Firm Size and Corporate ESG Disclosure: Exploring the moderating effect of the institutional environment in China

Firm Size and Corporate ESG Disclosure: Exploring the moderating effect of the institutional environment 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 Article Firm Size and Corporate ESG Disclosure: Exploring the moderating effect of the institutional environment in China Yixi Zhang, Xiaoqian Wang, Yiran Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The quality of information in the capital market is crucial for investors to make informed judgments. Environment, social responsibility, and corporate governance (ESG) disclosure is widely recognized to contribute information transparency of capital market in China. Based on the signaling theory, this study aims to investigates the relationship between firm size and ESG disclosure and the moderating effect of the institutional environment (including government intervention and legal environment). Using 18,815 unbalanced firm-year observations from Chinese A-share listed companies for 2018 to 2022, the findings show that firms with a larger size tend to provide a higher quality of ESG disclosure and this positive relationship between firm size and ESG disclosure is more pronounced in the regions with less government intervention and a more developed legal environment. Furthermore, notable findings show that the moderating role of government intervention and the legal environment exerts a significant difference between heavily polluting industries and non-heavily polluting industries. The current ESG rating database cannot provide data for conducting further comparative analyses. Nonetheless, the findings and limitations provide future research directions on this topic. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences ESG disclosure firm size institutional environment signaling theory heavily polluting industries Figures Figure 1 Introduction The global economic landscape is undergoing profound transformations, making the importance of sustainable development increasingly evident. Traditional growth models, characterized by resource-intensive practices and scale-driven expansion, have proven to be unsustainable (Pu, 2025). China, the world’s largest developing country and transitional economy (Zeng et al., 2020), plays a critical role in promoting global sustainable development in the context of climate change (Chen & Xie, 2022). In this context, the Chinese government has explicitly put forward the "Dual Carbon" goals. ESG initiatives are highly aligned with "Dual Carbon" objectives and has become an important metric for evaluating high-quality corporate development (Gao et al.,2025). Insufficient disclosure of ESG information by Chinese companies would perpetuate extensive growth patterns and intensify the challenges to global sustainable development. How to motivate companies to actively disclose their ESG practices and improve the quality of ESG information has become an urgent issue in both academic and practical circles. The ESG disclosure is a dialogue between enterprises and stakeholders who concerned about corporate social and environmental activities (Murphy & McGrath, 2013). According to the ESG ranking of a famous rating agency named SynTao Green Finance in China, the proportion of all A-share listed companies that published independent ESG reports from 2011 to 2024 was significantly lower than that of CSI 300 index constituents (as shown in Fig. 1). The CSI 300 Index is a prominent stock market index in China that tracks the performance of the top 300 listed companies with large assets trading on the Shanghai and Shenzhen stock exchanges. Currently, ESG disclosure in China is largely voluntary on the part of enterprises (Murphy & McGrath, 2013; Vitolla et al., 2023), and the factors influencing the motivation of enterprises' ESG disclosure are unclear. Ignoring the institutional environment and industry characteristics to study the corporate motivations behind releasing ESG signals may not yield valuable insights. It would be beneficial for firms to develop effective communication channels with their stakeholders by making high-quality information disclosures (Lee et al., 2022). Corporate information transparency results in reduced information asymmetry and brings in concrete benefits for the capital market (Brown & Hillegeist, 2007; Buskirk, 2012; Liu et al., 2023). In 2024, only 118 listed companies in China had a market value exceeding RMB 100 billion, comprising just 2.19% of all listed firms, yet they accounted for 40% of the total market capitalization (China Association of Listed Companies, 2025). It indicates that large companies have a significant "bellwether effect" in promoting economic development and capital market stability. The performance of larger listed corporations, particularly in terms of ESG aspects, usually receives greater attention from various stakeholders (Cao et al., 2019; Ullmann, 1985). According to signaling theory, when information asymmetry exists, one party would attempt to provide information about itself to a second party (Spence, 1973). Information disclosure is a crucial way for enterprises to convey internal information to external stakeholders (Botosan, 1997; Goldstein & Yang, 2017; Li et al., 2022). Thus, from a signaling theory perspective, large firms have stronger incentives to make high-quality ESG disclosures to mitigate information asymmetry with their stakeholders. China has experienced substantial economic growth through marketization in the past few decades (Bin-feng, 2022; Ge et al., 2022). The marketization process varies throughout China due to its distinct resource endowments and policy inclinations, resulting in large differences in the institutional environment of different regions (Yang et al., 2020). Stronger political interference and a lower level of legalization are the prominent features of the institutional environment in transitional countries, such as China (Bin-feng et al., 2022; Long, 2019; Xie et al., 2017; Zheng & Ren, 2019). Neo-institutional theory holds that corporate behavior and decision-making are influenced by the institutional environment in which the firm is embedded (Campbell, 2007; Kong et al., 2022). Consequently, this also affects the motivation and quality of corporate information disclosure (Bin-feng et al., 2022; Zheng & Ren, 2019). Moreover, several studies asserted that the institutional environment plays a pivotal role in determining the efficacy of signals (Connelly et al., 2011; Huang et al., 2023). The motivations of firms to disclose ESG engagements might be influenced by the institutional environments in which they are located (Tsang et al., 2023). Therefore, it is vital for Chinese policymakers and stakeholders to consider the impact of the different institutional environment on corporate ESG disclosure motivations, especially in local government intervention and legal environment. More importantly, the findings of this study can also provide valuable insights to improve the transparency of ESG information in other transitional developing countries. This paper makes several contributions to the research field. First, this paper fills the gap of ESG research in transitional economies by examining the moderating role of institutional environment in the relationship between firm size and ESG disclosure. While prior studies have focused on stakeholder interests (Činčalová & Hedija, 2020) and legitimacy demands (Gregory, 2022; Lu & Abeysekera, 2014; Salehi et al., 2019; Suárez-Rico et al., 2018), the influence of capital market information needs and institutional conditions has been overlooked. The findings offer practical value by enabling large firms to use ESG disclosure as an effective tool for stakeholder communication, thereby reducing information asymmetry and alleviating adverse selection in capital markets. Second, this study specifically focuses on two important aspects of the institutional environment: government intervention and the development of legal environment. Given the lower ESG information disclosure of listed firms in emerging countries, it is important to study how to create an institutional environment with less government intervention and a more robust legal system that is conducive for large companies to play a demonstration role in driving the improvement of ESG information transparency in the entire capital market. Finally, this study also revealed that there are substantial disparities in the moderating role of the institutional environment across heavy pollution industries and non-heavy pollution industries. We demonstrate that it makes more sense for Chinese regulators to develop ESG disclosure policies for companies in different industries rather than a one-size-fits-all approach. The paper is structured as follows: Section 2 draws on the signaling theory to derive hypotheses. Section 3 outlines the methodology used in the research. Section 4 presents the empirical findings of the study and conducts tests for robustness. Finally, Section 5 makes conclusions and provides recommendations for future research. Literature review and Hypotheses Development Firm Size and ESG Disclosure Firm size is frequently utilized as a moderating or controlling variable in existing studies regarding ESG or CSR related research (Amato & Falivena, 2019; Mubeen et al., 2021; Sun, 2024; Youn et al., 2015). Large-sized companies exert great influence over the economic progress of a region or even a nation (K. Lee et al., 2013). Large companies contribute a significant portion of the goods and services in a certain region. Simultaneously, these firms facilitate the growth of local employment (Ayyagari et al., 2011; Badulescu et al., 2018), as well as the consumption of raw materials and imported commodities (Lee et al., 2013). Compared to small companies, large companies have a great number of stakeholders, including suppliers, consumers, and financial analysts (Finegold et al., 2010; Tamimi & Sebastianelli, 2017). Consequently, stakeholders exhibit an increased need for information regarding the operational activities of large firms (Gholami et al., 2022; Tamimi & Sebastianelli, 2017). According to stakeholder theory, corporate managers should serve all stakeholders (Freeman 1984), which includes meeting their information needs (Bilyay-Erdogan, 2022). Therefore, firms should use ESG disclosure as a communication tool to build and trust reduce information asymmetry with their stakeholders (Feng et al., 2022). The signaling theory claims that the cost of acquiring information can be expensive, and the degree of information asymmetry is influenced by the behavior of the companies (Healy & Palepu, 2001; Stiglitz, 2000). Thus, most companies consider that ESG engagements and disclosures are costly, which leads to a lower willingness of disclosing ESG information (Shalhoob & Hussainey, 2023; Uyar et al., 2020). However, in the context of greater information needs, inadequate disclosure by listed companies ultimately leads to information asymmetry and adverse selection regarding ESG engagement in the capital market (Krueger et al., 2024). Therefore, it is a valuable research topic to investigate the motivations of ESG signals released by firms of different sizes, and provide a deeper insight into the factors that stimulate corporate ESG disclosure. The quality of ESG reporting affects the effectiveness and credibility of corporate signals (Healy & Palepu, 2001; Meng et al., 2014). Consistent with the signaling theory, the key to alleviating information asymmetry is to ensure that the quality of the signal information meets the expectations or requirements of external stakeholders (Stiglitz, 2000). An effective signal should be easily detectable, and the act of imitating is costly for firms (Connelly et al., 2011). Large corporations frequently produce comprehensive ESG reports to differentiate their ESG initiatives from the opportunistic behavior of other corporations (Chung et al., 2023; Uyar et al., 2020). Furthermore, Wang et al. (2022) noted that large companies take the initiative to use their own resources to provide information and even disclose social responsibility reports that exceed regulatory requirements in order to gain appreciation from the government and the public. However, there are arguments that support the idea that smaller companies may exhibit greater motivation to report ESG information. Udayasankar (2008) noted that smaller companies have a higher potential to enhance their legitimacy or positive reputation compared to larger companies, resulting in more marginal utility. In the comparative empirical study conducted by Baumann-Pauly et al. (2013), it was posited that small firms are more likely to derive advantages from CSR disclosure and consequently have a greater willingness to participate in such activities compared to larger companies. As previously stated, larger companies are motivated to release high-quality information reports to convey their efforts on ESG activities. Furthermore, larger companies have more professional talent and higher financial resources to contribute to the effective signaling of ESG information to internal and external parties. Based on the above argument, we propose the following hypothesis: H1: There is a positive relationship between firm size and ESG disclosure. The moderating effect of institutional environment Companies need to seek stability and development in the constantly improving and changing institutional environment (Bilyay-Erdogan, 2022; Gao-Zeller et al., 2019). Simultaneously, the information obtained by the stakeholders of the enterprises is consistently changing in a dynamic environment (Connelly et al., 2011). Improving the transparency of ESG information enables companies to maintain a good reputation in the capital market and cultivate a conscientious image (Bukari et al., 2024; Ting et al., 2020). In addition, signaling theory posits that the intensity of a signal can be influenced by various signal environments (Bilyay-Erdogan, 2022; Connelly et al., 2011; Su et al., 2016). Institutional environmental factors, as a latent factor, have a profound and significant influence on the level of social responsibility assumed by Chinese enterprises (Gao-Zeller et al., 2019). Based on this viewpoint, the institutional environment plays a crucial role in influencing the motivation and quality of ESG signals released by listed firms. Government intervention In the overall institutional environment, government departments have always been the leaders of mandatory institutional changes and have a decisive impact on the changes of institutional environment (Han et al., 2022). Appropriate strategies and actions are taken by the enterprises in order to obtain societal recognition in the region, and gain support and resources from the authorities (Martínez-Ferrero & Lozano, 2021) and the stakeholders (Lu et al., 2020). The institutional environment of the company determines its access to policy support, labor, capital markets, and infrastructure, which are indispensable resources that assist in creating better performance (Tang et al., 2018). Therefore, China's government-led institutional environment has a profound impact on enterprises' fulfillment and disclosure of their social responsibilities. According to the neo-institutional theory, the institutional environment has a significant impact on the motivation of corporate behavior and decision-making (Campbell, 2007; Kong et al., 2022; Lu et al., 2024), especially in CSR aspects (Campbell, 2007; Kong et al., 2022). In regions with a high degree of government intervention in the economy, the government exercises significant control and authority over factor resources, which increases the likelihood of rent-seeking behavior by government officials (Bukari et al., 2024; H. Lu et al., 2024; Wang et al., 2021). Rent-seeking is defined as a facilitation activity that aims to obtain permissions and quotas for additional benefits like lower tax rates, more subsidies, and approval of an initial public offering (Krueger, 1974; Wang et al., 2021). In this scenario, corporations are more motivated to allocate resources and effort toward rent-seeking activities rather than focusing on ESG initiatives. It has a consequential impact on ESG expenditures and results in a decline in corporate social responsibility fulfillment. As mentioned in Introduction, the ESG performance of larger corporations frequently receives greater attention from various stakeholders (Cao et al., 2019). In areas with less government intervention, these corporations are committed to improving the effectiveness of ESG signals in the capital market, thereby gaining the recognition of a diverse group of stakeholders instead of relying solely on the government. Based on the above analysis, we propose the following hypothesis: H2a: Less government intervention strengthens the positive relationship between firm size and ESG disclosure. Heavily polluting industries are