ESG Performance and Corporate Risk in Supply Chain Relationships: The Moderating Role of Supply Chain Efficiency

Abstract

Supply chain conditions can influence firm risk by shaping operational stability and dependence within customer and supplier relationships. This study examines whether ESG performance mitigates corporate risk in such settings. Using panel data on publicly listed firms, the paper shows that higher ESG scores are associated with lower total volatility, lower idiosyncratic volatility, and lower stock price crash risk. The paper further shows that this risk-reducing effect is more pronounced when firms face greater supply chain dependence or operational instability, as captured by trade credit dependence measures and inventory-based measures. Decomposing ESG into its environmental, social, and governance dimensions, the results show that the stronger risk-mitigating effect of ESG is driven primarily by the governance dimension, with some additional support for the social dimension and relatively weak evidence for the environmental dimension. These findings suggest that ESG is especially valuable in supply chain environments where dependence, instability, and operational frictions are greater. Overall, the results indicate that ESG performance and supply chain conditions jointly shape corporate risk and resilience.

1. Introduction

This study asks whether ESG performance is associated with lower corporate risk and whether this association varies across firms’ supply chain conditions. This question is important because firms embedded in supply chain relationships may face greater exposure to operational instability, financing frictions, and disruptions in customer and supplier networks, all of which can amplify uncertainty and adverse capital market outcomes. In such settings, mechanisms that improve transparency, strengthen governance, and support more stable stakeholder relationships may be especially relevant. I therefore examine whether ESG performance is associated with lower stock return volatility and lower future stock price crash risk, and whether these associations differ according to firms’ supply chain dependence and efficiency.

The analysis yields three main findings. First, higher ESG scores are associated with lower total volatility, lower idiosyncratic volatility, and lower future stock price crash risk. Second, this negative association is more pronounced under certain supply chain conditions, including greater trade credit dependence and higher operational instability. Specifically, the interaction results show that ESG is more strongly associated with lower risk when firms exhibit higher accounts receivable relative to sales, higher accounts payable relative to cost of goods sold, and greater inventory volatility. Additional evidence also indicates that the ESG-risk association varies with inventory turnover. Third, when ESG is decomposed into its environmental, social, and governance dimensions, the stronger negative association across supply chain settings is driven primarily by the governance dimension, with some additional support for the social dimension, while the environmental dimension appears weaker. These results suggest that ESG appears to be especially relevant in supply chain environments characterized by greater dependence, operational frictions, or instability, where credible governance, stakeholder trust, and resilience to adverse shocks are particularly important.

This study builds on research in accounting and finance showing that firms’ information environments are closely related to market-based risk outcomes. Prior studies show that corporate transparency, disclosure quality, and information asymmetry shape firm-specific stock return variation and stock price crash risk. Greater disclosure improves capital market outcomes and reduces information frictions, whereas opaque reporting environments make it more likely that bad news accumulates and is released suddenly. Collectively, prior studies suggest that the firm’s information environment plays a central role in determining firm-specific risk and downside tail risk [1,2,3,4].

Against this backdrop, ESG performance may serve as a firm-level mechanism associated with lower risk. ESG-related activities are often associated with stronger stakeholder engagement, improved governance, and greater transparency. Prior studies suggest that sustainability-oriented firms have stronger organizational processes and more credible long-term strategies, which may reduce uncertainty about future cash flows. Corporate sustainability has been shown to be associated with stronger organizational performance, providing a broad conceptual foundation for why socially responsible behavior can affect firm outcomes [5,6]. More directly, recent evidence suggests that ESG disclosure and sustainability-related information improve stock price informativeness, reduce firm-specific risk, and enhance the information environment and capital market pricing [7,8,9].

The relevance of ESG may be especially pronounced in supply chain settings. Prior research suggests that socially and environmentally responsible value-chain practices are closely tied to firms’ broader supply chain decisions and stakeholder relationships [10]. Research further shows that relationships with major customers and other trading partners have important implications for firm performance, financing, and capital markets. In particular, customer relationships affect cash holdings, financing constraints, profitability, and innovation incentives [11,12]. Moreover, studies on supply chain compliance and governance indicate that risks originating from suppliers and other trading partners can affect firm reputation, operational stability, and stakeholder trust [13,14]. Together, these studies imply that firms embedded in supply chain relationships may face meaningful dependence-related frictions and may be vulnerable to disruptions in key trading relationships.

In this study, I capture such supply chain conditions using both financial and operational measures. Specifically, I use accounts receivable scaled by sales and accounts payable scaled by cost of goods sold to capture customer-side and supplier-side trade credit dependence, respectively. I also use inventory turnover and inventory volatility to capture operational efficiency and instability within the supply chain. These measures allow me to examine whether ESG is more strongly associated with lower corporate risk when firms are more exposed to working capital dependence or operational fragility in supply chain relationships.

From this perspective, ESG may be particularly relevant for firms operating under dependency-intensive or unstable supply chain conditions. When firms rely more heavily on trade credit relationships or face greater operational instability, stable relationships, credible disclosure, and trust within the supply chain become more important. ESG performance may help such firms strengthen stakeholder relationships, build reputational capital, and reduce uncertainty about future operations. This argument is consistent with stakeholder-oriented views of the firm, as well as with transaction-cost and relational contracting perspectives emphasizing the importance of stable interorganizational relationships [15,16,17,18]. In this sense, the value of ESG may not be uniform across firms, but may instead depend on the structure and efficiency of the firm’s supply chain relationships.

This study addresses three gaps in the literature. First, most prior studies examine general firm risk, idiosyncratic risk, or crash risk separately, rather than jointly analyzing multiple dimensions of market-based corporate risk. Yet stock return volatility and stock price crash risk capture distinct aspects of risk: volatility reflects general uncertainty in stock returns, whereas crash risk captures extreme downside events associated with the hoarding of bad news. Second, relatively little is known about the conditions under which ESG is most strongly associated with lower firm risk. This matters because the value of ESG may depend on the firm’s external stakeholder and supply chain environment rather than being uniform across all firms. Third, although ESG is typically measured as an aggregate construct, less is known about whether its association with lower risk is more closely related to the environmental, social, or governance dimension, particularly across different supply chain settings.

To address these issues, I examine whether ESG performance is associated with lower stock return volatility and lower future stock price crash risk, and whether these associations vary across firms’ supply chain dependence and efficiency. Specifically, I test whether firms with higher ESG scores exhibit lower total volatility (VOL), lower idiosyncratic volatility (IVOL), and lower crash risk as measured by NCSKEW and DUVOL. This approach provides a more comprehensive test of the relation between ESG and corporate risk by jointly considering both general return variability and extreme downside risk. The empirical motivation for this analysis is supported by prior studies showing that information quality, disclosure, and reporting opacity influence both firm-specific volatility and crash risk. Prior studies indicate that weak information environments and market frictions increase the likelihood that firm-specific risk is reflected in adverse stock price outcomes [1,4,5,6,7,19].

I further decompose ESG into its environmental, social, and governance dimensions to examine which components are most closely associated with lower corporate risk and whether the moderating role of supply chain conditions differs across ESG dimensions. This additional analysis is important because the mechanisms linking ESG to risk-related outcomes may differ across components. In particular, if the ESG-risk association in supply chain settings is more closely related to governance and social performance than to environmental performance, this would suggest that credible governance, relationship stability, and trust in customer-supplier ties are especially important in mitigating uncertainty in dependency-intensive or operationally unstable settings.

This study contributes to the literature in three ways. First, it contributes to the ESG and corporate risk literature by jointly examining stock return volatility and future stock price crash risk within a unified framework. Second, it contributes to the disclosure and information environment literature by showing that ESG is associated with lower firm-specific risk and lower downside tail risk, consistent with the view that ESG may improve transparency and limit bad-news hoarding. Third, it contributes to the stakeholder and supply chain literature by showing that the ESG-risk association varies across supply chain conditions and that this association is driven mainly by the governance and, to a lesser extent, the social dimension of ESG. Overall, the findings suggest that ESG performance and supply chain conditions jointly shape corporate risk and resilience.

The remainder of the paper is organized as follows. Section 2 reviews the related literature and develops the hypotheses. Section 3 describes the sample, variable measurement, and empirical models. Section 4 presents the empirical results. Section 5 reports the robustness tests. Section 6 discusses the limitations of the study. Section 7 concludes.

2. Related Literature and Hypothesis Development

2.1. ESG, Information Environment, and Corporate Risk

A large body of research in accounting and finance shows that firms’ information environments are closely related to market-based risk outcomes. Studies on disclosure and transparency argue that greater disclosure reduces information asymmetry, improves market liquidity, and lowers information-related frictions in capital markets [2,3,20,21,22,23,24]. Related work further shows that better disclosure quality and financial statement comparability improve investors’ information processing and pricing efficiency [25,26,27]. At a broader level, studies on market microstructure and price formation emphasize that information asymmetry affects how firm-specific information is incorporated into prices [28,29,30]. In turn, more informative prices are associated with lower market frictions and more efficient incorporation of firm-level news [31,32,33].

This literature is directly relevant to corporate risk because both stock return volatility and stock price crash risk are shaped not only by firm fundamentals but also by the quality of the firm’s information environment. Prior studies argue that weak transparency increases the likelihood that adverse firm-specific information remains hidden from outside investors and show that opaque financial reporting is associated with greater stock price crash risk [1,4]. Related studies further document that firm-level opacity and weak external monitoring are linked to future crash risk [19,34]. These studies imply that the same information frictions that hinder the timely release of bad news can also contribute to higher return volatility and more severe downside risk. Thus, market-based corporate risk is not simply a function of business fundamentals; it is also shaped by disclosure quality, information asymmetry, and managerial reporting incentives [1,4,19,34].

The ESG literature provides a natural extension of this argument. ESG performance is often interpreted as reflecting stronger governance, more credible stakeholder engagement, and a greater willingness to provide nonfinancial information to the market. Prior research shows that ESG disclosure and sustainability-related practices are associated with better corporate processes and improved market outcomes [5,6,7,35]. More specifically, ESG-related disclosure can improve the firm’s information environment and reduce firm-specific risk. In this respect, the logic of the disclosure literature and the logic of the ESG literature are complementary: if ESG improves transparency, reduces agency conflicts, and enhances the credibility of firm communications, then firms with stronger ESG performance should exhibit lower stock return volatility and lower extreme downside risk. This argument is also consistent with governance-based studies emphasizing the importance of monitoring and internal control for financial outcomes [36].

This perspective is especially relevant when ESG is viewed not merely as a reputational attribute but as a firm-level mechanism that can reduce information risk. By improving transparency, constraining managerial opportunism, and reinforcing the credibility of firm disclosures, ESG may reduce both general return variability and downside tail risk. This information environment channel is directly related to stock return volatility and crash risk. If ESG improves the timeliness and credibility of firm-specific information, then firms with stronger ESG performance should exhibit lower overall volatility, lower idiosyncratic volatility, and lower future crash risk.

Based on this literature, ESG can be viewed as a firm-level mechanism that improves the information environment and constrains managerial opportunism. If so, stronger ESG performance should be associated with lower overall return variability and lower firm-specific volatility.

H1. 

ESG performance is negatively associated with stock return volatility.

The same logic extends to crash risk. Because stock price crashes are often understood as the result of managerial bad-news hoarding and its eventual release, any firm characteristic that improves transparency and limits opportunistic concealment should reduce crash risk. If ESG performance strengthens disclosure quality, enhances stakeholder monitoring, and promotes more stable and credible long-term decision-making, then it should reduce the likelihood of future crash events.

H2. 

ESG performance is negatively associated with future stock price crash risk.

2.2. Stakeholder Relationships and Corporate Risk

Although the baseline association between ESG performance and corporate risk may be negative on average, the value of ESG is unlikely to be uniform across firms. Stakeholder theory suggests that the importance of ESG depends on the nature of the firm’s relationships with key stakeholders [15,16]. In particular, supply chain conditions shape the degree to which firms depend on major trading partners and the extent to which they face operational or financial frictions within those relationships. Trade-credit dependence and inventory-related conditions therefore provide a useful way to identify situations in which stakeholder dependence and operating fragility are especially pronounced. Transaction-cost and relational contracting perspectives likewise suggest that concentrated or dependency-intensive trading relationships can alter firms’ incentives, governance structures, and exposure to risk [17,37,38].

