Corporate ESG Performance and Supply Chain Financing: Evidence from China

Abstract

In the context of the ongoing deepening of the “dual carbon” strategy and concepts of sustainable development, corporate environmental, social, and governance (ESG) performance has increasingly garnered the attention of various investment entities and gradually influenced key operational areas, such as supply chain financing. This paper analyzes the potential impact and mechanisms through which ESG performance affects corporate supply chain financing, using resource dependence and stakeholder theories as analytical lenses. The study utilizes data from A-share listed companies in China from 2013 to 2023 and finds that strong ESG performance significantly enhances the supply chain financing available to companies. This effect is particularly pronounced among state-owned enterprises, large firms, those with lower pollution levels, and companies in high-tech industries. Further analysis indicates that ESG performance positively influences supply chain financing by enhancing corporate reputation and reducing information asymmetry. Therefore, companies, financial institutions, and relevant government agencies should prioritize the development of ESG performance, integrate it into long-term strategies, promote standardized information disclosure, and support the sustainable development of supply chain financing.

1. Introduction

ESG refers to a development concept and sustainable practice that emphasizes non-financial factors, including environmental sustainability, social responsibility, and governance issues. Since its introduction, the ESG concept has gained widespread recognition and strong support from international organizations, government regulators, capital markets, corporations, and third-party institutions. Countries around the world continue to promote the coordinated development of the environment, society, and governance in accordance with the ESG principle [1]. In 2024, the European Union passed the Corporate Sustainability Due Diligence Directive (CSDDD). This directive has progressed from a legislative proposal to final legislation, imposing binding obligations on enterprises within its jurisdiction and their supply chains. It requires large enterprises to identify and prevent ESG risks in their supply chains. Meanwhile, during the same period, the United States introduced the Securities and Exchange Commission (SEC) Climate Disclosure Proposal, which has entered the implementation phase, setting more stringent ESG standards for companies involved in supply chains. Recent studies have confirmed that varying levels of ESG performance can significantly impact a company’s operations. Strong ESG performance helps businesses gain the trust of stakeholders such as financial institutions, suppliers, and customers, thereby reducing operating costs and enhancing operational efficiency [2]. The Chinese government has also placed significant emphasis on building and promoting the ESG system. The report of the 20th National Congress of the Communist Party of China underscores the strategic goal of “promoting green economic development and fostering harmonious coexistence between humans and nature”, which provides crucial policy support for the development of ESG in China and aligns closely with the country’s current economic development trends. Furthermore, in the 2024 “Opinions on Comprehensively Promoting the Construction of Beautiful China” issued by the Central Committee of the Communist Party of China and the State Council, it is noted that “the reform of the environmental information disclosure system will be deepened, and the evaluation of environmental, social, and corporate governance will be explored” [3]. For companies, ESG has transitioned from a “choice” to a “mandatory requirement”.

In the context of sustainable development, supply chain financing (SCF) serves as a crucial tool for improving cash flow and optimizing financing structures. This financing method enhances the flow of funds within the supply chain, reduces financing costs, and improves overall supply chain efficiency. By bolstering cash flow and optimizing financing structures, SCF helps suppliers and buyers address their financing challenges, providing essential financial support, particularly to small and medium-sized enterprises (SMEs). This support, in turn, promotes liquidity and stability across the entire supply chain [4]. As economic levels continue to rise, and as the strong pursuit of a high-quality life drives the development of traditional supply chains, early research on supply chain financing primarily focused on conventional trade financing methods, such as letters of credit and bills of exchange. The main emphasis was on improving the capital efficiency of individual companies, especially in the context of cross-border trade. However, in today’s era of digitalization, green development, and globalization, research on supply chain financing has expanded into multiple areas. Regarding business innovation, successful innovations have led to reduced supply chain concentration and altered the cost structures and external financing arrangements of enterprises by leveraging buyer market effects and alternative financing mechanisms. These innovations have enhanced commercial credit net financing and contributed to high-quality economic development [5]. Additionally, commercial credit, as a significant component of supply chain financing, is positively influenced by green innovation [6]. From a risk perspective, effective coordination within the supply chain can only be achieved if manufacturer exhibits a relatively low level of risk aversion. This suggests that overly cautious behavior among supply chain members may negatively impact overall performance [7]. While supply chain finance improves efficiency, it essentially bundles and transfers risks [8]. Conversely, from the perspective of market opportunities, SMEs can access more financing opportunities by leveraging their supply chain networks, as the structural characteristics of these networks are closely linked to financing outcomes [9].

With the continuous promotion of ESG principles, their influence has begun to extend into the field of supply chain financing. In the context of increasing supply chain complexity and uncertainty, ESG has emerged as a crucial factor in enhancing supply chain stability and efficiency [10]. Research shows that companies actively integrating sustainable practices into their operations often enjoy greater stakeholder trust, reduced regulatory risks, and increased attractiveness to investors [11]. Ensuring the sustainability of the supply chain is essential for a company’s long-term development and stability. The integration of ESG performance with supply chain financing has become a vital strategy for companies aiming to enhance their competitive advantages and strengthen the foundation of their supply chains [12]. This positive development is underpinned by a global consensus on the necessity of incorporating green principles into supply chain networks to ensure stable supply chain development and mitigate the environmental impacts associated with production, processing, distribution, and sales [13]. The ESG performance aligns closely with the trend of green transformation in the supply chain, a well-developed ESG structure implies more transparent information disclosure, more scientific decision-making mechanisms and more effective risk management systems. This will significantly boost investors’ confidence in the enterprise, reduce the enterprise’s financing costs and prompt upstream suppliers to offer more preferential terms to maintain cooperative relationships, thereby increasing the enterprise’s financing sources [14]. As an essential strategy for resource acquisition and sustainable development, ESG is intrinsically linked to access to supply chain financing. Research indicates that ESG performance exhibits an inverted U-shaped effect on supply chain financing, with the temporal consistency of ESG further reinforcing this relationship [15]. As ESG transitions from being perceived as a “moral choice” to a “business necessity”, its integration with supply chains will increasingly serve as a key strategy for companies to build competitive barriers and achieve long-term value.

Current research in China primarily focuses on the relationship between ESG performance and supply chain financing costs, as well as financing risks. However, there remain gaps and shortcomings in understanding the direct relationship between ESG performance and supply chain financing, particularly regarding whether these factors are influenced by corporate reputation and the level of information asymmetry.

Building upon this foundation, this paper employs a sample of Chinese A-share listed companies from 2013 to 2023 to empirically analyze the fundamental relationships, heterogeneity, and mechanisms through which ESG performance impacts supply chain financing. The study finds that as ESG scores improve, the amount of supply chain financing accessible to companies significantly increases. Strong ESG performance enhances a company’s external financing capacity and facilitates the flow of funds within the supply chain. Mechanism testing reveals that improvements in ESG performance bolster corporate reputation and reduce information asymmetry, thereby increasing both the willingness and ability of financial institutions to provide supply chain financing. Heterogeneity analysis indicates that these effects are particularly pronounced in state-owned enterprises, large companies, firms with low pollution levels, and high-tech enterprises. Furthermore, strong ESG performance can optimize a company’s social image and reinforce its long-term sustainability. This paper provides empirical evidence to enhance the understanding of how ESG performance influences corporate supply chain financing.

The marginal contribution of this paper can be attributed to two main aspects: First, it broadens the research perspective on the relationship between ESG performance and supply chain financing. Existing literature primarily examines this relationship through the lenses of financing costs [16,17], financing risks [18], and the constraints of financing relationships [19,20]. However, there is a notable gap in systematic discussion regarding whether ESG performance can directly influence a company’s ability to secure supply chain financing. Second, this paper investigates the mechanisms and effects of ESG performance on a company’s access to supply chain financing. It emphasizes the intricate relationship between ESG scores and factors such as information asymmetry and corporate reputation. Additionally, it explores the variations in how ESG performance affects supply chain financing across different companies, taking into account factors such as ownership structure, company size, pollution levels, and whether the company operates in high-tech industries.

