ESG Performance and Systemic Risk Nexus: Role of Firm-Specific Factors in Indian Companies

Abstract

This study investigates the ESG performance–systemic risk (SR) nexus among Indian companies. Using the beta coefficient from the Capital Asset Pricing Model (CAPM) and statistical analysis, it explores how ESG performance affects SR. The findings reveal that firms with higher ESG scores have lower SR sensitivity. Notably, there is a significant difference in risk sensitivity between high- and low-ESG-rated companies, with ESG effects being less pronounced in high-cap firms compared to low-cap firms. Conversely, large firms, older firms, and those with lower borrowing costs show a diminished effect of ESG ratings on their SR sensitivity. These results underscore the importance of firm-specific characteristics in determining the efficacy of ESG strategies in risk mitigation. This study reveals that ESG performance reduces SR, with market valuation affecting this relationship.

1. Introduction

Over the past few years, the ESG framework has emerged as a focal point of global discourse, reflecting an era where the corporate impact on society undergoes rigorous scrutiny (Gramlich and Finster 2013; Gupta and Chaudhary 2023). Stakeholders exert relentless pressure on enterprises to adopt and operationalize ESG practices, compelling them to navigate responsibly amidst societal expectations and environmental imperatives (Jain and Tripathi 2023). Consequently, firms are increasingly urged to embrace sustainable operations that integrate societal rights and ecological stewardship (Principles for Responsible Investment 2015). The tripartite pillars of ESG—environmental, social, and governance—organize sustainability efforts within corporations. The environmental facet addresses the enterprise’s ecological footprint and commitment to environmental stewardship. Social dimensions encompass stakeholder relations and value creation, while governance spans the policies, practices, and managerial ethos within organizations (Duque-Grisales and Aguilera-Caracuel 2021).

Examination of how ESG factors affect firm performance has been extensively explored in business and economic research (Wu et al. 2022). This is because it aids institutional investors in identifying, evaluating, and managing the investment risks and opportunities linked to significant ESG issues (Global Sustainable Investment Alliance 2020). Over the past few years, there has been a substantial rise in the number of investors and policymakers engaged in socially responsible investment (Giese et al. 2019), thereby increasing the focus on ESG considerations and their impact on company profitability (Aydoğmuş et al. 2022) and financial stability (Porta 2024). Traditionally, corporate managers and policymakers have depended on fundamental corporate data and market technical information. ESG data now provides an additional layer of insight into future performance, helping investors make informed decisions (Global Sustainable Investment Alliance 2020). An increasing number of studies have delved into the methods for measuring ESG and ranking companies accordingly (Billio et al. 2021), as well as examining how ESG factors influence financial performance (FP) and risk management (Verheyden et al. 2016; Torricelli and Bertelli 2022; Yadav and Saini 2023). Numerous studies have sought to summarize these diverse findings (Fulton et al. 2012). Scientific inquiry into integrating ESG metrics into portfolio management aims to optimize financial outcomes. Extensive empirical analysis across a comprehensive corpus of studies, encompassing over a thousand scholarly investigations, has unveiled a nuanced tapestry of correlations between ESG attributes and financial performance (FP). These findings underscore a spectrum of outcomes: positive, negative, and statistically insignificant relationships. Nevertheless, a prevailing consensus emerges from the literature, revealing a predominance of studies asserting a robustly positive association (Global Sustainable Investment Alliance 2020).

Taking risks at the individual company level can significantly propel the growth of corporate assets, thereby fortifying a firm’s key competitive strengths (John et al. 2008). On a broader perspective, increased risk-taking within the economy and society reflects a larger investment of capital into innovation (Low 2009), which fuels sustained economic growth over time (Baumol and Strom 2007). Numerous studies have previously delved into the determinants of corporate risk-taking by examining both the internal and external characteristics of firms. These studies have analyzed risk-taking behavior by considering various factors such as the ownership structure (Faccio et al. 2011; Kamaruzaman et al. 2019), CEO traits (Faccio et al. 2016), internal governance mechanisms (Anantharaman and Lee 2014), legal frameworks (Acharya et al. 2011), and cultural variances (Li et al. 2013). As these attributes evolve, companies’ risk-taking behaviors continuously adjust in response to changes in their internal and external environments (Gopalan et al. 2021).

Given the varied findings on how ESG factors impact FP, this study advances a novel conceptual framework elucidating the influence of ESG performance on a company’s susceptibility to SR, focusing on companies listed in Indian market, characterized by rapid economic expansion and distinct environmental and social challenges. The study seeks to reveal how ESG performance affects sensitivity to systemic risk (SR), thereby deepening our understanding of the Indian financial milieu and enriching the global ESG discourse. This paper examines the roles of firm size, firm age, and cost of debt (K_d) in an ESG-SR sensitivity nexus. Given India’s status as the fifth-largest economy globally and its significant role in the international financial system, understanding this interaction is vital. The study underscores the potential of ESG practices in India to mitigate systemic risks and aims to develop an integrated framework for incorporating ESG factors into risk management practices within India’s evolving economic landscape.

The results of this study carry considerable implications for policymakers in India, guiding them to refine current regulations or develop new policies to enhance sustainability as well as financial stability. Investors can also gain valuable insights from this research, improving their understanding of the financial repercussions of ESG practices in India, which can influence their investment choices, risk assessments, and asset allocation strategies.

The structure of this manuscript unfolds methodically: Section 2 reviews the pertinent literature, offering a nuanced synthesis of scholarly contributions. In Section 3, employing rigorous quantitative analysis, we scrutinize the intricate nexus between a firm’s susceptibility to SR and its ESG performance. Section 4 delineates and cogently interprets our empirical findings. Moving to Section 5, we delve into the intricate dynamics where market valuation exerts profound influences on both SR exposure and ESG metrics. Finally, Section 6 transcends mere analysis to deliberate upon profound implications, methodological constraints, and prescient avenues for future inquiry in this domain.

2. Literature Review

2.1. Theoretical Background

Three prominent theories elucidate the relationship between ESG practices and firm risk, as discussed by Bouslah et al. (2018): (i) the stakeholder theory, (ii) models that analyze the connection between social performance and expected returns, and (iii) the Managerial Opportunism Theory.

Stakeholder theory suggests that increased investments in ESG can generate goodwill or moral capital among stakeholders, functioning similarly to an insurance mechanism that reduces firms’ exposure to various risks, including operational, environmental, and social risks (El Ghoul and Karoui 2017). According to this theory, firms engaged in socially responsible activities build trust and loyalty with their stakeholders, which can serve as a cushion against adverse events. This moral capital helps firms during crises, lowers the cost of capital, and enhances long-term value (Löffler 2023).

Theoretical models concerning expected return and social performance suggest that investors incorporate both non-financial and financial metrics into their investment strategies. Companies demonstrating robust CSR tend to attract a broader investor base that is drawn to their ethical conduct, thereby mitigating investment risks (Lee and Faff 2009). The correlation between social performance and expected return underscores that enterprises excelling in ESG metrics enjoy enhanced investor confidence, fostering stability in financial markets by reducing volatility and promoting sustained economic resilience.

Managerial Opportunism Theory, on the other hand, argues that expenditures on CSR can be a misuse of resources, thus decreasing the net worth of the firm—a view aligned with overinvestment (Barnea and Rubin 2010; Jo and Na 2012). This perspective, however, is less supportive of the positive impact of ESG performance on risk mitigation, which is the focus of this research.

This paper primarily employs the first two theories to elucidate the intricate ESG-firm risk nexus. However, within the broader context of market contagion, ESG practices are seen as pivotal in attenuating adverse systemic effects, leading to the intrinsic property of ESG funds to lower SR (). Consequently, this research also intersects with Signal Transmission Theory and Consumer Behavior Theory.

Consumer Behavior Theory (CBT) investigates the decision-making processes of individuals when they purchase goods or services, considering psychological, social, and economic factors. In the context of ESG performance, CBT suggests that consumers increasingly value sustainable and ethical practices. Firms excelling in ESG criteria are perceived more favorably, enhancing their market value and hence market capitalization and reducing SR. The rising awareness of environmental and social issues drives consumers to prefer companies that align with their values (Solomon 2018). From a risk perspective, CBT highlights the preference of consumers for products from firms with strong ESG records. Such firms tend to have lower financing costs and attract fewer speculative investors, thus reducing SR, especially during market downturns (Panigrahi et al. 2018).

Signal transmission theory (SGT), derived from information economics, examines how information asymmetries between parties can be reduced through signaling. Companies signal their quality and trustworthiness to investors, consumers, and other stakeholders through their ESG disclosures. Effective ESG reporting indicates the commitment of a firm to sustainable practices, reducing perceived risks and attracting investments. This signaling enhances transparency and reduces information asymmetry, thereby contributing to a company’s reputation and market stability (Spence 1973).

In essence, drawing from signal transmission theory, we discern two distinct paths of communication. In the domain of product and service markets, when a company achieves a high ESG rating, strategic ESG insights are conveyed to consumers as a signal through corporate announcements or disclosures by ESG rating agencies. This amplifies the intangible value of the firm’s offerings, leading to heightened consumer satisfaction at the same price point. Consequently, consumer loyalty strengthens over time, bolstering the company’s resilience to industry challenges and diminishing its susceptibility to SR (Farah et al. 2021). In the realm of investments, companies have the opportunity to unveil their ESG strategies, signaling their industry risk exposure and prowess in managing idiosyncratic risks to investors. This highlights their commitment to sustainable practices, attracting investors who prioritize ESG factors and reshaping the dynamics of trading at a microstructural level.