Existing research shows that supply chain relationships have broad implications for firm behavior and capital market outcomes. Prior studies show that customer relationships affect suppliers’ cash policies, financing constraints, profitability, conservatism, and market responses to customer news [11,12,39,40]. In addition, the supply chain literature suggests that value-chain responsibility, supplier compliance, and platform governance are important determinants of stakeholder trust, operational resilience, and reputational risk [10,13,14,41]. Customer dependence also affects firms’ bargaining positions and operating decisions [42]. Accordingly, in settings where firms are more exposed to customer-side or supplier-side dependence, or where operations are less stable, disruptions in key trading relationships can more easily translate into firm-level uncertainty and market risk.

This supply chain setting provides a natural context in which ESG may become especially valuable. When a firm relies more heavily on trade credit relationships or faces greater operational instability, reputational capital, stable contracting, and transparent communication become more important. In such environments, ESG can help reduce uncertainty by improving trust, strengthening stakeholder ties, and supporting more stable operations. The logic is consistent with studies emphasizing the governance and financing consequences of stakeholder dependence, as well as broader views that customer relationships play a central role in corporate decision-making and value creation [12,15,16,18]. Moreover, firms facing stronger financing frictions may benefit more from mechanisms that improve credibility and reduce perceived risk, a point also related to the financial constraints literature and to models linking competition and financial vulnerability [43,44].

This argument represents a supply chain stakeholder channel that is distinct from, but complementary to, the information environment channel discussed above. The information environment channel explains why ESG may be associated with lower volatility and crash risk through transparency, monitoring, and lower bad-news hoarding. The supply chain stakeholder channel explains why this association may be stronger when firms face greater trade credit dependence or operational instability. In such settings, credible governance, stakeholder trust, and stable interorganizational relationships become more valuable because supply chain frictions can amplify uncertainty about future cash flows and expose firms to greater market-based risk.

Accordingly, the association between ESG performance and corporate risk should vary with firms’ supply chain conditions. In firms with greater trade credit dependence or inventory-related operating frictions, ESG is expected to play a greater stabilizing role by reinforcing stakeholder trust, supporting transparency, and reducing uncertainty surrounding future cash flows.

H3. 

The negative association between ESG performance and corporate risk varies with firms’ supply chain conditions, including trade credit dependence, inventory efficiency, and operational instability.

The prior literature also suggests that the supply chain stakeholder channel may differ across the environmental, social, and governance dimensions of ESG. Among the three dimensions, governance is expected to play the most direct role in supply chain settings because trade credit dependence and inventory-related instability increase the importance of monitoring, disclosure credibility, internal control, and disciplined managerial decision-making. Studies on information environments and crash risk show that weak transparency, opaque reporting, and limited monitoring increase firm-specific risk and downside tail risk [1,4,19,34]. This literature implies that the governance dimension of ESG should be particularly relevant because governance is closely related to monitoring, disclosure credibility, internal control, and constraints on managerial opportunism.

Agency-based arguments further suggest that stronger monitoring and internal control can constrain managerial opportunism and reduce the likelihood that adverse information is concealed from outside investors. These governance mechanisms may be especially important when firms face working-capital dependence or operational instability, because investors may be more concerned about hidden operational problems, delayed recognition of adverse news, and weak oversight. Relational contracting and transaction-cost perspectives also support the importance of governance in supply chain settings. When firms rely on customer-side or supplier-side trade credit relationships, or when inventory conditions are unstable, contracting frictions and coordination problems can increase uncertainty about future cash flows. Strong governance can help reduce these concerns by improving oversight, enhancing disclosure credibility, and supporting more disciplined decisions in relationships with customers and suppliers.

Accordingly, the governance dimension is expected to play the strongest role in conditioning the relation between ESG performance and corporate risk under supply chain frictions.

H4a. 

The moderating role of supply chain conditions in the relation between ESG performance and corporate risk is strongest for the governance dimension of ESG.

The social dimension may also matter in supply chain settings because stakeholder relationships, trust, and reputational stability are central to interorganizational relationships. Stakeholder theory emphasizes that firm value and risk are shaped by relationships with key stakeholders [15,16], while the supply chain literature shows that customer and supplier relationships affect firms’ financing constraints, cash policies, profitability, and market responses to customer news [11,12,39,40]. Prior studies also suggest that responsible value-chain practices, supplier compliance, and platform governance are related to stakeholder trust, operational resilience, and reputational risk [10,13,14,41]. These arguments suggest that social performance may help firms maintain stable stakeholder relationships and reduce uncertainty in supply chain settings.

Accordingly, the social dimension is also expected to strengthen the risk-mitigating role of ESG under supply chain frictions, although this role may be less direct than that of governance.

H4b. 

The moderating role of supply chain conditions in the relation between ESG performance and corporate risk is also present, but weaker than governance, for the social dimension of ESG.

By contrast, the environmental dimension may be less directly related to the specific financial and operational frictions examined in this study. Although environmental performance is important for long-term sustainability and regulatory exposure, the supply chain measures used here—AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility—primarily capture working-capital dependence and inventory-related operating conditions. These frictions are more directly linked to governance credibility and stakeholder relationship stability than to environmental performance.

Accordingly, the environmental dimension is expected to play a weaker role in conditioning the relation between ESG performance and corporate risk under the supply chain conditions examined in this study.

H4c. 

The moderating role of supply chain conditions in the relation between ESG performance and corporate risk is weaker for the environmental dimension of ESG than for the governance and social dimensions.

3. Sample, Variable Measurement, and Research Design

3.1. Sample

The sample consists of S&P 500 firms from 2016 to 2023 for which data on ESG performance, stock returns, accounting variables, and supply chain measures are jointly available. The S&P 500 provides an appropriate setting for this study because ESG information is relatively reliable, comprehensive, and consistently available for these firms. S&P 500 firms are large, visible, and closely followed by investors, analysts, regulators, and ESG rating agencies; therefore, their ESG-related activities are more systematically disclosed and evaluated than those of smaller firms. This feature is important because the empirical analysis relies on both aggregate ESG scores and ESG component scores.

I construct a comprehensive panel dataset by merging ESG data from Refinitiv ESG Scores with accounting data from Compustat and stock return data from CRSP. Refinitiv ESG Scores provide standardized assessments of firms’ environmental, social, and governance practices. The main explanatory variable is the overall ESG score, measured on a 0–100 scale. To provide additional insight into the channels through which ESG may affect corporate risk, I also use the environmental, social, and governance component scores in supplemental analyses. These component measures allow me to examine whether the relation between ESG and corporate risk, as well as the moderating role of supply chain conditions, differs across the three dimensions of ESG performance.

Specifically, I capture supply chain conditions using four variables: AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility. Financial firms and utilities are excluded because their regulatory environments, capital structures, and risk profiles differ systematically from those of other industries. I further exclude firm-year observations with missing values for the main variables used in the analysis. To reduce the influence of extreme observations, the overall ESG score, the three ESG component scores, and all other continuous variables are winsorized at the 1st and 99th percentiles. Following the existing literature, all independent variables are lagged by one year relative to the dependent variables to reduce concerns about simultaneity and reverse causality.

Table A2 summarizes the sample construction procedure. The initial sample consists of 3880 S&P 500 firm-year observations from 2016 to 2023. To address concerns that the results may be driven by pandemic-related disruptions, I exclude 970 firm-year observations from fiscal years 2020 and 2021 in the COVID-excluded robustness analysis. I then exclude financial and utility firms because their regulatory environments, capital structures, and risk profiles differ systematically from those of other industries, which removes 421 firm-year observations. Finally, I exclude 312 firm-year observations with missing ESG data, stock return data, accounting variables, or supply-chain-condition variables. The final sample for the COVID-excluded robustness analysis consists of 2177 firm-year observations.

Although the S&P 500 sample provides reliable and consistent ESG, accounting, and stock return data, this sample choice also limits the generalizability of the findings. S&P 500 firms are generally larger, more mature, and more extensively monitored than other firms. Smaller firms may face stronger supply chain dependence, weaker governance structures, and less developed ESG disclosure practices. Accordingly, the results should be interpreted as evidence for large U.S. public firms and may not fully generalize to smaller public firms, private firms, or firms in other institutional settings. The final sample includes firm-year observations spanning the period for which all relevant ESG, return, and supply chain data are jointly available.

3.2. Variable Measurement

3.2.1. ESG Performance

The main independent variable is ESG Score, which measures firm-level ESG performance. Higher values indicate better overall performance across the environmental, social, and governance dimensions. To provide additional insight into the channels through which ESG may affect corporate risk, I also use the Environmental Score, Social Score, and Governance Score separately in supplemental analyses. These component measures allow me to examine whether the relation between ESG performance and corporate risk, as well as the moderating role of supply chain conditions, differs across the three dimensions of ESG.

3.2.2. Stock Return Volatility

To capture stock return volatility, I use two measures. First, total volatility (VOL) is defined as the standard deviation of daily stock returns within a fiscal year. Second, idiosyncratic volatility (IVOL) is defined as the standard deviation of residuals obtained from a market model regression of daily stock returns. VOL captures overall variation in stock returns, whereas IVOL isolates the firm-specific component of return volatility after removing market-wide movements.

3.2.3. Stock Price Crash Risk

Following ref. [1], ref. [4], and ref. [19], future stock price crash risk is measured using negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) based on firm-specific weekly returns. To compute firm-specific weekly returns, I first estimate residual returns from a market model or factor model and then aggregate them to the annual level.

Negative conditional skewness is calculated as:

$$NCSKE{W}_{i,t}=-{\displaystyle \frac{n(n-1{)}^{3/2}\sum R_{i,t}^3}{(n-1)(n-2)(\sum R_{i,t}^2{)}^{3/2}}}$$

where $R_{i,t}$ denotes firm-specific weekly returns and $n$ is the number of weekly observations in year $t$. A higher value of NCSKEW indicates greater stock price crash risk.

DUVOL is defined as:

$$DUVO{L}_{i,t}=\mathrm{l}\mathrm{n}\left({\displaystyle \frac{Var\left(R_{down}\right)}{Var\left(R_{up}\right)}}\right)$$

where $Var\left(R_{down}\right)$ is the variance of firm-specific weekly returns in down weeks and $Var\left(R_{up}\right)$ is the variance in up weeks. Higher DUVOL values indicate greater downside volatility relative to upside volatility and therefore higher crash risk.

3.2.4. Supply Chain Conditions

To examine cross-sectional variation in the effect of ESG, I use four measures that capture firms’ supply chain conditions: AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility. AR/Sales, measured as accounts receivable scaled by sales, captures customer-side trade credit dependence. AP/COGS, measured as accounts payable scaled by cost of goods sold, captures supplier-side trade credit dependence. Inventory Turnover, measured as cost of goods sold divided by average inventory, captures operational efficiency in inventory management. Inventory Volatility, measured as the standard deviation of inventory scaled by total assets, captures operational instability and supply chain uncertainty. Taken together, these variables allow me to examine whether the risk-mitigating effect of ESG varies with firms’ supply chain dependence and operational efficiency.

3.2.5. Control Variables

Following the literature on stock return volatility, crash risk, and corporate disclosure, I include a standard set of firm-level control variables. These include Size, measured as the natural logarithm of total assets; Leverage, measured as total debt divided by total assets; ROA, measured as net income divided by total assets; MTB, measured as the market-to-book ratio; Cash, measured as cash and cash equivalents divided by total assets; and Stock Turnover, measured as annual trading volume scaled by shares outstanding. In the crash risk regressions, I also include Past Return to control for prior stock performance. These variables are intended to capture firm size, financial risk, profitability, growth opportunities, liquidity, and trading activity, all of which may be related to stock return volatility and crash risk.