The structure of this paper is organized as follows: The second section presents the theoretical analysis and research hypotheses. The third section outlines the empirical design and provides descriptive statistics of the sample. The fourth section discusses the baseline regression results and conducts robustness checks. The fifth section focuses on the analysis of heterogeneity. The sixth section examines the mechanism tests; and the seventh section concludes with the findings and policy recommendations.

2. Theoretical Analysis and Research Hypotheses

This paper constructs the theoretical framework illustrated in Figure 1, which integrates the dynamically complementary and cyclically reinforcing relationships among three major theories: resource dependence theory, stakeholder theory, and signaling theory. Resource dependence theory establishes the fundamental motivation for enterprises to engage in ESG practices, emphasizing the need to manage relationships with key resource controllers to mitigate the risks associated with external dependence. This motivation directly aligns with the practical scope of stakeholder theory, which clarifies that enterprises must address the demands of diverse stakeholders such as investors, customers, and governments through specific ESG initiatives. These ESG practices targeting stakeholders are subsequently externalized as effective market signals, transmitting positive information about the enterprise’s quality and long-term value to external audiences. Ultimately, successful signaling enhances the enterprise’s reputation and reduces information asymmetry, which in turn strengthens its capacity to acquire and manage critical external resources, thereby alleviating the initial issue of resource dependence in a feedback process. This framework forms a complete logical closed loop of “motivation–practice–mechanism–value realization,” systematically illustrating how ESG performance influences supply chain financing through intermediary pathways.

2.1. ESG Performance and Corporate Supply Chain Financing

According to resource dependence theory, organizations cannot achieve complete self-sufficiency in resources; they must rely on acquiring resources from the external environment to survive, which involves resource exchanges with other organizations [21]. In other words, within a supply chain system, a company’s ability to sustain its development is closely linked to the extent of its reliance on external resources. Supply chain finance is an emerging financing model that centers on core enterprises. By extending and managing the credit of these core enterprises and providing trade support to both upstream and downstream entities, it facilitates the flow of funds, enabling both the core enterprise and small to medium-sized businesses within the supply chain to access financial support [22].The quality of a company’s ESG performance, particularly in the Environmental (E) dimension, has a direct impact on its ability to secure supply chain financing. Based on this theory, green supply chain management emerges as an approach that seeks to balance economic growth with environmental protection. By leveraging the purchasing power of governments, businesses, and consumers, it creates a market-driven mechanism that enforces environmental standards across every stage of the product life cycle—from design and raw material sourcing to production, consumption, and waste recycling. This, in turn, encourages companies within the supply chain to improve their energy efficiency and environmental performance [23]. The stronger a company’s governance over environmental and resource sustainability, the greater its access to supply chain financing, thereby further promoting its development.

The stakeholder theory posits that a company is a collective of stakeholders, and its fundamental goal is to address their needs. Consequently, a company must develop effective strategies that reflect the demands of its stakeholders [24]. In fulfilling its ESG responsibilities, a company needs to consider not only the interests of shareholders but also the concerns of various stakeholders, including employees, customers, suppliers, governments, and communities [25]. By actively embracing its ESG responsibilities, a company can significantly enhance its social reputation and brand image. When a company excels in its ESG performance, it gains recognition from both the public and regulatory bodies, which helps to mitigate external uncertainties and bolster investor confidence. This, in turn, facilitates the expansion of financing channels and improves the overall financing environment. Based on this theory, companies should perceive ESG responsibilities as a strategic component of long-term development rather than a mere compliance obligation. By consistently meeting these responsibilities and fostering trust among all stakeholders, companies can create a more stable and sustainable financing environment.

Based on the preceding analysis, it is reasonable to assert that strong ESG performance can positively influence supply chain finance.

Accordingly, this paper proposes the following basic hypothesis:

H1: 

There is a significant positive correlation between a company’s ESG performance and its supply chain finance.

2.2. Corporate Reputation and Information Asymmetry

2.2.1. ESG Performance and Corporate Reputation

First, ESG performance enhances corporate reputation, thereby facilitating access to supply chain financing. The underlying reasons for this relationship are twofold. On one hand, corporate reputation reflects not only a company’s current operations but also the cumulative outcomes of its past behaviors and decisions. It serves as a visible manifestation of public sentiment, social recognition, and credibility assessment [26]. The three dimensions of ESG performance can each positively influence corporate reputation by addressing the expectations and demands of various stakeholders [27]. Specifically, social initiatives, such as employee welfare programs, public welfare activities, and consumer rights protection, often generate positive publicity, thereby boosting the loyalty of existing investors and customers while attracting new ones [28]. In terms of environmental performance, companies can cultivate a strong image and solid reputation among stakeholders through exemplary environmental practices [29]. With respect to corporate governance, improvements in governance structures and practices can position a company as a role model within its industry, further enhancing its reputation. On the other hand, reputation is a valuable asset for a company, it not only helps seize market opportunities but also shields against competitive threats, ultimately yielding tangible benefits. Moreover, corporate reputation significantly contributes to value creation within the supply chain. As reputation strengthens, the perceived value of the company’s products also increases, thereby enhancing the overall value of the supply chain and facilitating the company’s ability to meet its financing needs [30].

Based on the above analysis, the following hypothesis is proposed:

H2a: 

ESG performance increases access to supply chain financing by enhancing corporate reputation.

2.2.2. ESG Performance and Information Asymmetry

Second, ESG performance facilitates access to supply chain financing by reducing information asymmetry within the company. This occurs for several reasons. First, ESG performance is closely linked to the transparency and extent of information disclosure. Information asymmetry can exacerbate financing constraints and agency problems faced by companies, thereby hindering improvements in ESG practices [31]. Strong ESG performance typically signals higher investment efficiency in environmental, social, and governance aspects. Stakeholders can extract more non-financial information from ESG disclosures, thereby reducing information asymmetry [32] and acting as implicit informational intermediaries for both investors and consumers. Additionally, a robust corporate governance structure contributes to more efficient decision-making, effectively addressing agency issues and information asymmetries during a company’s development. This, in turn, lowers overall risk and strengthens the company’s long-term orientation [33], helping to alleviate conflicts of interest between internal and external stakeholders. Furthermore, in the context of capital markets, information asymmetry theory serves as a key framework for understanding the financing challenges faced by small and medium-sized enterprises. According to information economics, information asymmetry arises in both pre-contractual and post-contractual stages [34], with adverse selection occurring beforehand and moral hazard arising afterward. Numerous studies have demonstrated that information asymmetry is a primary factor behind SMEs’ difficulties in securing affordable financing [35,36]. Moreover, research by Qiao and Zhao (2023) [37] suggests that reducing information asymmetry among companies within a supply chain network enhances information flow, which, in turn, enables more effective resource management across the supply chain and promotes the acquisition of supply chain financing.

Based on the preceding analysis, the following hypothesis is proposed:

H2b: 

ESG performance increases access to supply chain financing by reducing information asymmetry.

3. Empirical Strategies and Descriptive Statistics of the Sample

3.1. Data Sources and Sample Selection

The sample data comprises A-share listed companies from 2013 to 2023. The following processing steps were applied to the initial sample: (1) Excluding financial, real estate, and special treatment (ST) companies; (2) Removing samples with incomplete supply chain and ESG performance data; (3) Excluding samples with missing values and outliers, and applying 1% and 99% winsorization to continuous variables to mitigate the impact of outliers. ESG scores were obtained from the Chinese Research Data Services (CNRDS) database, which is specifically tailored to the Chinese market, while other financial indicators were sourced from the China Stock Market & Accounting Research (CSMAR) database. Following these procedures, a total of 3619 sample observations were retained for analysis. Data processing and analysis were conducted using Stata 18.

A total of 3619 valid firm-year observations were ultimately obtained, covering 329 enterprises, with the sample structure organized as an unbalanced panel. Regarding the treatment of missing data, we first identified and excluded observations with incomplete information on key variables to ensure that all variables included in the model contained complete information. Subsequently, we applied the winsorization method rather than directly removing extreme values. This approach effectively controls the influence of outliers while maximizing the utilization of sample data, thereby enhancing the robustness of the estimation.

