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Article

ESG, Climate Risk, and Debt Management—Evidence from Chinese Listed Companies

by
Yang Zhao
*,
Kamarul Bahrain bin Abdul Manaf
and
Hazeline bt Ayoup
College of Business, Universiti Utara Malaysia, Sintok 06010, Malaysia
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2025, 13(3), 118; https://doi.org/10.3390/ijfs13030118
Submission received: 6 May 2025 / Revised: 13 June 2025 / Accepted: 19 June 2025 / Published: 1 July 2025

Abstract

The United Nations Sustainable Development Goals emphasize the need to assist developing countries in achieving long-term debt sustainability. Global corporate debt has repeatedly reached record levels, and the associated financial costs pose a significant threat to sustainable development. This study uses panel data from Chinese listed companies for regression analysis. The findings show that ESG reduces the interest-bearing debt ratio, the equity pledge of controlling shareholders, and the deviation from the target debt ratio, all of which contribute to improved debt management. Climate risk further strengthens the impact of ESG on debt management. Additionally, green credit policies help reduce the interest-bearing debt ratio in high-pollution industries through ESG practices.

1. Introduction

Target 17.4 of the United Nations Sustainable Development Goals calls for policies that support developing countries in achieving long-term debt sustainability, promoting debt financing, debt relief, and debt restructuring (Slimani et al., 2024). As 2025 marks the final five years of the United Nations’ Sustainable Development Goals initiative, debt financing plays a crucial role in providing the financial support necessary for these sustainable development efforts (Haughton & Keane, 2021; Yunita et al., 2023). However, corporate debt levels across countries have reached unprecedented highs (Jordà et al., 2022). Both developed and developing nations face systemic risks arising from credit booms (Karlström, 2025). Therefore, prudent debt management is vital for ensuring long-term corporate growth (Haughton & Keane, 2021).
Several studies have explored the effect of ESG on corporate debt financing (Khan et al., 2024). Additionally, research suggests that national policies, market value assessments, and environmental factors, including climate risks, influence corporate debt financing decisions (Y. Li & Zhang, 2023; Subhani et al., 2021; Y. Zhao et al., 2024). However, the existing literature lacks a comprehensive theoretical framework to evaluate these factors in detail. Furthermore, several important questions remain unexplored. First, it is unclear whether companies are motivated to achieve debt management by improving their ESG practices. Second, the role of climate risk management in the relationship between ESG and debt management requires further investigation. Third, more research is needed to understand how green credit policies and real estate financing restrictions influence corporate debt management. Finally, while higher ESG ratings are often linked to increased green innovation capacity (J. Wang et al., 2023), green innovation itself requires financial support. The impact of debt financing on corporate green innovation remains an underexplored area.
Companies with strong ESG can enhance investor confidence by lowering financing costs (Apergis et al., 2022). In corporate operations, debt management is primarily driven by business managers. When ESG practices create more low-cost financing opportunities for a firm, managers make debt financing decisions based on a comprehensive evaluation of the company’s current situation and future prospects. Managers may choose not to take full advantage of the financing benefits offered by ESG, thereby maintaining the existing debt level. From an expansionist perspective, lower financing costs allow the firm to engage in debt financing at a reduced cost of capital (Morrone et al., 2022). Managers might use ESG to increase interest-bearing debt, which would raise the company’s financial costs and negatively impact its sustainable development. On the other hand, corporate managers can leverage the low-interest financing associated with ESG to repay high-cost existing debt (Y. Cheng et al., 2025), thus reducing the overall debt burden and optimizing debt management. In conclusion, ESG provides managers with financing advantages for debt adjustments. However, effective debt management requires managers to be motivated to use ESG to reduce interest-bearing debt.
Adeneye et al. (2023) suggest that ESG does not significantly impact current leverage levels, possibly due to external environmental factors. As a result, despite the financing benefits associated with ESG, corporate debt decisions may vary depending on these external influences. The signing of the Paris Agreement has drawn increased attention to climate risks (G. Yang et al., 2024), prompting countries to implement various policies addressing corporate climate risk. For instance, the US has introduced mandatory climate risk disclosures (Carattini et al., 2022), while China has implemented carbon reduction policies (Xian et al., 2024). Managing corporate climate risks requires financial support and adjustments in debt financing costs (Ginglinger & Moreau, 2023), making it a key factor in corporate debt management. Companies need to secure funds to cover the costs associated with climate risk transfer and the physical risks posed by climate disasters (H. H. Huang et al., 2022). Climate risks, as a source of external uncertainty, significantly increase a company’s financing constraints and operational costs (Bolton & Kacperczyk, 2021). In high climate-risk environments, external investors and financial institutions may reassess a firm’s ability to repay debt, resulting in higher financing costs or reduced financing support. This, in turn, exacerbates the company’s financing challenges (Ginglinger & Moreau, 2023). Therefore, climate risk management may moderate the relationship between ESG and debt management, although few studies have explored this mechanism.
Trade-off and agency theories argue that corporate debt decisions primarily focus on internal factors, such as tax shields, bankruptcy risks, and agency costs, which influence debt financing choices (Bajaj et al., 2021; Kumar et al., 2020). However, previous theoretical research has predominantly concentrated on internal corporate factors (Y. Cheng et al., 2025; Esghaier, 2024). The institutional logics theory offers a fresh perspective on understanding the organizational management of sustainable debt, considering both internal and external factors. This approach provides a more comprehensive view of how ESG, climate risk management, and industry-specific financing restrictions impact debt management. According to the institutional logics theory, corporate behavior is shaped and constrained by various institutional logics (Lounsbury et al., 2021). Corporate debt management falls under the corporate logic. Reducing financial costs can enhance firm performance, therefore, companies need to reduce the interest-bearing debt and aim for a target debt ratio to achieve financial sustainability (Aleknevičienė & Stralkutė, 2023; Bissoondoyal-Bheenick et al., 2023). When stock prices decline, equity pledge financing requires continuous replenishment of the collateral, making it necessary for managers to reduce the proportion of this high-risk financing method (L. Wang et al., 2025). Industry financing policies belong to the national logic. The government constrains corporate financing through policies such as real estate loan restrictions and the Green Credit Guidelines. ESG, as a market rating, belongs to market logic. High-polluting firms can better utilize green credit policies to achieve debt management goals by improving their ESG ratings. This illustrates the complementary relationship between market logic and national logic.
Companies enhance their ESG to improve their market valuation (market logic), which in turn provides them with financing advantages (K. Guo et al., 2024). When managers are motivated to improve debt management, these financing advantages facilitate adjustments to the interest-bearing debt ratio.
However, the effectiveness of this logic is significantly influenced by dynamic changes in the external environment. The exposure of firms to climate risks falls under the realm of natural logic. As climate risks—especially physical risks—receive increasing attention from capital markets (S. Y. Zhang, 2022), firms with higher transition climate risks face greater difficulty in obtaining financing and require more capital to mitigate these risks (Ginglinger & Moreau, 2023). The natural logic of this situation exacerbates the financial burden on firms, leading them to rely more heavily on the financing advantages brought by ESG ratings in order to reduce the interest-bearing debt (H. H. Huang et al., 2022). Additionally, when national logic is dominant, as in China, the introduction of the green credit policy in 2012 has encouraged heavily polluting industries to enhance their ESG (F. He et al., 2024). The empirical results show that high-polluting firms can better mitigate the financing constraints imposed by green credit policies by improving their ESG ratings. This assists firms in achieving their debt management objectives. This illustrates the complementary relationship between market logic and national logic. The current study suggests that debt management is not driven by a single logic but rather by the interplay of multiple institutional logics.
The current study uses panel data from Chinese listed companies covering the period from 2009 to 2021. First, China is the largest emerging market globally, and its vast market data provide robust support for the research. Second, China has a comprehensive industrial structure, ensuring that the findings are applicable and representative across various industries. Furthermore, governments in emerging markets often possess significant industry intervention capabilities, and China’s policy implementation power and intensity of industry regulation make it particularly representative of this group. As a result, the findings offer valuable insights for other emerging markets. Additionally, China’s capital markets are increasingly emphasizing corporate ESG practices (Lai & Zhang, 2022), with the government encouraging companies to proactively address transition risks, which, in turn, impact their debt management.
Although China has strong policy interventions, European countries have a head start and place greater emphasis on ESG practices. Europe has implemented several financing policies focused on green environmental protection, ESG, and climate risks (Balp & Strampelli, 2022; Oliver Yébenes, 2024). Furthermore, India launched the National Renewable Energy Action Plan (NREAP) in 2015 and provides financing support for green projects through the Indian Renewable Energy Development Agency (IREDA) (Soylu et al., 2024). At the same time, the Securities and Exchange Board of India (SEBI) introduced ESG disclosure requirements in 2021, mandating listed companies to report their environmental and social impacts (Singh, 2022). While the policy interventions in these countries differ from those in China, their impact on corporate ESG practices and debt management may operate through similar mechanisms. Therefore, the conclusions of this study can, to some extent, provide insights for other countries and capital markets that emphasize ESG.
In addition, China is one of the countries most exposed to climate risks, and physical risks significantly affect corporate debt management (S. Chen et al., 2024). Finally, unlike in developed countries, equity pledge financing is more common among Chinese companies and serves as a key financing mechanism (Shi et al., 2023). Choosing Chinese companies as the subject of this research allows for an exploration of whether equity pledging by controlling shareholders serves as a significant mechanism in debt management.
The following are the main contributions of this study.
First, the current study expands on the institutional logics theory by integrating state, market, corporate, and natural logics into the framework of debt management. The institutional logics theory does not explain the interactions between different logics. This study argues that the influence of institutional logics varies according to the goals of the firm. This study focuses on the optimization of corporate debt management (corporate logic). This study highlights how different institutional logics interact by influencing the allocation of financing within firms.
Second, this study reveals the complementarity between market logic and corporate logic. This study finds that firms actively improve their ESG ratings to reduce the interest-bearing debt ratio, the equity pledge ratio of the controlling shareholders, and the deviation from the target debt ratio. Furthermore, this study highlights that firms obtain financing advantages by aligning with market logic through improving their ESG ratings. Financing advantages help firms achieve optimized debt management (corporate logic).
Third, this study reveals the relationship between national logic, market logic, and corporate logic. Financing policies fall under national logic. Real estate firms face a series of financing restriction policies, and empirical evidence shows that these firms cannot reduce their interest-bearing debt ratio by improving their ESG ratings. When the improvement in ESG ratings (market logic) does not meet the financing restriction requirements (national logic), firms are unable to reduce financing constraints through market logic. This prevents firms from achieving a reduction in their interest-bearing debt ratio (corporate logic). At this point, national logic conflicts with both market logic and corporate logic, with national logic taking precedence. Furthermore, the green credit policy imposes environmental requirements on firms, with ESG being one of the key environmental indicators. This prompts heavily polluting firms to place a greater emphasis on improving their ESG ratings to reduce the interest-bearing debt ratio. Empirical results show that, under the influence of the green credit policy, heavily polluting firms have reduced their interest-bearing debt ratio more than firms in other industries. Firms can better align with national policies by improving their ESG ratings (market logic). This allows firms to obtain greater financing advantages to reduce their interest-bearing debt ratio (corporate logic). This demonstrates the complementarity between national logic, market logic, and corporate logic.
Fourth, this study reveals the relationship between national logic, natural logic, and corporate logic. The empirical results show that climate risk significantly increases companies’ capital demand, thereby amplifying the impact of ESG on reducing the interest-bearing debt ratio. This indicates that natural logic and corporate logic are in conflict, as the financing constraints caused by climate risk (natural logic) force firms to rely more heavily on the financing advantages provided by ESG (market logic). Furthermore, as climate risk intensifies, the impact of green credit policies on the interest-bearing debt of heavily polluting firms becomes more significant. Specifically, the intensification of climate risk (natural logic) increases the financing constraints faced by firms, compelling them to place greater emphasis on improving their ESG performance (market logic) in order to reduce financing costs and secure more green capital. Firms reduce financing constraints by improving their ESG performance, thereby achieving the goal of reducing their interest-bearing debt (corporate logic). This process reflects the interaction and synergistic relationship between natural, corporate, and national logics.