3.3. Research Design

To test whether ESG performance is associated with lower stock return volatility, I estimate the following baseline model:

$$Ris{k}_{i,t}=\alpha +{\beta }_{1}ES{G}_{i,t-1}+\gamma Control{s}_{i,t-1}+IndustryFE+YearFE+{\epsilon }_{i,t}$$

where $Ris{k}_{i,t}$ is alternatively measured as VOL or IVOL. The coefficient of interest is ${\beta }_1$. A negative and significant coefficient on ESG indicates that stronger ESG performance is associated with lower stock return volatility.

To examine the relation between ESG performance and future stock price crash risk, I estimate:

$$CrashRis{k}_{i,t+1}=\alpha +{\beta }_{1}ES{G}_{i,t}+\gamma Control{s}_{i,t}+IndustryFE+YearFE+{\epsilon }_{i,t+1}$$

where $CrashRis{k}_{i,t+1}$ is alternatively measured as NCSKEW or DUVOL in the subsequent fiscal year. A negative coefficient on ESG suggests that firms with better ESG performance face lower future crash risk. In the crash-risk regressions, I include Past Return because prior stock performance may affect future downside risk. Firms with high prior returns may be more vulnerable to subsequent price corrections or to the delayed release of negative firm-specific information. Therefore, Past Return is included as an additional control in the NCSKEW, DUVOL, and CRASH specifications.

To test whether supply chain conditions moderate the effect of ESG on corporate risk, I estimate the following interaction model:

$$Y_{i,t+1}=\alpha +{\beta }_{1}ES{G}_{i,t}+{\beta }_{2}SC{M}_{i,t}+{\beta }_{3}ES{G}_{i,t}\times SC{M}_{i,t}+\gamma Control{s}_{i,t}+IndustryFE+YearFE+{\epsilon }_{i,t+1}$$

where $Y_{i,t+1}$ denotes corporate risk and is alternatively measured as stock return volatility (VOL and IVOL) and stock price crash risk (NCSKEW and DUVOL). $SC{M}_{i,t}$ denotes one of the four supply chain measures: AR/Sales, AP/COGS, Inventory Turnover, or Inventory Volatility. The coefficient of primary interest is ${\beta }_3$. A significant coefficient on the interaction term indicates that the risk-mitigating effect of ESG performance varies with firms’ supply chain conditions. All regressions include year fixed effects to absorb common macroeconomic shocks and industry fixed effects to control for time-invariant differences across industries. Standard errors are clustered at the firm level to account for within-firm correlation over time.

4. Empirical Results

4.1. Descriptive Statistics

Table 1 reports descriptive statistics for the main variables used in the analysis. The sample consists of 2177 firm-year observations. The mean ESG Score is 53.914, with a median of 54.600, indicating moderate variation in ESG performance across sample firms. The average total stock return volatility (VOL) is 0.394, while the mean idiosyncratic volatility (IVOL) is 0.326, suggesting that a substantial portion of return variability is attributable to firm-specific factors. With respect to stock price crash risk, the mean values of NCSKEW and DUVOL are −0.261 and −0.126, respectively, indicating meaningful cross-sectional variation in downside tail risk across firms.

Table 1. Descriptive Statistics.

Variables	N	Mean	Median	Std. Dev.	Q1	Q3
ESG Score	2177	53.914	54.600	16.108	41.900	66.100
E Score	2177	52.207	53.100	17.892	39.200	65.500
S Score	2177	55.682	56.700	16.804	44.100	68.200
G Score	2177	58.126	58.800	15.721	47.400	69.900
VOL	2177	0.394	0.367	0.176	0.267	0.491
IVOL	2177	0.326	0.305	0.149	0.219	0.407
NCSKEW	2177	−0.261	−0.236	0.682	−0.594	0.095
DUVOL	2177	−0.126	−0.114	0.365	−0.342	0.091
AR/Sales	2177	0.162	0.138	0.104	0.086	0.207
AP/COGS	2177	0.139	0.118	0.091	0.069	0.181
Inventory Turnover	2177	5.376	4.812	2.548	3.506	6.559
Inventory Volatility	2177	0.034	0.027	0.028	0.014	0.045
Size	2177	7.086	7.014	1.451	6.037	8.012
Leverage	2177	0.236	0.221	0.169	0.096	0.342
ROA	2177	0.056	0.052	0.076	0.021	0.091
MTB	2177	2.047	1.701	1.462	1.098	2.518
Cash	2177	0.121	0.095	0.111	0.040	0.171
Stock Turnover	2177	0.764	0.621	0.612	0.311	1.026
Past Return	2177	0.102	0.083	0.253	−0.053	0.218
Firm Age	2177	14.732	12.000	10.421	7.000	20.000

This table reports descriptive statistics for the variables used in the analysis. ESG Score denotes the overall ESG performance measure. VOL is the annualized total stock return volatility based on daily returns, and IVOL is idiosyncratic volatility measured as the standard deviation of residuals from a market model regression of daily stock returns. NCSKEW and DUVOL are measures of stock price crash risk based on firm-specific weekly returns. AR/Sales and AP/COGS capture customer-side and supplier-side trade credit dependence, respectively. Inventory Turnover measures operational efficiency in inventory management, and Inventory Volatility measures operational instability in inventory positions. All variable definitions are provided in Appendix A Table A1.

AR/Sales has a mean of 0.162 and a median of 0.138, indicating that accounts receivable account for a meaningful share of firms’ sales on average. The difference between the mean and median suggests that receivables intensity is somewhat right-skewed, with some firms exhibiting relatively high dependence on customer-side trade credit. AP/COGS has a mean of 0.139 and a median of 0.118, implying that accounts payable represent a nontrivial proportion of firms’ cost of goods sold. The higher mean relative to the median similarly suggests that supplier-side trade credit dependence is more pronounced for a subset of firms.

Inventory Turnover has a mean of 5.376 and a median of 4.812, indicating substantial variation in the efficiency with which firms manage inventory. Inventory Volatility has a mean of 0.034 and a median of 0.027, suggesting that inventory instability is generally modest on average but considerably higher for some firms. Taken together, these descriptive statistics indicate meaningful cross-sectional variation in trade credit dependence, operational efficiency, and operational instability. This variation is important for the cross-sectional tests examining whether the risk-mitigating effect of ESG differs across firms with different supply chain conditions.

The control variables also display reasonable variation. The average firm has a Size of 7.086, Leverage of 0.236, and ROA of 0.056. The mean market-to-book ratio (MTB) is 2.047, indicating that many firms in the sample exhibit growth characteristics. Cash holdings average 0.121 of total assets, while Stock Turnover has a mean of 0.764, suggesting considerable heterogeneity in trading activity across firms. Past Return has a mean of 0.102, and the average Firm Age is 14.732 years.

Table 2 reports the Pearson correlation coefficients among the main variables used in the analysis. The correlations among ESG Score and its component scores are positive, as expected, indicating that firms with higher overall ESG performance also tend to have higher environmental, social, and governance scores. Among the component scores, the correlations are moderate rather than excessively high, suggesting that the three ESG dimensions capture related but distinct aspects of ESG performance.

Table 2. Pearson Correlation Matrix.

Variables	ESG	E	S	G	AR/Sales	AP/COGS	Inventory Turnover	Inventory Volatility	VOL	IVOL	NCSKEW	DUVOL
ESG Score	1.000
E Score	0.782	1.000
S Score	0.814	0.563	1.000
G Score	0.746	0.421	0.517	1.000
AR/Sales	−0.068	−0.041	−0.052	−0.073	1.000
AP/COGS	−0.054	−0.036	−0.045	−0.061	0.312	1.000
Inventory Turnover	0.049	0.022	0.041	0.057	−0.284	−0.197	1.000
Inventory Voatility	−0.071	−0.038	−0.056	−0.081	0.241	0.218	−0.326	1.000
VOL	−0.112	−0.061	−0.087	−0.126	0.092	0.084	−0.097	0.118	1.000
IVOL	−0.105	−0.055	−0.079	−0.119	0.088	0.079	−0.091	0.113	0.862	1.000
NCSKEW	−0.096	−0.047	−0.074	−0.111	0.081	0.076	−0.086	0.104	0.431	0.398	1.000
DUVOL	−0.082	−0.039	−0.066	−0.094	0.073	0.069	−0.079	0.097	0.382	0.361	0.721	1.000

This table reports Pearson correlation coefficients among the main variables used in the analysis. ESG Score is the aggregate ESG performance measure, and E Score, S Score, and G Score are the environmental, social, and governance component scores, respectively. AR/Sales and AP/COGS capture customer-side and supplier-side trade credit dependence. Inventory Turnover measures inventory management efficiency, and Inventory Volatility captures inventory-related operational instability. VOL and IVOL are stock return volatility measures, while NCSKEW and DUVOL are future stock price crash-risk measures. All continuous variables are winsorized at the 1st and 99th percentiles.

The ESG variables are negatively correlated with the corporate risk measures, including VOL, IVOL, NCSKEW, and DUVOL. This pattern is consistent with the main argument that firms with stronger ESG performance tend to exhibit lower stock return volatility and lower future crash risk. In particular, the governance score shows the strongest negative correlations with the risk measures among the ESG components, which is consistent with the component-level prediction that governance is more closely related to monitoring, disclosure credibility, and risk reduction.

The supply-chain-condition variables also show correlations in the expected directions. AR/Sales, AP/COGS, and Inventory Volatility are positively correlated with the risk measures, suggesting that greater trade credit dependence and inventory instability are associated with higher corporate risk. By contrast, Inventory Turnover is negatively correlated with the risk measures, indicating that more efficient inventory management is associated with lower corporate risk. Overall, the correlation matrix provides preliminary evidence consistent with the regression results while also indicating that multicollinearity among the key variables is not likely to be severe.

4.2. ESG Performance and Stock Return Volatility

Table 3 reports the baseline regression results on the association between ESG performance and stock return volatility. Across both specifications, the coefficient on ESG Score is negative and statistically significant, indicating that stronger ESG performance is associated with lower stock return volatility. The magnitude of the coefficient is also economically meaningful. A one-standard-deviation higher ESG Score is associated with a 0.129 lower value of VOL and a 0.097 lower value of IVOL. These results suggest that ESG performance is related not only to lower overall variation in stock returns but also to lower firm-specific volatility after removing broad market movements.

Table 3. ESG Performance and Idiosyncratic Volatility.

	(1)	(2)
Variables	VOL	IVOL
ESG Score	−0.008 **	−0.006 **
	(−2.12)	(−2.04)
Size	−0.012 **	−0.010 **
	(−2.43)	(−2.28)
Leverage	0.017 **	0.014 **
	(2.49)	(2.31)
ROA	−0.026 *	−0.023 *
	(−1.80)	(−1.72)
MTB	0.004	0.003
	(1.24)	(1.17)
Cash	−0.007	−0.006
	(−1.10)	(−1.04)
Stock Turnover	0.014 ***	0.012 **
	(2.76)	(2.54)
Firm FE	Yes	Yes
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	2177	2177
Adj. R2	0.251	0.239
VIF	2.362	2.672

This table reports the regression results for the association between ESG performance and stock return volatility. VOL is measured as the standard deviation of daily stock returns within a fiscal year, and IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

The result for IVOL is particularly important because idiosyncratic volatility captures the firm-specific component of stock return risk. In this respect, the findings suggest that the association between ESG performance and stock return volatility is not limited to market-wide exposure. Rather, firms with stronger ESG performance appear to exhibit lower uncertainty related to firm-specific information, operations, and investor perceptions. This interpretation is consistent with the view that ESG performance is associated with a better information environment through greater transparency, lower information asymmetry, and more credible corporate disclosure. It is also consistent with the idea that stronger ESG performance reflects more stable stakeholder relationships and better internal governance, both of which may be associated with lower firm-level uncertainty.

More broadly, the negative coefficients on VOL and IVOL suggest that ESG performance may serve as a firm-level characteristic associated with lower risk. Firms with higher ESG performance may be viewed by investors as more transparent, more resilient, and less exposed to unexpected adverse shocks. As a result, their stock returns may fluctuate less in response to firm-specific news and uncertainty. Overall, the evidence in Table 3 supports H1 and suggests that ESG performance is associated with lower stock return volatility, particularly lower firm-specific risk.

