified. In model M6, the coefficients of the interaction terms between redundant resources and regulatory pressure were \u003cem\u003eβ\u003c/em\u003e=-0.065 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), respectively. This indicated that enterprises with abundant organizational slack reject reactive strategies and prefer environmental leadership strategies as regulatory pressure increases. Hypothesis H3b was verified. Model M7 showed that the interaction term coefficient between redundant resources and public pressure was not significant, and the empirical results did not adequately verify H3c.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of organizational slack on heterogeneous response to environmental strategy of heavily polluting enterprises under institutional pressure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eExplained variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00167\u003c/p\u003e \u003cp\u003e(-0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0153\u003c/p\u003e \u003cp\u003e(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6***\u003c/p\u003e \u003cp\u003e(-3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24**\u003c/p\u003e \u003cp\u003e(2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2***\u003c/p\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.221**\u003c/p\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.085***\u003c/p\u003e \u003cp\u003e(-3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003cp\u003e(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003cp\u003e(-0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04**\u003c/p\u003e \u003cp\u003e(-2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003cp\u003e(0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004*\u003c/p\u003e \u003cp\u003e(-2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00057\u003c/p\u003e \u003cp\u003e(-0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003cp\u003e(-3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00121*\u003c/p\u003e \u003cp\u003e(2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07***\u003c/p\u003e \u003cp\u003e(-3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0881\u003c/p\u003e \u003cp\u003e(-0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003cp\u003e(-1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003cp\u003e(1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli*Slack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00725*\u003c/p\u003e \u003cp\u003e(-2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0676*\u003c/p\u003e \u003cp\u003e(2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg*Slack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.065**\u003c/p\u003e \u003cp\u003e(-2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003cp\u003e(2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo*Slack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00003\u003c/p\u003e \u003cp\u003e(-0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00151\u003c/p\u003e \u003cp\u003e(1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171*\u003c/p\u003e \u003cp\u003e(-2.01)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.29)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16**\u003c/p\u003e \u003cp\u003e(-2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003cp\u003e(-0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003cp\u003e(-1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003cp\u003e(-0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003cp\u003e(0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.527\u003c/p\u003e \u003cp\u003e(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.158\u003c/p\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0641\u003c/p\u003e \u003cp\u003e(-0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0792 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003cp\u003e(1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078*\u003c/p\u003e \u003cp\u003e(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00614\u003c/p\u003e \u003cp\u003e(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0130 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00663 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0274\u003c/p\u003e \u003cp\u003e(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003cp\u003e(-1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.554**\u003c/p\u003e \u003cp\u003e(2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEduc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003cp\u003e(-1.62)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.555* (2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.96*\u003c/p\u003e \u003cp\u003e(-1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557**\u003c/p\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13***\u003c/p\u003e \u003cp\u003e(-4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003cp\u003e(-1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.141***\u003c/p\u003e \u003cp\u003e(-4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116*\u003c/p\u003e \u003cp\u003e(1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4***\u003c/p\u003e \u003cp\u003e(-4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.319**\u003c/p\u003e \u003cp\u003e(2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.784\u003c/p\u003e \u003cp\u003e(-0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.942*\u003c/p\u003e \u003cp\u003e(2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.824\u003c/p\u003e \u003cp\u003e(-0.66)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.44***\u003c/p\u003e \u003cp\u003e(2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003cp\u003e(-1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.8***\u003c/p\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.221**\u003c/p\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e62.