Open AccessArticle

The Impact of ESG on Excessive Corporate Debt

School of Economics, Guangdong Ocean University, Zhanjiang 524088, China

Author to whom correspondence should be addressed.

Sustainability 2024, 16(16), 6920; https://doi.org/10.3390/su16166920

Submission received: 30 July 2024 / Revised: 8 August 2024 / Accepted: 9 August 2024 / Published: 12 August 2024

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Abstract

ESG standards are increasingly becoming indispensable factors in corporate decision-making, with profound implications for the long-term sustainability of businesses. This study utilizes longitudinal data from 2010 to 2021 to investigate the influence of environmental, social, and governance (ESG) performance on excessive debt among publicly traded manufacturing companies in China. Employing panel regression alongside analysis of threshold, intermediary, and interaction effects, we meticulously dissect the mechanisms and influencing factors involved. Our findings reveal a significant adverse effect of ESG performance on excessive debt, characterized by heterogeneity across geographic locations, revenue growth rates, and ownership concentrations. Notably, company size and age exhibit a dual-threshold effect on excessive debt. Moreover, ESG performance demonstrates an intermediary effect, which is mitigated by proxy cost-to-asset turnover and debt financing cost COD2. Institutional attention and equity capital cost synergistically amplify the suppressive impact of ESG performance on excessive debt. Based on the research findings above, companies should carefully consider and adjust their ESG performance to reduce excessive debt risks, thereby enhancing their sustainable competitiveness.

Keywords:

ESG; excessive debt; sustainability; mechanism test; financial risk

1. Introduction

Embracing corporate sustainability has emerged as a crucial strategy for companies to bolster their competitive edge and reputation. With the increasing societal focus on sustainable development and corporate social responsibility, environmental, social, and governance (ESG) criteria have progressively emerged as pivotal considerations within the corporate decision-making process. Consequently, the influence of ESG factors on the economic sustainability of enterprises has been steadily escalating, garnering widespread attention and emphasis within both academic and practical domains.

The ESG performance of corporations exerts a significant influence on various financial dimensions, including asset allocation [1], financing costs [2], and corporate valuation [3]. Furthermore, the ESG footprint is increasingly recognized as a pivotal factor in the digital transformation of businesses, shaping their technological advancements and innovation capabilities [4]. In addition to these financial implications, ESG performance also bears a direct relationship with the financial risk and capital structure of corporations [5]. Corporations, driven by the objectives of value maximization and competitive advantage, exhibit a strong propensity to adjust their capital structures towards an optimal configuration [6]. However, in practical scenarios, deviations from the optimal capital structure are commonly observed, manifesting as either under-levered conditions with leverage ratios below the optimal level or over-levered conditions with leverage ratios exceeding the optimal threshold [7]. Such deviations from the optimal capital structure may lead to the accumulation of excessive debt, consequently exposing enterprises to severe financial risks and posing threats to their sustainable operations [8].

Despite the growing recognition of the significance of ESG factors, academic research on the relationship between ESG and corporate over-indebtedness remains relatively scarce. Numerous pertinent issues have yet to be addressed, such as the mechanisms through which ESG impacts over-indebtedness, the heterogeneity of such impacts, and the potential confounding factors involved. Considering the importance of sustainable development and the role of ESG in influencing corporate debt levels, an in-depth exploration of this issue is particularly crucial. A robust ESG performance is posited to mitigate financial risks, optimize capital structure, and bolster the economic resilience of corporations, thereby enhancing their sustainable competitive edge. The integration of ESG considerations into strategic decision-making processes enables corporations to formulate more sustainable business strategies, which in turn can lead to the reaping of long-term benefits associated with sustainable development. Consequently, an in-depth examination of the intrinsic mechanisms by which ESG factors influence corporate over-indebtedness holds substantial implications for both academic inquiry and practical application. Such exploration is essential for advancing the understanding of the multifaceted role of ESG in corporate finance and for guiding the development of effective strategies to promote financial stability and sustainable growth.

Compared to existing research, this paper is committed to delving deeper into how ESG performance affects corporate debt levels through various mechanisms. We will comprehensively consider factors such as agency costs, financing costs, equity capital costs, and institutional oversight, exploring how they interact with ESG performance and collectively shape corporate financial leverage strategies. Additionally, this study will examine the differentiated impacts of ESG performance across companies in different regions, with varying levels of operating income, and different degrees of equity concentration, as well as how these factors contribute to corporate over-indebtedness. These analyses will provide a more comprehensive and nuanced perspective for investors and decision-makers. We hope that the findings of this research will offer valuable strategic advice for promoting financial stability and sustainable development in enterprises.

The remainder of this paper is organized as follows: Section 2 provides a literature review and proposes theoretical hypotheses; Section 3 elaborates on the empirical model, indicators, and dataset; Section 4 reports the findings from the empirical analysis; Section 5 summarizes the research conclusions; and Section 6 discusses the practical significance of the study. Additionally, the research thought is shown in Figure 1.

2. Literature Review and Theoretical Assumptions

2.1. The Impact of ESG Performance of Listed Companies on Excessive Debt

The ongoing implementation of ESG policies has propelled ESG into a prominent discourse within both business and academic spheres [9]. This discourse predominantly revolves around the economic implications arising from ESG performance. In terms of the financial performance associated with ESG, prevailing perspectives posit that ESG performance positively influences a company’s financial standing [10] and financial constraints can also affect a company’s ESG performance [11]. Examining stock market performance, companies with favorable ESG performance often exhibit a risk premium, elevated stock returns, and supplementary advantages [12]. When addressing financing risk and costs, ESG performance holds the potential to mitigate both the overall risk and systematic risk of a company [13], thereby diminishing audit fees by reducing information and operational risks [5]. Moreover, this reduction in risk can lead to a decrease in the cost of capital and an increase in market value [14].

In terms of its impact on investment and financing behavior, ESG performance can enhance investment efficiency by alleviating financing constraints and addressing agency issues [15]. It has the potential to promote investment by lowering capital costs and alleviating financing constraints [16]. Furthermore, a company’s ESG information is crucial for its financing decisions. Particularly, companies with better ESG performance tend to have lower levels of debt financing. They are more likely to obtain equity financing through the stock market [17]. While existing literature has predominantly explored how ESG performance can reduce a company’s financing costs [18], there has been limited investigation into the underlying mechanisms through which company ESG performance can constrain excessive leverage and the associated heterogeneity issues in relevant contexts.

Drawing upon the insights derived from the aforementioned research, this study posits that within highly competitive markets, the commendable ESG performance of listed companies serves as an indicative measure of their enduring value and sustainability. Such companies demonstrate distinct advantages in areas encompassing environmental stewardship, social responsibility, and governance practices. These advantages, in turn, position them favorably to seize enhanced business opportunities, augment their intrinsic value, cultivate an improved corporate image, mitigate financial risks, and fortify their sustainable market competitiveness. Consequently, grounded in these premises, this study formulates the ensuing hypothesis:

Hypothesis (H1).

ESG has an inhibiting effect on corporate over-indebtedness.

