Environmental Background Directors and ESG Performance: A Perspective on Green Governance

Abstract

ESG has gradually become a key indicator to measure the corporate sustainable development performance in various countries. More and more business managements are integrating sustainable development goals into corporate strategies. The board of directors is the strategy and decision maker of the enterprise and has the primary responsibility for ESG governance. The green governance of the board of directors is an important guaranteed mechanism to promote ESG performance. We use text analysis to identify environmental background directors (EBDs) and analyze the impact and mechanisms of EBDs on ESG performance, drawing on data from Chinese-listed companies between 2010 and 2022. The results indicate that EBDs significantly enhance overall corporate ESG performance at the 1% level. Specifically, EBDs significantly improve social (S) performance at the 1% level and environmental (E) and governance (G) performance at the 5% level. Mechanism analysis finds that EBDs enhance ESG performance by fostering management’s green awareness and promoting the enterprise’s green innovation. Heterogeneity analysis shows that EBDs have a greater impact on green governance in regions with weak environmental regulation, cleaning industries, and after the implementation of the New Environmental Protection Law. Our findings contribute to the literature on factors influencing ESG performance and green governance. They also provide novel empirical evidence supporting the selection and cultivation of directors with environmental backgrounds, enhancing board diversity and fostering corporate sustainable development.

1. Introduction

In response to rising environmental concerns, like climate change, air pollution, and resource depletion, countries globally have all actively promoted ESG initiatives and related corporate responsibilities. ESG represents the application of sustainable development theory, focusing on the integration of economic growth, environmental protection, and social performance. Rooted in the principles of corporate legitimacy and sustainability, ESG aims to reconcile the conflict between short-term and long-term corporate objectives. By balancing stakeholder interests, ESG fosters corporate value creation while supporting green transformation and development. As a mainstream indicator for assessing corporate sustainability, ESG has become a central concern for management, supervisors, and other stakeholders [1,2,3].

With the rise of the sustainable development paradigm, an increasing number of companies are integrating ESG metrics into board decision making and risk management processes [4]. According to upper echelons theory, managers’ personal characteristics, experiences, and cognition influence financial performance and governance efficiency [5]. In modern corporate governance, boards represent the highest decision-making group within corporations, and their cognitive abilities and governance capabilities significantly impact strategic decisions and business performance [6]. Post et al. [7] argue that the composition and characteristics of boards of directors substantially influence corporate green governance. Furthermore, research highlights that board diversity, experience, gender, and independence are critical factors in shaping corporate sustainability performance [8,9,10,11,12,13]. Building on this foundation, this study investigates the impact and mechanisms of environmental background directors (EBDs) on corporate ESG performance.

According to stakeholder theory, the board of directors will take into account the interests of legislators and the public, resulting in improved environmental performance for enterprises [14]. The awareness and sensitivity of board members toward environmental protection significantly impacts corporate commitments to and initiatives for environmental sustainability [15]. Directors with green experience can enhance corporate environmental information disclosure [16]. Also, they are more adept at identifying and managing environmental risks, helping companies adapt to and comply with environmental regulations and reducing corporate exposure to regulatory and policy changes. Based on resource dependence theory, the background and network of board members can assist enterprises in acquiring key resources [17,18]. Directors with environmental experience can leverage their professional knowledge and network resources to help enterprises acquire information, technology, and capital related to environmental protection and promote corporate sustainable development [19]. Therefore, the study on the impact of EBDs on ESG performance is helpful to further understand the mechanisms of directors’ individual characteristics on corporate green governance.

As an external pressure mechanism, environmental regulation directly enhances corporate environmental performance and indirectly improves their social responsibility and governance outcomes by influencing incentive mechanisms, market responses, and governance structures. According to stakeholder theory, environmental regulation serves as external pressure from the government and social stakeholders, compelling enterprises to meet their environmental and social responsibilities. Stringent environmental regulation motivates companies to invest in sustainability efforts and enhance their environmental performance and ESG performance in order to satisfy stakeholder expectations for legitimacy [1,20]. Resource dependence theory posits that enterprises need to rely on external environmental resources, with environmental regulation serving as a form of external resource constraint. Strict environmental regulation motivates companies to implement environmental protection strategies, encouraging them to reduce pollutant emissions, improve resource utilization efficiency, and adopt cleaner production technologies to adapt to these constraints [20,21,22]. Additionally, environmental regulation strengthens the accountability of directors and management, urging them to focus on green management and improve sustainable development performance [23]. Thus, the relationship between EBDs and ESG performance could be influenced by environmental regulation.

Although some researchers have explored how gender diversity among directors, boards’ networks, CEOs’ green experience, or managers’ green experience influence corporate ESG performance [9,10,11,12,13,24,25,26], few studies have explored the impact of directors’ characteristics on ESG performance from the perspective of directors’ environmental background. Moreover, the mechanism by which environmental background directors influence specific ESG dimensions (environmental, social, governance) is not clear, and their governance effect may vary depending on factors such as regions, industries, and corporate characteristics. There remains a lack of dynamic research on the role and impact of EBDs in the long-term sustainable development of enterprises, which may limit guidance for their selection and training. This study aims to investigate how board characteristics, particularly the presence of directors with environmental backgrounds, influence corporate ESG performance while also considering the impact of environmental regulation. First, we analyze the impact and mechanisms through which EBDs affect ESG performance. Next, we examine the influence of factors such as regional environmental regulation intensity, heavy-polluting industries, and the New Environmental Protection Law on the relationship between EBDs and ESG performance. This study uncovers the internal logical connections between EBDs, external environmental regulation, and corporate ESG performance, providing insights into the role of directors’ personal experiences in promoting corporate sustainable development.

This study provides several incremental contributions. First, it discovers the intrinsic link between EBDs and corporate ESG performance, broadening the scope of research on board characteristics and corporate governance effectiveness. Second, this study highlights key internal governance factors that drive corporate ESG outcomes, emphasizing improvements in ESG performance through internal organizational efforts, thereby enriching the research on sustainable development performance. Third, it examines the mechanisms by which directors with environmental backgrounds influence ESG performance, illustrating how their personal experiences shape cognition, behavior, and decision making, thus contributing valuable insights to upper echelons theory. Fourth, it explores the varied effects of environmental regulation on ESG performance, distinguishing the specific impacts of the New Environmental Protection Law, regional regulatory intensity, and industry-specific characteristics. These findings provide a valuable reference for regulators in formulating and implementing ESG-related policies. Furthermore, this study provides new practical insights for enhancing board diversity, promoting corporate ESG practices, and achieving sustainable development goals.

The structure of this paper is as follows. Section 2 reviews the previous literature and develops the research hypothesis, Section 3 introduces the research design, Section 4 analyses the empirical results, Section 5 explains the heterogeneity analysis, and Section 6 provides the research conclusions and the policy recommendations.

