Abstract
Green investors, as key participants in corporate governance, play a crucial role in addressing the ESG ratings divergence. This study investigates how green investors influence ESG ratings divergence using data from Chinese A-share listed firms from 2015 to 2023. The results show that green investors significantly reduce ESG ratings divergence by alleviating information asymmetry between firms and ESG rating agencies. The effect is more pronounced in non-SOEs, firms with lower environmental transparency and poorer governance environment, non-heavily polluting firms, especially the firms with lower air pollution, and firms with stricter environmental regulations. The findings highlight green investors’ critical role in mitigating ESG ratings divergence and provide insights into their governance impact.
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Introduction
In recent years, ESG has gained widespread attention globally, which is the full name of Environmental, Social and Governance, and is based on the concept of sustainable development, guiding enterprises to focus on social responsibility and realize green and low-carbon development1,2. Since China is the largest manufacturing hub, China’s environmental issues are specifically important3. Given this reality, China plays a critical role in global climate governance and has committed to achieving carbon peak by 2030 and carbon neutrality by 2060. Firms are crucial agents in reaching energy conservation and emission reduction goals. As the primary consumers of natural resources and major sources of pollution4, corporate ESG disclosure provides essential information for governments to monitor progress toward emission reduction targets. However, due to information asymmetry, capital market participants are unable to fully understand the firm’s ESG practices, thus, many ESG rating agencies have emerged.
ESG ratings are the score or grade issued by third-party rating agencies to rate the sustainability of a company in three dimensions: environmental, social and governance, and then disclosed to the public. Market participants usually make investment decisions based on the third-party ESG rating results5. However, a unified ESG rating system and standard has not been formed globally. There are large differences in the evaluation samples, selection of evaluation indicators and setting of indicator weights among rating agencies, leading to a divergence in ESG ratings.
The divergence increases the difficulty for the market to identify the true ESG practices of enterprises and hinders their greening transformation and sustainable development6. Existing literature has found that ESG ratings divergence would reduce investors’ ability to identify high-quality companies7 and reduce the usefulness of ESG information to regulators and policymakers8. The negative impact of ESG ratings divergence on the capital market has led to growing academic interest in how to decrease the ESG ratings divergence of enterprises.
Different rating systems and information asymmetry are the main reasons for ESG ratings divergence9,10,11. Among them, rating system inconsistency is the subjective cause of ESG ratings divergence, while information asymmetry between the two is the objective cause of ESG ratings divergence7. Due to the existence of information asymmetry, there are differences in the ability of different rating agencies to obtain information on ESG practices of the same enterprise. Therefore, we attempt to study how to decrease the ESG ratings divergence from the perspective of reducing information asymmetry, which is of great significance in promoting the development of China’s green low-carbon economy.
Green investors are institutional investors who are concerned about both the firm’s economic and social benefits12. The “Green Investment Guidelines (Trial)” issued by the China Securities Investment Funds Association (CSIFA) in 2018 also pointed out that green investors need to promote investee enterprises to pay attention to environmental performance, improve environmental information disclosure, and carry out green investment according to their own strategic direction. Anecdotal evidence shows that Chinese green investors are increasingly demonstrating their commitment to sustainable development. For instance, E Fund Management Co., Ltd., China’s largest mutual fund manager, has been a pioneer in ESG investing. Since 2017, E Fund has integrated ESG principles into its investment strategies and launched several ESG-focused products, including the ESG Responsibility Investment Equity Fund and the Carbon Neutral ETF (Source: https://www.efunds.com.cn/en/lm/responsible-investing). By the end of 2023, the balance of green loans in China reached approximately RMB 30.08 trillion, marking a 36.5% year-on-year increase (Source: https://www.insurancebusinessmag.com/asia/news/breaking-news/chinas-green-transition-financial-institutions-boost-investments-in-sustainable-projects-503742.aspx?). Therefore, green investors have the characteristics of both “green” and “institutional investors”, and have the motivation and ability to urge investee companies to improve their environmental performance to promote sustainable development in the future13,14,15. Whether green investors, as important shareholders and external supervisors, can play a full role in corporate governance and promote adequate disclosure of ESG information, thereby reducing the divergence of ESG ratings, is worthy of in-depth discussion.
Prior studies have focused on how green investors affect corporate green governance behaviors, such as corporate green innovation, green governance performance, green investment efficiency, etc.16,17,18, while there is a dearth of literature focusing on how green investors can alleviate the asymmetry of information between corporations and external rating agencies. We attempt to study how green investors can play a corporate governance role and alleviate the information asymmetry between enterprises and external rating agencies, so as to provide a feasible solution for resolving ESG ratings divergence and provide theoretical support for the capital market to guide green institutional investors to give full play to their governance role, which is of dual significance in theory and practice. Therefore, we explore whether and how green investors can inhibit ESG ratings divergence in Chinese firms through their investment and governance strategies.
We select the Chinese capital market as the setting for examining the relationship between green investors and ESG ratings divergence for several key reasons. First, as a major manufacturing center, China faces pressing environmental challenges and is central to global climate efforts. Second, in China’s financial markets, awareness of ESG factors is growing among investors, particularly institutional investors who are increasingly integrating these considerations into their investment strategies19. By June 2023, approximately 140 Chinese institutions had become signatories to the United Nations’ Principles for Responsible Investment (PRI). The total size of ESG investment funds in China reached RMB 576 billion. Third, in April 2024, the China Securities Regulatory Commission (CSRC) issued its first sustainability disclosure guidelines, covering only a portion of listed companies (Source: https://www.sse.com.cn/lawandrules/sselawsrules/stocks/mainipo/c/c_20240412_5737862.shtml (In Chinese)). Prior to this, ESG disclosure of listed companies was mostly voluntary, resulting in inconsistent quality and significant divergence in ESG ratings20. Overall, exploring ways to reduce ESG ratings divergence and improve environmental information transparency in China can also offer valuable insights for global markets.