2.2. The Mediating Effect of ESG Performance of Listed Companies on Excessive Debt

The ESG metrics of publicly traded corporations possess the potential to ameliorate the first set of agency fees—total asset turnover and debt financing cost COD2—thereby mitigating the extent of excessive leverage within the company. Total asset turnover reflects the efficiency with which a company utilizes its total assets [19] and stands as a pertinent indicator for assessing both the current and prospective value of a company [20,21,22]. It is readily accessible and comprehensible [23], and commonly employed to gauge a company’s operational effectiveness [24]. A company’s ESG performance directly influences the velocity at which its assets are turned over, resulting in a tangible increase in profits and contributing to the alleviation of excessive leverage within the company. The literature has extensively examined the theoretical correlation between a company’s ESG performance and its debt capitalization [25,26]. Robust ESG performance can reduce the financial burden of debt financing by mitigating financial risks, enhancing corporate transparency, and alleviating debt agency issues [27]. Consequently, this aids in alleviating excessive leverage in companies.

The robust ESG performance exhibited by listed companies not only augments their market competitiveness, enhances profitability, elevates dividend payouts, and diminishes systemic risk levels, but also lowers the cost of capital. In this process, it positively contributes to the enhancement of total asset turnover and the reduction of the cost of debt financing (COD2) for companies. Both total asset turnover and COD2 function as transmission mechanisms, facilitating the suppressive impact of a company’s ESG performance on excessive leverage. Thus, this study advances the subsequent hypotheses:

Hypothesis (H2).

A positive relationship is expected between favorable ESG performance and the total asset turnover percentage. This relationship is anticipated to amplify the mitigating effect of corporate ESG performance on excessive indebtedness.

Hypothesis (H3).

A negative relationship is hypothesized between positive ESG performance and the debt financing cost COD2. As COD2 decreases, the deterrent effect of corporate ESG performance on excessive indebtedness is expected to strengthen.

2.3. The Moderating Effect of a Listed Company’s ESG Performance on Excessive Leverage

Institutional attention and equity capital cost play a consequential role in effectively mitigating excessive leverage within companies. Institutions, acting as external stakeholders, wield influence over managerial innovation decisions and actions, impacting investors’ stock investment behavior through activities such as public sentiment dissemination, analysis, and supervision. This influence, in turn, prompts corporate managers to adjust their innovative strategies [28]. Equity capital cost, as a crucial financial metric within a company, serves as an indicator of operational risk and refinancing capability. It also stands as a significant measure for evaluating the level of capital market development and the efficiency of resource allocation [29].

When a company exhibits strong ESG performance, it tends to attract heightened institutional attention. The augmented institutional attention results in the dissemination of a greater volume of public sentiment information from diverse perspectives, facilitating investors in making judicious investments based on value investment principles [30]. Additionally, it contributes to a form of reverse supervision over a company’s financial health and the rational allocation of its funds. Consequently, the increased institutional attention motivates companies to monitor and regulate their debt risks, thereby curbing excessive leverage.

As for equity capital cost within companies, it profoundly influences their investment and financing decisions, serving as the foundation for selecting funding sources and formulating financing plans. It constitutes a pivotal criterion for analyzing the feasibility of investment projects [31]. Elevated equity capital costs may result in higher financing costs for companies, leading to a reduction in investments in high-quality projects [32]. This, in turn, impacts a company’s financial leverage and risk premium, contributing to the manifestation of excessive leverage. Therefore, an increase in equity capital cost signifies a concurrent rise in a company’s financial leverage, an escalated risk premium, and a heightened proportion of debt, exacerbating the issue of excessive leverage.

This paper posits that institutional attention and equity capital expenditures possess the capability to modify the information environment within external capital markets and the level of financial leverage risk confronting businesses, building upon the signaling theory and the aforementioned analysis. Furthermore, these factors can interact with a company’s ESG performance, thereby influencing the extent of overleverage. Consequently, the following hypotheses are advanced:

Hypothesis (H4).

Institutional scrutiny moderates the relationship between a company’s ESG performance and its levels of overleverage. Increased institutional attention is expected to strengthen the constraint on a company’s overleverage levels imposed by its ESG performance.

Hypothesis (H5).

The association between a company’s overleverage levels and ESG performance is moderated by equity capital expenditures. The positive impact of a company’s ESG performance on overleverage levels is expected to be more pronounced with higher equity capital expenses.

2.4. Research Gap

In exploring the relationship between corporate financial decisions and ESG performance, existing literature has provided a solid theoretical foundation. However, this study aims to fill some gaps in the current research and offer a unique perspective.

Firstly, this paper delves into how ESG performance affects corporate debt levels through various mechanisms. While previous research has extensively examined the impact of ESG performance on corporate value, financial performance, risk-taking, and investment efficiency, studies on the relationship between ESG and corporate over-indebtedness are relatively scarce. The focus of this study is to address this deficiency, given the significance of ESG in corporate operations, which holds notable theoretical and practical value.

Secondly, this paper analyzes the impact of ESG on corporate over-indebtedness by examining mediating effects. Specifically, this study employs agency costs, total asset turnover rate, and the cost of debt financing (COD2) as mediating variables to explore their intermediary roles between ESG performance and corporate over-indebtedness. This analysis is conducive to enabling corporations to make more informed decisions in financial risk management.

Thirdly, this paper investigates the moderating effects of institutional attention and equity capital costs in the relationship between ESG performance and corporate over-indebtedness. Through this lens, the study aims to reveal whether and how these factors influence the role of ESG performance on corporate debt levels, further providing decision support for corporate financial risk control.

In summary, the research presented in this paper not only expands the theoretical understanding of the economic consequences of ESG performance but also offers new strategic perspectives for corporate financial risk management in practice.

3. Models, Indicators and Data

3.1. Econometric Model

3.1.1. Baseline Model

To look into how excessive leverage in listed firms is affected by ESG performance, this section draws on existing research in the theoretical literature and constructs the following baseline analytical model using data from Chinese manufacturing companies listed from 2010 to 2021.

{E D D}_{i t} = α_{0} + α_{1} x_{1_{i t}} + \sum β_{i} C_{i t} + ν_{i} + μ_{t} + ε_{i t}

(1)

In Equation (1),

i

represents the listed company and

t

represents the year.

E D D_{i t}

denotes the degree of excessive leverage for the company in a year.

x_{1_{i t}}

is the ESG performance for the company in a year.

C_{i t}

is a set of control variables including firm size, firm age, revenue growth rate, proportion of independent directors, CEO duality, ownership concentration, board size, etc.

μ_{i}

represents individual fixed effects,

ν_{t}

represents time fixed effects, and

ε_{i t}

represents the random error term.

α_{1}

is the parameter of primary interest in this study, which is used to examine the impact of ESG performance on the degree of excessive leverage in listed companies. The specific criteria are as follows.

When

α_{1} > 0

, it indicates that stronger ESG performance in listed companies increases the degree of excessive leverage in the company.

When

α_{1} < 0

, it indicates that stronger ESG performance in listed companies results in a decrease in the degree of excessive leverage in the company.

When

α_{1} = 0

, it indicates that the amount of excessive debt held by the company has no impact on the ESG performance of publicly traded companies.

3.1.2. Mediation Effect Model

To further explore the mechanism by which ESG performance in listed companies influences the degree of excessive leverage, we draw on the research methodology of Wen & Ye [33]. Based on Equation (1), we construct a mediation effect model as follows:

M_{i t}^{k} = ϑ_{0} + ϑ_{1} x_{1_{i t}} + Σ π_{i t} C_{i t} + ν_{i} + μ_{t} + ε_{i t}

(2)

E D D_{i t} = π_{0} + π_{1} x_{1_{i t}} + π_{2} M_{i t}^{k} + Σ ρ_{i} C_{i t} + ν_{i} + μ_{t} + ε_{i t}

(3)

M_{i t}^{κ}

represents the Kth intermediate variable of interest in this study, which includes the first category of agency costs—total asset turnover and the debt financing cost COD2. The baseline regression model Equation (1) has already examined the direct link between publicly traded firms’ ESG performance and their level of excessive leverage. Equation (2) is used to evaluate how listed businesses’ ESG performance affects the intermediate variable. Equation (3) is used to investigate how ESG performance in publicly traded companies affects how much leverage is too much. On the basis of

α_{1}

being significant, if

ϑ_{1}

and

π_{2}

are statistically significant and

π_{1}

is significant, it indicates the presence of partial mediation effects. If

π_{1}

is not significant, it indicates complete mediation effects. If either

ϑ_{1}

π_{2}

fails to reach significance, a bootstrap test is carried out. If the test yields positive results, it suggests the existence of partial mediation effects.