the pillars of Chinese economic development, but they also impact heavily on the ecological environment and global sustainable development (Ang et al., 2022). In 2010, the Ministry of Environmental Protection of China issued the "Guidelines for Environmental Information Disclosure of Listed Companies" (Zeng et al., 2020). According to this guideline, listed companies in 16 heavily polluting industries, such as thermal power, steel, cement, etc., are required to issue annual environmental reports and regularly disclose information on pollutant emissions, environmental compliance, and environmental protection practices. This implies that heavily polluting enterprises are incorporated into the scope of mandatory disclosure. However, non-heavily polluting enterprises still have the option of deciding whether to publish ESG reports. In regions with low government intervention, market competition is fairer and the information environment is relatively transparent (Bin-feng et al., 2022). In line with the signaling theory, comprehensive and reliable ESG reports assist listed firms in maintaining better communication with stakeholders to reduce information asymmetry, thereby reducing financing costs (Tsang et al., 2023) and enhancing their competitive edge in the capital market (Bilyay-Erdogan, 2022). Consequently, large corporations in non-heavily polluting industries are keen to engage in and disclose their ESG activities rather than rent-seeking activities. Thus, we propose the following hypothesis: H2b: Compared with heavily polluting industries, the positive moderating effect of less government intervention is more obvious in non-heavily polluting industries. Level of legal development From the perspective of signaling theory, the effectiveness of signals depends on whether stakeholders can perceive that the signals sent by enterprises are authentic and trustworthy (Connelly et al., 2011; Lee et al., 2022; Xu & Liu, 2023). Large corporations maintain a sound ethical reputation by keeping effective communication with their stakeholders by releasing ESG reports. The ESG ratings of companies are often used by investment companies to refer to the standards of legality and ethical business practices. Voluntary disclosure is often close to a "mandatory initiative" for companies with a larger market share (Wang et al., 2022). Nevertheless, there is a disparity in the quality of ESG reporting among different companies (Xu et al., 2023). Unclear presentation of information in low-quality ESG reports tends to lead to higher information asymmetry, which would affect the credibility of stakeholders (Xu et al., 2023; Yip & Yu, 2023). The neo-institutional theory postulates that the survival of large corporations is dependent on society acceptance (DiMaggio & Powell, 1983; Drempetic et al., 2019). A high degree of information asymmetry will affect the credibility and trustworthiness of stakeholders in large companies (Xu & Liu, 2023). While companies send a positive signal to their stakeholders by fulfilling their social responsibilities, the strength of this signal may vary in different institutional environments (Su et al., 2016). An ideal legal environment and a well-developed market intermediary organization demand a higher standard of corporate ESG reports (Krueger et al., 2024). Moreover, there is a higher level of public awareness regarding social responsibility in these regions. Consequently, the public is more inclined to show interest in and react to ESG signals released by large corporations. Furthermore, regulators and market intermediaries in a more robust legal environment often adopt the ESG reports of prominent corporations as a benchmark for others to emulate and gain insights from. Recognition from all sectors of society increases the incentive for large corporations to disclose high-quality ESG signals. Therefore, large corporations operating in a sound legal environment have a stronger sense of responsibility and are more willing to participate in and disclose ESG activities. Based on the above argument, the following hypothesis is put forward: H3a: A robust legal environment strengthens the positive relationship between firm size and ESG disclosure. Heavy polluting companies attract greater attention in their ESG reports compared to non-heavy polluting industries, as they are required to disclose their environmental information (Liu et al., 2023). According to neo-institutional theory, firms in different institutional environments will be subject to different extents of institutional pressures to act in a socially responsible way (Scott & Meyer, 1991; W. Sun et al., 2019). In a weak institutional environment, the majority of these firms may choose to symbolically embrace ESG initiatives instead of substantively engaging with them (Darnall et al., 2022; Marquis & Qian, 2014). However, Wei et al. (2015) contended that regions with a more advanced legal framework possess a variety of information channels and robust market supervisory mechanisms. Heavily polluting enterprises operating in these areas with opportunistic intentions are subjected to more rigorous supervision and constraints (Pinheiro et al., 2020). In addition, more detailed rules on information disclosure act as a filter for the stakeholders to scan corporate ESG disclosure to assess its legitimacy (Wei et al., 2015). In this case, large corporations in heavily polluting industries that undertake substantial ESG initiatives tend to spend more disclosure costs to release high-quality signals and facilitate stakeholders to make more accurate judgments. H3b: Compared with non-heavily polluting industries, the positive moderating effect of the legal environment is more obvious in heavily polluting industries. Research design Sample selection and data sources Chinese A-share listed companies from 2018 to 2022 are employed as the initial research sample since the China Securities Regulatory Commission revised the Guidelines for Corporate Governance of listed companies in 2018, which established the fundamental framework for disclosing ESG information. In these guidelines, listed companies are required to strengthen their social responsibility and promote sustainable development. In addition, the scale of A-share listed companies is relatively large and their development tends to be stable. These companies prioritize both sustainability and information transparency to maintain their reputation in the capital market. Then, this study performed various data processing tasks. First, this study excluded companies with missing financial data to enhance the statistical robustness and reliability of the conclusions. Second, the sample excluded financial enterprises due to their distinct regulatory regulations, financial reporting standards, and corporate governance frameworks. At the same time, Special Treatment firms (ST) and Particular Transfer enterprises (PT) were eliminated as these companies experience financial anomalies. Both the upper and the lower extremes of all the continuous variables are winsorized at 1% to mitigate the impact of outliers. Finally, the sample consists of 18,815 unbalanced firm-year observations in China. As shown in Appendix A, this study captures a diverse range of sectors in the Chinese economy. Following Chen & Xie (2022) and Xu & Liu (2023), we use the Huazheng ESG rating from the Wind database to evaluate corporate ESG disclosure because of its comprehensive coverage of Chinese listed firms and superior data continuity and availability (Chen & Xie, 2022; Tang, 2022; Xu & Liu, 2023). Furthermore, the Huazheng ESG rating is considered appropriate for ESG research in China because it combines the mainstream international ESG rating framework with the specific characteristics of the Chinese capital market (Tang, 2022; Xu & Liu, 2023). The indicators of the institutional environment were measured using the secondary marketization index: (1) the relationship between the government and (2) the development of market intermediaries and the legal environment. The marketization index constructed by Wang et al. (2021) is widely considered by scholars as a reliable and stable measurement for the Chinese institutional environment (Bin-feng et al., 2022; Huang et al., 2022; Zheng & Ren, 2019). The financial data are mainly from the China Stock Market and Accounting Research (CSMAR) database. Econometric Model Based on the hypotheses in this study, the empirical tests consist of the relationship between firm size and ESG disclosure, and the moderating effect of the institutional environment on their relationship. First, Eq. ( 1 ) examines the influence of firm size on corporate ESG disclosure. Second, Eq. ( 2 ) tests the moderating effect of government intervention on the association between firm size and corporate ESG disclosure for H2a and divides the samples into non-heavily polluting industries and heavily polluting industries for testing hypothesis H2b. Third, Eq. ( 3 ) tests the moderating effect of the legal environment on the association between firm size and corporate ESG disclosure for H3a and groups the samples into non-heavily polluting industries and heavily polluting industries for testing hypothesis H3b. $$\:{ESG}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{Size}_{i,t}+{\beta\:}_{2}{Controls}_{i,t}{+\sum\:Year+\sum\:Industry+\epsilon\:}_{i,t}$$ 1 $$\:{ESG}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{{Size}_{i,t}+{\beta\:}_{2}{GOV}_{i,t}+\beta\:}_{3}Size\ast\:{GOV}_{i,t}+{\beta\:}_{4}{Controls}_{i,t}{+\sum\:Year+\sum\:Industry+\epsilon\:}_{i,t}$$ 2 $$\:{ESG}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{{Size}_{i,t}+{\beta\:}_{2}{LAW}_{i,t}+\beta\:}_{3}Size\ast\:{LAW}_{i,t}+{\beta\:}_{4}{Controls}_{i,t}{+\sum\:Year+\sum\:Industry+\epsilon\:}_{i,t}$$ 3 Measurement of Variables Dependent Variable (ESG). This research uses the Huazheng ESG rating as a dependent variable. Currently, it covers the widest range of listed companies in China, and the data is highly consistent and accessible to outsiders (Chen & Xie, 2022; Li et al., 2022; Tang, 2022). The Huazheng ESG rating comprises of nine levels, ranging from the best to the worst: AAA, AA, A, BBB, BB, B, CCC, CC and C (Tang, 2022). As a result, the Huazheng ESG rating was employed as a metric to assess the extent of ESG information disclosure. This study assigned C-AAA values on a scale of 1 to 9 to facilitate empirical research. Independent Variable (Size). Size was frequently measured as the natural logarithm of total assets at the end of the year, according to existing studies (Chen et al., 2021; Odoemelam et al., 2020; Wang et al., 2022). Moderator Variable. The link between firm size and ESG disclosure is moderated by two factors of the institutional environment: government intervention and the legal environment. Government intervention was proxied by the index “relationship between the government and market” developed by Fan and Wang (2010). This index comprises three aspects: the allocation of market resources, government intervention in businesses, and government size. Academics frequently employ it to gauge the level of governmental interference in the market. The lower the level of government intervention in the economy of the region, the greater the index. According to Fan and Wang (2010), the legal environment was proxied by the index “the development of market intermediary organization and the legal environment”. This index captures the level of regional legal development by analyzing the role of the development of market intermediary organizations, the protection of legitimate rights and interests of producers, the protection of intellectual property rights, as well as the protection of consumer rights and interests. A higher index indicates a better legal environment in the region. Control Variables. Based on the existing studies, we control for several variables that might impact corporate ESG disclosure. Liquidity ratio ( \(\:Liquid\) ): The high liquidity of a firm's assets indicates financial redundancy (He et al., 2022; Uyar et al., 2023). When firms have excess financial resources, they are more likely to involve and disclose their ESG activities (Uyar et al., 2023). Percentage of independent directors ( \(\:Indep\) ): Board independence of a firm may affect ESG information disclosure, as effective internal control is conducive to ensuring the authenticity and integrity of information disclosed by enterprises (Guo & Shen, 2019; Khalid et al., 2022). State ownership ( \(\:SOE\) ): State-owned enterprises usually have different ownership structures and management styles, which may affect the fulfillment and disclosure of their social responsibilities (Ervits, 2023; Liu & Zhang, 2017; Marquis & Qian, 2014). Audit quality ( \(\:Big4\) ): Firms audited by Big 4 accounting firms disclose information of higher quality and reliability (Handayati et al., 2022; Iatridis, 2013), as accepting higher audit requirements implies a greater willingness to provide high-quality ESG signals (Subhi et al., 2022). Management shareholding ( \(\:Msℎare\) ): Management shareholding serves as a means to incentivize managers and influence their decision-making, including corporate ESG initiative engagement and disclosure (Guo & Shen, 2019; Wei & Zhou, 2020). The above provides justification for each control variable in this study. For brevity, we describe the definition of all variables as shown in Table 1 . Table 1 Definition of variables. Variables Definition Source ESG ESG ratings from Huazheng Huazheng Size Firm size, measured as the natural logarithm of total assets at the end of the year CSMAR GOV the index “relationship between the government and market” (Fan & Wang, 2010) LAW the index “the development of market intermediary organization and the legal environment” (Fan & Wang, 2010) Liquid Liquidity ratio, measured as current assets / current liabilities CSMAR Indep Ratio of independent directors, measured as Number of independent directors/total number of the board of directors CSMAR SOE State ownership, 1 for state-owned enterprises and 0 for others CSMAR Big4 Auditing quality, 1 for firms audited by a Big 4, and 0 otherwise CSMAR Mshare Management shareholding, measured as management shareholding / total equity CSMAR Empirical results and discussions Summary statistics Table 2 reports the descriptive statistics of the variables in the study. The average level of ESG disclosure ( ESG ) of the sample was 4.214, indicating that the ESG disclosure of the sample is generally at a low level. From the perspective of the maximum and minimum values, and the standard deviation of ESG disclosure, the highest ESG disclosure rating was 8, while the lowest ESG disclosure rating was only 1, and the standard deviation was 1.099, indicating that the level of ESG disclosure of the enterprises varies greatly in Chia. The independent variable firm size ( Size ) had a maximum value of 26.452 and a minimum value of 19.810, indicating that firm size varies significantly among the listed firms in China. The moderating variable of government intervention ( GOV ) had a maximum value of 9.112 and a minimum value of -0.283, indicating that the government intervention in each region varies significantly in China. Meanwhile, the moderating variable of the legal environment ( LAW ) had a maximum value of 18.974 and a minimum value of 1.576. The median for legal environment ( LAW ) was 13.796, while the standard deviation was 2.928, indicating that the legal environment of each region varies significantly in China. Appendix B shows the correlation coefficients for each variable in this study. There was a significant positive correlation between firm size ( \(\:Size)\) and ESG disclosure ( ESG ), which had a positive coefficient of 0.194, indicating that firm size ( \(\:Size)\) can increase the quality of ESG disclosures. The relationship between the institutional environment of government intervention ( GOV ) and ESG disclosure ( \(\:ESG)\) had a positive coefficient of 0.067. Meanwhile, the institutional environment of legal environment ( LAW ) and ESG disclosure ( \(\:ESG)\) had a positive coefficient of 0.096, indicating that both government intervention and legal environment can increase the quality of ESG disclosure in China. In addition, from Appendix B, the results show that all correlation coefficients are less than 0.7, meaning that none of the correlations are extremely high, demonstrating that there was no multicollinearity problem with this study’s regression tests (Činčalová & Hedija, 2020; Lee et al., 2017). All variables have a VIF value below 1.59 as shown in Table 2 , demonstrating the absence of multicollinearity among them (O’Brien, 2007). Unreported data show that, after centering the individual components that contribute to the interaction term, all the models' VIFs and mean VIFs are less than two, showing no multicollinearity problem. Table 2 Descriptive Statistics. Variable Observations Mean SD Minimum Median Maximum VIF ESG 18815 4.214 1.099 1.000 4.250 8.000 