ESG Rating Divergence: Existence, Driving Factors, and Impact Effects

Abstract

In recent years, corporate ESG performance has been widely incorporated into investment decisions and capital allocation considerations, becoming a focal point and hot topic for research by governments and organizations worldwide. However, due to various reasons, significant discrepancies have emerged in ESG ratings for the same company across different institutions, and this growing divergence in ESG ratings has increasingly drawn the attention of scholars. Studying the differences in ESG (environmental, social, and corporate governance) ratings is of great significance. This not only helps to understand the root causes of differences, improve the objectivity, consistency, and comparability of ratings, but also helps users better understand the meaning and limitations of rating results. It is beneficial for investors to understand the focus of different ratings and develop more effective investment strategies. It can promote rated companies to improve the quality and transparency of ESG-related information disclosure. It can also provide a reference for regulatory agencies and policymakers, identify market failures and potential risks, and promote the development of more unified standards and frameworks. At the same time, this study can also promote the in-depth development of relevant academic research and theories. Based on this, this study systematically reviews the relevant literature on ESG rating divergence, focusing on its existence, causes, influencing factors, and impacts. The study finds that, in addition to the widespread existence of rating divergence in corporate ESG performance, scholars also disagree on the measurement and methods of this divergence. The reasons for rating divergence are mainly that ESG is a qualitative indicator; top-level design, intermediate calculations, and bottom-level data collection across multiple stages exacerbate divergence; and controversies in practice further deepen divergence, among others. The influencing factors and impact effects of ESG rating divergence are diverse. Given the existence of ESG rating divergence, all parties should treat ESG ratings with caution. This paper offers corresponding recommendations and looks forward to the future, providing a foundation for subsequent research.

1. Introduction

ESG stands for environmental, social, and governance. First proposed in 2004 by former United Nations Secretary-General Kofi Annan, it aims to measure and reflect the sustainability performance of enterprises in environmental protection, social responsibility, and corporate governance during their development. Today, corporate ESG has been widely integrated into investment decisions and capital allocation considerations, becoming a focal point of research for governments and organizations worldwide [1]. Unlike traditional quantitative financial metrics, ESG places greater emphasis on a company’s balance and performance across multiple dimensions, including resource utilization and environmental conservation, social impact and stakeholder coordination, as well as corporate structure and internal controls. In China, where green development principles and ecological civilization construction are increasingly prioritized, ESG development serves as a powerful tool and foundation for achieving “carbon peak” and “carbon neutrality” goals.

In practice, the measurement and application of ESG performance involve multiple stages such as disclosure, evaluation, and investment [2]. Among these, the assessment (or rating) of corporate ESG information is a critical component. By evaluating corporate ESG performance, this process can “promote improvements through evaluation”, compelling enterprises to enhance their ESG performance and sustainable development capabilities [3]. This drives companies to boost competitiveness and reputation, optimize resource allocation, reduce financing costs, and mitigate operational risks [4]. Additionally, ESG evaluations help managers and investors identify and understand significant ESG-related financial risks, aiding investors in risk assessment and decision-making. They also facilitate the development of sustainable policies and regulations by providing policymakers with concrete metrics to design more effective environmental protection, social welfare, and corporate governance policies. Currently, numerous research institutions globally conduct ESG ratings and studies. Internationally recognized ESG rating agencies include KLD, MSCI, Sustainalytics, Thomson Reuters, FTSE Russell, S&P Dow Jones, and Vigeo Eiris. In China, organizations that rate and publish ESG-related reports are primarily consultancies and academic institutions, such as SynTao Green Finance, Huazheng, Wind, and others. These domestic ratings predominantly focus on listed companies with robust information disclosure practices.

In recent years, the Chinese government has prioritized advancing ESG norms and standardization for listed companies. In 2022 and 2023, the State-owned Assets Supervision and Administration Commission (SASAC) of the State Council issued the Work Plan for Improving the Quality of Central State-Owned Enterprise-Controlled Listed Companies and the Recommendation Letter on Further Promoting ESG Information Disclosure by State-Owned Enterprises. These documents emphasized that central enterprise groups should actively participate in shaping China’s ESG information disclosure rules, evaluation frameworks, and investment guidelines. In June 2024, SASAC mandated the integration of ESG initiatives into the holistic management of social responsibility for central state-owned enterprises (SOEs) in the new era, urging them to proactively address both the opportunities and challenges posed by ESG development. During the 2025 “Two Sessions” (China’s annual meetings of the National People’s Congress and the Chinese People’s Political Consultative Conference), the government pledged to accelerate the establishment of a unified ESG standard system to ensure policy coherence and practicality. Listed companies and large enterprises are now required to incorporate ESG-related content into financial reports to enhance the transparency and standardization of information disclosure. Despite these advancements, China’s ESG evaluation standardization still faces challenges [5]. Notably, significant discrepancies persist in ESG ratings for the same company across different agencies.

Divergent or even conflicting ESG ratings from different agencies create a cascade of impacts for stakeholders [6]. Rating discrepancies may lead to confusion among investors, who struggle to determine which evaluation to rely on. Companies facing inconsistent ratings may confront challenges in managing their public image, potentially undermining their ability to secure capital, exacerbating financing constraints, and distorting market valuations [7]. For regulators, such inconsistencies complicate the selection or establishment of unified rating benchmarks, thereby affecting the overall market’s ESG compliance and fairness. ESG rating discrepancies and their consequences have drawn significant attention, emerging as a critical, challenging, and highly debated issue in ESG research. Therefore, studying the differences in ESG (environmental, social, and corporate governance) ratings is of great significance. This study helps identify the weak links in the existing rating system. By comparing and analyzing different rating methods, it can promote rating agencies to optimize their evaluation framework, improve the objectivity, consistency, and comparability of ratings, and thus help users better understand the meaning and limitations of rating results. Secondly, studying differences can help investors understand the focus of different ratings and encourage them to consider multiple sources of information comprehensively, avoiding judgment bias that may arise from excessive reliance on a single ESG rating, and supporting them in choosing ESG ratings that are more in line with their own values and investment goals, or building more robust ESG investment portfolios. Again, such research can also promote companies to improve their ESG performance and information disclosure quality. By revealing the shortcomings in current corporate ESG information disclosure, we aim to encourage companies to provide more comprehensive, reliable, and standardized ESG data. In addition, the study of ESG rating divergence provides important insights for regulatory agencies and policymakers, highlighting the urgent need for more unified ESG disclosure standards, reporting frameworks, and rating methodologies in the market. It can provide a basis for policymaking and promote the establishment of more unified standards and frameworks. At the same time, understanding the reasons and impacts of rating discrepancies can help regulatory agencies develop more effective regulatory measures, regulate the behavior of ESG rating agencies, prevent conflicts of interest, and ultimately enhance the overall health and credibility of the market. In summary, the importance of delving into ESG rating divergence is profound. By understanding and effectively addressing rating divergence issues, we can better leverage the positive role of ESG in promoting sustainable economic and social development.

Based on this, this paper aims to explore the following core issues: What are the characteristics, measurement methods, and current development status of ESG rating divergence? How does the existing literature define ESG rating divergence? What are the main driving factors for ESG rating divergence? What are the consequences [8] of ESG rating divergence? Besides the commonly perceived negative impact on investors and rated companies, may there also be potential “constructive” or positive effects of ESG rating divergence? What are the methodological characteristics, advantages, and limitations of current research on ESG rating divergence? What key areas or research gaps should future research focus on that have not been fully explored?

The potential innovations of this paper can be summarized as follows: At the theoretical level, considering that the explanations for the reasons for differences are often scattered in different literature, this paper provides a complete and comprehensive analysis of ESG differences, systematically explaining the emergence, persistence, and impact of ESG rating differences, thus providing a more comprehensive and explanatory theoretical basis for understanding this complex ESG rating phenomenon. Secondly, this article will delve into the essential connotations and dimensions of “divergence”. The divergence in ESG ratings is not only manifested in the numerical differences in rating results but also in the academic measurement of it. Therefore, this paper will attempt to classify, compare, and evaluate the divergence measurement methods in existing literature in order to gain a more refined understanding of the composition of ESG rating divergence and provide clearer and more accurate measurement ideas and analysis directions for subsequent empirical research. Furthermore, this article will systematically review, classify, and critically evaluate previous research methods on ESG rating divergence, aiming to provide methodological references and improvement directions for subsequent researchers, thereby helping to enhance the rigor and credibility of research in this field. At the application and policy levels, this study specifically focuses on and summarizes the performance, driving factors, and impacts of ESG rating divergence in specific contexts (such as developing countries like China), aiming to provide empirical evidence for government regulatory agencies to formulate more targeted policies. On the other hand, given that previous research has focused more on discussing the impact of ESG rating divergence from a single perspective of investors or rated companies, this article aims to comprehensively sort out and compare the specific impacts of rating divergence on a wider range of stakeholders, including investors, companies, regulatory agencies, rating agencies themselves, and academia.