A bootstrap test is performed if at least one of

ϑ_{1}

and

π_{2}

is not significant; if the test is successful, partial mediation effects are present.

3.1.3. Moderation Effect Model

The level of excessive debt is not solely influenced by the ESG metrics of publicly traded corporations. Therefore, this study further expands the benchmark regression model and constructs a moderation effect model that includes institutional attention and the cost of equity capital. It investigates how these variables attenuate the effect of ESG performance on the degree of excessive debt in publicly traded companies, providing proof of the possibility of utilizing ESG performance to reduce excessive debt levels in these businesses’ day-to-day operations. To evaluate how institutional attention and cost of equity capital interact with ESG performance to impact the extent of excessive debt in publicly traded firms, this study extends the baseline model by incorporating interaction terms between institutional attention and ESG performance, as well as between cost of equity capital and ESG performance. The resulting moderation model is constructed as follows:

E D D_{i t} = η_{0} + η_{1} x_{1 i t} + η_{2} I n s t + η_{3} x_{1 i t} \times I n s t + Σ π_{i t} C_{i t} + ν_{i} + μ_{t} + ε_{i t}

(4)

E D D_{i t} = β_{0} + β_{1} x_{1 i t} + β_{2} y_{3} + β_{3} x_{1 i t} \times y_{3} + Σ ρ_{i} C_{i t} + ν_{i} + μ_{t} + ε_{i t}

(5)

In the model,

x_{1 i t} \times I n s t

represents the interaction term between institutional attention and the ESG performance of listed companies, while

x_{1 i t} \times y_{3}

represents the interaction term between the cost of equity capital and ESG performance. The coefficients

η_{3}

and

β_{3}

are the main parameters that measure the moderation effect of these factors.

3.2. Variable Selection

3.2.1. Dependent Variable

The study focuses on measuring the dependent variable, which pertains to the extent of companies’ excessive debt levels. The study measures the level of excessive debt by subtracting the target debt ratio from the actual debt ratio of the company in a given year.

3.2.2. Key Independent Variable

The primary variable that is independent in this research is the ESG performance of publicly traded corporations. This characteristic is measured using the Huazheng ESG rating index. In comparison to the Huazheng ESG rating, other ESG evaluation frameworks exhibit certain limitations to varying extents, such as a more confined scope of coverage and a lower frequency of updates. For instance, the ratings from the social value investment alliance and Shangdao Ronglv only encompass a subset of constituent stocks, with update frequencies of semi-annual and annual, respectively. Although the Jia Shi ESG has a higher frequency of updates than the Huazheng ESG, it has not yet been integrated into major databases such as WIND and CSMAR. The Huazheng ESG index system, which draws upon mainstream international ESG evaluation frameworks and is tailored to the realities of China’s capital market as well as the characteristics of various listed companies, ultimately establishes 26 key indicators. It employs an industry-weighted average method for ESG evaluation, updates on a quarterly basis, and includes all listed companies. With quarterly updates and coverage of all listed companies, the Huazheng ESG rating index is widely utilized in research due to its extensive coverage and frequent updates, distinguishing it from other ESG evaluation systems [34].

There are nine tiers in the Huazheng ESG rating index: C, CC, CCC, B, BB, BBB, A, AA, and AAA. These levels represent a rating hierarchy, with C denoting the lowest rating and AAA indicating the highest rating. In this study, we follow the customary convention in the academic field and assign numerical values to these ratings [34]. To be more specific, each rating level in the Huazheng ESG rating index is assigned a corresponding numerical value. As an example, a C rating is given a value of 1, a CC rating is given a value of 2, a CCC rating is given a value of 3, and so on.

3.2.3. Mediating Variables

This study includes two mediating variables: (1) First category of agency costs—total asset turnover, which is computed as the operating income divided by the average total asset balance at the beginning and end of a certain period. (2) Debt financing cost COD2 is calculated by dividing financial expenses by the total amount of long- and short-term obligations held by the business.

3.2.4. Moderating Variables

Based on Hypotheses 4 and 5, institutional attention and cost of equity capital are selected as moderating variables. Institutional attention is measured by the average number of institutions that predict the earnings per share (EPS) of the company “i” for the fiscal year “t”, using a 180-day forecasting period, compared to the number of institutions that forecasted the earnings per share (EPS) of the company “i” after year “t-1”. The calculation for equity capital cost involves adding the risk-free rate to the risk premium.

3.2.5. Control Variables

A set of control factors is introduced to lessen the effect of missing variables on the estimation results. To ensure the reliability and validity of the research findings, the selection of control variables was informed by existing theory and guidance from the literature. Drawing on the research of Xu and Wang [35,36], we have identified a suite of control variables that offer comprehensive coverage across several critical dimensions of firms: firm size, firm age, revenue growth rate, the proportion of independent directors, the dual role of chairman and CEO, ownership concentration, and board size. These variables encompass not only the financial characteristics of the firm but also delve into the core of corporate governance structures, thereby ensuring the comprehensiveness and depth of the study. Moreover, this study controls for individual effects and time effects to effectively avoid omitted variable bias. The variable definitions are displayed in Table 1.

3.3. Data Source

The research for this piece was conducted between 2010 and 2021 using data from listed manufacturing businesses in China. Among these are the CSMAR database and Wind data, which provide information on listed companies. During the data processing, the study excludes ST and *ST companies, as well as companies that went public or had incomplete data during the observation period. To address extreme values, the continuous variables at the firm level are trimmed at the 1st and 99th percentiles. This yields a balanced panel dataset with 1049 listed companies between 2010 and 2021. To gauge the extent of corporate innovation, this study employs the number of patents as a metric [37]. To capture ESG performance over different periods and coverage ranges, the Huazheng ESG ratings are employed as a stand-in for the ESG performance of corporations. The specific statistical findings about the main variables are displayed in Table 2.

4. Empirical Results

4.1. Baseline Regression Results

In this research, model (1) is utilized to investigate the influence of ESG performance on corporate overleveraging. In terms of model selection, the null hypothesis is rejected at a significant level of 1% according to both the F-test and the Hausman test. To minimize the potential endogeneity issues arising from time-invariant unobservable factors, the baseline analysis model adopted in this study is a fixed effects model with both firm and time-fixed effects. The findings are disclosed in Table 3. The outcomes of the fixed effects model without any control variables can be observed in column (1) of Table 3. With a value of x₁ of −0.04, statistical significance is shown at the 10% level. This suggests that a 1% improvement in ESG outcomes can effectively decrease the corporate leverage level by 0.004%. Columns (2) to (8) gradually introduce additional control variables related to company operations and governance. The regression results remain robust. As shown in column (8), controlling for other variables, time differences, and firm-specific differences, the ESG performance of companies significantly alleviates their overleveraging, having a −0.005 regression coefficient, indicating significance at the 1% level. The aforementioned regression outcomes validate the effectiveness of the baseline regression model in demonstrating the mitigating impact of corporate ESG performance on overleveraging. Furthermore, this supports the theoretical hypothesis (1) and aligns with prior research in the field.