2. Literature Review and Hypothesis Development

2.1. Literature Review

The United Nations Sustainable Development Goals emphasize the importance of helping developing countries achieve long-term debt sustainability (Luu et al., 2024). Enterprises play a crucial role in sustainable social development (Oliveira-Duarte et al., 2021). However, global corporate debt levels continue to rise, and addressing the sustainable development of corporate debt financing has become increasingly important (Cortina et al., 2021; Subhani et al., 2025). High debt costs expose companies to greater financial risks, including liquidity constraints and default risks (H. H. Huang et al., 2022). Flammer (2021) suggests that companies adopting sustainable financial practices, such as prudent debt management, face lower capital costs and have better access to green financing. This, in turn, supports the further development of sustainable innovation activities. Debt management aims to optimize a company’s capital structure by reducing reliance on high-cost interest-bearing debt, thereby preserving cash flow for operations and strategic initiatives (Sun et al., 2022).
In terms of financial stability, high interest-bearing debt increases repayment pressures and financial distress risks, as highlighted by Sadiq et al. (2023). Reducing such debt helps mitigate these risks, ensuring that firms or governments can meet their obligations without default—a cornerstone of debt management. From a resource allocation perspective, Daly et al. (2022) emphasize how excessive interest payments on interest-bearing debt divert resources away from sustainable development priorities (e.g., SDGs). Reducing these liabilities frees up funds that can be reinvested in such areas as health, education, or green initiatives, thus promoting long-term sustainability. Regarding fiscal sustainability, Checherita-Westphal et al. (2014) argue that debt management involves controlling the interest-bearing debt to prevent high interest costs from undermining fiscal policy. Lowering the interest-bearing debt is essential for maintaining sustainable financial strategies (Hartley & Kallis, 2021).
Numerous scholars have examined the impact of ESG on corporate leverage. Apergis et al. (2022) find that stronger ESG generally enables companies to issue bonds at lower costs, as investors perceive these companies to have better risk management, thereby reducing default risk. K. Guo et al. (2024) suggest that ESG reduces financing costs by fostering stakeholder trust and enhancing a company’s market reputation. Asimakopoulos et al. (2023), in their study of US companies, found that firms with strong ESG are motivated to avoid issues such as debt overhang and underinvestment. They also argue that ESG provides valuable information to lenders, facilitating easier access to internal financing channels, such as bank loans, which are preferred over bond issuance. ESG can reduce both optimal leverage ratios and the degree of information asymmetry within companies. However, they also find that ESG has no effect on the current leverage level. Zahid et al. (2023) discover that companies with stronger ESG are more likely to reduce debt, particularly state-owned companies, which are more inclined to raise equity capital through the stock market. In contrast, K. Guo et al. (2024) found that in a Chinese context, ESG reduces information asymmetry and improves disclosure quality, thereby increasing the scale of corporate debt financing. X. Zhao and Zhang (2024) found that ESG indicators are positively correlated with the debt-to-asset ratio, while the environmental and governance scores within the ESG rating are negatively correlated with leverage. Finally, Adeneye et al. (2023) argue that ESG increases book leverage, as sustainability efforts often result in higher debt expenditures for companies.
The relationship between ESG and corporate debt management remains contentious. Some studies suggest that ESG reduces the costs of debt financing (Alves & Meneses, 2024; Apergis et al., 2022; Y. Cheng et al., 2025). However, a reduction in financing costs does not necessarily imply that corporate managers will adjust their debt levels. The key issue lies in whether managers are motivated to make such adjustments. Despite this, the existing literature does not fully explore whether companies are motivated to adjust their debt ratios through improved ESG. On one hand, as global corporate debt levels rise and economic growth slows (Heimberger, 2023; Park et al., 2022), companies face pressure from reduced profits and increased financial costs, motivating them to reduce debt. On the other hand, the growing global emphasis on sustainable development has made a firm’s ESG a critical indicator of its long-term competitiveness. Research shows that strong ESG enhances a firm’s financing capabilities by attracting more investor attention (Apergis et al., 2022). Therefore, companies may be motivated to reduce debt by improving their ESG. Additionally, there is a lack of a comprehensive framework that examines managers’ debt financing decisions after ESG enhancement from the perspectives of the firm, the state, the market, and the natural environment.
The intensification of climate change has prompted scholars to examine its significant impact on corporate financial decisions, particularly in relation to debt management (Ginglinger & Moreau, 2023). Climate risks are typically categorized into two types: physical risks and transition risks. Physical risks refer to the direct consequences of natural disasters (Bernstein et al., 2019), while transition risks involve business and financial challenges arising from changes in climate policies, technological advancements, and shifts in market preferences (Bolton & Kacperczyk, 2021). Climate risks have two main effects. First, companies facing high climate risks experience greater financing constraints (Ginglinger & Moreau, 2023). Second, to cover costs associated with transition risks and physical risks, companies’ financing needs increase (Ginglinger & Moreau, 2023).
Ginglinger and Moreau (2023) found that companies exposed to higher physical climate risks anticipate greater future financial distress costs. The impact on financial institutions’ capital supply is reflected in climate risk’s effect on company valuations. Naseer et al. (2025) suggest that climate risks can erode a company’s value, making it more difficult for companies to secure financing from financial institutions. In summary, climate risks increase a company’s capital needs to address climate-related challenges, while simultaneously reducing financial institutions’ willingness to provide financing. As a result, companies become more reliant on the financing advantages offered by ESG.

