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This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

This study investigates the influence of social trust on the attainment of corporate environmental, social and governance (ESG) objectives.

This study conducts panel regression analysis on a distinctive dataset for 2009–2017 on Chinese firms.

The analysis reveals a significant positive association between social trust and firm-level ESG practices. Moreover, the impact of social trust on shaping ESG outcomes is further amplified by factors such as economic growth, corporate governance standards and institutional quality. This relationship remains statistically positive when the authors employ alternative measures and methodologies, such as the instrumental variables, propensity score matching and difference-in-differences approaches. Notably, the results of heterogeneity tests indicate that the Trust–ESG nexus is more prominent for state-owned enterprises and firms with substantial market capitalization, superior profitability and higher leverage.

This study expands the comprehension of the determinants of ESG and underscores the influential role of social trust as an informal institution in enhancing a firm's ESG performance.

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities).

The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions.

The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design.

To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.

The United Nations' sustainable development goals (SDGs) put together a global framework in an attempt to address environmental, social and governance (ESG) concerns. Measuring a company’s contribution to the SDGs relies heavily on ESG reporting. This paper aims to examine the impact of ESG reporting on the corporate profitability of listed companies in Thailand over the period of 2019–2021.

Using 147 listed firms in the ESG group, content analysis was used to quantify the ESG reporting (within 11 themes), while corporate profitability was measured by return on asset and return on equity. Descriptive analysis, correlation matrix and panel regression are used to analyze the data of this study.

Consistent with the legitimacy, stakeholder and signaling theories, the authors found a statistically significant and positive impact of ESG reporting on corporate profitability in Thailand.

The findings highlight the importance of incorporating ESG considerations into companies’ reporting and decision-making processes, as these can enhance firm profitability and performance, attract stakeholders, improve their competitive advantage and step toward sustainability.

The purpose of this study is to examine the impact of firm environmental, social and governance (ESG) rating scores on market perception and stock behavior from 2017 to 2021 while controlling for COVID-19 severity score.

The authors used panel regression models with robust standard errors based on cross-country and cross-industry sample of 1,324 ESG firms from 25 emerging countries across four regions. Four separate regression analyses are used. Hausman test is used to determine whether fixed-effect (FE) or random-effect approaches should be used in regression models. Lagrange multiplier test is used to test for time FEs, and F-test for individual effects to choose between pooled ordinary least squares model and FE. Two-unit root tests are conducted to check stationarity. Heteroskedasticity and serial correlation were controlled through a robust covariance matrix estimation.

The authors provide evidence that the stakeholder theory persists in emerging countries. Overall, the results suggest that firms’ stock behavior is positively associated with the level of environmental and social performance in the region. However, the results do not provide empirical evidence to support the link between ESG performance and stock market perception proxied by the price-to-sales ratio. The results suggest that Refinitiv and Bloomberg ESG rating scores have a positive impact on stock performance in emerging markets, albeit the Bloomberg rating score is insignificant.

Favorable impact of environmental and social performance on stock performance suggests that policymakers should take initiatives to raise awareness toward investments in ESG projects. Evidence shows that ESG stock performance in emerging markets does not insulate firms from the COVID-19 severity. Furthermore, this study highlights the inconsistency in calculating the ESG ratings, therefore, a more standardized approach is recommended to support investors seeking sustainable investments.

The findings have social implications for investors with proenvironmental preferences and nonpecuniary motives for ethical investments. Asset fund managers should develop ESG investment strategies to promote investor preferences that are linked to the proenvironmental and prosocial attitudes by increasing their investments in stocks of firms that behave ethically and support the environment. Furthermore, the findings show that investors pay a price for ethical and socially responsible investments as they are evaluating the environmental and social activities, hence, the firm ESG profile influences equity valuation and risk assessment.

The study extends the literature and provides evidence from the unique setting of emerging markets by analyzing the relationship between ESG rating scores and the COVID-19 severity scores on one hand, and stock behavior and market perception on the other.

Socially responsible investment (SRI) is a niche and upcoming investment strategy in India. Very few researches have been conducted on SRI in the Indian context. This study identifies the SRI awareness level, attitude towards the importance of environmental, social, and governance (ESG) issues, willingness to invest in SRI avenues and obstacles in SRI investment decision-making by Indian retail investors. The second objective was among the awareness, attitude, willingness, obstacle, and demographic constructs to identify the most significant variables that impact an individual investor's SRI decision in India. .