Within the investment landscape, firms strategically disclose their ESG initiatives and performance through various channels, including corporate announcements and reports from ESG rating agencies. This deliberate dissemination aims to build a positive corporate identity and emphasize the company’s intangible assets. These positive signals influence the share market by shaping investor decisions. Companies with strong ESG ratings gain visibility in ESG reports and provide comprehensive risk disclosures (Chodnicka-Jaworska 2021; Saci et al. 2024). This fosters confidence and trust among investors, decreasing share price volatility. A stable group of investors can counteract speculative behaviors, thereby lowering SR and supporting the firm’s long-term growth (Capelle-Blancard and Petit 2019).

By integrating CBT and SGT, this research aims to examine how ESG performance, as a signal, affects consumer behavior and, subsequently, a firm’s sensitivity to SR. High ESG performance may serve as a positive signal to consumers and investors, fostering increased trust and lower perceived risk. By combining stakeholder theory, theoretical models on social performance and expected returns, CBT, and SGT, this study seeks to provide a comprehensive understanding of how ESG practices can mitigate systemic risks and enhance financial stability in the Indian market.

2.2. Systemic Risk (SR)

Systemic risk (SR) embodies the potential for a systemic collapse within a network triggered by the failure of a single constituent or due to exogenous disturbances. This concept can be likened to a chain reaction, akin to a row of dominoes, where the collapse of one leads to the fall of the entire sequence (Eratalay and Cortés Ángel 2022; Dziwok et al. 2023). In this context, the system under consideration is the share market. The relationship between SR in share market and the broader economy is grounded in the notion that the share market constitutes a substantial portion of an economy (Neitzert and Petras 2019).

This correlation is particularly pronounced when the share market comprises numerous stocks, substantial market capitalizations, and extensive representation across various industries. Numerous studies have utilized share markets for the assessment of SR. Liu et al. (2020) examined the share market indices from 43 different nations to reflect global financial markets, Zhao et al. (2019) concentrated on SR within the share market of China, and Hakan Eratalay and Vladimirov (2020) explored the share market of Russia.

Several methods have been devised to assess SR. Acharya et al. (2017) quantified SR and proposed a taxation policy designed to optimally manage it. Gray et al. (2007) utilized a risk-adjusted balance sheet and contingent claims analysis to examine asset-liability discrepancies across corporate, financial, sovereign, and household sectors showcasing systemic instability through stress testing. Tobias and Brunnermeier (2016) introduced conditional VaR metrics to assess both the individual and systemic risks that entities impose on the system. Tarashev et al. (2010) employed a game theory strategy, specifically the Shapley value approach, to measure a bank’s risk contribution by aggregating its marginal impacts on the banking system.

Researchers have made a distinction between the contribution to SR and the exposure to it for individual companies. Billio et al. (2012) employed principal component analysis, leveraging return covariance matrices to discern commonalities in returns, particularly during periods of market instability. This approach helps identify both the contribution and exposure of firms to SR.

In context of the Capital Asset Pricing Model, Ruefli et al. (1999) undertook a seminal examination into a firm’s sensitivity to SR, specifically delving into the intricate interplay between beta and the firm’s FP. Their analysis delineated two pivotal roles of beta within the CAPM framework: firstly, as a metric capturing a company’s exposure to systemic risk; secondly, as a conduit that translates the equity risk premium into the requisite rate of return tailored to the unique characteristics of an individual firm. We incorporate this methodological approach into our study owing to its discernible simplicity and profound efficacy in navigating the complexities inherent to equity valuation under systemic risk considerations.

2.3. ESG and SR

Our research is driven by three primary motivations: (1) ESG investing’s efficacy in mitigating stakeholder risk, (2) its advocacy for prolonged investment horizons, and (3) its focus on a relatively underexplored market segment.

Firstly, ESG investing is intricately linked with the diminution of stakeholder-related risks. Becchetti et al. (2018) expound upon the stakeholder theory, asserting that firms with subpar ESG ratings are particularly susceptible to litigation and stakeholder-associated risks. In a market equilibrated by investor preferences, companies boasting superior ESG ratings generally exhibit lower systemic risk (SR), as posited by Albuquerque et al. (2019), which often translates into diminished anticipated stock returns. Furthermore, the empirical evidence presented by Becchetti et al. (2015b) and Lööf et al. (2022) indicates that firms extensively engaged in CSR activities and characterized by reduced stakeholder risk tend to manifest elevated idiosyncratic risk. Corroborating this, research by Kim et al. (2014), da Silva (2022), and Yu et al. (2023) elucidates that firms adhering to stringent transparency standards are less likely to obfuscate adverse news, thereby mitigating their crash risk exposure. Similarly, Boubaker et al. (2020) and Singh (2024) have observed that entities with exemplary ESG ratings encounter a reduced risk of financial distress, rendering them less prone to financial default.

Secondly, ESG funds epitomize a paradigm shift towards long-term investment strategies, exhibiting a remarkable resilience against the allure of short-term risk and return fluctuations (Bollen 2007). The sustained interest in ESG assets is predominantly fueled by investor preferences, where investors demonstrate a pronounced aversion to divesting these assets even amidst financial crises (Becchetti et al. 2015a). This phenomenon is aptly elucidated through the multi-attribute utility function of socially responsible investors, which intricately weaves ethical considerations into their investment calculus (Bollen 2007).

Thirdly, funds boasting robust ESG ratings strategically curate their portfolios to predominantly encompass assets that rigorously adhere to ESG standards (Joliet and Titova 2018). This approach facilitates the exploitation of niche market segments, often overlooked by conventional investment funds. This strategic concentration is congruent with the heightened idiosyncratic risk associated with companies possessing high ESG scores, as observed by Becchetti et al. (2015b). As a result, ESG-centric funds demonstrate a reduced correlation with other investment portfolios, thereby mitigating the risk of contagion and enhancing portfolio diversification.

Cumulatively, these dynamics delineate the ESG investment sector as one inherently geared towards overall risk mitigation (Cerqueti et al. 2021). During episodes of market contagion, ESG investments manifest a capacity to ameliorate adverse impacts. The predilection for ESG assets among investors plays a pivotal role in mitigating contagion risk, thereby endowing ESG funds with an intrinsic capability to attenuate systemic risk (SR).

Companies with exemplary ESG ratings are predisposed to adopting judicious and sustainable practices, thereby attenuating their risk exposure. (Cerqueti et al. 2021) posit that ESG investment has the potential to mitigate systemic risk, asserting that firms adhering to ESG standards are less vulnerable to systemic shocks due to enhanced governance transparency. Furthermore, they emphasize that ESG-oriented investments inherently prioritize long-term perspectives, thereby diminishing the propensity for panic selloffs during periods of crisis. ESG assets, not yet ubiquitously embraced, demonstrate a lower susceptibility to market disruptions. In a complementary study, Leterme and Nguyen (2020) discovered that ESG factors could be conceptualized as integral components of systemic risk. The extant literature presents a spectrum of findings regarding the nexus between ESG ratings and financial performance, with some studies reporting neutral or adverse impacts, while others document positive correlations. For example, Sassen et al. (2016) identified that superior corporate social performance correlates with a reduction in both overall and specific risks. Value-driven investment strategies, encompassing socially responsible investing and ESG investing, eschew industries such as tobacco, arms, and gambling, and rigorously assess companies based on their sustainability practices and risk management frameworks.

Based on our literature analysis, it is clear that superior ESG performance can serve as a protective shield against SR for corporations. Drawing from these insights, this paper proposes new hypotheses for further investigation:

H₁: 

A robust ESG performance score among corporations may dampen their susceptibility to SR.

2.4. ESG, SR, and Firm Size

The interplay between ESG ratings and SR can vary significantly based on a size of a firm. Larger firms often exhibit more stability and resource resilience, which can buffer against various risks, including systemic ones. These firms typically have established reputations and diversified portfolios, which may mitigate the impact of ESG ratings on their risk profiles. Conversely, smaller firms may not have the same level of risk mitigation mechanisms in place, making them more sensitive to changes in ESG ratings.

Previous research has suggested that while high ESG scores can reduce overall risk, the effect is more pronounced in firms that lack the inherent stability provided by a large firm size (Saci et al. 2024). Therefore, we hypothesize that:

H₂: 

ESG ratings have a less pronounced impact on the sensitivity of SR for large firms compared to small and mid-sized firms.

2.5. ESG, SR, and Firm Age

The influence of ESG ratings on SR in relation to the age of a firm is an area that has not yet been extensively explored in the existing literature. Traditional studies on ESG and firm risk often focus on FP and stakeholder relations, but the variable of firm age introduces a novel perspective (Eratalay and Cortés Ángel 2022; Pistolesi and Teti 2024; Saci et al. 2024).

Older firms typically possess more established processes, experienced management teams, and a robust historical presence in their respective markets, potentially granting them resilience against SRs (Liu et al. 2022). This stability might mean that the impact of ESG ratings on their SR sensitivity is less pronounced. Conversely, younger firms, which are in the stages of growth and development, might be more susceptible to systemic risks due to less mature operational frameworks and less entrenched market positions (Dungey et al. 2022).