Based on the sample of Chinese A-share listed companies from 2015 to 2023, we examine the impact and mechanism of green investors on ESG ratings divergence in terms of information asymmetry mitigation. The empirical results show that green investors help to inhibit ESG ratings divergence, specifically through the two channels of enhancing the quality of corporate internal control and increasing corporate external attention and supervision. Moreover, our results remain robust after applying various robustness and endogeneity checks, such as changing the main variables, changing the model, using the PSM methods and instrumental variables. We also apply the DiD model based on the exogenous policy shock to mitigate the endogeneity problems. Further researches show that the inhibitory effect of green investors on ESG ratings divergence is more pronounced in non-SOEs, when the firm’s environmental information transparency is lower, when the firm’s governance environment is poorer, in non-heavily polluting firms, especially the firms with lower air pollution, and in firms located in places with stronger environmental regulations. In addition, we find that the reduction of ESG ratings divergence by green investors helps to increase firm’s stock liquidity and enhance the market’s resource allocation efficiency.
Compared with the existing literature, our study makes several marginal contributions in the following three aspects. First, we focus on the impact of green investors on ESG ratings divergence, which enriches the literature related to the impact factors of ESG ratings divergence. Most of the existing literature focuses on the economic consequences of ESG ratings divergence, such as green innovation, the cost of debt financing, information disclosure, capital market efficiency, etc.21,22,23,24, and relatively little literature focuses on how to mitigate ESG ratings divergence. We provide new insights for mitigating ESG ratings divergence from the governance role of green investors, which has certain theoretical and practical significance.
Second, we expand the research of the role of green investors on firms from the perspective of ESG ratings divergence. Green investors, a group that is gradually expanding and playing an increasingly important governance role in both China’s and the global capital markets. Most of the existing literature focuses on how green investors influence the green governance behavior of firms16,17,18, and little literature has examined the governance role of green investors from the perspective of information asymmetry between firms and outsiders. We investigate the inhibitory effect of green investors on ESG ratings divergence, which complements the existing literature.
Third, we explore the role of green investors in inhibiting ESG ratings divergence from the perspective of their governance role and signaling role in the capital market. We also analyze how green investors affect ESG ratings divergence in different contexts from the perspective of company characteristics, industry characteristics, institutional environment, etc. Our results provide theoretical support for the government to formulate policies related to green and low-carbon economy, for capital market regulators to guide the development of green investors, and for companies to improve the transparency of their own information, so as to promote the formation of green governance synergy among multiple parties.
The remainder of this paper is structured as follows: Section “Theoretical analysis and research hypothesis” presents the theoretical analysis and develops our hypothesis. Section “Research design” outlines the research design. Section “Empirical results” presents the empirical findings, including the main regression results and robustness checks. Section “Further research” provides further analysis, including the mechanism tests and heterogeneity analysis. Section “Conclusion” concludes the study.
Theoretical analysis and research hypothesis
ESG ratings issued by professional third-party ESG rating agencies are a key channel of information communication between companies and the market. Currently, there are more than 600 ESG rating agencies in the world (from Chinese ESG White Paper, 2023), among which, FTSE Russell, RKS, Sino-Securities Index Information Service, Wind, SynTao Green Finance and Susallwave are widely recognized. However, with the increasing number of ESG rating agencies, ESG ratings divergence has become one of the challenges limiting the development of China’s ESG system25.
Existing literature highlights significant divergence among ESG ratings from different agencies, emphasizing growing academic interest in their underlying drivers and economic consequences. Previous studies generally classify the causes of ESG ratings divergence into two main categories. On the one hand, ESG ratings divergence may stem from the use of different evaluation criteria by rating agencies, including differences in the selection of indicators, weighting schemes, data collection methods, information sources, and rating models9,10. On the other hand, differences in the quality of corporate disclosures can lead to information asymmetry26, which in turn contributes to ESG ratings divergence. Liu et al.27) finds that investor-firm interaction can significantly reduce the ESG ratings divergence. He et al.28 suggests that hypocritical firms tend to show greater ESG ratings divergence, as their selective disclosure and symbolic promotion create cognitive dissonance among rating agencies. As for the possible economic consequences of ESG ratings divergence, they can reduce investors’ ability to identify high-quality firms7. They may also weaken the ability of ESG ratings to predict future ESG news and diminish the market’s responsiveness to such news, while reducing the usefulness of ESG information for regulators and policymakers8. To address these challenges, we focus on green investors and explore their impact on ESG ratings divergence.
Green investors, as important institutional investors, pay more attention to the firm’s ESG practices29. From a practical perspective, anecdotal evidence shows that China Asset Management Co., Ltd. (ChinaAMC), actively participate in ESG initiatives, indicating a strong commitment to corporate ESG concerns. For example, ChinaAMC’s ESG team engaged directly with 19 listed firms in 2023, utilizing shareholder activism to promote sustainable corporate practices (Source: https://www.bbtnews.com.cn/2023/0518/476265.shtml. (In Chinese).). This instance highlights green institutional investors’ capacity to significantly influence corporate ESG practices. Therefore, green investors can affect firm’s ESG ratings divergence through the internal governance capacity and push them to disclose ESG information.