3.2. Variable Selection

3.2.1. Dependent Variable: Supply Chain Finance

This paper adopts the methodology proposed by Wang and Yu (2023) [38], utilizing the ratio of “accounts receivable + notes receivable + prepaid accounts − advances from customers − accounts payable − notes payable” to total assets as a measure of supply chain finance. This metric more accurately reflects the actual financing relationships that occur within a company’s supply chain and illustrates how businesses sustain their operations through trade credit. It does not focus on a specific, structured supply chain finance product; rather, it serves as a comprehensive measure of an enterprise’s net capital occupation within its supply chain network [39]. Given that the supply chain encompasses the entire process from raw material procurement and product production to sales, this measure comprehensively captures the company’s financial flows throughout the entire supply chain. It not only highlights the credit relationships between the company and its customers and suppliers but also considers the balance and liquidity of various types of credit within the company’s operations.

3.2.2. Independent Variable: ESG Performance

To effectively quantify corporate ESG performance, this paper selects the ESG score as the primary explanatory variable. The score is derived from the authoritative CNRDS database and is based on a three-tiered indicator system that offers a comprehensive evaluation of a company’s environmental, social, and governance performance. The design of this evaluation model reflects the core principles of international ESG frameworks, while also taking into account the unique policy environment and information disclosure characteristics of China. This results in an assessment paradigm that is specifically tailored to align with Chinese practices.

3.2.3. Mediating Variables

(1)

Corporate reputation

This paper develops a comprehensive evaluation system for corporate reputation to examine the relationship among ESG performance, corporate reputation, and supply chain finance. The calculation method employed in this study follows the approach adopted by Li et al. [40]. Building upon the framework of this system, Li et al. drew inspiration from the research conducted by Liu and Geng [41], utilizing the natural logarithm of corporate intangible assets to measure corporate reputation. Their findings demonstrate that improvements in corporate reputation can effectively mitigate an enterprise’s dependence on its supply chain, suppliers, and customers, thereby providing empirical support for the “reputation mechanism.” Accordingly, this paper concludes that strong ESG performance can enhance corporate reputation, expand the pool of potential customers and suppliers, reduce reliance on specific major customers and large suppliers, and ultimately strengthen an enterprise’s bargaining power within the supply chain.

Based on the stakeholder theory framework and drawing on the research findings of Guan and Zhang (2019) [42], this study constructs a comprehensive, multi-dimensional evaluation system for corporate reputation. In selecting the indicators, we strictly adhered to the principles of operability, hierarchy organization, validity, and relative completeness, systematically integrating the key concerns of four major stakeholder groups. For the consumer and society dimensions, indicators reflecting market position—such as total assets, operating revenue, net profit, and industry value ranking—were chosen. In the creditor dimension, solvency indicators including the asset-liability ratio, current ratio, and long-term debt ratio, were incorporated. For the shareholder dimension, the protection of shareholder rights was assessed through metrics such as earnings per share, dividend payout levels, and the qualification of the auditing company. In terms of corporate governance, indicators like sustainable growth rate and the proportion of independent directors were utilized. Ultimately, a system comprising 12 key indicators was established. The specific construction indicators are presented in

Table 1. Construction index of corporate reputation.

Dimension	Index	Calculation Method/Definition
Consumers and society	Industry ranking by assets	The relative ranking of the company’s total assets within the same industry for that year.
	Industry ranking by income	The relative ranking of the company’s total operating revenue within the same industry for that year.
	Industry ranking by net profit	The relative ranking of the company’s net profit within the same industry for that year.
	Industry ranking by market capitalization	The relative ranking of the company’s market value within the same industry for that year.
Creditor	Liabilities-to-Assets ratio	Total liabilities/Total assets
	Long-term debt ratio	Total long-term liabilities/Total assets
	Liquidity ratio	Directly obtained from the financial statements.
Shareholder	Earnings per share	Directly obtained from the financial statements.
	Pre-tax cash dividend per share	Directly obtained from the financial statements.
	Are the audits conducted by the big four accounting firms?	If the audit is conducted by one of the big four international accounting firms, it is assigned a value of 1; otherwise, it is assigned a value of 0.
Corporation	The proportion of independent directors	Number of independent directors/Total number of board members.
	Sustainable growth rate	Directly obtained from the financial statements.

.

To effectively integrate the diverse information contained in the 12 reputation evaluation indicators and to mitigate the potential impact of multicollinearity issues on subsequent analyses, this study employed principal component factor analysis to construct a comprehensive reputation score. First, the 12 standardized and original indicators were utilized as input variables for the factor analysis. Based on the criterion that the eigenvalue is greater than 1, we extracted four principal component factors with significant explanatory power from the original indicators, denoted as F1, F2, F3, and F4. These factors represent the comprehensive performance of the enterprise’s reputation across various potential dimensions, as detailed in

Table 2. Explanation of overall variance.

Factor	Eigenvalue	Variance	Variance Explained	Cumulative Variance Explained
Factor 1	3.962	1.651	0.33	0.33
Factor 2	2.311	1.211	0.193	0.523
Factor 3	1.1	0.08	0.092	0.614
Factor 4	1.021	0.116	0.085	0.7
Factor 5	0.905	0.091	0.075	0.775
Factor 6	0.813	0.075	0.068	0.843
Factor 7	0.739	0.321	0.062	0.904
Factor 8	0.418	0.134	0.035	0.939
Factor 9	0.284	0.068	0.024	0.963
Factor 10	0.216	0.052	0.018	0.981
Factor 11	0.163	0.094	0.014	0.994
Factor 12	0.069	-	0.006	1

. We used the Kaiser-Harris criterion [43] to retain principal components with eigenvalues greater than 1. This standard ensures that each component retained can explain the amount of information contained in at least one original variable. As shown in Table 2, the eigenvalues of the first four factors are all greater than 1, and the cumulative variance explanation rate reaches 70%, which meets statistical requirements. Although the eigenvalues of the fifth and sixth factors are close to 1, their economic meanings are ambiguous and difficult to be interpreted theoretically. The first four factors clearly represent four dimensions of an enterprise, namely scale strength, solvency, shareholder returns, and governance level, which are highly consistent with the stakeholder theory framework.

Subsequently, to construct a single comprehensive reputation score (Score), we used the proportion of the variance explained by each principal component factor relative to the total variance explained by the four extracted factors as its weight. If the variance explained by the i-th factor is denoted as σ_i, then its weight can be expressed as follows:

$${\mathit{w}}_{\mathit{i}}={\displaystyle \frac{{\sigma }_{\mathit{i}}}{{\sum }_{\mathit{j}=1}^4{\sigma }_{\mathit{i}}}}$$

(1)

Therefore, the formula for calculating the comprehensive reputation score of each company over k years based on t years is as follows, with higher values indicating a stronger overall reputation:

$${\mathit{Score}}_{\mathit{kt}}={\mathit{w}}_1\times {\mathit{F}}_{1\mathit{kt}}+{\mathit{w}}_2\times {\mathit{F}}_{2\mathit{kt}}+{\mathit{w}}_3\times {\mathit{F}}_{3\mathit{kt}}+{\mathit{w}}_4\times {\mathit{F}}_{4\mathit{kt}}$$

(2)

(2)

Information asymmetry

In this paper, we employ principal component analysis to measure information asymmetry. Li et al. utilized the same measurement approach to demonstrate that investor ESG information transfer significantly reduces the level of information asymmetry faced by enterprises [44]. Furthermore, the degree of information asymmetry serves as a partial mediator between investor ESG information transfer and enterprises’ ESG performance. Improved ESG performance diminishes the level of information asymmetry within enterprises, facilitating information transfer and enhancing the potential for supply chain financing.

