Does ESG Performance Help Corporate Deleveraging? Based on an Analysis of Excessive Corporate Debt

Abstract

The ESG performance of enterprises is becoming an essential form of support for investors’ investment decisions and a critical aspect to follow to achieve sustainable development of enterprises. This study uses A-share listed companies in China from 2009 to 2022 as the research sample to study the impact of ESG performance on corporate over-indebtedness and its mechanism. The findings show that good ESG performance significantly negatively affects the level of corporate over-debt and the probability of over-debt. The mechanism test revealed that ESG performance reduces the level and probability of excessive corporate debt by alleviating information asymmetry, reducing corporate debt financing costs and short-term debt length, and improving corporate operating performance. The heterogeneity analysis indicates that the inhibitory effect of ESG performance on corporate over-indebtedness is more significant in polluting industries and regions with a low degree of marketization. Through the moderating effect, we find that improved internal control quality and increased analyst attention can enhance the inhibitory effect of ESG performance on excessive corporate debt. Based on the results above, enterprises should focus on improving ESG performance to reduce the risk of excessive debt and achieve sustainable development. This paper enriches the research on ESG performance and corporate leverage manipulation from the perspective of corporate over-indebtedness, deepens and expands the research on the mechanism of ESG performance affecting corporate over-indebtedness, and explores the moderating effect of internal and external governance mechanisms on ESG performance affecting corporate over-indebtedness.

1. Introduction

Excessive leverage ratios can induce corporate debt risks and even trigger systemic risks in the financial system [1]. In response to the impact of the global financial crisis, the Chinese government introduced the “Four Trillion Plan” economic stimulus policy in 2008. This policy caused the leverage ratio of China’s non-financial corporate sector to rapidly rise from 95.2% in 2008 to 168.4% in 2023. Long-term high debt ratios not only lead to increased interest expenses and reduced profit margins and risk tolerance regarding enterprises [2,3] but also to limits on enterprises’ investment and financing capabilities, weakening their market competitiveness [4,5]. Therefore, promoting corporate deleveraging has become an important way for enterprises to achieve sustainable development. However, deleveraging does not simply require enterprises to reduce their leverage ratios but requires them to control their debt-to-asset ratios at their optimal levels [6] because excessive debt is the fundamental reason why high debt ratios are not conducive to corporate development [7]. In the current global inflation and economic downturn, the overall debt level of enterprises has further increased, and the problem of excessive debt has become more prominent [8]. Therefore, studying the problem of excessive debt of enterprises is an important topic, especially in China, where the debt-to-asset ratio of enterprises is relatively high.

ESG is a concept that incorporates environmental protection (E), social responsibility (S), and corporate governance (G) factors into corporate investment decisions, which is highly consistent with the sustainable development of enterprises. Scholars have conducted in-depth research on the economic consequences of corporate ESG performance, and a large number of studies have shown that good ESG performance has a positive impact on companies. For example, a company’s ESG performance has a significant effect on enhancing corporate competitiveness [9], improving corporate value [10,11], reducing financing costs [12], reducing operating risks [13], improving corporate efficiency [14], reducing the risk of stock price collapse [15], improving the level of green technology innovation [16], and corporate reputation [17]. In addition, practical results also show that poor ESG performance is not conducive to corporate operations and development. For example, Thames Water, the third largest green bond issuer in the UK, had its poor ESG performance lead to Moody’s downgrading its credit rating to “junk”, which seriously affected the overall credit status of Thames Water’s green bond portfolio [18]. In addition, affected by ESG policies related to fossil fuels, 15 banks, including Australia’s four largest banks, refused to provide credit services to Delta Electricity’s Vales Point coal-fired power station in New South Wales [19]. It can be seen that ESG performance is closely related to corporate debt issues.

Notably, only a few scholars have explored the impact of ESG performance on corporate over-indebtedness, and the black box of the mechanism by which ESG performance affects corporate over-indebtedness needs to be further expanded and opened. In theory, ESG performance can affect corporate over-indebtedness through the following channels. First, as crucial non-financial information of a company, ESG information can effectively reduce and alleviate information asymmetry between the company and its external parties [20], strengthen the supervisory role of external stakeholders, and thus reduce the level of corporate over-indebtedness. Second, ESG performance is highly consistent with the current green development concept, reflecting the company’s philosophy and practice in environmental protection and social responsibility. Good ESG performance can help the company win more high-quality resources and development opportunities [21], thus improving corporate operating performance and reducing excessive debt levels. Third, good ESG performance can also reduce corporate financing costs, alleviate the situation of “borrowing new to repay old”, and thus reduce the level of corporate over-indebtedness. In addition, internal and external corporate governance are important mechanisms that affect corporate development. What is its impact on the relationship between ESG performance and corporate over-indebtedness? This is a question that existing research has not considered.

Compared with the existing literature, the contributions of this paper are as follows. First, this paper studies the impact of ESG performance on corporate deleveraging from the perspective of over-debt, which expands the research perspective of evaluating the economic consequences of ESG performance and enriches corporate over-debt-related research results. Second, we further explored how ESG performance affects corporate over-indebtedness. We thoroughly discuss how ESG performance affects corporate over-indebtedness from the perspectives of information asymmetry, corporate financing constraints, and corporate operating performance. This provides readers with new ideas for understanding the mechanism by which ESG performance affects corporate over-indebtedness. Third, this paper also discusses the moderating effect of internal and external governance on the impact of ESG performance on corporate over-indebtedness, clarifies the favorable conditions for enhancing ESG performance to alleviate the effect of corporate over-indebtedness, and provides an important reference for companies to strengthen internal and external governance to actively play the beneficial role of ESG performance.

The rest of the paper is structured as follows. Section 2 introduces the research literature on corporate ESG performance and over-indebtedness. Section 3 presents the theoretical analysis and research design, including hypotheses, sample and data collection, variable definition and measurement, and empirical model construction. Section 4 introduces the empirical analysis, including model testing, endogeneity testing, robustness testing, mechanism analysis, heterogeneity analysis, and moderating effects. Section 5 summarizes this study and discusses its implications.