2.2. Theoretical Background

Numerous scholars apply the trade-off theory, the agency theory, and the market timing theory to explain corporate financing decisions (Abate & Kaur, 2023). The trade-off theory emphasizes the balance between tax shields and bankruptcy costs, while the agency theory focuses on agency costs arising from the conflict of interest between shareholders and management. The market timing theory, on the other hand, suggests that corporate financing decisions are influenced by stock price fluctuations, with companies timing their financing decisions based on market conditions (Ardalan, 2017). While these theories provide valuable insights into corporate financing decisions, they predominantly focus on internal firm factors, such as balancing tax shields and bankruptcy costs, reducing agency costs, and capitalizing on market timing opportunities. However, corporate financing decisions are not determined solely by internal factors. External elements, such as financing policies (W. Li et al., 2024a; Tran, 2021), social responsibilities (M. Guo et al., 2024), and the natural environment, also play significant roles in shaping decisions in various contexts (X. Zhou et al., 2025).
Previous theoretical frameworks, such as the trade-off theory and the agency theory, primarily focused on companies’ internal factors (Adeneye et al., 2023), overlooking the influence of the external institutional environment on financing decisions. Lounsbury et al. (2021) introduced the sociological theory of institutional logics to analyze organizational behavior. Since companies are a subset of organizations, we integrated the institutional logics theory into the theoretical framework for analyzing debt management decisions. Institutions are conceptualized as systems of rules that guide and constrain organizational behavior. Institutional logic, distinct from specific institutional rules, refers to socially constructed, historically embedded practices, assumptions, values, beliefs, and norms that significantly shape the decision-making processes of organizations and individuals (Lounsbury et al., 2021). In business management, this theory serves as a crucial framework for analyzing organizational behavior across diverse institutional contexts.
Firms inevitably face multiple institutional logics in their operations. Managers must assess the relative importance of various institutional logics (Mountford & Cai, 2023), including corporate imperatives, policies and regulations, and capital market evaluations (Mountford & Cai, 2023), among others. Firms must meet the requirements of market evaluations and national policies to secure external financing (Gallucci et al., 2025; Hongbin et al., 2025). For instance, firms obtain the financing they need by satisfying both national logic and market logic.
The framework of the institutional logics theory includes state logic, market logic, and corporate logic (C. Cheng et al., 2023). State logic reflects the government’s mandatory regulation of corporate behavior (Lounsbury et al., 2021). This study utilizes two forms of national logic: one is financing restriction policies, and the other is the financing priority for state-owned enterprises.
National logic exerts a limiting effect on corporate financing through a series of loan restriction policies for the real estate industry and the Green Credit Guidelines. The loan restriction policies for the real estate industry strictly regulate the qualification requirements that real estate firms must meet to obtain financing.
The Green Credit Guidelines restructure corporate financing channels by incorporating green ratings into the lending criteria of financial institutions (Zheng et al., 2025). For example, firms in highly polluting industries face restricted access to bank loans, forcing them to place a greater emphasis on improving their ESG ratings to secure financing approval from financial institutions.
Most Chinese financial institutions are state-owned and tend to finance state-owned enterprises with better qualifications (Larsen & Oehler, 2023; Yeung, 2021). As a result, non-state-owned enterprises face higher financing thresholds. Financial institutions often require non-state-owned enterprises to provide a sufficient collateral to secure financing, making equity pledging more common among non-state-owned firms facing financing constraints (M. Zhao et al., 2025). Furthermore, corporate stocks tend to have high volatility. When stock prices decline, firms must provide an additional collateral, making this an unstable form of financing detrimental to their debt management (J. Zhou et al., 2021). This illustrates the conflict between national logic and the corporate logic of non-state-owned enterprises.
Market logic emphasizes the role of capital markets in determining investment and financing decisions by evaluating a company’s value to maximize investor interests (Bitektine & Song, 2023; Thornton & Ocasio, 2008). ESG ratings, a form of capital market evaluation, fall under market logic. Heavily polluting firms can leverage the Green Credit Guidelines (national logic) by improving their ESG ratings (market logic) to achieve their debt management objectives. However, the real estate industry faces strict financing restrictions (Gong et al., 2025; Y. Liu, 2022), which limits the ability of real estate firms to alleviate their financing constraints through ESG improvements. This demonstrates that market and national logics can be complementary or in conflict, depending on the industry.
Corporate logic, on the other hand, is an institutional logic centered around market orientation, bureaucratic rational decision-making, and hierarchical organizational structures. It prioritizes efficiency, cost control, and profit maximization, guiding companies to achieve their goals through structured management models and commercial operations in a competitive environment (Lounsbury et al., 2021; F. Zhang & Welch, 2023).
However, the institutional logics theory overlooks the influence of the natural environment on organizational behavior, particularly as the natural environment and climate change are receiving increasing attention from governments and investors (Hansen, 2022). To address this gap, the current study proposes the introduction of a new dimension—natural logic—to enhance the existing theoretical framework.
This study uses corporate exposure to climate risks as a representation of natural logic. When firms face physical climate risks, they need to increase expenditures. To cope with natural disasters and cover losses from natural debt, firms’ financing demands increase (Ginglinger & Moreau, 2023). When firms face transition climate risks, they need to incur expenses for pollution control, thus increasing their financing needs (Ginglinger & Moreau, 2023). Natural logic intensifies firms’ financing burdens, making them more reliant on the financing advantages brought by ESG ratings (market logic) to achieve corporate debt management (corporate logic).

2.3. Hypothesis Development

From 2011 to 2023, the global GDP growth rate exhibited a fluctuating downward trend, decreasing from 4.3% to 2.8% (IMF, 2023). According to a World Bank report, the global potential GDP growth rate is projected to reach its lowest level in 30 years by 2030 (Kose & Ohnsorge, 2024). This slowdown in economic growth, coupled with a pessimistic outlook for future development, suggests that companies may seek to reduce liabilities associated with expansion investments. At the same time, corporate debt levels worldwide remain high, consistently reaching new peaks, with frequent debt crises (Jordà et al., 2022; Morelli et al., 2022). Since 2015, the Chinese government has placed a strong emphasis on reducing corporate leverage, implementing deleveraging policies for state-owned enterprises (X. Ling & Wu, 2022). The combination of slowing economic growth, high debt levels, and deleveraging policies has created an environment in which companies are motivated to reduce their interest-bearing debt to achieve debt management.
Debt management requires reducing corporate financing costs, enabling companies to secure new low-interest debt to repay existing high-interest debt (Devos et al., 2017). Scholars have noted that ESG can lower companies’ financing costs (Apergis et al., 2022). Reducing the interest-bearing debt is a reflection of corporate logic. When firms need to reduce their interest-bearing debt, they improve their ESG ratings to align with market logic.
Companies with strong ESG performance are generally more effective at managing relationships with external stakeholders, which helps secure additional resources and ease financing constraints. This resource acquisition is evident not only in equity financing, but also in a company’s ability to reduce reliance on external debt through strong ESG practices (Zahid et al., 2023). Furthermore, companies with stronger ESG are more likely to adopt long-term sustainable development strategies, which reduces their dependence on high-risk debt and mitigates the uncertainty associated with resource acquisition (Ademi & Klungseth, 2022). When firms improve their ESG ratings to meet market logic, they gain greater financing advantages to fulfill corporate logic. Therefore, enhancing ESG performance may motivate companies to reduce their interest-bearing debt ratio.
H1: 
There is a significant negative relationship between a company’s ESG and its interest-bearing debt ratio.
As of 31 December 2021, 2517 listed companies in China raised funds through equity pledge financing, with the total pledged market value reaching 576.422 billion US dollars (Z.-x. Huang et al., 2022). Major financial institutions in China are state-owned, and banks are more inclined to lend to state-owned companies in order to mitigate the risk of state asset loss (Yeung, 2021). Currently, only a few large, high-quality private companies secure low-cost funding through public listings and bond issuance, while most small and medium-sized private companies face unequal credit treatment (Liu et al., 2021). Therefore, for non-state-owned listed companies, equity—being a highly liquid form of the collateral—has become the preferred financing choice for better-performing firms.
Companies pledge equity to financial institutions, which then provide loans based on the current stock price (Pan & Qian, 2024). Due to stock price volatility, when a company’s stock price declines, it must either repay the loan or provide an additional collateral, leading to operational instability (S. Wang et al., 2024). As a result, equity pledges can be detrimental to the debt management of companies. This illustrates the conflict between national logic and the corporate logic of non-state-owned enterprises. Therefore, non-state-owned enterprises are more likely to improve their ESG ratings to secure alternative low-interest financing, thereby reducing the equity pledge ratio of controlling shareholders. The equity pledging by controlling shareholders is an unstable form of financing, which is detrimental to the financial health of the firm. Therefore, from the perspective of corporate logic, firms have a need to reduce the equity pledge ratio of controlling shareholders. Pledged funds are often converted into on-balance-sheet liabilities through related-party lending (Z. He & Wei, 2023). Equity pledge financing is widely used by controlling shareholders in the Chinese market. Controlling shareholders raise funds through equity pledges to alleviate financing constraints for their companies (Shi et al., 2023). In summary, ESG practices help companies secure financing, thereby reducing their reliance on equity pledge financing from controlling shareholders. When controlling shareholders pledge their equity and re-lend it to the firm, reducing equity pledge financing can lower the company’s overall debt level. This illustrates how firms align with market logic to achieve corporate logic.
The widespread use of equity pledge financing among publicly listed non-state-owned enterprises in China highlights the dominant influence of state logic in corporate debt financing decisions (Hu et al., 2024). Within the framework of systemic credit discrimination shaped by state logic, liquidity-driven collateral mechanisms emerge as the preferred solution for companies to overcome financing constraints. State-owned financial institutions’ credit preference for state-owned companies reflects state logic, which prioritizes the preservation of state assets (Yuan et al., 2022). This institutional arrangement marginalizes private companies in the formal credit market, forcing them to rely on market-driven financing tools, such as equity pledging (Y. Zhou et al., 2025).
In this context, controlling shareholders’ use of equity pledging to alleviate corporate financing constraints results from the interplay between corporate logic (debt management) and state logic (credit allocation policy). The conversion of pledged funds into on-balance-sheet liabilities further increases the company’s debt levels. Specifically, improved ESG disclosure can reduce creditors’ perceived risks related to environmental resilience and governance practices, allowing companies to access conventional debt adjustment channels without depending on collateral financing from controlling shareholders (Y. Cheng et al., 2025). This substitution effect reduces the need for debt accumulation driven by equity pledging, as market logic (through ESG) in financing strategies helps mitigate the financing constraints imposed by state logic.
Higher ESG performance contributes to corporate financing in multiple ways. First, ESG has a financing substitution effect, as strong ESG practices expand access to low-cost financing channels, such as green bonds and sustainable loans (L. T. Cheng et al., 2023), thereby reducing reliance on equity pledging. Second, ESG can help drive up a company’s stock price (Khan et al., 2024), meaning that the same amount of pledged equity can secure more funds, allowing controlling shareholders to lower their equity pledge ratio. In this way, ESG enhances corporate financing by reducing companies’ reliance on pledge financing from controlling shareholders. Since controlling shareholders often pledge their equity and re-lend the proceeds to the firm, reducing equity pledge financing can lower the firm’s overall debt level.
H2: 
There is a significant negative relationship between a company’s ESG and the pledging of shares by controlling shareholders.
Climate risk, as a manifestation of natural logic, reshapes the relationship among multiple institutional logics in debt management. Climate risk (natural logic) exacerbates firms’ financing needs. When firms face higher transition climate risks (natural logic), they encounter greater financing constraints (market logic). This illustrates how natural logic influences market logic. As climate risk intensifies, natural logic compels creditors to adjust their risk pricing frameworks in response to physical environmental shocks and transition pressures (S. Y. Zhang, 2022). This shift moves environmental resilience assessments from being a “voluntary disclosure” aspect of market logic to a credit decision requirement with financial materiality (Ilhan et al., 2023). This process strengthens ESG as an interface bridging market logic and natural logic. A firm’s carbon reduction capabilities and climate adaptation investments, demonstrated through ESG practices, can effectively mitigate creditors’ concerns about asset stranding risks and cash flow volatility induced by natural logic, thereby securing better terms in debt contracts. At this point, market logic (ESG) and natural logic (climate risk management) create a synergistic effect. The intensification of climate risk forces companies, due to their need to adjust debt management, to increasingly rely on the financing advantages provided by ESG.
H3: 
Improved climate risk management by companies has strengthened the impact of ESG on their debt management.
The Green Credit Guidelines issued in 2012 significantly restricted financing for heavily polluting companies (Z. Huang et al., 2023). The green credit policy mandates that companies disclose their environmental ratings (Cao et al., 2024). Additionally, the policy links environmental ratings to bank lending, transforming the environmental rating from a reference for investors into a strict criterion for lending to heavily polluting companies (Cao et al., 2024). As a result, this policy may amplify the impact of ESG on the debt management of heavily polluting companies. This illustrates the complementarity between national logic (Green Credit Guidelines) and market logic (ESG). In aligning with national and natural logic, corporate logic (debt management needs) is achieved.
H4: 
In industries subject to financing policy restrictions, the impact of ESG on debt differs from that in other industries.