From a theoretical perspective, based on agency theory and efficient market hypothesis, as firm’s important shareholders, green investors give full play to their ability to participate in corporate internal governance. Also, as external supervisors, they play a signaling role in investment decisions in the capital market to enhance external attention and supervision. Therefore, through the synergy of internal and external factors, green investors alleviate the information asymmetry between investee companies and external ESG rating agencies30, and thus inhibiting ESG ratings divergence.
Specifically, on the one hand, prior research indicates that institutional investor plays a crucial role in corporate green governance by promoting social responsibility disclosures31, expanding green investments, fostering green innovation15, and enhancing green governance performance32,33. Compared with other individual investors and institutional investors with financial and information advantages, green investors are institutional investors who are concerned about both the firm’s economic and social benefits. They govern the company by “voting with their hands” and “voting with their feet”34,35. They participate in the green decision-making of companies, prompting them to fully disclose environmental information, improve information transparency, alleviate the information asymmetry between them and external rating agencies, and then inhibit ESG ratings divergence29,36.
On the other hand, green investors’ investment behavior can send signals to other investors in the capital market, attracting external attention and supervision, enhancing external governance, alleviating information asymmetry37, and thus suppressing ESG ratings divergence. As an important capital market information intermediary, green investors can form an information advantage through their own professional investment ability and information mining ability38. Sending signals to the capital market through investment behavior can help companies establish or optimize their “green and low-carbon” image15, thus attracting the attention of more information intermediaries such as analysts in the capital market. Analysts’ attention can not only play the role of external supervision on firms39,40, but also provide a new source of information on firm’s ESG practices for external ESG rating agencies. For one, analyst attention can play the role of information intermediation and external monitoring, exerting external pressure on corporate management, which in turn enhances the transparency of corporate information41,42, and thus helps to inhibit ESG ratings divergence. Second, the increase in analysts’ attention will prompt them to publish more research reports and broaden the information acquisition channels of ESG rating agencies. Compared with other information, analysts’ research reports are more professional43, providing new sources of information for ESG rating agencies to conduct ratings, thus alleviating the information asymmetry between firms and rating agencies, and thus suppressing ESG ratings divergence.
Moreover, the exit threat44 from green investors might damage the company’s “green and low-carbon” image, causing the company to face the collapse of market trust45, which can be used to put pressure on the management to fully implement green governance and timely disclosure to the market to alleviate the information asymmetry, thus reducing the ESG ratings divergence.
Therefore, we hypothesize:
H1
Green investors can significantly inhibit ESG ratings divergence.
Research design
In order to examine the effect and mechanism of green investors’ influence on ESG ratings divergence, this section details the sample and data sources, variable measures, and model design.
Sample and data sources
Our sample starts with A-share listed firms from 2015 to 2023, excluding ST, ST* companies, listed companies in the financial industry, and listed companies with missing key financial data. The reason for selecting 2015 as the starting point of the sample is that the ESG concept has been widely spread since 2015, resulting in the emergence and continuous development of domestic ESG rating agencies. The financial data is obtained from the China Stock Market & Accounting Research database (CSMAR). We identify green investors referring to Jiang et al.46. We focus on mutual funds and identify green investors by analyzing their “investment objectives” and “investment scope” (The text of funds’ “investment objectives” and “investment scope” is from WIND database) through the method of text analysis. If words like “environmental protection”, “ecology”, “green”, “new energy development”, “clean energy”, “low carbon”, “sustainability” and “energy saving” appear, the fund is considered to be a green investor. The raw shareholding data used for the green investor calculation also come from the CSMAR database, and the ESG rating data are from four rating agencies, namely Sino-Securities Index Information Service, Wind, SynTao Green Finance and Susallwave. Our final sample consists of 24,161 firm-years’ observations. In addition, in order to eliminate the effect of extreme values, we winsorize the continuous variables by 1% up and down.
Variable definitions
Dependent variable
The dependent variable is ESG ratings divergence (ESGdif), which is measured by the standard deviation of the ESG ratings of the four rating agencies, namely, Sino-Securities Index Information Service, Wind, SynTao Green Finance and Susallwave, for the same company, with reference to Christensen et al.7. Since the rating standards of domestic and international ESG rating agencies have large differences, we select the data of four Chinese ESG major rating agencies for calculating the ratings divergence considering the data integrity and availability. In addition, we re-examine the effect of green investors on the ratings divergence indicator (ESGdif2) after adding the rating data from FTSE Russell and RKS. Specifically, ESG rating scores are set on a scale of 1–10. Among them, the Wind ESG rating is full of 10 points, the Sino-Securities Index Information Service ESG rating is 9 grades, taking the value of 1 to 9, which is normalized by multiplying it by 10/9, the SynTao Green Finance ESG rating is from D to A+, the A+ rating takes the value of 10, and the middle of the progressive, the Susallwave ESG ratings have a total of 19 grades, from AAA to C are assigned the value of 19 to 1, which is then normalized by multiplying it by 10/19. If a rating agency has multiple ratings for a company during the year, the average value is taken as the raw data.
Independent variable
The independent variable is green institutional investor. Firstly, we define green investors: if the mutual funds’ “investment objectives” and “investment scope” text contains the words such as “environmental protection”, “ecology”, “green”, “new energy development”, “clean energy”, “low carbon”, “sustainability” and “energy saving”, and so on, the fund is identified as a “green investor”. According to the fund’s holding details (all the stocks held by the fund) disclosed in the fund’s annual report (The data of stock investment details of all funds is from CSMAR database.), we match the green fund’s name and ID with all listed companies and then we identify the firms with green investors. Secondly, we use two indicators to measure green investors, which are the dummy variable of green investor (GI) and the natural logarithm of the number of green investors (lnGI, see46). If the number of green investors is bigger, the easier it is for the green investors to put pressure on the management, thereby affecting the ESG ratings divergence.