Building on the research of Yu et al. (2012) [45], this study quantifies the degree of information asymmetry in the stock market through a multi-dimensional indicator system. Specifically, grounded in market microstructure theory, the research team identified three representative dimensions of liquidity observation: the liquidity ratio (LR), which reflects the market’s capacity to support trading volumes amid price fluctuations; the illiquidity ratio (ILL), which measures the price impact resulting from insufficient trading volumes; and the return reversal indicator (GAM), which captures price corrections caused by trading frictions. The methodology for constructing the indicators is outlined as follows:

The formula for calculating LR is as follows:

$${\mathit{LR}}_{\mathit{it}}=-{\displaystyle \frac{1}{{\mathit{D}}_{\mathit{it}}}}{\Sigma }_{\mathit{k}=1}^{{\mathit{D}}_{\mathit{it}}} \sqrt{{\displaystyle \frac{{\mathit{V}}_{\mathit{it}}\left(\mathit{k}\right)}{\left|{\mathit{r}}_{\mathit{it}}\left(\mathit{k}\right)\right|}}}$$

(3)

ILL is constructed based on the illiquidity ratio indicator, and its calculation formula is as follows:

$${\mathit{ILL}}_{\mathit{it}}={\displaystyle \frac{1}{{\mathit{D}}_{\mathit{it}}}}{\Sigma }_{\mathit{k}=1}^{{\mathit{D}}_{\mathit{it}}} \sqrt{{\displaystyle \frac{\left|{\mathit{r}}_{\mathit{it}}\left(\mathit{k}\right)\right|}{{\mathit{V}}_{\mathit{it}}\left(\mathit{k}\right)}}}$$

(4)

The GAM index is estimated using the following regression equation:

$${\mathit{GAM}}_{\mathit{it}}=\left|{\gamma }_{\mathit{it}}\right|$$

(5)

The coefficient γ_it is estimated using the following formula:

$${\mathit{r}}_{\mathit{it}}^{\mathit{e}}\left(\mathit{k}\right)={\theta }_{\mathit{it}}+{\phi }_{\mathit{it}}{\mathit{r}}_{\mathit{it}}\times (\mathit{k}-1)+{\gamma }_{\mathit{it}}{\mathit{V}}_{\mathit{it}}\times (\mathit{k}-1)\times \mathit{sign}\left[{\mathit{r}}_{\mathit{it}}^{\mathit{e}}(\mathit{k}-1)\right]+{\epsilon }_{\mathit{it}}\left(\mathit{k}\right)$$

(6)

Here, r_it(k) denotes the stock return of enterprise i on the k-th trading day of year t, V_it(k) indicates the daily trading volume, and D_it represents the total number of trading days within that year.

To synthesize the information embedded in the three proxy indicators and construct a unified measure for information asymmetry, the Principal Component Analysis (PCA) method was employed. The results of the PCA, along with the principal component loading matrix, are reported in

Table 3. Results of principal component analysis.

Principal Component	Eigenvalue	Variance	Variance Explained	Cumulative Variance Explained
Comp1	1.395	0.413	0.465	0.465
Comp2	0.983	0.361	0.328	0.793
Comp3	0.622	-	0.207	1

and

Table 4. Principal component load matrix.

Variable	Comp1	Comp2	Comp3
LR	0.431	0.806	0.406
ILL	0.699	−0.014	−0.715
GAM	0.571	−0.592	0.57

, respectively.

From Table 3, it can be observed that the eigenvalue of the first principal component is 1.395 (greater than 1), and the variance explained is 46.5%. The cumulative variance explained by the first two principal components reaches 79.3%, indicating that these two components effectively summarize the primary information of the original indicators. In Table 4, it can be noted that in the principal component loading matrix, the loadings of the three original indicators on the first principal component are all positive and relatively balanced. This suggests that the first principal component effectively captures the common variance among the three indicators, specifically the component related to information asymmetry. While the second and third principal components exhibit a high loading on individual indicators, their economic implications are mixed, making it challenging to provide a cohesive theoretical interpretation. Therefore, as shown in Formula (7), this study employs the first principal component as the comprehensive indicator of information asymmetry. A larger value of this indicator signifies a higher level of information asymmetry.

$${\mathit{ASY}}_{\mathit{it}}=0.431 \times {\mathit{LR}}_{\mathit{it}}+0.699 \times {\mathit{ILL}}_{\mathit{it}}+0.571 \times {\mathit{GAM}}_{\mathit{it}}$$

(7)

3.2.4. Control Variables

Controlling for year and industry characteristics, this paper systematically incorporates key variables that influence supply chain financing. In terms of capital occupancy, the scale of corporate debt financing is measured by the ratio of total short- and long-term borrowings to total assets. Financial leverage is assessed using the ratio of earnings before interest and taxes (EBIT) to (EBIT minus interest expenses and pre-tax preferred dividends), which reflects the risks associated with capital structure. It can more sensitively reflect the impact of fluctuations in corporate profits on debt-servicing risks, which are highly relevant to the short-term financial risks and debt-servicing capabilities that supply chain financing partners focus on. Cash flow is represented by the ratio of net cash flow from operating activities to total assets, indicating the level of internally generated funds. Regarding profitability, a dual-indicator system is employed, utilizing return on assets (ROA) and return on equity (ROE): ROA measures asset operating efficiency through the ratio of net profit to total assets, while ROE evaluates capital returns using the ratio of net profit to average shareholders’ equity. Additionally, the study introduces control variables such as firm size, ownership structure, and capital intensity to effectively mitigate the potential impact of firm heterogeneity on the empirical results.

The definitions of the variables discussed in this article are provided in

Table 5. Definition of main variables.

Type	Name	Symbol	Definition
Dependent variable	Supply chain financing	SCF	(Accounts receivable + notes receivable + prepayments − advances from customers − accounts payable − notes payable)/total asset
Independent variable	ESG performance	ESG	Comprehensive scores obtained from the CNRDs database
Mediating variables	Corporate reputation	REP	Based on 12 selected indicators, combined into a composite index through factor analysis
	Information asymmetry	ASY	Constructed based on three stock liquidity indicators: liquidity ratio (LR), illiquidity ratio (ILL), and return reversal indicator (GAM), with principal component analysis applied
Control variables	Firm size	Size	In (total assets)
	Return on assets	Roa	Net profit at the end of the period/total assets
	Return on equity	Roe	Net profit/average balance of owners’ equity
	Net cash flow from operating activities	Cashflow	Net cash flow from operating activities/total assets
	Bank loans	Bank	(Long-term borrowings + short-term borrowings)/total assets
	Capital intensity	Cap	Total assets/operating revenue
	Financial leverage	DFl	EBIT/(EBIT − interest expenses − pre-tax preferred dividends)
	Ownership nature	Soe	Dummy variable: 1 if the firm is state-owned, 0 otherwise

.

3.3. Model Design

To examine the impact of ESG performance on supply chain financing, an Ordinary Least Squares (OLS) model is constructed:

$${\mathit{SCF}}_{\mathit{it}}={\beta }_0+{\beta }_1{\mathit{ESG}}_{\mathit{it}}+{\beta }_2{\mathit{Controls}}_{\mathit{it}}+\mathit{Year}+\mathit{Ind}+{\epsilon }_{\mathit{it}}$$

(8)

In the formula, SCF_it represents the level of supply chain financing for company i in year t; ESG_it denotes the ESG performance of company i in year t; Controls_it represents control variables for company i in year t; Year indicates the year-fixed effect; Ind represents the industry-fixed effect; and ε_it denotes the error term. In Model (1), the coefficient β₁ of variable ESG_it is the primary focus of attention. According to Hypothesis 1, it is expected that β₁ will be significantly positive, indicating that strong ESG performance can enhance corporate supply chain financing.