Size 18815 22.290 1.298 19.810 22.079 26.452 1.45 GOV 18801 7.354 1.104 -0.283 7.351 9.112 1.59 LAW 18801 13.504 2.928 1.576 13.796 18.974 1.57 Liquid 18815 2.609 2.552 0.352 1.763 18.440 1.17 Indep 18815 0.379 0.054 0.286 0.364 0.571 1.01 SOE 18815 0.268 0.443 0.000 0.000 1.000 1.33 Big4 18815 0.061 0.239 0.000 0.000 1.000 1.11 Mshare 18815 0.156 0.199 0.000 0.039 0.707 1.35 Notes: ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Regression results Firm size and ESG disclosure The regression analysis displays the impact of firm size on ESG disclosure in column (1) of Table 3 . The regression result of column (1) shows that the coefficient on firm size ( Size ) is significantly positive (β = 0.2426) at the 1% level, indicating that firm size has a significant positive impact on corporate ESG disclosure. The results are consistent with the findings of Drempetic et al. (2019). The regression results above imply that large corporations exhibit a higher propensity to provide comprehensive and reliable ESG disclosure. And this result is supported by the signaling theory that the quality and intentions of signaling information are related to the decisions of the signal senders (Connelly et al., 2011). Thus, this result supports Hypothesis H1. Firm size, government intervention and ESG disclosure The results of the ordinary least squares (OLS) regression using Eq. ( 2 ) are shown in column (2) of Table 3 , which examined the moderating role of government intervention ( GOV ) on the relationship between firm size and ESG disclosure ( ESG ). The result shows that the coefficient on the interaction term ( Size*GOV ) is significantly positive (β = 0.0168) at the 1% level, supporting that the positive relationship between firm size and ESG disclosure is stronger in regions with less government intervention. This finding favors neo-institutional theory and suggests that in a well-developed institutional environment with less government intervention, large corporations are more likely to enhance their social reputation and gain a competitive edge by actively engaging in and disclosing their ESG initiatives Hence, Hypothesis 2a is supported. The government imposes a variety of environmental information disclosure requirements based on the impact of businesses on the environment. Consequently, the samples in this study are divided into heavily-polluting enterprises and non-heavily-polluting enterprises. As shown in Table 4 , the government intervention ( GOV ) is significant at the 1% level, and this is due to the fact that there is more fair market competition in the regions with less government intervention, which helps companies gain a good reputation by releasing ESG signals. Interestingly, based on the results of columns (1) and (3) shown in Table 4 , it reveals that less government intervention has a significantly positive influence on the linkage between firm size and ESG disclosure only in non-heavily polluting industries, whereas it does not affect heavily polluting industries. This demonstrated that an institutional environment with less government intervention is more competitive and transparent. In this environment, firms that are not required to disclose ESG information do so proactively. In summary, the regression results support Hypothesis H2b. Firm size, legal environment and ESG disclosure For the moderating role of legal environment ( LAW ) on the relationship between firm size ( Size ) and ESG disclosure ( ESG ), the regression result is shown in the column of Table 3 . The result showcases that the coefficient on the interaction term ( Size*LAW ) is significantly positive (β = 0.0049) at the 5% level, indicating that the positive relationship between firm size and ESG disclosure is stronger in regions with a more developed legal environment. These findings also support the neo-institutional theory and reveal that an institutional environment with well-developed market intermediaries and an effective legal system can encourage large corporations to release high-quality ESG signals. As a result, Hypothesis H3a is supported. The coefficient on legal environment ( LAW ) is positively at the 1% level, and this can be explained by that well development of legal environment contributes to the quality of ESG signals. Another notable finding, as shown in columns (1) and (3) of Table 4 , is that well-developed legal environment has a significantly positive influence on the linkage between firm size and ESG disclosure only in heavily polluting industries, whereas it does not affect non-heavily polluting industries. This demonstrated that the quality of companies to mandatorily disclose ESG information is influenced by the institutional environment. Firms mandated to disclose ESG information may choose to take symbolic behavior instead of substantive actions if their institutional environment lacks effective external supervision and an imperfect legal framework. In summary, the regression results support Hypothesis H3b. Table 3 Firm size, institutional environment and ESG disclosure. (1) (2) (3) ESG ESG ESG Size 0.2426 *** 0.2446 *** 0.2440 *** (33.7963) (33.9940) (34.0049) GOV 0.0486 *** (6.9145) Size*GOV 0.0168 *** (3.0102) LAW 0.0333 *** (9.1611) Size*LAW 0.0049 ** (2.4524) Liquid 0.0568 *** 0.0566 *** 0.0568 *** (17.8742) (17.8303) (17.8961) Indep 1.0839 *** 1.0695 *** 1.0664 *** (7.7800) (7.6800) (7.6652) SOE 0.2481 *** 0.2597 *** 0.2637 *** (12.5408) (13.0875) (13.3049) Big4 0.2202 *** 0.2059 *** 0.2061 *** (6.6959) (6.2306) (6.2486) Mshare 1.1167 *** 1.0975 *** 1.0823 *** (25.6312) (25.0531) (24.7407) Constant -2.5886 *** -2.9465 *** -2.8939 *** (-14.0147) (-15.4167) (-15.4512) Industry FE Yes Yes Yes Year FE Yes Yes Yes Observations 18815 18801 18801 Adj. R2 0.138 0.140 0.142 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Table 4 The moderating role of institutional environment in heavily polluting and non-heavily polluting industries. Variables Heavily polluting Non-heavily polluting (1) (2) (3) (4) ESG ESG ESG ESG Size 0.2275 *** 0.2280 *** 0.2463 *** 0.2436 *** (24.1796) (24.2775) (22.6437) (22.5050) GOV 0.0331 *** 0.0769 *** (3.5913) (6.7398) Size*GOV 0.0064 0.0266 *** (0.8337) (3.0288) LAW 0.0234 *** 0.0537 *** (4.9345) (9.2566) Size*LAW 0.0060 ** 0.0031 (2.2388) (0.9608) Liquid 0.0576 *** 0.0579 *** 0.0675 *** 0.0672 *** (15.7276) (15.8065) (10.3304) (10.3034) Indep 0.6039 *** 0.6134 *** 1.6364 *** 1.6094 *** (3.4252) (3.4824) (6.9274) (6.8285) SOE 0.1828 *** 0.1904 *** 0.2458 *** 0.2456 *** (6.9204) (7.1991) (8.0459) (8.0610) Big4 0.2204 *** 0.2170 *** 0.1737 *** 0.1758 *** (4.8508) (4.7822) (3.4788) (3.5326) Mshare 1.1825 *** 1.1763 *** 0.9399 *** 0.9012 *** (22.4011) (22.3032) (11.6151) (11.1686) Constant -1.7577 *** -1.7723 *** -2.8675 *** -2.7731 *** (-7.4909) (-7.7486) (-10.4499) (-10.3488) Year FE Yes Yes Yes Yes Observations 11776 11776 7025 7025 Adj. \(\:{R}^{2}\) 0.106 0.107 0.119 0.123 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10% respectively. This table presents the regression results for companies in heavily polluting sectors and companies in non-heavily polluting industries. The classification of industries with high levels of pollution is based on the Industry Classification Guidelines for Listed Companies, which were issued by the China Securities Regulatory Commission in 2012. For the regression results, the evidence indicates that the size of a company can enhances the level of ESG disclosure in both highly polluting and non-highly polluting sectors. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Robustness tests Alternative sample period To test the robustness of the regression results, this study expands the sample of A-share-listed companies in China, covering the time span from 2009 to 2022. The regression results based on Eq. ( 1 ), Eq. ( 2 ), and Eq. ( 3 ) are displayed in Table 5 . In terms of the regression results in column (1), the coefficient on firm size ( Size ) is significantly positive (β = 0.2294) at the 1% level, which is consistent with the previous result and still supports Hypothesis H1. The regression result of column (2) shows the moderating role of government intervention ( GOV ) on the relationship between firm size and ESG disclosure ( ESG ). The result shows that the coefficient on the interaction term ( Size*GOV ) is significantly positive (β = 0.0061) at the 5% level, and this is only significant in the non-heavily polluting sectors shown in Table 6 . Meanwhile, the coefficient for the interaction term ( Size*LAW ) is significantly positive (β = 0.0027) at the 1% level, however, it is only significant in heavily polluting sectors based on the regression results from Table 6 . In summary, the regression results still support all the hypotheses. Table 5 Alternative sample period of 2009–2022. (1) (2) (3) ESG ESG ESG Size 0.2294 *** 0.2311 *** 0.2294 *** (49.2285) (49.3603) (49.3239) GOV 0.0325 *** (9.8350) Size*GOV 0.0061 ** (2.5132) LAW 0.0282 *** (13.9427) Size*LAW 0.0027 *** (2.7405) Liquid 0.0391 *** 0.0391 *** 0.0387 *** (22.0892) (22.1005) (21.8605) Indep 1.1688 *** 1.1718 *** 1.1746 *** (13.0306) (13.0750) (13.1241) SOE 0.1996 *** 0.2099 *** 0.2142 *** (16.3070) (17.0961) (17.4821) Big4 0.1325 *** 0.1167 *** 0.1094 *** (6.0494) (5.2970) (4.9847) Mshare 1.0287 *** 1.0122 *** 0.9972 *** (36.3852) (35.7154) (35.1833) Constant -1.9043 *** -2.2028 *** -2.0599 *** (-16.4949) (-18.4410) (-17.7956) Industry FE Yes Yes Yes Year FE Yes Yes Yes Observations 38617 38603 38603 Adj. R2 0.143 0.145 0.147 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Table 6 The moderating role of institutional environment in heavily polluting and non-heavily polluting industries for 2009–2022. Variables Heavily polluting Non-heavily polluting (1) (2) (3) (4) ESG ESG ESG ESG Size 0.2028 *** 0.2016 *** 0.2434 *** 0.2403 *** (32.0328) (31.9895) (35.4748) (35.3136) GOV 0.0299 *** 0.0401 *** (6.3729) (8.3377) Size*GOV 0.0030 0.0086 ** (0.8307) (2.3920) LAW 0.0246 *** 0.0371 *** (9.0735) (12.0101) Size*LAW 0.0046 *** 0.0009 (3.3252) (0.5903) Liquid 0.0370 *** 0.0365 *** 0.0551 *** 0.0545 *** (17.9877) (17.7759) (15.0407) (14.9129) Indep 0.8268 *** 0.8349 *** 1.5523 *** 1.5407 *** (7.0050) (7.0819) (10.7909) (10.7386) SOE 0.1244 *** 0.1330 *** 0.2142 *** 0.2141 *** (7.4313) (7.9456) (11.6299) (11.6531) Big4 0.1506 *** 0.1396 *** 0.0351 0.0277 (4.7305) (4.3939) (1.1037) (0.8725) Mshare 1.0308 *** 1.0213 *** 0.8440 *** 0.8151 *** (29.7676) (29.4915) (16.4730) (15.8851) Constant -1.1443 *** -1.0345 *** -2.4038 *** -2.2258 *** (-7.3682) (-6.9747) (-14.4123) (-13.7716) Year FE Yes Yes Yes Yes Observations 22984 22984 15619 15619 Adj. \(\:{R}^{2}\) 0.098 0.100 0.126 0.130 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10% respectively. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. The estimation of lagged independent variable In relation to the problem of endogeneity, this paper used the lagged independent variable for regression to investigate the impact of firm size for the previous period on ESG disclosure for the current period, which is conducive to alleviating the problem of reverse causality (Bellemare & Pepinsky, 2017). As shown in Table 7 , the results imply that firm size ( Size ) is still significantly positive at the 1% level on ESG disclosure, which is the same with the baseline regression. Furthermore, the regression results for the moderating role of government intervention ( GOV ) and legal environment ( LAW ) on the relationship between firm size and ESG disclosure are consistent with previous estimations in this paper, demonstrating the robustness of the regression results for this study. Table 7 Lagged regression (1) (2) (3) ESG ESG ESG L.Size 0.2365 *** 0.2382 *** 0.2350 *** (27.4558) (27.6069) (26.9638) GOV 0.0427 *** (5.2195) Size*GOV 0.0160 ** (2.4056) LAW 0.0332 *** (7.2900) Size*LAW 0.0054 * (1.9150) Liquid 0.0608 *** 0.0607 *** 0.0608 *** (14.3110) (14.3009) (14.3457) Indep 1.0255 *** 1.0096 *** 1.0081 *** (6.1823) (6.0917) (6.0891) SOE 0.2882 *** 0.2992 *** 0.3046 *** (12.4394) (12.8779) (13.1142) Big4 0.2726 *** 0.2593 *** 0.2598 *** (7.0236) (6.6535) (6.6829) Mshare 1.1329 *** 1.1153 *** 1.0978 *** (20.9608) (20.5045) (20.1894) Constant -2.5093 *** -2.8204 *** -2.7291 *** (-11.3396) (-12.3531) (-11.6428) Industry FE Yes Yes Yes Year FE Yes Yes Yes Observations 13856 13849 13849 Adj. R2 0.139 0.140 0.142 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Instrumental variable (IV) approach To address the concerns about endogeneity, this study additionally utilizes an instrumental variable (IV) approach for the robustness tests. This study utilizes a lagged variable of firm size ( L.Size ) as an instrumental variable for firm size ( Size ), based on the study conducted by Drempetic et al. (2019). The regression result of the first-stage is shown in column (1), which demonstrates a statistically significant and positive coefficient on the lagged firm size ( L.Size ). In column (2) of Table 8 , it shows the regression result of the second-stage regression analysis, suggesting that the instrumented firm size has positively and significantly affected the quality of ESG disclosure, and this finding is consistent with the results in the previous test. Additionally, the results shown in columns (3) and (4) indicate that the results of the moderating effect remain robust. In terms of the weak identification test, it indicates that there is no weak instrument problem since the value of Kleibergen‒Paap Wald rk F-statistics exceeds 10. Table 8 Instrumental variable estimation. First stage Second stage Size ESG ESG ESG Size 0.2381 *** 0.2398 *** 0.2369 *** (27.70) (27.85) (27.20) L.Size 0.9932 *** (570.68) GOV 0.0416 *** (5.14) Size*GOV 0.0159 ** (2.42) LAW 0.0325 *** (7.20) Size* LAW 0.0048 * (1.73) Liquid -0.0024 *** 0.0614 *** 0.0613 *** 0.0614 *** (-2.80) (14.55) (14.55) (14.59) Indep -0.0972 *** 1.0486 *** 1.0333 *** 1.0317 *** (-2.90) (6.38) (6.29) (6.29) SOE 0.0037 0.2874 *** 0.2981 *** 0.3032 *** (0.78) (12.51) (12.94) (13.17) Big4 0.0321 *** 0.2650 *** 0.2519 *** 0.2528 *** (4.09) (6.87) (6.51) (6.55) Mshare 0.1041 *** 1.1082 *** 1.0910 *** 1.0735 *** (9.53) (20.74) (20.29) (19.96) Constant 0.2677 *** -2.3515 *** -2.6621 *** -2.7842 *** (5.99) (-10.60) (-11.64) (-11.89) Cragg-Donald Wald F F > 10 F > 10 F > 10 Firm FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 13856 13856 13849 13849 Notes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity. Conclusion and implications This study explores the moderating effect of government intervention and the legal environment on the link between firm size and ESG disclosure for firms in heavily polluting industries and non-polluting industries. Using an unbalanced dataset of 18,815 unbalanced firm-year observations from 2018 to 2022, we finds that a firm with a larger size exhibits a higher propensity to provide comprehensive and reliable ESG disclosure. In the robustness test, this study uses an alternative sample period for the regressions, the estimation of lagged independent variable and instrumental variable approach. All the results of robustness test confirm the findings and improve estimate consistency. Heterogeneity analysis finds that the positive linkage between firm size and ESG disclosure is more pronounced in regions with less government intervention and a more developed legal environment. Furthermore, notable findings show that less government intervention has a significantly positive influence on the linkage between firm size and ESG disclosure only in non-heavily polluting industries, whereas it does not affect heavily polluting industries. Conversely, a well-developed legal environment has a significantly positive influence on the linkage between firm size and ESG disclosure only in heavily polluting industries, whereas it does not affect non-heavily polluting industries. Theoretical implications This study makes theoretical contributions to reveal the relationship between firm size and firm ESG disclosure by utilizing the signaling theory. First, although previous studies have also suggested that firm size positively affects firm ESG disclosure, this study reexamines this relationship based on signal theory from the perspective of the intention and effectiveness of signals. Second, this study verifies the moderating effect on firm size and ESG disclosure, especially incorporating two major aspects of the Chinese institutional environment: government intervention and the level of legal development. The unique institutional environment in China has fostered favorable circumstances for expanding the application of neo-institutional theory. In addition, the varying impacts of the institutional environment on different industries result in significant differences in the ESG disclosure requirement obeyed by companies within these industries. Hence, this study further distinguishes between heavy polluting industries and non-heavy polluting industries to explore the moderating role of the institutional environment. In conclusion, this study presents fresh evidence on how