4.3. ESG Component Scores and Stock Return Volatility

Table 4 reports the regression results using the environmental, social, and governance component scores of ESG performance. The results show that the negative association between ESG performance and stock return volatility is not uniform across the three dimensions of ESG. Instead, it is driven primarily by the governance dimension, with weaker evidence for the social dimension and little evidence for the environmental dimension. In terms of economic significance, the governance dimension shows the largest magnitude. A one-standard-deviation increase in G Score is associated with a decrease of approximately 0.157 in VOL and 0.126 in IVOL. By comparison, a one-standard-deviation increase in S Score is associated with decreases of 0.101 in VOL and 0.084 in IVOL, while a one-standard-deviation increase in E Score is associated with smaller and statistically insignificant decreases of 0.054 in VOL and 0.036 in IVOL. This pattern is consistent with the view that governance is the primary ESG dimension associated with lower stock return volatility.

Table 4. ESG Component Scores and Stock Return Volatility.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	VOL	IVOL	VOL	IVOL	VOL	IVOL
E Score	−0.003	−0.002
	(−1.08)	(−0.96)
S Score			−0.006 *	−0.005 *
			(−1.72)	(−1.66)
G Score					−0.010 **	−0.008 **
					(−2.61)	(−2.23)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177	2177	2177
Adj. R2	0.249	0.238	0.250	0.239	0.255	0.243
VIF	2.367	2.558	2.126	2.451	2.351	2.345

This table reports regression results for the association between ESG component scores and stock return volatility. VOL is measured as the standard deviation of daily stock returns within a fiscal year, and IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns. Environmental Score, Social Score, and Governance Score are the component measures of firm-level ESG performance. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ** and * indicate statistical significance at the 5%, and 10% levels, respectively.

Specifically, the coefficient on E Score is negative in both the VOL and IVOL regressions, but it is not statistically significant. This suggests that environmental performance, by itself, is not strongly associated with lower stock return volatility in this setting. In contrast, S Score is negatively associated with both VOL and IVOL and is weakly significant in both specifications. This finding indicates that the social dimension of ESG may help reduce stock return volatility, possibly by strengthening stakeholder relationships and improving the stability of the firm’s external environment.

The strongest results are observed for the governance dimension. G Score is negatively and significantly associated with both VOL and IVOL, and its coefficient is larger in magnitude than those of the environmental and social dimensions. This pattern suggests that the overall negative relation between ESG performance and stock return volatility documented in Table 2 is driven mainly by governance-related factors. Because governance is closely related to monitoring, disclosure credibility, and managerial discipline, this result is consistent with the argument that stronger governance reduces firm-specific uncertainty and limits the scope for managerial actions that could increase stock return volatility.

The results for IVOL are particularly informative. Since idiosyncratic volatility captures the firm-specific component of stock return risk, the strong negative association between G Score and IVOL suggests that governance plays an important role in reducing uncertainty related to firm-level information, operations, and investor perceptions. The weaker but still negative relation for S Score is also consistent with the idea that stronger stakeholder relations and reputational stability may contribute to lower firm-specific risk. Overall, the evidence in Table 3 suggests that the volatility-reducing effect of ESG is driven primarily by the governance dimension, with some additional support for the social dimension.

4.4. ESG Performance and Future Stock Price Crash Risk

Table 5 presents the regression results on the relation between ESG performance and future stock price crash risk. The coefficient on ESG Score is negative and statistically significant in both the NCSKEW and DUVOL regressions, indicating that firms with stronger ESG performance are less likely to experience future stock price crashes. Specifically, the negative coefficient on NCSKEW suggests that firms with better ESG performance exhibit lower negative skewness in firm-specific weekly returns, while the negative coefficient on DUVOL indicates lower relative downside volatility. Taken together, these results suggest that ESG performance is associated with lower exposure to extreme downside risk in equity markets. The estimated coefficients also indicate economically meaningful effects. Based on the descriptive statistics, the standard deviation of ESG Score is 16.108. In the NCSKEW regression, the coefficient on ESG Score is −0.013, indicating that a one-standard-deviation increase in ESG Score is associated with a decrease of approximately 0.209 in NCSKEW. This corresponds to about 30.7% of the sample standard deviation of NCSKEW. In the DUVOL regression, the coefficient on ESG Score is −0.008, indicating that a one-standard-deviation increase in ESG Score is associated with a decrease of approximately 0.129 in DUVOL, or about 35.3% of the sample standard deviation of DUVOL. These magnitudes suggest that the negative association between ESG performance and future stock price crash risk is economically meaningful as well as statistically significant.

Table 5. ESG Performance and Future Stock Price Crash Risk.

	(1)	(2)
Variables	NCSKEW	DUVOL
ESG Score	−0.013 **	−0.008 *
	(−2.07)	(−1.74)
Size	−0.011 *	−0.008
	(−1.72)	(−1.31)
Leverage	0.024 ***	0.019 **
	(2.89)	(2.25)
ROA	−0.028 *	−0.023
	(−1.75)	(−1.61)
MTB	0.007	0.005
	(1.24)	(1.02)
Cash	−0.009	−0.006
	(−1.12)	(−0.84)
Stock Turnover	0.020 ***	0.017 ***
	(3.01)	(2.71)
Past Return	−0.012	−0.010
	(−1.58)	(−1.31)
Firm FE	Yes	Yes
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	2177	2177
Adj. R2	0.165	0.153
VIF	2.461	2.685

This table reports the regression results for H2, which predicts that ESG performance is negatively associated with future stock price crash risk. The dependent variables are NCSKEW and DUVOL, measured using firm-specific weekly returns over the subsequent fiscal year. ESG Score is the firm-level ESG performance measure. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

These findings are important because crash risk differs from ordinary stock return volatility. Whereas volatility captures the overall dispersion of stock returns, crash risk reflects the possibility that negative firm-specific information accumulates over time and is released suddenly, leading to a sharp downward price adjustment. In this sense, the negative association between ESG Score and both crash risk-measures suggests that ESG performance may help reduce the conditions under which bad-news hoarding becomes more likely. Firms with stronger ESG performance may face greater scrutiny from external stakeholders, provide more transparent and credible disclosures, and adopt governance structures that constrain managerial opportunism. As a result, adverse information may be incorporated into prices more gradually rather than being concealed and released in a concentrated manner.

The results are also consistent with the broader interpretation of ESG as a firm-level resilience mechanism. Firms with better ESG performance may be better positioned to maintain trust with investors, customers, employees, and other stakeholders during periods of uncertainty. This may reduce the likelihood that temporary negative developments escalate into severe market reactions. From this perspective, ESG appears to contribute not only to lower general return volatility but also to lower downside tail risk. Overall, the evidence in Table 4 supports H2 and is consistent with the argument that ESG performance mitigates future stock price crash risk by improving transparency, reducing bad-news hoarding, and promoting more stable long-term firm behavior.

4.5. ESG Component Scores and Future Stock Price Crash Risk

Table 6 reports the regression results using the environmental, social, and governance component scores of ESG performance. Similar to the volatility results, the component-level analysis shows that the negative association between ESG performance and future stock price crash risk is not uniform across the three dimensions of ESG. Rather, the effect is driven primarily by the governance dimension, with weaker evidence for the social dimension and limited evidence for the environmental dimension.

Table 6. ESG Component Scores and Future Stock Price Crash Risk.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
E Score	−0.005	−0.003
	(−1.02)	(−0.79)
S Score			−0.009 *	−0.006
			(−1.70)	(−1.58)
G Score					−0.016 **	−0.010 **
					(−2.48)	(−2.03)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177	2177	2177
Adj. R2	0.162	0.151	0.164	0.152	0.170	0.157
VIF	2.743	3.216	2.481	3.672	2.934	3.108

This table reports regression results for the association between ESG component scores and future stock price crash risk. The dependent variables are NCSKEW and DUVOL, measured using firm-specific weekly returns over the subsequent fiscal year. Environmental Score, Social Score, and Governance Score are the component measures of firm-level ESG performance. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ** and * indicate statistical significance at the 5%, and 10% levels, respectively.

Specifically, the coefficient on E Score is negative in both the NCSKEW and DUVOL regressions, but it is not statistically significant. This suggests that the environmental dimension of ESG is not strongly related to future stock price crash risk in this setting. By contrast, S Score is negatively associated with both NCSKEW and DUVOL and is weakly significant in both specifications. This pattern indicates that the social dimension may contribute to lower crash risk, possibly by supporting stronger stakeholder relationships, reputational stability, and more resilient interactions with external parties.

The strongest results again appear in the governance dimension. G Score is negatively and significantly associated with both NCSKEW and DUVOL, and its coefficients are larger in magnitude than those of the environmental and social dimensions. This result suggests that the overall negative relation between ESG performance and future stock price crash risk documented in Table 4 is driven mainly by governance-related factors. Because stock price crash risk is often linked to managerial bad-news hoarding, limited transparency, and weak monitoring, the strong governance result is consistent with the idea that better governance constrains managerial opportunism and promotes more timely disclosure of adverse information. The component-level results also indicate that the economic magnitude is largest for the governance dimension. Based on the descriptive statistics, the standard deviations of E Score, S Score, and G Score are 17.892, 16.804, and 15.721, respectively. A one-standard-deviation increase in E Score is associated with decreases of approximately 0.089 in NCSKEW and 0.054 in DUVOL, although these coefficients are not statistically significant. A one-standard-deviation increase in S Score is associated with decreases of approximately 0.151 in NCSKEW and 0.101 in DUVOL, providing weaker evidence of economic relevance. The largest magnitudes are observed for G Score: a one-standard-deviation increase in G Score is associated with decreases of approximately 0.252 in NCSKEW and 0.157 in DUVOL. These changes correspond to approximately 36.9% of the sample standard deviation of NCSKEW and 43.1% of the sample standard deviation of DUVOL, respectively. Overall, the component-level crash-risk results reinforce the interpretation that the negative association between ESG performance and future crash risk is driven primarily by the governance dimension, with weaker support for the social dimension and limited evidence for the environmental dimension.

These findings help refine the interpretation of the baseline ESG effect. While the aggregate ESG score is associated with lower future crash risk, the component analysis indicates that this relation is concentrated primarily in the governance dimension and, to a lesser extent, in the social dimension. The evidence therefore suggests that the crash-risk-reducing effect of ESG is most closely tied to mechanisms related to oversight, disclosure credibility, and stakeholder trust rather than to environmental performance alone. Overall, the evidence in Table 5 reinforces the conclusion that ESG performance mitigates future stock price crash risk and shows that this effect is driven mainly by governance, with some additional support for the social dimension.

4.6. Supply Chain Efficiency, ESG Performance, and Corporate Risk

Table 7 reports the cross-sectional results on whether supply chain efficiency moderates the relation between ESG performance and corporate risk. I use four measures that capture different dimensions of supply chain conditions: AR/Sales, AP/COGS, inventory turnover, and inventory volatility. Panels A and B focus on trade-credit-based dependence within supply chain relationships, while Panels C and D focus on operational efficiency and stability. Across the four panels, the interaction terms between ESG Score and the supply chain measures are generally negative, indicating that the risk-mitigating effect of ESG is stronger when firms face greater supply chain dependence or operational instability.

Table 7. ESG Performance, Supply Chain Efficiency, and Corporate Risk.

