The literature review in this study is divided into two main sections: the effects of green credit policies and the factors influencing corporate ESG performance.

First, the existing research on the effects of green credit policies can be broadly categorized into two areas: the macroeconomic impacts and the microeconomic impacts of these policies.

Macroeconomic impacts: Liu et al. employed a financial computable general equilibrium model to assess the systemic effects of green credit policies, focusing on industries such as paper manufacturing, chemicals, cement, and steel. Their findings indicate that these policies significantly suppress investment in energy-intensive industries (Liu et al., 2017). Lamperti et al. explored the relationship between climate change, green credit, and economic dynamics, testing a range of policy interventions. Their study concludes that green credit not only enhances corporate productivity but also reduces pollutant emissions, thereby improving environmental quality (Lamperti et al., 2021). Microeconomic impacts: At the corporate level, Lv et al. analyzed data from listed industrial firms between 2007 and 2022, using a difference-in-differences model to study the impact of the 2012“Green Credit Guidelines” on the green production efficiency of heavily polluting firms in China. Their findings reveal that green credit policies significantly enhance the green production efficiency of these firms, with the effect being particularly pronounced among firms with smaller commercial credit financing scales and lower capital utilization efficiency (Lv et al., 2023). At the financial institution level, Liu and Huang used data from Chinese banks and employed a structural vector autoregression model to explore the relationship between green credit issued by financial institutions and their financial risk management. Their results indicate that a positive shock in green credit issuance negatively impacts the financial risk management of these institutions. Conversely, a positive shock in financial risk management positively influences green credit issuance (Liu and Huang, 2022).

Secondly, in the research on corporate ESG performance, scholars have explored the influencing factors from various external perspectives, including securities regulations (Ji and Sun, 2024), carbon emission reduction policies (Li et al., 2024b), digital inclusive finance (Chen and Liu, 2024), and the uncertainty of external market environments (Wu et al., 2024). Additionally, numerous studies have confirmed the impact of corporate ESG performance on government-business relations (Qi and Wu, 2024a), the legal and environmental risks faced by companies (Xu and Yao, 2024), fund performance (Li et al., 2024c), and capital markets (Guo and Li, 2024).

In summary, the current research on green credit policies primarily focuses on examining their macroeconomic and microeconomic impacts. For instance, studies investigate the effects of such policies on industrial structure, the ecological environment, the positive performance of enterprises and financial institutions, as well as the pollution emission performance of enterprises. However, few scholars have examined the impact of policies on the ESG performance of heavily polluting enterprises. Likewise, research on corporate ESG performance has rarely considered the influence of green credit policies from the perspective of environmental regulation, particularly for heavily polluting industries. This study contributes to the existing literature on corporate ESG performance by providing new insights into the role of green credit policies, offering valuable reference points for future research.

Stakeholder theory posits each of these stakeholder groups exerts significant influence on the company’s operations and development (Ashraf et al., 2022). Green credit policies, by strengthening interactions between firms and their stakeholders, encourage companies to make more proactive improvements in environmental, social, and governance (ESG) aspects. Specifically, these policies can affect a firm’s ESG performance through two primary channels: altering the flow of funds and adjusting financing costs.

First, the alteration of fund flows forms the foundation of the policy logic. Under traditional credit mechanisms, companies primarily secure financing based on profitability, often neglecting environmental and social responsibilities. In contrast, green credit policies prioritize support for companies and projects aligned with sustainable development goals, particularly those focused on environmental protection, resource conservation, and low-carbon technologies. This shift in funding flows compels heavily polluting enterprises to improve their environmental performance to meet the requirements of green credit (Chen et al., 2022).

Second, the adjustment of financing costs serves as a direct incentive for improving corporate ESG performance under green credit policies. Typically, these policies are accompanied by lower loan interest rates and other financial incentives, which reduce the investment costs for environmental projects and green technologies. For heavily polluting companies, this means that adopting environmentally friendly measures not only reduces the risk of non-compliance with environmental regulations but also yields direct economic benefits. Conversely, companies that fail to improve their environmental performance may face higher financing costs or even exclusion from green credit support (Gao et al., 2022).

Based on the above analysis, this study proposes the following research hypothesis:

Hypothesis 1

The implementation of the green credit policy can enhance the ESG performance of heavily polluting enterprises.