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e64.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e92.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.1472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the Regulatory Effect of Asset Specificity\u003c/h2\u003e \u003cp\u003eThe regression results of multiple logit models for the proprietary regulatory effect of assets of heavily polluting enterprises are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Models M8, M9 and M10 successively adopt a number of logit models, which challenges the idea of \u0026ldquo;etiquette\u0026rdquo; consistency from the perspective of asset ownership. This verified the regulating effect of asset ownership on the relationship between policy pressure, regulatory pressure, public pressure and environmental strategy for heavily polluting enterprises. In model M8, the coefficients of the interaction terms between asset specificity and policy pressure were \u003cem\u003eβ\u003c/em\u003e=-0.0244 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and β\u0026thinsp;=\u0026thinsp;0.0653 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), respectively. This result showed that enterprises tend to choose a more positive environmental strategy when the policy pressure increases and the asset specificity is higher. Hypothesis H4a was verified. In model M9, the coefficients of the interaction terms between asset specificity and regulatory pressure were \u003cem\u003eβ\u003c/em\u003e=-0.0154 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00358 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), respectively. This indicates that enterprises tend to choose an environmental leadership strategy as the regulatory pressure increases. Hypothesis H4b was verified. In model M10, the coefficients of the interaction terms between asset specificity and public pressure were \u003cem\u003eβ\u003c/em\u003e=-0.0004 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0. 1) and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1676 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. This indicated that the probability of enterprises choosing reactive, pollution-defense and environmental leadership strategies increased successively as the pressure exerted by the public on enterprises increased. The empirical results confirmed H4c.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of asset specificity in heterogeneous response to environmental strategy of heavily polluting enterprises under institutional pressure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eExplained variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eM8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eM10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.292*** (-3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252* (2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.302***\u003c/p\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233*\u003c/p\u003e \u003cp\u003e(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.319***\u003c/p\u003e \u003cp\u003e(-3.76)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.238* (2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0444*\u003c/p\u003e \u003cp\u003e(-2.10)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0091 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0557*\u003c/p\u003e \u003cp\u003e(-2.43)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003cp\u003e(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0605*\u003c/p\u003e \u003cp\u003e(-2.39)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0122 (0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007*** (-3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00084\u003c/p\u003e \u003cp\u003e(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003cp\u003e(-3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00121\u003c/p\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00525**\u003c/p\u003e \u003cp\u003e(-2.94)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00226\u003c/p\u003e \u003cp\u003e(-0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0781\u003c/p\u003e \u003cp\u003e(-1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0781\u003c/p\u003e \u003cp\u003e(-1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.101*\u003c/p\u003e \u003cp\u003e(1.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli* Speci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0244**\u003c/p\u003e \u003cp\u003e(-2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0653*\u003c/p\u003e \u003cp\u003e(2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg* Speci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0154*\u003c/p\u003e \u003cp\u003e(-2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00358*\u003c/p\u003e \u003cp\u003e(2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo* Speci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0004*\u003c/p\u003e \u003cp\u003e(-2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1676**\u003c/p\u003e \u003cp\u003e(2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003cp\u003e(-1.46)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003cp\u003e(-1.56)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00924\u003c/p\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003cp\u003e(-1.70)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0211\u003c/p\u003e \u003cp\u003e(-0.11)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003cp\u003e(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003cp\u003e(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003cp\u003e(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0848 