Control variables
In addition to the primary variables of interest, we also include control variables for other time-varying firm characteristics that may influence ESG ratings divergence. Drawing on previous studies7,27, we select the following series of variables as control variables, including the natural logarithm of the firm’s total assets in the year (Size), the number of years since the firm was listed (Age), return on assets (ROA), financial leverage (Lev), the firm’s cashflow (Cashflow), the growth rate of the firm’s operating income for the year (Growth), book-to-market ratio (BM), intangible assets ratio (Intan), the shareholding ratio of the largest shareholder (Top1), the percentage of institutional investor ownership (Inst), the percentage of independent directors (Indep), the nature of ownership of the firm (SOE), the natural logarithm of board size (Board), duality of chairman and CEO (Dual) and employee compensation (Salary).
Model design
In order to test the impact of green investors on ESG ratings divergence, we construct the model as follows:
Where \({ESGdif}_{i,t}\) is ESG ratings divergence, \({GI}_{i,t}\) is a dummy variable for the existence of green investors, \({lnGI}_{i,t}\) is the natural logarithm of the number of green investors, and \({Controls}_{i,t}\) are a series of control variables. In addition, model (1) controls for firm fixed effects and year fixed effects. We focus on the coefficient of green investor \({\beta }_{1}\). If \({\beta }_{1}\) is significantly negative, it indicates that green investors can reduce the ESG ratings divergence of the firms, and supports H1. The definitions of all variables in the model and how they are calculated are shown in Appendix A.
Empirical results
In this section, we first provide descriptive statistics of the main variables, and secondly show the results of empirical tests on the divergence of green investors’ ESG ratings. We also use a variety of robustness and endogeneity tests to enhance the reliability of our findings.
Descriptive statistics
Table 1 shows the descriptive statistics of the main variables. The mean value of ESG ratings divergence is 0.748, which is consistent with the findings of Berg et al.9. The mean value of the green investor dummy variable is 0.435, indicating that the percentage of listed companies with green investors in our sample is about 43.5%, and the mean value of lnGI is 0.569, indicating that most listed companies have more than one green investor. The rest of the control variables are basically consistent with the existing studies.
Baseline regression results
As discussed before, we expect the green investors to have a negative effect on corporate ESG ratings divergence. The results of the baseline regression are shown in Table 2. The dependent variables in Column (1) and (2) are four rating agency standard deviations, and the dependent variables in Column (3) and (4) are six rating agency standard deviations. The regression results indicate that green investors help to reduce firm’s ESG ratings divergence (coefficients of − 0.021 and −0.042, t-values of − 2.34 and − 5.61, significant at the 5% significance level and 1% significance level, respectively). The conclusions remain unchanged after replacing with the standard deviation of the six rating agencies. These results are consistent with our Hypothesis 1 that green investors are negatively associated with ESG ratings divergence.
Robustness tests
Changing the measurement of dependent variables
We conduct a series of robustness tests. We change the measures of the dependent variables in Table 3. To eliminate the impact of different scales and dimensions between different ratings, we use the dispersion of ESG ratings among the four rating agencies (ESGdif3) as the dependent variable (standard deviation divided by the mean of the ratings). The results are shown is Column (1) and (2). We further use the extreme variances of the four rating agencies as the dependent variables (ESGrange). The results are shown in Column (3) and (4). The results in Table 3 indicate that green investors still significantly reduce firm’s ESG ratings divergence after changing the dependent variable’s measurement.
Changing the measurement of independent variables and the regression model
We also change the measurement of the independent variables and the regression model in Table 4. Since there may be a herd effect in the investment behavior of green investors47,48, we use the difference between the green investors variable and the industry’s annual mean (abGI and ablnGI) as independent variables. The results are shown in Columns (1) and (2) of Table 4. Then we re-run the model using the original value of the number of green investors as an independent variable (GIn) in Column (3). In addition, since the values of the dependent variables are all greater than 0 and the data distribution is more suitable for the tobit model, we re-examine the effect of green investors on ESG ratings divergence using the tobit model in Column (4) and (5) of Table 4.
The results of Table 4 indicate that the conclusion that green investors significantly reduce corporate ESG ratings divergence remains unchanged after changing the measurement of the independent variables and changing the regression model.
Adding interaction fixed effects
Since the regional environment and industry characteristics may have an important impact on firm’s ESG practices49, we further add the year-industry and year-province interaction fixed effects to the regression model. The results are shown in Table 5. We find that green investors still significantly reduce corporate ESG ratings divergence after adding the interaction fixed effects.
Endogeneity concerns
Propensity score matching (PSM) analysis
To address potential concerns that firms with green investors and the firms without green investors might not be comparable, we re-estimate our regression model using samples after matching. Intuitively, we create matched pairs by identifying a firm without green investors that is similar with respect to the above-mentioned characteristics to a firm with green investors. We use a logit model to estimate a propensity score of firm’s assignment based on the firm’s characteristics variables. We perform the match through the nearest neighbor matching method and find the three nearest neighbors of the treated group. The results are shown in Table 6.
Panel A shows the balance test results of PSM. After matching based on the chosen variables, there is no significant difference between the samples. The bias of all variables is less than 10%.