The positive impact of green credit policies may vary depending on the ESG performance of the companies themselves. First, companies with poor ESG performance typically have more room for improvement. These firms may have significant deficiencies in environmental protection, social responsibility, and corporate governance, and the implementation of policies offers them substantial incentives for improvement. Given their lower baseline, the intervention of green credit policies can significantly enhance their ESG performance. Second, companies with poor ESG performance may rely on low-cost financing or other incentives provided by green credit policies to improve their environmental management and governance structures. Finally, for companies with strong ESG performance, their market competitiveness, financial health, and access to diverse financing channels reduce their reliance on traditional bank loans (Du and Xu, 2024). In some cases, these policies could even increase their financing costs and risks, thereby diminishing the influence of bank credit policies on these firms (Yang, 2015).

In addition, the positive impact of policies on the ESG performance of heavily polluting enterprises may vary depending on the nature of ownership and the size of the workforce. Regarding ownership structure, state-owned enterprises (SOEs) are often direct implementers of government policies and thus tend to exhibit high compliance with national policies. For private firms with relatively limited resources, the preferential financing conditions and market incentives provided by green credit policies are a significant driving force for improving their ESG performance (Qi and Wu, 2024b). While foreign-invested enterprises are often subject to the global ESG standards of their parent companies or headquarters, and thus typically already maintain high ESG standards and management systems. The marginal impact of domestic green credit policies on further improving their ESG performance is limited. Mixed-ownership enterprises, with their complex ownership structures that include both state and private or foreign components, exhibit more diverse decision-making mechanisms and interest demands. This complex internal governance structure may lead to less effective responses to green credit policies.

Regarding workforce size, smaller enterprises generally face greater financial and resource constraints and exhibit a higher dependence on external financing. The low-cost financing provided by green credit policies is highly attractive to small enterprises, giving them a strong incentive to improve their ESG performance to meet green credit standards. In contrast, larger enterprises typically have more substantial internal funds and resources and more diversified financing channels, resulting in a lower reliance on green credit policies. These enterprises may already have well-established ESG strategies and financial security, thus the marginal impact of policies on their resource allocation is smaller.

Based on the above analysis, this paper proposes the following research hypotheses:

Hypothesis 2

Compared to heavily polluting enterprises with better ESG performance, the green credit policy is more effective in enhancing the ESG performance of heavily polluting enterprises with poorer ESG performance.

Hypothesis 3

The green credit policy significantly improves the ESG performance of state-owned enterprises, private enterprises, and heavily polluting enterprises with smaller employee scales, while having no significant impact on other heavily polluting enterprises.

Existing research has demonstrated that the personal characteristics of corporate executives, including board members, supervisors, and senior management, significantly influence, and even determine, the development of a company (Hakovirta et al., 2023). Older executives typically possess more traditional business perspectives and may exhibit a certain degree of inertia in adapting to new technologies and management methods (Hao et al., 2023). They might approach the new requirements and changes imposed by green credit policies with skepticism, potentially resisting necessary adjustments or investments, or hesitating to embrace innovative technologies or management practices.

The internal control systems of a company also impact the effectiveness of green credit policy implementation. A robust and scientifically designed internal control system ensures that the requirements of policies are systematically enforced. Such systems typically include environmental risk management, internal auditing, and information disclosure mechanisms (Xu and Wang, 2024).

Finally, executives with a financial background often possess a deep understanding of financial policies, market operations, and their implications for the company. They usually have strong risk management capabilities, enabling them to accurately identify and address financial and operational risks posed by external market uncertainties (Carpenter et al., 2004).

Therefore, the following research hypothesis is proposed:

Hypothesis 4

The age of senior management in heavily polluting enterprises negatively moderates the positive impact of the green credit policy on their ESG performance. In contrast, effective internal control systems and senior executives with financial backgrounds positively moderate the policy’s effect on the enterprises’ ESG performance.

The research framework of this study is shown in Figure 1.

This study uses data from listed companies in the Shanghai and Shenzhen A-shares markets between 2009 and 2022. To ensure the reliability and consistency of the data, the following procedures were applied:

(1) Financial and insurance companies were excluded.