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.627\u003c/p\u003e \u003cp\u003e(-0.93)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0203\u003c/p\u003e \u003cp\u003e(-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0858* (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003cp\u003e(-0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0165\u003c/p\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00768 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0194 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00927\u003c/p\u003e \u003cp\u003e(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0104\u003c/p\u003e \u003cp\u003e(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0245 (0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEduc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003cp\u003e(-1.13)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.560* (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003cp\u003e(-0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557**\u003c/p\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003cp\u003e(-1.27)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003cp\u003e(1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.156***\u003c/p\u003e \u003cp\u003e(-4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003cp\u003e(-1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.158***\u003c/p\u003e \u003cp\u003e(-4.41)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0980\u003c/p\u003e \u003cp\u003e(-1.48)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.148***\u003c/p\u003e \u003cp\u003e(-4.17)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0814\u003c/p\u003e \u003cp\u003e(-1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.961*\u003c/p\u003e \u003cp\u003e(2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.201\u003c/p\u003e \u003cp\u003e(-1.32)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.165*\u003c/p\u003e \u003cp\u003e(2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.586\u003c/p\u003e \u003cp\u003e(-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.034*\u003c/p\u003e \u003cp\u003e(2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.325\u003c/p\u003e \u003cp\u003e(-1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e90.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e91.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.1701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRobustness Test\u003c/h2\u003e \u003cp\u003eA robustness test was carried out to investigate the robustness of the main effect. First, the proxy variable of regulatory pressure was replaced by the number of staff in regional environmental protection agencies to verify whether the above conclusions were still valid. Second, multiple Probit regression was used for verification since the dependent variable belongs to the ordered multiple categorical variables (Q. Chen \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The substitution of regulatory pressure proxy variables and the adoption of multiple Probit model regression showed that the empirical results and conclusions were unchanged (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The empirical results were stable and robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of robustness test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eExplained variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSample Total Logit of alternative regulatory pressures(M13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultinomial Probit Regression of sample population (M14)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReaStr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeaStr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.299***\u003c/p\u003e \u003cp\u003e(-3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210**\u003c/p\u003e \u003cp\u003e(2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.226***\u003c/p\u003e \u003cp\u003e(-3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126*\u003c/p\u003e \u003cp\u003e(1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0592**\u003c/p\u003e \u003cp\u003e(-2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00465\u003c/p\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0401**\u003c/p\u003e \u003cp\u003e(-2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00517***\u003c/p\u003e \u003cp\u003e(-3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00089\u003c/p\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00547***\u003c/p\u003e \u003cp\u003e(-3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000151\u003c/p\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.152*\u003c/p\u003e \u003cp\u003e(-1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0866\u003c/p\u003e \u003cp\u003e(-0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003cp\u003e(-1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0839\u003c/p\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003cp\u003e(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.062\u003c/p\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.174\u003c/p\u003e \u003cp\u003e(1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00659\u003c/p\u003e \u003cp\u003e(-0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0777*\u003c/p\u003e \u003cp\u003e(1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00661\u003c/p\u003e \u003cp\u003e(-0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0538**\u003c/p\u003e \u003cp\u003e(2.