Panel A. ESG Performance, AR/Sales, and Corporate Risk
	(1)	(2)	(3)	(4)
Variables	VOL	IVOL	NCSKEW	DUVOL
ESG Score × AR/Sales	−0.011 **	−0.010 **	−0.015 **	−0.012 *
	(−2.08)	(−2.01)	(−2.14)	(−1.89)
ESG Score	−0.004	−0.003	−0.007	−0.005
	(−1.18)	(−1.12)	(−1.26)	(−1.21)
AR/Sales	0.014 *	0.013 *	0.019 *	0.015 *
	(1.83)	(1.79)	(1.90)	(1.76)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.235	0.227	0.180	0.168
VIF	2.743	3.216	2.481	3.672
Panel B. ESG Performance, AP/COGS, and Corporate Risk
	(1)	(2)	(3)	(4)
Variables	VOL	IVOL	NCSKEW	DUVOL
ESG Score × AP/COGS	−0.010 *	−0.009 *	−0.014 **	−0.011 *
	(−1.91)	(−1.84)	(−2.03)	(−1.86)
ESG Score	−0.004	−0.003	−0.006	−0.005
	(−1.14)	(−1.09)	(−1.22)	(−1.17)
AP/COGS	0.013 *	0.012 *	0.018 *	0.014 *
	(1.78)	(1.73)	(1.88)	(1.74)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.234	0.226	0.179	0.167
VIF	2.934	3.108	2.267	3.584
Panel C. ESG Performance, Inventory Turnover, and Corporate Risk
	(1)	(2)	(3)	(4)
Variables	VOL	IVOL	NCSKEW	DUVOL
ESG Score × Inventory Turnover	−0.012 **	−0.011 **	−0.016 **	−0.013 **
	(−2.19)	(−2.11)	(−2.22)	(−2.06)
ESG Score	−0.005	−0.004	−0.007	−0.006
	(−1.23)	(−1.18)	(−1.29)	(−1.24)
Inventory Turnover	−0.015 *	−0.013 *	−0.018 *	−0.014 *
	(−1.88)	(−1.80)	(−1.92)	(−1.77)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.238	0.230	0.183	0.170
VIF	2.819	3.431	2.156	3.047
Panel D. ESG Performance, Inventory Volatility, and Corporate Risk
	(1)	(2)	(3)	(4)
Variables	VOL	IVOL	NCSKEW	DUVOL
ESG Score × Inventory Volatility	−0.014 **	−0.013 **	−0.020 ***	−0.015 **
	(−2.31)	(−2.24)	(−2.61)	(−2.28)
ESG Score	−0.005	−0.004	−0.008	−0.006
	(−1.25)	(−1.20)	(−1.34)	(−1.28)
Inventory Volatility	0.018 *	0.016 *	0.024 **	0.017 *
	(1.94)	(1.87)	(2.02)	(1.81)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.239	0.231	0.184	0.171
VIF	2.695	3.763	2.372	3.294

This table reports the moderating effect of supply chain efficiency measures on the association between ESG performance and corporate risk. Panel A uses AR/Sales, defined as accounts receivable scaled by sales. Panel B uses AP/COGS, defined as accounts payable scaled by cost of goods sold. Panel C uses Inventory Turnover, and Panel D uses Inventory Volatility. VOL is measured as the standard deviation of daily stock returns within a fiscal year, IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns, and NCSKEW and DUVOL are measures of stock price crash risk based on firm-specific weekly returns. All regressions include the full set of control variables, industry fixed effects, and year fixed effects. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A presents the results using AR/Sales, which captures customer-side trade credit dependence. The interaction term between ESG Score and AR/Sales is negative and statistically significant across all four risk measures, including VOL, IVOL, NCSKEW, and DUVOL. In addition, the main effect of AR/Sales is positive, suggesting that greater receivables intensity is associated with higher corporate risk. This pattern implies that firms with greater exposure to customer-side working capital risk benefit more from ESG in reducing both stock return volatility and future crash risk.

Panel B reports the results using AP/COGS, which captures supplier-side trade credit dependence. Similar to Panel A, the interaction term between ESG Score and AP/COGS is negative across all specifications and statistically significant in most cases. The standalone coefficient on AP/COGS is positive, indicating that greater reliance on supplier financing is associated with higher risk. Taken together, these results suggest that ESG plays a stronger stabilizing role when firms are more dependent on supplier-side credit relationships and therefore more exposed to supply chain financing frictions.

Panel C turns to inventory turnover as a measure of operational efficiency. The interaction term between ESG Score and inventory turnover is negative and significant across all four specifications. The main effect of inventory turnover is also negative, indicating that firms with more efficient inventory management tend to exhibit lower stock return volatility and lower crash risk. These findings suggest that ESG and operational efficiency are jointly associated with lower corporate risk, and that the risk-reducing effect of ESG is more pronounced when firms exhibit stronger inventory-related operating performance.

Panel D reports the results using inventory volatility, which captures operational instability in the supply chain. The interaction term between ESG Score and inventory volatility is negative and statistically significant across all four risk measures, with the strongest coefficient observed for NCSKEW. In contrast to inventory turnover, the standalone coefficient on inventory volatility is positive, indicating that greater fluctuations in inventory are associated with higher corporate risk. This result is consistent with the view that inventory instability reflects demand uncertainty, planning frictions, or supply chain disruptions. At the same time, the negative interaction term suggests that ESG becomes especially valuable in reducing risk when firms operate in more unstable supply chain environments.

The interaction coefficients also indicate economically meaningful moderating effects. Using the standard deviations reported in Table 1, I calculate the additional effect of a one-standard-deviation increase in ESG Score when each supply chain condition increases by one standard deviation. For AR/Sales, the additional decreases range from 0.017 to 0.025 across the four risk measures. For AP/COGS, the corresponding decreases range from 0.013 to 0.021. The largest interaction magnitudes are observed for Inventory Turnover, where the additional decreases range from 0.451 to 0.657. For Inventory Volatility, the additional decreases range from 0.006 to 0.009. Overall, these magnitudes indicate that the moderating role of supply chain conditions is economically meaningful, particularly for inventory-related operating conditions.

Overall, the evidence in Table 7 indicates that the risk-mitigating effect of ESG is not uniform across firms, but depends on the firm’s financial and operational position within the supply chain. The results in Panels A and B suggest that ESG is particularly valuable when firms face greater trade credit dependence, while the results in Panels C and D show that ESG also interacts importantly with operational efficiency and instability. Across both volatility-based and crash-risk-based measures, the findings consistently indicate that ESG plays a stronger stabilizing role when firms are more exposed to supply chain frictions, dependence, or operational uncertainty. These results support the broader view that the effect of ESG on corporate risk is conditioned by the structure and efficiency of the firm’s supply chain environment.

4.7. Environmental Scores and Supply Chain Efficiency

Table 8 extends the interaction analysis by focusing on the environmental dimension of ESG and examining whether its association with corporate risk varies across different supply chain conditions. The table uses four supply chain measures: AR/Sales, AP/COGS, inventory turnover, and inventory volatility. Overall, the results show that the moderating effects involving the environmental dimension are negative in sign across all specifications, but they are not statistically significant. This pattern suggests that environmental performance does not play a strong role in explaining cross-sectional variation in the risk-mitigating effect of ESG across supply chain settings.

Table 8. Environmental Score and Supply Chain Efficiency.

Panel A. E Score × AR/Sales
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
E Score × AR/Sales	−0.005	−0.004	−0.007	−0.006
	(−1.24)	(−1.17)	(−1.36)	(−1.28)
E Score	−0.002	−0.002	−0.004	−0.003
	(−0.71)	(−0.67)	(−0.79)	(−0.72)
AR/Sales	0.014 *	0.013 *	0.019 *	0.015 *
	(1.82)	(1.78)	(1.89)	(1.75)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.219	0.298	0.211	0.271
VIF	2.928	3.615	2.504	3.139
Panel B. E Score × AP/COGS
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
E Score × AP/COGS	−0.005	−0.004	−0.007	−0.006
	(−1.18)	(−1.13)	(−1.33)	(−1.25)
E Score	−0.002	−0.002	−0.004	−0.003
	(−0.69)	(−0.65)	(−0.77)	(−0.70)
AP/COGS	0.013 *	0.012 *	0.018 *	0.014 *
	(1.77)	(1.72)	(1.87)	(1.73)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.233	0.225	0.178	0.166
VIF	3.271	2.846	3.508	2.219
Panel C. E Score × Inventory Turnover
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
E Score × Inventory Turnover	−0.006	−0.005	−0.008	−0.007
	(−1.29)	(−1.22)	(−1.40)	(−1.31)
E Score	−0.002	−0.002	−0.004	−0.003
	(−0.73)	(−0.68)	(−0.80)	(−0.72)
Inventory Turnover	−0.015 *	−0.013 *	−0.018 *	−0.014 *
	(−1.87)	(−1.79)	(−1.91)	(−1.76)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.236	0.228	0.181	0.168
VIF	2.631	3.742	2.987	3.365
Panel D. E Score × Inventory Volatility
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
E Score × Inventory Volatility	−0.007	−0.006	−0.009	−0.008
	(−1.34)	(−1.27)	(−1.48)	(−1.36)
E Score	−0.002	−0.002	−0.004	−0.003
	(−0.74)	(−0.69)	(−0.81)	(−0.73)
Inventory Volatility	0.018 *	0.016 *	0.024 **	0.017 *
	(1.93)	(1.86)	(2.01)	(1.80)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.237	0.229	0.182	0.169
VIF	3.096	2.458	3.221	2.774

This table reports regression results for the moderating effect of supply chain efficiency measures on the association between environmental performance and corporate risk. Panel A uses AR/Sales, measured as accounts receivable scaled by sales, as a proxy for customer-side trade credit dependence. Panel B uses AP/COGS, measured as accounts payable scaled by cost of goods sold, as a proxy for supplier-side trade credit dependence. Panel C uses Inventory Turnover as a measure of operational efficiency, and Panel D uses Inventory Volatility as a measure of operational instability. VOL is measured as the standard deviation of daily stock returns within a fiscal year, IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns, and NCSKEW and DUVOL are measures of future stock price crash risk based on firm-specific weekly returns over the subsequent fiscal year. Environmental Score is the environmental component measure of firm-level ESG performance. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ** and * indicate statistical significance at the 5%, and 10% levels, respectively.

Panel A reports the results using AR/Sales, which captures customer-side trade credit dependence. Although the interaction term between E Score and AR/Sales is negative for all four risk measures, it is not statistically significant. At the same time, the standalone coefficient on AR/Sales is positive and weakly significant, suggesting that greater receivables intensity is associated with higher corporate risk. These findings imply that while customer-side working capital dependence is related to higher firm risk, the environmental dimension of ESG does not appear to materially strengthen risk reduction in such settings.

Panel B presents the results using AP/COGS, which captures supplier-side trade credit dependence. Similar to Panel A, the interaction term between E Score and AP/COGS is negative but insignificant across all four specifications. The positive coefficient on AP/COGS indicates that greater reliance on supplier financing is associated with higher stock return volatility and crash risk. However, the lack of significance on the interaction term suggests that environmental performance does not meaningfully alter the relation between supplier-side trade credit dependence and corporate risk.

Panel C turns to inventory turnover as a measure of operational efficiency. The interaction term between E Score and inventory turnover remains negative but statistically insignificant across all four specifications. The main effect of inventory turnover is negative and weakly significant, indicating that firms with more efficient inventory management tend to face lower corporate risk. Nevertheless, the results do not provide evidence that the environmental component of ESG is especially effective in strengthening this relation.

Panel D reports the results using inventory volatility, which captures operational instability. The interaction term between E Score and inventory volatility is again negative but insignificant, whereas the main effect of inventory volatility is positive and significant. This pattern suggests that greater inventory instability is associated with higher corporate risk, but the environmental dimension of ESG does not appear to play a particularly important moderating role in such unstable supply chain environments.

The economic magnitudes of the environmental interaction terms are generally modest and should be interpreted cautiously because the coefficients are not statistically significant. Using the standard deviations reported in Table 1, I calculate the additional change in corporate risk associated with a one-standard-deviation increase in E Score when each supply chain condition increases by one standard deviation. For AR/Sales, the additional decreases range from approximately 0.007 in IVOL to 0.013 in NCSKEW. For AP/COGS, the corresponding decreases range from approximately 0.007 in IVOL to 0.011 in NCSKEW. For Inventory Turnover, the implied magnitudes are larger, ranging from approximately 0.228 in IVOL to 0.365 in NCSKEW, reflecting the relatively large standard deviation of Inventory Turnover. For Inventory Volatility, the additional decreases are very small, ranging from approximately 0.003 to 0.005 across the four risk measures.

Overall, although the environmental interaction terms are consistently negative, their lack of statistical significance suggests that environmental performance does not provide strong evidence of an economically reliable moderating role in the supply chain setting examined in this study. This pattern is consistent with the interpretation that the supply chain channel operates less through environmental performance and more through governance and social dimensions.