Panel B, Column (1) of Table 6 shows the results of the first stage of matching. A total of 16,831 samples are obtained after matching. We re-run the regression based on the matched samples. The results are shown in Table 6, Panel B, Columns (2) to (5). The results show that based on the matched samples, green investors help to reduce corporate ESG ratings divergence, indicating that the conclusions remain unchanged.
Instrumental variables approach
To further mitigate the effect of reverse causality, i.e., firms with low ESG ratings divergence are more likely to attract more green investors, we conduct the regression using the instrumental variable method. We use the cube of the difference between the annual-industry mean and the value of main variables (IV1 and IV2) as instrumental variables50 to conduct the regression. The method of constructing instrumental variables proposed by Lewbel50 is a method to construct effective internal instrumental variables without the help of external factors. Using the above method to construct instrumental variables can help eliminate endogeneity bias to a certain extent.
The second stage results are shown in Column (5) and (6) of Table 7. Column (1) and (2) show the first stage regression results. The IVs are correlated with our main variables (GI and lnGI). Column (3) and (4) show the results of the exogeneity results. The IVs are not related with firm’s ESG ratings divergence. The above instrumental variables pass the weak instrumental variable test and the under-identification test (The p-value of Kleibergen-Paap rk LM statistic is less than 0.01 and the Kleibergen-Paap rk Wald F statistic is greater than the critical value of the Stock-Yogo weak identification test at the 10% level). The regression results in Column (5) and (6) show that green investors significantly reduce corporate ESG ratings divergence and the conclusions remain unchanged.
In order to further alleviate the endogeneity problem, we refer to Firth et al.51 and select legal person ownership (LPO) as another instrumental variable. The higher the LPO, the lower the shareholding ratio that green investors can obtain, while LPOs generally do not participate in corporate governance and have no direct relationship with ESG ratings divergence.
The results are shown in Table 7, Panel B. The second stage results are shown in Column (4) and (5). Columns (1)–(2) show the first stage regression results. The LPO is correlated with our main variables (GI and lnGI). Column (3) shows that LPO is not related with firm’s ESG ratings divergence. The IV passes the weak instrumental variable test and the under-identification test (The p-value of Kleibergen-Paap rk LM statistic is less than 0.01 and the Kleibergen-Paap rk Wald F statistic is greater than the critical value of the Stock-Yogo weak identification test at the 10% level). The second stage result shows that our conclusions remain unchanged.
Exogenous shock: difference-in-difference model
In order to further mitigate the reverse causality problem, the “Green Investment Guidelines (Trial)” released by the CSIFA in November 2018 is selected as an exogenous shock. Then we use the difference-in-difference model to test the relationship between green investors and ESG ratings divergence.
The guidelines clarify the meaning of green investment, stipulate the basic objectives and principles of green funds and clarify the basic approach to green investment. Also, the guidelines supervise the green fund managers’ practice of green investment. The intention is to gradually guide green fund managers to operate in a market-oriented, standardized and professional manner, cultivate a long-term value investment orientation, establish green investment behavioral norms, and avoid the uneven quality of green investment activities causing troubles to the industry.
After the release of this policy, the number of green funds shows a significant upward trend, so the policy has a significant impact on the number of green investors, while it does not have a significant link with the firm’s ESG ratings divergence. The regression model is shown in Eq. (2).
where Treat is a dummy variable assigned a value of 1 for the year in which the firm first appeared as a green investor and subsequent years, and 0 otherwise, and Post is a time dummy variable assigned a value of 1 for 2018 and subsequent years, and 0 otherwise. The cross-multiplier term Treat*Post takes the value of 1 to denote green investor holdings under the influence of exogenous shock.
Panel A of Table 8 shows the parallel trend test of the policy shock. The results show that the policy satisfies the parallel trend hypothesis. In the first three phases of policy implementation, the entry of green investors has no impact on firms ESG ratings divergence (All the pre* variables are not significant). After the policy is implemented, the entry of green investors can significantly reduce the divergence of ESG ratings (All the las* variables and the current variable are negatively significant).
The regression result is shown in Column (1) of Table 8, Panel B. Due to the existence of samples in which green investors enter and then exit, which do not meet the requirements of the DID model, we further exclude the above samples and re-run the model again, and the result is shown in Column (2) of Table 8. The regression coefficients of the cross-multiplier term and corporate ESG ratings divergence are significant at 1% significance level respectively, which proves that green investors shareholding can significantly reduce corporate ESG ratings divergence.
Further research
This section first examines the mechanism of green investors’ role in ESG ratings divergence. Secondly, we analyze the relationship between the two in terms of heterogeneity at the firm, industry, and regional levels. Finally, we test the economic consequences of green investors’ suppression of ESG ratings divergence. The tests in this section help to improve the research logic chain, enrich the research conclusions, and provide a decision-making basis for policy makers to encourage the development of green investors and thus enhance the efficiency of the capital market.
Mechanism tests
Information asymmetry mitigation
The role of green investors in reducing ESG ratings divergence works mainly through improving the transparency of firm information. Green investors, as external investors, face the problem of information asymmetry with management52. Different from other types of investors, green investors focus on both economic and social benefits, and they have more incentives to govern their shareholding companies, which leads to more transparent ESG disclosures, and thus reduces corporate ESG ratings divergence. How green investors mitigate information asymmetry through internal governance and external monitoring is discussed in the following two sections.