2. Literature Review and Research Hypothesis

2.1. Environmental Background Directors and ESG Performance

With the rise of environmental pollution, resource depletion, and social conflicts, the governance functions and paradigms of corporate boards are undergoing significant transformation. The responsibilities of boards have expanded from prioritizing shareholder interests to addressing broader stakeholder concerns, with a particular emphasis on sustainable development and corporate green governance [27]. Boards of directors play a critical role in aligning corporate actions with stakeholder demands and societal expectations by making strategic decisions, overseeing management activities, and reporting to stakeholders [9,27]. Environmental, social, and governance (ESG) principles align closely with the concept of sustainable development and serve as critical indicators for evaluating corporate sustainable development performance from a stakeholder perspective [9]. Research indicates that strong ESG performance enables companies to secure resources and support from investors, creditors, and government agencies [1,2,3]. Consequently, an increasing number of companies are integrating ESG principles into board decision making and advisory processes [4]. Some firms have even begun linking directors’ remuneration to climate risk or ESG performance. Furthermore, boards of directors monitor management’s involvement in ESG activities, with CEOs facing potential dismissal if the company is implicated in ESG controversies reported by prominent media outlets [4].

Grounded in corporate governance theory and upper echelons theory, directors’ decisions are influenced by individual factors such as personality traits, cognitive biases, and group dynamics. Gallego and Rodriguez [8] highlight that board diversity, experience, and tenure are critical in fulfilling firms’ environmental commitments. Additionally, female directors tend to be more proactive in promoting ESG compliance [13]. And, increased gender diversity on boards is linked to narrowing the gap between ESG participation and ESG information disclosure [9]. Furthermore, female directors’ overseas experience helps transfer advanced environmental knowledge and sustainable practices to domestic firms, thereby strengthening their green commitments [10]. Board size and independence also significantly enhance corporate ESG disclosures [11]. A higher proportion of external directors on the board is linked to stronger green governance, leading to improved performance in the environmental dimension [7]. Consequently, identifying and appointing “green directors” is crucial for advancing corporate sustainability [27]. With the increasing stringency of environmental regulatory enforcement, more and more companies are taking the background and experience of their board members or CEOs into account when making appointments. The appointment of green directors helps to improve the effectiveness of a company’s environmental strategy and the sustainability of its environmental governance commitments [16]. Some scholars have found that appointing directors or managements with green backgrounds can significantly reduce firms’ carbon emissions [25], actively fulfill green environmental ethical requirements [21], enhance the standard of disclosed environmental information [16], and improve ESG performance and compliance [26]. Building on the above analysis, we argue that the presence of environmental background directors (EBDs) on the board can enhance corporate environmental responsibility and green governance, ultimately improving ESG performance. Hypothesis 1 is proposed for empirical testing as follows:

H1. 

Directors with an environmental background (EBD) are positively correlated with corporate ESG performance.

2.2. The Mechanism of Green Awareness

Three main motivations drive companies to respond positively to green eco-development: legitimacy, competitiveness, and ecological responsibility. These motivations are determined by the level of individual attention, the importance of the issue, and the cohesiveness of the field [28]. According to upper echelons theory, managers develop their ethical awareness and personal management style through education and work experience [29]. Moreover, their cognitive level and work experience significantly influence strategic decision making [30]. Managers with green experience are more proactive in fostering a green culture within their organizations [25]. An organization’s green culture supports environmental protection and contributes to achieving sustainable development goals [31]. When managers possess strong and positive environmental attitudes, they are more inclined to support environmental endeavors and community environmental protection activities, which enhances corporate environmental sustainability [32]. Deng et al. [24] report that CEO’s green experience lifts the management team’s awareness on environmental issues and aligns the firm’s strategy and resources with green development goals. Liu et al. [16] also point out that directors’ green experience influences the green attitudes, values, and culture of corporate management and employees, which subsequently improves the disclosure quality of environmental information. Directors or CEOs with environmental backgrounds are more attuned to the impact of environmental pressures on their firms, which enhances management’s awareness of environmental issues and green governance. Strengthening management’s green awareness can increase the firm’s focus on environmental protection, green innovation, and other sustainable initiatives [25], improve the identification of environmental project risks [33], and foster corporate commitment to environmental ethics [21]. The green management model advocated by EBDs also guides corporate managers to actively implement green technological innovations, enhance corporate green performance [34], and improve green governance efficiency [35]. By implementing green governance initiatives, EBDs enhance management’s green awareness, thereby improving corporate ESG performance. Based on the above analysis, Hypothesis 2 is proposed as follows:

H2. 

Management’s green awareness serves as a key mechanism linking EBDs to ESG performance.

2.3. The Mechanism of Green Innovation

The deteriorating problem of environmental pollution has heightened pressure on companies to prioritize environmental protection, propelled by stakeholders, including the government, investors, and the general populace. To enhance corporate environmental performance, companies must address the demands for compliance by investing in environmental equipment, training employees on environmental protection, improving operating processes, and adopting green technologies [36]. Green innovation involves technological advancements aimed at conserving resources and reducing environmental pollution. And, it reflects a low-carbon development strategy endorsed by governments, investors, and market dynamics [37]. Green innovation produces significant economic effects and environmental impacts. On the one hand, green innovation in products and processes allows companies to strengthen their competitive edge and improve their long-term financial performance [38,39]. On the other hand, green innovation enables companies to reshape their image of sustainable development, respond positively to government and market requirements, reduce pollutant emissions, optimize resource use efficiency, enhance environmental legitimacy, and improve overall environmental performance [34,37,40]. Moreover, environmental performance is a key pillar of ESG performance and significantly contributes to its overall enhancement. Environmental background directors (EBDs) or directors’ green experience are more attuned to environmental governance and are highly motivated to advance green innovation [41,42]. They understand the benefits and risks of green product development and technological innovation [24], which leads to greater investment in green technologies [21]. Green innovation is an effective method for realizing sustainable and green development, demonstrating the results of firms’ adherence to ESG commitments [43]. Therefore, green innovation significantly enhances companies’ ESG performance by upgrading their green technologies. Hypothesis 3 is as follows:

H3. 

Green innovation serves as a key mechanism linking EBDs to ESG performance.

The following is research structure logic figure for this study (Figure 1).

3. Research Design

3.1. Data Sources and Research Sample

This study focuses on Chinese-listed companies on the A-share market using data from 2010 to 2022 as the research sample. ESG performance rating indicators are collected from the Bloomberg database, directors’ personal biographies are from Chinese Sina Finance, and other financial data are from the CSMAR database, a widely used data source for studies on Chinese-listed companies. Data are processed as follows. (1) Companies exhibiting unusual statuses, such as ST and PT, have been excluded from the sample. (2) Companies in financial industries, such as banking and insurance, are excluded. (3) Observations with missing key variables are omitted. (4) All data for continuous variables are winsorized by 1% and 99%. Finally, we gain a sample consisting of 10,812 firm-year observations.

3.2. Explained Variable

This study uses Bloomberg’s ESG ratings as an indicator of corporate ESG performance. As an early institution in developing corporate ESG ratings internationally, Bloomberg’s ESG indicators have gained broad recognition and usage in academic research [1,9,24,26]. Bloomberg’s ESG ratings incorporate approximately 120 detailed indicators, such as climate change, carbon dioxide emissions, renewable energy, community relations, human rights, diversity, etc., covering the three core pillars of environment (E), social (S), and governance (G) performance. Bloomberg’s ESG ratings utilize both qualitative and quantitative approaches to thoroughly assess corporate ESG performance. Its ESG score is measured on a scale from 0 to 100, where a higher score reflects better corporate ESG performance.