2. Literature Review

2.1. Economic Consequences of ESG Performance

The ESG metric is a company evaluation system that systematically considers environmental, social, and governance factors. It represents a new direction and change in the high-quality development of enterprises in a new era, representing a new stage in the environmental, social, and governance aspects [22]. Good ESG performance is a mechanism that can be used to maintain the sustainable profitability of enterprises. It can reflect the management quality of target companies and help investors identify the best investment companies. On the contrary, when ESG performance is poor, companies will face higher political, regulatory, and litigation risks [23]. Therefore, ESG performance determines the development status of enterprises to a certain extent. Delta Electronics (Taiwan), for example, has been a leader in ESG, issuing its first Corporate Social Responsibility report in 2005 and consistently publishing annual updates. The company has been listed on the Dow Jones Sustainability Indices for 14 consecutive years and achieved a top 10% S&P Global CSA score in 2024. Notably, Delta’s US headquarters in Fremont, California, which was built in 2016, showcases green energy and building automation products, including a geothermal heat pump system and a full rooftop solar system. The building was awarded a LEED Zero Energy certification in 2022, highlighting Delta’s commitment to environmental sustainability. Delta’s proactive environmental strategies have enhanced its reputation, leading to recognition such as being named 24th in Time Magazine’s 2024 ranking of the world’s most sustainable companies. These efforts have attracted environmentally conscious investors and customers, contributing to the company’s growth and market position [24]. Since 2006, First Capital has developed properties using Leadership in Energy and Environmental Design (LEED) standards. In 2020, the company released its 2020–2024 Environmental, Social, and Governance (ESG) Roadmap and Sustainability Policy, outlining current and future sustainability plans. First Capital has achieved a ‘AAA’ rating in the Morgan Stanley Capital International (MSCI) ESG Ratings assessment for 3 consecutive years and received validation from the Science Based Targets Initiative (SBTi) for their 2030 GHG reduction target of 46%. First Capital’s strong ESG performance has enhanced its reputation, leading to recognition as one of Canada’s Top Small and Medium Employers. The company’s sustainability initiatives have attracted tenants and investors who prioritize environmental responsibility, contributing to its stable growth [25].

A large number of scholars have studied the impact of corporate ESG performance and have affirmed its positive impact. First, ESG performance helps reduce information asymmetry inside and outside the company. As an important part of a company’s non-financial information, ESG information can convey to investors the company’s current status in environmental protection and social responsibility, help investors more accurately assess the company’s potential risks and value levels, and reduce information asymmetry [20]. Second, ESG performance helps enhance the company’s image and reputation. ESG performance reflects the company’s sustainable development concept, conveys to the outside world the company’s efforts and achievements in environmental and social responsibility, and helps the company establish a good image [26]. Finally, ESG performance helps improve the relationship between corporate stakeholders. On the one hand, ESG performance highlights the quality of the corporate working environment and the company’s potential for sustainable development [27], strengthening the connection between employees, executives, and the company. On the other hand, ESG performance is in line with the current concept of green development, highlighting the company’s philosophy and practice of environmental protection and social responsibility, thereby helping the company establish better government–enterprise relations and obtain high-quality resources and opportunities [21].

Based on the above three mechanisms, scholars have further explored the importance of ESG performance in achieving high-quality corporate development. Some scholars pointed out that ESG performance helps to strengthen the relationship between enterprises and stakeholders and enhance corporate competitiveness [28]. Other scholars have found that ESG performance helps to enhance customer loyalty, reduce the compliance costs of corporate governance failures, and curb corporate risks [29]. In addition, ESG performance has played a positive role in reducing corporate financing costs, easing financing constraints, reducing the risk of stock price collapse, and curbing corporate violations by reducing information asymmetry [21,30,31].

However, some studies point out that ESG performance has no significant or even negative impact on corporate development. On the one hand, there is currently a lack of unified standards for corporate ESG performance, and some companies publish empty ESG reports to whitewash themselves. Therefore, ESG performance cannot reduce financing costs, and even positive ESG events cannot bring additional benefits to companies [32,33]. On the other hand, out of the pursuit of personal interests, managers may excessively pursue the company’s performance in the fields of environmental protection and social responsibility, crowding out the company’s production and research and development, which is not conducive to the improvement of corporate value [34]. In addition, scholars have also found negative effects of ESG performance. They found that improving ESG performance led to increased corporate costs and decreased operating profits, which had an adverse impact on corporate growth capabilities [35]. It can be seen that whether ESG performance has a positive impact on enterprises requires further in-depth analysis.

2.2. Factors Affecting Excessive Debt of Enterprises

In theory, influenced by factors such as market friction, information asymmetry, bankruptcy costs, and income tax, enterprises have a target capital structure or target debt ratio to maximize their interests [36]. However, due to the existence of objective random events and adjustment costs, enterprises may deviate from their target debt ratio, thus forming excessive debt and increasing the risk of corporate bankruptcy. Existing research has found that the factors affecting corporate over-indebtedness can be divided into internal factors and external factors. In terms of internal factors, some scholars pointed out that state-owned enterprises are more likely to obtain bank loans, resulting in higher debt levels [37]. However, from a long-term and dynamic perspective, the level and likelihood of over-indebtedness of state-owned enterprises are lower than those of non-state-owned enterprises [38]. In addition, some studies point out that corporate investment behavior and group control are also important factors affecting the level of excessive debt [39]. Some studies have explored the impact of government policies on excessive corporate debt from the perspectives of “deleveraging”, interest rate marketization, industrial support policies, and policy uncertainty [40]. Although the above studies have presented a relatively comprehensive discussion on the factors of corporate excessive debt, there is a lack of research on corporate excessive debt from the perspective of ESG information disclosure, which needs to be further enriched.

3. Theoretical Analysis and Research Design

3.1. Theoretical Analysis

Existing studies conducted extensive research on the economic consequences of ESG performance, but few studies have examined the relationship between ESG performance and corporate over-indebtedness. So, how does ESG performance affect corporate over-indebtedness?

First, ESG performance can reduce information asymmetry inside and outside the company, enhance the company’s reputation, and strengthen the supervision of the company by external stakeholders, thereby reducing the company’s excessive debt. On the one hand, based on the theory of information asymmetry, external stakeholders cannot accurately assess the company’s risks based solely on the company’s financial statement information. As important non-financial information of the company, ESG information can convey information about the company’s environmental protection, social responsibility, and corporate governance to external stakeholders, which, to a certain extent, makes up for the lack of financial information. This helps external stakeholders to form a comprehensive and three-dimensional understanding of the company, reduces the supervision costs of investors, increases the possibility of exposure to the potential debt risks of the company, and forces the company to reduce the level of excessive debt. At the same time, based on the governance effect of ESG performance, high-quality ESG performance means that the internal governance mechanism of the enterprise is better [41], and managers’ decision-making is more inclined to be long-term oriented. Therefore, companies with higher ESG performance scores are more inclined to have reasonable debt and restrain excessive debt. On the other hand, based on reputation theory, a good corporate reputation and image can attract more attention from the government and investors, and the external supervision effect can be stronger. As investors pay more attention to corporate environmental protection and social responsibility, ESG performance sends altruistic signals of corporate green transformation and high-quality development to stakeholders, helping companies establish a good image and attracting higher levels of attention from investors and analysts [42]. A higher level of external supervision can play a “magnifying glass” supervisory effect, prompting companies to reduce financial risks and alleviate the problem of excessive debt. Therefore, this study makes the following assumptions.

H1. 

Good ESG performance has a significant negative impact on both the level of corporate over-debt and the probability of over-debt.

H2a. 

ESG performance reduces the level and probability of excessive corporate debt by alleviating information asymmetry.