3. Methodology

3.1. Sample

The current study examines Chinese A-share listed companies on Shenzhen and Shanghai Stock Exchanges from 2009 to 2021. Following the methodology of García and Herrero (2021), the initial sample underwent a three-step screening process. First, financial companies were omitted. Second, companies with significant data deficiencies were removed. Finally, we applied a 1% winsorization. After screening, the final sample contained 22,818 firm-year observations. Figure 1 is the research framework.

3.2. Empirical Model

The current study employs the following model
IDR i , t = α 0 + α 1 L E S G i , t - 1 + α 2 SIZE i , t + α 3 FIXED i , t + α 4 SOE i , t + α 5 C A S H F L O W i , t + α 6 G R O W T H i , t + α 7 BOARD i , t + α 8 T O P 1 i , t + α 9 BM i , t + φ i + φ t + ε i , t
  AbsDev i , t = α 0 + α 1 L E S G i , t - 1 + α 2 SIZE i , t + α 3 FIXED i , t + α 4 SOE i , t + α 5 C A S H F L O W i , t + α 6 G R O W T H i , t + α 7 BOARD i , t + α 8 T O P 1 i , t + α 9 BM i , t + φ i + φ t + ε i , t
P l a d g e S t o c k i , t = α 0 + α 1 L E S G i , t - 1 + α 2 SIZE i , t + α 3 FIXED i , t + α 4 SOE i , t + α 5 C A S H F L O W i , t + α 6 G R O W T H i , t + α 7 BOARD i , t + α 8 T O P 1 i , t + α 9 BM i , t + φ i + φ t + ε i , t ,
where i and t represent the company and year; ID R i , t is the interest-bearing debt ratio; AbsDe v i , t is the absolute value of the difference between a company’s target leverage ratio and its actual leverage ratio; P l a d g e S t o c k i , t is the proportion of equity pledged by the controlling shareholder; LES G i , t - 1 is the lagged one-period rating from the HUAZHENG database; SIZ E i , t is the firm size; F I X E D i , t is the fixed assets ratio; S O E i , t is the nature of ownership; C A S H F L O W i , t is the ratio of cash to the total assets; G R O W T H i , t is the total asset growth rate; BOAR D i , t is the number of board members taken as the natural logarithm of the value; T O P 1 i , t is the shareholding ratio of the largest shareholder; and B M i , t is the book-to-market corporate value. The error term is denoted as ε i , t . We controlled for year and firm fixed effects. This study employs standard errors clustered at the firm level.

3.3. Measuring of Variables

3.3.1. Measuring Debt Management

Interest-Bearing Debt Ratio (IDR)
The interest-bearing debt ratio is defined as the interest-bearing debt divided by the total assets.
Proportion of Shares Pledged by the Controlling Shareholder (PladgeStock)
The explanatory variable is the proportion of shares pledged by the controlling shareholder. Based on Yan et al. (2025), the current study uses the cumulative proportion of shares pledged by the controlling shareholder during the year as the measure.
The Firm’s Target Leverage Deviation (AbsDev)
The firm’s target leverage deviation (AbsDev) refers to the extent to which the firm’s actual leverage deviates from its target leverage, defined as follows:
A b s D e v i , t = | L E V i , t L E V i , t | ,
where L E V i , t represents the firm’s actual leverage and L E V i , t represents the firm’s target leverage.
L E V i , t = X i , t 1
Based on Ezeani et al. (2023) and Adeneye et al. (2023), this study employs the local equilibrium adjustment model (6):
L E V i , t L E V i , t 1 = λ ( L E V i , t L E V i , t 1 ) + ε i , t
By introducing model (5) into model (6), we obtain model (7):
L E V i , t = λ β X i , t 1 + ( 1 λ ) L E V i , t 1 + ε i , t
X represents the firm-level characteristics that influence leverage. Based on Ezeani et al. (2023) and Adeneye et al. (2023), the current study selects the following variables from the previous year: tangible assets (TangibleAsset), profitability (Profit), non-debt tax shields (TaxShield), firm size (Size), growth (MB), annual leverage average (LevMedian), where L E V i , t 1 represents the leverage of firm i in year t − 1, and λ represents the speed of leverage adjustment. This paper includes firm and year fixed effects in model (7). The current study estimates the capital structure adjustment model in Equation (7) using the least squares dummy variable (LSDV) approach and simultaneously estimates β to substitute into Equation (2) to obtain the target leverage L E V i , t .

3.3.2. Measuring ESG

Referring to the studies by Lu et al. (2024), Goodell et al. (2024), Jin et al. (2025), S. Chen et al. (2024), and H. Wu et al. (2024), we used the one-period lag of the ESG ratings from the HUAZHENG ESG database, which offers extensive data on Chinese listed companies over an extended time period.

3.3.3. Measuring Climate Risk Management

Due to insufficient climate risk disclosure, its uncertain impact, and its future-oriented nature, there is ongoing controversy surrounding the measurement of climate risk at the firm level. The annual report is a key document that reflects the business management of listed companies, and the presence of climate risk–related terms in the report indicates the firm’s level of attention to climate risk management (Ding et al., 2021). Q. Li et al. (2024b) conducted text analysis on earnings conference call transcripts from US-listed companies to measure climate risk using “climate risk” keywords. Following the methodology of Q. Li et al. (2024b) and Du et al. (2023), we combined English keyword sets with data from the financial reports of Chinese listed companies. By referencing Du et al. (2023) and applying text analysis and machine learning techniques, we filtered out words with low relevance to climate risk. As shown in Table 1, we identified an expanded “climate risk” vocabulary set containing 86 words. Financial reports of listed companies are essential public documents, and when these reports mention climate risk-related terms multiple times, it signals that the company is exposed to significant climate risk. We created a climate risk indicator by calculating the ratio of the frequency of these terms to the total word count in the annual reports.

3.3.4. Control Variables

Following Ademi and Klungseth (2022), Apergis et al. (2022), and Zahid et al. (2023), the current study controlled for some common indicators, including firm size (SIZE), fixed asset ratio (FIXED), cash flow ratio (CASHFLOW), growth (GROWTA), state-owned (SOE), the number of board members (BOARD), the ownership percentage of the largest shareholder (Top1), and the book-to-market corporate value ratio (BM).

3.3.5. Other Variables

The definitions of other variables are provided in Table 2.

4. Result

4.1. Descriptive Statistics

As shown in Table 3, the median of the interest-bearing debt ratio (IDR) was 0.201, while the median of the leverage ratio (LEV) was 0.448.

4.2. Baseline Regressions

As shown in Table 4, the results consistently showed a significant negative relationship between the LESG and the interest-bearing debt ratio (IDR), with a coefficient of −0.003. This finding suggests that companies can reduce their interest-bearing debt ratio, target debt deviation, and the ratio of equity pledge by improving ESG.

4.3. Robustness Test

4.3.1. Instrumental Variable

Based on the studies of Q. Li et al. (2024a) and Alves and Meneses (2024), the instrumental variables were selected as the average ESG of the other listed companies located in the same city as the firm (CITYESG) and the average ESG of the other listed companies in the same industry (INDUSTRYESG). First, the instrumental variables were correlated with the independent variable, which in this case referred to the firm’s ESG. Companies in the same industry and region operate in similar market environments and face comparable regulatory requirements and target audiences. When the ESG of some companies gains market recognition, other companies, in order to maintain competitiveness, often enhance their ESG (Alves & Meneses, 2024). Thus, the ESG of the other listed companies in the same industry and region influences that of the target firm. This influence arises not only from market competition and imitation behavior, but also from industry standards and regional policies. Second, regarding the exogeneity of instrumental variables, neither the average ESG of the other listed companies located in the same city as the firm (CITYESG) nor the average ESG score of the other listed companies in the same industry (INDUSTRYESG) was expected to have a causal relationship with the firm’s debt decisions (Q. Li et al., 2024a).
As shown in Table 5, the coefficient of LESG on the interest-bearing debt ratio (IDR) was −0.095.
Following the studies of Q. Li et al. (2024a) and Alves and Meneses (2024), the ESG averages for the same year, industry, and city were selected as instrumental variables for both the controlling shareholder’s equity pledge ratio and the deviation from the company’s target leverage ratio. Based on the study by X. Yang et al. (2024), the number of stocks held by “pan-ESG” funds was selected as an instrumental variable. The average ESG of companies in the same year, industry, and city encourages listed companies to pay more attention to their own ESG, but does not directly affect the pledge ratio of the controlling shareholders’ equity and the deviation from the company’s target debt ratio. The number of shares held by “pan-ESG” funds incentivizes companies to improve their ESG. First, in terms of relevance, as institutional investors, “pan-ESG” funds encourage companies to enhance their ESG through their shareholdings (Dyck et al., 2023; X. Yang et al., 2024). The underlying logic is that institutional investors express their preference for improved ESG by engaging with companies through private channels. This indicates a correlation between “pan-ESG” funds and companies’ ESG strengths. Second, in terms of exogeneity, “pan-ESG” funds independently determine the establishment and investment scale of ESG investment funds, and fund managers autonomously allocate the shareholding portfolios of these funds (X. Yang et al., 2024). The creation and holdings of ESG investment funds do not directly influence financing decisions.
As shown in Table 6, the coefficient of LESG on the firm’s target leverage deviation (AbsDev) was −0.003, and in Table 7, the coefficient of LESG on the controlling shareholder’s equity pledge ratio (PladgeStock) was −0.011.