We select the degree of corporate earnings management, namely, discretionary accruals, as a proxy variable for information asymmetry (DisAcc). The higher the firm’s earnings management, the higher the degree of information asymmetry53. Compared with other proxy variables of information transparency, discretionary accruals are calculated based on the company’s financial data, not on the text information disclosed by the company, and are relatively objective. The regression results are shown in Table 9.
Column (1) and (2) of Table 9 indicate that green investors decrease the firm’s discretionary accruals (significant at 10% level and 1%, respectively). Column (3) indicates that the higher the firm’s discretionary accruals, the higher the corporate ESG ratings divergence (significant at 5% level). That is, green investors can mitigate corporate information asymmetry and thus reduce corporate ESG ratings divergence.
Internal control
From the perspective of internal corporate governance, green investors can play a role in corporate governance after entering54, improve the internal control, compress the space for management to engage in opportunistic behavior, and inhibit their strategic information disclosure behavior, thereby fully disclose the company’s ESG practice, and then decrease the firm’s ESG ratings divergence. Referring to Shu et al.55, we use the internal control index (IC) as the proxy variable for internal control. (The data is from the Dibo database. The index covers seven indicators related to internal control, including the internal environment, control activities, risk assessment, information communication, supervision and inspection, whether the accounting firm issues an evaluation report, and whether the independent directors and the board of supervisors express their opinions, which comprehensively and objectively reflects the internal control situation of the enterprise. We acknowledge that some of the indicators of the index rely on the public disclosure of companies, which may be biased. Therefore, we have added other tests (Section “Corporate governance”) to the analysis of internal corporate governance in order to enhance the credibility of our conclusions.) The higher the index, the better the company’s internal control. The results are shown in Table 10.
Columns (1)–(2) of Table 10 show that when the green investors enter the firm, the higher the level of corporate internal control. Column (3) shows that the higher the level of internal control, the lower the firm’s ESG ratings divergence (significant at the 1% level). It indicates that green investors help improve the level of corporate internal control, mitigate corporate information asymmetry and thus reduce corporate ESG ratings divergence.
External monitor
Green investors can play a signaling role in the capital market. They can attract more external monitoring attention, broaden the access to information of ESG rating agencies. More external attention can exert stronger external supervision on companies, alleviate information asymmetry, and thus reduce ESG ratings divergence. Green investors can attract more analysts’ attention56. The increased attention of analysts prompts them to publish more research reports, thus providing information sources for ESG rating agencies to conduct ratings. At the same time, the increase of analysts’ attention can also provide strong external supervision to the firms40, prompting them to improve the transparency of information, which in turn inhibits ESG ratings divergence. Drawing on previous studies, we use the natural logarithm of the number of following analysts (Ana) during the year to measure external monitoring. The relevant regression results are shown in Table 11.
Column (1) and (2) of Table 11 indicate that green investors significantly increase analyst attention. Column (3) shows that the higher the analyst attention, the lower the firm’s ESG ratings divergence (significant at 1% level). That is, green investors enhance firm’s external monitoring attention and thus suppresses firm’s ESG ratings divergence.
Cross-sectional analysis
Ownership heterogeneity
There are two distinct types of ownership structures in China: state-owned enterprises (i.e., firms controlled by various government entities) and non-state-owned enterprises (i.e., firms controlled by private investors). Compared with state-owned enterprises (SOEs), green investors in non-state-owned enterprises (non-SOEs) are more likely to reduce ESG ratings divergence. State-owned enterprises (SOEs) emphasize both economic and social benefits, and shoulder more national missions than non-SOEs57. Under the guidance of green policy, state-owned enterprises are more inclined to carry out ESG practices and make full and transparent disclosure of information, which results in lower ESG ratings divergence. On the other hand, non-SOEs are more targeted at economic benefits, and their environmental governance behavior cannot bring benefits to the enterprises in the short term, so they are more likely to “green washing” in information disclosure, which intensifies the difficulty for ESG rating agencies to obtain relevant information. As a result, green investors are more able to play the role of governance, improve their information transparency, and thus reduce ESG ratings divergence. In order to test the impact of ownership structure, we split the sample into state-owned enterprises and non-state-owned enterprises group for regression. The results are shown in Table 12.
The results in Table 12 indicate that in the non-SOEs, green investors can significantly reduce ESG ratings divergence, while in the SOEs, the relationship is not significant. The p-value of difference in coefficient between groups is significant at the 5% level. The results show that green investors’ governance role is more pronounced in the non-SOEs, which significantly reduce ESG ratings divergence.
Transparency of corporate environmental information
According to stakeholder theory, environmental information disclosure is a method of voluntary environmental regulation, through which companies that voluntarily disclose environmental information attempt to get external stakeholders to regulate and monitor their own environmental behavior58. The higher level of voluntarily environmental information disclosure, the lower the extent of information asymmetry between them and external rating agencies. Under the situation, the green investors have a relatively small impact on the divergence of ESG ratings. In contrast, in the mandatory disclosure group, the information asymmetry between the companies and the rating agencies is high, and the entry of green investors is a channel to broaden information for the rating agencies, thus helping to reduce the ESG ratings divergence of companies. So, we expect that green investors have a more pronounced effect on firms with lower environmental information transparency.
If firms disclose environmental information in the CSR report or in the environmental report, they are categorized as the voluntary disclosure group (VD), while the disclosure in the annual report is delineated as the involuntary disclosure group. The former has a higher degree of transparency of environmental information. The regression results of the subgroups are shown in Columns (1) to (4) of Table 13. The p-value of difference in coefficient between Column (1) and (2) is significant at the 5% level while the p-value of difference in coefficient between Column (3) and (4) is significant at the 10% level. In the lower environmental information transparency group, green investors significantly reduce ESG ratings divergence, while in the higher environmental information transparency group, the relationship is not significant. The results confirm our conclusions.