2. Methodology and Findings

2.1. Methodology

To ensure a comprehensive and systematic identification of research literature related to ESG rating divergence, this study adopted a multi-stage literature search and screening strategy. The literature search mainly relied on the following core academic databases: Web of Science (WoS) Core Collection, Google Scholar, and other mainstream databases, and supplemented the search with relevant journals in the recommended list of “FMS Management Science High Quality Journals”. The time range for retrieval is set from 1 January 1990 to 31 March 2025, aiming to cover the major academic developments in the field since the rise of ESG rating concepts.

The construction of search terms is based on the deconstruction of the core theme “ESG rating divergence”, mainly combining and expanding around the three core concept clusters of “ESG”, “rating”, and “divergence” phenomenon. Such as “ESG”, “Environmental, Social, and Governance”; ESG rating, ESG score, ESG divergence, and so on. When searching, the above keywords are prioritized for combination search in the title, abstract, and keyword fields of the literature to improve the relevance of the search results.

This study mainly includes peer-reviewed academic journal articles published in English, peer-reviewed papers from important academic conferences, and high-quality working papers from authoritative academic series. Non-academic news reports, blogs, purely opinion articles, book reviews, and literature with only abstracts are excluded. The literature screening process consists of two main stages: firstly, preliminary screening is conducted based on titles and abstracts to exclude obviously irrelevant literature; Subsequently, a detailed full-text reading of the literature that passed the initial screening is conducted, and the final screening is carried out based on the predetermined inclusion and exclusion criteria.

2.2. Findings

ESG rating divergence refers to situations where different assessment agencies provide varying evaluations of a company or project’s ESG performance [9,10]. These discrepancies may manifest as differences in rating tiers, numerical scores, or the format of evaluation results [11]. Currently, three primary ESG rating formats exist in the market:

• Tiered Ratings: For example, SynTao Green Finance uses a 10-tier system (A+ to D), while the Social Value Investment Alliance (SVIA) employs a 20-tier classification [12];
• Numerical Scores: FTSE Russell publishes ESG performance on a 5-point scale, whereas Huazheng assigns percentage-based ESG scores;
• Rankings: Some agencies rank companies based on annual ESG scores. For instance, the China Research Data Service Platform (CNRDS), developed by Shanghai Jinghe Information Technology Co., Ltd. (Shanghai, China), releases ESG rankings of all listed companies (excluding delisted firms) each year.

In addition to differences in presentation, even for the same type of rating method, there is a phenomenon of inconsistent evaluation. Biio et al. [13] conducted a study on Nissan’s ESG rating based on data from four rating agencies. They found that Sustainalytics and MSCI gave lower ratings, while RobecoSAM and Refinitiv believed that Nissan was leading among similar companies. This rating difference may stem from different institutions’ emphasis on assessing corporate ESG risks and opportunities. In 2022, SynTao Green Finance assigned a comprehensive ESG rating of B+ to Vanke, while Huazheng rated the same company A. Similarly, Kweichow Moutai, a well-known Chinese liquor company, is also faced with inconsistent ESG ratings from different institutions: domestic rating agencies tend to give high ratings, while international institutions such as MSCI and Bloomberg usually give low ratings because they belong to alcohol-related sin stocks. These cases indicate that ESG rating divergence is a common phenomenon that can lead to confusion in understanding corporate performance and has a significant impact on corporate reputation and market expectations [14].

Table 1. Paired correlation coefficients of ESG ratings from different rating agencies.

Authors	Rating Agency	Sample Object Authors	Pairwise Correlation Coefficient	Mean Value
Chatterji et al. [6]	KLD, Asset4, Innovest, DJSI, FTSE, Calvert	S&P 500, Russell 1000 index companies from 2002 to 2010	−0.12–0.67	0.3
Berg et al. [15]	Asset4, KLD, MSCI, RobecoSAM, Sustainalytics, Vigeo Eiris	Companies rated by 6 rating agencies in 2014	0.38–0.71	0.54
Dimson et al. [16]	MSCI, FTSE, Sustainalyties	STOXX Europe 600 Index Companies from 2010 to 2021	0.30–0.59	0.45
Chen et al. [14]	Wind, SinoData, OuantData	Chinese A-share listed companies from 2018 to 2022	0.27–0.35	0.31
Wang et al. [17]	Sino, Bloomberg, MSCI, WindSyTao, CASVI	Chinese A-share listed companies from 2009 to 2021	0.18–0.56	0.4
Zumente and Lāce [18]	ISS.Bloomberg, RobecoSAMSustainalytics, MSCI	As of 2021, listed companies on European stock exchanges	0.08–0.59	0.29
Jacobsen [19]	Refinitiv, Bloomberg, S&P Global	STOXX Europe 600 Index Companies from 2010 to 2021	0.58–0.69	0.54
Jensen and Holmeset [4]	Refinitiv, Sustainalytics, Bloomberg, S&P Global	Companies rated by at least two rating agencies from 2004 to 2022	0.42–0.66	0.54

shows the pairwise correlation coefficients of ESG ratings from different rating agencies. It can be seen that the pairwise correlation coefficients of different ESG ratings are much lower than those of credit ratings (above 0.95), indicating significant differences in ESG ratings.

As shown in

Table 2. Institutions that provide ESG ratings for listed companies in China and their introductions.

Rating Agency	Brief Introduction	Website	Rating Format	Index
Bloomberg News	A globally leading provider of financial information and data services	https://www.bloombergchina.com/solution/sustainable-finance/ (accessed on 15 March 2025)	Scoring system (percentage system)	More than 600 indicators
Morgan Stanley Capital International	A globally renowned rating and index provider, particularly known for its Environmental, Social, and Governance (ESG) ratings. In addition, MSCI also offers indices for stocks and bonds	https://www.morganstanley.com/ (accessed on 15 March 2025)	Scoring system	By conducting a comprehensive evaluation of the collected large amount of data through buried points, the focus is mainly on the intersection between the company’s core business and the ESG issues in its industry, in order to determine the positive or negative impact that ESG issues may have on the company
FTSE Russell	An index provider, but it also involves rating services, particularly in the field of environmental, social, and governance (ESG)	ftserussell.com (accessed on 15 March 2025)	Scoring system (5-point system)	3 major themes, 14 sub themes, and over 300 indicators
Huazheng	An independent third-party professional service provider for various asset management institutions, specializing in comprehensive index and index investment services	https://www.chindices.com/ (accessed on 15 March 2025)	Scoring system (percentage system); Hierarchical system (9 levels from CCC to AAA)	3 major themes, 16 sub themes, 44 key indicators, and over 300 underlying indicators. The principle of weight assignment is that the weight with higher impact is higher, and the weight with shorter impact time is higher
China Research Data Service Platform (CNRDS)	The Chinese Research Data Services Platform (CNRDS) is a high-quality, open, and platform based comprehensive data platform for Chinese economic, financial, and business research	http://www.efindata.com/Home/Academic (accessed on 15 March 2025)	Scoring system (percentage system); Ranking system. Based on the ESG scores of the year, rank listed companies that have not been delisted for the entire year.	3 primary indicators, 14 secondary indicators, and 39 tertiary indicators. It includes indicators such as emergency plans for sudden environmental risks, employee rights (whether there are women in directors, supervisors, and senior positions and their proportion), employee training and education, rural revitalization, employee growth rate, and employee news coverage (jumping off buildings, mining accidents/occupational diseases)
Wind	China’s leading financial information service enterprise, supporting the dynamic extraction of data from Wande Financial terminals and Excel	https://www.wind.com.cn/mobile/EDB/zh.html (accessed on 15 March 2025)	Hierarchical system. There are 9 levels from CCC to AAA;Scoring system (ten point system)	Three primary indicators, 29 secondary indicators, and over 500 tertiary indicators are used to evaluate controversial events based on news and public opinion, legal proceedings, and regulatory penalties
Menglang ESG	A fintech company that focuses on quantitative evaluation of sustainable development value, with two main products: Menglang Carbon Financing and Menglang FIN-ESG Database	https://www.susallwave.com/finesg-db/activity/0 (accessed on 15 March 2025)	Hierarchical system; There are a total of 9 basic levels, which are detailed into 19 enhancement levels.	There are 6 primary indicators, 30+secondary indicators, and 90+tertiary indicators
SynTao Green Finance	A leading professional service institution in China specializing in green finance and responsible investment, dedicated to providing clients with professional services such as color finance consulting and research	https://www.syntaogf.net/ (accessed on 15 March 2025)	Hierarchical system. There are ten levels: D, C−, C+, B−, B+, A−, A−, and A	Three dimensions, 14 secondary indicators, and over 200 specific indicators
Rankins CSR Ratings (CSMAR)	Designed and developed jointly by Shenzhen Xishima Data Technology Co., Ltd. (Shenzhen, China) and Runling Global Consulting Co., Ltd. (Beijing, China)	http://www.rksratings.cn/ (accessed on 15 March 2025)	Hierarchical system; There are 7 levels: CCC, B, BB, BBB, A, AA, and AAA; Scoring system (0–10 points)	3 primary indicators and 35 secondary key indicators, each involving over 100 sub indicators

, which summarizes ESG rating agencies and their methodologies for Chinese listed companies, domestic institutions do not mechanically replicate international ESG frameworks. Instead, they adapt criteria to China’s unique socioeconomic context, incorporating metrics such as anti-corruption, rural revitalization, and common prosperity into their ESG evaluation systems.