3.3. Explanatory Variable

Some scholars define managerial activities in green education, green work experience, or environmental governance as indicative of executives with environmental backgrounds or green experience [16,21,24,25,33,42,44]. Based on this framework, we define “Environmental Background Directors” (EBDs) as directors with experience in environmental fields such as sustainability management, green governance, or environmental protection. To identify EBDs, we collect directors’ personal information from Chinese Sina Finance and apply a textual analysis method. This involves examining directors’ education and work experience for environmental keywords, such as “new energy”, “low carbon”, “energy saving”, “environmental protection”, “clean”, “ecological”, “sustainable”, and “green”. Directors whose personal information includes these keywords are categorized as EBDs [16,21,24,25,33,42,44]. Finally, we calculate the proportion of EBDs as the explanatory variable, denoted as EBD_Ratio.

3.4. Mechanism Variables

Management’s green awareness is proxied using text analysis to identify keywords in a firm’s annual report, such as energy saving, low carbon, environmental protection, emission reduction, pollution control, etc. We use the frequency of these environmentally conscious terms in the firm’s annual report as an indicator of management’s green awareness [16,24,25]. Green invention patents can reflect the input and output of enterprises in green technology innovation, and we use a firm’s green invention patent applications plus one taking natural logarithms to measure green innovation [21,24].

3.5. Control Variables

We choose some financial indicators that represent a firm’s operating performance: a firm’s asset size (Size), return on assets (ROA), revenue growth (Growth), liability level (Lev), operating cash flow (Cash), R&D investment (R&D), and proportion of overseas revenue (Overseas). We also choose some governance indicators representing corporate governance characteristics: shareholding concentration (Top1), board size (Board), board independence (Indep), nature of ownership (Soe), duality of chief executives (Dual), and management shareholding (Mshare) [9,24,26]. In addition, the control variables include the industry dummy variable and the year dummy variable. Table 1 presents comprehensive definitions for the main variables.

3.6. Model Design

Referring to the studies by Liu et al. [16], Li et al. [21], and Deng et al. [24], we use an OLS model to analyze the relationship between EBDs and corporate ESG performance.

(1)${\mathit{ESG}}_{i,t}={\alpha }_0+{\alpha }_1{\mathit{EBD}\_\mathit{Ratio}}_{i,t-1}+\alpha Controls+{\displaystyle \sum Industry+{\displaystyle \sum Year+{\epsilon }_{i,t}}}$

Referring to the studies by Chen et al. [45] and Di Giuli and Laux [46], we use a two-step method to test the mechanisms of EBD’s influence on corporate ESG performance.

(2)${\mathit{Green}\_\mathit{Awa}}_{i,t}={\alpha }_0+{\alpha }_1{\mathit{EBD}\_\mathit{Ratio}}_{i,t-1}+\alpha Controls+{\displaystyle \sum Industry+{\displaystyle \sum Year+{\epsilon }_{i,t}}}$

(3)${\mathit{Green}\_\mathit{PT}}_{i,t}={\alpha }_0+{\alpha }_1{\mathit{EBD}\_\mathit{Ratio}}_{i,t-1}+\alpha Controls+{\displaystyle \sum Industry+{\displaystyle \sum Year+{\epsilon }_{i,t}}}$

${\mathit{ESG}}_{i,t}$ represents the ESG score of firm i in year t, ${\mathit{EBD}\_\mathit{Ratio}}_{i,t-1}$ represents the environmental background directors‘ ratio of firm i in year (t − 1), controls represent the control variables shown in Table 1, industry and year represent industry fixed effect and year fixed effect, respectively, and ${\epsilon }_{i,t}$ is the random error term.

4. Empirical Results

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics for the main variables. The ESG ratings range from 11.16 to 58.58, with a mean of 29.5 and a standard deviation of 9.811. These figures indicate significant variability in ESG performance among firms, with generally low ratings for listed firms in China. The skewness and kurtosis of ESG are 0.767 and 3.58, respectively, suggesting a right-skewed distribution with heavier tails compared to a normal distribution. The proportion of EBDs ranges from 0 to 77.8%, with a mean value of 10.7%, highlighting substantial differences in board composition across firms and an overall low EBD proportion. The skewness of EBDs is 2.133, indicating a right-skewed distribution, with many enterprises’ EBD values concentrated on the left. The kurtosis of EBDs, at 8.884, suggests the presence of outliers or extreme values, reflecting significant differences in EBDs among different firms. For the control variables, the average ROA is only 0.047, indicating that the return on assets for the sample companies is not very high. The mean proportion of independent directors is 37.5%, meeting the requirement that the proportion of independent directors is not less than one third. The mean value management shareholding is 7%, indicating that equity incentives for the executives in Chinese-listed companies are relatively low. Other variables’ statistical data are shown in Table 2.

Table 3 presents the descriptive statistics and tests for two subsamples categorized by the presence or absence of environmental background directors (EBDs). The results indicate that firms with EBDs have a significantly higher mean ESG score compared to those without EBDs. This finding provides preliminary evidence of the significant impact of EBDs on corporate ESG performance.

4.2. Correlation Analysis

We use the Pearson correlation coefficient to test the logical relationship between EBDs and ESG performance. The results present in Table 4 show that EBDs have a significant positive correlation with ESG performance at the 1% level, preliminarily confirming Hypothesis 1. In addition, we also ran the test of multiple covariances. The VIF for each variable is below 2, and the average VIF is 1.35, indicating that there is no obvious multicollinearity problem in the research model.

4.3. Baseline Regression Analysis

Table 5 displays the results of baseline regression. Column (1) shows a strong positive relationship between environmental background directors (EBDs) and ESG performance, significant at the 1% level, supporting Hypothesis 1. To further examine the impact of EBDs on the three sub-dimensions of ESG, we conducted separate regression analyses on environmental (E), social (S), and governance (G) performance. The results, shown in columns 2 to 4 in Table 5, reveal a significant positive correlation between EBDs and S at the 1% level, as well as between EBDs and E and G at the 5% level. These findings provide additional support for Hypothesis 1.

4.4. Mechanism Analysis

We examine how EBDs influence corporate ESG performance, with the mechanism analysis results shown in Table 6. Column 1 indicates a positive correlation between EBDs and management’s green awareness, significant at the 1% level, supporting Hypothesis 2. In Column 2 in Table 6, EBDs have a positive relationship with the firm’s green innovation at the 1% significance level, validating Hypothesis 3. Therefore, EBDs improve corporate ESG performance by enhancing management’s green awareness and promoting corporate green innovation.

4.5. Robustness Tests

4.5.1. Instrumental Variables Approach

In the baseline regression, EBDs significantly enhance corporate ESG performance. However, it is also possible that firms with good ESG performance may direct their boards to prioritize environmental protection or green governance to maintain strong ESG ratings. These activities may enhance the boards’ environmental experience and knowledge. Consequently, there may be reverse causality in the research model. To tackle this issue, we use the average EBD indicator from companies within the same year, province, and industry as an instrumental variable (IV) for the 2SLS analysis, as recommended by Deng et al. [24]. Based on network relationships, business managers in the same region can learn from each other and engage in competition, fostering strong cohort relationships [33]. Additionally, firms in the same industry often exhibit similar operating characteristics and face comparable regulatory policies [16,21], meeting the correlation requirement. However, the year, province, and industry average ratio of EBDs generally does not affect the ESG ratings of individual firms, thus satisfying the exogeneity requirement [24]. Column 1 in Table 7 displays the results of the first stage, demonstrating a significant positive correlation between the IV and EBDs at the 1% level. Column 2 in Table 7 presents the results of the second-stage regression, showing that EBDs remain positively correlated with ESG performance at the 1% significance level after accounting for reverse causality.