Secondly, ESG performance is conducive to reducing corporate financing costs, alleviating corporate reliance on short-term debt, and thus reducing corporate over-indebtedness. “Difficult and expensive financing” is an important reason for corporate over-indebtedness. The information asymmetry between companies and creditors increases the financing constraints companies face, resulting in banks or other creditors needing to charge higher interest rates to cope with uncertainty risks. ESG performance can reduce information asymmetry between companies and creditors and help companies establish a good image, thereby reducing corporate financing constraints and financing costs. This helps reduce corporate debt financing financial expenses and reduce corporate demand for high-cost funds, thereby reducing corporate over-indebtedness. In addition, Chinese companies are highly dependent on short-term debt, and there is a widespread phenomenon of “short-term debt for long-term use” [43], meaning when long-term loans cannot be met, companies have to “borrow new to repay old” in order to realize long-term asset investment [44]. This will increase the company’s active demand for obtaining financing through high leverage, increase the corporate leverage ratio, and lead to corporate over-indebtedness. Good ESG performance can ease the long-term financing constraints of enterprises, reduce the extent to which enterprises use short-term debt for long-term purposes [45], and thus reduce the excessive debt of enterprises. Therefore, this study makes the following assumption.

H2b. 

ESG performance reduces the level and probability of excessive corporate debt by improving corporate operating performance.

Finally, ESG performance can improve corporate operating performance, thereby reducing corporate debt-to-equity ratios and excessive debt levels. On the one hand, high-quality ESG performance means that companies pay more attention to the working environment of employees and have a complete constraint mechanism, which is not only conducive to attracting and cultivating better talents and improving the enthusiasm and efficiency of corporate executives and employees [46] but is also conducive to restraining the short-sighted and self-interested behavior of corporate executives, reducing agency costs, improving investment efficiency, and ultimately improving corporate operating results. On the other hand, high-quality ESG performance signals to the outside world that the company is actively fulfilling its social responsibilities. While improving the company’s reputation, it helps the company obtain policy support and scarce resources from relevant stakeholders, enhance customer loyalty, trust, and the competitive advantage of its products, and achieve an increase in product cost markup and operating performance [47]. The improvement of corporate operating performance means that the company has higher profitability, which allows the company to have more surplus funds to repay corresponding debts, thereby reducing excessive corporate debt. Therefore, this study makes the following assumption.

H2c. 

ESG performance reduces the level and probability of excessive corporate debt by reducing corporate debt financing costs and short-term debt length.

3.2. Sample and Data

The ESG rating data of CSRC were officially released in 2009. Therefore, this study uses Chinese-listed companies from 2009 to 2022 as research samples. In order to ensure the reliability of the research conclusions of this paper, we referred to the existing literature to process the initial sample as follows: (1) exclude the samples from the financial or insurance industry; (2) exclude the samples of PT, ST, and ST* in the current year; PT is the abbreviation of Particular Transfer. According to the provisions of China’s “Company Law” and “Securities Law”, if a listed company suffers losses for 3 consecutive years, its stocks will be suspended from the listing; ST is the abbreviation of special treatment. ST companies refer to the stocks of domestically listed companies in China that have suffered losses for 2 consecutive years and have been subject to special treatment; *ST companies refer to the stocks of domestically listed companies in China that have suffered losses for 3 consecutive years; (3) exclude enterprises with an asset-liability ratio greater than 1; (4) exclude the samples with missing values of the explained variable, explanatory variable, control variable, mechanism variable, and moderating variable; (5) perform a 1% bilateral win-scoring treatment on all continuous variables. After the above processing, this study obtained 14,465 annual observation samples using 2780 enterprises.

In terms of data sources, the ESG data was obtained from the Huazheng ESG Rating and Bloomberg databases, the corporate internal control quality information was obtained from the DIB Internal Control and Risk Management Database, and the other data was obtained from the China Stock Market and Accounting Research (CSMAR) database.

3.3. Definition of Variables

3.3.1. Dependent Variable

Based on the existing research, there are three main ways to measure the over-indebtedness of enterprises. First, the actual asset deleveraging ratio of enterprises is subtracted from their target debt ratio [48]. Second, the actual debt ratio is used to subtract the median or average debt ratio of the industry in the current year [49]. Thirdly, the interest expense of enterprises when tax incentives are maximum is divided by the actual interest expense [48]. The differences between these measurement methods are as follows: the first method believes that enterprises have an optimal debt ratio (target debt ratio) that is conducive to their own development due to the influence of internal and external factors, such as differences in enterprise characteristics and market and industrial environments. Therefore, the over-debt level of enterprises should be judged by how much the actual debt ratio exceeds the target debt ratio. This is the most common indicator used by scholars to study the excessive debt of enterprises [50]. The second approach holds that the target debt ratio of the enterprise is determined by the industry in which the enterprise is located, which has a certain desirability. However, this approach does not take into account the differences between the characteristics within the firm. The third method holds that the corporate debt ratio is determined by tax factors, but relevant research shows that after controlling other influencing factors, tax has no significant impact on the corporate asset-liability ratio [51]. Based on this, this study is consistent with mainstream research and uses the first method to measure corporate over-indebtedness. Based on the existing research, this study first uses the Tobit model to conduct annual regression on the samples and obtain the target debt ratio (AIMLEV) of enterprises. The specific model is as follows:

$$\begin{array}{l}{AIMLEV}_t={\alpha }_0+{\alpha }_1{SOE}_{t-1}+{\alpha }_2{ROA}_{t-1}\\ +{\alpha }_3{INDLEVB}_{t-1}+{\alpha }_4{GROWTH}_{t-1}+{\alpha }_5{FATA}_{t-1}\\ +{\alpha }_6{SHRCR1}_{t-1}+{\alpha }_7{SIZE}_{t-1}\end{array}$$

(1)

We then used the actual debt ratio of the enterprise minus the target debt ratio, which is estimated by regression in the model (1), to obtain the enterprise’s excessive debt level (EXLEVB). On this basis, this study also constructs a dummy variable for whether the enterprise is over-indebted (LEVBDUM); that is, if the enterprise’s excessive debt ratio is positive, it is assigned a value of 1. Otherwise, it is 0. The control variables in the model (1) include state-owned nature (SOE), enterprise profitability (ROA), industry median debt ratio (INDLEVB), fixed asset ratio (FATA), total asset growth rate (GROWTH), the largest shareholder’s shareholding ratio (SHRCR1), and enterprise size (SIZE).

3.3.2. Independent Variable

We used the Huazheng ESG rating data to measure the ESG performance of Chinese-listed companies. These data have the characteristics of wide coverage, high timeliness, and closeness to the Chinese market and have been widely recognized and applied in the industry and academia [52]. The Huazheng ESG Rating divides corporate ESG performance into nine levels, from low to high, namely C, CC, CCC, B, BB, BBB, A, AA, and AAA. This study assigns the levels from 1 to 9. In addition, this study also uses the ESG scores of Chinese A-share listed companies in Shanghai and Shenzhen (published by Bloomberg) divided by 100 as a proxy variable for corporate ESG performance for robustness testing.