4.3.2. Other Robustness Tests

We conducted other robustness tests.
(1)
Addition of control variables: drawing on W. Li et al. (2024b), the current study incorporated the chairperson of the board and the general manager who hold concurrent positions (DUAL) as shown in Table 8.
(2)
The variable was replaced from the interest-bearing debt ratio (IDR) with the leverage ratio (LEV) to examine the impact of different debt ratio indicators on the results as shown in Table 9.
(3)
To assess corporate ESG, the current study referenced and compared the applicability and coverage of several mainstream ESG rating databases. The MSCI ESG rating database primarily covers companies included in the MSCI ACWI Global Index or the MSCI China Index. As of 2021, the MSCI China Index included 423 companies listed in Mainland China (J. Z. Chen et al., 2025). MSCI’s ESG ratings are based on company annual reports, government databases, academic and non-governmental organization data, as well as alternative data sources, emphasizing data diversity and credibility (Gyönyörová et al., 2023).
The BloomBerg ESG database primarily relies on company information collected from its platform, with a sample largely consisting of large-cap firms that have comprehensive information disclosure (J. Tang, 2025). The covered indices include MSCI World, MSCI Emerging Markets, and CSI 300, with approximately 1200 Chinese listed companies recorded each year.
Given that the current study focused on the relationship between ESG ratings and debt management in Chinese firms, HUAZHENG ESG ratings were selected as the core indicator due to their extensive coverage of publicly listed companies in Mainland China. Furthermore, as a domestic rating agency, its ESG ratings are widely adopted by Chinese financial institutions (C. Li et al., 2022; Z. Wu & Chen, 2024). However, the data from MSCI and BloomBerg as international authoritative rating agencies are less influenced by Chinese-listed companies, and thus their ratings can somewhat mitigate the distortion caused by “greenwashing” in ESG assessments, providing a useful contrast for the empirical analysis.
In addition, the existing studies (Berg et al., 2022; Billio et al., 2021) have confirmed that there are significant discrepancies in the ratings of the same company by different ESG rating agencies, which may lead to bias in empirical results due to the choice of data sources.
Drawing on F. He et al. (2022; 2023), S. Wang et al. (2024), and X. Wang et al. (2024), some scholars have used the HEXUN ESG rating. As shown in Table 9, the regression results remain consistent with those of the main regression.
Drawing on L. Chen et al. (2023), M.-T. Chen et al. (2023), P. Zhao et al. (2024), and X. Yang et al. (2024), some scholars have used BloomBerg’s ESG disclosure score to study Chinese listed companies. The Morgan Stanley Capital International (MSCI) ESG database has included Chinese data since 2018. Drawing on Piserà and Chiappini (2024), this study used this database as the ESG rating for robustness checks.
As shown in Table 10, there were significant differences in the ESG ratings of Chinese listed companies across different rating agencies. The average ESG scores of HUAZHENG (LESG) and MSCI were relatively close, at 6.547 and 6.244, respectively. However, the standard deviation of MSCI ratings was 2.263, significantly higher than HUAZHENG (LESG)’s 1.122. This indicates that MSCI ratings exhibited greater dispersion in the sample and provided a higher level of differentiation in assessing company performance. Both MSCI and HUAZHENG (LESG) had a maximum rating of 9, reflecting a more optimistic approach in evaluating company performance. In contrast, HEXUN’s ESG rating had an average of 4.121, a median of 4, a maximum score of 8, and a standard deviation of 1.084, indicating a generally lower rating level compared to MSCI and HUAZHENG (LESG). HEXUN’s ESG ratings showed smaller fluctuations, suggesting that its rating method places more emphasis on basic ESG compliance and tends to be more conservative when scoring high-performing companies. BloomBerg’s ESG ratings cover 1249 Chinese companies, with an average of 27.225, a median of 26.748, a maximum score of 67.207, and a standard deviation of 8.374, indicating significant volatility.
As shown in Table 11, both MSCI and BloomBerg ESG ratings were associated with a reduction in the interest-bearing debt. However, the MSCI ESG rating was not significantly related to the target debt deviation or equity pledge. In the BloomBerg results, only ESG was not significantly related to equity pledge. The HUAZHENG and HEXUN ESG ratings are local Chinese ESG ratings, encompassing data from the majority of listed companies.
Table A1 presents the ratings of Chinese listed companies by four major ESG rating agencies (LESG, HEXUN, MSCI, and BloomBerg). The sample consisted of 89 listed companies, with a total of 263 company-year observations. In terms of the average ratings, LESG (HUAZHENG) had an average score of 7.764, a median of 8, a standard deviation of 0.877, and a score range from 4 to 9, indicating that companies generally received higher ratings with a relatively concentrated distribution. MSCI had an average score of 6.338, a median of 6, a standard deviation of 2.132, and a score range from 1 to 9, suggesting greater variability. BloomBerg’s rating system showed an average score of 38.535, a median of 36.687, a standard deviation of 9.168, and a score range from 19.304 to 61.634.
As shown in Table A1 and Table A2, we selected a sample that included HUAZHENG, HEXUN, MSCI, and BloomBerg ratings. The empirical results reveal a significant negative correlation between ESG and IDR, while ESG showed no significant correlation with the deviation from the target debt ratio or equity pledging. This suggests that, for certain company data, the ratings from all four agencies are relatively consistent. Furthermore, the ESG sample for Chinese companies from MSCI and BloomBerg was much smaller than that of HUAZHENG. Therefore, the differences in empirical results across the ESG ratings may have been due to variations in sample size.
According to the studies by G. Li and Cheng (2024), different ESG rating agencies (such as HUAZHENG, BloomBerg, and MSCI) produce significantly divergent ratings due to differences in methodology, weight allocation, and data coverage. HUAZHENG primarily relies on local disclosure information and direct communication, placing a greater emphasis on the governance dimension and being more inclusive of state-owned enterprises (Zahid et al., 2023; Y. Zhang & Yuan, 2025). In contrast, BloomBerg relies solely on publicly available data, with a relatively balanced weight distribution across the environmental, social, and governance dimensions, but is limited by the completeness of disclosures (Eliwa et al., 2021). MSCI uses proprietary databases and industry surveys, placing more emphasis on environmental risks and introducing a unique risk exposure score, which leads to a rating scope that significantly differs from that of other agencies (Berg et al., 2022).
Additionally, this may indicate that Chinese listed companies are more likely to enhance their HUAZHENG, HEXUN, and other local ESG ratings to facilitate financing. Since MSCI and BloomBerg are foreign institutions, their connections with Chinese financial institutions are less strong compared to local ESG rating agencies. As a result, the ESG ratings from MSCI and BloomBerg have not received sufficient attention from Chinese listed companies. This further supports the notion that companies actively improve their ESG ratings to achieve debt management goals.
(4)
The environmental dimension (E) has the largest number of quantifiable indicators as it relies on extensive physical measurement data (e.g., emissions, resource consumption) and technical metrics. In contrast, the social (S) and governance (G) dimensions include more subjective assessments, making them more difficult to quantify (Berg et al., 2022). Moreover, as a foreign rating agency, BloomBerg is less susceptible to influence from Chinese listed companies. Therefore, this study used BloomBerg’s environmental rating, a rating from a foreign agency, as an independent variable. This allowed for a more objective examination of ESG ratings, with fewer external influences, based on the environmental rating.
The relationship between ESG ratings and debt management is more objectively tested through the environmental rating. As shown in Table 12, BloomBerg’s environmental rating was negatively correlated with IDR, AbsDev, and PladgeStock, which was consistent with the benchmark regression results. However, only BloomBerg’s environmental rating and IDR were statistically significant. This may suggest that the ESG ratings of Chinese listed companies are susceptible to greenwashing. This further supports the idea that companies are motivated to actively improve their ESG ratings to achieve debt management goals.
(5)
This study replaced time fixed effects with a time trend to validate the model’s stability and ensure that the results were not influenced by specific time shocks. As shown in Table 13, this study used the time trend variable instead of annual fixed effects for robustness checks.
(6)
A robustness test was conducted by lagging the independent and control variables by one period, as shown in Table A3, with the results consistent with the baseline regression.

4.4. Further Analysis

4.4.1. Heterogeneity Analysis of ESG and Shares Pledged by the Controlling Shareholder

M. Li et al. (2019) highlighted that, although it is uncommon for controlling shareholders to pledge equity as a collateral for corporate loans in many developed economies, it is quite common in China. The high frequency of equity pledge financing by controlling shareholders in China is rooted in the unique state logic of emerging markets. In a financing environment dominated by state logic, private companies face greater difficulty in obtaining credit allocation compared to state-owned companies, prompting controlling shareholders to innovate financing channels through equity pledging. This situation is essentially a product of the conflict between corporate logic (corporate financing needs) and state logic. ESG, as a tangible representation of a firm’s alignment with market logic, significantly enhances a firm’s ability to obtain external financing, such as green credit and sustainable bonds, by building stakeholder trust and reducing environmental compliance risks. This, in turn, reduces the firm’s reliance on equity pledge financing from controlling shareholders.
Controlling shareholders often obtain funds through equity pledging, which helps ease financing constraints for companies (Shi et al., 2023). Better ESG is perceived as having lower environmental and market risks, which makes it easier for them to gain favor from investors and secure support from financial institutions in the capital markets (Zahid et al., 2023). This financing convenience reduces a firm’s reliance on internal funds, thereby decreasing the need for controlling shareholders to obtain funds through equity pledging. When a firm gains access to more external financing channels through ESG, the demand for equity pledging by controlling shareholders decreases, leading to a reduction in the equity pledge. Since controlling shareholders typically lend the funds obtained through equity pledging back to the firm, a decrease in the equity pledge ratio generally results in a lower firm debt ratio. As shown in Table 14, non-state-owned companies rely more on the pledging of shares by controlling shareholders to finance the firm. However, when the firm’s ESG improves, it reduces its reliance on share pledges by the controlling shareholders.