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00744\u003c/p\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0280\u003c/p\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00504\u003c/p\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEduc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003cp\u003e(-1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.559*\u003c/p\u003e \u003cp\u003e(2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003cp\u003e(-1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003cp\u003e(1.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.149***\u003c/p\u003e \u003cp\u003e(-4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0974\u003c/p\u003e \u003cp\u003e(-1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.133***\u003c/p\u003e \u003cp\u003e(-4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0773*\u003c/p\u003e \u003cp\u003e(-1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.340**\u003c/p\u003e \u003cp\u003e(2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.770\u003c/p\u003e \u003cp\u003e(-1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.512**\u003c/p\u003e \u003cp\u003e(2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.309\u003c/p\u003e \u003cp\u003e(-0. 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e86.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e72.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe baseline group is the environmental strategy of pollution prevention; Z value in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Research Conclusions And Implications","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMain Conclusions\u003c/h2\u003e \u003cp\u003eThis paper focuses on how heavily polluting enterprises choose heterogeneous environmental strategies, analyzes the relationship between institutional pressures and environmental strategic choices for such enterprises, and explores the heterogeneous choices of enterprise environmental strategy from the perspective of resources available. The main conclusions are as follows.\u003c/p\u003e \u003cp\u003eThe influence of different institutional pressures on the choice of environmental strategy differs. Firstly, enterprises were more likely to adopt an environmental leadership strategy if the policy pressure was greater. Secondly, enterprises increasingly rejected the reactive environmental strategy if the regulatory pressure or public pressure increased, but there was no significant impact on whether the company made a choice for an environmental leadership strategy under these conditions. Our results differ from earlier research that concludes that companies are more active in corporate environmental strategy as institutional pressure increases (Menguc, Auh, and Ozanne 2010). Our study subdivides the types of environmental strategies. It finds that increased regulatory or public pressure does not prompt enterprises to choose the environmental leadership strategy because the two kinds of pressure do not form an effective incentive mechanism for enterprises.\u003c/p\u003e \u003cp\u003eOrganizational slack makes the response of corporate environmental strategy induced by institutional pressure heterogeneous. Organizational slack positively regulated the relationship between policy pressure and environmental strategy selection for heavily polluted enterprises. Similarly, enterprises were more inclined to choose advanced environmental strategies, the higher the organizational slack for heavily polluting enterprises, and the greater the regulatory pressure on these companies. This paper verifies that redundant resources can effectively buffer enterprise resource shortages and make enterprises better adapt to the external institutional environment, and is consistent with most of the research results on organizational slack (Kuusela, Keil, and Maula 2017). However, previous studies mostly focused on the role of organizational slack in the field of enterprise capability and innovation (Geiger and Makri \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Iyer and Miller 2008). Our paper extended organizational slack to the level of enterprise strategy and found that redundant resources promoted enterprises to implement more advanced environmental strategies.\u003c/p\u003e \u003cp\u003eThe relationship between institutional pressure of asset specificity promotion and environmental strategic choice was investigated. For enterprises with strong proprietary assets, the greater the public and regulatory pressure or public pressure, the more inclined they were to choose the environmental leadership strategy. Research on asset specificity in transaction economic theory mostly uses the degree of asset specificity to weigh whether an organization implements vertical integration strategy (H. Wang, Zhao, and Chen 2017). Our paper takes asset specificity as an important strategic resource for heavily polluting enterprises who want to cope with external institutional pressure and implement active environmental protection practices. It thus extends the research to include asset specificity. In addition, the heterogeneity rather than the homogeneity of the responses to institutional pressure on corporate environmental strategy was studied from the perspective of organizational slack and asset specificity. This has enriched research on the heterogeneous selection of environmental strategies by heavily polluting enterprises under the institutional isomorphism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Proposal\u003c/h2\u003e \u003cp\u003eOur paper proposes the following policy recommendations for government and relevant private sector enterprises based on our research findings.