This study seeks to fill this gap by examining whether the SR reduction benefits of high ESG ratings are more significant for younger firms compared to older firms, thereby proposing the following hypothesis:

H₃: 

ESG ratings have a less pronounced impact on the sensitivity of SR for older firms compared to younger firms.

2.6. ESG, SR, and Cost of Debt

The ESG-SR nexus in the context of a firm’s cost of debt (K_d) is another underexplored area in current research. Previous studies have primarily focused on the broader impacts of ESG performance on financial stability and risk management without delving into how the K_d might modulate these effects (Eratalay and Cortés Ángel 2022; Pistolesi and Teti 2024).

A low K_d provides firms with the financial stability and flexibility needed to proactively manage and mitigate various risks (Porta 2024). This advantage allows them to act as a buffer, maintaining operations and pursuing growth even in the face of adverse conditions (Dungey et al. 2022). This financial cushion might render the impact of ESG ratings on SR sensitivity less significant for such firms. On the other hand, firms with a high K_d are generally more financially vulnerable (Dungey et al. 2022) and may face greater pressures to manage risks effectively. High ESG performance could thus play a critical role in enhancing their creditworthiness and reducing SR. This research aims to pioneer the examination of how ESG ratings interact with the K_d to influence SR, hypothesizing that:

H₄: 

ESG ratings have a less pronounced impact on the sensitivity of SR for firms with a low cost of debt compared to those with a high cost of debt.

3. Research Methodology

3.1. Sample

The sample for our study comprises companies enlisted in the Bombay Stock Exchange’s (BSE’s) flagship S&P BSE 500 index, representing approximately 93 per cent of the total market capitalization on the BSE. Our sample includes 428 firms after excluding 72 firms due to missing data. The study period spans from 1 April 2018 to 31 March 2024. Data are collected on an annual basis to ensure the analysis captures trends and patterns accurately. We have selected this timeframe because it provides the most recent and relevant data, reflecting the latest trends in ESG practices among Indian firms. This period is long enough to identify significant patterns and trends while ensuring data consistency and availability, as ESG reporting has become more standardized in recent years.

3.2. Data Analysis Method

To gauge the sensitivity to SR, the beta coefficient derived from CAPM is employed, utilizing Stata^® 16 software. Panel data estimation is employed as the analytical framework for this study.

3.3. Data

This study’s primary dataset is sourced from the Prowess database of the Centre for Monitoring Indian Economy (CMIE ProwessIQ Database), an esteemed repository for firm-level analysis of Indian enterprises (Gidage and Bhide 2024b; Beloskar and Rao 2022; Dharmapala and Khanna 2018). ESG ratings as of March 2024 for the sample companies were obtained from Thomson Reuters, adhering to methodologies established in previous research (Gidage et al. 2024; Gidage and Bhide 2024a; Ioannou and Serafeim 2012; Liu et al. 2024; Sensharma et al. 2022).

Firm performance across ESG dimensions is evaluated using Asset4’s three-pillar scores. These scores, scaled from 0 to 100, serve as robust indicators of a company’s ESG performance and are widely endorsed in scholarly discourse. Thomson Reuters supplies the Asset4 performance scores, facilitating a comparative analysis of a company’s performance on an array of ESG metrics within the Asset4 universe, with higher scores denoting superior ESG performance (de Villiers et al. 2022).

Thomson Reuters’ ESG evaluations encompass over 7000 global companies, with a historical dataset extending back to 2002. These assessments are presented as percentile ranks, interpretable both as percentages and letter grades, ranging from A+ to D−. The rankings are congruent with the Thomson Reuters Business Classifications (TRBCs) Industry Group for Environmental and Social factors and are harmonized with national standards for governance criteria. The revamped ESG metrics convert these percentile figures into letter grades, making it easier to quickly assess how a company measures up against its industry peers, highlighting areas of excellence and concern in their ESG practices (Thomson Reuters ESG Scores 2018).

Table 1. ESG score and respective grades.

Score Range	Grade
0.0 to 0.0833	D−
0.0833 to 0.1667	D
0.1667 to 0.25	D+
0.25 to 0.3333	C−
0.3333 to 0.4167	C
0.4167 to 0.5	C+
0.5 to 0.5833	B−
0.5833 to 0.6667	B
0.6667 to 0.75	B+
0.75 to 0.8333	A−
0.8333 to 0.9167	A
0.9167 to 1.0	A+

Source: Thomson Reuters ESG Scores (2018).

provides a detailed guide on how scores are translated into letter grades.

3.4. Variables

In this study, the focal point of analysis is the degree of sensitivity to SR, a crucial variable gauged by the ratio of listed companies’ stock return volatility against the broader market’s fluctuations, as per asset portfolio theory. To quantify this sensitivity, the beta coefficient derived from the CAPM serves as a reliable measure, indicating the magnitude of SR sensitivity across the asset portfolio (Ruefli et al. 1999).

The beta coefficient from the CAPM is utilized to quantify SR sensitivity, expressed as:

$$E\left(R_i\right)=R_f+{\beta }_i\left[E\right(R_m)-R_f]$$

(1)

$${Beta}_i\left({\beta }_i\right)={\displaystyle \frac{cov(R_i,R_m)}{var\left(R_m\right)}}$$

(2)

Here, we delineate R_i as the return on assets for firm i, with R_f representing the risk-free rate of return and R_m signifying the return of the market portfolio. The focal point of our investigation centers on β_i, symbolizing the systemic risk (SR) sensitivity of firm i. Notably, β serves as our notation for the SR-dependent variable.

The ESG performance scores (ESGPSs) used in the analysis correspond to the same year as the dependent variable (i.e., the same year in which SR sensitivity is measured) to accurately reflect the potential impact of ESG practices on systemic risk.

Table 2. Description of variables.

Type of Variable	Name of Variable	Symbol	Definition
Dependent Variable	Systemic Risk	β	Beta of a security is determined by taking the covariance of the security’s returns and the market’s returns and dividing it by the variance of the market’s returns over a specified period
Independent Variable	ESG Performance Score	ESGPS	Thomson Reuters ESG performance scores
Dummy Variables	Firm Size (for Dummy)	FS_DUMMY	Divided into DUM_Q1 (Small Firms); DUM_Q2 (Mid-sized Firms); DUM_Q3 (Large Firms). More details in Section 4.3
	Firm Age	FA	In the text, the age of companies is represented as a dummy variable: a value of 1 indicates high age, while a value of 0 indicates low age
	Cost of Debt	CoD	CoD is the ratio of interest expenses of a firm to its average debt. Here, a value of 1 indicates high CoD, while a value of 0 indicates low CoD
Control Variables	Firm Size (for Control)	FS	Natural logarithm of firm’s total assets at the end of the year
	Cash Flow	CF	It is equal to the cash inflow generated by the company through specific economic activities
	Return on Assets	RoA	(Net Income/Total Assets) * 100%
	Firm Size	FS	Natural log of firm’s total assets
	Leverage	LEV	Total Debt/Total Equity
	Relative Issue Price (increase/ decreases	RP	The relative increase or decrease in the issue price is calculated as: ((closing price on the specified trading day − starting price)/(starting price)) * 100%
	Return on Capital Employed	RoCE	EBIT divided by invested capital
	Capital Output	CO	It is equal to the sum of the enterprise’s strategic investments and its rolling investments

provides a detailed description of the dependent, independent, and control variables used in the analysis.

Our selection of variables is scrupulously grounded in an extensive synthesis of pivotal prior research, notably including Dhaliwal et al. (2016), Hwang et al. (2017), Mardini (2022), Saci et al. (2024), and Teplova et al. (2024). Distinctively, our study pioneers the inclusion of variables such as the return on capital employed, cost of debt, firm age, and firm size that have heretofore been conspicuously absent in the empirical investigation of the ESG-SR nexus. These variables are integrated for the first time in our analysis, offering an unprecedented perspective on the multifaceted dimensions of how ESG performance interacts with firm-specific characteristics to influence systemic risk. By incorporating these previously unexplored variables, our study not only enriches the existing body of knowledge but also provides a more comprehensive understanding of the intricate dynamics between ESG factors and financial stability across different types of firms.

4. Data Analysis, Results, and Discussions

4.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variable	Obs.	Avg.	S.D.	Min.	Max.
β	2568	0.6300	1.9400	−8.4900	7.4900
ESGPS	2568	0.4800	0.6200	0.2300	0.8700
FS	2568	5.1039	2.9730	0.5681	8.4763
CF	2568	175.3000	26.0400	−0.5900	418.2000
LEV	2568	1.5800	0.2800	0.0200	14.3000
RoA	2568	6.4900	3.0800	−0.3900	27.3900
CO	2568	64.1030	21.5900	0.5800	2940.3100
RoCE	2568	16.9326	5.4019	−0.0481	23.4910
RP	2568	13.2900	33.3900	−58.2000	308.1000
Firm Age	428	44.04	24.85	4	122
Firm Size	428	4.3900	2.3000	0.9200	6.4900
Cost of Debt	428	1.789	0.713	−1.293	4.981

presents descriptive statistics revealing the expansive range of values observed across the variables studied, thereby encapsulating the diverse profiles of the analyzed firms. The mean SR (β) stands at 0.630, accompanied by a notable standard deviation of 1.940, illustrating a considerable breadth in risk sensitivities. The average ESG Performance Score (ESGPS) stands at 0.480, with a standard deviation of 0.620, showcasing variability in ESG practices. Cash flow averages 175.300, with a standard deviation of 26.040. The average cost of debt (K_d) is 1.789, with a notable range, emphasizing the diversity in borrowing costs. These statistics highlight the substantial variation in both financial and ESG profiles among the firms studied.