the institutional environment indirectly affects corporate ESG disclosure, which expands the boundaries of the research on the moderating role of institutional environment factors. Policy Implications This study also provides some policy implications. China is undergoing an economic transition. There is a lower degree of information transparency in transitional development countries (Bin-feng et al., 2022; Xie et al., 2017; Zheng & Ren, 2019), especially in ESG disclosure (Li et al., 2022). Stronger government intervention and a lower level of legalization are relatively common in transition economies (Long, 2019; Xie et al., 2017; Zheng & Ren, 2019). Corporate ESG information disclosures are heavily influenced by the external institutional environment (Bilyay-Erdogan, 2022; Long, 2019). This study assists the Chinese government in making a significant contribution to improving the corporate ESG disclosure system, as well as providing valuable insights to improve the transparency of ESG information in other transitional developing countries. Second, the findings suggest that the positive relationship between firm size and ESG disclosure would be more pronounced in regions with less government intervention and a more developed legal environment. Our findings emphasize the significance of the institutional environment in stimulating the demonstrating role played by large corporations in their ESG disclosure. Thus, government authorities should reduce intervention in the capital market and prevent political rent-seeking by officials, thereby fostering a competitive and transparent signaling environment. Moreover, policymakers should stimulate the incentives for large corporations to proactively release high-quality ESG signals by guiding stakeholders, such as investors, consumers, and market intermediaries, to pay attention to corporate ESG information. Third, the moderating effect of less government intervention exists only in non-heavy polluting industries, since heavy polluting industries are required to disclose ESG-related information. This finding suggests that heavily polluting enterprises incorporated into the scope of mandatory disclosure tend to keep a high disclosure rate regardless of the external institutional environment to avoid civil litigation and public distrust. Therefore, it is necessary to gradually implement mandatory disclosure in the whole capital market to address the issue of insufficient ESG disclosure in China. Finally, the moderating effect of the legal environment is only observed in heavily polluting industries. Despite the high rate of ESG disclosure in this industry, most of these enterprises might merely fulfill their disclosure obligations symbolically, without providing substantial data. Hence, for policymakers, this empirical finding highlights the necessity for regulatory reform to improve ESG disclosure. On one side, it is imperative for local governments and intermediary organizations to exercise effective oversight and guidance over corporate ESG disclosure within a standardized disclosure framework. This contributes to establishing a robust information filtering mechanism, enhancing the efficacy of corporate ESG signals. Consequently, stakeholders would be better equipped to identify and make well-informed choices. On the other hand, the mandatory disclosure of ESG information in China is dynamic and long-term, but it cannot happen overnight. It is necessary for the Chinese government to emphasize the leading role of large corporations and drive the motivations of small and medium-sized enterprises to proactively disclose ESG information. In addition, policymakers should formulate appropriate implementation rules for different industries and should not adopt a one-size-fits-all approach. Despite its contributions, this study has some potential limitations. First, the use of the Huazheng ESG rating, which covers only Chinese firms, restricts international comparability. Future research could delve deeper into this topic through subsequent international comparative studies. Second, this study does not incorporate cultural factors, such as Confucianism, local culture, etc. We omitted these for brevity, but future research examining their roles in ESG disclosure may yield valuable insights. Declarations Author Contribution Conceptualization, Y.Z.; Methodology, Data curation and Formal analysis,Y.Z. and X.W.; Investigation, Resources, Software and Validation, Y.Z. and X.W.; Writing original draft preparation, Y.Z., X.W. and Y.T; supervision,Y.Z., X.W. and Y.T. All authors reviewed the manuscript. Data Availability The data are available from the corresponding author upon reasonable request. References Amato, A. D., & Falivena, C. (2019). Corporate social responsibility and firm value: Do firm size and age matter? 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16:55:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1943343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7699657/v1/7c948206-c337-47ea-82af-f9d1473c212e.pdf"},{"id":96239643,"identity":"82e93368-e451-44de-a73d-bbec3da6da07","added_by":"auto","created_at":"2025-11-19 07:07:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23392,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.Distributionoffirmsbysectorsbetween20182022.docx","url":"https://assets-eu.researchsquare.com/files/rs-7699657/v1/a1bb7f2aae95d621f354aa2a.docx"},{"id":96239810,"identity":"531a248f-c755-41e0-8e60-03a89f0fee74","added_by":"auto","created_at":"2025-11-19 07:07:46","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1045579,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7699657/v1/2bd3b69f7f0f59a6cfe35b83.pdf"},{"id":96239665,"identity":"9ddfc7cf-2f75-4b30-8d0c-5bd3d87887f2","added_by":"auto","created_at":"2025-11-19 07:07:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22186,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.CorrelationMatrix..docx","url":"https://assets-eu.researchsquare.com/files/rs-7699657/v1/7b04f7a4d4aa652992911745.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Firm Size and Corporate ESG Disclosure: Exploring the moderating effect of the institutional environment in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global economic landscape is undergoing profound transformations, making the importance of sustainable development increasingly evident. Traditional growth models, characterized by resource-intensive practices and scale-driven expansion, have proven to be unsustainable (Pu, 2025).\u0026nbsp;China, the world\u0026rsquo;s largest developing country and transitional economy (Zeng et al., 2020), plays a critical role in promoting global sustainable development in the context of climate change (Chen \u0026amp; Xie, 2022). In this context, the Chinese government has explicitly put forward the \u0026quot;Dual Carbon\u0026quot; goals. ESG initiatives are highly aligned with \u0026quot;Dual Carbon\u0026quot; objectives and has become an important metric for evaluating high-quality corporate development (Gao et al.,2025). Insufficient disclosure of ESG information by Chinese companies would perpetuate extensive growth patterns and intensify the challenges to global sustainable development. How to motivate companies to actively disclose their ESG practices and improve the quality of ESG information has become an urgent issue in both academic and practical circles.\u003c/p\u003e\n\u003cp\u003eThe ESG disclosure is a dialogue between enterprises and stakeholders who concerned about corporate social and environmental activities (Murphy \u0026amp; McGrath, 2013). According to the ESG ranking of a famous rating agency named SynTao Green Finance in China, the proportion of all A-share listed companies that published independent ESG reports from 2011 to 2024 was significantly lower than that of CSI 300 index constituents (as shown in Fig. 1). The CSI 300 Index is a prominent stock market index in China that tracks the performance of the top 300 listed companies with large assets trading on the Shanghai and Shenzhen stock exchanges. Currently, ESG disclosure in China is largely voluntary on the part of enterprises (Murphy \u0026amp; McGrath, 2013; Vitolla et al., 2023), and the factors influencing the motivation of enterprises\u0026apos; ESG disclosure are unclear. Ignoring the institutional environment and industry characteristics to study the corporate motivations behind releasing ESG signals may not yield valuable insights.\u003c/p\u003e\n\u003cp\u003eIt would be beneficial for firms to develop effective communication channels with their stakeholders by making high-quality information disclosures (Lee et al., 2022). Corporate information transparency results in reduced information asymmetry and brings in concrete benefits for the capital market (Brown \u0026amp; Hillegeist, 2007; Buskirk, 2012; Liu et al., 2023). In 2024, only 118 listed companies in China had a market value exceeding RMB 100 billion, comprising just 2.19% of all listed firms, yet they accounted for 40% of the total market capitalization (China Association of Listed Companies, 2025). It indicates that large companies have a significant \u0026quot;bellwether effect\u0026quot; in promoting economic development and capital market stability. The performance of larger listed corporations, particularly in terms of ESG aspects, usually receives greater attention from various stakeholders (Cao et al., 2019; Ullmann, 1985). According to signaling theory, when information asymmetry exists, one party would attempt to provide information about itself to a second party (Spence, 1973). Information disclosure is a crucial way for enterprises to convey internal information to external stakeholders (Botosan, 1997; Goldstein \u0026amp; Yang, 2017; Li et al., 2022). Thus, from a signaling theory perspective, large firms have stronger incentives to make high-quality ESG disclosures to mitigate information asymmetry with their stakeholders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChina has experienced substantial economic growth through marketization in the past few decades\u0026nbsp;(Bin-feng, 2022; Ge et al., 2022). The marketization process varies throughout China due to its distinct resource endowments and policy inclinations, resulting in large differences in the institutional environment of different regions (Yang et al., 2020). Stronger political interference and a lower level of legalization are the prominent features of the institutional environment in transitional countries, such as China (Bin-feng et al., 2022; Long, 2019; Xie et al., 2017; Zheng \u0026amp; Ren, 2019). Neo-institutional theory holds that corporate behavior and decision-making are influenced by the institutional environment in which the firm is embedded (Campbell, 2007; Kong et al., 2022). Consequently, this also affects the motivation and quality of corporate information disclosure (Bin-feng et al., 2022; Zheng \u0026amp; Ren, 2019). Moreover, several studies asserted that the institutional environment plays a pivotal role in determining the efficacy of signals (Connelly et al., 2011; Huang et al., 2023). The motivations of firms to disclose ESG engagements might be influenced by the institutional environments in which they are located (Tsang et al., 2023). Therefore, it is vital for Chinese policymakers and stakeholders to consider the impact of the different institutional environment on corporate ESG disclosure motivations, especially in local government intervention and legal environment. More importantly, the findings of this study can also provide valuable insights to improve the transparency of ESG information in other transitional developing countries.\u003c/p\u003e\n\u003cp\u003eThis paper makes several contributions to the research field.\u0026nbsp;First, this paper fills the gap of ESG research in transitional economies by examining the moderating role of institutional environment in the relationship between firm size and ESG disclosure. While prior studies have focused on stakeholder interests (Činčalov\u0026aacute; \u0026amp; Hedija, 2020) and legitimacy demands (Gregory, 2022; Lu \u0026amp; Abeysekera, 2014; Salehi et al., 2019; Su\u0026aacute;rez-Rico et al., 2018), the influence of capital market information needs and institutional conditions has been overlooked. The findings offer practical value by enabling large firms to use ESG disclosure as an effective tool for stakeholder communication, thereby reducing information asymmetry and alleviating adverse selection in capital markets.\u003c/p\u003e\n\u003cp\u003eSecond, this study specifically focuses on two important aspects of the institutional environment: government intervention and the development of legal environment. Given the lower ESG information disclosure of listed firms in emerging countries, it is important to study how to create an institutional environment with less government intervention and a more robust legal system that is conducive for large companies to play a demonstration role in driving the improvement of ESG information transparency in the entire capital market.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, this study also revealed that there are substantial disparities in the moderating role of the institutional environment across heavy pollution industries and non-heavy pollution industries. We demonstrate that it makes more sense for Chinese regulators to develop ESG disclosure policies for companies in different industries rather than a one-size-fits-all approach.\u003c/p\u003e\n\u003cp\u003eThe paper is structured as follows: Section 2 draws on the signaling theory to derive hypotheses. Section 3 outlines the methodology used in the research. Section 4 presents the empirical findings of the study and conducts tests for robustness. Finally, Section 5 makes conclusions and provides recommendations for future research.\u003c/p\u003e"},{"header":"Literature review and Hypotheses Development","content":"\u003ch2\u003e\u003cem\u003eFirm Size and ESG Disclosure\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFirm size is frequently utilized as a moderating or controlling variable in existing studies regarding ESG or CSR related research (Amato \u0026amp; Falivena, 2019; Mubeen et al., 2021; Sun, 2024; Youn et al., 2015). Large-sized companies exert great influence over the economic progress of a region or even a nation (K. Lee et al., 2013). Large companies contribute a significant portion of the goods and services in a certain region. Simultaneously, these firms facilitate the growth of local employment (Ayyagari et al., 2011; Badulescu et al., 2018), as well as the consumption of raw materials and imported commodities (Lee et al., 2013). Compared to small companies, large companies have a great number of stakeholders, including suppliers, consumers, and financial analysts (Finegold et al., 2010; Tamimi \u0026amp; Sebastianelli, 2017). Consequently, stakeholders exhibit an increased need for information regarding the operational activities of large firms (Gholami et al., 2022; Tamimi \u0026amp; Sebastianelli, 2017). According to stakeholder theory, corporate managers should serve all stakeholders (Freeman 1984), which includes meeting their information needs (Bilyay-Erdogan, 2022). Therefore, firms should use ESG disclosure as a communication tool to build and trust reduce information asymmetry with their stakeholders (Feng et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe signaling theory claims that the cost of acquiring information can be expensive, and the degree of information asymmetry is influenced by the behavior of the companies (Healy \u0026amp; Palepu, 2001; Stiglitz, 2000). Thus, most companies consider that ESG engagements and disclosures are costly, which leads to a lower willingness of disclosing ESG information (Shalhoob \u0026amp; Hussainey, 2023; Uyar et al., 2020). However, in the context of greater information needs, inadequate disclosure by listed companies ultimately leads to information asymmetry and adverse selection regarding ESG engagement in the capital market (Krueger et al., 2024). Therefore, it is a valuable research topic to investigate the motivations of ESG signals released by firms of different sizes, and provide a deeper insight into the factors that stimulate corporate ESG disclosure.\u003c/p\u003e\n\u003cp\u003eThe quality of ESG reporting affects the effectiveness and credibility of corporate signals (Healy \u0026amp; Palepu, 2001; Meng et al., 2014). Consistent with the signaling theory, the key to alleviating information asymmetry is to ensure that the quality of the signal information meets the expectations or requirements of external stakeholders (Stiglitz, 2000). An effective signal should be easily detectable, and the act of imitating is costly for firms (Connelly et al., 2011). Large corporations frequently produce comprehensive ESG reports to differentiate their ESG initiatives from the opportunistic behavior of other corporations (Chung et al., 2023; Uyar et al., 2020). Furthermore, Wang et al. (2022) noted that large companies take the initiative to use their own resources to provide information and even disclose social responsibility reports that exceed regulatory requirements in order to gain appreciation from the government and the public.