4.5.2. Propensity Score Matching (PSM)

Firms with strong ESG performance tend to prioritize environmental compliance and sustainable development. They may also place greater emphasis on the environmental background or green experience of board members. And, the analysis may be subject to self-selection bias. Referring to Liu et al. [16], Li et al. [21], Deng et al. [24], and Zhang and Zhang [33], we adopt the PSM method to alleviate the self-selection problem. For propensity score matching, we categorize the sample into an experimental group and a control group. Specifically, the companies with EBDs are set as the experimental group (Treat = 1), and the companies without EBDs are the control group (Treat = 0). Using the original control variables as covariates, we performed 1:1 nearest neighbor matching, resulting in a matched sample of 5971 observations. Column 3 in Table 7 reports the regression results for the PSM sample. After addressing the self-selection issue, EBDs and ESG performance are still significantly positively correlated at the 1% level.

4.5.3. Sample Processing

Environmental protection companies prioritize their green performance and sustainable development, with a higher proportion of directors or managers having experience or education in environmental governance. These samples may introduce bias in the estimation of EBD’s impact on ESG performance. Drawing on the study by Wang et al. [44], we identify a firm’s business scope using text analysis. Specifically, we search for keywords related to low carbon, renewable energy, energy savings, environmental protection, cleanliness, ecology, sustainability, environment, and green in the firm’s name and business scope. The firm’s name and business scope containing these keywords are classified as environmental protection companies. Then, we remove environmental protection enterprises from the sample and regress the Modle1 again. The result is reported in column 4 in Table 7, the positive effect of EBDs on ESG performance remains significant at the 5% level.

4.5.4. Fixed Effects

Considering the influence of unobservable individual characteristics of the firm, we use an individual fixed-effect model. The regression result in column 5 in Table 7 shows that EBDs remain positively correlated with ESG performance at the 1% significance level in the individual fixed-effect model, confirming the robustness of the research finding.

4.5.5. Other Robustness Test

For the replacement of the explanatory variable, referring to Liu et al. [16], we utilize a dummy variable to assess the EBD. EBD_dum is assigned a value of 1 if firm i has EBDs on its board in year (t − 1); otherwise, it is assigned a value of 0. The result presented in column 1 in Table 8 shows that the EBD_dum coefficient remains significantly and positively associated with ESG performance at the 5% significance level.

For the replacement of the explained variable, referring to Deng et al. [24] and Wang et al. [47], we utilize the ESG rating index from the Chinese Sino-Securities Company. The Chinese Sino-Securities Company classifies ESG ratings into nine levels: AAA, AA, A, BBB, BB, B, CCC, CC, and C. We assign values from 9 to 1, denoted as ESG_HZ. The regression result, presented in column 2 in Table 8, shows that the positive effect of EBDs on ESG performance remains significantly correlated at the 1% significance level.

For the lag effect test, to test the sustained effect of directors with environmental backgrounds on corporate green governance, we performed regression analyses using ESG in year (t + 1) and (t + 2) as the explained variable. The results presented in column 3 and column 4 in Table 8 show that EBDs continue to have a sustained positive effect on corporate ESG performance in the (t + 1) and (t + 2) periods.

5. Heterogeneity Analysis

5.1. Environmental Regulation

Environmental regulation consists of policies, systems, and measures implemented by government authorities, market organizations, and the public to ensure that companies meet their environmental protection responsibilities and reduce pollution and resource waste. It is generally classified into command and control, market-based incentives, and informal public regulation. The command and control environmental regulation enforces more stringent limitations on companies [48]. The Porter Hypothesis posits that effective and well-regulated environmental policies not only enhance environmental protection and corporate growth but also encourage technological innovation and economic development that surpass the associated environmental costs [22]. The selection of environmental strategies and governance actions by firms largely depends on the strength of environmental regulation [49]. Firms in regions with strict environmental regulations encounter greater constraints and legal liabilities, which motivate them to invest in environmental initiatives to fulfill stakeholder expectations for legitimacy [20]. A large number of studies consistently agree that the level of environmental regulation is strongly and positively linked to environmental performance [20,21,25,48,49,50]. Environmental performance is a key pillar of ESG rating. Stringent environmental regulation can enhance corporate managers’ compliance with environmental governance, thereby improving corporate ESG performance [24]. In regions with stringent environmental regulations, local firms face great external pressures that drive them to enhance environmental performance [21], which may undermine the positive effects of corporate managers’ green experiences [25]. Therefore, we identify the influence of the intensity of environmental regulation on the relationship between EBDs and ESG performance through heterogeneity analysis.

We use text analysis to examine the frequency of environment-related terms in local government annual reports, measuring the intensity of regional environmental regulation by dividing the count of these terms by the total word count of the reports [20,25]. According to the average level of environmental regulatory intensity, we divide the sample into two groups. We categorize regions with above-average environmental regulation intensity as having strong environmental regulation, while those regions below the average are classified as having weak environmental regulation. We then conduct a grouped regression to examine the differential impact of environmental regulation intensity on the relationship between EBDs and ESG performance. The results are presented in Table 9. We find that in regions with weak environmental regulation, EBDs improve ESG performance at the 1% significance level. However, in regions with strong environmental regulation, the impact of EBDs on ESG performance is not statistically significant. When a company is located in regions with stringent environmental regulations, it faces strict legal constraints. To meet legitimacy requirements, the company voluntarily takes actions to improve its ESG performance. Consequently, the motivating effect of EBDs on corporate green governance may be weakened.

5.2. Heavily Polluting Industries

The Chinese government has developed a variety of environmental regulations aimed at different industries, depending on the degree of environmental risk they pose, with the goal of improving corporate environmental practices [48]. According to the “Management List of Industry Classification for Environmental Verification of Listed Companies”, issued by the former Chinese Ministry of Environmental Protection (MEP) in 2008, and the “Guidelines for Industry Classification of Listed Companies (Revised in 2012)”, issued by the Chinese Securities Regulatory Commission (CSRC), 16 industries are classified as heavily polluting. These industries include building materials, coal, electrolytic aluminum, thermal power, brewing, papermaking, metallurgy, chemicals, petrochemicals, pharmaceuticals, etc. In 2021, the Chinese Ministry of Ecology and Environment issued the “Measures for the Lawful Disclosure of Corporate Environmental Information”. In accordance with the law, polluting industries need to mandatorily disclose environmental information. Mandatory disclosure enhances the transparency of listed companies’ environmental information and promotes better performance in the environment. Thus, polluting industries encounter greater external pressure for environmental monitoring and compliance [26], stricter financing constraints [20], and more scrutiny and negative reports by the media [21]. And, these industries need to adopt more aggressive environment-friendly strategies to counteract their stigmatized image [26], which may undermine the governance impact of management’s green experiences in heavily polluting firms [16,26]. Therefore, we further analyze the heterogeneous impact of EBDs on ESG performance in heavily polluting industries and cleaning industries.