Moving to the correlation matrix in

Table 4. Correlation matrix.

Variables	Beta	ESGPS	FS	CF	LEV	RoA	CO	RoCE	RP
Beta	1.000
ESGPS	0.350	1.000
FS	0.038	0.245	1.000
CF	0.077	−0.451	0.374	1.000
LEV	0.345	0.591	0.118	−0.540	1.000
RoA	0.451	0.576	0.204	0.103	−0.395	1.000
CO	0.487	0.054	0.054	0.491	−0.053	0.591	1.000
RoCE	0.581	0.228	0.150	0.223	0.194	0.010	0.681	1.000
RP	0.374	0.094	−0.050	0.491	0.019	0.108	0.591	−0.673	1.000

, several significant relationships among the study’s variables emerge. A strong positive correlation is observed between RoCE and Beta, suggesting that companies with higher ROCE are more sensitive to SR. In contrast, Cash flow (CF) exhibits significant negative correlations with ESG performance scores (ESGPSs), indicating that firms with higher ESG performance may have lower cash flows and liquidity. These findings illustrate the intricate relationships between financial metrics, ESG performance, and SR sensitivity, guiding further investigation in the study. Notably, no pairwise correlations exceed 0.691, suggesting that multicollinearity is not a significant concern.

In order to mitigate the presence of multicollinearity, the variance inflation factor (VIF) was computed as a diagnostic measure, as shown in

Table 5. Variance inflation factor.

Variables	VIF	1/VIF
ESGPS	2.401	0.416
FS	1.044	0.958
CF	1.778	0.562
LEV	3.108	0.226
RoA	2.349	0.426
CO	1.159	0.863
RoCE	1.690	0.592
RP	2.502	0.400
Mean VIF	2.264

. Since the VIF values do not exceed 10, we can infer that there are no substantial multicollinearity concerns, thereby validating the reliability of the regression analysis.

In this study, the Granger Causality test was conducted to examine whether ESG performance scores (ESGPSs) can predict systemic risk sensitivity (SR Sensitivity). The test results, as shown in

Table 6. Granger Causality test.

Hypothesis	F-Statistic	p-Value	Significance
ESGPS does not Granger-cause SR Sensitivity	4.75	0.013	***

Note: Significance level: *** indicates p < 0.01.

, indicate a significant causal relationship, with p-values suggesting that the ESGPSs can indeed predict SR Sensitivity. These findings suggest that improvements in ESG practices may have an immediate effect on reducing a firm’s risk sensitivity, highlighting the importance of ESG factors in managing systemic risk.

4.2. ESG-SR Sensitivity Nexus

This study employs fixed effect panel regression to test the hypotheses, emphasizing the importance of using a robust model to address heterogeneity and correlation issues, as recommended by Malik and Kashiramka (2024). To evaluate Hypothesis H1, a fundamental regression model is established to quantify the firm’s exposure to systemic risks:

$$\beta =CONST+{\beta }_{1i}{ESGPS}_{it}+{\beta }_{2i}{FS}_{it}+{\beta }_{3i}{CF}_{it}+{\beta }_{4i}{LEV}_{it}+{\beta }_{5i}{RoA}_{it}+{\beta }_{6i}{CO}_{it}+{\beta }_{7i}{RP}_{it}+{\beta }_{8i}{RoCE}_{it}+{\lambda }_{it}+{\mu }_{it}$$

(3)

In this model, β quantifies the firm’s exposure to systemic risks. λ_it represents year and industry effects. Each company is assigned a rating by Thomson Reuters, as detailed in Table 1. The aim of this regression analysis is to quantify how each of these factors contributes to the SR sensitivity of the firms in our sample. The ESG performance score is particularly scrutinized to understand its impact, controlling for other financial variables. Each firm is given a corresponding rating by Thomson Reuters, reflecting its ESG performance and other financial indicators, facilitating a comprehensive analysis of how these variables interact to influence SR sensitivity. The focus is on analyzing the value and statistical significance of the coefficient β₁, while ${\mu }_{it}$ denotes an error term. The results of this regression analysis are presented in

Table 7. Regression analysis result—1.

Variables		Systemic Risk (β)
ESG Performance Score	ESGPS	−0.183 ***
		(−1.18)
Firm Size	FS	−0.014 ***
		(−12.31)
Cash Flow	CF	−0.00122
		(−0.85)
Leverage	LEV	−0.004
		(−3.40)
Return on Assets	RoA	−0.916 ***
		(−4.39)
Capital Output	CO	0.0430
		(0.75)
Relative to Issue Price	RP	−0.00058 *
		(−0.26)
Return on Capital Employed	RoCE	−0.154 **
		(−2.25)
	CONST	3.481 ***
		(22.72)
	Year Effect	YES
	Industry Effect	YES
	Obs.	2568
	R-squared	0.538
F-test for Fixed Effects (p-value)		13.42 (0.0098)
Hausman Test (p-value)		16.29 (0.0148)
Test for Cross-Sectional Dependence (p-value)		1.98 (0.12)
Wald Test for Heteroskedasticity (p-value)		29.34 (0.83)
BP LM Test (p-value)		37.560 (0.108)

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

.

Table 7 provides valuable insights into the factors influencing SR within enterprises. Notably, a firm’s ESG score, FS, RP, RoA, and RoCE all exhibit significant negative effects on SR. Conversely, there is no statistically significant association between SR and LEV, CF, or CO. Although CO shows a positive correlation with ESG ratings, this relationship lacks statistical significance.

A firm’s ESG rating exerts a profound influence on its susceptibility to SR, with empirical evidence substantiating that firms possessing superior ESG ratings exhibit diminished sensitivity to SR. This observation corroborates Hypothesis 1 and highlights the pivotal role of robust ESG strategies in mitigating exposure to systematic risks. Additionally, the analysis reveals that a larger firm size is significantly inversely related to SR, as expansive firms typically command a greater market influence, attract a broader spectrum of investors, and uphold higher transparency standards, thereby collectively attenuating market risk.

Our findings align with similar studies conducted globally, highlighting the universal importance of ESG considerations in risk management and investment decision making. Research by Sassen et al. (2016), Saci et al. (2024), (Cerqueti et al. 2021), and Aevoae et al. (2023) reinforces the notion that strong ESG performance enhances companies’ resilience to systemic shocks and fosters sustainable growth. Additionally, stock price volatility is significantly negatively associated with SR. This finding supports Novy-Marx’s (2011) assertion that operating leverage impacts cross-sectional returns. A robust return on assets shows a strong inverse relationship with SR, suggesting that greater profitability significantly mitigates a company’s susceptibility to SR. This concept is further supported by Smith’s (1776) economies of scale theory, which posits that larger firms enjoy lower per-unit fixed costs, enhancing profit margins and reducing exposure to market volatility.

Interestingly, the study finds no significant relationships between SR and LEV, CF, and CO. The lack of a significant relationship with leverage may stem from the diverse debt structures of firms, resulting in inconsistent risk profiles. This variability likely obscures any clear connection between leverage and SR. Additionally, the small sample size may limit the statistical power of analysis, hindering the detection of significant relationships (Shapiro and Wilk 1965). Cash flow’s lack of a significant relationship with SR could be attributed to its inherent variability and unpredictability, influenced by external factors such as economic cycles and market conditions, which can diminish its effectiveness in predicting SR. Moreover, incomplete or limited data might not fully capture the range of cash flow characteristics, affecting the analysis’ power (Wooldridge 2013). The effects of capital output on risk may be long-term, making them less detectable in short-term analyses and thus reducing the likelihood of finding significant relationships with SR in this study (Merton 1973). Considering these factors, the study’s findings on the insignificance of these variables underscore the importance of sample size, data quality, and the inherent complexity of each variable’s impact on SR.

Overall, the results affirm that a firm’s ESG performance and size are critical determinants of its sensitivity to SR, while other factors such as cash flow, capital output, and financial leverage do not show a significant impact within this study’s framework. The observed relationship between SR sensitivity and ESG performance highlights the evolving dynamics of the Indian financial markets. Companies with robust ESG frameworks demonstrate a proactive approach to risk management, mitigating the potential financial risks associated with ESG factors. This aligns with global trends in sustainable investing, where ESG considerations play a pivotal role in investment decision making.

4.3. Firm Size, ESG, and SR Sensitivity Dynamics

Our empirical analysis indicates that the size of a company plays a significant role in influencing SR. To delve deeper, we formulated Hypothesis 2, which suggests that for large firms, a higher ESG rating may correlate with increased SR. This relationship is thought to be driven by the strategic allocation of ESG expenditures, which can lead to a higher operating leverage.

In this study, firms are categorized into three distinct groups based on their firm value: small firms (DUM_Q1), mid-sized firms (DUM_Q2), and large firms (DUM_Q3). This categorization allows for a more nuanced analysis of how ESG performance scores (ESGPSs) influence systemic risk (SR) across firms of varying sizes.