3.3.3. Control Variables

By referring to existing studies, this study controls the following factors that may affect corporate over-indebtedness: corporate ownership (SOE), profitability (ROA), industry debt ratio (INDLEVB), total asset growth rate (GROWTH), fixed asset ratio (FATA), enterprise size (SIZE), the largest shareholder’s shareholding (SHRCR1), book-to-market ratio (BM), management expense ratio (MANEXP), non-debt tax shield (NDTS), income tax rate (ETR), earnings volatility (VEBITTA), cash flow volatility (VCF), and management shareholding ratio (MANOWN). At the same time, this study also controls the time effect and industry effect. The specific definitions of each variable are shown in

Table 1. Definition of variables.

Variable Symbols	Variable Name	Variable Definition
EXLEVB	Over-indebtedness1	The actual debt ratio of the enterprise minus the target debt ratio.
LEVBDUM	Over-indebtedness2	If the enterprise’s over-indebtedness ratio is positive, it is assigned a value of 1; otherwise, it is 0.
ESG	ESG performance	Assign a value of 1–9 to C and CC to AAA in the ESG rating of Huazheng, respectively.
SOE	Nature of property rights	Assign a value of 1 to state-owned enterprises and 0 to non-state-owned enterprises.
ROA	Profitability	Operating profit/total assets.
INDLEVB	Industry debt ratio	Median debt-to-asset ratio of the industry.
GROWTH	Total assets growth rate	(Total assets at the end of this period − Total assets at the end of the previous period)/Total assets at the end of the previous period.
FATA	Fixed assets ratio	Fixed assets/total assets.
SIZE	Enterprise scale	Logarithm of total assets.
SHRCR1	The largest shareholder’s shareholding	The proportion of the largest shareholder’s shareholding in the total share capital.
BM	Book-to-Market Ratio	Book value/market value.
MANEXP	Management expense ratio	Book value/market value.
NDTS	Non-debt tax shield	(Depreciation of fixed assets + depreciation of oil and gas assets + depreciation of productive biological assets)/total assets.
ETR	Income tax rate	Income tax/total profit.
VEBITTA	Earnings volatility	Earnings Before Interest and Tax/Three-year volatility of total assets.
VCF	Cash flow volatility	Total cash flow/Three-year volatility of total assets.
MANOWN	Management shareholding ratio	The proportion of total shares held by all senior managers of the company at the end of the year to the total share capital.

.

3.4. Model Construction

In order to verify the impact of ESG performance on the excessive debt of listed companies, this study establishes the following model:

$${EXLEVB}_{it}={\beta }_0+{\beta }_1{ESG}_{it}+{\beta }_2{CONTROLS}_{it}+{\lambda }_t+{\gamma }_s+{\epsilon }_{it}$$

(2)

In model (2), ${EXLEVB}_{it}$ represents the level of over-indebtedness of enterprise i in period t or whether it is over-indebted, ${CONTROLS}_{it}$ is the control variable in Table 1, ${\lambda }_t$ and ${\gamma }_s$ represent the year and industry-fixed effects, respectively, and ${\epsilon }_{it}$ is the random error term. In order to eliminate the possible heteroskedasticity problem, this study uses robust standard errors for regression. If the regression coefficient ${\beta }_1$ is significantly negative, it means that H1 holds that corporate ESG performance helps reduce corporate excessive debt.

4. Empirical Results Analysis

4.1. Descriptive Statistics

Table 2. Descriptive statistics for the main variables.

Variables	N	Mean	SD	Min	Max
EXLEVB	14,465	0.004	0.139	−0.356	0.381
LEVBDUM	14,465	0.509	0.500	0	1.000
ESG	14,465	4.210	0.929	1.000	8.000
SOE	14,465	0.316	0.465	0	1.000
ROA	14,465	0.045	0.055	−0.224	0.197
IND_LEVB	14,465	0.412	0.092	0.260	0.714
GROWTH	14,465	0.178	0.243	−0.276	1.190
FATA	14,465	0.218	0.153	0.002	0.692
SIZE	14,465	22.476	1.290	19.963	26.262
SHRCR1	14,465	33.747	14.456	9.200	74.300
BM	14,465	0.327	0.149	0.049	0.786
MANEXP	14,465	0.041	0.025	0.004	0.131
NDTS	14,465	0.02	0.014	0	0.066
ETR	14,465	0.151	0.147	−0.49	0.728
VEBITTA	14,465	0.028	0.035	0.001	0.249
VCF	14,465	0.043	0.034	0.003	0.204
MANOWN	14,465	14.563	19.199	0	67.565

reports the descriptive statistics of the main variables in this study. The mean of corporate over-indebtedness, EXLEVB (LEVBDUM), is −0.004 (0.509), and the standard deviation is 0.139 (0.500), indicating that about 50.9% of enterprises are over-indebted, and there are large differences in the level of over-indebtedness among different enterprises. The mean of corporate ESG performance is 4.21, and the standard deviation is 0.929, indicating that the ESG performance level of listed companies in my country is relatively low. In addition, the mean of enterprise size (SIZE) is 22.476, and the standard deviation is 1.29, indicating that the research sample covers enterprises of different sizes. The statistical results of the remaining variables are basically consistent with existing research.

Figure 1 and Figure 2 show the linear fit of a firm’s ESG performance to its over-indebtedness level and the distribution of its over-indebtedness level under different ESG ratings, respectively. The results in those figures show that with the improvement of ESG performance, the excessive debt level of enterprises gradually decreases, which initially proves the validity of Hypothesis 1.

4.2. Benchmark Regression Results

Table 3. The impact of ESG performance on corporate over-indebtedness.