3.4. Descriptive Statistics

Table 6. Descriptive statistics results.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
SCF	3619	−0.027	0.126	−0.593	0.515
ESG	3619	28.480	10.300	6.092	79.320
Rep	3619	0.193	0.552	−1.808	1.991
Asy	3619	−0.394	0.577	−5.420	0.634
Size	3619	22.970	1.297	19.590	26.440
Roa	3619	0.036	0.054	−0.358	0.255
Roe	3619	0.066	0.110	−0.787	0.415
Cashflow	3619	0.051	0.068	−0.196	0.267
Bank	3619	0.158	0.132	0	0.757
Cap	3619	2.747	2.514	0.378	18.560
Fl	3619	1.616	1.634	−5.549	26.570
Soe	3619	0.618	0.486	0	1

presents the descriptive statistics for the main variables. The maximum value of supply chain financing (SCF) is 0.515, indicating that some companies experience a significant net outflow of supply chain funds, while the minimum value of −0.593 suggests that certain companies may encounter risks that lead to strained supply chain relationships. Some enterprises exhibit marked phenomena of net outflow or inflow of supply chain funds, reflecting significant differences in the capital occupancy status of firms within the supply chain. After applying winsorization at the 1% and 99% levels, the extreme values of Supply Chain Financing (SCF) have been controlled. The mean value of −0.027 and a standard deviation of 0.126 indicate that, overall, the sample enterprises are close to a state of capital balance; however, there are significant fluctuations among individual firms. The ESG scores exhibit a wide range, with a mean of approximately 28.5 and a standard deviation of 10.3, indicating considerable volatility and instability. This finding suggests that the overall ESG performance of Chinese listed companies remains relatively low, consistent with the findings of Fang and Hu (2023) [46]. In this sample, both Roa and Roe exhibit relatively low average values, while the mean value of Fl is 0.48, indicating that, overall, companies demonstrate moderate profitability and a relatively balanced level of debt. Furthermore, the values of other related variables are generally consistent with the average conditions of Chinese A-share listed companies, with no extreme outliers observed.

4. Baseline Regression Results and Robustness Tests

4.1. Analysis of Baseline Regression Results

The regression results of ESG performance on supply chain financing are presented in

Table 7. Baseline regression results.

Variable	(1)	(2)	(3)	(4)	(5)
	SCF	SCF	SCF	SCF	SCF
ESG	0.001 *** (4.76)	0.001 *** (5.23)	0.001 *** (2.91)	0.001 * (1.94)
ESG2					0.000 ** (2.37)
Size		−0.016 *** (−9.43)	−0.016 *** (−8.98)	−0.016 *** (−3.79)	−0.015 *** (−8.87)
Roa		1.496 *** (16.35)	1.197 *** (13.92)	1.197 *** (7.56)	1.196 *** (13.89)
Roe		−0.611 *** (−14.19)	−0.451 *** (−11.19)	−0.451 *** (−6.31)	−0.450 *** (−11.18)
Cashflow		−0.231 *** (−6.98)	−0.284 *** (−9.36)	−0.284 *** (−6.35)	−0.284 *** (−9.36)
Bank		0.152 *** (8.53)	0.143 *** (7.85)	0.143 *** (4.04)	0.143 *** (7.86)
Cap		−0.001 * (−1.83)	0.003 *** (3.31)	0.003 ** (2.10)	0.003 *** (3.29)
Fl		0.000 (0.31)	−0.001 (−0.66)	−0.001 (−0.46)	−0.001 (−0.69)
Soe		−0.013 *** (−3.24)	−0.016 *** (−4.00)	−0.016 (−1.61)	−0.016 *** (−3.94)
_cons	−0.054 *** (−8.83)	0.295 *** (7.93)	0.296 *** (7.69)	0.282 *** (3.19)	0.301 *** (7.81)
Ind FE	NO	NO	YES	YES	YES
Year FE	NO	NO	YES	YES	YES
N	3619	3619	3619	3619	3619
R2	0.01	0.12	0.33	0.33	0.33

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; t-values are reported in parentheses.

. Column (1) reports the regression results between the main variables, demonstrating a significantly positive relationship at the 1% level. Column (2) incorporates relevant control variables based on the results from column (1), while column (3) further controls for year and industry effects. In all cases, the results remain significantly positive at the 1% level. A one-standard-deviation increase in the ESG score will lead to an increase of 0.0103 in a firm’s supply chain finance (SCF) level. This effect size accounts for approximately 8.17% of the standard deviation of the SCF variable itself, indicating that improved ESG performance can result in significant differentiation in financing status among enterprises. Compared to the sample mean of SCF, this increase represents a 38.1% improvement in a firm’s supply chain finance level. Enhancing ESG performance is not merely symbolic; it can provide enterprises with substantial and economically significant financing advantages within their supply chain networks. Furthermore, the R² value in column (3) is higher than those in columns (1) and (2), indicating that the inclusion of dual fixed effects for industry and year enhances the model’s fit and provides a better explanation of the relationship between ESG performance and supply chain financing. This paper presents more conservative estimation results in Column (4). Building on the model in Column (3), this specification adjusts the standard errors by clustering them at the firm level. This approach relaxes the assumption of independent and identically distributed error terms and effectively addresses the issues of heteroscedasticity and serial correlation that may exist in panel data. The results indicate that, after accounting for the correlation within individual firms, the coefficient for ESG performance remains positively significant at the 10% significance level. Although the t-statistic decreases due to the increased standard errors, this further demonstrates that the positive correlation between ESG performance and supply chain financing exhibits strong statistical robustness and is not driven by a specific error structure. Additionally, the signs and significance of the core control variables have not undergone fundamental changes, which further supports the reliability of the baseline regression conclusions. The higher the ESG score, the better a company performs in terms of environmental protection, social responsibility, and governance. Such companies tend to have more stable business operations and are likely to enjoy greater access to financing. Therefore, Hypothesis 1 is supported: a company’s ESG performance is significantly positively associated with its supply chain financing.

This paper further incorporated the squared term of ESG (ESG²) in Column (5). The results indicate that the coefficient for the ESG squared term is significantly positive at the 5% significance level (coefficient = 0.000, t = 2.37), suggesting that the impact of ESG performance on supply chain financing demonstrates a significant increasing marginal effect. This finding not only confirms the positive correlation between ESG and SCF but also reveals the non-linear characteristic that this relationship strengthens at an accelerated pace as ESG levels improve. Given that the coefficient of the ESG squared term is relatively small, and that the model’s goodness of fit is comparable to that of the linear model, this paper adopts the linear relationship as the main analytical framework in subsequent analyses to maintain model simplicity and consistency in interpretation. Nonetheless, it acknowledges and emphasizes the theoretical significance of this non-linear finding.

4.2. Robustness Tests

4.2.1. Two Stage Least Square (2SLS) Instrumental Variable Method

To further address potential endogeneity issues arising from bidirectional causality, this study constructs instrumental variables for additional validation. Following the approach of Liu et al. (2025) [47], we extend the time window based on the baseline regression by lagging the dependent variable by one period, denoted as Z1. The rationale for selecting the one-period lagged ESG as an instrumental variable is that it is highly correlated with the current-period ESG performance. However, compared to the current-period supply chain financing, it exhibits stronger exogeneity; this is because past ESG performance is not directly influenced by the effects of current-period supply chain financing. Additionally, the average ESG rating of companies within the same industry and province is employed as another instrumental variable, denoted as Z2. The results of the instrumental variable underidentification test (Kleibergen-Paap rk LM statistic = 697.482, p = 0.000) and the weak instrumental variable test (Kleibergen-Paap rk Wald F statistic = 1178.653) indicate an extremely strong correlation between the instrumental variables and the endogenous explanatory variables. This correlation far exceeds the Stock-Yogo critical value, which allows us to effectively reject the null hypothesis of weak instrumental variables. More importantly, the p-value of the Hansen overidentification test is 0.186, which does not reject the null hypothesis that “all instrumental variables are exogenous.” This finding provides statistical support for the validity of the instrumental variables with respect to the exclusion restriction condition, suggesting that the instrumental variables influence supply chain financing primarily through the single channel of affecting firms’ ESG performance. The corresponding regression results are presented in

Table 8. Instrumental variables regression results.

Variable	(1)	(2)	(3)	(4)
	ESG	SCF	ESG	SCF
Z1	0.478 *** (26.25)
Z2			0.686 *** (20.38)
ESG		0.001 *** (3.97)		0.003 *** (4.22)
LM Statistic	383.142		269.657
Wald F Statistic	904.051		501.346
Control Variables	YES	YES	YES	YES
Industry/Year	YES	YES	YES	YES
Sample Size	3289	3289	3611	3611

Note: *** p < 0.01; t-values are reported in parentheses.