In addition to the rating discrepancies generated by various assessment agencies, scholars have also developed diverse methods and perspectives for measuring and defining the scale of such divergences in their research. Currently, academic studies primarily employ the following approaches to quantify ESG rating divergence; see Table 3 and Table 4 for details:

• Standard Deviation: Researchers collect ESG ratings from different agencies. For tier-based ratings (e.g., letter grades like A+, B), numerical values are assigned and normalized. For score-based ratings (e.g., percentage or point systems), the data are directly normalized. The processed data are then used to compute the standard deviation, which serves as an indicator of the magnitude of ESG divergence for a given year.
• Coefficient of Variation: Building on the standard deviation calculation, the coefficient of variation is derived by dividing the standard deviation by the mean of ESG ratings. This metric quantifies the relative magnitude of ESG rating divergence.
• Percentage (Quantile) Ranking Standard Deviation: This method measures pairwise ESG rating divergence (i.e., the average divergence across all “rater pairs” in a given year). Step 1: For each year, identify samples covered by any two ESG rating agencies. Rank the ESG ratings from both agencies separately and calculate each company’s percentile ranking within each agency’s evaluation. Step 2: Compute the standard deviation of these percentile (quantile) rankings between the two agencies to determine pairwise ESG rating divergence. Step 3: Repeat Steps 1 and 2 for all 10 possible combinations of five rating agencies. The overall ESG rating divergence for a company is then calculated as the average of all pairwise divergences. This approach addresses differences in rating scales and coverage across agencies, preserving data richness while ensuring comparability.
• Pairwise Standard Deviation Mean: The divergence is measured by averaging the standard deviations of ratings from all possible pairs of agencies for the same company.
• Binary Reclassification: If a company’s ESG ratings fall into different tiers across agencies, divergence is assigned a value of 1; otherwise, it is 0.
• Logarithmic Ratio of Bloomberg and Huazheng ESG Scores: ESG rating divergence is calculated as the natural logarithm of the ratio of Bloomberg’s ESG score to Huazheng’s score.

2.3. Reasons for Differences in ESG Ratings

2.3.1. Back to the Roots: The Limitations of Qualitative Metrics

While ESG emerged early as a response to the global sustainability movement, governments and international bodies like the United Nations have yet to formally define the specific components of its three pillars—environmental, social, and governance. ESG integrates these elements into a unified framework, yet their superficial independence belies an underlying interconnectedness [20]. As an advanced paradigm replacing traditional corporate social responsibility (CSR) [21], ESG lacks a robust theoretical foundation to justify its legitimacy and rationality [22]. This theoretical ambiguity leads to complex objectives, conflicting values, inconsistent measurement practices, and an opaque ecosystem, resulting in fragmented understanding and misaligned priorities among businesses and raters. Divergent interpretations of ESG abound, with many deviating from its core principles. Narrowing, overgeneralizing, overestimating, or superficializing ESG has become a common deviation in both academic research and corporate practice [23].

Fundamentally, ESG metrics are qualitative and distinct from traditional financial data (e.g., profits, revenue), which benefit from globally standardized accounting principles [24], reporting formats, and calculative methodologies. In contrast, ESG metrics—such as environmental impact, social responsibility, and controversy management—are inherently shaped by raters’ cognitive biases, sociocultural contexts, and value systems. The same ESG data may carry divergent meanings depending on individual expertise or institutional stances. For instance, Western researchers might prioritize climate change and social equity, while Chinese analysts may emphasize poverty alleviation and anti-corruption, reflecting distinct cultural and policy priorities that inevitably influence rating outcomes.

In summary, the ambiguity, subjectivity, lack of standardization, and irreplicability of qualitative ESG metrics hinder the establishment of a unified evaluation framework.

2.3.2. Stratified Disruption: Multi-Stage Amplification of Divergence

$$\begin{array}{c}\mathrm{ESG}\left(i\right)=E\left(i\right)\ast W_e+S\left(i\right)\ast W_s+G\left(i\right)\ast W_g\end{array}$$

(1)

$$\begin{array}{c}E\left(i\right)=E_1\left(i\right)\ast w_{e1}+E_2\left(i\right)\ast w_{e2}+E_3\left(i\right)\ast w_{e3}+\cdots +E_n\left(i\right)\ast w_{en}\end{array}$$

(2)

$$\begin{array}{c}S\left(i\right)=S_1\left(i\right)\ast w_{s1}+S_2\left(i\right)\ast w_{s2}+S_3\left(i\right)\ast w_{s3}+\cdots +S_n\left(i\right)\ast w_{sn}\end{array}$$

(3)

$$\begin{array}{c}G\left(i\right)=G_1\left(i\right)\ast w_{g1}+G_2\left(i\right)\ast w_{g2}+G_3\left(i\right)\ast w_{g3}+\cdots +G_n\left(i\right)\ast w_{gn}\end{array}$$

(4)

$$\begin{array}{c}{\mathrm{ESG}}_{a-b}\left(i\right)={\mathrm{ESG}}_a\left(i\right)-{\mathrm{ESG}}_b\left(i\right)\end{array}$$

(5)

As shown in Equations (1)–(5), firstly, the overall ESG score ($\mathrm{ESG}\left(i\right)$) of company i is obtained by the weighted summation of its scores in three core dimensions: the environmental score $E\left(i\right)$ multiplied by its weight $w_e$, the social score $S\left(i\right)$ multiplied by its weight $w_s$, and the governance score $G\left(i\right)$ multiplied by its weight $w_g$. The score for each dimension (such as company $i^{\prime }s$ environmental score $E\left(i\right))$ is obtained by multiplying its performance on a series of specific environmental indicators (denoted as $E_1\left(i\right)$, $E_2\left(i\right)$, etc.) by their respective weights and adding them up. The difference in ESG ratings ${ESG}_{a-b}\left(i\right)$ between Institution a and Institution b for the same company i can be further decomposed into differences caused by various pillars (environmental, social, and governance).

The generation of the results of ESG divergence is decomposed, and the causes of divergence are analyzed in terms of top-level design, intermediate links, and bottom-level data [25].

In terms of the top-level design of the ESG rating framework, there are differences in the design and framework adopted due to differences in stakeholder needs, indicator selection, weighting, and philosophy, especially in the domestic top-down ESG rating system [26,27]. First, due to the lack of uniform standards globally, the variety of existing standards, and the lack of a unified consensus, as well as the differences in the target markets and user needs of the rating agencies, the rating agencies tend to focus on different aspects when selecting standards, which increases their room for discretion and leads to differences in the “what” and “how” of the rating system. Chatterji et al. [6] argue that the divergence stems from the lack of consensus on ESG metrics, as shown in Table 2, where the range of secondary and bottom-tier metrics chosen by agencies varies too much, with each agency having its own standards and philosophies. Berg et al. [15] analyze the differences between sustainability ratings and identify three different sources of disagreement: scope disagreement, measurement disagreement, and weight disagreement, where measurement disagreement contributes more than 50% of the disagreement, scope contributes 38%, and weight disagreement contributes the least (6%).

In the intermediate stages of ESG rating calculations, such as data cleaning, processing, and computation, different rating agencies also lack unified standards. Due to the vast amount of data residing in semi-structured and unstructured formats like text, news, public sentiment, and legal judgments, researchers commonly use natural language processing (NLP) and AI semantic analysis for data extraction and processing. However, deviations arise from differences in the selection of technologies and models. During data cleaning, variations in handling missing values (deletion, imputation, interpolation), data standardization (min-max normalization, zero-mean normalization, logarithmic transformation), and dimensionality reduction techniques also lead to inconsistencies in final ratings.

In the preliminary stages of ESG rating data selection and collection, the divergent foundational data selection and collection methods across rating agencies represent another source of discrepancies. For example, FTSE Russell primarily relies on publicly disclosed corporate information, while MSCI ESG Ratings uses corporate self-reported data supplemented by alternative data from academic institutions and government organizations to verify authenticity. SynTao Green Finance, on the other hand, selects publicly available market data and third-party databases. These differences in data sources amplify final rating divergences. Additionally, variations in collection frequency (daily, monthly, annual) and subsequent long-term dynamic tracking, monitoring, and evaluation further affect discrepancies in outcomes.

Overall, from data collection technologies in the initial stage to data cleaning, missing value imputation, standardization techniques, and onward to rating calculation workflows, weighting methodologies, and scoring algorithms in the evaluation phase, each difference in data processing and technical method selection alters the subsequent data structure and format. As a result, identical ESG information may be differentially—or even inaccurately—reflected in ESG rating outcomes due to varying processing techniques, ultimately exacerbating divergences among rating agencies.

2.3.3. Factor Drivers—Controversies in Practice Leading to Divergences

The discrepancies in ESG rating results are not accidental but rather stem from the interplay of multiple complex factors at the practical level, often rooted in the real-world challenges of corporate operations, strategic decisions, and systemic controversies. These driving factors can be systematically reviewed and compared across multiple dimensions, such as the macroeconomic and institutional environment, information disclosure practices, corporate motivations and behaviors, and micro-level characteristics.