4.4.2. Moderating Effect Analysis

The Moderating Effect of Corporate Climate Risk Management
As shown in Table 15, LESG*ClimateRisk had a coefficient of −0.012. Following Hainmueller et al. (2019), Table A4 reports the linear combinations of the baseline and interaction effects. The results demonstrate the moderation effect of climate risk through robustness checks. This indicates that higher climate risk amplifies the negative impact of ESG on the interest-bearing debt ratio (IDR). The findings of this research indicate that both physical risk and transition risk notably magnify the influence of ESG on the interest-bearing debt ratio (IDR). However, the moderating effect of physical risk is more pronounced. First, physical risks, such as natural disasters, directly and urgently affect financial stability (Pagnottoni et al., 2022). Companies increasingly rely on the financing advantages offered by ESG to alleviate short-term financial pressures. Second, the impact of transition risks is typically long-term and gradual, allowing companies more time to adjust strategically (Semieniuk et al., 2021). Since the need for funds to address transition risks is less urgent than for physical risks, companies are less reliant on the financing advantages of ESG.

4.4.3. Analysis of Industries with Financing Restrictions

As shown in Table 16, in the real estate and construction industry, ESG is not correlated with the interest-bearing debt ratio, whereas it is significantly negatively correlated in other industries. Since 1993, China has prohibited real estate companies from going public and has tightly controlled the flow of bank funding into the real estate sector. The suspension of refinancing for listed real estate companies began in 2010 (Wei et al., 2014).
Since 1993, China’s real estate industry has been profoundly impacted by a series of financing restriction policies, reflecting the dominant role of national logic. In 1993, the State Council issued the Opinions on the Current Economic Situation and Strengthening Macroeconomic Control, which prohibited banks from lending to high-end real estate projects (such as villas and resorts) and required developers to have a minimum equity ratio of 30%, while strictly controlling the credit scale (Y. Tang et al., 2021). In 2003, the People’s Bank of China issued the Notice on Further Strengthening Real Estate Credit Management, raising the equity ratio requirement to 35% and prohibiting the use of working capital loans for real estate development. The Guidelines for Commercial Banks’ Real Estate Loan Risk Management issued in 2007 strengthened loan risk controls. In 2010, the State Council’s ten measures and the China Banking Regulatory Commission’s “Three No Loans” regulation suspended refinancing for listed real estate companies and further restricted bank loans (Mou & Li, 2024; Wei et al., 2014). These policies strictly limited the financing qualifications of real estate companies (Chu et al., 2023; X. Ma & Xie, 2025). A series of financing restriction policies for real estate companies (national logic) have limited the impact of ESG improvements on reducing debt costs.
In addition, we used the difference-in-differences method to examine the policy impact of the 2012 green credit policy on high-pollution industries. The policy requires banks to assess clients’ environmental compliance during the credit granting process. Furthermore, the Green Credit Guidelines mandate banks to disclose the implementation of green credit, which enhances the transparency of corporate environmental information. In sum, the green credit policy imposes stricter ESG rating requirements on heavily polluting firms (D. Ma et al., 2024). The green credit policy influences the interest-bearing debt ratio of firms by compelling heavily polluting companies to improve their ESG. Drawing on the methods of F. H. Liu et al. (2024) and D. Ma et al. (2024), we analyzed the impact of the green credit policy on high-pollution industries. As shown in Table 17 and Figure 2, the green credit policy had a greater impact on adjusting the interest-bearing debt ratio in high-pollution industries. As shown in Figure 3, both the p-values and the placebo-estimated coefficients followed a normal distribution. The DID coefficient in Table 17 was –0.011, which deviates substantially from the normal distribution illustrated in Figure 3, indicating that the placebo test was successfully passed. In conclusion, industry financing restrictions implemented by the Chinese government, such as those in the high-pollution and real estate sectors, have significant impacts. This indicates that in corporate financing within emerging market countries such as China, state logic takes precedence over other logics. Motivated by the firm logic’s demand for low-interest financing, firms are compelled to adhere to market logic in response to the financing constraints imposed by both state and natural logics. This illustrates the complementarity between national logic (Green Credit Guidelines) and market logic (ESG). In aligning with national and natural logic, corporate logic (debt management needs) is achieved.
As shown in Table 18, climate risk and transition risk significantly amplified the impact of Green Credit Guidelines. This suggests that climate risk intensifies financing constraints for companies, forcing them to rely more on improving ESG to reduce the interest-bearing debt. Natural logic drives companies to improve ESG through environmental pressures, meeting the demands of both national and market logic. This reflects the complementary nature of market logic, national logic, and natural logic.

4.4.4. ESG, Debt Management: Impact on Green Innovation

Green i , t = α 0 + α 1 L E S G i , t 1 + α 2 ROE i , t + α 3 FIXED i , t + α 4 SOE i , t + α 5 C A S H F L O W i , t + α 6 G R O W T H i , t + α 7 BOARD i , t + α 8 T O P 1 i , t + α 9 BM i , t + φ i + φ t + ε i , t ,
G r e e n i , t = α 0 + α 1 I D R i , t + α 2 ROE i , t + α 3 FIXED i , t + α 4 SOE i , t + α 5 C A S H F L O W i , t + α 6 G R O W T H i , t + α 7 BOARD i , t + α 8 T O P 1 i , t + α 9 BM i , t + φ i + φ t + ε i , t ,
where i and t represent the company and year. Green i , t is corporate green innovation; IDR i , t is interest-bearing debt ratio; LESG i , t 1 is the lagged one-period rating from the HUAZHENG database; ROE i , t is return of equity; F I X E D i , t is fixed assets ratio. S O E i , t is nature of ownership; C A S H F L O W i , t is the ratio of cash to total assets; G R O W T H i , t is total asset growth rate;   BOARD i , t is the number of board members is taken as the natural logarithm of the value; T O P 1 i , t is shareholding ratio of the largest shareholder; BM i , t is book—to—market corporate value. The error term is denoted as ε i , t . We control for year and firm fixed effects.
As shown in Table 19, the study by Haughton and Keane (2021) found that improvements in corporate ESG significantly promote green innovation. This suggests that ESG, by enhancing a company’s environmental and social responsibility performance, stimulates investment in green technology research and development and innovation activities. Based on the institutional logics theory, the enhancement of green innovation can be seen as a complement between natural logic and corporate logic. Natural logic emphasizes environmental sustainability, driving companies to respond to climate change and societal expectations. Corporate logic, on the other hand, focuses on enhancing financing convenience through green innovation and avoiding penalties from environmental regulations. Meanwhile, companies with strong ESG are more likely to secure green bonds or low-cost loans (Alves & Meneses, 2024), thereby reducing their reliance on high-cost interest-bearing debt. This phenomenon reflects the role of market logic, where companies respond to the market demand for sustainable investments through ESG practices, obtaining more favorable financing conditions.
However, as shown in Table 19, the ratio of the interest-bearing debt to equity is positively correlated with green innovation. Reducing the ratio of the interest-bearing debt negatively impacts green innovation. When companies optimize their capital structure through ESG practices and significantly reduce their interest-bearing debt ratio, funding for green innovation may be constrained. This is because green innovation typically requires substantial long-term capital, and the interest-bearing debt (e.g., bank loans or corporate bonds) is a key source of innovation funding for companies (C. H. Wang & Juo, 2021).
From the perspective of corporate logic, reducing the ratio of the interest-bearing debt is an inherent need for companies to pursue financial stability and risk control. However, this behavior may conflict with the green innovation goals driven by natural logic, as reduced access to capital may limit companies’ investments in environmental technology research (Ghisetti et al., 2017). This contradiction reflects the financial conflict between corporate logic (debt management optimization) and natural logic (green innovation) in corporate operations. This conflict leads to the use of ESG to alleviate financing constraints, which in turn helps reduce a company’s interest-bearing debt (corporate logic). However, market logic (ESG’s financing advantages) can mitigate this conflict, as ESG enhances a firm’s reputation and market competitiveness (Ni et al., 2024). Additionally, ESG improves a company’s informational effects and transparency, driving better market perceptions (Ellili, 2022). Therefore, ESG still contributes to corporate green innovation.

5. Discussion

Companies play a crucial role in achieving socially sustainable development, and debt is an essential source of funding for reaching the United Nations Sustainable Development Goals (SDGs). However, the financial costs associated with high levels of debt can undermine a company’s ability to pursue sustainable development. When managers are motivated to improve debt management with a focus on sustainability, enhancing environmental, social, and governance (ESG) ratings can create a positive feedback loop, further strengthening the company’s debt management.
The findings support the hypothesis that ESG significantly influences debt management, particularly by reducing the interest-bearing debt ratio, the equity pledge of controlling shareholders, and deviations from a firm’s target leverage. Specifically, ESG practices reduce reliance on equity pledge financing, a mechanism often used by non-state-owned companies to overcome financing constraints.
The study also offers new insights into the role of climate risk management, demonstrating how it amplifies the impact of ESG on debt management. This supports the view that strong ESG practices, especially in managing climate risks, can alleviate creditors’ concerns about asset stranding and cash flow volatility, thus enabling companies to secure more favorable debt terms.
Additionally, the research confirms that state-driven policies, such as the Green Credit Guidelines, play a significant role in shaping corporate financing strategies. This underscores the value of the institutional logics theory, which effectively captures the multi-dimensional nature of corporate debt decisions by incorporating not only corporate logic, but also state and market considerations.
The findings also raise several questions for future research. First, the impact of ESG on corporate green innovation should be further explored, as the observed reduction in the interest-bearing debt ratio correlates with a decrease in corporate green innovation. This presents an opportunity to investigate the trade-offs companies face between reducing debt and pursuing long-term innovation goals. Second, future studies could examine how institutional logics differ across various regulatory environments, particularly in emerging markets, where state-driven logic may have a more prominent influence.
To support debt management and advance the SDGs, policymakers should incentivize companies to integrate ESG frameworks into their financial strategies. This could be carried out by offering tax incentives or subsidies to companies that align their debt financing with SDG-oriented projects, such as renewable energy or social equity initiatives. Additionally, governments and international organizations should encourage standardized ESG reporting to improve transparency and attract sustainable investments, thereby reducing the financial risks associated with excessive debt. Public–private partnerships can further enhance access to affordable, long-term financing, creating a virtuous cycle where improved ESG practices strengthen companies’ ability to manage debt sustainably while contributing to the achievement of the SDGs.
Although China can serve as a representative study of emerging markets, its unique characteristics limit the generalizability of the research findings. As a government-led country, China’s unique national logic in environmental policies, industry regulation, and capital market development may differ from that of other emerging markets or developed countries. For example, non-state-owned enterprises in China face challenges of high financing costs and limited access to financing. However, the policies launched by the Chinese government in promoting ESG-related policies and addressing climate risks are similar to those of other emerging markets (such as India and Brazil) or developed countries (such as the United States and the EU countries). For instance, China has signed the Paris Climate Agreement, proposed carbon neutrality, and implemented policies requiring companies to disclose ESG ratings.
Both ESG ratings and the Paris Climate Agreement were primarily driven by the developed countries, with China being an active follower in this regard. Therefore, it is difficult to claim that China’s strong policy power surpasses that of developed countries in terms of emphasis on ESG and climate risks. For example, the EU has promoted stringent carbon neutrality goals through the European Green Deal, while the emerging markets such as India have introduced proactive policies in renewable energy and green financing. While the policy interventions of these countries may differ from China’s, their impact on corporate ESG practices and debt management may operate through similar mechanisms. Therefore, the conclusions of this study can, to some extent, provide insights for corporate financing research in countries that emphasize ESG ratings and climate risks. However, their applicability needs further validation in light of the specific institutional contexts and market environments of each country.
To enhance the generalizability of the findings, future research could conduct cross-country comparative analyses to further explore the relationship between ESG practices, climate risks, and debt management under different national logics. For example, countries with varying policy intervention intensities and capital market characteristics (such as India, Brazil, the United States, or the EU countries) could be selected to compare the similarities and differences in ESG policy implementation, climate risk responses, and financing mechanisms. Such comparative studies would not only validate the applicability of the conclusions in other emerging markets, but also reveal the differences in debt management mechanisms between the developed and emerging markets. Furthermore, cross-country comparative research could help identify which factors (such as policy intensity, market maturity, or cultural background) play a dominant role in shaping corporate debt management strategies, thus providing a more universal guidance for policymakers and business managers.