\u003c/p\u003e \u003cp\u003eAt the government level, the first step is to actively promote the implementation of environmental governance policies. The central government has improved the top-level design of environmental governance policies. Local governments, which have focused on the central policies and guidelines and the current situation for regional pollution, have formulated policies and measures suitable for local green development. These measures include pollution control, ultra-low emission subsidies, carbon emission trading and others. At the same time, relevant government departments should increase the frequency and availability of supervision, and correctly guide and advocate the participation of all people in pollution measures. Central and local environmental protection supervision should act alternately and complement each other. This will help to reduce the \u0026ldquo;fluke mentality\u0026rdquo; of enterprises and the opportunistic behavior to engage in activities with illegal emissions. Also, the Internet should be used as the channel to increase the exposure of inspection results and force enterprises to pay attention to the treatment of pollutants at source. In general, institutional incentives and pressure should be combined to jointly promote the sustainable development of heavily polluting enterprises.\u003c/p\u003e \u003cp\u003eAt the enterprise level, it is suggested that enterprises with abundant organizational slack tilt and allocate resources to environmental governance. In the face of increasingly strict requirements for environmental protection systems, enterprises respond in different ways to institutional pressure. Enterprises that have more redundancy within their organization can realize upgrades to environmental strategy by allocating redundant resources to environmental protection. This alleviates the impact of external pressure on enterprises. If an enterprise has a high degree of proprietary assets, it should implement a strategy to avoid environmentally sensitive external stakeholders taking measures such as divestment, which will cause losses to the enterprise and threaten its survival.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant competing interests to disclose.\u003c/p\u003e\n\u003cp\u003eHuman Participants and Animals are not involved in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e and Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors approve the version to be participated and published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSen WANG and Jianhua YIN conceived the study and were responsible for the design and development of the data analysis. Xiaomei ZHU was responsible for data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the National Natural Science Foundation of China\u003c/p\u003e\n\u003cp\u003e(No.72002013, No.71874029), Project of Beijing Municipal Education Commission (No. SM202111417005),Academic Research Projects of Beijing Union University (No. SK80202001, No. XP202010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThe datasets used in the study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eBarney, Jay, Wright, Mike, and Ketchen Jr, David J.\u003c/strong\u003e 2001. \"The resource-based view of the firm: Ten years after 1991.\" \u003cem\u003eJournal of management\u003c/em\u003e, 27 (6): 625-641. \u003ca href=\"https://doi.org/10.1002/smj.4250160303\"\u003ehttps://doi.org/10.1002/smj.4250160303\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBaum, Joel AC, and Oliver, Christine.\u003c/strong\u003e 1992. \"Institutional embeddedness and the dynamics of organizational populations.\" \u003cem\u003eAmerican sociological review\u003c/em\u003e: 540-559. \u003ca href=\"https://doi.org/10.2307/2096100\"\u003ehttps://doi.org/10.2307/2096100\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBerrone, Pascual, Fosfuri, Andrea, Gelabert, Liliana, and Gomez\u003c/strong\u003e\u003cstrong\u003e‐Mejia, Luis R.\u003c/strong\u003e 2013. \"Necessity as the mother of \u0026lsquo;green\u0026rsquo;inventions: Institutional pressures and environmental innovations.\" \u003cem\u003eStrategic Management Journal\u003c/em\u003e, 34 (8): 891-909. \u003ca href=\"https://doi.org/10.1002/smj.2041\"\u003ehttps://doi.org/10.1002/smj.2041\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBradley, Steven W, Shepherd, Dean A, and Wiklund, Johan.\u003c/strong\u003e 2011. \"The importance of slack for new organizations facing \u0026lsquo;tough\u0026rsquo;environments.\" \u003cem\u003eJournal of Management Studies\u003c/em\u003e, 48 (5): 1071-1097. \u003ca href=\"https://doi.org/10.1111/j.1467-6486.2009.00906.x\"\u003ehttps://doi.org/10.1111/j.1467-6486.2009.00906.x\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBuysse, Kristel, and Verbeke, Alain.\u003c/strong\u003e 2003. \"Proactive environmental strategies: A stakeholder management perspective.\" \u003cem\u003eStrategic management journal\u003c/em\u003e, 24 (5): 453-470. \u003ca href=\"https://doi.org/10.1002/smj.299\"\u003ehttps://doi.org/10.1002/smj.299\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCarnes, Christina Matz, Xu, Kai, Sirmon, David G, and Karadag, Reha.\u003c/strong\u003e 2019. \"How Competitive Action Mediates the Resource Slack\u0026ndash;Performance Relationship: A Meta‐Analytic Approach.\" \u003cem\u003eJournal of Management Studies\u003c/em\u003e, 56 (1): 57-90. \u003ca href=\"https://doi.org/10.1111/joms.12391\"\u003ehttps://doi.org/10.1111/joms.12391\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eChen, Cheng, Wan, Shan, and Zhu, Le.\u003c/strong\u003e 2019. \"Executive Compensation of state-owned enterprises and corporate Social responsibility\u0026mdash;Organizational slack and the moderating effect of marketization process.\" \u003cem\u003eChina Soft Science\u003c/em\u003e, 6: 129-137.