	(1)	(2)	(3)	(4)
Variables	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM
ESG	−0.0063 ***	−0.0722 ***	−0.0090 ***	−0.1014 ***
	(−5.99)	(−5.63)	(−8.91)	(−7.18)
SOE	−0.0117 ***	−0.1344 ***	−0.0022	−0.0848 **
	(−4.79)	(−4.52)	(−0.92)	(−2.55)
ROA	−0.5499 ***	−5.2045 ***	−0.5643 ***	−6.0618 ***
	(−28.62)	(−20.41)	(−31.03)	(−21.32)
IND_LEVB	−0.1446 ***	−1.1363 ***	−0.1957 ***	−2.2645 ***
	(−12.68)	(−8.27)	(−5.35)	(−4.34)
GROWTH	0.0755 ***	0.6698 ***	0.1006 ***	0.9570 ***
	(18.18)	(12.02)	(25.72)	(16.09)
FATA	0.0587 ***	0.5941 ***	0.0784 ***	0.9719 ***
	(5.28)	(4.36)	(6.82)	(5.96)
SIZE	0.0112 ***	0.1351 ***	0.0077 ***	0.1271 ***
	(12.08)	(11.68)	(7.98)	(9.02)
SHRCR1	0.0007 ***	0.0056 ***	0.0007 ***	0.0060 ***
	(11.00)	(6.69)	(11.48)	(6.55)
BM	−0.4765 ***	−4.5871 ***	−0.5611 ***	−5.9273 ***
	(−72.15)	(−49.66)	(−84.93)	(−52.05)
manage	−0.2080 ***	−2.3172 ***	0.1572 ***	1.2646 **
	(−4.71)	(−4.23)	(3.49)	(2.00)
NDTS	−0.3743 ***	−2.0569	−0.4730 ***	−5.6622 ***
	(−3.02)	(−1.35)	(−3.87)	(−3.24)
ETR	0.0183 ***	0.1690 **	0.0217 ***	0.2327 ***
	(2.79)	(2.12)	(3.50)	(2.69)
VEBITTA	−0.3303 ***	−3.2283 ***	−0.3331 ***	−3.5871 ***
	(−10.61)	(−7.98)	(−11.28)	(−7.86)
VCF	0.4242 ***	4.2933 ***	0.3936 ***	4.4268 ***
	(14.33)	(11.48)	(13.94)	(10.74)
MANOWN	0.0003 ***	0.0030 ***	0.0003 ***	0.0038 ***
	(4.78)	(4.08)	(5.53)	(4.89)
Year FE	N	N	Y	Y
Industry FE	N	N	Y	Y
Adj_R2	0.341		0.450

Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. The t- or z-values calculated using robust standard errors are in parentheses.

shows the baseline regression results of the impact of ESG performance on corporate over-indebtedness. Among them, columns (1) and (2) are regression results that add control variables but do not consider year-fixed effects and industry-fixed effects. The regression coefficients of ESG performance on the company’s over-debt level and whether it is over-debt are −0.0063 and −0.0722, respectively, which is significantly negative at the 1% level. Columns (3) and (4) are the regression results after adding control variables, year-fixed effects, and industry-fixed effects. The regression coefficients of ESG performance on the level of corporate over-debt and whether it is over-debt are −0.0090 and −0.1014, which are significantly negative at the 1% level, respectively. By taking the regression results in the third column as an example, from an economic perspective, when a firm’s ESG performance grade increases by one notch, the firm’s over-debt level will decrease by 0.9%. We can see that the regression results in Table 3 indicate both statistically and economically that corporate ESG performance helps reduce corporate excessive debt.

4.3. Endogeneity Test

4.3.1. Instrumental Variables

In the benchmark regression of this study, factors that affect corporate over-indebtedness were controlled as much as possible, and year and industry-fixed effects were controlled, which reduces the endogeneity of this study to a certain extent. This study attempts to construct instrumental variables to further solve the possible endogeneity problem in this study. Existing studies have mostly used the mean of ESG performance of companies in the same industry or region as instrumental variables, but this method has certain shortcomings in terms of the principle of exclusivity [53]. This study draws on the practice of existing research and uses ESG fund holdings data as an instrumental variable for ESG performance [46]. The reason is that ESG funds use the method of “voting with their feet” and supervision to influence corporate operations [54], which will inevitably lead to the transmission of their investment philosophy to the companies in which they hold shares. However, ESG funds rarely intervene in the daily operations of listed companies [55] and, therefore, have no direct impact on the level of corporate over-indebtedness. Specifically, this study uses the number of shares held by corporate ESG funds as an instrumental variable for ESG performance.

Table 4. The results of the instrumental variable method.

	(1)	(2)	(3)
	ESG	EXLEVB	LEVBDUM
Number of ESG fund holdings	0.0154 ***
	(8.13)
ESG		−0.2609 ***	−2.9524 ***
		(−7.58)	(−7.30)
Controls	Y	Y	Y
Year FE	Y	Y	Y
Industry FE	Y	Y	Y
Cragg-Donald Wald F/Wald test	70.703 ***		206.88 ***
Adj_R2	0.169	−2.173

Note: *** denotes statistical significance at the 1% level. The t- or z-values calculated using robust standard errors are in parentheses.

shows the regression results of the instrumental variables. Column (1) is the result of the first-stage regression. The regression coefficient of the number of ESG fund holdings is significantly positive at the 1% level, confirming the correlation of instrumental variables. At the same time, the Cragg-Donald Wald F and Wald test statistics are 70.703 and 206.88, respectively, and there is no problem with weak instrumental variables. The regression coefficients in columns (2) and (3) are both significantly negative at the 1% level, which is consistent with the baseline regression. Therefore, ESG performance helps reduce corporate over-indebtedness.

4.3.2. Heckman Two-Step

When considering that the relevant information to evaluate a firm’s ESG performance is the information voluntarily disclosed by the firm, if the firm does not disclose the relevant information, the firm’s ESG performance cannot be evaluated. Therefore, this study may have the problem of sample selection bias.

To solve this problem, this study used the Heckman selection model to further test the relationship between ESG performance and excessive debt. The key to selecting the model is to select reasonable exclusivity constraint variables and ensure that the model does not have multicollinearity problems; otherwise, it will lead to insufficient validity of regression estimation results. By referring to existing research [56], this study generates the dummy variable ESGDUM based on the annual median ESG performance of other enterprises in the same industry. Second, a first-stage regression is performed using a Probit model, and then the inverse Mills ratio (IMR) is calculated. By referring to the common practice in the existing literature [57], the control variables of the Probit model are consistent with those in model (2). Finally, IMR is included in model (2) as a control variable for regression. Columns (1) to (3) of

Table 5. The results of the Heckman two-step and propensity score-matching methods.

	Heckman Two-Step			PSM
	(1)	(2)	(3)	(4)	(5)
	ESGDUM	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM
ESG	3.1870 ***	−0.0100 ***	−0.1469 ***	−0.0075 ***	−0.0988 ***
	(60.99)	(−3.52)	(−3.66)	(−5.37)	(−4.93)
IMR		−0.0005	−0.0222
		(−0.41)	(−1.21)
Controls	Y		Y	Y	Y
Year FE	Y		Y	Y	Y
Industry FE	Y		Y	Y	Y
Adj_R2		0.450		0.456

Note: *** denotes statistical significance at the 1% level. The t- or z-values calculated using robust standard errors are in parentheses.

report the regression results of the Heckman selection model, and the mean VIF is 2.02, indicating that there is no multicollinearity in the model; the regression coefficient of IMR is not significant, indicating that there is no serious sample selection problem in the study. At the same time, the regression coefficient of ESG is still significantly negative, indicating that when an enterprise’s ESG performance improves by one grade, its over-debt level and probability of over-debt decrease by 1% and 14.7%, respectively. Therefore, the conclusion that ESG performance has an inhibitory effect on corporate excess debt is still valid.