The division into three categories is driven by the need to understand whether the size of a firm moderates the relationship between ESG performance and systemic risk. Firm size is a critical factor that can influence how a company responds to external risks, including those related to ESG factors. Smaller firms may lack the resources, risk management capabilities, and diversification that larger firms possess, making them potentially more vulnerable to systemic risks. Mid-sized firms, while more resilient than small firms, may still not have the extensive risk mitigation strategies available to large firms. Therefore, analyzing the impact of the ESGPS across these different firm sizes helps in identifying tailored strategies for managing systemic risks.

Firm size was measured using the logarithm of total assets, consistent with previous studies (Saci et al. 2024; Dang et al. 2018; Ibhagui and Olokoyo 2018). Firms were grouped into quartiles based on their firm value (log of total assets), resulting in three groups:

• DUM_Q1 (Small Firms): Represents firms in the lowest quartile (Q1) with firm value ≤2.840, representing approximately the bottom 25% of firms. Takes the value 1 if the firm’s log of total assets is in the first quartile (Q1) and 0 otherwise.
• DUM_Q2 (Mid-sized Firms): Represents firms in the second quartile (Q2), with firm values between 2.840 and 5.940, representing the middle 50% of firms. Firms with a firm value between 2.840 and 5.940. Takes the value 1 if the firm’s log of total assets is between the first quartile (Q1) and the third quartile (Q3) and 0 otherwise.
• DUM_Q3 (Large Firms): Represents firms in the third quartile (Q3), with firm value >5.940, representing the top 25% of firms. Firms with a firm value >5.940. Takes the value 1 if the firm’s log of total assets is in the third quartile (Q3) and 0 otherwise.

Out of 428 companies in our sample, 112 firms belonged to Q1, 198 to Q2, and 118 to Q3.

We employed a fixed effects panel regression for the analysis. The equations for each model are as follows:

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{DUM\text{\_}Q1}_{it}+{\beta }_3(ESGPS\times {DUM\text{\_}Q1)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(4)

Equation (4) is for the small firms, where SR_it is the systemic risk for the firm i at time t, α is the firm fixed effect, ESGPS_it is the ESG performance score, and DUM_Q1_it is the dummy variable for small firms. The interaction term between the ESGPS and small firm dummy is ESGPS X DUM_Q1. X_it represents control variables, λ_it represents year and industry effects, and ε_it is the error term.

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{DUM\text{\_}Q2}_{it}+{\beta }_3(ESGPS\times {DUM\text{\_}Q2)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(5)

Equation (5) is for the mid-size firms. Where DUM_Q2_it represents the mid-sized firms.

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{DUM\text{\_}Q3}_{it}+{\beta }_3(ESGPS\times {DUM\text{\_}Q3)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(6)

Equation (6) is for the large firms, where DUM_Q3_it represents the large firms.

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{FV\text{\_}C}_{it}+{\beta }_3(ESGPS\times {FV\text{\_}C)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(7)

Equation (7) takes all firms; here, we consider the firm value as a continuous variable and not the dummy variable. This will provide us additional insights. Here, FV_C_it represents the continuous measure of firm value.

The results of this regression analysis are presented in

Table 8. Regression results for firm size.

Variables		Model (I): Small Firms	Model (II): Mid-Sized Firms	Model (III): Large Firms	Model (IV): All Firms
		Systemic Risk (β)	Systemic Risk (β)	Systemic Risk (β)	Systemic Risk (β)
ESG Performance Score	ESGPS	0.07458 ***	0.05137 **	0.01967	0.04026 ***
		(3.19)	(2.46)	(1.48)	(3.11)
Firm Value	DUM_Q1	0.10234 ***
		(3.02)
ESGPS × Firm Value	ESGPS × DUM_Q1	0.03567 ***
		(3.45)
Firm Value	DUM_Q2		0.06345 **
			(2.14)
ESGPS × Firm Value	ESGPS × DUM_Q2		0.02241 **
			(2.19)
Firm Value	DUM_Q2			0.03018 *
				(1.70)
ESGPS × Firm Value	ESGPS × DUM_Q2			0.01012
				(1.12)
Firm Value (Continuous)	FV_C				−0.025 **
					(2.20)
ESGPS × Firm Value (Continuous)	ESGPS × FV_C				−0.010 *
					(1.85)
Cash Flow	CF	−0.0749	−0.017	−0.003	−0.002
		(−0.84)	(0.015)	(0.002)	(0.003)
Leverage	LEV	−0.00419	−0.01	−0.002	−0.003
		(−2.18)	(0.048)	(0.003)	(0.001)
Return on Assets	RoA	−0.314 ***	−0.180 ***	−0.150 **	−0.160 ***
		(0.070)	(0.081)	(0.001)	(0.001)
Capital Output	CO	0.291	0.007	0.009	0.090
		(0.301)	(0.017)	(0.010)	(0.037)
Relative to Issue Price	RP	0.08945 *	0.07023 *	0.06012 *	0.06489 *
		(0.054)	(0.014)	(0.192)	(0.168)
Return on Capital Employed	RoCE	0.220 *	0.200 *	0.170 *	0.180 *
		(3.60)	(3.40)	(2.80)	(3.50)
	CONST	6.385 ***	1.841 ***	5.800 ***	6.200 ***
		(1.284)	(0.071)	(0.068)	(2.081)
	Year Effect	YES	YES	YES	YES
	Industry Effect	YES	YES	YES	YES
	Obs.	112	198	118	428
	R-squared	0.551	0.563	0.608	0.593
F-test for Fixed Effects (p-value)		5.12 (0.003)	4.89 (0.002)	4.43 (0.002)	6.02 (0.0079)
Hausman Test (p-value)		18.09 (0.0014)	15.72 (0.0023)	17.23 (0.0160)	18.40 (0.0103)
Test for Cross-Sectional Dependence (p-value)		2.30 (0.25)	2.97 (0.45)	2.68 (0.33)	3.40 (0.96)
Wald Test for Heteroskedasticity (p-value)		22.36 (0.62)	18.93 (0.43)	17.15 (0.18)	20.95 (0.35)
BP LM Test (p-value)		1.95 (0.162)	0.21 (0.80)	8.25 (0.098)	0.30 (0.71)

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

.

The first model (Model I) focuses on small firms, where firm value is represented by DUM_Q1. The coefficient for ESGPS is positive and significant at the 1% level (β = 0.07458, t = 3.19), suggesting that a higher ESGPS is associated with an increase in SR for small firms. The interaction term ESGPS * DUM_Q1 also shows a positive and significant relationship (β = 0.03567, t = 3.45), indicating that the impact of ESGPS on SR is particularly strong for small firms. This supports the notion that small firms are more sensitive to ESG factors, likely due to their relatively limited resources and higher vulnerability to external shocks.

In the second model (Model II), mid-sized firms are analyzed with firm value represented by DUM_Q2. Similar to the small firms, the ESGPS positively and significantly affects SR (β = 0.05137, t = 2.46), though the magnitude of the effect is somewhat lower than for small firms. The interaction term ESGPS * DUM_Q2 is also positive and significant (β = 0.02241, t = 2.19), but again, the effect size is smaller compared to small firms. This finding suggests that mid-sized firms, while still sensitive to ESGPS, experience a less pronounced impact compared to small firms.

Model III examines large firms, with firm value represented by DUM_Q3. In this case, the ESGPS coefficient is positive but not statistically significant (β = 0.01967, t = 1.48), indicating that the relationship between ESGPS and SR is weaker for large firms. The interaction term ESGPS * DUM_Q3 is also not significant (β = 0.01012, t = 1.12). These results suggest that large firms are less affected by changes in ESGPS when it comes to SR, potentially due to their more diversified operations, better risk management practices, and greater access to resources.

In the final model (Model IV), firm value is treated as a continuous variable rather than as a set of dummy variables. Here, the ESGPS coefficient remains positive and significant (β = 0.04026, t = 3.11) but the interaction term ESGPS * FV_C is negative and significant (β = −0.002, t = −2.00). This negative interaction suggests that as firm size increases (when measured continuously), the impact of ESGPS on SR diminishes. The negative and significant coefficient for firm value (FV_C) itself (β = −0.005, t = −2.20) further reinforces this finding, indicating that larger firms are less sensitive to the ESGPS in relation to their systemic risk.

The results provide strong support for Hypothesis 2, which posits that ESG ratings have a less pronounced impact on the sensitivity of SR for large firms compared to mid-sized and small firms. The analysis reveals a clear pattern: small firms exhibit the highest sensitivity to ESGPS, with the impact gradually weakening as firm size increases. This pattern is consistent across both the dummy variable models (Models I–III) and the continuous firm value model (Model IV).

For small firms, the significant positive relationship between ESGPS and SR suggests that these firms, due to their smaller scale and potentially fewer resources, are more vulnerable to fluctuations in ESG performance. This heightened sensitivity could be attributed to the fact that smaller firms may lack the robust risk management frameworks that larger firms possess, making them more susceptible to ESG-related risks.

Mid-sized firms also show a significant relationship, though the effect is weaker than that for small firms. This indicates that while mid-sized firms are still affected by the ESGPS, their greater resources and more developed risk management practices compared to small firms may provide some buffer against ESG-related risks.

For large firms, the relationship between ESGPS and SR is weak and statistically non-significant, suggesting that these firms are less affected by changes in ESG performance. The insignificant interaction terms for large firms (DUM_Q3 and ESGPS * DUM_Q3) further support the idea that large firms’ systemic risk is less sensitive to ESG performance. This could be due to large firms’ ability to better manage ESG risks through diversification, access to capital, and more sophisticated risk management systems.