Data for the study have been collected through a self-structured questionnaire. Descriptive statistics are used to identify the importance of variables for individual investors. This paper used the theory of planned behavior (TPB) to understand the factors impacting individual investors' SRI behavior. Binary logistics regression analysis is used to recognize the variables that affect an individual investor's SRI decision.

The descriptive statistics indicate a low level of SRI awareness; the majority of the investors agreed that ESG issues are significant in investing and showed a willingness to invest in SRI avenues. However, the investors were not willing to accept lower returns from SRI. The majority of investors found, lower returns on SRIs, no tax benefit, lack of information about SRIs, and low liquidity as important obstacles in SRI investing. Binary logistics regression results indicated that awareness about SR/ESG indices, awareness about SR/ESG funds, and willingness to invest in SRI avenues significantly impact investors' SRI decisions but demographic variables have no significant impact on SRI decision-making.

This study has implications for the ethical/SR mutual funds managers, policymakers, government, and international asset management companies. The study finds an urgent need for increasing awareness about SRI among individual investors in India. The study suggests that the issuers must provide adequate information about SRI avenues and probable risk and returns involved in these, while the regulators must make efforts to educate investors in India.

The context of the present study is original because hardly any of the earlier studies conducted in India have tried to find out the individual investors' SRI awareness level, investors' willingness towards SRI, investors' attitude towards ESG issues, and obstacles faced by investors in socially responsible investing.

This paper aims to investigate the relationship between corporate environmental, social and governance (ESG) ratings and leverage manipulation and the moderating effects of internal and external supervision.

The authors draw on a sample of Chinese non-financial A-share-listed firms from 2013 to 2020 to explore the effect of ESG ratings on leverage manipulation. Robustness and endogeneity tests confirm the validity of the regression results.

ESG ratings inhibit leverage manipulation by improving social reputation, information transparency and financing constraints. This effect is weakened by internal supervision, captured by the ratio of institutional investor ownership, and strengthened by external supervision, captured by the level of marketization. The effect is stronger in non-state-owned firms and firms in non-polluting industries. The governance dimension of ESG exhibits the strongest effect, with comprehensive environmental governance ratings and social governance ratings also suppressing leverage manipulation.

Firms should strive to cultivate environmental awareness, fulfil their social responsibilities and enhance internal governance, which may help to strengthen the firm’s sustainability orientation, mitigate opportunistic behaviours and ultimately contribute to high-quality firm development. The top managers of firms should exercise self-restraint and take the initiative to reduce leverage manipulation by establishing an appropriate governance structure and sustainable business operation system that incorporate environmental and social governance in addition to general governance.

Policymakers and regulators should formulate unified guidelines with comprehensive criteria to improve the scope and quality of ESG information disclosure and provide specific guidance on ESG practice for firms. Investors should incorporate ESG ratings into their investment decision framework to lower their portfolio risk.

This study contributes to the literature in four ways. Firstly, to the best of the authors’ knowledge, it is among the first to show that high ESG ratings may mitigate firms’ opportunistic behaviours. Secondly, it identifies the governance factor of leverage manipulation from the perspective of firms’ subjective sustainability orientation. Thirdly, it demonstrates that the relationship between ESG ratings and leverage manipulation varies with the level of internal and external supervision. Finally, it highlights the importance of governance in guaranteeing the other two dimensions’ roles by decomposing overall ESG.

This paper aims to explore the relationship between corporate corruption risk and environmental, social and governance (ESG) reporting and if this relationship is moderated by business ethics.

Data from a sample of 347 European firms selected from the ESG Index between 2010 and 2020 were used to test the model using panel data and multiple regressions. This paper considered the feasible generalized least squares estimation for linear panel data models. A multiple regression model is used to analyze the moderating effect of business ethics on the association between corporate corruption risk and ESG reporting. For robustness analyses, the authors included the alternative measure of the dependent variable, and they applied the simultaneous equation model for the endogeneity test.

The empirical results reveal a negative relationship between corporate corruption risk and ESG reporting. Furthermore, the findings suggest that business ethics positively moderate the relationship between corporate corruption risk and ESG reporting.

This paper presents an enormous contribution to the various economic agents involved in the company. The results could attract the attention of socially responsible investors and, above all, corporate citizens. Moreover, the managers of corrupt companies could take into account the results of this study by being more committed to an optimized transparency strategy on ESG reporting.

To the best of the authors’ knowledge, this is the first study to investigate the moderating role of business ethics on the relationship between corporate corruption risk and ESG reporting in the European context. It is also the first study documenting that business ethics reinforce the relationship between firm corruption and nonfinancial information transparency. This study fills a research gap as it expands the existing literature, which generally focuses on the impact of corporate corruption on ESG reporting.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.