Corporate governance
We further test the moderating effect of corporate governance on the impact of green institutional investors on the ESG ratings divergence. Companies with higher board independence have better internal control environments, and their board can fully play a governance role. When the board independence is weaker, green institutional investors could better push the firms to disclose more transparent information and decrease the ESG ratings divergence. Therefore, we anticipate that the negative correlation between green investors and ESG ratings divergence will be more pronounced in the low board independence group. Additionally, the higher the equity dispersion, the lower the ownership concentration, and the greater the effect of green institutional investors’ intervention in corporate governance. On the contrary, when the equity dispersion is low, the green institutional investors don’t have enough motivation and ability to participate in the corporate governance.
We draw upon Liu et al.59, utilizing the percentage of independent directors to assess board independence. The sample is divided into high and low board independence groups according to the annual industry average value. Additionally, we use the sum of the shareholding ratios of the company’s second largest shareholder to the tenth largest shareholder as a proxy variable for equity dispersion (ED). Samples that exceed the annual industry average are classified into the high equity dispersion group, while those below this threshold are categorized as the low equity dispersion group.
The regression results are shown in Table 14, Columns (1) to (4). The results show that when board independence is lower (Panel A), equity dispersion is higher (Panel B), the green institutional investors can help decrease the corporate ESG ratings divergence. The p-value of difference in coefficient is significant at the 5% level in Panel A and Column (3) and (4) of Panel B, while the p-value of difference in coefficient is significant at the 10% level in Column (1) and (2) of Panel B. It reveals that in an environment more conducive to the governance role of green institutional investors, they can help improve the information transparency and decrease the corporate ESG ratings divergence.
Industry characteristics
The level of firm’s ESG disclosure is affected by the characteristics of the industries. Compared with non-heavily polluted industries, companies in heavily polluted industries face a higher degree of regulation and greater environmental risks, and receive more attention from regulators, the capital market and even the public60. Thus, the quality of environmental information disclosure is already at a higher level, and the effect of green investors in reducing the divergence of ESG ratings is not obvious under the situation. For firms in non-polluting industries, green investors can play the role of governance fully, prompting them to improve the transparency of information and enhance the attention of external supervision, thus significantly inhibiting ESG ratings divergence.
We split the sample based on the “Notice on the issuance of the Listed Company Environmental Verification Industry Classification and Management Directory” and the 2012 China Securities Regulatory Commission’s “Industry Classification Codes for Listed Companies”. We identify the firms as the heavy polluting industry firms (HPI) if they are in the industries with industry codes B06, B07, B08, B09, C17, C19, C22, C25, C26, C28, C29, C30, C31, C32 and D44. The regression results are shown in Columns (1) to (4) of Table 15. The p-value of difference in coefficient between Column (1) and (2) is significant at the 10% level while the p-value of difference in coefficient between Column (3) and (4) is significant at the 5% level. It reveals that green investors’ governance role is more pronounced in the non-heavily polluting firms, which significantly reduce ESG ratings divergence.
Pollutant type
We further explore the heterogeneous effects based on the classification of firms with different pollutant types. We classify companies into water pollution or air pollution based on the types and amounts of pollutant emissions. Samples that exceed the annual industry average are classified into the higher pollution group, while those below this threshold are categorized as the lower pollution group. The regression results are shown in Table 16. To be clearer, we focus on the main variable (lnGI).
In Column (1) and (2), the green institutional investors can help decrease the ESG ratings divergence regardless of water pollution level (The p-value of difference in coefficient between groups is greater than 0.1). In Column (3) and (4), the green institutional investors can help decrease the ESG ratings divergence in the lower air pollution group. The p-value of difference in coefficient between Column (1) and (2) is significant at the 5% level. The results reveal that ESG ratings agencies are more sensitive to the air pollution. In the higher air pollution group, even if green investors enter, they cannot reduce the ESG ratings divergence.
Environmental regulation
The level of regional environmental governance has a significant impact on the firm’s environmental information disclosure. After 2015, China’s government has paid more and more attention to the ESG practice behavior of listed companies, which provides assistance for green investors to govern the environmental information disclosure of enterprises. The level of regional environmental regulation can help to enhance the inhibitory effect of the green investors on the divergence of ESG ratings.
Referring to Chen et al.39, we use the frequency of the words related to “environmental protection” in the work report of the local government to measure the environmental regulation (ER). Samples above the annual-industry average are categorized as the high environmental regulation group, and the rest as the low environmental regulation group. The regression results are shown in Table 17, Columns (1) to (4). In the group with high degree of environmental regulation, green investors can significantly reduce ESG ratings divergence, indicating that regional environmental governance provides strong external environmental support for green investors to inhibit ESG ratings divergence. The p-value of difference in coefficient between Column (1) and (2) is significant at the 5% level while the p-value of difference in coefficient between Column (3) and (4) is significant at the 10% level.
Economic consequences
High ESG ratings divergence indicates that a firm’s ESG behavior cannot be unanimously recognized by rating agencies, which leads to greater uncertainty for external investors. In contrast, green investors can significantly reduce ESG ratings divergence, thereby improving the efficiency of capital market resource allocation. Stock liquidity is the blood of the enterprise, which is important for improving the efficiency of the capital market61. Therefore, it is necessary to test whether the green investors reduce ESG ratings divergence and then help to improve corporate stock liquidity, which in turn promotes the high-quality development of the capital market. Thus, we estimate model (3) to test the economic consequences of green investors after reducing ESG ratings divergence from the perspective of stock liquidity:
We refer to Jun et al.62 who use the average daily turnover rate as a proxy for stock liquidity (Liq) to conduct the regression. The results are shown in Table 18.