According to the industry classification of CSRC, we classify the industries with the coding of B06, B07, B08, B09, C17, C19, C22, C25, C26, C28, C29, C30, C31, C32, and D44 as heavily polluting [24,26]. A value of 1 is assigned to a firm if it is a part of heavily polluting industries, while a value of 0 is given otherwise. We then conduct group regression, with the results presented in Table 10. In the cleaning industries, EBDs have a significantly positive effect on ESG performance at the 5% level. In contrast, companies in heavily polluting industries, which face stricter environmental regulations and constraints, are compelled to actively improve their environmental performance and disclose environmental information to meet legitimacy standards. As a result, the green governance effect of EBDs in heavily polluting industries is mitigated, and the improvement in ESG performance is not significant.

5.3. New Environmental Protection Law

In January 2015, China implemented the revised “Environmental Protection Law of the People’s Republic of China” (New Environmental Protection Law). The New Environmental Protection Law establishes a stringent environmental accountability mechanism and introduces a “daily penalty system”, whereby escalating fines are imposed on companies for ongoing environmental violations, aiming to prompt them to address these issues in time. The New Law imposes administrative detention penalties on offenders. Additionally, it holds environmental certification organizations jointly liable for assisting companies in making false statements, thereby preventing these organizations from evading legal responsibility. The revised Environmental Protection Law also introduces an environmental public interest litigation system, integrating public oversight into the legal framework to strengthen the monitoring of environmental governance by both the government and companies. And, the Law suggests that local governments should offer rewards to companies and individuals who have made significant contributions to environmental protection. In September 2015, the Chinese State Council issued the “Overall Program for the Reform of the Ecological Civilization System”, which created a compulsory system for listed companies to disclose environmental information. The New Law and the requirements for listed companies to disclose environmental information have increased environmental governance pressures. Consequently, EBDs are more responsive to the urgency of environmental challenges and are increasingly motivated to enhance environmental performance by leveraging their expertise, experience, and technologies.

Thus, we divide the sample into two timeframes according to the enactment of the New Environmental Protection Law: pre-2015 and post-2015. We conduct group regression by time interval, and the regression results are shown in Table 11. The promotional effect of EBDs on ESG performance is more significant after the implementation of the New Environmental Protection Law. The implementation of the New Environmental Protection Law has had a strong deterrent effect on companies and management. EBDs have adopted more proactive approaches to improving environmental performance and fulfilling social responsibilities. They increase investments in environmental governance and green technological innovation [42], significantly enhancing corporate ESG performance.

6. Conclusions

This study examines the influence and mechanisms through which environmental background directors (EBDs) affect corporate ESG performance, using data from Chinese-listed companies between 2010 and 2022. Our findings indicate that (1) EBDs significantly enhance corporate ESG performance and notably promote improvements in the individual dimensions of E, S, and G. EBDs play an active role in advancing green governance by improving corporate environmental governance and social responsibility, thereby enhancing overall ESG outcomes. (2) EBDs enhance ESG performance by improving management’s green awareness and fostering corporate green innovation. EBDs possess stronger environmental governance awareness, facilitating the implementation of green culture and green activities within organizations and enhancing management’s green awareness. The increased management’s green awareness enables corporations to promote the organization’s commitment to sustainability, environmental performance, green technology innovation, and green governance efficiency [16,25,34,35], thereby promoting corporate ESG performance. Moreover, EBDs possess expertise and experience in environmental governance, green processes, and green technologies, which help companies implement green technology innovation more effectively. These significantly enhance the company’s investment and output of green innovation [24,42,43]. Thus, EBDs improve corporate ESG performance from the perspective of green technology. (3) The role of EBDs in corporate green governance is influenced by environmental regulation. A grouping regression test shows that the motivating effect of EBDs on ESG performance is more pronounced in regions with weaker environmental regulation, in industries with lower pollution levels, and after the implementation of the New Environmental Protection Law.

6.1. Theoretical Implications

First, this study contributes to the existing sustainability research by examining the impact of EBDs on corporate ESG performance. The growing urgency of environmental issues, such as pollution, extreme climate events, and resource depletion, has brought ESG to the forefront among stakeholders. By emphasizing the balance between environmental protection, social responsibility, and green governance, ESG closely aligns with the goals of sustainable development. The board’s green governance is a key strategy for companies to achieve sustainable development and long-term competitiveness. With limited resources, boards with diverse memberships or firms with an environmental governance committee are more likely to be transparent about ecological issues and to balance financial performance with non-financial objectives [19]. Our research confirms that EBDs can significantly improve ESG performance. The findings highlight the internal factors driving corporate green governance and sustainable development, offering theoretical insights for enhancing board governance and ESG performance.

Second, this study expands the understanding of individual-level factors influencing corporate ESG performance by focusing on directors’ environmental experience. It enhances our understanding of the impact mechanisms through which directors’ characteristics affect corporate governance and deepens the interpretation of how personal attributes influence economic and social outcomes within the framework of upper echelons theory. In contrast to previous studies examining the impact of board characteristics, such as gender diversity [9], independence [10], structure [11], and network [12] on ESG performance, this study extends the analysis to include directors’ green experience. Our research demonstrates that directors’ learning or work experience in environmental governance can enhance the management team’s green awareness, foster corporate green innovation, and improve ESG performance, thereby confirming the positive role of EBDs in corporate green governance. This provides valuable insights into green governance and serves as a meaningful contribution to corporate governance theory.

Third, our study provides additional evidence on the impact of EBDs on ESG performance across different contextual settings by analyzing environmental regulation. Environmental regulation is a critical driver of corporate ESG practices [50], requiring firms to respond strategically, technologically, and operationally to compliance demands, thereby achieving a balance between long-term value creation, environmental protection, and social responsibility. Strong environmental regulations exert external pressure on firms, compelling them to meet ESG responsibilities, which may significantly affect the effectiveness of EBD-driven green governance. Our findings reveal that the impact of EBDs on ESG performance is more pronounced in regions with weaker environmental regulation, in cleaning industries, and after the implementation of the New Environmental Protection Law. This study highlights how the individual characteristics of directors interact with the regulatory environment to shape corporate strategic decisions and behaviors. It also provides a more comprehensive theoretical framework for improving regulatory policies and enhancing corporate ESG performance.

6.2. Practical Implications

Based on our findings, this study offers recommendations for enterprises and policymakers to advance ESG practices.

First, boards should integrate ESG and sustainable development goals into strategic decision making. By establishing clear policies and objectives, companies can align environmental, social, and governance targets with their operational plans. As China’s economy undergoes transformative development, the “dual carbon” strategic goals proposed at the United Nations General Assembly align closely with ESG principles. By actively promoting green governance, fulfilling environmental and social responsibilities, and enhancing ESG information disclosure, directors and managers can ensure that companies meet stakeholders’ demands for sustainable development. Furthermore, strong ESG performance enables companies to secure support from both governments and investors.