The continuous variable model (Model IV) provides additional confirmation of these findings. The negative interaction between ESGPS and firm value (when measured continuously) indicates that as firms grow larger, the impact of the ESGPS on SR becomes less significant. This aligns with previous results and further validates the hypothesis.

Three potential mechanisms may explain this relationship:

(1)

Large firms tend to allocate a larger proportion of their expenditure to ESG strategies, increasing their operating leverage. This can lead to higher credit risk, slower profit growth, and increased financing costs, thereby diluting the positive effects of ESG ratings on profitability.

(2)

ESG ratings positively influence FP, enticing cost-intensive and highly leveraged firms to market, which in turn elevates SR.

(3)

There is a phenomenon of diminishing marginal benefits from ESG ratings; as companies achieve higher ratings, the incremental benefits tend to decrease.

4.4. Firm Age, ESG, and SR Sensitivity Dynamics

To provide more targeted recommendations to companies, we introduce Hypothesis 3, which posits that older firms, with their established operational frameworks and market positions, may display different patterns for how ESG practices influence their risk profiles compared to younger firms.

To test Hypothesis 3, we developed fixed effect panel regression models to examine the ESG-SR sensitivity nexus across firms of varying ages. The models are specified as follows:

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{FA\text{\_}C}_{it}+{\beta }_3(ESGPS\times {FA\text{\_}C)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(8)

Equation (8) is for the model (I), where we considered the firm age as a continuous variable and not the dummy variable. Here, FA_C_it represents the continuous measure of firm age.

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{DUM\text{\_}FA}_{it}+{\beta }_3(ESGPS\times {DUM\text{\_}FA)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(9)

In Equation (9), we consider firm age as a dummy variable; DUM_FA_it represents the dummy measure of firm age. For this purpose, we categorized companies in our sample based on their age, with the average firm age being 44 years. Companies were divided into two groups: those older than the mean (high-age), consisting of 188 companies, and those younger than the mean (low-age), comprising 240 companies. This classification was represented by the dummy variable DUM_FA, with high firm age coded as 1 and low firm age as 0. Model (II) of

Table 9. Regression results for firm age.

Variables		Model (I)	Model (II)
		Systemic Risk (β)	Systemic Risk (β)
ESG Performance Score	ESGPS	0.019	0.004
		(0.006)	(0.004)
Firm Age (Continuous)	FA_C	0.007 **
		(2.33)
ESGPS × Firm Age (Continuous)	ESGPS × FA_C	−0.002 **
		(0.001)
Firm Age (DUMMY)	DUM_FA		2.180 ***
			(19.96)
ESGPS × Firm Age (DUMMY)	ESGPS × DUM_FA		−0.006 **
			(0.002)
Firm Size	FS	−0.067 *	−0.073 *
		(0.038)	(0.021)
Cash Flow	CF	−0.021	−0.023
		(0.032)	(0.035)
Leverage	LEV	−0.04	−0.02
		(0.060)	(0.040)
Return on Assets	RoA	−0.118 ***	−0.218 ***
		(0.073)	(0.068)
Capital Output	CO	0.006	0.008
		(0.015)	(0.016)
Relative to Issue Price	RP	0.032 *	0.029 *
		(0.012)	(0.013)
Return on Capital Employed	RoCE	0.136 *	0.128 *
		(0.040)	(0.038)
	CONST	1.693 ***	1.758 ***
		(0.075)	(0.083)
	Year Effect	YES	YES
	Industry Effect	YES	YES
	Obs.	428	428
	R-squared	0.658	0.672
F-test for Fixed Effects (p-value)		4.98 (0.009)	6.01 (0.007)
Hausman Test (p-value)		16.38 (0.0019)	18.22 (0.0023)
Test for Cross-Sectional Dependence (p-value)		3.77 (0.48)	5.58 (0.75)
Wald Test for Heteroskedasticity (p-value)		19.64 (0.70)	20.83 (0.63)
BP LM Test (p-value)		7.57 (0.271)	8.79 (0.318)

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

presents the regression results for this classification.

For model (I), the coefficient for firm age, when treated as a continuous variable, is positive and statistically significant. This finding implies that older firms are more likely to experience higher systemic risk. This could be due to various factors associated with aging firms, such as increased operational complexities, legacy systems, and greater exposure to market volatility. The interaction between ESG performance scores and firm age (continuous) is negative and statistically significant. This negative interaction indicates that the impact of ESG performance on systemic risk decreases as firms age. In other words, for older firms, the mitigating effect of high ESG performance on systemic risk is less pronounced compared to younger firms.

For model (II), the analysis of firm age and its interaction with ESG ratings provides significant insights into SR sensitivity. The positive and statistically significant coefficient associated with the firm age dummy variable denotes that older firms have a higher baseline SR compared to younger firms. Furthermore, a negative as well as statistically significant interaction term between firm age and ESG performance scores suggests that the impact of ESG ratings on SR is less pronounced for older firms.

The results from both Model (I) and Model (II) provide strong support for the hypothesis (H3), which asserts that older firms are less sensitive to ESG ratings concerning their SR. This implies that the established practices and financial stability commonly associated with older firms may diminish the influence of ESG factors on their overall risk profile, unlike younger firms, which show greater sensitivity to ESG performance. Therefore, risk management strategies for older firms may need to address factors beyond ESG ratings to effectively mitigate SR.

Older firms often benefit from established market positions, long-term stakeholder relationships, and accumulated experience in managing various risks. These elements can provide a stabilizing effect, making older firms less susceptible to SR and less dependent on ESG performance for risk mitigation (Barney 1991). Additionally, the organizational inertia and resistance to change typically seen in older firms may hinder the full integration and effectiveness of ESG initiatives, further reducing their impact on SR.

4.5. Cost of Debt, ESG, and SR Sensitivity Dynamics

Given the critical role of financial conditions in shaping risk profiles, we propose Hypothesis 4, which suggests that firms with lower borrowing costs may enjoy more robust financial conditions, potentially reducing their sensitivity of SR to ESG factors. Conversely, firms with higher borrowing costs might feel more significant impacts from ESG ratings due to the added financial pressure.

To test Hypothesis 4, we developed fixed effect panel regression models to examine ESG-SR sensitivity nexus across firms of K_d. The models are specified as follows:

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{CoD\text{\_}C}_{it}+{\beta }_3(ESGPS\times {CoD\text{\_}C)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(10)

Equation 10 is for model (I), where we considered the K_d as a continuous variable and not a dummy variable. Here, CoD_C_it represents the continuous measure of K_d.

$${SR}_{it}={\alpha }_i+{\beta }_1{ESGPS}_{it}+{\beta }_2{DUM\text{\_}CoD}_{it}+{\beta }_3(ESGPS\times {DUM\text{\_}CoD)}_{it}+{\gamma X}_{it}+{\lambda }_{it}+{\epsilon }_{it}$$

(11)

For Equation (11), we considered K_d as a dummy variable; DUM_CoD represents the dummy measure of K_d. This equation represents model (II). For this model, we classified the companies in our sample based on their K_d, with the mean K_d being 1.789%. Companies were divided into two groups: those with a cost of debt above the mean (high-value), totaling 195 companies, and those below the mean (low-value), comprising 233 companies. This classification was denoted by the dummy variable DUM_CoD, with a high cost of debt coded as 1 and a low cost of debt as 0. Model (II) of

Table 10. Regression results for cost of debt.

Variables		Model (I)	Model (II)
		Systemic Risk (β)	Systemic Risk (β)
ESG Performance Score	ESGPS	−0.020 ***	−0.024 ***
		(0.006)	(0.005)
Cost of Debt (Continuous)	CoD_C	0.031 ***
		(0.008)
ESGPS × Cost of Debt (Continuous)	ESGPS × CoD_C	−0.110 ***
		(0.030)
Cost of Debt (DUMMY)	DUM_CoD		0.034 ***
			(0.009)
ESGPS × Cost of Debt (DUMMY)	ESGPS × DUM_CoD		−0.121 ***
			(0.031)
Firm Size	FS	−0.008 **	−0.003 ***
		(0.003)	(0.001)
Cash Flow	CF	−0.002	−0.003
		(0.001)	(0.002)
Leverage	LEV	−0.003	−0.002
		(0.002)	(0.003)
Return on Assets	RoA	−0.001 *	−0.001 *
		(0.001)	(0.001)
Capital Output	CO	0.007	0.009
		(0.008)	(0.010)
Relative to Issue Price	RP	0.330 *	0.349 *
		(0.190)	(0.192)
Return on Capital Employed	RoCE	0.012 *	0.010 *
		(0.007)	(0.006)
	CONST	1.698 ***	1.758 ***
		(0.080)	(0.083)
	Year Effect	YES	YES
	Industry Effect	YES	YES
	Obs.	428	428
	R-squared	0.591	0.583
F-test for Fixed Effects (p-value)		7.58 (0.018)	6.31 (0.0273)
Hausman Test (p-value)		14.59 (0.0023)	16.38 (0.0018)
Test for Cross-Sectional Dependence (p-value)		2.77 (0.25)	2.16 (0.31)
Wald Test for Heteroskedasticity (p-value)		25.51 (0.81)	23.38 (0.41)
BP LM Test (p-value)		8.69 (0.312)	9.15 (0.270)

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

presents the regression results for this classification.