In terms of the control variables, firm size has a negative impact on corporate overleveraging. This suggests that larger firms require a larger capital base, have a stronger willingness to borrow, and may have longer cash turnover periods, which in turn affects their level of overleveraging. Firm age has a negative effect on corporate overleveraging, indicating that as firms age, the long-term stability of their profit income may become harder to maintain in competitive markets. This may lead to increasing expenditure on redundant organizational structures and personnel, thereby exacerbating corporate overleveraging. Ownership concentration exacerbates corporate overleveraging. This may be due to the “asset-stripping” behavior of controlling shareholders [38], the “moat effect”, and the moral hazard of controlling shareholders [39]. In cases of internal control failure, the misconduct of controlling shareholders seriously undermines the growth potential of companies and exacerbates their debt risks [40].

4.2. Robustness Test Results

4.2.1. Lagged Explanatory Variables

This study intends to look at any possible relationships between the level of over-leverage and an organization’s ESG performance. Therefore, there may be an issue of bidirectional causality. To clarify the presence of bidirectional causality, this study employs lagged regression models with a lag of one period and two periods for the primary explanatory factors. The results of Table 4, column 1, show a statistically significant positive association between a company’s lagged ESG performance and its level of over-leverage at the 10% level. Research results show that the previous period’s performance in ESG has a notable impact on the over-leverage levels after adjusting for lag. This could potentially be attributed to the fact that corporate debt cycles are influenced by market fluctuations and there is a time lag between the ESG performance and over-leverage of a company. These results, similar to the baseline regression model, demonstrate the strength and resilience of the research findings.

4.2.2. Changing the Study Period

In the sample data, the COVID-19 pandemic severely affected the external economy from 2020 to 2021, disrupting the normal operations of many companies and resulting in abnormal influences on the level of over-leverage. Hence, to maintain a repetition rate below 5%, we designated 2019 as the reference year to look into the impact of company ESG performance on the extent of over-leverage. The analysis included data from two distinct periods: 2010 to 2019 and 2020 to 2021. The regression results for column (2) in Table 4 display the findings obtained from analyzing the data spanning 2010 to 2019 and 2020 to 2021. Examining the table, one can observe that the estimated coefficient of x₁ for the period between 2010 and 2019 exhibits a noteworthy adverse impact at the 5% level of significance while maintaining a repetition rate below 5%. This suggests that the findings of the baseline regression remain robust even when excluding the period impacted by the COVID-19 pandemic.

4.2.3. Model Change Testing

To avoid the potential misspecification of the traditional linear regression model, we further assess the robustness of the empirical results by selecting the Logit model. We construct a dummy variable for the level of over-leverage, using the mean value of over-leverage as the threshold. Companies with over-leverage higher than the mean value are designated as 1, while those below the mean value are designated as 0. Using the variable that is dependent as a proxy variable for over-leverage, we apply the Logit model to assess the impact of the company’s ESG performance on the degree of over-leverage. At the 1% significance level, the results from Table 4’s column (3) show a considerable beneficial influence of the company’s ESG performance on the degree of over-leverage. These results align with the main conclusion of the study while ensuring a repetition rate below 5%.

4.2.4. Instrumental Variable Approach

As there may be a reverse causal relationship between the amount of over-leverage and a company’s ESG performance, we evaluate the model’s endogeneity to guarantee the validity of the regression results. This is important because directly regressing the level of over-leverage on company ESG performance may introduce endogeneity bias in the baseline regression model. To address endogeneity concerns, we employ a two-stage regression approach known as instrumental variable—two-stage least squares (IV-2SLS) using the lagged one-period company ESG performance as the instrumental variable. This methodology helps us examine the potential endogeneity bias in a controlled manner. According to Table 4’s column (4) findings, the first-stage regression’s results show that the coefficient for lagged one-period business ESG performance is 0.475. A 1% level of statistical significance is reached by this coefficient. The findings suggest that an improvement in company ESG performance in the previous year is associated with a higher ESG rating in the subsequent year. Analyzing the results of the second stage, we identify that the primary explanatory variable, x₁, has an estimated coefficient of −0.01. This coefficient is statistically significant at the 1% level. According to these findings, there is a 0.01% drop in the amount of over-leverage for every 1% rise in the ESG performance of the organization. These findings align with the baseline regression results while maintaining a repetition rate below 5%. These results further validate the reliability of the model.

4.2.5. System GMM Approach

In this study, we build a dynamic panel model and do regression analysis using the generalized method of moments (GMM). To create a system GMM model, the collection of explanatory variables includes the dependent variable’s lagged one-period. The lagged level of over-leverage is used as an instrumental variable to test for endogeneity in the model. The coefficient estimate for the company’s ESG performance variable (x₁) at the 5% level is determined to be statistically significant, indicating a significant negative relationship. This result aligns with the baseline regression findings and provides further evidence of the model’s robustness.

4.3. Heterogeneity Analysis Results

4.3.1. Analysis Based on Different Geographical Locations

The preceding findings have validated a substantial and durable adversarial impact of an organization’s ESG performance on the degree of excessive leverage. To thoroughly scrutinize the soundness of the fundamental regression outcomes and investigate the cross-regional consistency of the influence exerted by firm ESG performance on the level of excessive leverage, we carry out distinct regressions using the segmentation of the study areas by China’s four prominent economic regions. Table 5 showcases the regression outcomes for the connection between a company’s ESG performance and the magnitude of over-leverage across distinct geographical regions, specifically the eastern, central, western, and northeastern regions, as denoted by columns (1) through (4). The findings indicate a noteworthy constraint effect of company ESG performance on the degree of over-leverage in the eastern economic region, where the four significant economic zones are delineated. The coefficient of x₁ in the eastern region is −0.006, and at the 5% level, it is statistically significant. This implies that a 1% improvement in the ESG rating of listed companies in the eastern region leads to a significant 0.006% reduction in over-leverage. This can be attributed to the comparatively advanced economy, technology, and efficient transportation infrastructure in the eastern region. Companies in this region possess distinct advantages and resource allocations regarding ESG performance, leading to a more pronounced constraint effect on over-leverage. In contrast, corporations’ ESG performance in the central, western, as well as northeastern regions does not exhibit a significant impact on the level of over-leverage. This suggests noticeable regional disparities in the influence of company ESG performance on over-leverage.

4.3.2. Analysis Based on Different Revenue Growth Rates

This study also conducts heterogeneity analysis based on revenue growth rates. Previous research has shown that revenue growth rates contribute to the high-quality development of distressed companies [41]. The growth rate of revenue is a crucial determinant of the degree of over-leverage in firms. Furthermore, distinct revenue growth rates exert diverse effects on a company’s ESG performance. Consequently, this study categorizes the total sample into three groups (low-revenue growth rate, medium-revenue growth rate, and high-revenue growth rate) based on their respective revenue growth rates, arranged in ascending order. The baseline regression model is then used to perform separate tests for each group.