Second, companies can improve board diversity by appointing directors with environmental expertise or green experience. Furthermore, firms can cultivate environmental governance knowledge among board members by involving them in corporate green initiatives, environmental protection activities, and sustainability-focused training programs. Companies may also implement effective incentive structures by tying board members’ compensation to ESG performance or offering rewards such as performance-based bonuses, equity grants, or flexible benefits to directors who actively contribute to green governance. These measures aim to enhance the board’s environmental decision-making capabilities and governance efficiency, thereby promoting corporate green technology, environmental performance, and sustainable development.

Third, regulatory authorities can strengthen environmental governance and sustainability-related legislation by clearly defining corporate responsibilities in areas such as emissions control, resource management, and pollution prevention. Additionally, improving and standardizing ESG reporting and disclosure requirements will ensure the effectiveness and long-term consistency of corporate ESG responsibility. The government can also implement a system of rewards and penalties. Enterprises involved in environmental violations or ESG-related incidents may face administrative penalties or market restrictions, compelling them to meet their environmental and social responsibilities in a lawful manner. Conversely, companies that actively engage in environmental governance or demonstrate strong ESG performance could be rewarded with policies such as tax incentives, government subsidies, green credit, and project awards, thereby promoting better ESG practices through resource support.

6.3. Research Limitations

Although the research findings confirm the positive impact of EBDs on ESG, there are some limitations.

First, our study is based on Chinese-listed companies, where ESG practices are still in the exploratory stage. Given the differences in regulations, governance structures, corporate characteristics, and operating environments across countries, caution should be exercised when generalizing the conclusions to companies in other countries. Future research could expand the sample size to include more countries and regions, thereby increasing the applicability of the findings.

Second, we employ text analysis to capture directors’ environmental backgrounds by extracting environmentally related keywords from their personal information. However, due to limitations in the availability of personal data on board members, we focus on analyzing their environmental experiences during education or work. This approach may not fully capture all of the directors’ environmental experience, particularly complex or indirect green behaviors that may be difficult to capture. Future research will explore how to develop more comprehensive, precise, and effective metrics for measuring EBDs.

Third, in addition to the influence of board members’ environmental background, a company’s ESG strategy and performance may also be shaped by individual characteristics of board members, such as race, educational background, network structure, and social capital. Future research could further investigate the impact of combining board members’ backgrounds with other characteristics to explore the positive role of board governance in corporate sustainability.

Finally, a company’s operating environment may also influence directors’ decisions and ESG responsibilities. This study examines the impact of environmental regulation on the relationship between EBDs and ESG performance from the perspective of green governance. However, factors such as the company’s lifecycle, investor demand, market competition, social pressure, and the global economic environment may also affect ESG performance. Future research could explore the interaction between internal drivers and the external operating environment, investigating their combined impact on ESG performance.

Author Contributions

Conceptualization, W.P. and C.D.; methodology, C.D. and Z.D.; software, W.P.; validation, C.D. and Q.Z.; formal analysis, C.D. and Z.D.; investigation, C.D. and W.P.; data curation, C.D. and Q.Z.; writing—original draft preparation, C.D.; writing—review and editing, W.P. and Z.D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1. Research structure logic figure.

Table 1

Definition of main variables.

Symbol Name	Variable Definitions
ESG	Bloomberg’s ESG composite score
E	Bloomberg’s environmental score
S	Bloomberg’s social score
G	Bloomberg’s governance score
EBD_Ratio	Number of environmental background directors/number of directors of the board
Green_Awa	Total frequency of words related to green awareness in the firm’sannual report
Green_PT	Patents filed for green inventions plus 1 taking natural logarithms
Size	Firm’s total assets taking natural logarithms
ROA	Net profit/total assets
Growth	(Current operating income-previous operating income)/previous operating income
Lev	Total liabilities/total assets
Cash	Net cash generated from operating activities/total assets
R&D	R&D investment/operating income
Overseas	Overseas income/operating income
Top1	Number of shares held by the largest shareholder/total number of shares
Board	The total number of board members taking natural logarithms
Indep	Number of independent directors/total number of board members
Soe	The ultimate controller of the firm is a state-owned organization taking 1; otherwise, taking 0
Dual	If the same person holds both the chairman and general manager positions, a value of 1 is assigned; otherwise, a value of 0 is given
Mshare	Total shares held by directors, supervisors, and management/the number of total shares
Industry	Industry fixed effect
Year	Year fixed effect

Table 2

Descriptive statistics.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	N	Mean	SD	Min	Max	Skewness	Kurtosis
ESG	10,812	29.50	9.811	11.16	58.58	0.767	3.580
EBD_Ratio	10,812	0.107	0.143	0	0.778	2.133	8.884
Size	10,812	23.20	1.305	20.50	26.90	0.463	3.120
ROA	10,812	0.047	0.058	−0.165	0.220	−0.058	5.553
Growth	10,812	0.179	0.360	−0.484	2.177	2.567	13.79
Lev	10,812	0.476	0.197	0.069	0.869	−0.098	2.180
Cash	10,812	0.060	0.070	−0.131	0.264	0.206	3.721
R&D	10,812	0.032	0.043	0	0.256	2.676	12.14
Overseas	10,812	0.118	0.205	0	0.897	2.147	7.057
Top1	10,812	0.373	0.160	0.091	0.774	0.393	2.471
Board	10,812	2.177	0.201	1.609	2.708	−0.089	3.826
Indep	10,812	0.375	0.055	0.333	0.571	1.503	5.131
Soe	10,812	0.521	0.500	0	1	−0.083	1.007
Dual	10,812	0.209	0.407	0	1	1.428	3.039
Mshare	10,812	0.070	0.148	0	0.643	2.321	7.524

Table 3

Descriptive statistics for separate subsamples divided by EBDs.

Variables	EBD = 1		EBD = 0		Difference
	N	Mean	N	Mean	Mean Diff	T-Value
ESG	5866	30.05	4946	28.86	1.186 ***	6.273
Size	5866	23.26	4946	23.12	0.141 ***	5.621
ROA	5866	0.048	4946	0.046	0.002	1.520
Growth	5866	0.187	4946	0.169	0.018 **	2.556
Lev	5866	0.487	4946	0.463	0.023 ***	6.138
Cash	5866	0.061	4946	0.059	0.002	1.306
R&D	5866	0.032	4946	0.033	−0.001	−0.936
Overseas	5866	0.124	4946	0.110	0.014 ***	3.576
Top1	5866	0.368	4946	0.379	−0.011 ***	−3.427
Board	5866	2.189	4946	2.164	0.025 ***	6.548
Indep	5866	0.374	4946	0.375	−0.001	−0.582
Soe	5866	0.511	4946	0.532	−0.021 **	−2.167
Dual	5866	0.222	4946	0.195	0.027 ***	3.423
Mshare	5866	0.070	4946	0.070	−0.000	−0.199

Note: ** and *** denote significance levels of 5% and 1% respectively.

Table 4

Correlation test of main variables.