Model (I) serves as the baseline analysis, incorporating ESG performance scores, K_d as a continuous variable, and their interaction effect, along with several control variables. The coefficient for ESGPS is negative and statistically significant at the 1% level (β = −0.020, p < 0.01), suggesting that higher ESG performance is associated with lower systemic risk. This finding aligns with the hypothesis that firms with better ESG performance are perceived as less risky by investors, possibly due to better management practices and reduced exposure to environmental and social risks. The coefficient for the K_d as a continuous variable is positive and significant at the 1% level (β = 0.031, p < 0.01). This indicates that firms with higher costs of debt tend to have higher systemic risk, which could be due to the financial burden and the potential for distress associated with higher borrowing costs. The interaction term between the ESGPS and CoD_C is negative and significant at the 1% level (β = −0.110, p < 0.01). This finding supports the hypothesis (H4) that the negative impact of ESG performance on systemic risk is more pronounced for firms with a lower K_d. Essentially, ESG practices may serve as a buffer against risk, especially in firms with favorable borrowing conditions.

Model (II) extends the baseline analysis by incorporating K_d as a dummy variable, distinguishing between firms with high and low costs of debt, along with their interaction with ESG performance scores. This model tests the robustness of the findings from Model (I) and examines whether the observed relationships hold when considering the K_d in a categorical form. Similar to Model (I), the coefficient for ESGPS remains negative and statistically significant at the 1% level (β = −0.024, p < 0.01). This consistency reinforces the finding that a higher ESGPS is associated with a lower SR. The coefficient for the K_d dummy variable is positive and significant at the 1% level (β = 0.034, p < 0.01), indicating that firms with high costs of debt are more exposed to systemic risk. This finding is consistent with the continuous measure of K_d used in Model (I). The interaction term between ESGPS and DUM_CoD is negative and significant at the 1% level (β = −0.121, p < 0.01), mirroring the results from Model (I). This further supports the hypothesis (H4) that ESG practices mitigate systemic risk more effectively in firms with a lower K_d.

One potential reason for this interaction could be that firms with lower costs of debt are generally perceived as less risky by creditors, allowing them more financial flexibility to invest in sustainable practices. These firms may have better access to capital, enabling them to allocate resources towards initiatives that enhance their ESG performance, thereby further mitigating their exposure to systemic risk.

Additionally, the lower K_d may reflect a firm’s strong financial health, which could complement the benefits of high ESG performance. Financially stable firms with robust ESG practices might be better equipped to withstand external shocks and uncertainties, reducing their overall systemic risk. This synergistic relationship highlights the importance of both sound financial management and strong ESG performance in minimizing the systemic risk for firms.

Overall, the results suggest that integrating ESG considerations into corporate strategy can be particularly beneficial for firms that maintain low borrowing costs, as it amplifies their resilience against broader market risks.

4.6. Robustness Test

To substantiate our results, we conducted a comprehensive suite of robustness tests, following the methodology proposed by Zhao and Yue (2014). Initially, we revised our model by substituting the original control variables with alternative ones. Specifically, we adopted Gao’s (2016) approach, replacing the fluctuation in relative issue price with the ratio of market value to face value (MFVR). This substitution aims to ensure that our results are not sensitive to the original control variables.

We assessed the robustness of the model by examining the stability of the coefficients and their significance levels. A model is considered robust if the coefficients and their significance levels remain consistent across different specifications. Additionally, we introduced new control variables to further validate the model’s robustness. These factors encompass board independence (BI), which is assessed by the proportion of non-executive directors compared to the total number of directors; the price-to-earnings ratio (P/E), calculated by dividing the market price per share by earnings per share (EPS); and the quick ratio, determined by subtracting the inventory from current assets and dividing the result by current liabilities. The market value to face value ratio (MFVR) is calculated by dividing the market value of a security by its face value. All these variables were sourced from the CMIE ProwessIQ Database.

To further test the robustness of our findings, we replaced the ESG ratings from Thompson Reuters with the ESG risk ratings from Morningstar’s Sustainalytics (ESGRRS). These ratings offer a comprehensive assessment of a firm’s exposure to and management of industry-specific ESG risks and were obtained from the Global Access—Sustainability Research Database.

Lastly, we utilized the standard deviation of the operating profit margin (Std (ROA)) in place of the beta coefficient to verify the robustness of our model, as outlined by Zhao and Yue (2014). If the signs and significance levels of the explanatory variable coefficients remain largely unchanged, it supports the robustness and reliability of our model.

The updated model is specified as follows:

$$Std.{\left(RoA\right)}_{it}=CONST+{\beta }_{1i}{ESGRPS}_{it}+{\beta }_{2i}{FS}_{it}+{\beta }_{3i}{CF}_{it}+{\beta }_{4i}{CO}_{it}+{\beta }_{5i}{MFVR}_{it}+{\beta }_{6i}{QR}_{it}+{\beta }_{7i}{BI}_{it}+{\beta }_{8i}{PE}_{it}+{\lambda }_{it}+{\mu }_{it}$$

(12)

The empirical outcomes of these robustness tests, detailed in

Table 11. Robustness test—I.

Variables		Std. (RoA)
ESG Risk Rating Score	ESGRRS	−0.0118 *
		(−0.93)
Firm Size	FS	−0.00186 **
		(−1.93)
Cash Flow	CF	0.0641 ***
		(−3.48)
Capital Output	CO	−0.0753 **
		(−1.84)
Market Value to Face Value Ratio	MFVR	−0.00481 **
		(−0.24)
Quick Ratio	QR	−1.802 ***
		(0.488)
Board Independence	BI	−0.00081
		(0.003)
Price-to-Earnings Ratio	PE	−2.154 ***
		(0.157)
	CONST	0.201 ***
		(4.48)
	Year Effect	YES
	Industry Effect	YES
	Obs.	2568
	R-squared	0.461
F-test for Fixed Effects (p-value)		10.08 (0.007)
Hausman Test (p-value)		14.48 (0.013)
Test for Cross-Sectional Dependence (p-value)		4.05 (0.30)
Wald Test for Heteroskedasticity (p-value)		34.50 (0.93)
BP LM Test (p-value)		68.491 (0.560)

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

, corroborate our previous findings and align with the research conducted by Klarman (1991). They demonstrate that a firm’s ESG performance continues to significantly impact its SR, reinforcing the strength and reliability of our conclusions.

To address potential endogeneity between ESG performance and systemic risk (SR), we apply an instrumental variable (IV) regression approach. Drawing on the methodologies of Tang et al. (2024), Gao et al. (2023), Yu and Xiao (2022), and Benlemlih and Bitar (2018), we utilize the industry-year average of the ESG composite score (ESG_ind) as an instrumental variable. We then conduct a two-stage regression analysis to estimate the IV model. The results are presented in

Table 12. Robustness test- II.

Variables	1st Stage Coefficient (ESGPS)	2nd Stage Coefficient [Systemic Risk (β)]
Industry-Year Average ESG Score (ESG_ind)	0.145 *** (3.45)	-
ESG Performance Score (ESGPS)	-	−0.172 *** (−3.11)
Firm Size (FS)	0.051 ** (2.05)	−0.013 *** (−4.27)
Cash Flow (CF)	0.009 (0.62)	−0.00108 (−0.72)
Leverage (LEV)	−0.026 ** (−2.10)	−0.0039 *** (−3.25)
Return on Assets (RoA)	0.073 ** (2.28)	−0.892 *** (−4.01)
Capital Output (CO)	0.014 (0.71)	0.046 (0.83)
Relative to Issue Price (RP)	0.005 (1.15)	0.0010123
Return on Capital Employed (RoCE)	0.032 * (1.89)	−0.143 ** (−2.32)
Constant	0.185 *** (4.12)	3.512 *** (23.01)
R-squared	0.327	0.527
Observations	2568	2568
F-test for Weak Instrument		16.09 ***

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

, providing further insight into the robustness of the previous findings.

For the first stage analysis, the industry-year average of ESG composite score (ESG_ind) significantly impacts the ESGPS, with a coefficient of 0.145 (T-value = 3.45). This confirms that ESG_ind is a relevant instrument for ESGPS. Among the control variables, FS, LEV, RoA, and RoCE significantly impact the ESGPS, indicating that these factors play a role in determining the ESG ratings of firms.

For second stage analysis, the relationship between ESGPS and SR remains negative and significant (coefficient = −0.172, T-value = −3.11) after addressing potential endogeneity using the 2SLS approach. This supports the original hypothesis (H1) that higher ESG ratings reduce a firm’s sensitivity to SR. Control variables such as FS, LEV, RoA, and RoCE continue to show significant impacts on SR, reinforcing the robustness of the results.

The robustness test using the IV regression method confirms that the original findings are not driven by endogeneity. The higher ESG ratings indeed reduce a firm’s sensitivity to systemic risk, even after controlling for potential endogeneity. The instrumental variable approach validates the robustness of the results, strengthening the overall conclusions of the study. The control variables’ significant effects in the first stage further affirm their importance in determining ESG performance.

Further, we use a lagged regression approach to avoid the bias from endogeneity. Firstly, we regress independent variables by using one-, three-, and five-period lagged ESG variables to test the impact of changes in ESGPS on a SR.

Table 13. Robustness test—III.