. In the first-stage regression, the coefficients of both instrumental variables are significantly positive at the 1% level, and the F-values exceed the empirical threshold of 10, indicating that the weak instrument problem is not a concern. In the second stage, the Cragg-Donald Wald F-statistic significantly exceeds the 10% Stock-Yogo critical value (16.380), rejecting the null hypothesis of weak instruments. Furthermore, the Kleibergen-Paap rk LM statistic is significant at the 1% level, rejecting the null hypothesis of under-identification, thereby further confirming the validity and reliability of the selected instrumental variables. The design of the instrumental variable Z2 meets the requirements of both relevance and exclusivity. Enterprises within the same region and industry face similar institutional environments and regulatory pressures, resulting in a significant “peer effect” in ESG performance, which is highly correlated with the ESG performance of individual firms. By controlling for industry and year fixed effects, and incorporating province-year interactive fixed effects in the robustness test, we absorb common shocks at the regional-industry level to a certain extent. This ensures that Z2 primarily influences firms’ own ESG practices through this channel. In the second-stage regression results, both instrumental variables remain significantly positive, suggesting that even after accounting for bidirectional endogeneity, there is still a significant positive relationship between a company’s ESG performance and its supply chain financing.

4.2.2. Propensity Score Matching (PSM) Method

To address potential endogeneity concerns, this study adopts the approach of Kane et al. [48] by employing the PSM method for robustness testing. Specifically, the full sample is divided based on the median ESG score, assigning a value of 0 to company with ESG score below the median and 1 to those above it, thereby effectively controlling for sample selection bias. During the matching process, covariates such as firm size, return on assets, return on equity, net cash flow, bank loans, collateral capacity, financial leverage, and ownership nature are used as covariates. A 1:1 nearest neighbor matching method is implemented to ensure that the matched samples are comparable across these variables. Following the matching, regression analysis yields a t-value of 2.29, which is significant at the 5% level. Balance tests also confirm that all bias values are below 10%. When the matched samples are substituted into Model (1) for regression, as shown in column (1) of

Table 9. Other robustness check results.

Variable	(1)	(2)	(3)	(4)
	PSM	Alternative Variable	Interaction Fixed Effects	Shorten Time Window
ESG	0.001 *** (2.74)	0.000 * (1.94)	0.001 *** (3.21)	0.001 ** (2.28)
Controls	YES	YES	YES	YES
_cons	0.258 *** (3.88)	0.275 *** (7.82)	0.285 *** (7.10)	0.293 *** (6.43)
Ind FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	1699	3619	3619	2631
R2	0.33	0.43	0.17	0.34

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; t-values are reported in parentheses.

, the results remain significantly positive, further supporting the hypothesis that strong ESG performance positively influences supply chain financing.

4.2.3. Alternative Variable

This study adopts the methodology of Chen and Liu (2018) [49] by modifying the measurement of supply chain financing supply. To more comprehensively capture a company’s liquidity and cash flow conditions, we enhance the original formula by including “other receivables”, which includes non-core but actual fund transactions between the firm and its upstream and downstream partners in the supply chain. Incorporating this item into the formula allows for a more holistic portrayal of the enterprise’s overall fund utilization within the supply chain. Using the ratio of (accounts receivable + notes receivable + other receivables + prepayments) to total assets as a proxy for supply chain financing supply in the robustness analysis. The regression results, presented in column (2) of Table 9, indicate that altering the measurement of the dependent variable does not significantly affect the outcomes. The findings remain consistent with those of the baseline regression, suggesting that improvements in ESG performance can effectively enhance a company’s competitive advantage in supply chain financing and exert a positive influence. The “other receivables” account does contain amounts that are not directly related to core supply chain operations, thereby introducing a certain degree of “noise.” Therefore, this test of substitution variables should be viewed as a broader sensitivity analysis. Its primary purpose is to verify that the core positive impact of ESG performance persists even when a more inclusive measure of supply chain finance is applied. The more precise measure used in the benchmark regression serves as the main foundation for the conclusions of this paper. The results of this robustness test further enhance the robustness of these conclusions.

4.2.4. Controlling for Interaction and Fixed Effects

In the baseline regression, this study controls for both industry and year fixed effects. However, due to differences in industries structure, market characteristics, cyclical fluctuations, and supply chain configurations across industries, the impact of external factors such as macroeconomic volatility and geopolitical risks, on company’s supply chain risks may also vary. To further mitigate potential bias resulting from omitted variables, this study sequentially introduces combined fixed effects for industry-year, province-year, and province-industry into the regression analysis. The results remain consistently significant across these specifications. Column (3) of Table 9 specifically reports the regression results control for province-year fixed effects, thereby verifying the robustness of the findings.

4.2.5. Shorten Time Window

Given that the COVID-19 pandemic from 2019 to 2021 had a significant impact on China’s overall industrial supply chain, this study excludes data from the year 2019 to 2021 to mitigate external disruptions caused by the pandemic and re-examines the effect of ESG ratings on supply chain financing. Columns (4) of Table 9 present the regression results, indicating that even after excluding the impact of the pandemic, the relationship between ESG performance and supply chain financing remains significant at the 5% level. This further confirms that strong ESG performance can effectively enhance supply chain financing.

5. Mechanism Tests

Based on the preceding theoretical analysis, the core causal chain proposed in this paper suggests that ESG performance facilitates access to supply chain financing by enhancing corporate reputation and mitigating information asymmetry. Scholars widely acknowledged that corporate reputation and the degree of information asymmetry significantly influence companies’ ability to secure supply chain financing. Therefore, based on the “two-step method” proposed by Jiang (2022) [50], this paper employs this methodology to conduct empirical tests focusing on whether ESG performance significantly impacts corporate reputation and the level of information asymmetry. This paper constructs models (2) and (3) to test these relationships. The influence of corporate reputation and information asymmetry on supply chain financing is widely recognized within the academic community. This implies that once it is confirmed that ESG has a causal effect on these two mediating variables, the transmission path “ESG → mediating variable → SCF” can be logically validated.

$${\mathit{REP}}_{\mathit{it}}={ \beta }_0+{ \beta }_1{\mathit{ESG}}_{\mathit{it}}+{ \beta }_2\mathit{Controls}+\mathit{Year}+\mathit{Ind}+{\epsilon }_{\mathit{it}}$$

(9)

$${\mathit{ASY}}_{\mathit{it}}={ \beta }_0+{\beta }_1{\mathit{ESG}}_{\mathit{it}}+{\beta }_2\mathit{Controls}+\mathit{Year}+\mathit{Ind}+{\epsilon }_{\mathit{it}}$$

(10)

The results regarding the effect of ESG performance on corporate reputation are presented in column (1) of

Table 10. Mechanism test results of corporate reputation and information asymmetry.

Variable	(1)	(2)
	REP	ASY
ESG	0.001 ** (2.44)	−0.004 *** (−4.70)
Control	YES	YES
_cons	−7.607 *** (−76.91)	5.497 *** (−29.98)
Ind FE	YES	YES
Yea FE	YES	YES
N	3619	3619
R2	0.86	0.55

Note: *** p < 0.01, ** p < 0.05; t-values are reported in parentheses.

. The findings indicate that ESG performance exert a positive influence on corporate reputation at the 5% significance level. This suggests that companies with better ESG performance tend to achieve a higher reputational standing, thereby strengthening external stakeholders’ confidence in their operational stability and social responsibility. Mechanistically, reputation is an intangible asset that is difficult to build in the short term but highly vulnerable during crises. A strong reputation signals stability, reliability, and accountability, which in turn bolsters lenders’ confidence in the company’s willingness and ability to repay. This signaling mechanism is particularly crucial in the context of supply chain financing, where investment decisions depend heavily on the perceived long-term prospects of the company. Thus, an enhanced reputation improves a company’s financing capability and stabilizes supply chain cash flows. The results of this study indicate that ESG performance is not only an external manifestation of a company’s internal governance quality but also plays a pivotal role in enhancing its attractiveness in supply chain financing through reputation channels. Consequently, Hypothesis 2a is strongly supported: ESG performance positively influences access to supply chain financing by enhancing corporate reputation.

