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This chapter is a study of the impact of environmental, social, and governance (ESG) issues on credit rating in India. It will help issuers, investors, and other market participants understand rating agencies' approach to incorporating the sustainability-related factors in its analysis. This will provide an overall perspective on the considerations that are usually the most important. Under environment considerations, climate change, waste recycling, air pollutants, and natural capital sustainability can be important factors. Social considerations are becoming more and more important among investors and consumers and are raising awareness about prosperous and failing communities. Moreover, COVID-19 pandemic has further highlighted the need for redirecting capital flows toward sustainable activities, making our economy and society more resilient against shocks. Governance factors primarily involve the corporate governance practices prevalent in the entity reflecting the different rights and responsibilities among its different stakeholders management, board of directors, employees, lenders, shareholders, customers, and suppliers. It also encompasses the corporate's business conduct and practices related to transparency and disclosure. This chapter will focus on the contemporary issues of including ESG in credit rating and what are the probable impacts of doing the same.

With favorable rating, companies can access international debt market easily. Moreover a favorable sovereign rating is beneficial for the overall economy of a country as better rating enables the government of the country to access the international debt market. Even capital allocation by foreign institutional investors increases which is beneficial for the equity market too.

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.

There is growing recognition that numerous business drivers contribute to financial performance and investment returns but they are not included in a company's profit and loss statements. In the investment industry, these wider sets of value drivers are known as environment-social- governance (ESG) factors. A small number of specialized ESG rating agencies provide information to investors about the extent to which firms' behaviors are socially responsible. However, a major criticism of these rating agencies is the lack of transparency in their methods. This paper aims to examine the issues of subjectivity, transparency and uniformity of ESG ratings by exploring the methods used to assess ethics performance by an Australian rating agency.

A case study was conducted on an Australian ESG rating provider, Regnan. The data for the analysis were sourced from internal Regnan documents.

The paper found that a level of subjectivity is inevitable in ESG ratings and the call for uniformity may inhibit innovation, but these issues can be addressed by increased transparency of the rating methods.

Further research is required to understand what level and, combination of, uniformity and transparency is sufficient to satisfy stakeholder requirements for ESG information.

The discussion of the factors underlying the ethics performance rating may prompt more open and transparent debate on how to assess ethical performance of companies, and increase investor confidence in ESG ratings. It may also provide more direction to companies on how to strengthen their ethical performance.

There is growing recognition that numerous business drivers contribute to financial performance and investment returns but they are not included in a company's profit and loss statements. These “ESG” factors can account for up to 66 percent of the market value of globally listed companies. In response to calls for more transparency on how ESG factors are assessed, and how ethical performance is appraised, this paper attempts to lift the veil on ESG rating methods.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

The purpose of this paper is to investigate the relationship between a company’s environmental, social and governance (ESG) performance and its financial performance. This paper also investigates the relationship between ESG performance and a company’s market valuation. This paper provides convincing empirical evidence that delivering superior ESG performance pays off financially.

The financial data and ESG scores of 150 publicly traded companies listed in the Standard and Poor’s 500 index for 2017–2020, comprising 5,750 observations, were collected. STATA was used to run a fixed-effect regression and a weighted least squares model to analyze the panel data.

The results of the empirical analysis suggest that companies with superior ESG performance perform better financially and are valued higher in the market compared to their industry peers. The ESG rating score impacts both return-on-capital-employed as a proxy for financial performance and Tobin’s Q as a proxy for the market valuation of a company.

This study contributes to the existing research on ESG performance and financial performance relationship by providing empirical evidence to resolve confusion in the existing literature caused by contradictory evidence. Taking advantage of worldwide crisis caused by the COVID-19 pandemic, this study shows that a positive relationship between ESG performance and a company’s market valuation holds even during times of unexpected crises. Further, this study contributes to business practitioners’ knowledge by showing that ESG aspects constitute highly relevant non-financial information that impact the market’s perception of a company and that investing in sustainability positively impacts a company’s bottom line.

This paper aims to investigate how a US firm’s political landscape affects the integration of environmental, social and governance (hereafter ESG) measures in CEO compensation contracts, thereby affecting the firm’s ESG performance and credit rating.

Based on the results of state senatorial and presidential elections and the location of a US firm’s headquarters, the authors categorize whether a firm has a political environment that is predominantly Democratic (blue) or Republican (red). The empirical analyses are based on a sample of US firms in the period 2014–2021.

The authors find that firms in blue states are more likely to link CEO compensation to ESG performance measures. Further, the results show that firms in blue states with ESG-linked compensation contracts have better ESG performance. Lastly, the authors find evidence that a firm’s ESG performance has a positive impact on its credit rating, but the impact is weakened if firms in red states link ESG performance to executive compensation.

To the best of the authors’ knowledge, this is the first research that explores how a firm’s political environment affects the use of ESG performance measures in CEO compensation contracts. Furthermore, the authors contribute to the literature by showing evidence that the political environment interacts with the impact of ESG-linked compensation incentives on the firm’s ESG performance and, thus, its credit rating.

This paper aims to examine the consistency between firms’ stakeholder-friendly responses to the COVID-19 pandemic and their environmental, social and governance (ESG) ratings. Consistent firms are those with high prior ESG ratings that actively support stakeholders during the COVID-19 crisis.

The authors use data from JUST Capital, which tracks Russell 1000 firms’ actions in response to the pandemic, to examine the relationship between pre-pandemic ESG ratings and their COVID responses towards employees, customers and communities. The authors also analyse the impact of firms’ consistency between pre-pandemic ESG ratings and stakeholder-friendly COVID responses on ESG ratings and stock returns.

This study finds that firms with higher pre-pandemic ESG ratings are more likely to support their stakeholders during the pandemic. The authors also find that firms with high ESG ratings before the pandemic experience a decline in their ESG ratings if they do not actively support their communities during the COVID-19 crisis, although insufficient employee/customer support does not impact their ESG ratings. Finally, the authors find that firms with higher pre-pandemic ESG ratings that continue to uphold their ESG commitments through community assistance during the pandemic achieve higher stock returns compared to inconsistent firms.

The results reveal gaps in how comprehensively ESG agencies assess firms’ crisis responses, highlighting areas for rating improvements. The findings contribute to sustainable development by revealing the importance of firms upholding their ESG commitments during crises to maintain stakeholder trust and drive long-term value creation.

The findings underscore the need for responsive, transparent ESG rating processes to support the integration of sustainability considerations into corporate practices and investment decisions, particularly during evolving societal expectations during crises.

To the best of the authors’ knowledge, this study is the first to investigate how pre-pandemic ESG ratings explain firms’ stakeholder-friendly responses during the COVID-19 pandemic and analyse the integration of these responses and pandemic risks into ESG ratings during the crisis.

The authors examine the effect of split environmental, social and governance (ESG) ratings on information asymmetry, corporate value and trading behavior. The authors test the risk-based hypothesis and the optimism-bias hypothesis on the relationship between diverging opinions and future stock prices. The authors results show that split ESG ratings is positively related to idiosyncratic volatility, an alternative measure for information asymmetry. Further, the negative effect of split ESG ratings on cumulative abnormal return under short-selling constraints is consistent with the optimism bias hypothesis. The authors find a negative relationship between split ESG ratings and the net purchase ratio (NPR) of pension funds. Considering that the NPR is a direct measure of net demand, ESG disagreement may hinder socially responsible investing (SRI) in a firm. This study directly demonstrates the negative effect of ESG disagreement on firm value and investment by Korea's National Pension Service (NPS). The results offer valuable insights into policymakers, as the wide divergence in ESG ratings requires urgent attention to expand SRI.

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.

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.