Firstly, at the macroeconomic and institutional level, the development of ESG itself is a process of co-evolution at both macro and micro levels, often advancing spirally amidst controversies [28]. Due to the varying institutional backgrounds, regulatory intensities, and maturity of ESG ecosystems across different countries and regions [18,29], ESG exhibits different characteristics and patterns in diverse environments, which introduces initial complexity for rating agencies in establishing uniform evaluation standards.

Secondly, ESG information disclosure practices and standards are a critical link contributing to rating discrepancies. Compared to the relatively mature mandatory disclosure systems in developed regions like Europe and America, China is currently undergoing a gradual transition from voluntary to semi-mandatory, and eventually to mandatory disclosure. During this process, the awareness among enterprises regarding proactive ESG information disclosure varies, and coupled with a lack of uniformity in disclosure standards and formats, this significantly ‘amplifies’ the ‘noise’ in the signals transmitted to rating agencies, making it difficult for them to conduct assessments based on homogenized information.

Furthermore, corporate practical motivations and behaviors directly influence ESG rating discrepancies. The strong externalities inherent in the ESG concept may conflict with the goal of some companies to maximize short-term economic benefits. Some companies may believe that ESG fails to create direct value for the firm and its investors, thus merely making ‘superficial commitments’ or even resisting ESG practices. Other companies, aiming to reduce financing constraints or gain the trust of ‘ESG-friendly’ investors, may engage in strategic ‘greenwashing’ [30] or ‘carbonwashing’ [31,32]. Whether it is passive resistance or active strategic disclosure, these motivations increase the uncertainty and potential discrepancies in rating outcomes.

Finally, beyond the aforementioned aspects, a series of more specific internal corporate characteristics (such as corporate governance and digital capabilities) and external environmental factors (such as capital markets and investor behavior, policy regulations and compliance requirements, socio-cultural aspects and feedback, among others) collectively influence ESG rating results (see Table 3 for details). Rating agencies differ in the methods and focuses they adopt when understanding these diverse factors, assessing their specific impacts, and assigning corresponding weights, which further increases the likelihood of discrepancies in ESG rating results.

3. Research Status on ESG Rating Divergence

3.1. Current Status of Research on Driving Factors of ESG Rating Differences

Regarding the causes of ESG rating divergences, scholars have conducted extensive empirical research, delving into various influencing factors and their causal relationships. Key research themes primarily cover: corporate governance and digital capabilities, information disclosure and ESG practices, capital markets and investor behavior, policies/regulations and compliance requirements, as well as social culture and feedback mechanisms. See Table 3 for details.

Table 3. Classification results of influencing factors of ESG rating divergence (partial literature).

Subject	Independent Variable	Mediating/Moderating Variables	Research Conclusion	Research Sample	Related Literature	Processing Method
Corporate Governance And Digital Capabilities	Corporate Supply Chain Digitalization	Mediating Variable: Information Disclosure, Governmental Environmental Subsidies, Corporate Greenwashing Behaviors	Main Effect: −; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Sun [33]	Standard Deviation
	Provincial Digital Finance Levels	-	Main Effect: −	Chinese A-Share Listed Companies	Guo et al. [34]	Standard Deviation
Information Disclosure And Esg Practices	Information Disclosure	-	Main Effect: +	European And American Cases	Christensen et al. [35]	Standard Deviation
	Disclosure Quantification	-	Main Effect: +	Chinese A-Share Listed Companies	Liu [36]	Standard Deviation
Capital Markets And Investor Behavior	Capital Market Liberalization	Mediating Variable: Impression Management	Main Effect: +	Chinese A-Share Listed Companies	Yan et al. [37]	Standard Deviation
	Capital Market Opening	Adjusting Variables: Analyst Attention, Firm Audit Environment, And Investor Attention	Main Effect: +; Moderating Effect:✔	Chinese A-Share Listed Companies	Sun et al. [38]	The Difference Between Two Indicators
	Investor Enterprise Interaction	Mediating Variable: Esg Disclosure Quality	Main Effect: −; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Liu et al. [39]	Standard Deviation
Policies/Regulations And Compliance Requirements	-	-	-	Kld, Sustainalytics, Moody’S Esg, Refinitiv, Msci, And Standard&Poor’S Global	Berg et al. [15]	Binary Reclassification
	Eu Classification System	-	-	-	Dumrose et al. [40]	-
	Legal Origin Theory	-	Main Effect: ✔	Esg Ratings of 2392 Listed Companies in 53 Countries/Regions	Kurbus and Rant [41]	Standard Deviation
	Environmental Policies (Penalties Or Incentives)	Mediating Variables: Green Technology Innovation; Green Behavior	Main Effect: +; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Li et al. [42]	Pairwise Standard Deviation Mean
Social Culture And Feedback Mechanisms	Confucian Culture	-	Main Effect: +	Chinese A-Share Listed Companies	Tian [43]	Standard Deviation
	Corporate Identity	Mediating Variable: Impression Management	Main Effect: +; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	He et al. [44]	Standard Deviation
	Media Attention	Adjusting Variables: Information Disclosure, Rating Agency Attention	Main Effect: +; Moderating Effect: ✔	Chinese A-Share Listed Companies	Chen et al. [45]	Standard Deviation
	The Impact Of Esg Investor Preferences On Asset Prices	-	Main Effect: −	Grouped Esg Investment Index	Billio et al. [13]	Binary Reclassification

Table 3. Classification results of influencing factors of ESG rating divergence (partial literature).

Subject	Independent Variable	Mediating/Moderating Variables	Research Conclusion	Research Sample	Related Literature	Processing Method
Corporate Governance And Digital Capabilities	Corporate Supply Chain Digitalization	Mediating Variable: Information Disclosure, Governmental Environmental Subsidies, Corporate Greenwashing Behaviors	Main Effect: −; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Sun [33]	Standard Deviation
	Provincial Digital Finance Levels	-	Main Effect: −	Chinese A-Share Listed Companies	Guo et al. [34]	Standard Deviation
Information Disclosure And Esg Practices	Information Disclosure	-	Main Effect: +	European And American Cases	Christensen et al. [35]	Standard Deviation
	Disclosure Quantification	-	Main Effect: +	Chinese A-Share Listed Companies	Liu [36]	Standard Deviation
Capital Markets And Investor Behavior	Capital Market Liberalization	Mediating Variable: Impression Management	Main Effect: +	Chinese A-Share Listed Companies	Yan et al. [37]	Standard Deviation
	Capital Market Opening	Adjusting Variables: Analyst Attention, Firm Audit Environment, And Investor Attention	Main Effect: +; Moderating Effect:✔	Chinese A-Share Listed Companies	Sun et al. [38]	The Difference Between Two Indicators
	Investor Enterprise Interaction	Mediating Variable: Esg Disclosure Quality	Main Effect: −; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Liu et al. [39]	Standard Deviation
Policies/Regulations And Compliance Requirements	-	-	-	Kld, Sustainalytics, Moody’S Esg, Refinitiv, Msci, And Standard&Poor’S Global	Berg et al. [15]	Binary Reclassification
	Eu Classification System	-	-	-	Dumrose et al. [40]	-
	Legal Origin Theory	-	Main Effect: ✔	Esg Ratings of 2392 Listed Companies in 53 Countries/Regions	Kurbus and Rant [41]	Standard Deviation
	Environmental Policies (Penalties Or Incentives)	Mediating Variables: Green Technology Innovation; Green Behavior	Main Effect: +; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	Li et al. [42]	Pairwise Standard Deviation Mean
Social Culture And Feedback Mechanisms	Confucian Culture	-	Main Effect: +	Chinese A-Share Listed Companies	Tian [43]	Standard Deviation
	Corporate Identity	Mediating Variable: Impression Management	Main Effect: +; Mesomeric Effect: ✔	Chinese A-Share Listed Companies	He et al. [44]	Standard Deviation
	Media Attention	Adjusting Variables: Information Disclosure, Rating Agency Attention	Main Effect: +; Moderating Effect: ✔	Chinese A-Share Listed Companies	Chen et al. [45]	Standard Deviation
	The Impact Of Esg Investor Preferences On Asset Prices	-	Main Effect: −	Grouped Esg Investment Index	Billio et al. [13]	Binary Reclassification