Author Contributions

Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing—original draft preparation, Y.Z., K.B.b.A.M. and H.b.A.; writing—review and editing, Y.Z., K.B.b.A.M. and H.b.A.; visualization, Y.Z.; supervision, K.B.b.A.M. and H.b.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data obtained from the China Stock Market & Accounting Research (CSMAR) database (https://data.csmar.com).

Acknowledgments

We would like to express our sincere gratitude to friends for their patience, care, and support.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ESGEnvironmental, social, and governance

Appendix A

Table A1. Comparison of the different ESG ratings.
Table A1. Comparison of the different ESG ratings.
Number of FirmsNMeanMedianStd. dev.MinMax
LESG892637.76480.87749
LESGHEXUN892634.91351.00617
LESGMSCI892636.33862.13219
LESGBloomBerg8926338.53536.6879.16819.30461.634
Table A2. Comparison of the different ESG ratings.
Table A2. Comparison of the different ESG ratings.
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)
VariablesIDRAbsDevPladgeStockIDRAbsDevPladgeStockIDRAbsDevPladgeStockIDRAbsDevPladgeStock
LESGBloomBerg−0.002 *−0.000−0.000
(0.001)(0.001)(0.000)
LESGMSCI −0.004 **−0.000−0.001
(0.002)(0.001)(0.001)
LESG −0.025 **0.0050.001
(0.010)(0.004)(0.005)
LESGHEXUN −0.014 **0.005 *−0.004
(0.006)(0.002)(0.002)
Observations263263263263263263263263263263263263
R-squared0.9520.6000.8640.9490.5830.8640.9570.1170.0210.9550.1230.031
Note: robust t-statistics in parentheses; ** p < 0.05, * p < 0.1.
Table A3. One-period lag test.
Table A3. One-period lag test.
(1)(2)(3)
VariablesIDRAbsDevPladgeStock
LESGHEXUN−0.003 ***−0.001 ***−0.002 ***
(0.001)(0.000)(0.001)
Control variablesYesYesYes
Year FEYesYesYes
Firm FEYesYesYes
Observations18,61318,61318,613
R-squared0.8230.0030.048
Note: robust t-statistics in parentheses; *** p < 0.01.
Table A4. Climate risk as a robustness check of the moderation effect.
Table A4. Climate risk as a robustness check of the moderation effect.
(1)(2)(3)(4)
IDRIDRIDRIDR
LESG + ESG × ClimateRisk × 0−0.002 *
(0.001)
LESG + ESG × ClimateRisk × 0.3 −0.006 ***
(0.002)
LESG + ESG × ClimateRisk × 0.48 −0.008 ***
(0.002)
LESG + ESG × ClimateRisk × 0.65 −0.010 ***
(0.003)
LESG + ESG × PhysicalRisk × 0−0.003 **
(0.001)
LESG + ESG × PhysicalRisk × 0.3 −0.089 ***
(0.030)
LESG + ESG × PhysicalRisk × 0.48 −0.140 ***
(0.047)
LESG + ESG × PhysicalRisk × 0.65 −0.189 ***
(0.064)
LESG + ESG × TransitionRisk × 0−0.002 *
(0.001)
LESG + ESG × TransitionRisk × 0.3 −0.006 ***
(0.001)
LESG + ESG × TransitionRisk × 0.48 −0.008 ***
(0.002)
LESG + ESG × TransitionRisk × 0.65 −0.010 ***
(0.003)
Control variablesYesYesYesYes
Year FEYesYesYesYes
Firm FEYesYesYesYes
Observations22,66822,66822,66822,668
Note: robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