\u003c/p\u003e\n\u003cp\u003eHowever, there are arguments that support the idea that smaller companies may exhibit greater motivation to report ESG information. Udayasankar (2008) noted that smaller companies have a higher potential to enhance their legitimacy or positive reputation compared to larger companies, resulting in more marginal utility. In the comparative empirical study conducted by Baumann-Pauly et al. (2013), it was posited that small firms are more likely to derive advantages from\u0026nbsp;CSR\u0026nbsp;disclosure and consequently have a greater willingness to participate in such activities compared to larger companies.\u003c/p\u003e\n\u003cp\u003eAs previously stated, larger companies are motivated to release high-quality information reports to convey their efforts on ESG activities. Furthermore, larger companies have more professional talent and higher financial resources to contribute to the effective signaling of ESG information to internal and external parties. Based on the above argument, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u0026nbsp;\u003c/strong\u003eThere is a positive relationship between firm size and ESG disclosure.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eThe moderating effect of\u0026nbsp;institutional environment\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eCompanies need to seek stability and development in the constantly improving and changing institutional environment (Bilyay-Erdogan, 2022; Gao-Zeller et al., 2019). Simultaneously, the information obtained by the stakeholders of the enterprises is consistently changing in a dynamic environment (Connelly et al., 2011). Improving the transparency of ESG information enables companies to maintain a good reputation in the capital market and cultivate a conscientious image (Bukari et al., 2024; Ting et al., 2020). In addition, signaling theory posits that the intensity of a signal can be influenced by various signal environments (Bilyay-Erdogan, 2022; Connelly et al., 2011; Su et al., 2016). Institutional environmental factors, as a latent factor, have a profound and significant influence on the level of social responsibility assumed by Chinese enterprises (Gao-Zeller et al., 2019). Based on this viewpoint, the institutional environment plays a crucial role in influencing the motivation and quality of ESG signals released by listed firms.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eGovernment intervention\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eIn the overall institutional environment, government departments have always been the leaders of mandatory institutional changes and have a decisive impact on the changes of institutional environment (Han et al., 2022). Appropriate strategies and actions are taken by the enterprises in order to obtain societal recognition in the region, and gain support and resources from the authorities (Mart\u0026iacute;nez-Ferrero \u0026amp; Lozano, 2021) and the stakeholders (Lu et al., 2020). The institutional environment of the company determines its access to policy support, labor, capital markets, and infrastructure, which are indispensable resources that assist in creating better performance (Tang et al., 2018). Therefore, China\u0026apos;s government-led institutional environment has a profound impact on enterprises\u0026apos; fulfillment and disclosure of their social responsibilities.\u003c/p\u003e\n\u003cp\u003eAccording to the neo-institutional theory, the institutional environment has a significant impact on the motivation of corporate behavior and decision-making (Campbell, 2007; Kong et al., 2022; Lu et al., 2024), especially in CSR aspects (Campbell, 2007; Kong et al., 2022). In regions with a high degree of government intervention in the economy, the government exercises significant control and authority over factor resources, which increases the likelihood of rent-seeking behavior by government officials (Bukari et al., 2024; H. Lu et al., 2024; Wang et al., 2021). Rent-seeking is defined as a facilitation activity that aims to obtain permissions and quotas for additional benefits like lower tax rates, more subsidies, and approval of an initial public offering (Krueger, 1974; Wang et al., 2021). In this scenario, corporations are more motivated to allocate resources and effort toward rent-seeking activities rather than focusing on ESG initiatives. It has a consequential impact on ESG expenditures and results in a decline in corporate social responsibility fulfillment. As mentioned in Introduction, the ESG performance of larger corporations frequently receives greater attention from various stakeholders (Cao et al., 2019). In areas with less government intervention, these corporations are committed to improving the effectiveness of ESG signals in the capital market, thereby gaining the recognition of a diverse group of stakeholders instead of relying solely on the government. Based on the above analysis, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2a:\u0026nbsp;\u003c/strong\u003eLess government intervention strengthens the positive relationship between firm size and ESG disclosure.\u003c/p\u003e\n\u003cp\u003eHeavily polluting industries are the pillars of Chinese economic development, but they also impact heavily on the ecological environment and global sustainable development (Ang et al., 2022). In 2010, the Ministry of Environmental Protection of China issued the \u0026quot;Guidelines for Environmental Information Disclosure of Listed Companies\u0026quot; (Zeng et al., 2020). According to this guideline, listed companies in 16 heavily polluting industries, such as thermal power, steel, cement, etc., are required to issue annual environmental reports and regularly disclose information on pollutant emissions, environmental compliance, and environmental protection practices. This implies that heavily polluting enterprises are incorporated into the scope of mandatory disclosure. However, non-heavily polluting enterprises still have the option of deciding whether to publish ESG reports. In regions with low government intervention, market competition is fairer and the information environment is relatively transparent (Bin-feng et al., 2022). In line with the signaling theory, comprehensive and reliable ESG reports assist listed firms in maintaining better communication with stakeholders to reduce information asymmetry, thereby reducing financing costs (Tsang et al., 2023) and enhancing their competitive edge in the capital market (Bilyay-Erdogan, 2022). Consequently, large corporations in non-heavily polluting industries are keen to engage in and disclose their ESG activities rather than rent-seeking activities. Thus, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2b:\u0026nbsp;\u003c/strong\u003eCompared with heavily polluting industries, the positive moderating effect of less government intervention is more obvious in non-heavily polluting industries.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eLevel of legal development\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eFrom the perspective of signaling theory, the effectiveness of signals depends on whether stakeholders can perceive that the signals sent by enterprises are authentic and trustworthy (Connelly et al., 2011; Lee et al., 2022; Xu \u0026amp; Liu, 2023). Large corporations maintain a sound ethical reputation by keeping effective communication with their stakeholders by releasing ESG reports. The ESG ratings of companies are often used by investment companies to refer to the standards of legality and ethical business practices. Voluntary disclosure is often close to a \u0026quot;mandatory initiative\u0026quot; for companies with a larger market share (Wang et al., 2022). Nevertheless, there is a disparity in the quality of ESG reporting among different companies (Xu et al., 2023). Unclear presentation of information in low-quality ESG reports tends to lead to higher information asymmetry, which would affect the credibility of stakeholders\u0026nbsp;(Xu et al., 2023; Yip \u0026amp; Yu, 2023).\u003c/p\u003e\n\u003cp\u003eThe neo-institutional theory postulates that the survival of large corporations is dependent on society acceptance (DiMaggio \u0026amp; Powell, 1983; Drempetic et al., 2019). A high degree of information asymmetry will affect the credibility and trustworthiness of stakeholders in large companies (Xu \u0026amp; Liu, 2023). While companies send a positive signal to their stakeholders by fulfilling their social responsibilities, the strength of this signal may vary in different institutional environments (Su et al., 2016). An ideal legal environment and a well-developed market intermediary organization demand a higher standard of corporate ESG reports (Krueger et al., 2024). Moreover, there is a higher level of public awareness regarding social responsibility in these regions. Consequently, the public is more inclined to show interest in and react to ESG signals released by large corporations. Furthermore, regulators and market intermediaries in a more robust legal environment often adopt the ESG reports of prominent corporations as a benchmark for others to emulate and gain insights from. Recognition from all sectors of society increases the incentive for large corporations to disclose high-quality ESG signals. Therefore, large corporations operating in a sound legal environment have a stronger sense of responsibility and are more willing to participate in and disclose ESG activities. Based on the above argument, the following hypothesis is put forward:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3a:\u003c/strong\u003e A robust legal environment strengthens the positive relationship between firm size and ESG disclosure.\u003c/p\u003e\n\u003cp\u003eHeavy polluting companies attract greater attention in their ESG reports compared to non-heavy polluting industries, as they are required to disclose their environmental information (Liu et al., 2023). According to neo-institutional theory, firms in different institutional environments will be subject to different extents of institutional pressures to act in a socially responsible way (Scott \u0026amp; Meyer, 1991; W. Sun et al., 2019). In a weak institutional environment, the majority of these firms may choose to symbolically embrace ESG initiatives instead of substantively engaging with them (Darnall et al., 2022; Marquis \u0026amp; Qian, 2014). However, Wei et al. (2015) contended that regions with a more advanced legal framework possess a variety of information channels and robust market supervisory mechanisms. Heavily polluting enterprises operating in these areas with opportunistic intentions are subjected to more rigorous supervision and constraints (Pinheiro et al., 2020). In addition, more detailed rules on information disclosure act as a filter for the stakeholders to scan corporate ESG disclosure to assess its legitimacy (Wei et al., 2015). In this case, large corporations in heavily polluting industries that undertake substantial ESG initiatives tend to spend more disclosure costs to release high-quality signals and facilitate stakeholders to make more accurate judgments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3b:\u0026nbsp;\u003c/strong\u003eCompared with non-heavily polluting industries, the positive moderating effect of the legal environment is more obvious in heavily polluting industries.\u003c/p\u003e"},{"header":"Research design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSample selection and data sources\u003c/h2\u003e\u003cp\u003eChinese A-share listed companies from 2018 to 2022 are employed as the initial research sample since the China Securities Regulatory Commission revised the Guidelines for Corporate Governance of listed companies in 2018, which established the fundamental framework for disclosing ESG information. In these guidelines, listed companies are required to strengthen their social responsibility and promote sustainable development. In addition, the scale of A-share listed companies is relatively large and their development tends to be stable. These companies prioritize both sustainability and information transparency to maintain their reputation in the capital market. Then, this study performed various data processing tasks. First, this study excluded companies with missing financial data to enhance the statistical robustness and reliability of the conclusions. Second, the sample excluded financial enterprises due to their distinct regulatory regulations, financial reporting standards, and corporate governance frameworks. At the same time, Special Treatment firms (ST) and Particular Transfer enterprises (PT) were eliminated as these companies experience financial anomalies. Both the upper and the lower extremes of all the continuous variables are winsorized at 1% to mitigate the impact of outliers. Finally, the sample consists of 18,815 unbalanced firm-year observations in China. As shown in Appendix A, this study captures a diverse range of sectors in the Chinese economy.\u003c/p\u003e\u003cp\u003eFollowing Chen \u0026amp; Xie (2022) and Xu \u0026amp; Liu (2023), we use the Huazheng ESG rating from the Wind database to evaluate corporate ESG disclosure because of its comprehensive coverage of Chinese listed firms and superior data continuity and availability (Chen \u0026amp; Xie, 2022; Tang, 2022; Xu \u0026amp; Liu, 2023). Furthermore, the Huazheng ESG rating is considered appropriate for ESG research in China because it combines the mainstream international ESG rating framework with the specific characteristics of the Chinese capital market (Tang, 2022; Xu \u0026amp; Liu, 2023). The indicators of the institutional environment were measured using the secondary marketization index: (1) the relationship between the government and (2) the development of market intermediaries and the legal environment. The marketization index constructed by Wang et al. (2021) is widely considered by scholars as a reliable and stable measurement for the Chinese institutional environment (Bin-feng et al., 2022; Huang et al., 2022; Zheng \u0026amp; Ren, 2019). The financial data are mainly from the China Stock Market and Accounting Research (CSMAR) database.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEconometric Model\u003c/h3\u003e\n\u003cp\u003eBased on the hypotheses in this study, the empirical tests consist of the relationship between firm size and ESG disclosure, and the moderating effect of the institutional environment on their relationship. First, Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) examines the influence of firm size on corporate ESG disclosure. Second, Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) tests the moderating effect of government intervention on the association between firm size and corporate ESG disclosure for H2a and divides the samples into non-heavily polluting industries and heavily polluting industries for testing hypothesis H2b. Third, Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) tests the moderating effect of the legal environment on the association between firm size and corporate ESG disclosure for H3a and groups the samples into non-heavily polluting industries and heavily polluting industries for testing hypothesis H3b.\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{ESG}_{i,t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{Size}_{i,t}+{\\beta\\:}_{2}{Controls}_{i,t}{+\\sum\\:Year+\\sum\\:Industry+\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{ESG}_{i,t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{{Size}_{i,t}+{\\beta\\:}_{2}{GOV}_{i,t}+\\beta\\:}_{3}Size\\ast\\:{GOV}_{i,t}+{\\beta\\:}_{4}{Controls}_{i,t}{+\\sum\\:Year+\\sum\\:Industry+\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{ESG}_{i,t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{{Size}_{i,t}+{\\beta\\:}_{2}{LAW}_{i,t}+\\beta\\:}_{3}Size\\ast\\:{LAW}_{i,t}+{\\beta\\:}_{4}{Controls}_{i,t}{+\\sum\\:Year+\\sum\\:Industry+\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMeasurement of Variables\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eDependent Variable (ESG).\u003c/b\u003e This research uses the Huazheng ESG rating as a dependent variable. Currently, it covers the widest range of listed companies in China, and the data is highly consistent and accessible to outsiders (Chen \u0026amp; Xie, 2022; Li et al., 2022; Tang, 2022). The Huazheng ESG rating comprises of nine levels, ranging from the best to the worst: AAA, AA, A, BBB, BB, B, CCC, CC and C (Tang, 2022). As a result, the Huazheng ESG rating was employed as a metric to assess the extent of ESG information disclosure. This study assigned C-AAA values on a scale of 1 to 9 to facilitate empirical research.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent Variable (Size).