In the aspect of corporate governance and digital capabilities, scholars have found that robust corporate governance and internal controls reduce ESG rating divergences. The mechanism lies in the fact that effective internal controls enhance the quality of information disclosure and promote the fulfillment of social responsibilities, thereby mitigating ESG rating discrepancies [46]. Specifically, companies with well-established internal controls typically have stricter disclosure systems, ensuring the completeness and authenticity of ESG reports. This helps reduce informational disparities encountered by rating agencies, subsequently lowering rating divergences. Other studies reveal that executives’ overseas backgrounds also reduce ESG rating divergences. Research suggests that executives with international experience (particularly in Europe or the U.S.) are more familiar with global ESG standards, which improves the quality of ESG disclosures and encourages firms to adopt universally recognized reporting frameworks and governance practices. Further studies indicate that factors such as analyst attention, audit opinions, and ISO 14001 [47] certification can moderate the impact of executives’ overseas backgrounds on ESG rating divergences. These factors themselves help reduce information asymmetry and lower rating discrepancies. For instance, ISO 14001 certification, as an international environmental management standard, enhances the credibility of corporate environmental data. Similarly, higher analyst scrutiny subjects corporate ESG information to greater external oversight. Collectively, these factors align rating agencies’ assessments across relevant dimensions, ultimately reducing divergences. Existing research demonstrates that supply chain digitalization and corporate digital transformation also reduce ESG rating divergences to varying degrees. Scholars have further analyzed the transmission mechanisms through which these factors mitigate divergences, including improved supply chain transparency, enhanced environmental disclosure quality, promotion of green technology innovation, strengthened corporate disclosure willingness, and increased media attention. For example, supply chain digitalization improves data accessibility and transparency, reduces information asymmetry, and thereby enhances ESG rating consistency. It also incentivizes firms to adopt green technologies, elevate environmental governance standards, and proactively disclose ESG information. Concurrently, when companies invest more in green technology innovation, ESG rating agencies’ evaluation criteria for their environmental performance may converge, reducing divergences. Similarly, corporate digital transformation (e.g., AI, big data analytics, cloud computing) enhances internal data integration capabilities and the accuracy/timeliness of external disclosures. Greater transparency enables rating agencies to assess ESG performance more precisely, lowering discrepancies. Additionally, digital transformation increases corporate visibility, allowing regulators, media, and investors to more effectively monitor ESG performance. This heightened external oversight improves the authenticity of disclosures, aligns rating agencies’ interpretations of ESG performance, and ultimately reduces divergences.

In the aspect of information disclosure and ESG practices [48,49,50], the prevailing view holds that when corporate disclosures are more comprehensive and of higher quality, rating agencies can access more consistent information [51], thereby reducing rating divergences [35]. However, some scholars’ research suggests that information disclosure may instead exacerbate ESG rating divergences. This phenomenon may stem from selective disclosure practices (e.g., companies favoring the disclosure of ESG information beneficial to themselves while avoiding negative information) and the diversity of adopted disclosure frameworks. The use of different frameworks leads to variations in data structures and prioritized issues, both of which may amplify rating divergences. Specifically, studies argue that as the extent of listed companies’ information disclosure increases, ESG rating agencies obtain more information. However, this may prompt agencies to select different sub-dimensions for evaluation under the same criteria, thereby generating discrepancies. For example, when assessing business ethics under the governance (G) dimension, for companies with highly detailed disclosures, one rating agency might select the firm’s whistleblower system as a sub-indicator of business ethics, while another might use the number of legal disputes as the basis. Conversely, for companies with limited disclosures—reporting legal disputes but omitting whistleblower systems—all ESG rating agencies may have to rely solely on legal disputes as the metric for business ethics, leading to more aligned ratings in that dimension. Thus, this perspective posits that greater corporate disclosure increases rating agencies’ flexibility in indicator selection, potentially resulting in larger ESG rating divergences. Additionally, product market competition may influence corporate motivations and strategies for ESG disclosure. In highly competitive markets, companies may adopt more cautious or selective ESG data disclosure practices to avoid providing exploitable information to competitors. This approach increases informational uncertainty for rating agencies and may elevate rating divergences. Conversely, factors that help reduce divergences include the degree of quantification and standardization in disclosed information, as well as third-party assurance. When ESG information is more quantifiable and standardized, assessments by different rating agencies are more likely to converge, lowering divergences [36]. Meanwhile, ESG reports verified by third parties typically exhibit higher quality and reliability, effectively alleviating rating agencies’ skepticism about information authenticity and similarly contributing to reduced divergences.

In terms of capital market performance and investor behavior, some scholars have studied the impact of capital market liberalization and openness on ESG rating divergences [15], finding that capital market openness generally exacerbates these divergences. While capital market liberalization facilitates firms’ access to international capital and enhances market competitiveness, it may also prompt firms to adopt impression management strategies to cater to specific investors and rating agencies [37]. This can lead to selective disclosure of favorable information while concealing unfavorable aspects. Such impression management may introduce biases in ESG information, causing different rating agencies to rely on distinct (and potentially biased) information sources, thereby intensifying ESG rating divergences. Furthermore, research indicates that the extent to which capital market openness influences ESG rating divergences is moderated by factors such as analyst coverage, audit environment quality, and investor attention [38]. Other scholars have examined the relationship between the cost of debt capital for listed firms and ESG rating divergences, finding that firms with higher debt capital costs are more likely to experience greater ESG rating divergences. Moreover, the strength of this relationship is moderated by the extent of ESG disclosure regulations. Additionally, some researchers have explored the impact of investor-firm interactions [39], revealing that active investor engagement with firms can enhance information transparency and improve ESG disclosure quality. Moreover, scholars have found that firms with higher stock price synchronicity (i.e., the degree to which an individual stock’s price moves in tandem with the overall market index) tend to exhibit greater ESG rating divergences. This may be because, on the one hand, higher stock price synchronicity implies that firm-specific information is less incorporated into market transactions, making it harder for external investors and rating agencies to accurately assess a firm’s ESG performance. On the other hand, firms with higher stock price synchronicity are more prone to analyst forecast biases, which may further contribute to rating divergences.

In terms of policies, regulations, and compliance requirements, the implementation of the new Securities Law has further strengthened information disclosure requirements for listed companies, significantly enhancing the transparency of ESG information [52]. Strict disclosure requirements help reduce rating agencies’ subjective interpretations of corporate ESG performance, thereby lowering rating divergences. At the same time, regulatory reinforcement improves corporate internal control quality, reducing the likelihood of management manipulating ESG information and thus decreasing rating uncertainty. As part of the ESG regulatory framework, the EU Taxonomy aims to establish a unified definition standard for sustainable economic activities [40]. This taxonomy requires companies to disclose ESG data according to consistent standards, thereby improving the comparability of ESG information. When corporate ESG disclosures become standardized and informational differences among rating agencies decrease, ESG rating divergences may also be reduced accordingly. Some scholars suggest that different legal origin theories influence ESG rating divergences. Analyzing ESG scores of 2392 listed companies across 53 countries or regions, research has found that different legal systems (such as the common law system, the French civil law system, and the German civil law system) affect the intensity of securities market regulation and corporate governance structures, which in turn influence ESG rating divergences [41]. Furthermore, environmental policies (such as carbon taxes, subsidies, and environmental protection laws) play a crucial role in corporate ESG performance. Policy instruments can be classified into punitive measures (e.g., pollution fines, carbon emission caps) and incentive measures (e.g., tax exemptions, subsidies) [42]. These policies influence corporate green technology innovation and sustainable behavior, thereby affecting ESG rating divergences. For example, when governments impose stricter environmental penalties (such as carbon taxes and pollution fines), companies may increase investment in green technology R&D to improve production efficiency and reduce pollution emissions, ultimately lowering rating divergences. Similarly, incentive policies (such as government subsidies and green credit support) encourage firms to adopt environmentally friendly production methods and comprehensively disclose environmental management measures in their ESG reports. When corporate green practices are incentivized by policies, ESG performance becomes more transparent and standardized, improving rating consistency among agencies and reducing rating divergences.

In terms of social culture and feedback, some scholars have found that Confucian culture may exacerbate ESG rating divergences. Confucianism emphasizes ethical responsibility, social harmony, and long-termism [43]. Therefore, in regions or companies where Confucian influence is strong, firms may be more inclined to undertake so-called “implicit” social responsibilities that are not necessarily disclosed through formal channels. This information asymmetry leads to differences in how various rating agencies perceive corporate ESG performance, thereby increasing rating divergences. Additionally, corporate ownership structure (e.g., state-owned vs. privately owned) also affects ESG rating divergences. State-owned enterprises (SOEs) are often guided by government policies and may bear stronger “implicit” responsibilities in social areas such as employment stability and environmental governance. However, these responsibilities are not always fully quantifiable, and the extent of their disclosure in ESG reports may be inconsistent, leading to significant disparities in rating agency assessments. While SOEs generally adhere to more standardized ESG reporting practices, their disclosures may be more policy-oriented rather than providing comprehensive environmental, social, and governance data. In contrast, privately owned enterprises, driven by market competition, may adopt more flexible ESG disclosure strategies, though often with lower standardization. Thus, ownership structure moderates the impact of corporate ESG information transparency on rating divergences. Furthermore, corporate image management is another critical factor [44]. Some firms engage in impression management to cultivate a “sustainable development” image by selectively disclosing favorable ESG information while concealing unfavorable aspects. This incomplete information leads rating agencies to make differing judgments based on varying data sources, thereby increasing rating divergences. Regarding information feedback mechanisms, firms with high media attention are often subject to stricter external scrutiny, which may improve the quality and completeness of their ESG disclosures. When the media extensively reports on corporate sustainability activities, rating agencies can access more consistent information, potentially reducing rating divergences [45]. However, some research suggests that certain ESG rating agencies tend to focus more on highly publicized firms, while overlooking those with lower media exposure. This discrepancy may lead to more transparent and consistent ratings for high-profile firms, while low-profile firms suffer from information gaps, thereby exacerbating overall rating divergences. Finally, market feedback, particularly investor preferences, also influences rating divergences [13]. As ESG investing becomes mainstream, investors increasingly favor stocks of companies with strong ESG performance. However, the market lacks a unified definition of “high ESG” firms. Since different rating agencies assess corporate ESG performance differently, investors rely on varying rating results to make investment decisions, which can lead to significant discrepancies in market valuations of ESG-related assets. This divergence not only increases asset price volatility but may also further amplify inconsistencies among rating agencies.