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eChen, Qiang\u003c/strong\u003e. 2014. \u003cem\u003eAdvanced Econometrics and Stata Applications (2nd Edition)\u003c/em\u003e. Higher Education Press.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eClarkson, Peter M, Li, Yue, Richardson, Gordon D, and Vasvari, Florin P.\u003c/strong\u003e 2008. \"Revisiting the relation between environmental performance and environmental disclosure: An empirical analysis.\" \u003cem\u003eAccounting, organizations and society\u003c/em\u003e, 33 (4-5): 303-327. \u003ca href=\"https://doi.org/10.1016/j.aos.2007.05.003\"\u003ehttps://doi.org/10.1016/j.aos.2007.05.003\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eClemens, Bruce, and Douglas, Thomas J.\u003c/strong\u003e 2006. \"Does coercion drive firms to adopt \u0026lsquo;voluntary\u0026rsquo;green initiatives? Relationships among coercion, superior firm resources, and voluntary green initiatives.\" \u003cem\u003eJournal of business research\u003c/em\u003e, 59 (4): 483-491. \u003ca href=\"https://doi.org/10.1016/j.jbusres.2005.09.016\"\u003ehttps://doi.org/10.1016/j.jbusres.2005.09.016\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDasgupta, Susmita, and Wheeler, David. \u003c/strong\u003e1997. \u003cem\u003eCitizen complaints as environmental indicators: evidence from China\u003c/em\u003e. The World Bank.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDimaggio, Paul J, and Powell, Walter W.\u003c/strong\u003e 1983. \"The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields.\" \u003cem\u003eAmerican sociological review\u003c/em\u003e: 147-160. \u003ca href=\"https://doi.org/10.2307/2095101\"\u003ehttps://doi.org/10.2307/2095101\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFan, Qunlin, Shao, Yunfei, and Tang, Xiaowo.\u003c/strong\u003e 2013. \"The impact of environmental policy, technological progress, and market structure on environmental technology innovation.\" \u003cem\u003eScience Research Management\u003c/em\u003e, 6.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFleming, Lee, and Bromiley, Philip. \u003c/strong\u003e2003. \u003cem\u003eA prospect theory model of R\u0026amp;D allocation and invention. \u003c/em\u003eHarvard Business School Working Paper Series.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFreeman, R Edward.\u003c/strong\u003e 2010. \u003cem\u003eStrategic management: A stakeholder approach\u003c/em\u003e. Cambridge university press.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGeiger, Scott W, and Makri, Marianna.\u003c/strong\u003e 2006. \"Exploration and exploitation innovation processes: The role of organizational slack in R \u0026amp; D intensive firms.\" \u003cem\u003eThe Journal of High Technology Management Research\u003c/em\u003e, 17 (1): 97-108. \u003ca href=\"https://doi.org/10.1016/j.hitech.2006.05.007\"\u003ehttps://doi.org/10.1016/j.hitech.2006.05.007\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHu, Jun, Song, Xianzhong, and Wang, Hhongjian.\u003c/strong\u003e 2017. \"Informal institution, hometown identity and corporate environmental governance.\" \u003cem\u003eManagement World\u003c/em\u003e, 3: 76-94.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHuang, Rongbing, and Chen, Danping.\u003c/strong\u003e 2015. \"Does environmental information disclosure benefit waste discharge reduction? Evidence from China.\" \u003cem\u003eJournal of Business Ethics\u003c/em\u003e, 129 (3): 535-552. \u003ca href=\"https://doi.org/10.1007/s10551-014-2173-0\"\u003ehttps://doi.org/10.1007/s10551-014-2173-0\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIyer, Dinesh N, and Miller, Kent D.\u003c/strong\u003e 2008. \"Performance feedback, slack, and the timing of acquisitions.\" \u003cem\u003eAcademy of Management Journal\u003c/em\u003e, 51 (4): 808-822. \u003ca href=\"https://doi.org/10.5465/amr.2008.33666024\"\u003ehttps://doi.org/10.5465/amr.2008.33666024\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKaplan, Sarah.\u003c/strong\u003e 2011. \"Research in cognition and strategy: Reflections on two decades of progress and a look to the future.\" \u003cem\u003eJournal of Management Studies\u003c/em\u003e, 48 (3): 665-695. \u003ca href=\"https://doi.org/10.1111/j.1467-6486.2010.00983.x\"\u003ehttps://doi.org/10.1111/j.1467-6486.2010.00983.x\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKassinis, George, and Vafeas, Nikos.\u003c/strong\u003e 2006. \"Stakeholder pressures and environmental performance.\" \u003cem\u003eAcademy of Management Journal\u003c/em\u003e, 49 (1): 145-159. \u003ca href=\"https://doi.org/doi.org/10.5465/amj.2006.20785799\"\u003ehttps://doi.org/doi.org/10.5465/amj.2006.20785799\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGeorge, Kassinis, and Nikos, Vafeas\u003c/strong\u003e 2009. \"Environmental performance and plant closure.\" \u003cem\u003eJournal of Business Research\u003c/em\u003e, 62 (4): 484-494. \u003ca href=\"https://doi.org/10.1016/j.jbusres.2008.01.037\"\u003ehttps://doi.org/10.1016/j.jbusres.2008.01.037\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKing, Brayden G.\u003c/strong\u003e 2008. \"A political mediation model of corporate response to social movement activism.\" \u003cem\u003eAdministrative Science Quarterly\u003c/em\u003e, 53 (3): 395-421. \u003ca href=\"https://doi.org/10.2189/asqu.53.3.395\"\u003ehttps://doi.org/10.2189/asqu.53.3.395\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKuusela, Pasi, Keil, Thomas, and Maula, Markku.\u003c/strong\u003e 2017. \"Driven by aspirations, but in what direction? Performance shortfalls, slack resources, and resource‐consuming vs. resource‐freeing organizational change.