Overall, the evidence in Table 8 suggests that the environmental dimension of ESG is not a primary driver of the stronger risk-mitigating effect documented in the broader ESG analysis. Although the environmental interaction terms are consistently negative, their lack of statistical significance indicates that environmental performance is less directly related to the financial and operational supply chain conditions captured by these measures. These findings imply that the supply chain channel emphasized in this study operates less through environmental performance and more through other dimensions of ESG.

The weak evidence for the environmental dimension should not be interpreted as evidence against stakeholder theory. Rather, it suggests that different ESG dimensions may operate through different channels depending on the type of stakeholder relationship and risk being examined. The supply chain conditions considered in this study—AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility—primarily capture working-capital dependence and inventory-related operating conditions. These frictions are more directly connected to governance credibility, monitoring, disclosure quality, and stakeholder relationship stability than to environmental performance. Therefore, the limited evidence for the environmental dimension suggests that environmental performance may be less directly related to the specific supply chain-related risk channels examined in this study. This pattern is consistent with the view that the risk-mitigating role of ESG differs across ESG dimensions rather than operating uniformly through all components.

4.8. Social Score and Supply Chain Efficiency

Table 9 examines whether the social dimension of ESG moderates the relation between supply chain efficiency and corporate risk. In contrast to the environmental dimension, the results show that the interaction terms involving S Score are negative across all panels and are statistically significant in most cases. This pattern indicates that the social dimension of ESG plays a meaningful role in reducing corporate risk when firms face greater supply chain dependence or operational instability.

Table 9. Social Score and Supply Chain Efficiency.

Panel A. S Score × AR/Sales
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
S Score × AR/Sales	−0.010 *	−0.009 *	−0.014 **	−0.011 *
	(−1.89)	(−1.82)	(−2.01)	(−1.86)
S Score	−0.004	−0.003	−0.007	−0.005
	(−1.16)	(−1.10)	(−1.28)	(−1.19)
AR/Sales	0.014 *	0.013 *	0.019 *	0.015 *
	(1.83)	(1.79)	(1.90)	(1.76)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.235	0.227	0.180	0.168
VIF	2.389	3.547	2.816	3.028
Panel B. S Score × AP/COGS
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
S Score × AP/COGS	−0.009 *	−0.008 *	−0.013 *	−0.010 *
	(−1.82)	(−1.76)	(−1.94)	(−1.81)
S Score	−0.004	−0.003	−0.006	−0.005
	(−1.13)	(−1.08)	(−1.24)	(−1.16)
AP/COGS	0.013 *	0.012 *	0.018 *	0.014 *
	(1.78)	(1.73)	(1.88)	(1.74)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.212	0.263	0.189	0.171
VIF	3.683	2.241	3.119	2.956
Panel C. S Score × Inventory Turnover
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
S Score × Inventory Turnover	−0.011 *	−0.010 *	−0.015 **	−0.012 *
	(−1.93)	(−1.85)	(−2.05)	(−1.89)
S Score	−0.004	−0.003	−0.007	−0.005
	(−1.17)	(−1.11)	(−1.29)	(−1.20)
Inventory Turnover	−0.015 *	−0.013 *	−0.018 *	−0.014 *
	(−1.88)	(−1.80)	(−1.92)	(−1.77)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.236	0.228	0.181	0.169
VIF	2.705	3.334	2.587	3.791
Panel D. S Score × Inventory Volatility
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
S Score × Inventory Volatility	−0.012 *	−0.011 *	−0.016 **	−0.013 *
	(−1.97)	(−1.89)	(−2.11)	(−1.93)
S Score	−0.004	−0.003	−0.007	−0.005
	(−1.18)	(−1.12)	(−1.30)	(−1.21)
Inventory Volatility	0.018 *	0.016 *	0.024 **	0.017 *
	(1.94)	(1.87)	(2.02)	(1.81)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.237	0.229	0.182	0.169
VIF	3.042	2.668	3.456	2.314

This table reports regression results for the moderating effect of supply chain efficiency measures on the association between social performance and corporate risk. Panel A uses AR/Sales, measured as accounts receivable scaled by sales, as a proxy for customer-side trade credit dependence. Panel B uses AP/COGS, measured as accounts payable scaled by cost of goods sold, as a proxy for supplier-side trade credit dependence. Panel C uses Inventory Turnover as a measure of operational efficiency, and Panel D uses Inventory Volatility as a measure of operational instability. VOL is measured as the standard deviation of daily stock returns within a fiscal year, IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns, and NCSKEW and DUVOL are measures of future stock price crash risk based on firm-specific weekly returns over the subsequent fiscal year. Social Score is the social component measure of firm-level ESG performance. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ** and * indicate statistical significance at the 5%, and 10% levels, respectively.

Panel A reports the results using AR/Sales. The interaction term between S Score and AR/Sales is negative and statistically significant across all four risk measures, with somewhat stronger significance for the crash-risk measures. The main effect of AR/Sales remains positive, suggesting that greater receivables intensity is associated with higher corporate risk. Taken together, these results imply that social performance becomes more valuable when firms are more exposed to customer-side trade credit dependence. One interpretation is that stronger social performance helps firms maintain trust, reputational stability, and more resilient external relationships when customer-related working capital exposure is high.

Panel B presents the results using AP/COGS. The interaction term between S Score and AP/COGS is again negative across all four specifications and statistically significant, although somewhat weaker than in Panel A. The main effect of AP/COGS is positive, indicating that greater supplier-side trade credit dependence is associated with higher risk. These findings suggest that social performance also plays a stabilizing role when firms rely more heavily on supplier financing relationships, possibly because stronger stakeholder-oriented practices improve relational stability and reduce uncertainty in supplier interactions.

Panel C reports the results using inventory turnover. The interaction term between S Score and inventory turnover is negative and significant across all four specifications, while the standalone coefficient on inventory turnover is negative. This pattern suggests that firms with more efficient inventory management exhibit lower corporate risk and that social performance is associated with additional risk reduction in these operating environments. In this sense, the social dimension appears to complement operational efficiency in lowering both return volatility and crash risk.

Panel D uses inventory volatility as a measure of operational instability. The interaction term between S Score and inventory volatility is negative and statistically significant across all four risk measures. At the same time, inventory volatility itself is positively associated with corporate risk. These results indicate that the social dimension of ESG becomes especially valuable when firms operate in more unstable supply chain environments. Stronger social performance may help firms absorb disruptions more effectively by supporting stakeholder trust, reputational capital, and the stability of trading relationships.

The interaction results for the social dimension also indicate economically meaningful, although generally weaker, moderating effects. Using the standard deviations reported in Table 1, I calculate the additional change in corporate risk associated with a one-standard-deviation increase in S Score when each supply chain condition increases by one standard deviation. For AR/Sales, the additional decreases range from approximately 0.016 in IVOL to 0.024 in NCSKEW. For AP/COGS, the corresponding decreases range from approximately 0.012 in IVOL to 0.020 in NCSKEW. For Inventory Turnover, the implied additional decreases range from approximately 0.428 in IVOL to 0.642 in NCSKEW. For Inventory Volatility, the additional decreases range from approximately 0.005 in IVOL to 0.008 in NCSKEW.

4.9. Governance Score and Supply Chain Efficiency

Table 10 reports the results for the governance dimension of ESG and shows the strongest and most consistent interaction effects among the three component measures. Across all four panels, the interaction terms between G Score and the supply chain measures are negative and statistically significant, with the largest magnitudes generally appearing in the crash-risk specifications. These findings suggest that governance is the primary dimension through which ESG reduces corporate risk in firms facing greater supply chain dependence or operational instability.

Table 10. Governance Score and Supply Chain Efficiency.

Panel A. G Score × AR/Sales
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
G Score × AR/Sales	−0.014 **	−0.013 **	−0.019 ***	−0.015 **
	(−2.27)	(−2.19)	(−2.52)	(−2.24)
G Score	−0.006	−0.005	−0.009	−0.007
	(−1.39)	(−1.33)	(−1.46)	(−1.35)
AR/Sales	0.014 *	0.013 *	0.019 *	0.015 *
	(1.83)	(1.79)	(1.90)	(1.76)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.238	0.230	0.183	0.170
VIF	2.493	3.176	2.921	3.602
Panel B. G Score × AP/COGS
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
G Score × AP/COGS	−0.013 **	−0.012 **	−0.018 **	−0.014 **
	(−2.21)	(−2.13)	(−2.38)	(−2.16)
G Score	−0.006	−0.005	−0.009	−0.007
	(−1.36)	(−1.30)	(−1.43)	(−1.32)
AP/COGS	0.013 *	0.012 *	0.018 *	0.014 *
	(1.78)	(1.73)	(1.88)	(1.74)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.237	0.229	0.182	0.169
VIF	3.287	2.735	3.049	2.563
Panel C. G Score × Inventory Turnover
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
G Score × Inventory Turnover	−0.015 **	−0.014 **	−0.020 ***	−0.016 **
	(−2.31)	(−2.23)	(−2.59)	(−2.27)
G Score	−0.006	−0.005	−0.009	−0.007
	(−1.41)	(−1.35)	(−1.47)	(−1.36)
Inventory Turnover	−0.015 *	−0.013 *	−0.018 *	−0.014 *
	(−1.88)	(−1.80)	(−1.92)	(−1.77)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.239	0.231	0.184	0.171
VIF	2.842	3.519	2.204	3.151
Panel D. G Score × Inventory Volatility
Variables	(1) VOL	(2) IVOL	(3) NCSKEW	(4) DUVOL
G Score × Inventory Volatility	−0.016 **	−0.015 **	−0.022 ***	−0.017 **
	(−2.38)	(−2.29)	(−2.71)	(−2.34)
G Score	−0.006	−0.005	−0.009	−0.007
	(−1.43)	(−1.37)	(−1.49)	(−1.38)
Inventory Volatility	0.018 *	0.016 *	0.024 **	0.017 *
	(1.94)	(1.87)	(2.02)	(1.81)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177
Adj. R2	0.240	0.232	0.185	0.172
VIF	3.624	2.418	3.337	2.981

This table reports regression results for the moderating effect of supply chain efficiency measures on the association between governance performance and corporate risk. Panel A uses AR/Sales, measured as accounts receivable scaled by sales, as a proxy for customer-side trade credit dependence. Panel B uses AP/COGS, measured as accounts payable scaled by cost of goods sold, as a proxy for supplier-side trade credit dependence. Panel C uses Inventory Turnover as a measure of operational efficiency, and Panel D uses Inventory Volatility as a measure of operational instability. VOL is measured as the standard deviation of daily stock returns within a fiscal year, IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns, and NCSKEW and DUVOL are measures of future stock price crash risk based on firm-specific weekly returns over the subsequent fiscal year. Governance Score is the governance component measure of firm-level ESG performance. All independent variables are lagged by one year. t-statistics are reported in parentheses. Standard errors are clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A presents the results using AR/Sales. The interaction term between G Score and AR/Sales is negative and statistically significant across all four risk measures, while the main effect of AR/Sales is positive. This pattern indicates that greater customer-side trade credit dependence is associated with higher corporate risk, but that governance quality becomes more valuable in reducing this risk when receivables intensity is high. Stronger governance may improve monitoring, disclosure credibility, and managerial discipline in settings where firms are more exposed to customer-related working capital risk.

Panel B reports the results using AP/COGS. Similar to Panel A, the interaction term between G Score and AP/COGS is negative and statistically significant across all specifications. The standalone coefficient on AP/COGS is positive, indicating that greater reliance on supplier financing is associated with higher corporate risk. The negative interaction term suggests that governance plays a stronger stabilizing role when firms are more dependent on supplier-side credit relationships and therefore more exposed to financing frictions within the supply chain.

Panel C turns to inventory turnover as a measure of operational efficiency. The interaction term between G Score and inventory turnover is negative and significant across all four specifications, and the main effect of inventory turnover is also negative. These findings indicate that operational efficiency is associated with lower corporate risk and that governance further strengthens this risk-reducing effect. One interpretation is that strong governance and efficient operations are complementary mechanisms through which firms reduce both general return volatility and extreme downside risk.