Variables	ESG	EBD_Ratio	Size	ROA	Growth	Lev	Cash	R&D	Overseas	Top1	Board	Indep	Soe	Dual	Mshare
ESG	1
EBD_Ratio	0.110***	1
Size	0.462***	0.058***	1
ROA	0.035***	−0.005	−0.103***	1
Growth	0.002	0.042***	0.001	0.254***	1
Lev	0.050***	0.075***	0.496***	−0.466***	0.004	1
Cash	0.128***	−0.006	−0.008	0.503***	0.062***	−0.248***	1
R&D	0.117***	0.014	−0.196***	0.085***	0.016*	−0.294***	0.016*	1
Overseas	0.089***	0.028***	−0.027***	−0.001	0.068***	−0.032***	0.021**	0.134***	1
Top1	−0.002	−0.059***	0.223***	0.077***	−0.015	0.080***	0.074***	−0.200***	−0.075***	1
Board	−0.010	−0.056***	0.177***	−0.048***	−0.047***	0.105***	0.006	−0.147***	−0.069***	0.027***	1
Indep	0.062***	0.031***	0.075***	0.020**	0.006	0.013	0.022**	0.040***	−0.003	0.080***	−0.434***	1
Soe	0.026***	−0.081***	0.264***	−0.191***	−0.109***	0.234***	−0.084***	−0.239***	−0.177***	0.256***	0.268***	−0.012	1
Dual	0.023**	0.058***	−0.100***	0.094***	0.062***	−0.092***	0.039***	0.172***	0.102***	−0.093***	−0.181***	0.085***	−0.298***	1
Mshare	−0.056***	0.056***	−0.270***	0.208***	0.117***	−0.266***	0.060***	0.274***	0.093***	−0.160***	−0.191***	0.041***	−0.467***	0.241***	1

Note: *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively.

Table 5

Baseline regression analysis.

Variables	(1)	(2)	(3)	(4)
	ESG	E	S	G
EBD_Ratio	2.803 ***	3.327 **	3.449 ***	2.227 **
	(2.90)	(2.05)	(3.40)	(2.23)
Size	2.622 ***	3.802 ***	1.944 ***	1.853 ***
	(16.48)	(15.40)	(12.22)	(9.22)
ROA	5.703 ***	5.649 *	5.606 **	2.074
	(3.05)	(1.72)	(2.57)	(0.87)
Growth	−0.074	0.256	−0.161	−0.267
	(−0.37)	(0.73)	(−0.77)	(−1.07)
Lev	−3.024 ***	−4.251 ***	−4.424 ***	−2.513 **
	(−3.51)	(−2.93)	(−4.73)	(−2.49)
Cash	4.441 ***	6.166 ***	1.250	3.620 **
	(3.39)	(2.78)	(0.84)	(2.17)
R&D	6.990 **	6.297	6.800 *	5.196
	(2.13)	(1.07)	(1.66)	(1.63)
Overseas	1.038 *	1.529	1.071	0.023
	(1.66)	(1.39)	(1.59)	(0.03)
Top1	−0.277	0.294	0.007	−0.114
	(−0.30)	(0.18)	(0.01)	(−0.11)
Board	0.586	−0.063	0.802	1.284
	(0.80)	(−0.05)	(0.99)	(1.47)
Indep	3.443	0.223	3.013	7.433 **
	(1.44)	(0.06)	(1.19)	(2.46)
Soe	0.660 **	0.121	0.726 **	0.724 **
	(2.03)	(0.22)	(2.07)	(2.04)
Dual	0.128	0.094	−0.098	0.335
	(0.46)	(0.19)	(−0.31)	(1.00)
Mshare	−0.047	2.373	3.751 ***	−1.897
	(−0.05)	(1.37)	(2.94)	(−1.63)
Constant	−33.882 ***	−77.767 ***	−33.317 ***	16.394 ***
	(−9.10)	(−12.89)	(−8.86)	(3.46)
Observations	10,812	10,689	10,763	10,812
Adjusted R2	0.635	0.395	0.301	0.688
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

Table 6

Mechanism analysis.

Variables	(1)	(2)
	Green_Awa	Green_PT
EBD_Ratio	3.493 ***	0.973 ***
	(4.19)	(6.58)
Size	0.563 ***	0.460 ***
	(5.40)	(15.74)
ROA	−4.496 ***	0.098
	(−3.05)	(0.28)
Growth	−0.140	−0.003
	(−1.13)	(−0.09)
Lev	0.306	0.187
	(0.46)	(1.35)
Cash	3.273 ***	−0.520 **
	(3.34)	(−2.31)
R&D	−10.084 ***	5.684 ***
	(−4.76)	(8.71)
Overseas	−0.740	0.030
	(−1.31)	(0.28)
Top1	0.978	−0.020
	(1.52)	(−0.14)
Board	0.582	−0.080
	(1.03)	(−0.59)
Indep	−1.476	−0.313
	(−0.86)	(−0.73)
Soe	0.289	0.190 ***
	(1.12)	(3.38)
Dual	−0.528 ***	0.053
	(−2.79)	(1.07)
Mshare	−0.968	0.107
	(−1.56)	(0.73)
Constant	−10.578 ***	−9.976 ***
	(−4.02)	(−13.67)
Observations	10,812	10,810
Adjusted R2	0.179	0.368
Industry	Yes	Yes
Year	Yes	Yes

Note: ** and *** denote significance levels of 5% and 1% respectively.

Table 7

Endogeneity test.

Variables	(1)	(2)	(3)	(4)	(5)
	First Step	Two-Step	PSM	New Sample	Fixed-Effect
	EBD_Ratio	ESG	ESG	ESG	ESG
IV	0.983 ***
	(34.37)
EBD_Ratio		5.467 ***	2.758 ***	2.686 **	3.500 ***
		(3.11)	(2.60)	(2.51)	(3.36)
Size	0.001	2.616 ***	2.669 ***	2.602 ***	1.802 ***
	(0.43)	(16.50)	(15.67)	(15.72)	(7.42)
ROA	0.036	5.446 ***	6.532 ***	5.456 ***	6.487 ***
	(0.85)	(2.89)	(2.98)	(2.77)	(4.11)
Growth	0.006	−0.098	−0.251	−0.208	−0.226
	(1.44)	(−0.49)	(−0.99)	(−0.98)	(−1.52)
Lev	0.047 ***	−3.200 ***	−2.718 ***	−3.015 ***	−3.329 ***
	(2.83)	(−3.67)	(−2.97)	(−3.37)	(−4.04)
Cash	−0.030	4.569 ***	4.125 ***	4.594 ***	1.456
	(−0.99)	(3.48)	(2.64)	(3.33)	(1.52)
R&D	−0.010	6.955 **	3.046	7.039 **	9.259 **
	(−0.13)	(2.12)	(0.92)	(2.06)	(2.29)
Overseas	−0.003	1.016	1.312*	1.542 **	1.557 *
	(−0.23)	(1.63)	(1.77)	(2.30)	(1.78)
Top1	−0.048 ***	−0.156	0.300	−0.200	0.737
	(−2.89)	(−0.16)	(0.30)	(−0.20)	(0.50)
Board	−0.023	0.672	0.654	0.787	0.851
	(−1.48)	(0.91)	(0.82)	(1.02)	(1.19)
Indep	0.033	3.371	2.355	3.516	6.343 ***
	(0.73)	(1.42)	(0.91)	(1.38)	(2.89)
Soe	−0.015 **	0.711 **	0.608 *	0.672 **	0.223
	(−2.04)	(2.17)	(1.72)	(1.96)	(0.45)
Dual	0.009	0.100	−0.143	0.093	0.005
	(1.43)	(0.36)	(−0.47)	(0.32)	(0.02)
Mshare	−0.005	−0.115	0.275	−0.150	1.038
	(−0.22)	(−0.13)	(0.28)	(−0.16)	(0.68)
Constant			−34.809 ***	−34.032 ***	−25.576 ***
			(−8.47)	(−8.68)	(−4.45)
Observations	10,812	10,812	5971	9370	10,812
Adjusted R2		0.203	0.631	0.637	0.734
Industry	Yes	Yes	Yes	Yes	No
Year	Yes	Yes	Yes	Yes	Yes
Stkcd	No	No	No	No	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

Table 8

Other robustness tests.