\" \u003cem\u003eStrategic management journal\u003c/em\u003e, 38 (5): 1101-1120. \u003ca href=\"https://doi.org/10.1002/smj.2544\"\u003ehttps://doi.org/10.1002/smj.2544\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLin, Haiying.\u003c/strong\u003e 2012. \"Cross-sector alliances for corporate social responsibility partner heterogeneity moderates environmental strategy outcomes.\" \u003cem\u003eJournal of Business Ethics\u003c/em\u003e, 110 (2): 219-229. \u003ca href=\"https://doi.org/10.1007/s10551-012-1423-2\"\u003ehttps://doi.org/10.1007/s10551-012-1423-2\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLounsbury, Michael, Ventresca, Marc, and Hirsch, Paul M.\u003c/strong\u003e 2003. \"Social movements, field frames and industry emergence: a cultural\u0026ndash;political perspective on US recycling.\" \u003cem\u003eSocio-economic review\u003c/em\u003e, 1 (1): 71-104.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLuo, Danglun, and Lai, Zaihong.\u003c/strong\u003e 2016. \"Investment of heavily polluting enterprises and promotion of local officials\u0026ndash;based on the practical investigation of data for prefectural cities during 1999-2010.\" \u003cem\u003eAccounting Research\u003c/em\u003e, 4: 42-48.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMarquis, Chris, Jackson, Susan E, and Li, Yuan.\u003c/strong\u003e 2015. \"Building sustainable organizations in China.\" \u003cem\u003eManagement and Organization Review\u003c/em\u003e, 11 (3): 427-440.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMenguc, Bulent, Auh, Seigyoung, and Ozanne, Lucie.\u003c/strong\u003e 2010. \"The interactive effect of internal and external factors on a proactive environmental strategy and its influence on a firm's performance.\" \u003cem\u003eJournal of Business Ethics\u003c/em\u003e, 94 (2): 279-298. \u003ca href=\"https://doi.org/10.1007/s10551-009-0264-0\"\u003ehttps://doi.org/10.1007/s10551-009-0264-0\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMeyer, John W, and Rowan, Brian.\u003c/strong\u003e 1977. \"Institutionalized organizations: Formal structure as myth and ceremony.\" \u003cem\u003eAmerican journal of sociology\u003c/em\u003e, 83 (2): 340-363. \u003ca href=\"https://doi.org/10.1086/226550\"\u003ehttps://doi.org/10.1086/226550\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOliver, Christine.\u003c/strong\u003e 1991. \"Strategic responses to institutional processes.\" \u003cem\u003eAcademy of management review\u003c/em\u003e, 16 (1): 145-179. \u003ca href=\"https://doi.org/10.5465/amr.1991.4279002\"\u003ehttps://doi.org/10.5465/amr.1991.4279002\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePang, Fanglan, and Zhuang, Guijun.\u003c/strong\u003e 2017. \"Commitment to asymmetric trading of proprietary assets and inter-firm trust.\" \u003cem\u003eIndustrial Engineering and Management\u003c/em\u003e, 22 (3): 128-134.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePope, Holly C, Draper, Carrie, Younginer, Nicholas, Whitt, Olivia, and Paget, Christopher.\u003c/strong\u003e 2020. \"Use of decision cases for building SNAP-Ed implementers\u0026rsquo; capacities to realize policy, systems, and environmental strategies.\" \u003cem\u003eJournal of nutrition education and behavior\u003c/em\u003e, 52 (5): 512-521. \u003ca href=\"https://doi.org/10.1016/j.jneb.2019.09.020\"\u003ehttps://doi.org/10.1016/j.jneb.2019.09.020\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eReid, Erin M, and Toffel, Michael W.\u003c/strong\u003e 2009. \"Responding to public and private politics: Corporate disclosure of climate change strategies.\" \u003cem\u003eStrategic Management Journal\u003c/em\u003e, 30 (11): 1157-1178. \u003ca href=\"https://doi.org/10.1002/smj.796\"\u003ehttps://doi.org/10.1002/smj.796\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSharfman, Mark P, Wolf, Gerrit, Chase, Richard B, and Tansik, David A.\u003c/strong\u003e 1988. \"Antecedents of organizational slack.\" \u003cem\u003eAcademy of Management review\u003c/em\u003e, 13 (4): 601-614. \u003ca href=\"https://doi.org/10.5465/amr.1988.4307484\"\u003ehttps://doi.org/10.5465/amr.1988.4307484\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVannoorenberghe, Gonzague.\u003c/strong\u003e 2012. \"Firm-level volatility and exports.\" \u003cem\u003eJournal of International Economics\u003c/em\u003e, 86 (1): 57-67. \u003ca href=\"https://doi.org/10.1016/j.jinteco.2011.08.013\"\u003ehttps://doi.org/10.1016/j.jinteco.2011.08.013\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWang, Heli, Zhao, Shan, and Chen, Guoli.\u003c/strong\u003e 2017. \"Firm‐specific knowledge assets and employment arrangements: Evidence from CEO compensation design and CEO dismissal.\" \u003cem\u003eStrategic Management Journal\u003c/em\u003e, 38 (9): 1875-1894. \u003ca href=\"https://doi.org/10.1002/smj.2604\"\u003ehttps://doi.org/10.1002/smj.2604\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWang, Shubin, and Xu, Yingzhi.\u003c/strong\u003e 2015. \"Environmental regulation and haze pollution decoupling effect: based on the perspective of enterprise investment preferences.\" \u003cem\u003eChina Industrial Economics\u003c/em\u003e, 4: 18-30.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWang, Yun, Li, Yanxi, Ma, Zhuang, and Song, Jinbo.\u003c/strong\u003e 2017. \"Media coverage, environmental regulation and corporate environment behavior.\" \u003cem\u003eNankai Business Review\u003c/em\u003e, 12: 42-54.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWilliamson, David, Lynch-Wood, Gary, and Ramsay, John.\u003c/strong\u003e 2006. \"Drivers of environmental behaviour in manufacturing SMEs and the implications for CSR.