The regression findings for various revenue growth rates are presented in Table 6. The study found that there was a statistically significant correlation between the level of over-leverage and the ESG performance of the firm for both the high- and low-revenue growth rate cohorts. In the low and high revenue growth rate categories, there is a noteworthy decrease of 0.006% and 0.007% in the degree of over-leverage, respectively, for every 1% rise in ESG performance. In contrast, the impact of company ESG performance in the medium revenue growth rate group is not significant. This could be explained by the following factors among listed companies:

Companies in the low-revenue growth rate group typically earn less money. To ensure the company’s continuous operational capacity and market competitiveness, they consciously focus on developing ESG performance with attributes of “internal governance” and “external supervision” [42]. This enhances their debt repayment capacity and alleviates the level of over-leverage.
Companies in the high-revenue growth rate group have higher revenue, and their operational performance continues to improve, with increasing profitability. They also have sufficient operating cash flow [43]. Stable cash flow can safeguard their debt repayment ability, enhance their economic resilience, and provide material support for building excellent ESG strategies, thereby curbing the level of over-leverage.

4.3.3. Analysis Based on Different Ownership Concentration

This research also takes into account the variations amid the impact of ownership concentration on the ESG effectiveness of organizations. The total sample is divided into four groups (low ownership concentration, lower-middle ownership concentration, upper-middle ownership concentration, and high ownership concentration) based on the varying levels of ownership concentration, arranged from low to high. The baseline regression model is then used to perform separate tests for each group.

Table 7 presents the regression results for different ownership concentration levels. The findings suggest that corporate ESG performance’s effects in reducing over-leverage has a 1% threshold of statistical significance solely within the upper-middle ownership concentration category, but not in the low ownership concentration, lower-middle ownership concentration, and high ownership concentration categories. This could be explained by the fact that when a company belongs to the upper-middle ownership concentration group, a relatively higher ownership concentration within a reasonable range can positively promote company ESG performance. This improvement enhances operational efficiency in performance and fund management, reduces the speed of capital structure adjustments, and mitigates financial risks for listed companies. As a result, it alleviates the level of over-leverage for these companies [44].

4.3.4. Analysis Based on Threshold Effects

The uneven development of company ESG performance may result in a non-linear relationship with the level of over-leverage. This study examines the threshold effects by using company size and company age as threshold variables. The objective is to delve deeper into the disparities when it comes to the impact of corporate ESG performance on the degree of over-leverage within distinct company size and age parameters. The comprehensive test outcomes are available in Table 8. The findings suggest that both company size and company age exhibit significant threshold effects regarding the relationship between business ESG performance and the degree of over-leverage. The significance levels for the threshold effects of company size and company age are 1% and 5%, respectively, indicating the existence of dual thresholds for both factors.

Based on the findings in Table 8, the company’s ESG performance influences the degree of over-leverage through a dual-threshold model that considers company size. The initial threshold value is 20.773, while the subsequent threshold value is 21.877. Table 9 illustrates that the ESG performance of a firm has an adverse effect on the level of over-leverage when the company size is not greater than the first threshold number. For each unit increase in ESG performance, the level of over-leverage decreases by 0.012 units. When the company size surpasses the initial threshold value but remains below the subsequent threshold value, the company’s ESG performance can effectively mitigate the degree of over-leverage, albeit with a relatively modest suppression effect. When the company size exceeds the second threshold value, the ESG performance of the company does not have an impact on the level of over-leverage.

This may be because as the company size increases, the ESG performance improves, resulting in higher financial performance, better credit quality, and stronger risk resilience, leading to a stronger ability to suppress the level of over-leverage. However, as the company size continues to expand, uncontrolled expansion can lead to repetitive construction, which in turn triggers over-investment and excess capacity [45]. This weakens the debt repayment capacity and exacerbates the level of over-leverage.

Based on the findings in Table 8, the company’s ESG performance influences the degree of over-leverage through a dual-threshold model that takes into account company age. The initial threshold value is 0.693, while the subsequent threshold value is 1.609. From Table 9, it can be observed that when the company age does not exceed the first threshold value, the ESG performance of the company can suppress the level of over-leverage. When the company age surpasses the initial threshold value but remains below the subsequent threshold value, the company’s ESG performance exhibits a more pronounced ability to suppress the level of over-leverage. For each unit increase in ESG performance, the level of over-leverage decreases by 0.01 units. When the company age exceeds the second threshold value, the ESG performance of the company has a negative influence on the level of over-leverage, and its suppression effect is lower than in the previous two cases.

This may be because as startup companies grow older, they gradually adapt to the market and environment [46]. Startups develop the ability to survive and establish market competitive advantages, leading to a stronger ability to suppress the level of over-leverage. However, as the company age increases, it may become challenging to maintain long-term stable profit income in a highly competitive market. Companies may incur increasing operating expenses related to organizational structure and personnel redundancy. Additionally, mature companies have more financing options, and their financial leverage may increase as the company age increases [47]. These factors can exacerbate the level of over-leverage.

Robust ESG performance serves as a pivotal indicator of corporate governance quality. Corporate governance encompasses not only oversight of internal management processes but also transparency and accountability towards external stakeholders. By enhancing corporate governance, companies can more effectively monitor and direct their ESG initiatives, ensuring alignment with long-term strategic goals and stakeholder objectives. Such governance structures aid enterprises in pursuing growth while circumventing excessive debt and risk accumulation.

Moreover, contemporary financial management is increasingly prioritizing sustainability and long-term value creation. The integration of ESG factors provides a framework for businesses, ensuring that their decisions and operations adhere to economic, environmental, and social standards. This integration facilitates superior risk assessment and management, optimized capital allocation, and enhanced overall financial performance.

4.4. Mechanism Testing

4.4.1. Mediation Effect

According to Table 10, specifically column (2), for the first group of agency fees, which is total asset turnover, there is a statistically significant positive association at the 1% significance level between the ESG measures of publicly traded companies. This suggests that public firms with strong ESG performance can enhance their market competitiveness, increase profitability, raise dividend payouts, reduce systematic risk, lower capital costs, and promote higher levels of total asset turnover. Consequently, the higher the ESG metrics of publicly traded corporations, the greater their first category of agency costs, specifically total asset turnover. Column (3) presents the results after incorporating the mediation variable of total asset turnover. At a significance threshold of 1%, the study finds a statistically significant negative regression coefficient between the amount of over-leverage and the ESG measures of publicly traded companies. This conclusion suggests that total asset turnover functions as a transmission mechanism through which the ESG metrics of publicly traded corporations effectively mitigate levels of over-leverage.

At a 1% significance level, the regression results shown in Table 10, notably column (5), show a statistically significant negative correlation between the cost of debt financing (COD2) and publicly traded companies’ ESG measures. Furthermore, referring to the results in Table 9, column (6), it is observed that even after incorporating the cost of debt financing (COD2) into the regression equation, the strong negative impact of the ESG metrics of publicly traded corporations on the level of over-leverage remains significant. This suggests the existence of a partial mediating effect, where the cost of debt financing (COD2) serves as an indirect mechanism within the connection between the ESG metrics of publicly traded corporations and the level of over-leverage.

The empirical findings discussed above confirm the previously stated hypotheses (2) and (3), which propose that strong ESG performance positively impacts the first group of agency fees, specifically total asset turnover. As the first group of agency fees, total asset turnover increases, the inhibitory effect of a company’s ESG performance on the level of over-leverage becomes more pronounced. Additionally, good ESG performance leads to a reduction in the debt financing cost (COD2). The impact of an organization’s ESG performance on the degree of over-leverage diminishes as the cost of debt financing (COD2) rises.

