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Environmental, social and governance (ESG) scores are becoming increasingly relevant in academic literature and the corporate world. This is partly because the themes covered by ESG scores are intended to resolve multiple major social and environmental issues. However, there is little consensus among academics about the definition of ESG scores and their measures. Many scholars have used ESG scores to represent various issues. The purpose of this study is to gather all definitions that were used by scholar when using ESG scores in their research.

This systematic literature review aims to identify how ESG scores are presented in the academic literature. A total of 4,145 articles were identified, of which 342 articles from influential peer-reviewed journals were retained.

In the articles, five different thematic definitions emerged in terms of how scholars have used ESG scores in their research: sustainability, corporate social responsibility, disclosure, finance and the analysis of ESG scores. Although some definitions are consistent with the methodologies of the agencies that produce ESG scores, others raise further questions. Caution is required when using ESG scores as a metric. They represent financial adjusted risk-return for some and are used to express business sustainability for others.

Only top-ranked journals were analyzed. In addition, only the key terms “ESG Score” and “ESG Scores” were used to gather all research papers.

Researchers could improve the accuracy of their results by developing specific methodologies that are closely related to the issues intended to be measured. The underlying variables composing the ESG scores could be used instead of the final score for more accurate environmental or social issues measurements.

This research shows that scholars use ESG scores to represent multiple issues that are not always captured by ESG scores’ official methodologies. ESG scores can express the overall performance of environmental and social issues, but they cannot be used to track specific underlying issues.

The purpose of the study is to measure the performance level of halal compliance practices by the halal-certified restaurants in Malaysia and rank and rate them based on several dimensions of halal compliance.

A total of 320 halal restaurants were surveyed. The absolute measurement approach of the analytic hierarchy process (AHP) was applied to rank and rate the restaurants. Finally, ANOVA and independent t-test were applied to assess if there is any significant difference in halal compliance performance between different groups of the respondents.

The AHP application resulted in only 19 restaurants (5.94%) achieving an “Excellent” rating. A significant difference has been observed between different groups of the respondents regarding their halal compliance performance.

An onsite audit and ranking of all the restaurants in Malaysia were beyond the scope of the study. The research was able to rank only 320 restaurants across Malaysia.

The findings and methodology of the study will provide policymakers with a clear roadmap for establishing a comprehensive rating system in the fields of the halal food industry to enhance the quality and integrity of the halal food management system.

To the best of the authors’ knowledge, this is the first time an empirical approach, like AHP, has been used to determine how Malaysia’s halal-certified restaurants stack up against one another. Similar studies can be carried out in other sectors of the halal industry as well as in similar context.

This paper aims to examine the informational value of credit rating changes for investors. The article analyses whether credit rating changes indicate the future financial performance of a firm.

The study employs pooled time-series cross-section regression technique and two-sample t-test for analysis. The paper utilizes a firm's operating profit as a proxy of its future financial performance to understand what inference can be drawn about future financial performance from a change in a firm's credit rating.

The paper finds that a firm operating profit declines in the year after a credit rating downgrade. However, no such significant relationship is evident in the case of a rating upgrade. The results are consistent across rating categories and individual years of the sample period.

The study uses non-financial corporate rating data; hence, the findings may not apply to credit rating changes in financial corporates and structured finance.

Investors and analysts can incorporate credit rating downgrade by CRAs as a key input in a firm's future financial forecast. Analysts and investment managers can also look at credit rating changes of firms in the same industry and draw a definite conclusion about which firm is likely to see a higher deterioration in performance.

The author has not come across any literature that directly investigates credit rating changes from the perspective of information content about future financial performance.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

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 provide further insights into the connection between corporate social responsibility (CSR) and companies’ credit ratings, while also exploring the role of corporate governance as a moderating factor. The hypotheses for this relationship are rooted in both legitimacy and stakeholder theories.

Using a sample of French non-financial listed firms from 2007 to 2020, this paper uses the ordered probit model introduced by Greene (2000). The issue of endogeneity has also been addressed.

The study reveals that CSR practices positively impact companies’ credit ratings by enhancing solvency and financial performance. Specifically, firms that prioritize CSR, particularly in the social and environmental dimensions (such as community relations, diversity, employee relations, environmental performance and product characteristics), tend to have higher credit ratings and a reduced risk of default. This suggests that credit rating agencies likely incorporate CSR performance when assigning credit ratings. Furthermore, the quality of corporate governance acts as a moderator, strengthening the relationship between CSR and credit ratings. The findings remain robust even after accounting for key firm attributes and addressing potential endogeneity between CSR and credit ratings.

This research provides valuable guidance for policymakers, corporate managers, investors and other stakeholders, as it offers insights into the influence of CSR activities on risk premiums and financing costs. For financial institutions, expanding credit decisions to encompass non-financial factors such as CSR can result in more accurate predictions of firm credit quality compared to relying solely on financial indicators.

To the best of the authors’ knowledge, this study stands out as the first to systematically examine the relationship between CSR and credit ratings within the French context. Moreover, it distinguishes itself by investigating the moderating influence of corporate governance on this relationship, setting it apart from prior research.

This study examines whether international tourism demand in the Visegrád countries is influenced by countries' risk rating on environmental, social and governance (ESG) factors, as non-economic factors relating to ESG risks have been ignored by previous researches on determinants of international tourism demand.

The study investigates panel data for the Visegrád countries comprising the Czech Republic, Hungary, Poland and Slovakia over the period 1995–2019. Recently developed techniques of augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators are employed so as to take care of cross-sectional dependence, nonstationary residuals and possible heterogeneous slope coefficients.

The regression estimates suggest that besides economic factors, the perception of international tourists regarding ESG risk is another important determinant of international tourism demand in the Visegrád countries. The study also established that income levels in the tourists' originating countries are the most critical determinant of international tourism demand to the Visegrád countries.

The research outcomes of the study include the need for the Visegrád countries to direct policies towards further mitigating their ESG risks in order to improve future international tourism demand in the area. They also need to ensure exchange rate stability to prevent volatility and sudden spikes in the relative price of tourism in their countries.

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 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 study aims to build on a contextualised approach for revealing the particularities of social sustainability indicators on the building scale within the particular context of Jordan, focusing on the buildings of the Dahiyat Al Hussein suburb.

This study relied on a review of the relevant previous research as well as global, regional and local rating tools, followed by working with a diversified participant base comprising experts as well as users through Delphi rounds and analytic hierarchy process-based assessments.

This study resulted in several key indicators that were classified into three main categories: environmental friendliness, comfort and convenience and social blend. It resembled a step on the path towards the standardisation of context-specific social sustainability indicators, an effort that would be further supported by future research addressing other development within Jordan.

The sustainability debate has, for long, acquired the undivided attention of its key stakeholders. With the continuous rise of global attention, the conceptualisation of sustainability has grown more specialised in both function and scale, and sustainable measures have been developed at the macro and micro levels. With the further spread of the concept, recognition of its contextual differences between various countries became more vivid, where the social dimension attains particular importance.