4.3.3. Propensity Score-Matching Method

In the problem of sample selection bias, there is a special situation of sample self-selection bias; that is, the ESG performance of enterprises in this study is not randomly distributed but is affected by other observable or unobservable factors of enterprises so that enterprises with lower levels of over-debt are more able to improve their ESG performance. PSM can solve the sample self-selection problem caused by observable variables affecting the ESG performance of enterprises [58]. The basic idea of this method is to find an enterprise with the same characteristics as the enterprises in the treatment group but with a different ESG performance to the control of the enterprises in the treatment group to test the impact of ESG performance on the over-indebtedness of enterprises. Therefore, based on the Heckman selection model, this study further uses the PSM method to solve the possible sample self-selection problem.

According to the annual median ESG performance of other enterprises in the same industry, this study defines the experimental group as an ESG performance that is higher than the industry median and the treatment group as an ESG performance that is otherwise. Specifically, the relevant control variables in model (2) are taken as the covariates of ESG performance, and the nearest neighbor 1:1 matching method is adopted to select the control group so as to eliminate the systematic differences between the experimental group and the control group as much as possible. The regression results after PSM are shown in columns (4) and (5) of Table 5, and the regression coefficient of ESG is still significantly negative, indicating that when ESG performance increases by one grade, the over-debt level and the probability of the over-debt of enterprises decrease by 0.75% and 9.9%, respectively. This indicates that the previous research conclusions are still robust after considering the sample self-selection problem.

4.4. Other Robustness Test

This study mainly conducts other robustness tests from the following three aspects. First, replace the explained variable. Existing studies have not yet reached a unified standard for measuring corporate over-indebtedness. By referring to existing studies, this study uses the difference between the actual debt ratio of the enterprise and the median debt ratio of the industry and whether it is greater than 0 to generate proxy variables for the level of corporate over-indebtedness (exlevb) and whether it is over-indebted (exdum) for robustness tests. The regression results are shown in columns (1) and (2) of

Table 6. Other robustness tests.

	Replace Explained Variable		Joint Fixed Effects		Firm Fixed Effects		Replace Explanatory Variables
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	exlevb	exdum	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM
ESG	−0.0157 ***	−0.1531 ***	−0.0094 ***	−0.1145 ***	−0.0047 ***	−0.1309 ***
	(−15.50)	(−9.52)	(−9.05)	(−7.59)	(−3.82)	(−6.37)
ESGpb							−0.0005 **	−0.0096 ***
							(−2.34)	(−2.87)
Controls	Y	Y	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y	Y	Y	Y	Y
Adj_R2	0.641		0.460		0.630		0.450

Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. The t- or z-values calculated using robust standard errors are in parentheses.

. Second, adjust the fixed effect. On the one hand, the joint fixed effects of industry and year should be added. The previous benchmark regression added year-fixed effects and industry-fixed effects, but some factors that change over time at the industry level may have a certain impact on corporate over-indebtedness. For this reason, this study further controls the joint fixed effects of year and industry. On the other hand, adjust the firm fixed effect. Considering that the over-indebtedness of enterprises may also be affected by factors that do not change with time at the enterprise level, this study further controls the fixed effect of enterprises. The regression results in columns (3) to (6) of Table 6 are consistent with the above basic regression. Third, replace the explanatory variables. The Huazheng ESG rating is an ex-post evaluation of how well a company does ESG and does not consider the ex-ante behavior of how the company says ESG. In view of this, this study uses the corporate ESG information disclosure score released by Bloomberg to measure corporate ESG performance (ESGpb) from the perspective of ex-ante evaluation. The regression results are shown in columns (7) and (8) of Table 6; the regression coefficients of corporate ESG performance are still significantly negative. The above robustness test results show that the role of ESG performance in reducing corporate over-indebtedness is established.

4.5. Mechanism Analysis

The previous analysis pointed out that ESG performance mainly affects corporate over-indebtedness through the mechanism of alleviating information asymmetry, reducing corporate financing costs and short-term debt for long-term use, and improving corporate operating performance. This part will conduct relevant verification.

First, information asymmetry is an important factor affecting corporate over-indebtedness. Reducing the degree of information asymmetry between companies and investors requires high-quality information disclosure. Based on this, this study refers to this practice and uses the information disclosure quality of listed companies to measure information asymmetry [35]. The higher the quality of corporate information disclosure, the lower the degree of information asymmetry. Specifically, the Shenzhen and Shanghai Stock Exchanges make a comprehensive assessment of the quality of corporate information disclosure based on the authenticity, accuracy, completeness, timeliness, legality, compliance, and fairness of corporate information disclosure; the penalties, sanctions, and regulatory measures taken against the company; the co-operation between the company and the exchange; and the management of corporate information disclosure affairs and other situations recognized by the exchange and make a four-level evaluation of corporate information transparency (Toumd) of A, B, C and D. According to the rating results, this study assigns a value of 4 to grade A, 3 to grade B, 2 to grade C, and 1 to grade D, and generates a dummy variable of high transparency. When the enterprise information transparency level is A, it is assigned a value of 1 (High = 1), and the other levels are assigned a value of 0. The regression results in

Table 7. Mechanism tests.

	Information Transparency		Financing Costs, Short-Term Debt, and Long-Term Use		Business Performance
	(1)	(2)	(3)	(4)	(5)	(6)
	Toumd	High	COD	SLEV	ROA	TobinQ
ESG	0.1253 ***	0.2584 ***	−0.0024 ***	−0.0099 ***	0.0071 ***	0.0745 ***
	(23.53)	(17.14)	(−4.92)	(−12.87)	(15.53)	(7.59)
Controls	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y	Y	Y
Adj_R2	0.207		0.163	0.341	0.210	0.529

Note: *** denotes statistical significance at the 1% level. The t- or z-values calculated using robust standard errors are in parentheses.

show that columns (1) and (2) are estimation results using the OLS and Probit models, respectively. The regression coefficients of ESG performance are both significantly positive, indicating that ESG performance can significantly improve corporate information transparency and reduce information asymmetry, thereby reducing corporate over-indebtedness.

Second, high financing costs and short-term debt with long-term use have increased companies’ demand for highly leveraged funds, exacerbating the financing phenomenon of companies “borrowing new and paying off old” and making companies more likely to fall into over-indebtedness, increasing corporate debt risks. In order to test this mechanism, this study refers to the approach in [59] and uses the ratio of the company’s financial expenses to the total liabilities at the end of the period as a proxy variable for corporate debt financing costs. We also refer to the approach of using the ratio of short-term liabilities to total assets (SLEV) to measure the short-term debt and long-term use of enterprises [60]. The larger the ratio, the higher the level of short-term debt and long-term use of enterprises. The regression results in Table 7 show that the regression coefficient of ESG performance is significantly negative at the 1% level. This indicates that ESG performance can effectively reduce corporate debt financing costs and alleviate corporate short-term debt and long-term use, thereby reducing corporate over-indebtedness.