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Figure 1. Research framework. Note: Corporate green innovation is an economic consequence in the further analysis.
Figure 1. Research framework. Note: Corporate green innovation is an economic consequence in the further analysis.
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Figure 2. Parallel trends. Note: the current period (0) is 2012.
Figure 2. Parallel trends. Note: the current period (0) is 2012.
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Figure 3. Placebo test. Note: “b[did2012]” refers to the placebo estimate obtained from the difference-in-differences (DiD) approach used in the 2012 study.
Figure 3. Placebo test. Note: “b[did2012]” refers to the placebo estimate obtained from the difference-in-differences (DiD) approach used in the 2012 study.
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Table 1. Lexicon of climate risk indicators.
Table 1. Lexicon of climate risk indicators.
SourceQ. Li et al. (2024b), Du et al. (2023), Annual Financial Reports of Listed Companies
Climate risk lexiconTransition riskRecycling, usage efficiency, atomic energy, turbine-driven energy, fossil gas, efficiency improvement, fuel oil, revitalization, carbon mitigation, ecological conservation, eco-friendly, climate-neutral, energy saving, fuel, hydrological efficiency, sun-powered, superior efficiency, fuel expenditure, power consumption, energy demand, efficacy
Physical riskSevere catastrophe, seismic activity, violent storm system, tsunami, hydrological extremes, extreme, critical, urban flooding, strong wind, dust, hurricane, frost, freezing conditions, disturbance, mudslide, landslide, frozen obstruction, snow disaster, drought, flooding, heavy rain, tornado, hail, flood disaster, precipitation, subfreezing, snowstorm, frost damage, aridity, drought condition, heavy rainfall, flood, severe cold, sandstorm, climate, atmospheric conditions, wet, water temperature, thermal decline, chilling, temperature, rainfall, thermal level, rainwater, wet-phase interval, rain volume, precipitation, cloudburst-prone, rainy, extreme cold, frost period, flood season, high humidity, water condition, water level, light, hydric deficit, high altitude and cold, frigid front, ground collapse, subsurface aqua, inundation status, surface, water storage.
Table 2. Variable definitions.
Table 2. Variable definitions.
VariableDefinition
LEVDebt ratio: total assets/total liabilities
LESGHEXUNThe one-period lag of the ESG rating issued by the HEXUN database
GreenCorporate green innovation ln (the number of independently applied green invention patents in that year + the number of independently applied green utility model patents in that year + 1) (G. Li et al., 2022)
DUALWhether the roles of the Chairman and the CEO are held by the same person
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
NMeanMedianStd. Dev.MinMax
IDR22,8180.2180.2010.1620.0000.648
AbsDev22,8180.0430.0290.0450.0000.622
SharePlage22,8180.0770.0000.1150.0000.855
LEV22,8180.4490.4480.1990.0270.925
LESG22,8186.5476.0001.1221.0009.000
LESGHEXUN22,8184.1214.0001.0841.0008.000
ClimateRisk22,8180.0970.0440.1510.0000.908
PhysicalRisk22,8180.0040.0000.0100.0000.338
TransitionRisk22,8180.0970.0410.1750.0002.609
Green22,8180.3530.0000.8040.0006.848
SIZE22,81822.41222.2371.28619.52526.430
FIXED22,8180.2160.1830.1630.0020.736
SOE22,8180.4020.0000.4900.0001.000
CASHFLOW22,8180.0470.0460.068−0.2240.257
BOARD22,8182.1332.1970.2001.6092.708
TOP122,8180.3370.3150.1470.0810.758
GROWTH22,8180.1630.1040.408−0.6604.330
BM22,8181.1850.7631.3020.05110.142
DUAL22,8180.2470.0000.4310.0001.000
ROE22,8180.0560.0680.134−1.0720.406
Note: Data obtained from the China Stock Market & Accounting Research (CSMAR) database (https://data.csmar.com).
Table 4. Baseline regressions.
Table 4. Baseline regressions.
(1)(2)(3)
VariablesIDRAbsDevPladgeStock
LESG−0.003 ***−0.001 *−0.002 **
(0.001)(0.000)(0.001)
SIZE0.060 ***0.003 ***0.006 **
(0.004)(0.001)(0.003)
FIXED−0.138 ***−0.017 ***0.000
(0.008)(0.004)(0.006)
SOE0.101 ***−0.034 ***−0.014
(0.019)(0.006)(0.014)
CASHFLOW0.0060.003−0.058 ***
(0.007)(0.002)(0.007)
BOARD−0.193 ***−0.022 ***−0.017
(0.013)(0.007)(0.011)
TOP1−0.0040.001−0.005
(0.009)(0.003)(0.008)
GROWTH0.0110.015 **0.275 ***
(0.023)(0.007)(0.023)
BM0.004 **0.014 ***−0.002
(0.002)(0.002)(0.002)
Constant0.011 ***−0.001 ***−0.004 **
(0.002)(0.001)(0.001)
Year FEYesYesYes
Firm FEYesYesYes
Observations22,66922,66922,669
R-squared0.8180.2660.725
Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 5. Instrumental variable method to test LESG and the interest-bearing debt ratio (IDR).
Table 5. Instrumental variable method to test LESG and the interest-bearing debt ratio (IDR).
VariablesIDR
LESG−0.095 ***
(0.027)
Control variablesYes
Year FEYes
Firm FEYes
Observations22,669
R-squared−0.493
Underidentification test (Kleibergen–Paap rk LM statistic)26.926 ***
(p = 0.000)
Weak identification test (Cragg–Donald Wald F-statistic)
(10% maximal IV size)
42.008
(19.93)
Hansen J-statistic (overidentification test of all the instruments)1.418
(p = 0.234)
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 6. Instrumental variable method to test LESG and AbsDev.
Table 6. Instrumental variable method to test LESG and AbsDev.
VariablesAbsDev
LESG−0.003 ***
(0.001)
Control variablesYes
Year FEYes
Firm FEYes
Observations21,529
R-squared0.018
Underidentification test (Kleibergen–Paap rk LM statistic)489.851 ***
(p = 0.000)
Weak identification test (Cragg–Donald Wald F-statistic)
(10% maximal IV size)
2040.989
(19.93)
Hansen J-statistic (overidentification test of all the instruments)0.000
(p = 0.998)
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 7. Instrumental variable method to test LESG and PladgeStock.
Table 7. Instrumental variable method to test LESG and PladgeStock.
VariablesPladgeStock
LESG−0.011 ***
(0.001)
Control variablesYes
Year FEYes
Firm FEYes
Observations21,529
R-squared0.059
Underidentification test (Kleibergen–Paap rk LM statistic)480.851 ***
(p = 0.000)
Weak identification test (Cragg–Donald Wald F-statistic)
(10% maximal IV size)
2040.989
(19.93)
Hansen J-statistic (overidentification test of all the instruments)0.549
(p = 0.459)
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 8. Added control variable tests.
Table 8. Added control variable tests.
(1)(2)(3)
VariablesIDRAbsDevPladgeStock
LESG−0.004 ***−0.001 *−0.002 **
(0.001)(0.000)(0.001)
DUAL−0.002−0.001−0.002
(0.003)(0.001)(0.003)
Control variablesYesYesYes
Year FEYesYesYes
Firm FEYesYesYes
Observations22,66922,66922,669
R-squared0.8110.0220.073
Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 9. HEXUN ESG.
Table 9. HEXUN ESG.
(1)(2)(3)(4)
VariablesLEVIDRAbsDevPladgeStock
LESG−0.007 ***
(0.001)
LESGHEXUN −0.004 ***−0.001 ***−0.003 ***
(0.001)(0.000)(0.001)
Control VariablesYesYesYesYes
Year FEYesYesYesYes
Firm FEYesYesYesYes
Observations22,66922,66922,66922,669
R-squared0.8440.8180.0240.074
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 10. Comparison of ESG rating data.
Table 10. Comparison of ESG rating data.
Number of FirmsNMeanMedianStd. dev.MinMax
LESG324822,8186.54761.12219
LESGHEXUN324822,8184.12141.08418
LESGMSCI34211276.24462.26319
LESGBloomBerg1249844627.22526.7488.3748.67867.207
Table 11. BloomBerg ESG and MSCI ESG.
Table 11. BloomBerg ESG and MSCI ESG.
(1)(2)(3)(4)(5)(6)
VariablesIDRAbsDevPladgeStockIDRAbsDevPladgeStock
LESGBloomBerg−0.001 ***−0.000 **−0.000
(0.000)(0.000)(0.000)
LESGMSCI −0.002 *−0.0010.000
(0.001)(0.001)(0.001)
Constant−0.972 ***−0.0100.133−1.302 **−0.256 *0.520 *
(0.169)(0.067)(0.136)(0.634)(0.154)(0.295)
Control variablesYesYesYesYesYesYes
Year FEYesYesYesYesYesYes
Firm FEYesYesYesYesYesYes
Observations839983998399109510951095
R-squared0.8550.2770.7770.9240.3870.888
Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 12. BloomBerg environmental rating and corporate debt management.
Table 12. BloomBerg environmental rating and corporate debt management.
(1)(2)(3)
VariablesIDRAbsDevPladgeStock
LBloomBergESGE−0.000 **−0.000−0.000
(0.000)(0.000)(0.000)
Control variablesYesYesYes
Year FEYesYesYes
Firm FEYesYesYes
Observations831683168316
R-squared0.8540.2740.778
Note: Robust t-statistics in parentheses; ** p < 0.05.
Table 13. Time trend instead of time fixed effects test.
Table 13. Time trend instead of time fixed effects test.
(1)(2)(3)
VariablesIDRAbsDevPladgeStock
LESG−0.004 ***−0.001 *−0.003 ***
(0.001)(0.000)(0.001)
Timetrend−0.013 ***0.0000.010 ***
(0.001)(0.001)(0.001)
Timetrendsq0.000 ***−0.000 ***−0.001 ***
(0.000)(0.000)(0.000)
Control variablesYesYesYes
Firm FEYesYesYes
Observations22,66822,66822,668
R-squared0.8100.0270.091
Note: Robust t-statistics in parentheses; *** p < 0.01, * p < 0.1.
Table 14. ESG and shares pledged by the controlling shareholder (PladgeStock).
Table 14. ESG and shares pledged by the controlling shareholder (PladgeStock).
Full SampleState-Owned EnterpriseNon-State-Owned Enterprise
VariablesPladgeStockPladgeStockPladgeStock
LESG−0.002 ***−0.001−0.004 ***
(0.001)(0.001)(0.001)
Control variablesYesYesYes
Year FEYesYesYes
Firm FEYesYesYes
Observations22,668911013,475
R-squared0.7250.6260.717
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 15. The moderating effect of corporate climate risk management.
Table 15. The moderating effect of corporate climate risk management.
(1)(2)(3)
VariablesIDRIDRIDR
LESG−0.002 *−0.003 **−0.002 *
(0.001)(0.001)(0.001)
ClimateRisk0.042
(0.047)
LESG*ClimateRisk −0.012 **
(0.006)
PhysicalRisk 1.675 **
(0.679)
LESG*PhysicalRisk −0.286 ***
(0.100)
TransitionRisk 0.045
(0.038)
LESG*TransitionRisk −0.011 **
(0.005)
_cons−1.035 ***−1.015 ***−1.029 ***
Control variablesYesYesYes
Year FEYesYesYes
Firm FEYesYesYes
Observations22,66822,66822,668
R-squared0.8110.8110.811
Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 16. Real estate and construction industry.
Table 16. Real estate and construction industry.
Non-Real Estate and Construction IndustryReal Estate and Construction Industry
Variables(1)(2)
LESG−0.003 ***−0.005
(0.001)(0.006)
_cons−1.096 ***−1.584 ***
(0.093)(0.401)
Control variablesYesYes
Year FEYesYes
Firm FEYesYes
Observations21,5141146
R-squared0.8260.767
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 17. Green credit policy and the interest-bearing debt ratio.
Table 17. Green credit policy and the interest-bearing debt ratio.
VariablesIDR
DID−0.011 *
(0.006)
_cons−1.011 ***
(0.093)
Control variablesYes
Year FEYes
Firm FEYes
Observations22,668
R-squared0.810
Note: Robust t-statistics in parentheses; *** p < 0.01, * p < 0.1.
Table 18. The moderation effect of climate risk under policy shocks.
Table 18. The moderation effect of climate risk under policy shocks.
(1)(2)(3)
VariablesIDRIDRIDR
DID−0.000−0.010−0.002
(0.007)(0.006)(0.007)
ClimateRisk−0.013
(0.021)
ModdidCLIMATE−0.072 ***
(0.024)
PhysicalRisk −0.045
(0.180)
ModdidPHYSICAL −0.324
(0.311)
TransitionRisk −0.006
(0.018)
ModdidTransitionRisk −0.064 ***
(0.022)
Constant−1.033 ***−1.013 ***−1.028 ***
(0.094)(0.093)(0.094)
Observations22,66822,66822,668
R-squared0.8110.8110.811
Note: Robust t-statistics in parentheses; *** p < 0.01.
Table 19. ESG, debt management: impact on green innovation.
Table 19. ESG, debt management: impact on green innovation.
(1)(2)
VariablesGreenGreen
IDR0.079 *
(0.041)
LESG 0.014 ***
(0.004)
Constant0.375 ***0.299 ***
(0.071)(0.076)
Control variablesYesYes
Year FEYesYes
Firm FEYesYes
Observations22,66822,668
R-squared0.7340.734
Note: Robust t-statistics in parentheses; *** p < 0.01, * p < 0.1.
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Zhao, Y.; Manaf, K.B.b.A.; Ayoup, H.b. ESG, Climate Risk, and Debt Management—Evidence from Chinese Listed Companies. Int. J. Financial Stud. 2025, 13, 118. https://doi.org/10.3390/ijfs13030118

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Zhao Y, Manaf KBbA, Ayoup Hb. ESG, Climate Risk, and Debt Management—Evidence from Chinese Listed Companies. International Journal of Financial Studies. 2025; 13(3):118. https://doi.org/10.3390/ijfs13030118

Chicago/Turabian Style

Zhao, Yang, Kamarul Bahrain bin Abdul Manaf, and Hazeline bt Ayoup. 2025. "ESG, Climate Risk, and Debt Management—Evidence from Chinese Listed Companies" International Journal of Financial Studies 13, no. 3: 118. https://doi.org/10.3390/ijfs13030118

APA Style

Zhao, Y., Manaf, K. B. b. A., & Ayoup, H. b. (2025). ESG, Climate Risk, and Debt Management—Evidence from Chinese Listed Companies. International Journal of Financial Studies, 13(3), 118. https://doi.org/10.3390/ijfs13030118

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