Panel D shows the results using inventory volatility, which captures operational instability. The interaction term between G Score and inventory volatility is negative and statistically significant across all four risk measures, with the strongest coefficient observed for NCSKEW. At the same time, the standalone coefficient on inventory volatility is positive and significant, indicating that greater fluctuations in inventory are associated with higher corporate risk. These results suggest that governance becomes especially important in unstable supply chain environments, where stronger oversight, better internal controls, and more credible disclosure may help mitigate uncertainty and reduce the likelihood of severe downside outcomes.

The governance interaction effects are also economically meaningful. Using the standard deviations reported in Table 1, I calculate the additional change in corporate risk associated with a one-standard-deviation increase in G Score when the relevant supply chain condition increases by one standard deviation. For AR/Sales, the additional decreases range from approximately 0.021 in IVOL to 0.031 in NCSKEW. For AP/COGS, the corresponding additional decreases range from approximately 0.017 in IVOL to 0.026 in NCSKEW. The largest implied magnitudes appear for Inventory Turnover, where the additional decreases range from approximately 0.561 in IVOL to 0.801 in NCSKEW. For Inventory Volatility, the implied additional decreases are smaller in absolute terms, ranging from approximately 0.007 to 0.010 across the four risk measures.

Overall, the evidence in Table 10 indicates that the stronger risk-mitigating effect of ESG across supply chain settings is driven primarily by the governance dimension. Compared with the environmental and social dimensions, governance shows the most consistent and economically meaningful interaction effects across both trade-credit-based measures and inventory-based measures. These findings suggest that the supply chain channel emphasized in this study operates mainly through governance-related mechanisms, including monitoring, disclosure credibility, and managerial discipline, with some additional support from the social dimension but little evidence for the environmental dimension.

Across the full set of regression analyses, the reported VIF values also suggest that multicollinearity is unlikely to be a serious concern. The VIF values range from approximately 2.1 to 3.7 across the main, component-level, interaction, and robustness specifications. Although these values vary depending on the ESG measure, supply chain variable, interaction term, and dependent variable included in each model, all values remain below commonly used thresholds of 5 or 10. Therefore, the variation in VIF values across specifications does not appear to materially affect the interpretation of the empirical results.

5. Robustness Tests

To assess the robustness of the main findings, I conduct three additional sets of analyses. First, I use an alternative crash-risk measure based on an event-based CRASH indicator. CRASH is an indicator variable equal to one if firm (i) experiences at least one firm-specific weekly return that is 3.09 standard deviations below the firm’s mean firm-specific weekly return during fiscal year (t), and zero otherwise. The 3.09 threshold corresponds approximately to the 0.1% lower tail of the normal distribution. Higher values of CRASH indicate that the firm experienced an extreme negative firm-specific return event during the year. Second, I use MSCI ESG ratings as an alternative ESG measure to examine whether the results are specific to Refinitiv ESG Scores. Third, I conduct subsample analyses by separately estimating the regressions for S&P 100 firms and the remaining S&P 500 firms. To conserve space, Table 11 reports only the coefficients of interest. Each coefficient is estimated in a separate regression with the full set of control variables, firm fixed effects, industry fixed effects, and year fixed effects.

Table 11. Summary of Robustness Tests.

Panel A. Alternative Crash-Risk Measure: CRASH Indicator
Variables		CRASH
ESG Score		−0.006 **
		(−2.05)
ESG × AR/Sales		−0.009 **
		(−2.03)
ESG × AP/COGS		−0.008 *
		(−1.84)
ESG × Inventory Turnover		−0.010 **
		(−2.11)
ESG × Inventory Volatility		−0.012 **
		(−2.26)
Panel B. Alternative ESG Measure: MSCI ESG Ratings
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
MSCI ESG Score	−0.008 **	−0.006 **	−0.013 **	−0.008 *	−0.005 *
	(−2.14)	(−2.03)	(−2.08)	(−1.79)	(−1.82)
MSCI ESG × AR/Sales	−0.010 **	−0.009 **	−0.014 **	−0.011 *	−0.008 *
	(−2.05)	(−1.98)	(−2.10)	(−1.86)	(−1.83)
MSCI ESG × AP/COGS	−0.009 *	−0.008 *	−0.013 **	−0.010 *	−0.007 *
	(−1.88)	(−1.80)	(−2.01)	(−1.78)	(−1.75)
MSCI ESG × Inventory Turnover	−0.011 **	−0.010 **	−0.015 **	−0.012 **	−0.009 **
	(−2.16)	(−2.07)	(−2.18)	(−2.02)	(−1.97)
MSCI ESG × Inventory Volatility	−0.013 **	−0.012 **	−0.018 **	−0.014 **	−0.011 **
	(−2.28)	(−2.19)	(−2.45)	(−2.17)	(−2.06)
N	1772	1772	1772	1772	1772
Panel C. Subsample Analysis: S&P 100 Firms vs. Remaining S&P 500 Firms
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
S&P 100 Firms
ESG Score	−0.007 *	−0.006 *	−0.012 *	−0.007 *	−0.004 *
	(−1.81)	(−1.75)	(−1.79)	(−1.70)	(−1.69)
ESG × AR/Sales	−0.009 *	−0.008 *	−0.013 *	−0.010 *	−0.007 *
	(−1.82)	(−1.76)	(−1.84)	(−1.73)	(−1.70)
ESG × AP/COGS	−0.008 *	−0.007	−0.012 *	−0.009 *	−0.006
	(−1.70)	(−1.61)	(−1.76)	(−1.68)	(−1.58)
ESG × Inventory Turnover	−0.010 *	−0.009 *	−0.014 *	−0.011 *	−0.008 *
	(−1.91)	(−1.84)	(−1.89)	(−1.80)	(−1.74)
ESG × Inventory Volatility	−0.012 **	−0.011 *	−0.016 **	−0.013 *	−0.010 *
	(−2.03)	(−1.96)	(−2.11)	(−1.91)	(−1.86)
N	436	436	436	436	436
Remaining S&P 500 Firms
ESG Score	−0.009 **	−0.007 **	−0.014 **	−0.009 *	−0.006 **
	(−2.24)	(−2.16)	(−2.19)	(−1.87)	(−1.99)
ESG × AR/Sales	−0.011 **	−0.010 **	−0.015 **	−0.012 *	−0.009 **
	(−2.12)	(−2.04)	(−2.18)	(−1.91)	(−2.01)
ESG × AP/COGS	−0.010 *	−0.009 *	−0.014 **	−0.011 *	−0.008 *
	(−1.93)	(−1.86)	(−2.06)	(−1.88)	(−1.84)
ESG × Inventory Turnover	−0.012 **	−0.011 **	−0.016 **	−0.013 **	−0.010 **
	(−2.20)	(−2.13)	(−2.23)	(−2.07)	(−2.10)
ESG × Inventory Volatility	−0.014 **	−0.013 **	−0.020 ***	−0.015 **	−0.012 **
	(−2.34)	(−2.26)	(−2.61)	(−2.29)	(−2.22)
N	1741	1741	1741	1741	1741

This table summarizes the robustness tests. Panel A reports results using CRASH as an alternative crash-risk measure. CRASH is an indicator variable equal to one if a firm experiences at least one firm-specific weekly return that is 3.09 standard deviations below its annual mean firm-specific weekly return, and zero otherwise. Panel B reports results using MSCI ESG ratings as an alternative ESG measure. Panel C reports subsample analyses for S&P 100 firms and the remaining S&P 500 firms. To conserve space, the table reports only the coefficients of interest. Each coefficient is estimated in a separate regression. All regressions include the full set of control variables, firm fixed effects, industry fixed effects, and year fixed effects. t-statistics are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A reports the results using the CRASH indicator as an alternative measure of future stock price crash risk. CRASH equals one if a firm experiences at least one firm-specific weekly return that is 3.09 standard deviations below its annual mean firm-specific weekly return, and zero otherwise. The coefficient on ESG Score is negative and statistically significant, indicating that firms with stronger ESG performance are less likely to experience extreme negative firm-specific return events. The interaction terms between ESG Score and the four supply-chain-condition variables are also negative, suggesting that the risk-mitigating role of ESG is more pronounced when firms face greater trade credit dependence, inventory-related operating conditions, or operational instability. These findings are consistent with the main results based on NCSKEW and DUVOL and indicate that the crash-risk results are not driven solely by the use of continuous crash-risk measures.

Panel B presents the results using MSCI ESG ratings as an alternative ESG measure. The coefficients on MSCI ESG Score are negative across VOL, IVOL, NCSKEW, DUVOL, and CRASH. In addition, the interaction terms between MSCI ESG and the supply-chain-condition variables are generally negative and statistically significant. These results suggest that the main findings are not specific to Refinitiv ESG Scores. Rather, the evidence remains qualitatively similar when ESG performance is measured using an alternative ESG rating provider.

Panel C reports the subsample results for S&P 100 firms and the remaining S&P 500 firms. This analysis examines whether the findings are driven only by the largest and most visible firms within the S&P 500. The results are generally consistent across both subsamples. The coefficients on ESG Score are negative in both the S&P 100 and remaining S&P 500 subsamples, and the interaction terms between ESG Score and the supply-chain-condition variables are also generally negative. Although the statistical significance is somewhat weaker in the S&P 100 subsample, likely because of the smaller sample size, the overall pattern remains consistent with the main findings.

Overall, the robustness tests provide additional support for the main conclusions. The results are robust to using an event-based crash-risk measure, an alternative ESG rating provider, and alternative subsamples within the S&P 500. These findings strengthen the interpretation that ESG performance is associated with lower corporate risk and that this association is more pronounced under certain supply chain conditions.

To further address potential selection concerns, I conduct an entropy balancing analysis. Specifically, I classify firms into high-ESG and low-ESG groups based on the industry-year median ESG Score and use entropy balancing to make the two groups comparable in terms of observable firm characteristics, including firm size, leverage, profitability, market-to-book ratio, cash holdings, stock turnover, past return, firm age, and industry and year characteristics. I then re-estimate the baseline and interaction regressions using entropy-balanced weights.

Table 12 reports the results. Panel A shows that ESG Score remains negatively associated with all five corporate risk measures after balancing observable firm characteristics. The coefficient on ESG Score is negative and statistically significant for VOL, IVOL, NCSKEW, DUVOL, and CRASH. These findings indicate that the baseline association between ESG performance and lower corporate risk is not driven solely by observable differences between high- and low-ESG firms.

Table 12. Entropy Balancing Robustness Tests.

Panel A. Baseline ESG-Risk Relation after Entropy Balancing
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
ESG Score	−0.007 **	−0.005 **	−0.012 **	−0.007 *	−0.005 *
	(−2.08)	(−2.01)	(−2.04)	(−1.78)	(−1.81)
Controls	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Entropy-balanced weights	Yes	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177	2177
Adj. R2	0.247	0.235	0.161	0.149	0.142
Panel B. ESG × Supply Chain Conditions after Entropy Balancing
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
ESG × AR/Sales	−0.010 **	−0.009 *	−0.014 **	−0.011 *	−0.008 *
	(−2.01)	(−1.92)	(−2.06)	(−1.83)	(−1.79)
ESG × AP/COGS	−0.009 *	−0.008 *	−0.013 *	−0.010 *	−0.007 *
	(−1.84)	(−1.77)	(−1.94)	(−1.76)	(−1.70)
ESG × Inventory Turnover	−0.011 **	−0.010 **	−0.015 **	−0.012 **	−0.009 *
	(−2.12)	(−2.04)	(−2.15)	(−1.99)	(−1.91)
ESG × Inventory Volatility	−0.013 **	−0.012 **	−0.018 **	−0.014 **	−0.010 **
	(−2.24)	(−2.16)	(−2.42)	(−2.11)	(−2.02)
Controls	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Entropy-balanced weights	Yes	Yes	Yes	Yes	Yes
Observations	2177	2177	2177	2177	2177
Adj. R2	0.241	0.232	0.178	0.166	0.151

This table reports the results of the entropy balancing robustness tests. Firms are classified into high-ESG and low-ESG groups based on the industry-year median ESG Score. Entropy balancing is used to balance observable firm characteristics between the two groups, including Size, Leverage, ROA, MTB, Cash, Stock Turnover, Past Return, Firm Age, and industry and year characteristics. The regressions are then re-estimated using entropy-balanced weights. Panel A reports the baseline relation between ESG performance and corporate risk after entropy balancing. Panel B reports the interaction between ESG performance and supply chain conditions after entropy balancing. VOL is measured as the standard deviation of daily stock returns within a fiscal year, and IVOL is measured as the standard deviation of residuals from a market model regression of daily stock returns. NCSKEW and DUVOL are stock price crash-risk measures based on firm-specific weekly returns. CRASH is an indicator variable equal to one when the firm experiences an extreme negative firm-specific weekly return, and zero otherwise. All regressions include the full set of control variables, firm fixed effects, industry fixed effects, and year fixed effects. Standard errors are clustered at the firm level. t-statistics are reported in parentheses. ** and * indicate statistical significance at the 5%, and 10% levels, respectively.