This chapter provides a comprehensive review of empirical research on the causes of ESG rating divergence, revealing the diversity and complexity of its influencing factors. Research shows that improving corporate governance and enhancing digital capabilities often help reduce disagreements. However, the practice of information disclosure is relatively complex: although high-quality and standardized disclosure should promote consistency, the selective disclosure of enterprises, the diversity of disclosure frameworks, and the increase in information volume sometimes increase divergence due to the introduction of more subjective judgment space. At the level of the capital market, corporate impression management behavior and certain market characteristics in the context of market openness may exacerbate differences, while active investor interaction can help narrow the gap. In terms of policies and regulations, stricter information disclosure norms and unified classification standards (such as the EU classification system) can effectively reduce rating differences, while different legal sources and environmental policy tools (punitive and incentive) also have a significant impact on the degree of differences. In addition, factors such as socio-cultural background (such as “implicit” social responsibility), corporate property characteristics, impression management to maintain image, and media feedback have also increased the complex causes of ESG rating divergence from different dimensions.

3.2. Current Status of Research on the Effects of ESG Rating Differences

Regarding the consequences and influencing mechanisms of ESG rating divergence, existing scholarly research has covered multiple domains, with related research topics primarily encompassing audit-related aspects, green innovation and sustainable development, capital market performance and risk [53], financing and capital costs, corporate governance operations and strategy, as well as market participant behavior and information prediction. See Table 4 for details.

Table 4. Classification results of the impact effects of ESG rating divergence (partial literature).

Subject	Independent Variable	Mediating/Moderating Variables	Research Conclusion	Research Sample	Related Literature	Processing Method
audit-related aspects	Audit fees	Mediating variables: information asymmetry, operational risk, debt cost	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Ling et al. [54]	standard deviation
green innovation and sustainable development	Enterprise energy efficiency	Mediating variables: green innovation, energy transition	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Wang et al. [55]	standard deviation
	Quantity and quality of green patents	Mediating variables: external pressure channels and internal strategic channels	main effect: Quantity+, Quality−; mesomeric effect: ✔	Chinese A-share listed companies	Zhu et al. [56]	standard deviation
	Number of green innovation patents	Mediating variables: Capital cost and information asymmetry of enterprises	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Xiao et al. [57]	standard deviation
	Green innovation behavior	Mediating variables: green innovation resources and green innovation willingness	main effect: −	Chinese A-share listed companies	Li et al. [58]	standard deviation
	Strategic Green Innovation (Quantity and Quality of Green Innovation)	Adjusting variable: ESG investors	main effect: +−; moderating effect: ✔	Chinese A-share listed companies	Hou and Xie [59]	standard deviation
	Enterprise Green Innovation	Adjusting variables: director resource advantage, media attention	main effect: +; moderating effect: ✔	Chinese A-share listed companies	Zhou et al. [60]	standard deviation
	Enterprise green innovation foam	Mediating variables: short sightedness in management; Adjusting variables: industry competition, media attention, and internal control effectiveness	main effect: +; mesomeric effect: ✔; moderating effect: ✔	Chinese A-share listed companies	Geng et al. [61]	standard deviation
	Enterprise Innovation	Mediating variables: financing constraints and human capital	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Li et al. [62]	Pairwise Standard Deviation Mean
capital market performance and risk	Stock excess returns	Adjusting variables: investor sentiment, ESG improvement potential, and information transparency	main effect: −; moderating effect: ✔	Chinese A-share listed companies	Wang et al. [63]	standard deviation
	stock yield	-	main effect: +	Listed companies on the S&P 500 index	Gibson Brandon et al. [64]	standard deviation
	Stock collapse risk	Mediating variables: degree of information asymmetry and agency costs	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Sun et al. [65]	standard deviation
	Return volatility	-	main effect: +	European and American cases	Christensen et al. [35]	standard deviation
	Stock volatility	-	main effect: +	Chinese A-share listed companies	Li and Lai [66]	standard deviation
	stock yield	-	main effect: +	Chinese A-share listed companies	Zeng et al. [67]	Percentage (Quantile) Ranking Standard Deviation
	Stock price volatility	Mediating variables: investor attention and noise trading	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Liu et al. [68]	standard deviation
	company value	Mediating variables: information uncertainty and agency costs	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Zhao et al. [69]	standard deviation
financing and capital costs	cost of debt	Mediating variables: information asymmetry and corporate risk	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Zhang et al. [70]	standard deviation
	Cost of equity capital	Mediating variables: stock price synchronization, stock returns; Adjusting variables: digital transformation and financial institution shareholding	main effect: +; mesomeric effect: ✔; moderating effect: ✔	Chinese A-share listed companies	Zhou and Ma [71]	standard deviation
corporate governance operations and strategy	Earnings management	Independent variable: ESG rating, moderating variable: ESG divergence	main effect: +; moderating effect: ✔	Chinese A-share listed companies	Mao et al. [72]	standard deviation; Pairwise Standard Deviation Mean
	Enterprise digital transformation	Mediating variables: technological innovation and financing constraints	main effect: −	Chinese A-share listed companies	Ren [73]	Pairwise Standard Deviation Mean
market participant behavior and information prediction	Analyst’s prediction error	-	main effect: −	Chinese A-share listed companies	Liu et al. [74]	Coefficient of Variation

Table 4. Classification results of the impact effects of ESG rating divergence (partial literature).

Subject	Independent Variable	Mediating/Moderating Variables	Research Conclusion	Research Sample	Related Literature	Processing Method
audit-related aspects	Audit fees	Mediating variables: information asymmetry, operational risk, debt cost	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Ling et al. [54]	standard deviation
green innovation and sustainable development	Enterprise energy efficiency	Mediating variables: green innovation, energy transition	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Wang et al. [55]	standard deviation
	Quantity and quality of green patents	Mediating variables: external pressure channels and internal strategic channels	main effect: Quantity+, Quality−; mesomeric effect: ✔	Chinese A-share listed companies	Zhu et al. [56]	standard deviation
	Number of green innovation patents	Mediating variables: Capital cost and information asymmetry of enterprises	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Xiao et al. [57]	standard deviation
	Green innovation behavior	Mediating variables: green innovation resources and green innovation willingness	main effect: −	Chinese A-share listed companies	Li et al. [58]	standard deviation
	Strategic Green Innovation (Quantity and Quality of Green Innovation)	Adjusting variable: ESG investors	main effect: +−; moderating effect: ✔	Chinese A-share listed companies	Hou and Xie [59]	standard deviation
	Enterprise Green Innovation	Adjusting variables: director resource advantage, media attention	main effect: +; moderating effect: ✔	Chinese A-share listed companies	Zhou et al. [60]	standard deviation
	Enterprise green innovation foam	Mediating variables: short sightedness in management; Adjusting variables: industry competition, media attention, and internal control effectiveness	main effect: +; mesomeric effect: ✔; moderating effect: ✔	Chinese A-share listed companies	Geng et al. [61]	standard deviation
	Enterprise Innovation	Mediating variables: financing constraints and human capital	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Li et al. [62]	Pairwise Standard Deviation Mean
capital market performance and risk	Stock excess returns	Adjusting variables: investor sentiment, ESG improvement potential, and information transparency	main effect: −; moderating effect: ✔	Chinese A-share listed companies	Wang et al. [63]	standard deviation
	stock yield	-	main effect: +	Listed companies on the S&P 500 index	Gibson Brandon et al. [64]	standard deviation
	Stock collapse risk	Mediating variables: degree of information asymmetry and agency costs	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Sun et al. [65]	standard deviation
	Return volatility	-	main effect: +	European and American cases	Christensen et al. [35]	standard deviation
	Stock volatility	-	main effect: +	Chinese A-share listed companies	Li and Lai [66]	standard deviation
	stock yield	-	main effect: +	Chinese A-share listed companies	Zeng et al. [67]	Percentage (Quantile) Ranking Standard Deviation
	Stock price volatility	Mediating variables: investor attention and noise trading	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Liu et al. [68]	standard deviation
	company value	Mediating variables: information uncertainty and agency costs	main effect: −; mesomeric effect: ✔	Chinese A-share listed companies	Zhao et al. [69]	standard deviation
financing and capital costs	cost of debt	Mediating variables: information asymmetry and corporate risk	main effect: +; mesomeric effect: ✔	Chinese A-share listed companies	Zhang et al. [70]	standard deviation
	Cost of equity capital	Mediating variables: stock price synchronization, stock returns; Adjusting variables: digital transformation and financial institution shareholding	main effect: +; mesomeric effect: ✔; moderating effect: ✔	Chinese A-share listed companies	Zhou and Ma [71]	standard deviation
corporate governance operations and strategy	Earnings management	Independent variable: ESG rating, moderating variable: ESG divergence	main effect: +; moderating effect: ✔	Chinese A-share listed companies	Mao et al. [72]	standard deviation; Pairwise Standard Deviation Mean
	Enterprise digital transformation	Mediating variables: technological innovation and financing constraints	main effect: −	Chinese A-share listed companies	Ren [73]	Pairwise Standard Deviation Mean
market participant behavior and information prediction	Analyst’s prediction error	-	main effect: −	Chinese A-share listed companies	Liu et al. [74]	Coefficient of Variation