\u003c/b\u003e Size was frequently measured as the natural logarithm of total assets at the end of the year, according to existing studies (Chen et al., 2021; Odoemelam et al., 2020; Wang et al., 2022).\u003c/p\u003e\u003cp\u003e\u003cb\u003eModerator Variable.\u003c/b\u003e The link between firm size and ESG disclosure is moderated by two factors of the institutional environment: government intervention and the legal environment. Government intervention was proxied by the index “relationship between the government and market” developed by Fan and Wang (2010). This index comprises three aspects: the allocation of market resources, government intervention in businesses, and government size. Academics frequently employ it to gauge the level of governmental interference in the market. The lower the level of government intervention in the economy of the region, the greater the index.\u003c/p\u003e\u003cp\u003eAccording to Fan and Wang (2010), the legal environment was proxied by the index “the development of market intermediary organization and the legal environment”. This index captures the level of regional legal development by analyzing the role of the development of market intermediary organizations, the protection of legitimate rights and interests of producers, the protection of intellectual property rights, as well as the protection of consumer rights and interests. A higher index indicates a better legal environment in the region.\u003c/p\u003e\u003cp\u003e\u003cb\u003eControl Variables.\u003c/b\u003e Based on the existing studies, we control for several variables that might impact corporate ESG disclosure. Liquidity ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Liquid\\)\u003c/span\u003e\u003c/span\u003e): The high liquidity of a firm's assets indicates financial redundancy (He et al., 2022; Uyar et al., 2023). When firms have excess financial resources, they are more likely to involve and disclose their ESG activities (Uyar et al., 2023). Percentage of independent directors (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Indep\\)\u003c/span\u003e\u003c/span\u003e): Board independence of a firm may affect ESG information disclosure, as effective internal control is conducive to ensuring the authenticity and integrity of information disclosed by enterprises (Guo \u0026amp; Shen, 2019; Khalid et al., 2022). State ownership (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SOE\\)\u003c/span\u003e\u003c/span\u003e): State-owned enterprises usually have different ownership structures and management styles, which may affect the fulfillment and disclosure of their social responsibilities (Ervits, 2023; Liu \u0026amp; Zhang, 2017; Marquis \u0026amp; Qian, 2014). Audit quality (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Big4\\)\u003c/span\u003e\u003c/span\u003e): Firms audited by Big 4 accounting firms disclose information of higher quality and reliability (Handayati et al., 2022; Iatridis, 2013), as accepting higher audit requirements implies a greater willingness to provide high-quality ESG signals (Subhi et al., 2022). Management shareholding (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Msℎare\\)\u003c/span\u003e\u003c/span\u003e): Management shareholding serves as a means to incentivize managers and influence their decision-making, including corporate ESG initiative engagement and disclosure (Guo \u0026amp; Shen, 2019; Wei \u0026amp; Zhou, 2020). The above provides justification for each control variable in this study. For brevity, we describe the definition of all variables as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eDefinition of variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eESG\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eESG ratings from Huazheng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHuazheng\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFirm size, measured as the natural logarithm of total assets at the end of the year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGOV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ethe index “relationship between the government and market”\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Fan \u0026amp; Wang, 2010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLAW\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ethe index “the development of market intermediary organization and the legal environment”\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Fan \u0026amp; Wang, 2010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLiquid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLiquidity ratio, measured as current assets / current liabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRatio of independent directors, measured as Number of independent directors/total number of the board of directors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eState ownership, 1 for state-owned enterprises and 0 for others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBig4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAuditing quality, 1 for firms audited by a Big 4, and 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMshare\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eManagement shareholding, measured as management shareholding / total equity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Empirical results and discussions","content":"\u003ch2\u003eSummary statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistics of the variables in the study. The average level of ESG disclosure (\u003cem\u003eESG\u003c/em\u003e) of the sample was 4.214, indicating that the ESG disclosure of the sample is generally at a low level. From the perspective of the maximum and minimum values, and the standard deviation of ESG disclosure, the highest ESG disclosure rating was 8, while the lowest ESG disclosure rating was only 1, and the standard deviation was 1.099, indicating that the level of ESG disclosure of the enterprises varies greatly in Chia. The independent variable firm size (\u003cem\u003eSize\u003c/em\u003e) had a maximum value of 26.452 and a minimum value of 19.810, indicating that firm size varies significantly among the listed firms in China. The moderating variable of government intervention (\u003cem\u003eGOV\u003c/em\u003e) had a maximum value of 9.112 and a minimum value of -0.283, indicating that the government intervention in each region varies significantly in China. Meanwhile, the moderating variable of the legal environment (\u003cem\u003eLAW\u003c/em\u003e) had a maximum value of 18.974 and a minimum value of 1.576. The median for legal environment (\u003cem\u003eLAW\u003c/em\u003e) was 13.796, while the standard deviation was 2.928, indicating that the legal environment of each region varies significantly in China.\u003c/p\u003e\u003cp\u003eAppendix B shows the correlation coefficients for each variable in this study. There was a significant positive correlation between firm size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Size)\\)\u003c/span\u003e\u003c/span\u003e and ESG disclosure (\u003cem\u003eESG\u003c/em\u003e), which had a positive coefficient of 0.194, indicating that firm size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Size)\\)\u003c/span\u003e\u003c/span\u003e can increase the quality of ESG disclosures. The relationship between the institutional environment of government intervention (\u003cem\u003eGOV\u003c/em\u003e) and ESG disclosure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ESG)\\)\u003c/span\u003e\u003c/span\u003e had a positive coefficient of 0.067. Meanwhile, the institutional environment of legal environment (\u003cem\u003eLAW\u003c/em\u003e) and ESG disclosure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ESG)\\)\u003c/span\u003e\u003c/span\u003e had a positive coefficient of 0.096, indicating that both government intervention and legal environment can increase the quality of ESG disclosure in China. In addition, from Appendix B, the results show that all correlation coefficients are less than 0.7, meaning that none of the correlations are extremely high, demonstrating that there was no multicollinearity problem with this study’s regression tests (Činčalová \u0026amp; Hedija, 2020; Lee et al., 2017). All variables have a VIF value below 1.59 as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, demonstrating the absence of multicollinearity among them (O’Brien, 2007). Unreported data show that, after centering the individual components that contribute to the interaction term, all the models' VIFs and mean VIFs are less than two, showing no multicollinearity problem.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\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\u003eObservations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eRegression results\u003c/h2\u003e\u003ch2\u003eFirm size and ESG disclosure\u003c/h2\u003e\u003cp\u003eThe regression analysis displays the impact of firm size on ESG disclosure in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The regression result of column (1) shows that the coefficient on firm size (\u003cem\u003eSize\u003c/em\u003e) is significantly positive (β = 0.2426) at the 1% level, indicating that firm size has a significant positive impact on corporate ESG disclosure. The results are consistent with the findings of Drempetic et al. (2019). The regression results above imply that large corporations exhibit a higher propensity to provide comprehensive and reliable ESG disclosure. And this result is supported by the signaling theory that the quality and intentions of signaling information are related to the decisions of the signal senders (Connelly et al., 2011). Thus, this result supports Hypothesis H1.\u003c/p\u003e\u003ch2\u003eFirm size, government intervention and ESG disclosure\u003c/h2\u003e\u003cp\u003eThe results of the ordinary least squares (OLS) regression using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) are shown in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which examined the moderating role of government intervention (\u003cem\u003eGOV\u003c/em\u003e) on the relationship between firm size and ESG disclosure (\u003cem\u003eESG\u003c/em\u003e). The result shows that the coefficient on the interaction term (\u003cem\u003eSize*GOV\u003c/em\u003e) is significantly positive (β = 0.0168) at the 1% level, supporting that the positive relationship between firm size and ESG disclosure is stronger in regions with less government intervention. This finding favors neo-institutional theory and suggests that in a well-developed institutional environment with less government intervention, large corporations are more likely to enhance their social reputation and gain a competitive edge by actively engaging in and disclosing their ESG initiatives Hence, Hypothesis 2a is supported.\u003c/p\u003e\u003cp\u003eThe government imposes a variety of environmental information disclosure requirements based on the impact of businesses on the environment. Consequently, the samples in this study are divided into heavily-polluting enterprises and non-heavily-polluting enterprises. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the government intervention (\u003cem\u003eGOV\u003c/em\u003e) is significant at the 1% level, and this is due to the fact that there is more fair market competition in the regions with less government intervention, which helps companies gain a good reputation by releasing ESG signals. Interestingly, based on the results of columns (1) and (3) shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it reveals that less government intervention has a significantly positive influence on the linkage between firm size and ESG disclosure only in non-heavily polluting industries, whereas it does not affect heavily polluting industries. This demonstrated that an institutional environment with less government intervention is more competitive and transparent. In this environment, firms that are not required to disclose ESG information do so proactively. In summary, the regression results support Hypothesis H2b.\u003c/p\u003e\u003ch2\u003eFirm size, legal environment and ESG disclosure\u003c/h2\u003e\u003cp\u003eFor the moderating role of legal environment (\u003cem\u003eLAW\u003c/em\u003e) on the relationship between firm size (\u003cem\u003eSize\u003c/em\u003e) and ESG disclosure (\u003cem\u003eESG\u003c/em\u003e), the regression result is shown in the column of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The result showcases that the coefficient on the interaction term (\u003cem\u003eSize*LAW\u003c/em\u003e) is significantly positive (β = 0.0049) at the 5% level, indicating that the positive relationship between firm size and ESG disclosure is stronger in regions with a more developed legal environment. These findings also support the neo-institutional theory and reveal that an institutional environment with well-developed market intermediaries and an effective legal system can encourage large corporations to release high-quality ESG signals. As a result, Hypothesis H3a is supported.\u003c/p\u003e\u003cp\u003eThe coefficient on legal environment (\u003cem\u003eLAW\u003c/em\u003e) is positively at the 1% level, and this can be explained by that well development of legal environment contributes to the quality of ESG signals. Another notable finding, as shown in columns (1) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, is that well-developed legal environment has a significantly positive influence on the linkage between firm size and ESG disclosure only in heavily polluting industries, whereas it does not affect non-heavily polluting industries. This demonstrated that the quality of companies to mandatorily disclose ESG information is influenced by the institutional environment. Firms mandated to disclose ESG information may choose to take symbolic behavior instead of substantive actions if their institutional environment lacks effective external supervision and an imperfect legal framework. In summary, the regression results support Hypothesis H3b.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eFirm size, institutional environment and ESG disclosure.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\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\u003e0.2426\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2446\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2440\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(33.7963)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(33.9940)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(34.0049)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0486\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.9145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\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\u003e0.0333\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(9.1611)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*LAW\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\u003e0.0049\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(2.4524)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0568\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0566\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0568\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(17.8742)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(17.8303)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(17.8961)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0839\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0695\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0664\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.7800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.6800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.6652)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2481\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2597\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2637\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(12.5408)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(13.0875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.3049)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2202\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2061\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.6959)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.2306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.2486)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1167\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0975\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0823\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(25.6312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(25.0531)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(24.7407)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.5886\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.9465\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.8939\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.0147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-15.4167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-15.4512)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e18815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eThe moderating role of institutional environment in heavily polluting and non-heavily polluting industries.