A dataset containing 3,154 enterprises with a total of 19,774 valid observations was finally obtained. Then, the sample data were matched based on the Management List of Industry Classification for Environmental Verification of Listed Companies issued by the Ministry of Environmental Protection of China in 2008 and the Guidelines for Industry Classification of Listed Companies revised by the China Securities Regulatory Commission (CSRC) in 2012 (Sun et al., 2019; Xin and Wang, 2021). Finally, the sample data were divided into two groups: heavily polluting enterprises (experimental group) and non-heavily polluting enterprises (control group). Industry codes for heavy polluting enterprises: B06, B07, B08, B09, B09, B11, C17, C18, C19, C22, C25, C26, C27, C28, C29, C30, C31, C32, C33, D44. All data in this article are sourced from CSMAR, WIND, and CNRDS databases.

The GCG issued by the China Banking Regulatory Commission in 2012 represents the top-level design of China’s green finance policy, which has since accelerated the advancement of green finance initiatives in the country. From the perspective of firms, the GCG constitutes an exogenous policy shock, allowing the creation of a quasi-natural experiment to evaluate the net effect of the policy on corporate ESG performance. Accordingly, this study draws on the research by Zhang and Hu (2022), categorizing firms directly impacted by the GCG as the treatment group and those not affected as the control group. A Difference-in-Differences (DID) model is employed to analyze the impact of the GCG on the ESG performance of high-pollution enterprises (Xiao and Dong, 2023). The specific regression model is outlined as follows: d i d = T r e a t i , t * A f t e r i , t E S G i , t , j = β 0 + β 1 * d i d + β 2 * C o n t r o l i , t , j + μ i + γ t + λ j + ε i , t , j (1) where E S G i , t , j represents the dependent variable, reflecting the corporates’ ESG performance. d i d is the interaction term between the policy dummy variable T r e a t i , t and the time dummy variable A f t e r i , t . C o n t r o l i , t , j denotes a set of control variables, while μ i , γ t and λ j represent individual fixed effects, time fixed effects, and province fixed effects, respectively, and ε i , t , j is the error term. Here, i , t   a n d   j denote firm, year, and province respectively. β 0 is the intercept of the model, and β 1 is the primary coefficient in the DID model, used to assess the impact of the GCG on corporate ESG performance.

To address potential issues of heteroscedasticity and serial correlation, all regression results in this study employ robust standard errors with clustering at the firm level.

The descriptive statistics for all variables are presented in Table 2, including the sample size (totaling 19,774 observations), sample mean, median, minimum, and maximum values. As shown in Table 2, the average ESG performance of Chinese listed companies between 2009 and 2022 is 4.33, with a median of 4, a minimum of 1, and a maximum of 8, and the variance is 0.935, indicating that the ESG scores of Chinese listed companies are generally low and exhibit significant variation across firms.

Table 3 presents the regression results for Model 1. Columns 1 through 3 show the impact of the GCG on high-pollution corporate ESG performance under three conditions: without control variables, with control variables, and with control variables along with various fixed effects. The results indicate that, regardless of the inclusion of control variables and fixed effects, the influence of the GCG on high-pollution corporate ESG performance is positive and statistically significant at the 1% level.

More specifically, the coefficient of the key explanatory variable ″ d i d ″ in the third column is 0.298 and is statistically significant at the 1% level. This indicates that, compared to the control group (firms not affected by the green credit policy), the experimental group (firms influenced by the policy) experienced an average 29.8% improvement in ESG performance after 2012, thereby confirming Hypothesis 1.

Additionally, Columns 4 through 6 display the effects of the GCG on high-pollution corporate environmental responsibility (E), social responsibility (S), and corporate governance (G). The regression results reveal that the estimated coefficients for the core explanatory variable, ″ d i d ″ , are all positive and significant at the 1% level. Moreover, the policy’s impact on high-pollution corporate governance (G) shows the highest regression coefficient, at 1.996. This suggests that the GCG encourages high-pollution companies to improve their environmental practices, assume social responsibility, and enhance internal governance, thereby enhancing their overall ESG performance, which confirms Hypothesis 1.

The dynamic effect model examines the sustained impact of the “Guidelines” on the ESG performance of heavily polluting enterprises over different years. The specific regression results are shown in Table 4.