In audit-related research [75], scholars have examined the impacts of ESG rating divergence on dependent variables such as audit fees, audit input, auditor selection and pricing, audit risk premiums, and abnormal audit fees [54]. Most researchers contend that increased ESG rating divergence elevates audit-related costs. The underlying transmission mechanisms mainly include: Under significant ESG rating divergence, enterprises typically face heightened operational and information risks. Auditors consequently need to implement additional audit procedures and allocate more resources to assess the accuracy of financial information, thereby reducing audit risks. Simultaneously, auditors must conduct meticulous reviews of more potential risk points while facing pressure to maintain their professional reputation, collectively driving up audit costs. ESG rating divergence often attracts greater media attention, which may conversely compel enterprises to pay higher audit fees to ensure the credibility and transparency of audit reports. The operational risks and uncertainties caused by rating divergence expose auditors to more challenges during audit processes, increasing the complexity of audit work and ultimately elevating audit pricing and fees. However, this impact is not static. Factors such as internal control quality and analyst attention have been found to moderate the effect of ESG rating divergence on audit fees. When enterprises demonstrate higher internal control quality, auditors face relatively lower risks, thereby weakening the influence of ESG rating divergence and potentially resulting in smaller increases in audit fees. Conversely, heightened analyst attention may incentivize enterprises to enhance information transparency, enabling more effective communication and resolution of rating divergence issues, thereby mitigating its driving effect on audit fee increases.

In the field of green innovation and sustainable development, existing research has focused on the impact of ESG rating divergence on corporate green performance [58,76], typically measured using indicators such as green innovation levels [77], energy efficiency, and the quantity/quality of green patents [55]. Regarding the directional effects, divergent perspectives exist. Some scholars argue that increased ESG rating divergence may inhibit corporate green innovation activities. They posit that while ESG rating divergence might elevate public and investor scrutiny, the uncertainty stemming from conflicting expectations could undermine stable ESG investment returns, thereby weakening firms’ motivation for green innovation. Concurrently, inconsistent rating standards may hinder effective access to government subsidies and incentives, distorting the allocation of green innovation resources. Additionally, ESG rating divergence exacerbates information asymmetry, impeding enterprises’ ability to secure essential technical and financial support, ultimately obstructing the growth of green patent output. Conversely, other scholars maintain that ESG rating divergence could actually stimulate green innovation. They propose that divergence might incentivize enterprises to enhance both the quantity and quality of green patents through dual mechanisms: external pressures (e.g., government policies, consumer demands, investor expectations, and capital market dynamics) and internal strategic adjustments (e.g., innovation strategies, resource allocation, and executive compensation incentives [56]). However, this divergence-driven green innovation carries hidden risks [57,59]. Heightened ESG divergence implies intensified scrutiny and pressure from ESG-focused investors. Under such pressures, management might prioritize short-term ESG expectations through excessive green innovation investments, potentially neglecting long-term sustainability outcomes [60]. This could lead to corporate overemphasis on nominal “green image” cultivation and inflated green patent quantities, thereby generating “green bubbles”. Industry competition and amplified media attention may exacerbate this trend, though effective internal controls could mitigate green innovation overinvestment [61]. Research further identifies corporate digital transformation as a critical moderating variable influencing the ESG divergence-green innovation relationship. At a deeper mechanistic level, ESG rating divergence primarily affects green innovation trajectories through three channels: (1) altering corporate financing constraints, (2) reshaping human capital structures, and (3) redirecting R&D investment patterns, thereby shaping both the temporal dynamics and substantive outcomes of green innovation processes [62].

In the context of capital market performance and risk [78,79], scholars have utilized listed companies’ indicators—including stock price volatility [80,81], synchronicity, liquidity [35], returns [64,82,83,84], excess returns [63], and firm value—as dependent variables to evaluate the impact of ESG rating divergence [85]. Empirical studies demonstrate that significant disparities in ESG ratings tend to amplify stock price volatility, with investor confidence acting as a critical moderating factor: stronger investor confidence stabilizes markets and suppresses volatility [65], while weaker confidence exacerbates pessimistic sentiment and heightens price fluctuations. Further analysis reveals that excessive ESG divergence intensifies stock price volatility by magnifying inconsistencies in market perceptions of corporate fundamentals, though this relationship weakens when corporate information disclosure compliance improves. Additionally, ESG rating divergence has been shown to increase stock price synchronicity, primarily due to substantial discrepancies among rating agencies impeding effective market pricing of ESG information. However, this positive association diminishes under three conditions: superior ESG performance by listed firms, higher-quality information disclosure, and stringent environmental regulations [86]. Regarding returns, ESG rating divergence typically depresses stock returns and excess returns, likely because inconsistent market interpretations of corporate ESG performance elevate investor risk premiums and suppress equity valuations [66]. Multiple factors moderate this adverse effect: heightened external analyst coverage alleviates information asymmetry, elevated controlling shareholder ownership adjusts governance dynamics, and investor sentiment, ESG improvement potential, and information transparency systematically influence the effect’s intensity [67,87]. Rating divergence may further erode firm value and escalate market risks through two interconnected mechanisms: heightened information uncertainty impairs investors’ ability to assess long-term corporate value, while rising agency costs degrade governance quality, compounding value destruction [69]. These dual effects culminate in severe consequences, including distorted price assessments that exacerbate volatility and crash risks [68], as well as suboptimal corporate decisions driven by elevated agency costs, which may trigger abrupt stock price declines. Notably, the interplay between ESG divergence and market instability underscores a self-reinforcing cycle where poor information environments amplify rating discrepancies, which in turn intensify market uncertainties.

In the domain of financing and capital costs [88,89], scholars have employed indicators such as debt costs [90], credit financing costs, and equity capital costs as dependent variables to assess the impact of ESG rating divergence. A majority of researchers posit that increased ESG rating divergence elevates all three categories of capital costs [70]. Specifically, ESG rating divergence exacerbates information asymmetry and complicates risk assessment, making it challenging for creditors to accurately evaluate borrowers’ financial health and risk profiles, thereby compelling creditors to demand higher debt costs to compensate for perceived risks. Concurrently, heightened operational risks associated with ESG divergence independently drive up debt costs. Furthermore, divergent ESG assessments may cast doubt on firms’ green innovation capabilities, increasing overall financing costs. However, analyst coverage can partially mitigate these effects by enhancing market trust in corporate disclosures, thereby reducing financing expenses. Regarding commercial credit financing, ESG divergence interacts with credit rating adjustments through amplified information asymmetry—distorting market perceptions of genuine creditworthiness and undermining the accuracy of credit rating formulation—and tightened financing constraints, which may prompt rating agencies to downgrade credit ratings preemptively based on anticipated financial distress. Subsequent rating downgrades impair firms’ risk resilience, external monitoring efficacy, reputational capital, and information transparency, collectively worsening access to and costs of commercial credit. Nevertheless, contrasting perspectives exist. Some studies argue that ESG rating divergence could paradoxically reduce debt financing costs by compelling firms to improve ESG information transparency, stimulating substantive sustainability initiatives, and attracting analyst scrutiny to improve information environments. Heterogeneity analysis supporting this view reveals more pronounced cost-reduction effects in high-pollution industries facing acute ESG pressures and regions with underdeveloped financial ecosystems where information gaps are wider [71].