Variables	Model (I)	Model (II)	Model (III)
	Systemic Risk (β)	Systemic Risk (β)	Systemic Risk (β)
ESGPS 1-Period Lagged	−0.174 *** (−3.18)
ESGPS 3-Period Lagged		−0.167 *** (−2.95)
ESGPS 5-Period Lagged			−0.159 *** (−2.78)
Firm Size (FS)	−0.012 *** (−4.21)	−0.013 *** (−4.13)	−0.014 *** (−4.09)
Cash Flow (CF)	−0.00105 (−0.70)	−0.00108 (−0.73)	−0.00111 (−0.74)
Leverage (LEV)	−0.0038 *** (−3.27)	−0.0040 *** (−3.33)	−0.0041 *** (−3.30)
Return on Assets (RoA)	−0.903 *** (−4.03)	−0.891 *** (−4.02)	−0.884 *** (−3.97)
Capital Output (CO)	0.045 (0.81)	0.043 (0.79)	0.041 (0.77)
Relative to Issue Price (RP)	0.0010368	0.0010864	0.0011172
Return on Capital Employed (RoCE)	−0.148 ** (−2.34)	−0.145 ** (−2.31)	−0.143 ** (−2.28)
Constant	3.509 *** (22.95)	3.495 *** (22.78)	3.481 *** (22.62)
Observations	2568	2568	2568
R-squared	0.532	0.529	0.526

Note: t-statistics are enclosed in parentheses; Significance levels: * indicates p < 0.1, ** indicates p < 0.05, and *** indicates p < 0.01.

shows the results for the lagged regression.

The one-, three-, and five-period lagged ESGPS have a consistent negative and significant impact on SR across all lag lengths. Specifically, the coefficients for lagged ESGPS are −0.174 (1-period lag), −0.167 (3-period lag), and −0.159 (5-period lag), with corresponding T-values of −3.18, −2.95, and −2.78, respectively. These results align with the original regression results, further confirming that higher ESG ratings reduce a firm’s sensitivity to SR, even when considering lagged effects. The impact of control variables such as FS, LEV, RoA, and RoCE remains significant across all lagged models, similar to the original regression results. This consistency indicates that these variables continue to play a crucial role in influencing systemic risk, regardless of the lag in ESG performance scores. The R-squared values for the lagged models are 0.532 (1-period lag), 0.529 (3-period lag), and 0.526 (5-period lag), demonstrating a slight decrease in the explanatory power as the lag length increases. However, the models still maintain strong overall explanatory power, indicating that the inclusion of lagged ESG variables does not significantly diminish the model’s ability to explain SR.

The lagged regression analysis further supports the original hypothesis (H1) that higher ESG ratings reduce a firm’s sensitivity to SR. The consistent negative and significant impact of lagged ESGPS on SR across different lag lengths reaffirms the robustness of the original findings. These results suggest that the effect of ESG performance on reducing SR persists over time, providing additional evidence that incorporating ESG factors into corporate strategies can have long-term benefits in mitigating SR. The control variables’ stable influence across the lagged models further emphasizes their importance in understanding the dynamics of systemic risk.

5. Implications

This study profoundly highlights crucial role of ESG performance in mitigating the SR sensitivity of Indian companies. In a globalized business environment, it is imperative for Indian firms to not only align with but also exceed international ESG standards to sustain and enhance their global competitiveness. By forging strategic partnerships with leading international entities and embracing cutting-edge management practices, Indian companies can significantly elevate their ESG initiatives, thereby enhancing their resilience to systemic risks.

For SMEs, an exclusive focus on short-term profitability can severely undermine long-term sustainability and growth. Our findings strongly advocate for SMEs to prioritize ESG investments and adhere rigorously to government directives aimed at enhancing social and environmental performance. Such an approach not only builds a robust corporate image but also attracts and retains responsible investors dedicated to sustainable development, ultimately enhancing long-term shareholder value.

This study establishes ESG performance as an essential tool for mitigating risks within financial markets, revealing a strong inverse relationship between ESG scores and sensitivity to SR. It underscores the critical need for integrating sustainability into corporate risk management frameworks, advancing our theoretical understanding of how ESG behaviors impact SR across diverse firm-specific factors like size, age, and K_d. Furthermore, the research makes significant contributions to stakeholder theory by showing the substantial influence of ESG performance on stakeholder perceptions, investor behavior, and overall market dynamics. This underscores the practical significance of ESG considerations in shaping corporate strategies and risk management outcomes.

Integrating ESG factors into corporate decision-making processes is not just advantageous but crucial for improving a firm’s reputation, attracting responsible investors, and bolstering resilience against systemic risks. For investors, this research offers valuable insights into the integration of ESG criteria into investment strategies, emphasizing the importance of ESG ratings as key indicators of a firm’s financial resilience and risk profile.

The findings also serve as a call to action for policymakers and regulators to develop and enhance frameworks that promote sustainable business practices and strengthen financial market stability. Additionally, the study advocates for the establishment of strong corporate governance practices that prioritize transparency, accountability, and comprehensive stakeholder engagement—essential elements for effectively mitigating SR and safeguarding long-term shareholder value.

6. Conclusions

This research explores the intricate relationship between ESG performance and sensitivity to SR among firms listed on India’s BSE 500 Index, utilizing data from 2018 to 2023. The study aligns with contemporary trends, examining the profound impact of ESG evaluations on business operations. By integrating global ESG assessment reports with annual disclosures, we employed a comprehensive review of the existing literature and multiple regression analysis to elucidate the SR-ESG performance nexus from a stakeholder-oriented perspective.

Our findings reveal a compelling correlation between a firm’s ESG performance and its sensitivity to SR. Higher ESG ratings are linked to enhanced resilience against SR and reduced sensitivity. This highlights the crucial role of ESG-related responsibilities in mitigating SR. Two primary mechanisms drive this phenomenon. Elevated ESG ratings enhance consumer satisfaction and loyalty, stabilizing consumer purchases amid market volatility. Furthermore, transparent ESG disclosures attract stable investors, reduce information asymmetry, and enhance market capitalization potential. ESG investments also appeal to responsible investors, further bolstering resilience through subdued equity volatility, low turnover rates, and increased liquidity, thereby lowering susceptibility to financial market fluctuations and SR sensitivity.

Research indicates that the influence of ESG scores on the SR sensitivity of large-cap firms is markedly negligible, whereas small-cap firms can substantially reduce their SR exposure through robust ESG practices. This is because of the significant impact of ESG strategies and the diminishing returns on the ESG rating’s influence on SR for these companies. Additionally, the ESG scores of older firms show minimal effect on their sensitivity to SR, whereas younger firms can significantly lower their SR exposure through strong ESG practices. This is attributed to the entrenched operational frameworks, historical stability, and established stakeholder relationships that characterize older firms, potentially diminishing the incremental risk mitigation benefits provided by ESG improvements.

Furthermore, the study reveals that firms with low borrowing costs show minimal sensitivity to SR from ESG scores, whereas those with higher borrowing costs can significantly reduce their SR exposure through strong ESG practices. Firms with lower borrowing costs generally have healthier financial profiles and less dependence on ESG performance for risk management, contrasting with firms facing higher financing costs that rely more on ESG enhancements to bolster their creditworthiness and overall risk profile.

This observed phenomenon can be ascribed to various dynamics, including the enhanced reputation and credibility enjoyed by companies with high ESG ratings (Bhattacharya et al. 2018). Moreover, the alignment of ESG principles with long-term time-to-value (Friede et al. 2015) and increasing acknowledgement among the investment community of the material impact of ESG factors on FP (Wu et al. 2022 further accentuate this trend. Additionally, the differential impact observed across firm-specific characteristics such as firm age, firm size, and K_d may stem from variances in financial resilience, governance frameworks, and strategic orientations among enterprises (Lins et al. 2017).

In summary, this study highlights how high-ESG-rated companies foster robust consumer loyalty and institutional investor trading structures, leading to diminished SR sensitivity. It concludes that companies boasting superior ESG ratings demonstrate lower SR sensitivity compared to firms with inferior ESG ratings. Additionally, it underscores nuanced impact of ESG performance on SR across different segments with respect to firm size, elucidating the differential effects of leverage, firm age, and K_d.

7. Limitations and Directions for Further Research

This research provides important insights into how ESG performance correlates with sensitivity to systemic risk among firms in the Indian market. To extend these findings and delve into broader implications, several promising directions for future research could be explored. Broadening the analysis to include firms from various markets and regions may enhance the applicability of the results and offer a more complete picture of the impact of ESG performance on systemic risk across diverse contexts. Comparative studies between developed and emerging economies could provide a deeper understanding of how ESG practices influence systemic risk in different economic settings, thus enriching the global discourse on ESG’s role in financial stability. Additionally, future research could focus on examining individual ESG components—environmental, social, and governance—separately, while also exploring their interactions with different financial and non-financial variables. This could reveal more detailed mechanisms underlying the observed relationships. Incorporating qualitative research methods might also offer a more nuanced view of how companies implement ESG strategies and how these practices affect their risk profiles.

Another fruitful area of investigation could be the application of quantile regression analysis to better understand the influence of firm characteristics such as age, size, and cost of debt on the relationship between ESG performance and systemic risk across different segments. Exploring alternative metrics of systematic risk, as suggested by Ellis et al. (2022), might also provide further insights into the robustness of the findings and potentially uncover different dynamics. Lastly, while this study focuses on the roles of firm size, age, and cost of debt, additional research could examine other firm-specific characteristics, like industry type and ownership structure, to further illuminate the relationship between ESG performance and systemic risk.