The effect of ESG performance on information asymmetry is presented in column (2) of Table 10, which reports the regression results between ESG performance and the level of information asymmetry. The findings demonstrate a significant negative relationship at the 1% significance level, indicating that better ESG performance effectively reduces information asymmetry. Companies with superior ESG performance typically exhibit greater operational transparency and higher levels of stakeholder trust in their sustainability practices, thereby creating a more favorable financing environment. Specifically, proactive disclosures in the environmental dimension enable external parties to assess a company’s resource efficiency and environmental compliance; the fulfillment of social responsibilities reflects the company’s commitment to its employees, communities, and consumers, enhancing its overall credibility; and strong governance practices directly mitigate risks related to internal control failures and managerial opportunism. Information asymmetry remains a structural challenge in financing activities, especially in supply chain financing, where lenders often struggle to obtain comprehensive insights into a company’s non-financial attributes, such as operational health, debt repayment ability, and environmental compliance. Therefore, robust ESG performance can substantially alleviate issues related to adverse selection and moral hazard. These findings support Hypothesis 2b: improvements in ESG performance significantly reduce information asymmetry, thereby enhancing a company’s access to supply chain financing.

6. Heterogeneity Analysis

6.1. Heterogeneity Analysis Based on Ownership Type

There are notable differences between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) regarding the fulfillment of social responsibilities, policy orientation, and financing channels. SOEs are more susceptible to policy influences and are obligated to undertake greater social responsibilities. Consequently, they tend to engage more actively in ESG practices, which significantly enhances their credit ratings and financing capabilities. In contrast, non-SOEs, facing greater market competition and relatively weaker access to resources, experience limited credit enhancement from ESG performance in supply chain financing. These differences may influence the impact of ESG performance on supply chain financing. In this study, listed companies in the sample are categorized based on ownership structure into SOEs (Soe = 1) and non-SOEs (Soe = 0), and regressions are conducted separately for each group. The regression results, presented in columns (1) and (2) of

Table 11. Heterogeneity Test Results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	SOE	Non-SOE	Large Enterprise	Small and Medium-Sized Enterprise	Non-Heavy-Polluting Enterprise	Heavy- Polluting Enterprise	High-Tech Enterprise	Non- High-Tech Enterprise
ESG	0.001 ** (2.39)	0.000 (0.72)	0.001 ** (2.26)	0.001 (1.58)	0.001 *** (3.01)	0.000 (1.15)	0.001 *** (2.60)	0.000 (1.18)
Control	YES	YES	YES	YES	YES	YES	YES	YES
_cons	0.198 *** (3.78)	0.419 *** (6.89)	0.270 *** (4.60)	0.622 *** (4.83)	0.289 *** (5.64)	0.241 *** (4.50)	0.074 (1.40)	0.551 *** (10.08)
Ind FE	YES	YES	YES	YES	YES	YES	YES	YES
Yea FE	YES	YES	YES	YES	YES	YES	YES	YES
N	2234	1382	2497	1116	2335	1283	2023	1595
R2	0.34	0.40	0.34	0.41	0.32	0.32	0.33	0.32
Coefficient Difference Value	0.000		0.000		0.000		0.000

Note: *** p < 0.01, ** p < 0.05; t-values are reported in parentheses.

, indicate that the coefficient of ESG performance for SOEs is significantly positive at the 5% level, whereas for non-SOEs, the coefficient is not statistically significant. This disparity may be attributed to the fact that, compared to SOEs, non-SOEs rely more heavily on their operational capabilities and market performance, often facing greater short-term profitability pressures. As a result, they may lack sufficient motivation and resources to invest in ESG initiatives over the long term, rendering improvements in their ESG performance less impactful in enhancing their financing ability.

6.2. Heterogeneity Analysis Based on Firm Size

Differences in firm size may also lead to varying degrees of impact of ESG performance on supply chain financing. Following the approach of He et al. [51], this study uses total assets as a proxy for firm size and categorizes the sample into large enterprises and small-to-medium enterprises based on the industry median. Separate regression analyses are conducted for each group, with the results presented in columns (3) and (4) of Table 11. The findings indicate that ESG performance does not have a significant effect on supply chain financing for small and medium-sized enterprises, whereas it has a significant positive impact for large enterprises. This may be attributed to the fact that large enterprises typically exhibit higher levels of information transparency, standardized governance structures, and more mature ESG management systems, enabling supply chain partners to accurately and promptly assess their ESG performance and, consequently, be more willing to offer favorable financing terms.

6.3. Heterogeneity Analysis Based on Pollution Level

Environmental pollution is another major factor contributing to the supply chain financing risks faced by heavily polluting enterprises. Such enterprises are often subject to greater public scrutiny, and in the event of a serious environmental incident, the resulting loss of trust from business partners can damage cooperative relationships, reduce market recognition, and restrict access to financing. In this study, industries classified as heavily polluting are identified based on the Guidelines for the Industry Classification of Listed Companies and the Environmental Inspection Industry Classification Directory issued by the Ministry of Environmental Protection. Enterprises are categorized into heavily polluting and non-heavily polluting enterprises accordingly for regression analysis. The results, presented in columns (5) and (6) of Table 11, indicate that both the magnitude and significance level of the ESG performance coefficient are higher for non-heavily polluting enterprises compared to their heavily polluting counterparts. This suggests that non-heavily polluting enterprises are more likely to gain recognition from supply chain financiers when enhancing their ESG performance.

6.4. Heterogeneity Analysis Based on High-Tech Industry Classification

High-tech enterprises have long benefited from institutional advantages, such as government R&D subsidies and special support for green technologies, which create an implicit linkage between their ESG performance and supply chain financing channels. In this study, high-tech industries are selected as the focal point for heterogeneity analysis. Based on the China Securities Regulatory Commission’s 2012 industry classification guidelines for listed companies, the sample is divided into high-tech and non-high-tech enterprises for separate regression analyses. The results indicate that the regression coefficient in column (7) of Table 11 is significant at the 1% level, whereas the coefficient for the control group in column (8) is not statistically significant. High-tech enterprises, which rely on technological innovation as their core competitiveness, typically possess higher technical barriers and stronger market positions. Their exemplary ESG performance facilitates recognition from supply chain partners. In contrast, non-high-tech enterprises are primarily engaged in traditional industries, where ESG performance is often limited by production processes and industry-specific factors, making it challenging for improvements in ESG performance to significantly influence supply chain financing.

6.5. Further Analysis of Heterogeneity Through the Introduction of Interaction Terms

In the heterogeneity analysis of this paper, we further introduce an interaction term model test, which confirms significant heterogeneity in the impact of ESG performance on supply chain financing, as shown in

Table 12. Results of the heterogeneity test incorporating interaction terms.

Variable	(1)	(2)	(3)	(4)
	Firm Nature	Firm Size	Pollution Level	High-Tech Level
ESG × SOE	0.001 * (1.94)
ESG × Size		−0.001 ** (−2.17)
ESG × Pollute			−0.001 ** (−2.02)
ESG × High-tech				−0.002 *** (−4.13)
_cons	YES	YES	YES	YES
Ind FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	3619	3619	3619	3619

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; t-values are reported in parentheses.

. The nature of state-owned enterprises provides a substantial “enhancing effect” on the financing-promoting role of ESG performance. Conversely, the scale of large enterprises, the characteristics of heavy-polluting industries, and those of non-high-tech industries exhibit a significant “weakening effect.” This indicates that the inherent characteristics of enterprises systematically regulate the financing returns of ESG investments. The effectiveness of ESG performance as a credit signal is more prominently reflected in state-owned enterprises, small and medium-sized enterprises, non-heavy-polluting enterprises, and high-tech enterprises. Firms with these attributes achieve higher marginal financing returns from ESG investments, and the transmission efficiency of ESG performance as a credit signal is also enhanced.

The ESG behaviors of state-owned enterprises are more readily interpreted by the market as a sustainable long-term commitment. Therefore, when state-owned enterprises demonstrate excellent ESG performance, they can gain higher credibility from financial institutions and upstream and downstream partners in the supply chain, significantly improving their financing efficiency. In contrast, the reputation of non-state-owned enterprises relies more on their own market-oriented operational performance, and the transmission of their ESG signals needs to overcome a higher “credibility threshold”. Although ESG investment is beneficial, the marginal financing returns during the initial stage of signal transmission are relatively low. Large enterprises typically possess high information transparency and are prime targets for competition among financial institutions; their traditional financial and collateral credit is generally sufficient, so the marginal credit increment and information value derived from ESG performance are relatively limited.