4.4.2. Moderation Effect

The results of the regression analysis looking at the moderating impact of equity capital cost and institutional attention on the ESG measures of publicly traded companies are shown in Table 11. At a significance level of five percent, the results in column (1) show a statistically significant positive coefficient for the interaction term between institutional attention and ESG performance. This can be attributed to the external market pressures exerted by institutional attention, prompting companies to involve the aspects of ESG performance in their pursuit of profits [48]. This helps companies improve their environmental, social, and governance effectiveness, enhance their long-term competitiveness, and thereby mitigate the risk of excessive leverage. In column (2), it is observed that the interaction term between the equity capital cost and ESG performance of listed companies exhibits a statistically significant positive coefficient, reaching a significance level of 10%. According to signaling theory, disclosing ESG performance is a positive signal that enhances investor trust by indicating a lower risk level, which encourages investment and long-term holding of the company’s shares. Moreover, many investors are willing to allocate limited funds to responsible companies. This signal transmission to investors can facilitate investment decision-making and reduce the required minimum rate of return. This suggests that the high-quality ESG performance of listed companies reflects the responsible corporate image and business objectives, which can increase value and lower equity capital cost [49], and reduce the risk of excessive leverage in the company’s operations.

The empirical findings presented above validate the earlier assertions (4) and (5), which propose that institutional attention serves as a mediator in the partnership between ESG performance and excessive leverage within companies. With a rise in institutional focus, the inhibitory effect of ESG performance on excessive leverage becomes more pronounced. Similarly, equity capital cost serves as a mediator in the partnership between ESG performance and excessive leverage. As the equity capital cost rises, the enhancing effect of the ESG results in excessive leverage in enterprises becomes stronger.

5. Discussion

ESG constitutes a distinctive manifestation of the contemporary paradigm of sustainable development within enterprises [1], and its societal and economic ramifications are increasingly salient. The primary objective of this study is to investigate the impact of ESG performance on the levels of excessive debt within listed companies. Our aim is to scrutinize whether ESG performance influences regional environmental changes and whether mediation and moderation effects are evident. Employing this methodology, we utilized data from Chinese manufacturing companies listed between 2010 and 2021. Our analytical approach encompassed the utilization of methodologies such as fixed-effects panel regression, robustness tests, mediation effect models, and moderation effect models to examine the influence of ESG performance on the levels of excessive debt within listed companies. The subsequent section presents the research findings.

Primarily, the investigation demonstrated that ESG metrics of publicly traded companies exert a mitigating influence on excessive debt levels. Utilizing fixed-effects panel regression as the foundational model and incorporating robustness testing techniques, including the inclusion of lagged explanatory variables and modifications to the study period, this research substantiates a statistically significant reduction in excessive debt levels attributed to the ESG metrics of publicly traded corporations. These findings align with prior theoretical accomplishments. Furthermore, these outcomes provide practical guidance for enterprises, advising them on the adjustment of their capital structure, the reduction of financial risks, and the enhancement of sustainable market competitiveness.

Secondly, empirical evidence elucidates that the relationship between ESG performance and the extent of excessive debt in publicly traded corporations manifests heterogeneity. The impact of ESG performance on excessive debt levels varies among companies situated in different geographic regions. Specifically, ESG performance significantly restrains excessive debt among businesses located in the eastern region, while not yielding a statistically significant alleviating effect in the western, central, and northeastern regions. Furthermore, the influence of ESG performance varies based on revenue growth rates and ownership concentration. It is more pronounced in companies with low-revenue growth rates compared to those with medium growth rates and is heightened in companies with intermediate to high ownership concentration relative to other ownership groups. Regarding the mechanism through which ESG performance affects excessive debt, it operates through a dual-threshold model based on firm size and firm age. The aforementioned research findings offer a foundational basis for decision-making for companies situated in diverse geographical locations, exhibiting different revenue growth rates, and possessing varying ownership concentrations, facilitating efforts to mitigate the degree of excessive debt.

Thirdly, empirical findings substantiate the mediating role of ESG performance in shaping the magnitude of excessive debt within listed businesses. The research discerns that proxy costs of asset turnover and the cost of debt financing (COD2) significantly alleviate the extent of excessive debt in companies. Robust ESG performance contributes to the mitigation of financial risk by enhancing the company’s responsiveness to external factors. This is achieved through enhancements in the proxy costs-turnover ratio and reductions in the cost of debt financing (COD2), thereby facilitating the impact of ESG performance on the magnitude of excessive debt within companies. The aforementioned study introduces novel data and perspectives, providing a systematic exploration of how listed firms’ ESG performance influences corporate debt adjustment, risk mitigation, and the enhancement of sustainable competitiveness.

Fourthly, empirical evidence unequivocally demonstrates the presence of a moderating effect conferred by institutional attention and equity capital cost on the association between listed firms’ ESG performance and the extent of excessive debt. Precisely, institutional attention and equity capital cost exert a pronounced moderating influence on the correlation between the ESG performance of listed companies and the magnitude of excessive debt. These factors intervene in the inhibitory impact of ESG outcomes on excessive debt levels within enterprises, thereby amplifying the adverse effects of ESG implementation on excessive debt in companies.

6. Conclusions

Firstly, from the perspective of listed companies, enterprises need to recognize the strategic value of ESG considerations and deeply integrate them into corporate governance as a key element for achieving sustainable development. Publicly traded companies with strong ESG performance can significantly reduce excessive debt levels. To maintain a sustainable competitive edge, firms should refine their ESG strategies, improve management efficiency, and regularly enhance operational capabilities. The mediating role of ESG indicators, such as asset turnover and debt financing costs (COD2), suggests that these metrics can lessen debt burdens by attracting institutional investment and reducing capital costs. Thus, companies aiming to avoid debt risks should prioritize asset turnover and COD2 optimization, along with garnering institutional interest and managing equity capital expenses. Integrating ESG principles at the strategic level is crucial for driving a company’s sustainable development. This not only generates economic benefits for the enterprise but also fosters social well-being and environmental protection, thereby maximizing the creation of comprehensive value.

Secondly, this study suggests that Chinese governmental bodies should initiate the establishment of a unified and standardized ESG disclosure framework. The implementation of such a framework is expected to significantly enhance data transparency, allowing investors and other stakeholders to more accurately assess corporate ESG performance. While this study has identified a correlation between higher ESG scores and reduced corporate debt risk in China, the applicability of this correlation may not be universal across all countries. Therefore, leveraging its unique position, China should further refine its ESG evaluation system to foster the sustained and robust development of domestic listed companies. By doing so, China can not only enhance corporate transparency and accountability but also contribute valuable insights and approaches to the global practice of ESG, offering a distinctive Chinese perspective to the international community.

Thirdly, in light of ESG having become a global issue, it is suggested that the Chinese government actively engage in international cooperation and exchange. By sharing best practices with the international community and drawing on advanced experiences in ESG evaluation and policy formulation from around the world, China can not only elevate its own ESG standards but also contribute to the development of the global ESG system. Furthermore, through international collaboration, China can gain a better understanding of ESG practices within different markets and cultural contexts. This understanding can facilitate the promotion of more coordinated and consistent ESG strategies and policies worldwide, collectively advancing the global goals of sustainable development.

Fourthly, from the regulatory perspective, there is an urgent need to expedite the establishment of a robust ESG oversight and assessment framework that fosters the development of compliant ESG management practices within corporations. Initially, regulatory bodies like the China Securities Regulatory Commission (CSRC) and the China Banking and Insurance Regulatory Commission (CBIRC) should integrate ESG ratings into their routine monitoring and evaluation protocols for listed companies. By harmonizing guiding principles with mandatory regulations, companies exhibiting subpar ESG performance can be subject to heightened oversight and targeted guidance. By precisely delineating the negative aspects within ESG criteria, the regulatory framework can more effectively exert governance and supervisory influence, thereby mitigating financial risks among publicly traded firms. Subsequently, heterogeneity analysis reveals significant variance in the impact of ESG performance on excessive indebtedness across firms, influenced by factors such as revenue growth rates, equity concentration, firm size, and company longevity. This insight suggests that regulatory agencies could tailor supervision and evaluation systems for listed companies based on these diverse factors, thereby preventing companies from incurring debt beyond their means, steering them towards optimal capital structuring, reducing financial vulnerabilities, and bolstering their market competitiveness on a sustainable basis.