Variables	(1)	(2)	(3)	(4)
	ESG	ESG_HZ	ESGi,t+1	ESGi,t+2
EBD_dum	0.538 **
	(2.52)
EBD_Ratio		0.371 ***	3.019 ***	3.324 ***
		(2.86)	(2.86)	(2.86)
Size	2.626 ***	0.221 ***	2.629 ***	2.661 ***
	(16.50)	(11.80)	(15.42)	(14.51)
ROA	5.697 ***	2.960 ***	7.576 ***	6.509 **
	(3.05)	(8.33)	(3.56)	(2.51)
Growth	−0.055	−0.159 ***	−0.091	−0.390 *
	(−0.27)	(−4.94)	(−0.44)	(−1.84)
Lev	−2.928 ***	−0.822 ***	−2.509 ***	−2.177 **
	(−3.39)	(−6.24)	(−2.67)	(−2.08)
Cash	4.306 ***	−0.441 **	5.367 ***	6.840 ***
	(3.28)	(−2.05)	(3.84)	(4.43)
R&D	6.984 **	0.826	8.478 **	11.348 ***
	(2.12)	(1.58)	(2.35)	(2.90)
Overseas	1.021	0.169	1.205 *	1.245 *
	(1.64)	(1.64)	(1.77)	(1.69)
Top1	−0.317	−0.003	−0.313	−0.092
	(−0.34)	(−0.03)	(−0.31)	(−0.09)
Board	0.414	−0.023	0.498	0.164
	(0.56)	(−0.21)	(0.64)	(0.19)
Indep	3.393	1.824 ***	3.168	3.062
	(1.41)	(5.45)	(1.23)	(1.11)
Soe	0.627 *	0.286 ***	0.624 *	0.612
	(1.93)	(5.87)	(1.79)	(1.63)
Dual	0.133	−0.051	0.193	0.184
	(0.48)	(−1.14)	(0.64)	(0.55)
Mshare	0.059	0.362 **	0.049	0.094
	(0.06)	(2.47)	(0.05)	(0.09)
Constant	−33.588 ***	−1.253 ***	−33.368 ***	−32.773 ***
	(−8.99)	(−2.74)	(−8.38)	(−7.76)
Observations	10,812	10,805	9187	7879
Adjusted R2	0.634	0.156	0.614	0.588
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

Table 9

Heterogeneity analysis of environmental regulation intensity.

Variables	(1)	(2)
	Strong ER	Weak ER
EBD_Ratio	1.671	3.934 ***
	(1.54)	(3.21)
Size	2.495 ***	2.719 ***
	(13.99)	(14.47)
ROA	2.017	8.938 ***
	(0.85)	(3.73)
Growth	0.539 *	−0.621 **
	(1.95)	(−2.45)
Lev	−3.151 ***	−2.838 ***
	(−3.07)	(−2.70)
Cash	5.750 ***	3.421 **
	(3.62)	(1.98)
R&D	4.274	8.730 **
	(1.09)	(2.19)
Overseas	1.021	1.111
	(1.31)	(1.44)
Top1	−0.865	0.141
	(−0.88)	(0.12)
Board	0.811	0.463
	(1.02)	(0.49)
Indep	2.885	3.439
	(1.05)	(1.14)
Soe	0.762 **	0.541
	(2.06)	(1.24)
Dual	0.012	0.228
	(0.03)	(0.68)
Mshare	0.635	−0.657
	(0.55)	(−0.61)
Constant	−31.159 ***	−35.932 ***
	(−7.58)	(−7.98)
Observations	5114	5697
Adjusted R2	0.634	0.637
Industry	Yes	Yes
Year	Yes	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

Table 10

Heterogeneity analysis of heavily polluting industries.

Variables	(1)	(2)
	Heavily Polluting Industries	CleaningIndustries
EBD_Ratio	2.172	2.691 **
	(1.15)	(2.34)
Size	2.707 ***	2.594 ***
	(8.11)	(14.63)
ROA	−0.050	7.454 ***
	(−0.01)	(3.63)
Growth	0.005	−0.104
	(0.01)	(−0.46)
Lev	−4.639 **	−2.344 **
	(−2.41)	(−2.55)
Cash	6.164 **	4.131 ***
	(2.25)	(2.83)
R&D	−1.114	10.509 ***
	(−0.08)	(3.13)
Overseas	0.090	1.302 *
	(0.07)	(1.88)
Top1	−0.944	−0.076
	(−0.54)	(−0.07)
Board	0.523	0.718
	(0.35)	(0.87)
Indep	10.246 *	1.584
	(1.90)	(0.59)
Soe	0.499	0.647 *
	(0.76)	(1.73)
Dual	−0.137	0.209
	(−0.21)	(0.70)
Mshare	2.271	−0.559
	(0.88)	(−0.59)
Constant	−36.355 ***	−33.536 ***
	(−5.12)	(−7.64)
Observations	2860	7952
Adjusted R2	0.644	0.635
Industry	Yes	Yes
Year	Yes	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

Table 11

Heterogeneity analysis of the implementation of New Environmental Protection Law.

Variables	(1)	(2)
	Pre-New EPL	Post-New EPL
EBD_Ratio	1.721	2.922 ***
	(1.47)	(2.58)
Size	1.848 ***	3.016 ***
	(9.39)	(17.96)
ROA	−2.091	6.658 ***
	(−0.71)	(3.16)
Growth	−0.474 *	0.035
	(−1.68)	(0.14)
Lev	−2.758 **	−3.183 ***
	(−2.55)	(−3.23)
Cash	3.564 **	5.581 ***
	(2.19)	(3.44)
R&D	5.818	7.877 **
	(1.25)	(2.14)
Overseas	0.588	1.108
	(0.64)	(1.63)
Top1	−1.416	0.478
	(−1.46)	(0.43)
Board	0.903	0.374
	(1.07)	(0.42)
Indep	4.011	2.496
	(1.46)	(0.84)
Soe	0.546	0.755 **
	(1.50)	(1.98)
Dual	0.076	0.214
	(0.21)	(0.64)
Mshare	1.489	−1.238
	(1.37)	(−1.09)
Constant	−23.772 ***	−39.073 ***
	(−5.61)	(−9.42)
Observations	3480	7332
Adjusted R2	0.177	0.464
Industry	Yes	Yes
Year	Yes	Yes

Note: *, **, and *** denote significance levels of 10%, 5%, and 1% respectively.

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