Conversely, small and medium-sized enterprises often face more severe information asymmetry and financing constraints. In this context, an excellent ESG report can serve as a pivotal signal to break through information barriers and demonstrate management quality and development potential. The ESG investments of heavy-polluting enterprises are often viewed as “compliance costs” to meet regulatory requirements rather than as value creation, and their effectiveness may be compromised due to concerns about “greenwashing.” This leads to the dilution of the positive effects of ESG investment by the negative reputation of the industry. In comparison, the ESG investments made by non-heavy-polluting enterprises are more readily recognized, thus securing financing support. Non-high-tech enterprises often lack the capability to effectively transform ESG factors into core technological advantages and business model innovations, resulting in an unclear path for ESG to generate financial returns. However, the business nature of high-tech enterprises aligns closely with innovation, talent development, and social responsibility. Their ESG performance can more directly enhance brand reputation, attract talent, and explore green markets, thereby facilitating a smoother conversion into financing advantages.

7. Conclusions and Implications

This paper conducts an empirical analysis using data from A-share listed companies in China between 2013 and 2023, exploring the extent to which a company’s ESG performance influences its supply chain financing capabilities. The main findings are as follows: First, strong ESG performance significantly enhances a company’s ability to secure supply chain financing. This conclusion remains robust after a series of tests, including propensity score matching, instrumental variable methods, substitution of explanatory variables, adjustment of fixed effects, and shortening of the time window. Second, ESG performance primarily facilitates supply chain financing through two channels: enhancing corporate reputation and reducing information asymmetry between companies and their stakeholders. Third, the effect exhibits asymmetry across different firm characteristics, including ownership structure, firm size, pollution intensity, and industry type. Specifically, the positive impact of ESG performance is more pronounced among state-owned enterprises, large firms, companies with lower pollution levels, and high-tech industries.

Enterprises, financial institutions, and governments each play distinct yet interrelated roles in enhancing the impact of ESG on supply chain financing. The insights derived from the study are as follows:

For enterprises, it is imperative to elevate ESG principles to a strategic level and to integrate them comprehensively across all aspects of operations and management. On the environmental front, companies should actively promote green production, adopt clean energy practices, and minimize emissions and resource wastage. In terms of social responsibility, firms should adopt a “dual circulation” perspective, strengthen the protection of employee rights, enhance labor standards throughout their supply chains, and actively engage in community building and public welfare activities to foster a broad network of trust among stakeholders. Regarding governance, companies must strive to establish transparent and efficient decision-making systems, as well as robust oversight mechanisms. The three pillars of ESG are not isolated evaluation metrics; rather, they form an interconnected ecosystem. Only through the positive interaction of environmental stewardship, social contribution, and governance excellence can companies unlock the multiplier effects of ESG on development. A systematic improvement in ESG performance not only optimizes a company’s green credit profile but also generates value network effects across the industrial chain, thereby strengthening its supply chain financing capability. The essence of these suggestions lies in transforming ESG from a reporting framework into specific operational practices. Specifically, it is recommended that leading enterprises conduct ESG due diligence throughout the supply chain, integrate ESG standards into procurement contracts and supplier management processes, and utilize digital tools such as blockchain to reduce compliance costs for small and medium-sized enterprises both upstream and downstream. Additionally, establishing internal ESG transformation funds can incentivize green transformation across the entire supply chain through mechanisms such as green procurement and financing for equipment upgrades.

For financial institutions, the industry is at a critical juncture, shifting from a traditional, financially driven investment and lending model to a comprehensive, value-oriented approach. Historically, banks, trusts, securities firms, and insurers have prioritized static financial indicators—such as financial statements, balance sheets, and cash flows—when assessing companies, often neglecting non-financial factors like environmental management, social responsibility, and governance practices. With the growing recognition of the relationship between ESG practices and long-term corporate value, relying solely on financial metrics has become increasingly inadequate in today’s complex and dynamic markets. In this context, financial institutions must integrate ESG factors into their investment decision-making frameworks and develop multi-dimensional assessment models. Companies with strong ESG performance tend to exhibit better information disclosure, higher operational transparency, greater regulatory compliance, and stronger internal controls. These attributes not only lower the risk of future environmental, social, or governance-related crises but also enhance corporate resilience amid uncertainty. Investing in or lending to such companies helps reduce default rates and credit losses while also bolstering the institution’s reputation in green finance and strengthening its brand image. Therefore, prioritizing ESG factors can create new financing opportunities and direct more capital toward companies with strong ESG credentials, fostering a greener and more sustainable capital market. The key to this transformation lies in promoting ESG from a qualitative concept to a quantitative risk control parameter. Greater efforts should be made to develop ESG-weighted credit rating models, dynamically adjust credit lines and interest rates for enterprises demonstrating excellent performance, and innovatively design supply chain financial products linked to sustainable performance, such as ESG stepped-interest notes. Furthermore, through a bank-enterprise data interconnection mechanism, verified ESG data streams can be directly integrated into the comprehensive risk control system, encompassing all processes from pre-loan to post-loan management.

For governments, it is essential to accelerate the development of unified, actionable ESG evaluation standards that specify disclosure metrics and content requirements, thereby shifting ESG information disclosure from voluntary to standardized practices. Governments serve not only as regulators but also as key actors in ensuring orderly and fair market operations. By strengthening information disclosure systems in capital markets, governments can ensure that investors have access to comprehensive and accurate ESG data, ultimately improving supply chain financing opportunities for enterprises. Moreover, governments could establish ESG information platforms to provide authoritative data services and evaluation support, reducing disclosure costs for enterprises and enhancing market participants’ access to relevant information. Improving the disclosure of ESG and other non-financial information would mitigate information asymmetry and provide investors with critical references for making sound investment decisions. To build an institutional infrastructure with incentive compatibility, mandatory industry-specific ESG disclosure guidelines should be implemented to ensure information comparability. Additionally, efforts should be made to lead the integration of a national ESG data platform that aggregates data from multiple departments, thereby breaking down information silos. Furthermore, targeted fiscal and tax incentive policies should be established: enterprises with improved ESG ratings may be granted certain tax reductions, and core enterprises that successfully drive collaborative carbon reduction in the supply chain can receive specific subsidies. Through these measures, an effective market-oriented guidance mechanism can be established.

8. Research Limitations and Future Research Direction

During the course of this research, several inherent limitations have emerged. In terms of sample coverage, the empirical analysis is strictly based on publicly available data from Chinese A-share listed companies. While this choice ensures the standardization and accessibility of the data, it also suggests that the conclusions may not be directly generalizable to the broader population of non-listed companies and small and medium-sized enterprises (SMEs), which often face more severe financing constraints. Consequently, this limitation affects the external validity of the research findings to some extent. Additionally, there are dual limitations in the measurement of the core variables. First, the assessment of enterprises’ ESG performance relies on the specific rating system of the CNRDS database. Although this system fully considers the characteristics of the local market, there are systematic differences in indicator weights, evaluation methods, and disclosure requirements compared to those employed by international mainstream rating agencies such as MSCI and Sustainalytics. These differences may impact the generalizability of the research findings in international comparisons.

Based on the above analysis of limitations, future research directions can be further expanded in terms of sample selection and research contexts. The scope of the sample could be extended to include enterprises listed on the New Third Board, or large-scale surveys could be conducted to cover SMEs, thereby testing the inclusive financial value of ESG across heterogeneous enterprise groups. Additionally, conducting cross-border comparative studies and incorporating diverse national institutional backgrounds and ESG rating systems into the analytical framework could provide deeper insights into the boundary conditions and cross-cultural differences in the financial effects of ESG. Future studies could also explore the integration of digital supply chain financial tools into the measurement framework of financing capacity or investigate the role of financial technology in enhancing ESG information transmission. Such efforts could offer a more comprehensive understanding of the contemporary dynamics of supply chain finance. Moreover, future research could focus on decomposing individual dimensions or key sub-indicators of ESG to identify the core elements that most significantly drive the relationship between ESG and supply chain finance.