\" \u003cem\u003eJournal of business ethics\u003c/em\u003e, 67 (3): 317-330. \u003ca href=\"https://doi.org/10.1007/s10551-006-9187-1\"\u003ehttps://doi.org/10.1007/s10551-006-9187-1\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWilliamson, Oliver E.\u003c/strong\u003e 1984. \"The economics of governance: framework and implications.\" \u003cem\u003eZeitschrift f\u0026uuml;r die gesamte Staatswissenschaft/Journal of Institutional and Theoretical Economics\u003c/em\u003e (H. 1): 195-223.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWinter, S\u0026oslash;ren C, and May, Peter J.\u003c/strong\u003e 2001. \"Motivation for compliance with environmental regulations.\" \u003cem\u003eJournal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management\u003c/em\u003e, 20 (4): 675-698. \u003ca href=\"https://doi.org/10.1002/pam.1023\"\u003ehttps://doi.org/10.1002/pam.1023\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eXie, Weimin, and Wei, Huaqian \u003c/strong\u003e2016. \"Market competition organizational slack and enterprise R \u0026amp; D investment.\" \u003cem\u003eChina Soft Science\u003c/em\u003e, 8: 102-111.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYang, Defeng, Wang, Aric Xu, Zhou, Kevin Zheng, and Jiang, Wei.\u003c/strong\u003e 2019. \"Environmental strategy, institutional force, and innovation capability: A managerial cognition perspective.\" \u003cem\u003eJournal of Business Ethics\u003c/em\u003e, 159 (4): 1147-1161. \u003ca href=\"https://doi.org/10.1007/s10551-018-3830-5\"\u003ehttps://doi.org/10.1007/s10551-018-3830-5\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYang, Yang, Wei, Jiang, and Luo, Laijun.\u003c/strong\u003e 2015. \"Who is using government subsidies to innovate? \u0026mdash;Joint adjustment effect of ownership and distortion of factor market.\" \u003cem\u003eManagement World\u003c/em\u003e, 1: 75-86.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYao, X., Tang, X. W., and Pan, J. M. .\u003c/strong\u003e 2009. \"Research on supply chain Partnership and Alliance Profit Model based on asset specificity.\" \u003cem\u003eChina Soft Science\u003c/em\u003e, 23 (1): 118-122.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYin, Jianhua, Wang, Sen, and Zhang, Lingling.\u003c/strong\u003e 2019. \"Heterogeneous Response of Corporate Environmental Strategy under Institutional Isomorphism.\" \u003cem\u003eJournal of Beijing Institute of Technology (Social Sciences Edition)\u003c/em\u003e, 21 (4): 47-55.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhao, Yapu, Zhang, Wenhong, and Chen, Silei.\u003c/strong\u003e 2014. \"Impact of organizational slack on firm exploration in a dynamic environment.\" \u003cem\u003eScience Research Management\u003c/em\u003e, 35 (2): 10-16.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZheng, S. Q., Wan, G. H., Sun, W. Z., and Luo, D. L.\u003c/strong\u003e 2013. \"Public demands and urban environmental governance.\" \u003cem\u003eManagement World\u003c/em\u003e, 6: 72-84.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"environmental strategy, policy pressure, regulatory pressure, public pressure, organizational slack, asset specificity","lastPublishedDoi":"10.21203/rs.3.rs-253787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-253787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe impact of institutional pressure on the environmental strategy was analyzed and the heterogeneous choices available for corporate environmental strategy. A total of 597 publicly listed companies in heavily polluting industries were selected using multiple Logit models for empirical research. The results show that more companies choose environmental leadership strategies when the policy pressure is greatest; however, more companies choose pollution prevention strategies when the regulatory and public pressures are greatest; finally, organizations with more redundant resources and strong asset specificity are more inclined to choose environmental leadership strategies as institutional pressures increase. The findings provide a decision-making framework to promote environmental protection measures related to policy formulation, government supervision and public participation. Our study also provides empirical evidence to guide environmental strategic choices for heavily polluting enterprises.\u003c/p\u003e","manuscriptTitle":"Heterogeneous Choice of Environmental Strategy for Heavily Polluting Firms Under Institutional Pressure in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-15 16:42:36","doi":"10.21203/rs.3.rs-253787/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2021-04-12T03:22:54+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-03-13T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-13T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2021-03-11T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-25T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-02-17T23:57:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"163c8ef5-f230-4243-b4c4-ced0b404eab5","owner":[],"postedDate":"March 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":2966517,"name":"Environmental Engineering"},{"id":2966518,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2022-01-17T09:34:42+00:00","versionOfRecord":{"articleIdentity":"rs-253787","link":"https://doi.org/10.1007/s11356-021-16090-9","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2021-09-03 09:34:42","publishedOnDateReadable":"September 3rd, 2021"},"versionCreatedAt":"2021-03-15 16:42:36","video":"","vorDoi":"10.1007/s11356-021-16090-9","vorDoiUrl":"https://doi.org/10.1007/s11356-021-16090-9","workflowStages":[]},"version":"v1","identity":"rs-253787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-253787","identity":"rs-253787","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}