Where d i d _ 2012 represents the differential effect of the 2012 GCG on the ESG performance of heavily polluting enterprises compared to non-polluting enterprises. Similarly, d i d _ 2013 to d i d _ 2022 carry the same interpretation for subsequent years. As shown in Table 4, from the implementation year of the “Guidelines” in 2012, the coefficient for the d i d _ y e a r shows a positive trend, significant at the 1% level. Additionally, the absolute value of the d i d _ y e a r coefficient generally increases over time, reaching its peak in 2020 at 0.562. This indicates that the GCG has had a positive and increasingly significant impact on the ESG performance of enterprises over time.

However, it is worth noting that after 2021, the impact of the GCG on the ESG performance of heavily polluting enterprises becomes insignificant, with the regression coefficients diminishing. This suggests that the long-term effect of the GCG is gradually weakening. A possible explanation for this is that, after 2021, as China was grappling with the severe impact of the pandemic, local governments and financial institutions may have gradually relaxed the loan restrictions associated with the Green Credit Policy to help more enterprises recover. This relaxation in policy enforcement likely reduced the impact of the policy on corporate ESG performance.

To more comprehensively analyze the impact of the GCG on the ESG performance of heavily polluting enterprises, especially in evaluating policy effects across different quantiles, this section employs a panel quantile regression model. The specific regression results are detailed in Table 9.

From Table 9, it can be observed that at the 10th and 50th quantiles of the sample data, the estimated coefficients of the core explanatory variable are positive and significant at the 1% level, with the regression coefficient at the 10th quantile being greater than that at the 50th quantile. At the 90th and 99th quantiles, the estimated coefficients of the core explanatory variable are positive but not significant. These results indicate that the “Green Credit Guidelines” have a significant impact on enterprises with lower ESG performance.

The observed phenomenon can be explained by the following factors: For firms with poor ESG performance, their inadequate environmental practices hinder their ability to meet the lending criteria and financing conditions stipulated by green credit policies. This limitation significantly restricts their growth prospects. To secure financing or expand their operations, such firms are compelled to actively enhance their environmental performance to comply with the standards set by policies, thereby improving their overall ESG performance. Conversely, firms with already great ESG performance are more likely to naturally satisfy the policy’s lending requirements due to their superior environmental practices. As a result, these firms do not need to undertake additional efforts to obtain green credit. Furthermore, these firms may have already invested extensively in environmental protection and social responsibility. Consequently, the marginal benefits of further improvements for obtaining GCG diminish, leading to a lack of significant additional positive impact from the GCG for such firms.

The impact of GCG may vary depending on the ownership structure and the employee size of a company. In this study, we group the sample data based on ownership structure and employee size to analyze these differences. In terms of ownership structure, the sample is divided into four categories: state-owned enterprises (SOEs), private enterprises, foreign-funded enterprises, and mixed-ownership enterprises. We then conduct separate regressions for each group, with the results presented in Table 10. The findings indicate that the policy has a significant positive effect on SOEs and private enterprises, while the impact on non-SOEs and foreign-funded enterprises is not significant. Regarding employee size, following the approach of previous scholars (Liu et al., 2023), the sample is split into large-scale enterprises (above the median employee size) and small-scale enterprises (below the median employee size). The regression results are displayed in Table 11. As shown in Table 11, the policy does not have a significant effect on large-scale enterprises but does have a significant positive impact on small-scale enterprises.

The reasons for these observed phenomena may be as follows:

Ownership Structure: Firstly, in China’s economic system, SOEs often enjoy substantial policy and resource support and bear a portion of social responsibilities. As a result, when the GCG is introduced, SOEs can respond quickly and leverage these policies to enhance their ESG performance. Secondly, although private enterprises do not have the same level of policy advantages as SOEs, they face intense market competition. To maintain competitiveness, private enterprises may be more inclined to improve their ESG performance to meet the requirements of the policy, thereby expanding their financing options.

Employee Size: Large-scale enterprises typically possess more abundant resources and stronger financing capabilities, allowing them to access funds through various channels beyond green credit. Even if green credit policies are in place, these enterprises may not rely on this specific policy to meet their financing needs, leading to a negligible impact. In contrast, small-scale enterprises have relatively limited resources and financing channels. GCG offers them a crucial financing opportunity, making them more motivated to improve their ESG performance to meet policy requirements and secure the necessary financial support.