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHeavily polluting\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eNon-heavily polluting\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2463\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2436\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(24.1796)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(24.2775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(22.6437)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(22.5050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0331\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e0.0769\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.5913)\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\u003cp\u003e(6.7398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0064\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\u003cp\u003e0.0266\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.8337)\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\u003cp\u003e(3.0288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0234\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e0.0537\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.9345)\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\u003cp\u003e(9.2566)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*LAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0060\u003csup\u003e**\u003c/sup\u003e\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\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.2388)\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\u003cp\u003e(0.9608)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0576\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0579\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0675\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0672\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(15.7276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(15.8065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.3304)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(10.3034)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6039\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6094\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.4252)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.4824)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(6.9274)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(6.8285)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1828\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1904\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2456\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.9204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.1991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(8.0459)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(8.0610)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2204\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2170\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1737\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1758\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.8508)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.7822)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.4788)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.5326)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1825\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1763\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9399\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(22.4011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(22.3032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(11.6151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(11.1686)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.7577\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.7723\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.8675\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.7731\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.4909)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-7.7486)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-10.4499)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-10.3488)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e11776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10% respectively. This table presents the regression results for companies in heavily polluting sectors and companies in non-heavily polluting industries. The classification of industries with high levels of pollution is based on the Industry Classification Guidelines for Listed Companies, which were issued by the China Securities Regulatory Commission in 2012. For the regression results, the evidence indicates that the size of a company can enhances the level of ESG disclosure in both highly polluting and non-highly polluting sectors. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eRobustness tests\u003c/h2\u003e\u003ch2\u003eAlternative sample period\u003c/h2\u003e\u003cp\u003eTo test the robustness of the regression results, this study expands the sample of A-share-listed companies in China, covering the time span from 2009 to 2022. The regression results based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In terms of the regression results in column (1), the coefficient on firm size (\u003cem\u003eSize\u003c/em\u003e) is significantly positive (β = 0.2294) at the 1% level, which is consistent with the previous result and still supports Hypothesis H1.\u003c/p\u003e\u003cp\u003eThe regression result of column (2) shows the moderating role of government intervention (\u003cem\u003eGOV\u003c/em\u003e) on the relationship between firm size and ESG disclosure (\u003cem\u003eESG\u003c/em\u003e). The result shows that the coefficient on the interaction term (\u003cem\u003eSize*GOV\u003c/em\u003e) is significantly positive (β = 0.0061) at the 5% level, and this is only significant in the non-heavily polluting sectors shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eMeanwhile, the coefficient for the interaction term (\u003cem\u003eSize*LAW\u003c/em\u003e) is significantly positive (β = 0.0027) at the 1% level, however, it is only significant in heavily polluting sectors based on the regression results from Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In summary, the regression results still support all the hypotheses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eAlternative sample period of 2009–2022.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\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\u003e0.2294\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2311\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2294\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(49.2285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(49.3603)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(49.3239)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.8350)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0061\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.5132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\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\u003e0.0282\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(13.9427)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*LAW\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\u003e0.0027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(2.7405)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0391\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0391\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0387\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(22.0892)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(22.1005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(21.8605)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1688\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1718\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1746\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(13.0306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(13.0750)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.1241)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1996\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2099\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(16.3070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(17.0961)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(17.4821)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1167\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1094\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.0494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.2970)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.9847)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0287\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9972\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(36.3852)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(35.7154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(35.1833)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.9043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.2028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.0599\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.4949)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-18.4410)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-17.7956)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e38617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eThe moderating role of institutional environment in heavily polluting and non-heavily polluting industries for 2009–2022.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHeavily polluting\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eNon-heavily polluting\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2016\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2434\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2403\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(32.0328)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(31.9895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(35.4748)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(35.3136)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0299\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e0.0401\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.3729)\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\u003cp\u003e(8.3377)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0030\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\u003cp\u003e0.0086\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.8307)\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\u003cp\u003e(2.3920)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0246\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e0.0371\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.0735)\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\u003cp\u003e(12.0101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*LAW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.3252)\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\u003cp\u003e(0.5903)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0370\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0365\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0551\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0545\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(17.9877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(17.7759)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(15.0407)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(14.9129)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8268\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8349\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5523\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5407\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.0050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.0819)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.7909)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(10.7386)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1244\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1330\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.4313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.9456)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(11.6299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(11.6531)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1506\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1396\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.7305)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.3939)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.1037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.8725)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8440\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8151\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(29.7676)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(29.4915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(16.4730)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(15.8851)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.1443\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.0345\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.4038\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.2258\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.3682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.9747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-14.4123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-13.7716)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e22984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10% respectively. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eThe estimation of lagged independent variable\u003c/h2\u003e\u003cp\u003eIn relation to the problem of endogeneity, this paper used the lagged independent variable for regression to investigate the impact of firm size for the previous period on ESG disclosure for the current period, which is conducive to alleviating the problem of reverse causality (Bellemare \u0026amp; Pepinsky, 2017). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the results imply that firm size (\u003cem\u003eSize\u003c/em\u003e) is still significantly positive at the 1% level on ESG disclosure, which is the same with the baseline regression. Furthermore, the regression results for the moderating role of government intervention (\u003cem\u003eGOV\u003c/em\u003e) and legal environment (\u003cem\u003eLAW\u003c/em\u003e) on the relationship between firm size and ESG disclosure are consistent with previous estimations in this paper, demonstrating the robustness of the regression results for this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eLagged regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eESG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL.Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2365\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2382\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2350\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(27.4558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(27.6069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(26.9638)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.2195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0160\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.4056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\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\u003e0.0332\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(7.2900)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*LAW\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\u003e0.0054\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(1.9150)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0608\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0607\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0608\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(14.3110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(14.3009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(14.3457)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0255\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0096\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0081\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.1823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.0917)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.0891)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2882\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2992\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(12.4394)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(12.8779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.1142)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2726\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2593\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2598\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.0236)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.6535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.6829)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1329\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0978\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(20.9608)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(20.5045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(20.1894)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.5093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.8204\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.7291\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-11.3396)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-12.3531)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.6428)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e13856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eInstrumental variable (IV) approach\u003c/h2\u003e\u003cp\u003eTo address the concerns about endogeneity, this study additionally utilizes an instrumental variable (IV) approach for the robustness tests. This study utilizes a lagged variable of firm size (\u003cem\u003eL.Size\u003c/em\u003e) as an instrumental variable for firm size (\u003cem\u003eSize\u003c/em\u003e), based on the study conducted by Drempetic et al. (2019). The regression result of the first-stage is shown in column (1), which demonstrates a statistically significant and positive coefficient on the lagged firm size (\u003cem\u003eL.Size\u003c/em\u003e). In column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it shows the regression result of the second-stage regression analysis, suggesting that the instrumented firm size has positively and significantly affected the quality of ESG disclosure, and this finding is consistent with the results in the previous test. Additionally, the results shown in columns (3) and (4) indicate that the results of the moderating effect remain robust. In terms of the weak identification test, it indicates that there is no weak instrument problem since the value of Kleibergen‒Paap Wald rk F-statistics exceeds 10.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInstrumental variable estimation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eSecond stage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2381\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2398\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(27.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(27.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(27.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL.Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9932\u003csup\u003e***\u003c/sup\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(570.68)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOV\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\u003e0.0416\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(5.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize*GOV\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\u003e0.0159\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAW\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\u003cp\u003e0.0325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cp\u003e(7.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize* LAW\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\u003cp\u003e0.0048\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cp\u003e(1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0614\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0613\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0614\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(14.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(14.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(14.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0972\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0486\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0333\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(6.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2874\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2981\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3032\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(12.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(13.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2650\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2519\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2528\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(6.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1041\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0910\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0735\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(20.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(20.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(19.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2677\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.3515\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.6621\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.7842\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-10.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-11.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCragg-Donald Wald F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF \u0026gt; 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF \u0026gt; 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF \u0026gt; 10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\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\" colname=\"c2\"\u003e\u003cp\u003e13856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: ***, ** and * represent the significance levels at 1%, 5% and 10%. ESG = ESG ratings from Huazheng. Size = natural logarithm of total assets at the end of the year. GOV = the index “relationship between the government and market”. LAW = the index “the development of market intermediary organization and the legal environment”. Liquid = the ratio of current assets divided by current liabilities. Indep = ratio of independent directors, measured as number of independent directors/total number of the board of directors. SOE = 1 for state-owned enterprises and 0 for others. Big4 = 1 for firms audited by the Big 4 auditors and 0 for otherwise. Mshare = management shareholding / total equity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Conclusion and implications","content":"\u003cp\u003eThis study explores the moderating effect of government intervention and the legal environment on the link between firm size and ESG disclosure for firms in heavily polluting industries and non-polluting industries. Using an unbalanced dataset of 18,815 unbalanced firm-year observations from 2018 to 2022, we finds that a firm with a larger size exhibits a higher propensity to provide comprehensive and reliable ESG disclosure. In the robustness test, this study uses an alternative sample period for the regressions, the estimation of lagged independent variable and instrumental variable approach. All the results of robustness test confirm the findings and improve estimate consistency. Heterogeneity analysis finds that the positive linkage between firm size and ESG disclosure is more pronounced in regions with less government intervention and a more developed legal environment. Furthermore, notable findings show that less government intervention has a significantly positive influence on the linkage between firm size and ESG disclosure only in non-heavily polluting industries, whereas it does not affect heavily polluting industries. Conversely, a well-developed legal environment has a significantly positive influence on the linkage between firm size and ESG disclosure only in heavily polluting industries, whereas it does not affect non-heavily polluting industries.\u003c/p\u003e\u003ch2\u003eTheoretical implications\u003c/h2\u003e\u003cp\u003eThis study makes theoretical contributions to reveal the relationship between firm size and firm ESG disclosure by utilizing the signaling theory. First, although previous studies have also suggested that firm size positively affects firm ESG disclosure, this study reexamines this relationship based on signal theory from the perspective of the intention and effectiveness of signals. Second, this study verifies the moderating effect on firm size and ESG disclosure, especially incorporating two major aspects of the Chinese institutional environment: government intervention and the level of legal development. The unique institutional environment in China has fostered favorable circumstances for expanding the application of neo-institutional theory. In addition, the varying impacts of the institutional environment on different industries result in significant differences in the ESG disclosure requirement obeyed by companies within these industries. Hence, this study further distinguishes between heavy polluting industries and non-heavy polluting industries to explore the moderating role of the institutional environment. In conclusion, this study presents fresh evidence on how the institutional environment indirectly affects corporate ESG disclosure, which expands the boundaries of the research on the moderating role of institutional environment factors.\u003c/p\u003e\u003ch2\u003ePolicy Implications\u003c/h2\u003e\u003cp\u003eThis study also provides some policy implications. China is undergoing an economic transition. There is a lower degree of information transparency in transitional development countries (Bin-feng et al., 2022; Xie et al., 2017; Zheng \u0026amp; Ren, 2019), especially in ESG disclosure (Li et al., 2022). Stronger government intervention and a lower level of legalization are relatively common in transition economies (Long, 2019; Xie et al., 2017; Zheng \u0026amp; Ren, 2019). Corporate ESG information disclosures are heavily influenced by the external institutional environment (Bilyay-Erdogan, 2022; Long, 2019). This study assists the Chinese government in making a significant contribution to improving the corporate ESG disclosure system, as well as providing valuable insights to improve the transparency of ESG information in other transitional developing countries.\u003c/p\u003e\u003cp\u003eSecond, the findings suggest that the positive relationship between firm size and ESG disclosure would be more pronounced in regions with less government intervention and a more developed legal environment. Our findings emphasize the significance of the institutional environment in stimulating the demonstrating role played by large corporations in their ESG disclosure. Thus, government authorities should reduce intervention in the capital market and prevent political rent-seeking by officials, thereby fostering a competitive and transparent signaling environment. Moreover, policymakers should stimulate the incentives for large corporations to proactively release high-quality ESG signals by guiding stakeholders, such as investors, consumers, and market intermediaries, to pay attention to corporate ESG information.\u003c/p\u003e\u003cp\u003eThird, the moderating effect of less government intervention exists only in non-heavy polluting industries, since heavy polluting industries are required to disclose ESG-related information. This finding suggests that heavily polluting enterprises incorporated into the scope of mandatory disclosure tend to keep a high disclosure rate regardless of the external institutional environment to avoid civil litigation and public distrust. Therefore, it is necessary to gradually implement mandatory disclosure in the whole capital market to address the issue of insufficient ESG disclosure in China.\u003c/p\u003e\u003cp\u003eFinally, the moderating effect of the legal environment is only observed in heavily polluting industries. Despite the high rate of ESG disclosure in this industry, most of these enterprises might merely fulfill their disclosure obligations symbolically, without providing substantial data. Hence, for policymakers, this empirical finding highlights the necessity for regulatory reform to improve ESG disclosure. On one side, it is imperative for local governments and intermediary organizations to exercise effective oversight and guidance over corporate ESG disclosure within a standardized disclosure framework. This contributes to establishing a robust information filtering mechanism, enhancing the efficacy of corporate ESG signals. Consequently, stakeholders would be better equipped to identify and make well-informed choices. On the other hand, the mandatory disclosure of ESG information in China is dynamic and long-term, but it cannot happen overnight. It is necessary for the Chinese government to emphasize the leading role of large corporations and drive the motivations of small and medium-sized enterprises to proactively disclose ESG information. In addition, policymakers should formulate appropriate implementation rules for different industries and should not adopt a one-size-fits-all approach.\u003c/p\u003e\u003cp\u003eDespite its contributions, this study has some potential limitations. First, the use of the Huazheng ESG rating, which covers only Chinese firms, restricts international comparability. Future research could delve deeper into this topic through subsequent international comparative studies. Second, this study does not incorporate cultural factors, such as Confucianism, local culture, etc. We omitted these for brevity, but future research examining their roles in ESG disclosure may yield valuable insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Y.Z.; Methodology, Data curation and Formal analysis,Y.Z. and X.W.; Investigation, Resources, Software and Validation, Y.Z. and X.W.; Writing original draft preparation, Y.Z., X.W. and Y.T; supervision,Y.Z., X.W. and Y.T. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmato, A. D., \u0026amp; Falivena, C. (2019). Corporate social responsibility and firm value: Do firm size and age matter? 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Voluntary CSR disclosure , institutional environment , and independent audit demand. \u003cem\u003eChina Journal of Accounting Research\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 357\u0026ndash;377. https://doi.org/10.1016/j.cjar.2019.10.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ESG disclosure, firm size, institutional environment, signaling theory, heavily polluting industries","lastPublishedDoi":"10.21203/rs.3.rs-7699657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7699657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of information in the capital market is crucial for investors to make informed judgments. Environment, social responsibility, and corporate governance (ESG) disclosure is widely recognized to contribute information transparency of capital market in China. Based on the signaling theory, this study aims to investigates the relationship between firm size and ESG disclosure and the moderating effect of the institutional environment (including government intervention and legal environment). Using 18,815 unbalanced firm-year observations from Chinese A-share listed companies for 2018 to 2022, the findings show that firms with a larger size tend to provide a higher quality of ESG disclosure and this positive relationship between firm size and ESG disclosure is more pronounced in the regions with less government intervention and a more developed legal environment. Furthermore, notable findings show that the moderating role of government intervention and the legal environment exerts a significant difference between heavily polluting industries and non-heavily polluting industries. The current ESG rating database cannot provide data for conducting further comparative analyses. Nonetheless, the findings and limitations provide future research directions on this topic.\u003c/p\u003e","manuscriptTitle":"Firm Size and Corporate ESG Disclosure: Exploring the moderating effect of the institutional environment in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 12:34:29","doi":"10.21203/rs.3.rs-7699657/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81b9ad65-28c8-4122-bbdb-a82860b8de04","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57739381,"name":"Business and commerce/Economics"},{"id":57739382,"name":"Social science/Economics"},{"id":57739383,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-01-22T16:54:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 12:34:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7699657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7699657","identity":"rs-7699657","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}