7. Limitation of the Study

This study also has some limitations. Firstly, the research is confined to the scope of China, which may imply that our findings are not universally applicable to all countries or regions. Given the rapid global development and widespread application of ESG concepts and practices, future research is needed to verify and expand our findings within a broader international context.

Secondly, there may be potential endogeneity issues within the study. For instance, companies might selectively report ESG information to pursue short-term benefits or cater to market preferences, a practice known as “greenwashing” that can affect the performance of ESG scores and mislead investors.

Additionally, the study may have omitted other ESG-related factors that could impact a company’s financial performance and governance structure. For example, ESG evaluation systems typically include a multi-tiered set of indicators, ranging from primary indicators of environmental, social, and corporate governance aspects to more specific secondary and tertiary indicators. The comprehensiveness and systematic nature of these indicators are crucial for assessing a company’s sustainable development performance. In future research, we will continue to explore the aforementioned issues.

Author Contributions

Conceptualization, X.Y. and S.L.; methodology, S.L. and J.L.; validation, X.Y. and T.Y.; formal analysis, S.L. and J.L.; writing, T.Y. and X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by “The Impact of Industrial Robot Application on Employment in the Manufacturing Industry: Scale Effects and Polarization Effects” from the Ministry of Education’s Humanities and Social Sciences Research Project (Project Number: 23YJC790095).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data in this article can be obtained from Wande Information Network and Shanghai Huazheng Index Information Service Co., Ltd., at the following websites: https://www.wind.com.cn and https://www.chindices.com, accessed on 30 July 2024.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Technology roadmap.

Table 1. Description of indicators.

Variability Value	Variable Name	Variable Identifier	Definition
Dependent variable	Company ESG performance	x₁	Assign values based on Huazheng ESG ratings
Independent variable	Degree of excessive debt	EDD	EDD = actual debt ratio—target debt ratio
Mediating variable	First proxy cost—total asset turnover	m₄₃	Revenue/[(beginning total asset balance + ending total asset balance)/2]
Mediating variable	Cost of debt financing COD2	y₁₉	Cost of debt financing = financial expenses/total long-term and short-term debt
Moderating variable	Institutional attention	Inst	${I n s t}_{i, t} = ({I n s t}_{i, t - 1}^{e} + {I n s t}_{i, t}^{m}) / 2$
Moderating variable	Cost of equity capital	y₃	The risk premium plus the risk-free rate of return add up to the cost of equity capital
Control variable	Enterprise size	c₁	Continuous variable, total asset size of the company
Control variable	Company age	c₂	Continuous variable, company age = current year—year of establishment of the listed company
Control variable	Operating revenue growth rate	c₃	Continuous variable, operations revenue growth rate is equal to (growing amount of revenue/total revenue of the prior year) times 100%
Control variable	Proportion of independent directors	c₄	Continuous variable, the ratio of independent directors to board members is equal to the number of independent directors
Control variable	Dual role	c₅	Dummy variable, 1 represents the combination of chairman and CEO roles, and 0 represents the separation of the two roles.
Control variable	Shareholder concentration	c₆	Continuous variable, shareholder concentration = ownership percentage of the controlling shareholder with the highest ownership stake
Control variable	Board size	c₇	Continuous variable, number of board members equals board size

Table 2. Descriptive statistics of key variables.

Variable	N	Mean	SD	Min	Max
EDD	12,588	0.002	0.157	−0.335	0.438
x₁	12,588	6.395	1.132	2.000	9.000
c₁	12,588	22.232	1.252	19.091	26.101
c₂	12,588	2.506	0.614	0.000	3.367
c₃	12,588	0.257	0.850	−0.783	9.184
c₄	12,588	0.375	0.063	0.250	0.600
c₅	12,588	0.245	0.429	0.000	1.000
c₆	12,588	33.035	14.171	8.600	75.00
c₇	12,588	2.330	0.204	1.792	2.890
y₁₉	12,588	0.022	0.016	0.000	0.066
m₄₃	12,588	0.700	0.430	0.037	2.645
Inst	12,588	0.406	0.226	0.000	3.267
y₃	12,588	0.093	0.023	0.005	0.160

Table 3. Baseline regression model findings.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	EDD	EDD	EDD	EDD	EDD	EDD	EDD	EDD
x₁	−0.004 **	−0.005 ***	−0.005 ***	−0.005 ***	−0.005 ***	−0.005 ***	−0.005 ***	−0.005 ***
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
c₁		0.022 ***	0.020 ***	0.020 ***	0.020 ***	0.020 ***	0.019 ***	0.019 ***
		(0.005)	(0.005)	(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
c₂			0.021 **	0.021 **	0.021 **	0.021 **	0.035 ***	0.035 ***
			(0.008)	(0.008)	(0.008)	(0.008)	(0.009)	(0.009)
c₃				0.003	0.003	0.003	0.002	0.002
				(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
c₄					0.016	0.016	0.014	0.013
					(0.023)	(0.023)	(0.023)	(0.023)
c₅						−0.001	−0.001	−0.001
						(0.005)	(0.005)	(0.005)
c₆							0.002 ***	0.002 ***
							(0.000)	(0.000)
c₇								−0.009
								(0.007)
Constant	0.012	−0.448 ***	−0.459 ***	−0.457 ***	−0.464 ***	−0.464 ***	−0.517 ***	−0.501 ***
	(0.011)	(0.110)	(0.109)	(0.109)	(0.110)	(0.110)	(0.110)	(0.111)
N	12,588	12,588	12,588	12,588	12,588	12,588	12,588	12,588
R-squared	0.004	0.014	0.015	0.016	0.016	0.016	0.025	0.025

Notes: Standard error in parentheses; ** p < 0.05, *** p < 0.01.

Table 4. Robust regression results.

	(1)		(2)		(3)	(4)		(5)
	Lagged Explanatory Variables		Study Timeframe		Change Model	iv-2sls		System GMM
	One-Period Lag	Two-Period Lag	2010 to 2019	22020 to 2021	Logit Model	First Stage	Second Stage	System GMM
Variable	$EDD$	EDD	EDD	EDD	XN_E	$x_{1}$	$EDD$	EDD
L.x₁	−0.005 **					0.475 ***
	(0.002)					(0.009)
L2.x₁		−0.005 ***
x₁			−0.004 *	−0.002	−0.082 **		−0.010 ***
			(0.002)	(0.005)	(0.034)		(0.003)
L.EDD								0.838 ***
								(0.048)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−0.578 ***	−0.735 ***	−0.534 ***	−2.041 ***		1.811 ***	−0.688 ***	0.054
	(0.121)	(0.130)	(0.123)	(0.550)		(0.438)	(0.063)	(0.325)
Individual fixed	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	11,539	10,490	10,490	2098	8856	11,539	11,539	11,539
R-squared	0.029	0.037	0.024	0.036		0.722	0.664
Number of id	1049	1049	1049	1049	738			1049

Notes: Standard error in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 5. Heterogeneity analysis results based on geographic location.