Panel B reports the entropy-balanced results for the interaction between ESG performance and supply chain conditions. The interaction terms between ESG Score and AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility are generally negative and statistically significant across the risk measures. These results suggest that the risk-reducing association of ESG remains stronger when firms face greater supply chain dependence or operational instability, even after balancing observable firm characteristics. The strongest interaction results are observed for Inventory Volatility and Inventory Turnover, consistent with the view that ESG is particularly relevant when firms operate under inventory-related instability or when operational efficiency is closely tied to risk outcomes.

Overall, the entropy balancing results provide additional support for the main findings. ESG performance remains associated with lower stock return volatility and lower future crash risk, and the moderating role of supply chain conditions remains broadly consistent with the main analysis. However, entropy balancing addresses only selection based on observable characteristics. Therefore, the results should still be interpreted as conditional associations rather than definitive causal evidence.

To further strengthen the interpretation of the governance channel, I conduct an additional subsample analysis based on analyst coverage. Analyst coverage captures the strength of the external information environment and external monitoring. If the governance dimension of ESG reduces corporate risk by improving monitoring, disclosure credibility, and managerial discipline, the governance effect should be particularly relevant when external monitoring is weaker. I therefore split the sample into low- and high-analyst-coverage firms based on the industry-year median value of analyst coverage and re-estimate the governance specifications.

Table 13 reports the results. Panel A shows that the negative association between Governance Score and corporate risk is stronger among firms with low analyst coverage. In this subsample, Governance Score is negatively and significantly associated with VOL, IVOL, NCSKEW, DUVOL, and CRASH. The interaction terms between Governance Score and the supply chain-condition variables are also consistently negative and statistically significant. These results suggest that governance is especially important in reducing corporate risk when firms operate in weaker external information environments.

Table 13. Supplementary Channel Analysis by Analyst Coverage.

Panel A. Low Analyst Coverage Firms
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
G Score	−0.012 **	−0.010 **	−0.018 **	−0.011 **	−0.008 **
	(−2.42)	(−2.31)	(−2.39)	(−2.08)	(−2.02)
G Score × AR/Sales	−0.014 **	−0.013 **	−0.020 **	−0.015 **	−0.011 **
	(−2.34)	(−2.21)	(−2.46)	(−2.18)	(−2.05)
G Score × AP/COGS	−0.013 **	−0.011 **	−0.018 **	−0.014 **	−0.010 *
	(−2.21)	(−2.04)	(−2.27)	(−2.03)	(−1.93)
G Score × Inventory Turnover	−0.015 **	−0.014 **	−0.022 ***	−0.017 **	−0.012 **
	(−2.48)	(−2.33)	(−2.72)	(−2.31)	(−2.13)
G Score × Inventory Volatility	−0.017 ***	−0.016 **	−0.025 ***	−0.019 **	−0.014 **
	(−2.66)	(−2.51)	(−2.93)	(−2.47)	(−2.24)
Controls	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Observations	1084	1084	1084	1084	1084
Adj. R2	0.263	0.251	0.184	0.171	0.158
Panel B. High Analyst Coverage Firms
Variables	VOL	IVOL	NCSKEW	DUVOL	CRASH
G Score	−0.006 *	−0.005	−0.010 *	−0.006	−0.004
	(−1.72)	(−1.61)	(−1.74)	(−1.48)	(−1.39)
G Score × AR/Sales	−0.008 *	−0.007	−0.011 *	−0.008	−0.006
	(−1.70)	(−1.57)	(−1.73)	(−1.55)	(−1.46)
G Score × AP/COGS	−0.007	−0.006	−0.010	−0.007	−0.005
	(−1.55)	(−1.43)	(−1.61)	(−1.44)	(−1.32)
G Score × Inventory Turnover	−0.009 *	−0.008 *	−0.013 *	−0.010 *	−0.007
	(−1.82)	(−1.70)	(−1.86)	(−1.71)	(−1.59)
G Score × Inventory Volatility	−0.011 *	−0.010 *	−0.016 **	−0.012 *	−0.009 *
	(−1.94)	(−1.81)	(−2.08)	(−1.87)	(−1.73)
Controls	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Observations	1093	1093	1093	1093	1093
Adj. R2	0.238	0.226	0.159	0.148	0.137

This table reports supplementary subsample analyses based on analyst coverage. Firms are classified into low- and high-analyst-coverage groups based on the industry-year median value of analyst coverage. Analyst coverage is used as a proxy for the strength of the external information environment and external monitoring. Panel A reports the results for firms with low analyst coverage, and Panel B reports the results for firms with high analyst coverage. G Score denotes the governance component of ESG performance. AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility capture different dimensions of supply chain conditions. VOL and IVOL are stock return volatility measures, while NCSKEW, DUVOL, and CRASH are stock price crash-risk measures. All regressions include the full set of control variables, firm fixed effects, industry fixed effects, and year fixed effects. Standard errors are clustered at the firm level. t-statistics are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel B reports the results for firms with high analyst coverage. The coefficients remain generally negative, but they are smaller in magnitude and less statistically significant than those in the low-analyst-coverage subsample. This pattern suggests that the risk-reducing role of governance is less pronounced when firms are already subject to stronger external monitoring and information production by analysts.

Overall, the subsample results provide supplementary evidence consistent with the proposed governance channel. The findings suggest that governance-related ESG performance is particularly valuable when external monitoring is weaker, supporting the interpretation that the governance dimension operates through monitoring, disclosure credibility, and managerial discipline. However, because this analysis is not a formal mediation test, the results should be interpreted as suggestive channel evidence rather than definitive causal evidence.

6. Limitation

First, although this study provides evidence that the association between ESG performance and corporate risk varies across supply chain conditions, it does not directly test the specific mediation mechanisms through which ESG may operate. For example, ESG may be associated with lower risk by improving customer retention, extending supplier-customer relationship duration, strengthening contract stability, or improving trade credit terms. However, detailed data on customer retention, contract duration, supplier-customer relationship length, and contract-level terms are not consistently available for the full sample. Accordingly, the component-level analyses should be interpreted as suggestive evidence on plausible channels rather than as direct mediation tests. Future research could use transaction-level, contract-level, or supply chain network data to examine these mechanisms more directly.

Second, although the empirical design includes lagged explanatory variables, firm fixed effects, and additional robustness analyses, endogeneity concerns cannot be fully eliminated. ESG performance may be correlated with time-varying unobserved firm characteristics, and reverse causality remains possible. Accordingly, the findings should be interpreted as robust conditional associations rather than definitive causal estimates. Future research could use quasi-natural experiments, regulatory shocks, mandatory ESG disclosure changes, or ESG rating methodology revisions to establish stronger causal evidence.

7. Discussion and Conclusions

This study examines whether ESG performance helps mitigate corporate risk under different supply chain conditions. Using stock return volatility and future stock price crash risk as the main measures of corporate risk, I investigate whether firms with stronger ESG performance exhibit lower overall volatility, lower firm-specific volatility, and lower downside tail risk, and whether these effects vary with firms’ supply chain dependence and operational efficiency. To capture these supply chain conditions, I use four measures: AR/Sales, AP/COGS, Inventory Turnover, and Inventory Volatility.

The empirical results provide three main findings. First, ESG performance is negatively associated with both total stock return volatility and idiosyncratic volatility. This finding suggests that firms with stronger ESG performance experience lower overall stock return variation as well as lower firm-specific risk. Second, ESG performance is also negatively associated with future stock price crash risk, as measured by NCSKEW and DUVOL. This result indicates that ESG is linked not only to lower general uncertainty but also to lower exposure to extreme downside events. Third, the risk-mitigating effect of ESG varies systematically with firms’ supply chain conditions. The interaction results suggest that ESG plays a stronger stabilizing role when firms exhibit greater trade credit dependence and higher operational instability, as reflected in AR/Sales, AP/COGS, and Inventory Volatility. Additional evidence also indicates that ESG interacts meaningfully with Inventory Turnover, suggesting that inventory-related operating conditions are an important channel through which ESG affects corporate risk.

Additional analyses further show that these effects are not uniform across the environmental, social, and governance dimensions of ESG. The component-level results indicate that the stronger risk-mitigating effect of ESG across supply chain settings is driven primarily by the governance dimension, with weaker but still meaningful evidence for the social dimension, while the environmental dimension appears less strongly related to corporate risk in this setting. These findings suggest that the role of ESG in reducing risk under different supply chain conditions operates mainly through governance-related credibility, monitoring, and disclosure quality, together with some contribution from stakeholder-oriented social performance.

Taken together, these findings suggest that ESG performance serves as an important firm-level risk management mechanism. One plausible interpretation is that stronger ESG performance improves transparency, strengthens stakeholder relationships, and constrains managerial behavior in ways that reduce firm-specific uncertainty and the likelihood of bad-news hoarding. The results further imply that the benefits of ESG are not uniform across firms or across ESG dimensions. Instead, they depend in part on the firm’s supply chain environment. In particular, ESG appears to play a more important role in mitigating risk when firms are more exposed to trade credit dependence or operational instability, and this stronger effect is concentrated mainly in the governance and social dimensions.

This study contributes to the literature in several ways. First, it extends the ESG and corporate risk literature by jointly examining stock return volatility and future stock price crash risk within a unified framework. Second, it contributes to the disclosure and information environment literature by showing that ESG is associated with lower firm-specific risk and lower downside tail risk, consistent with the idea that ESG improves the firm’s information environment. Third, it contributes to the stakeholder and supply chain literature by showing that supply chain conditions influence the effectiveness of ESG as a risk-reducing mechanism, and that this conditional effect is driven primarily by governance and, to a lesser extent, by social performance.

These findings also have practical implications. For managers, the results suggest that ESG should be viewed not only as a reputational or compliance-related activity but also as a tool for risk management, particularly in firms facing greater working capital dependence or operational instability within the supply chain. In such environments, credible governance and stable stakeholder relations appear to be especially important in reducing uncertainty and improving resilience. For investors, the evidence indicates that ESG performance may contain useful information about both general firm-level uncertainty and exposure to future crash risk, especially when evaluated together with the firm’s supply chain conditions. More broadly, the results suggest that supply chain dependence, operating efficiency, and inventory stability should be taken into account when evaluating the economic consequences of ESG performance.

Despite these contributions, this study is subject to several limitations. First, although the empirical design uses lagged explanatory variables and a broad set of controls, endogeneity concerns cannot be fully eliminated. Second, ESG measures may not perfectly capture the quality or substance of firms’ underlying ESG practices. Third, the supply chain measures used in this study capture only selected aspects of firms’ financial and operational exposure within supply chain relationships. Future research may examine whether other forms of supply chain exposure, such as customer network centrality, logistics disruptions, or institutional monitoring, similarly shape the effect of ESG on corporate risk. Future research may also further investigate whether different dimensions of ESG matter in distinct ways across alternative supply chain settings or industry environments.

Overall, this study provides evidence that ESG performance is associated with lower stock return volatility and lower future stock price crash risk, and that these effects vary systematically with firms’ supply chain conditions. The findings further suggest that the stronger risk-mitigating role of ESG is driven mainly by the governance dimension and, to a lesser extent, by the social dimension of ESG. Taken together, the results support the view that ESG performance enhances corporate resilience and reduces risk, particularly in firms facing greater supply chain dependence or operational instability.