As shown in Column (1) of Table 18, the result indicates that the greater the ESG ratings divergence, the worse the firm’s stock liquidity. The results in Column (2) and (3) of Table 18 demonstrate that green investors mitigate the negative and significant relationship between ESG ratings divergence and firm’s stock liquidity (cross-multiplier terms are significant at 10% and 1% significance levels, respectively). Meanwhile, green investors can significantly enhance stock liquidity. The results in Table 18 indicate that green investors significantly reduce ESG ratings divergence after which it can significantly enhance stock liquidity and drive long-term sustainable corporate growth.
Conclusion
In recent years, ESG has received widespread attention globally, and ESG investment has played an important role in promoting corporate green transformation. However, due to differences in rating systems and information asymmetry, there are large differences in the ESG ratings of enterprises by different rating agencies, which increases the difficulty of the capital market in accurately assessing the ESG practices of enterprises, which in turn hinders the greening transformation of enterprises. Green investors, as institutional investors concerned with both economic efficiency and social responsibility, can alleviate the information asymmetry between corporations and rating agencies by promoting corporate environmental information disclosure and strengthening environmental governance. Although studies have explored the impact of green investors on corporate green governance behavior, there is still a lack of systematic analysis on their role in reducing ESG ratings divergence. We examine the impact and mechanism of green investors on ESG ratings divergence by studying the governance role of green investors both internally and externally in terms of information asymmetry mitigation.
We utilize text analysis to identify green investors among institutional investors. Based on a sample of A-share listed companies from 2015 to 2023, we investigate how green investors affect ESG ratings divergence. We find that the firms with green investors face the lower ESG ratings divergence. The above findings remain valid after reconstructing the main variables, changing the regression model, adding interaction fixed effects and other robustness tests. In addition, our conclusions remain unchanged after using the PSM approach, constructing instrumental variables, and using exogenous policy shocks to mitigate the endogeneity problem. Mechanism tests show that green investors play an inhibitory effect on ESG ratings divergence mainly through two channels: enhancing the quality of internal control and increasing corporate external monitoring. Meanwhile, we explain the impact of green institutional investors on ESG ratings divergence based on different contexts such as corporate ownership heterogeneity, environmental information transparency, industry characteristics and environmental regulations. Results show that green investors have a more pronounced inhibitory effect on ESG ratings divergence in non-SOEs, firms with lower environmental information transparency and poorer governance environment, in non-heavily polluting firms, especially the firms with lower air pollution, and firms located in regions with stronger environmental regulations. Finally, the economic consequence test shows that green investors help improve corporate stock liquidity and enhance the market’s resource allocation efficiency after reducing ESG ratings divergence.
We focus on the impact of green investors on ESG ratings divergence and provide new insights for mitigating ESG ratings divergence from the governance role of green investors, which has certain theoretical and practical significance.
Theoretically, we contribute to the literature by examining how green investors can mitigate ESG ratings divergence, a critical issue that challenges the reliability and comparability of firm’s ESG ratings. By focusing on the governance and signaling roles of green investors, our research highlights new mechanisms through which capital market participants may help align ESG evaluations. Furthermore, we explore the heterogeneous effects of green investors under different conditions, such as ownership structure, environmental information transparency, governance situations, industry characteristics, pollutant type, and regional environmental regulation. This analysis enriches existing studies on the determinants of ESG ratings divergence and expands the research on the influence of green investors in corporate governance.
Practically, our findings provide evidence-based insights to support the formulation of more effective green finance policies. For policymakers, our results underscore the importance of fostering and regulating green investors to enhance their governance capabilities. For capital market regulators, the study offers guidance on how to harness the potential of green investors to improve ESG-related practices. Additionally, for companies, our results serve as a call to enhance environmental information transparency and engage more actively with sustainability-oriented stakeholders. By doing so, a green governance ecosystem among multiple parties can be better cultivated to jointly address environmental challenges and promote long-term sustainable development.
However, our research has certain limitations. Our identification of ESG ratings divergence is not perfect limited by the data integrity and availability. Further research could conduct more survey on the ESG ratings agencies. Moreover, we do not distinguish between different types of green investors, which leaves open the possibility that some may engage in “greenwashing”. Future research could benefit from a multi-dimensional analysis of green investor behavior and its nuanced effects on corporate green practices.
Data availability
The datasets used and analysed during the current study available from the corresponding author on reasonable request.
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Acknowledgments
This work was supported by the PhD Research Startup Fund of Jiangsu University of Science and Technology (Grant No. 1042932504) and the General Project of Philosophy and Social Science Research in Jiangsu Universities.
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Yiyun Ge (First Author): Conceptualization, Methodology, Investigation, Visualization, Writing-Original Draft, Writing-Review & Editing. Ruixuan Zhang (Corresponding Author): Methodology, Supervision, Software, Formal Analysis, Data Curation. Hanbin Zhu: Methodology, Supervision, Software, Formal Analysis, Data Curation. We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
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Ge, Y., Zhang, R. & Zhu, H. Green investors and ESG ratings divergence. Sci Rep 15, 20410 (2025). https://doi.org/10.1038/s41598-025-05329-x
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DOI: https://doi.org/10.1038/s41598-025-05329-x