Third, improving corporate operating performance will help increase corporate profitability, help companies repay debts, and reduce corporate over-indebtedness. In order to test this mechanism and refer to existing research, this study uses the enterprise’s return on total assets (ROA) and TobinQ to measure the enterprise’s short-term and long-term operating performance. The regression results in columns (5) and (6) of Table 7 show that the regression coefficient of ESG performance is significantly positive at the 1% level, indicating that ESG performance has a significant positive effect on improving corporate operating performance. The above results show that ESG performance can reduce corporate over-indebtedness by improving corporate operating performance.

4.6. Heterogeneity Analysis

4.6.1. Differences in Industry Environmental Sensitivity

ESG performance evaluation incorporates corporate environmental protection into investment decisions and pays more attention to the sustainable development of companies. This results in certain differences in the impact of the ESG performance of companies in different environmentally sensitive industries on corporate over-indebtedness. Therefore, this study draws on the existing research and divides the sample companies into non-polluting industries and polluting industries according to the “Environmental Protection Verification Industry Classification Management Directory of Listed Companies” issued by the Ministry of Environmental Protection of China to further examine ESG; in particular, whether there is a difference in the impact of performance on corporate over-indebtedness in polluting industries (WR = 1) and non-polluting industries (WR = 0) and constructing a cross-term of corporate ESG performance and WR to test the significance of the difference.

Table 8. Heterogeneity analysis–Differences in industry environmental sensitivity.

	(1)	(2)	(3)	(4)	(5)	(6)
	Non-Polluting Industries	Polluting Industries	Interaction Test	Non-Polluting Industries	Polluting Industries	Interaction Test
	EXLEVB	EXLEVB	EXLEVB	LEVBDUM	LEVBDUM	LEVBDUM
ESG	−0.0070 ***	−0.0126 ***		−0.0586 ***	−0.1903 ***
	(−5.63)	(−7.31)		(−3.37)	(−7.70)
ESGWR			−0.0049 **			−0.1134 ***
			(−2.44)			(−3.92)
Controls	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y	Y	Y
Adj_R2	0.447	0.458	0.450

Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. The t- or z-values calculated using robust standard errors are in parentheses.

reports the estimation results of the group regression. The regression coefficients of ESG performance in non-polluting industries and polluting industries are −0.0070 (−0.0586) and −0.0126 (−0.1903), respectively, which are significant at the 1% level. When considering the absolute value of the coefficient, ESG performance has a significant impact on corporate over-indebtedness. The inhibitory effect is more obvious in polluting industries. At the same time, the regression coefficient of the cross-multiplication term in columns (3) and (6) in Table 8 is significantly negative. The above test results show that ESG performance has a significant inhibitory effect on the over-debt levels of companies in both non-polluting industries and polluting industries, although the effect is more significant on companies in polluting industries. The possible reason is that because polluting industries face higher environmental risks, the improvement in ESG performance of companies in polluting industries indicates that companies pay more attention to environmental protection and sustainable development, which attracts the attention of external stakeholders and increases the number of companies. Information transparency makes it easier for companies with better ESG performance to obtain favorable conditions and resources, improve corporate operating performance, and thus reduce corporate over-debt levels more significantly.

4.6.2. Differences in Marketization

The degree of marketization is an important external environment for enterprises, which has an impact on their business behavior and economic performance. The impact of ESG performance on corporate over-indebtedness may vary in regions with different degrees of marketization. Based on this, this study defines regions with an annual marketization index above the median as a high-marketization group (MARKET = 1) according to the marketization index of the province where the enterprise is located and vice versa as a low-marketization group. Then, a group regression test is used to test whether there is a difference in the impact of ESG performance on corporate over-indebtedness in regions with different degrees of marketization, and the intersection term (ESGMAR) of corporate ESG performance and MARKET is constructed for testing.

Table 9. Heterogeneity analysis–Differences in marketization degree.

	(1)	(2)	(3)	(4)	(5)	(6)
	Low Marketization	A High Degree of Marketization	Interaction Test	Low Marketization	A High Degree of Marketization	Interaction Test
	EXLEVB	EXLEVB	EXLEVB	LEVBDUM	LEVBDUM	LEVBDUM
ESG	−0.0122 ***	−0.0061 ***		−0.1564 ***	−0.0548 ***
	(−8.38)	(−4.31)		(−7.52)	(−2.75)
ESGMAR			0.0046 **			0.0864 ***
			(2.46)			(3.28)
Controls	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y	Y	Y
Adj_R2	0.462	0.456	0.450

Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. The t- or z-values calculated using robust standard errors are in parentheses.

reports the results of the regression estimation. The regression coefficients of ESG performance in samples with low and high marketization degrees are −0.0122 (−0.1564) and −0.0061 (−0.0548), respectively, which are significant at the 1% level. Judging from the absolute value of the coefficient, the inhibitory effect of ESG performance on corporate over-indebtedness is more obvious among samples with a low degree of marketization. At the same time, the regression coefficient of the intersection item in columns (3) and (6) of Table 9 is significantly positive. The above test results show that ESG performance has a significant inhibitory effect on the over-debt levels of companies in regions with different degrees of marketization and has a more significant impact on the over-debt levels of sample companies with low degrees of marketization. The reason may be that in areas with a low level of marketization, the market lacks effective information intermediary organizations, and the degree of information asymmetry is high. In addition, regional financial service competition and supply services are relatively insufficient, and corporate debt financing costs are higher. The improvement in corporate ESG performance has played a better role in reducing information asymmetry inside and outside the company, reducing the high costs companies pay to obtain debt financing. Therefore, good ESG performance has a greater inhibitory effect on corporate over-indebtedness in areas with low marketization.

4.7. Moderation Effect

4.7.1. The Moderating Effect of Internal Control

The impact of ESG performance on corporate over-indebtedness may be affected by the quality of corporate internal control. On the one hand, internal control plays an important role in corporate internal governance, including five parts: internal environment, risk assessment, control measures, information and communication, and supervision and inspection. A high level of internal control can form a strict supervision system, reduce short-sighted behaviors such as adverse selection and the moral hazards of management, improve corporate compliance management capabilities [60], improve the reliability and integrity of corporate financial information effectively [59], and deliver reliable financial reporting information to the public. Therefore, high-quality internal control will further reduce corporate over-indebtedness and the debt risks it causes. On the other hand, corporate ESG performance has become an important indicator for evaluating the overall level of enterprises [61], and high-quality internal control will also require enterprises to enhance their ESG information disclosure. Based on this, this study argues that as the quality of corporate internal control improves, the inhibitory effect of ESG performance on corporate excessive debt will increase.

By referring to existing research, this study uses the DIB internal control index divided by 100 as a proxy variable for corporate internal control quality (ICQ) and uses the median of the corporate internal control index as the standard to list listed companies that are higher than the median level of the internal control index. Enterprises are assigned a value of 1 (HICQ = 1); otherwise, the value is 0. Then, based on Equation (2), the cross-term of ESG performance and internal control quality (ESGICQ and ESGHICQ) is added for regression. The regression results in

Table 10. Moderating effect–Internal control quality.