	(1)	(2)	(3)	(4)
	Eastern Region	Central Region	Western Region	Northeast Region
Variable	$E D D$	$E D D$	$E D D$	$E D D$
x₁	−0.006 **	−0.007	−0.003	0.003
	(0.002)	(0.005)	(0.004)	(0.009)
Control variables	Yes	Yes	Yes	Yes
Constant	−0.507 ***	0.031	−0.947 ***	−0.347
	(0.165)	(0.202)	(0.206)	(0.343)
Individual fixed	Yes	Yes	Yes	Yes
Time fixed	Yes	Yes	Yes	Yes
R-squared	0.024	0.026	0.060	0.094
Number of id	636	149	213	51

Notes: Standard error in parentheses; ** p < 0.05, *** p < 0.01.

Table 6. Heterogeneity analysis results of revenue growth rate.

	(1)	(2)	(3)
	Low Revenue Growth Rate	Medium Revenue Growth Rate	High Revenue Growth Rate
Variables	EDD	EDD	EDD
x₁	−0.006 **	−0.004	−0.007 **
	(0.003)	(0.003)	(0.003)
Control variables	Yes	Yes	Yes
Observations	4196	4196	4196
Number of id	923	960	909

Notes: Standard error in parentheses; ** p < 0.05.

Table 7. Heterogeneity analysis results of income level.

	(1)	(2)	(3)	(4)
	Low Degree of Equity Concentration	Moderately Low Degree of Equity Concentration	Moderately High Degree of Equity Concentration	High Degree of Equity Concentration
Variables	EDD	EDD	EDD	EDD
x₁	−0.004	−0.002	−0.012 ***	−0.003
	(0.003)	(0.003)	(0.004)	(0.004)
Control variables	Yes	Yes	Yes	Yes
Observations	3147	3147	3147	3147
Number of id	451	561	542	441

Notes: Standard error in parentheses; *** p < 0.01.

Table 8. Threshold effect test results.

Dependent Variable	Key Explanatory Variable	Threshold Variable	Threshold Model	Threshold Value	F-Statistic	p-Value	Crit1	Crit5	Crit10
EDD	x₁	c₁	Single threshold	20.773	34.58	0.000 ***	42.208	27.603	22.614
		c₁	Dual threshold	21.877	36.39	0.002 ***	34.993	26.446	22.267
		c₂	Single threshold	0.693	38.3	0.000 ***	31.937	24.849	20.493
		c₂	Dual threshold	1.609	34.2	0.032 **	34.953	22.749	18.552

Notes: Standard error in parentheses; ** p < 0.05, *** p < 0.01.

Table 9. Empirical regression results based on threshold value.

Variable	EDD	EDD
x₁ (c₁ ≤ 20.773)	−0.012 ***
	(0.001)
x₁ (20.773 < c₁ ≤ 21.877)	−0.007 ***
	(0.001)
x₁ (c₁ > 21.877)	−0.003
	(0.001)
x₁ (c₂ ≤ 0.693)		−0.007 **
		(0.003)
x₁ (0.693 < c₂ ≤ 1.609)		−0.010 ***
		(0.001)
x₁ (c₂ > 1.609)		−0.005 ***
		(0.001)
c₁		0.018 ***
		(0.002)
c₂	0.014 ***
	(0.003)
c₃	0.002	0.002
	(0.001)	(0.001)
c₄	0.005	0.006
	(0.018)	(0.019)
c₅	−0.002	−0.002
	(0.003)	(0.003)
c₆	0.002 ***	0.002 ***
	(0.000)	(0.000)
c₇	−0.008	−0.009
	(0.006)	(0.006)
Constant	−0.037 *	−0.392 ***
	(0.020)	(0.041)
Individual fixed	Yes	Yes
Time fixed	Yes	Yes
R-squared	0.028	0.026
Observations	12,588	12,588

Notes: Standard error in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 10. Mediation regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variable	EDD	m₄₃	EDD	EDD	y₁₉	EDD
x₁	−0.005 ***	0.015 ***	−0.006 ***	−0.005 ***	−0.001 ***	−0.004 **
	(0.002)	(0.005)	(0.002)	(0.002)	(0.000)	(0.002)
m₄₃			0.043 ***
			(0.009)
y₁₉						0.682 ***
						(0.146)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−0.501 ***	1.294 ***	−0.557 ***	−0.501 ***	−0.009	−0.495 ***
	(0.111)	(0.325)	(0.113)	(0.111)	(0.009)	(0.110)
Individual fixed	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed	Yes	Yes	Yes	Yes	Yes	Yes
Observations	12,588	12,588	12,588	12,588	12,588	12,588
R-squared	0.025	0.083	0.034	0.025	0.068	0.030

Notes: Standard error in parentheses; ** p < 0.05, *** p < 0.01.

Table 11. Moderation effect test.

	(1)	(2)
Variable	EDD	EDD
x₁	−0.010 ***	−0.0133 **
	(0.003)	(−2.71)
Inst	−0.075 *
	(0.045)
x₁ $\times$ Inst	0.013 **
	(0.007)
y₃		−0.586
		(−1.77)
x₁ $\times$ y₃		0.0975 *
		(1.99)
Control variables	Yes	Yes
Individual fixed	Yes	Yes
Time fixed	Yes	Yes
Constant	−0.493 ***	−0.457 ***
	(0.115)	(−3.95)
Observations	12,588	12,588

Notes: Standard error in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

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Yang, X.; Yang, T.; Lv, J.; Luo, S. The Impact of ESG on Excessive Corporate Debt. Sustainability 2024, 16, 6920. https://doi.org/10.3390/su16166920

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Yang X, Yang T, Lv J, Luo S. The Impact of ESG on Excessive Corporate Debt. Sustainability. 2024; 16(16):6920. https://doi.org/10.3390/su16166920

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Yang, Xinhua, Tingting Yang, Jingjing Lv, and Shuai Luo. 2024. "The Impact of ESG on Excessive Corporate Debt" Sustainability 16, no. 16: 6920. https://doi.org/10.3390/su16166920

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The Impact of ESG on Excessive Corporate Debt

Abstract

1. Introduction

2. Literature Review and Theoretical Assumptions

2.1. The Impact of ESG Performance of Listed Companies on Excessive Debt

2.2. The Mediating Effect of ESG Performance of Listed Companies on Excessive Debt

2.3. The Moderating Effect of a Listed Company’s ESG Performance on Excessive Leverage

2.4. Research Gap

3. Models, Indicators and Data

3.1. Econometric Model

3.1.1. Baseline Model

3.1.2. Mediation Effect Model

3.1.3. Moderation Effect Model

3.2. Variable Selection

3.2.1. Dependent Variable

3.2.2. Key Independent Variable

3.2.3. Mediating Variables

3.2.4. Moderating Variables

3.2.5. Control Variables

3.3. Data Source

4. Empirical Results

4.1. Baseline Regression Results

4.2. Robustness Test Results

4.2.1. Lagged Explanatory Variables

4.2.2. Changing the Study Period

4.2.3. Model Change Testing

4.2.4. Instrumental Variable Approach

4.2.5. System GMM Approach

4.3. Heterogeneity Analysis Results

4.3.1. Analysis Based on Different Geographical Locations

4.3.2. Analysis Based on Different Revenue Growth Rates

4.3.3. Analysis Based on Different Ownership Concentration

4.3.4. Analysis Based on Threshold Effects

4.4. Mechanism Testing

4.4.1. Mediation Effect

4.4.2. Moderation Effect

5. Discussion

6. Conclusions

7. Limitation of the Study

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

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