	(1)	(2)	(3)	(4)
	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM
ESG	0.0015	0.1499 **	−0.0055 ***	−0.0502 ***
	(0.31)	(2.14)	(−4.11)	(−2.72)
ESGICQ	−0.0016 **	−0.0380 ***
	(−2.08)	(−3.59)
ICQ	0.0037	0.1086 ***
	(1.23)	(2.62)
ESGHICQ			−0.0073 ***	−0.1062 ***
			(−3.87)	(−4.00)
HICQ			0.0272 ***	0.4172 ***
			(3.36)	(3.64)
Controls	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Adj_R2	0.450		0.450

Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. The t- or z-values calculated using robust standard errors are in parentheses.

show that the regression coefficients of ESGICQ and ESGHICQ are both significantly negative at the 1% level, which shows that when the company’s ESG performance is certain, the improvement of the company’s internal control quality can enhance the suppression of corporate over-debt by ESG performance; that is, as the quality of corporate internal control improves, ESG performance has a more obvious effect on the governance of corporate over-debt.

4.7.2. The Moderating Effect of Analyst Attention

As an important part of the external supervision mechanism of enterprises, analyst attention may have a moderating effect on the effect of ESG performance on enterprises’ excessive debt. On the one hand, analyst attention has an external supervision function and can curb corporate over-indebtedness. As a bridge between investors and enterprises in the capital market, analysts will track and collect corporate financial and non-financial information, use professional knowledge to conduct in-depth analysis and mining, and produce information that helps investors better understand the company. Therefore, under the deterrence of analyst attention, corporate management will improve corporate operational compliance and corporate financial quality and reduce the risk of corporate over-indebtedness. On the other hand, analyst attention plays an “amplifier” role in the capital market and can enhance the ESG performance of enterprises. As a sustainable development concept that capital market entities such as governments, investors, and enterprises focus on, the ESG concept has received more attention from analysts [62]. Through the attention and analysis of analysts, the ESG performance of enterprises can effectively transmit information from enterprises to investors, which further reduces the information asymmetry between enterprises and investors and enhances the reputation of enterprises. Based on this, this study believes that analyst attention can strengthen the impact of ESG performance on corporate over-indebtedness.

By referring to existing research, this study uses the number of analysts (teams) who follow and analyze the company within 1 year as the proxy variable for analyst attention (Analyst) and uses the median of analyst attention as the standard, which will be higher than analyst attention. An enterprise with a median degree is assigned a value of 1 (HAnalyst = 1); otherwise, it is 0. Then, the cross-term of ESG performance and analyst attention (ESGAnalyst and ESGHAnalyst) is added to model (2). The regression results in

Table 11. Moderating effect–Analyst coverage.

	(1)	(2)	(3)	(4)
	EXLEVB	LEVBDUM	EXLEVB	LEVBDUM
ESG	0.0125 ***	0.1598 ***	−0.0007	0.0062
	(5.62)	(4.98)	(−0.51)	(0.34)
ESGAnalyst	−0.0094 ***	−0.1146 ***
	(−9.43)	(−7.94)
Analyst	0.0178 ***	0.2392 ***
	(4.04)	(3.76)
ESGHAnalyst			−0.0160 ***	−0.2066 ***
			(−8.45)	(−7.66)
HAnalyst			0.0449 ***	0.6025 ***
			(5.49)	(5.18)
Controls			Y	Y
Year FE			Y	Y
Industry FE			Y	Y
Adj_R2	0.466		0.457

Note: *** denotes statistical significance at the 1% level. The t- or z-values calculated using robust standard errors are in parentheses.

show that the regression coefficients of the interaction terms ESGAnalyst and ESGHAnalyst are both significantly negative, which shows that when a company’s ESG performance is certain, an increase in analyst attention can enhance the inhibitory effect of ESG performance on corporate over-indebtedness. Therefore, the impact of ESG performance on corporate over-debt is affected by analyst attention. As analyst attention increases, the governance effect of ESG performance on corporate over-debt becomes more obvious.

5. Conclusions and Future Research

This study uses data from China’s A-share listed companies from 2009 to 2022 to study the impact of ESG performance on corporate over-indebtedness. The study found that ESG performance significantly reduces the level and probability of corporate over-indebtedness. This conclusion still holds true after a series of robustness tests and endogeneity discussions. The mechanism test shows that corporate ESG performance affects corporate over-indebtedness by alleviating information asymmetry, reducing corporate debt financing costs, alleviating short-term debt for long-term use, and improving corporate operating performance. Further research shows that in polluting industries and areas with a low degree of marketization, ESG performance has a more significant inhibitory effect on corporate over-debt. In addition, improvements in corporate internal control quality and analyst attention can enhance the inhibitory effect of ESG performance on excessive debt.

Based on the above conclusions, this study has the following implications: First, the government should improve the corporate ESG performance system and enhance the positive impact of corporate ESG performance. At present, ESG has become a powerful signal to guide the direction of capital market resource allocation, and high-quality ESG performance can help companies obtain many additional benefits. The government should focus on building a unified corporate ESG performance evaluation system, improving the quality of corporate ESG performance, enhancing the recognition of capital market investors for ESG information ratings, and further strengthening the signal guidance role of ESG.

Second, companies should change their one-sided view that ESG practices will squeeze corporate resources and increase production costs, recognize the beneficial impact of practicing ESG concepts on companies, and actively practice ESG concepts.

Third, external stakeholders such as consumers, governments, investors, and analysts should increase their attention on the ESG performance of enterprises, strengthen the external supervision effect and alleviate information asymmetry, encourage enterprises to actively disclose high-quality ESG information, reduce corporate financing constraints and financing costs, and alleviate corporate excessive debt.

Of course, this study also has certain limitations, which require further in-depth research in the future. First, the objects of this study are listed companies in the A-share markets of Shanghai and Shenzhen, which are different from enterprises in other countries in terms of market environment, laws and regulations, cultural background, and economic development level. This makes our research conclusions possibly not applicable to companies in other countries or regions. Therefore, future research should expand the research subjects to companies in other countries or regions, especially those that have significant differences from China in market structure and economic conditions. Secondly, the variables used in this study to measure corporate ESG may be relatively simple. Although this study uses ESG data from Huazheng Securities, which is widely recognized by mainstream research, and uses Bloomberg ESG data for robustness testing, the data that measure the ESG performance of Chinese companies also include ESG rating data from SynTao Green Finance, Wind, and others. Future research should more comprehensively take into account issues such as authority, accuracy, and recognition when measuring corporate ESG performance. In addition, as the government and investors continue to recognize the ESG concept, some companies have whitewashed their ESG performance to a certain extent, which may lead to bias in our research conclusions. Future research should fully consider the impact of corporate ESG rating differences or greenwashing on corporate over-indebtedness.