In the domain of corporate governance, operations, and strategy, scholars have investigated the multifaceted impacts of ESG rating divergence on corporate earnings management [72], voluntary information disclosure, operational risks, risk-taking capacity, labor employment [91], and digital transformation. Specifically, regarding operational risks and risk-taking, ESG rating divergence may first increase corporate operational risks by exacerbating information asymmetry, which impedes the market’s ability to accurately assess company risks. Second, divergence may amplify investor sentiment volatility, rendering market evaluations of operational risks more unstable, thereby heightening actual operational risks and correspondingly diminishing firms’ risk-taking capacity. Turning to earnings quality, ESG rating divergence has also been found to significantly reduce the informational value of corporate earnings, undermining their reliability and predictive power. While high ESG performance is generally associated with reduced earnings management due to firms’ desire to project strong social responsibility and avoid reputation-damaging financial practices, the uncertainty introduced by rating divergence disrupts this mechanism, making it harder for investors to rely on earnings information. Furthermore, studies indicate that riskier firms may be more inclined to withhold negative information in disclosures. In this context, the degree of accrual-based earnings management itself acts as a moderating variable, influencing the transparency of earnings information: higher levels of accrual-based earnings management typically exacerbate the decline in earnings informativeness. Research also explores the effects of ESG rating divergence on voluntary information disclosure. Some scholars argue that divergence may paradoxically incentivize firms to increase voluntary disclosures through channels such as analyst attention, financing constraints, and investor sentiment. This occurs via three pathways: (1) intensified analyst scrutiny pressures firms to disclose more to meet external oversight expectations; (2) heightened financing constraints (as previously discussed) may motivate firms to proactively enhance transparency to rebuild market trust and lower capital costs; and (3) stable investor sentiment encourages management to view disclosures as confidence-boosting tools, increasing their willingness to share information. However, corporate strategies to address the information asymmetry, investor pressures, and financing difficulties caused by rating divergence may inadvertently foster managerial short-termism. This manifests as an excessive focus on short-term financial outcomes or stock price gains at the expense of long-term strategic goals and sustainable development. For instance, management might delay or reduce critical long-term investments to lower agency costs, protect personal reputations, and cater to investor short-termism, ultimately harming corporate longevity. Additionally, inconsistent ESG ratings may signal elevated risk perceptions among investors and creditors, leading to higher financing costs or restricted access to capital. These intensified financing constraints can trigger adverse operational consequences, including reduced labor hiring, diminished investment efficiency, and constrained investments in digital transformation—thereby compromising the effectiveness and quality of digital initiatives. Crucially, the interplay between ESG divergence and digital transformation reveals a self-reinforcing cycle: inadequate digital capabilities hinder firms’ ability to standardize ESG disclosures and mitigate rating discrepancies, while persistent divergence further limits resources available for digital upgrades [73].

In the realm of market participant behavior and information prediction [92,93], research has focused on how ESG rating divergence influences investor sentiment [94], institutional investors’ field research activities [95], and analysts’ forecasting behaviors. Empirical studies reveal that heightened ESG rating divergence significantly amplifies investor sentiment volatility and may increase the frequency or willingness of institutional investors to conduct on-site due diligence. This dual effect operates through two complementary channels: (1) ESG divergence inherently attracts heightened attention from investors and institutional stakeholders, while (2) the uncertainty it generates compels institutional investors to pursue deeper informational verification. When firms provide high-quality disclosures, institutional investors demonstrate stronger motivation to validate and contextualize ESG information through field research, thereby mitigating investment risks arising from information asymmetry or rating inconsistencies. Conversely, in cases of poor corporate transparency, ESG divergence may paradoxically reduce research engagement as institutions perceive insurmountable information barriers. Regarding analyst forecasting, studies demonstrate that ESG rating divergence substantially reduces earnings forecast accuracy (i.e., increases forecast errors) [74]. Mechanism analysis identifies two primary transmission pathways: First, divergence elevates analysts’ information processing costs by forcing reconciliation of conflicting ESG assessments, directly impairing forecast precision. Second, the cognitive load induced by rating discrepancies may divert analytical resources from core financial analysis to ESG interpretation, indirectly degrading forecasting quality. This effect exhibits cross-sectional variation, being more pronounced for firms with complex ESG profiles or those operating in industries lacking standardized sustainability metrics. Notably, the predictive impairment extends beyond earnings forecasts to affect other financial projections, including cash flow estimates and growth rate assumptions, suggesting systemic impacts on analysts’ information synthesis capabilities.

This chapter explores the complex impacts of ESG rating divergence on multiple levels. Research has shown that ESG rating divergence often drives up audit fees and investment due to increased perceived risk and information asymmetry. Its impact on green innovation is controversial: some studies believe that differences inhibit innovation due to market uncertainty and uncertain returns, while others believe that differences may stimulate innovation through external pressure and internal strategic adjustment, although sometimes it may lead to “green” behavior or innovation foam. In terms of capital market performance and risk, rating divergence generally exacerbates stock price volatility and synchronicity, reduces stock returns and company value, and may increase the risk of collapse, mainly due to increased information uncertainty and risk premium. High-quality information disclosure can partially alleviate this negative impact. In terms of financing and capital costs, disagreements often increase debt and equity capital costs due to information asymmetry and risk perception. However, some studies have also shown that in specific contexts (such as forcing companies to improve the quality of information disclosure or in high-polluting industries), disagreements may actually reduce financing costs. For corporate governance, operations, and strategy, disagreements can increase operational risks, weaken the content and quality of earnings information, and may lead to short-sighted behavior by management, reduce labor employment and investment efficiency, and hinder digital transformation, although sometimes voluntary information disclosure may also increase in response to external pressures. Finally, in terms of market participant behavior and information prediction, rating divergence can exacerbate investor sentiment fluctuations, increase the frequency of institutional investors’ on-site research, and may reduce analysts’ prediction errors by strengthening information scrutiny and processing.

4. Conclusions and Discussion

4.1. Conclusions

Environmental, social, and governance (ESG) rating divergence has become one of the core challenges in the field, significantly undermining the reliability of ESG assessments and sparking widespread controversy. Such discrepancies not only hinder objective evaluations of corporate ESG performance but also negatively impact capital allocation efficiency and the achievement of sustainable development goals.

In China, ESG rating divergence manifests through three primary formats adopted by domestic agencies: grade-based classifications (e.g., AAA to C), numerical scoring systems, and relative ranking mechanisms, while academic consensus on standardized divergence measurement methodologies remains lacking.

The root causes of ESG rating inconsistencies stem from multiple interconnected factors. First, at the foundational level, the absence of a unified global definition, robust theoretical framework, and standardized metrics for ESG—combined with its inherently qualitative and subjective nature—renders evaluations vulnerable to raters’ backgrounds and cognitive biases. Second, methodological variations across rating agencies, particularly in indicator scope definition, measurement techniques, weighting systems, data processing approaches, and source selection, further exacerbate discrepancies. Third, practical considerations such as regional disparities in disclosure requirements, corporate strategic motives (e.g., greenwashing [96]), and firm-specific characteristics (e.g., size, industry) compound these challenges.

Existing research has extensively explored both the drivers and economic consequences of ESG rating divergence, with antecedent studies focusing on corporate governance structures, digital capabilities, disclosure quality, capital market dynamics, policy frameworks, and socio-cultural influences, while consequence analyses examine impacts across audit practices, green innovation, capital market performance, financing costs, corporate strategies, and market participant behavior.

Despite these challenges, rating divergence does not negate the value of ESG measurement but rather highlights the inherent complexity of quantifying sustainability performance, necessitating critical scrutiny of underlying data and context-specific interpretation of ESG ratings in practical applications.

4.2. Discussion

Despite the growing body of research on ESG rating divergence, existing literature predominantly focuses on exploring its influencing factors and consequences, with relatively insufficient attention to how to appropriately utilize current ratings amidst such divergence or how to improve the rating systems themselves. Against this backdrop, this paper aims to systematically review relevant literature, synthesize strategies and methodologies for the rational application of ESG ratings, and explore potential directions and pathways for future enhancements to ESG rating frameworks [97].

For governments and regulatory bodies, the priority lies in enhancing the compatibility and coordination of ESG disclosure standards while progressively promoting their unification. Concurrently, given the substantial variations in standard selection and weighting within current ESG rating frameworks, establishing more standardized and normative ESG rating benchmarks is critical. Policymakers should encourage collaborative development and widespread adoption of ESG rating criteria, fostering information sharing, data interoperability, and regulatory harmonization.

Individual and institutional investors must recognize and comprehensively account for methodological differences across rating agencies. It is advisable to establish open dialogue mechanisms with rating institutions to gain deeper insights into their evaluation logic and methodological specifics. Investors should conduct holistic assessments using market intelligence and multi-source information to achieve more prudent and multidimensional evaluations of corporate ESG performance.

Corporations should proactively mitigate the adverse effects of ESG rating divergence through concrete measures that support sustainable development [98]. Key initiatives include improving the transparency and quality of ESG disclosures; strengthening communication with rating agencies to ensure accurate understanding and assessment of ESG information and performance; and continuously enhancing governance mechanisms while substantively improving ESG performance metrics [99].

Rating agencies themselves must enhance transparency by clearly defining and explicitly communicating their ESG performance evaluation frameworks—including assessment dimensions, indicator selection criteria, and aggregation rules—while making measurement practices and methodologies more accessible. Additionally, regular critical evaluations of methodological validity should be institutionalized, incorporating feedback loops from stakeholders and empirical validation of rating outcomes’ predictive power for material sustainability risks. This evolution should ultimately aim to reconcile the inherent tension between rating comparability and context-specific relevance in ESG assessments.

4.3. Limitations and Prospects of Research

While existing literature has extensively explored the causes, influencing factors, and effects of ESG rating divergence, several notable limitations warrant attention. First, the research focus remains disproportionately skewed toward problem diagnosis—identifying drivers and impacts of divergence—with inadequate practical guidance on effective mitigation strategies or standardization measures to resolve such discrepancies. Second, current analyses predominantly concentrate on firm-level rating variations, overlooking systematic heterogeneity across industries. Critical questions about the existence, magnitude, and determinants of inter-industry rating divergence remain largely unaddressed. Third, most studies adopt static or short-term analytical frameworks, failing to account for the dynamic interactions among ESG ratings, corporate practices, and evolving market conditions. The intensity, drivers, and consequences of ESG rating divergence likely exhibit temporal evolution, particularly amid shifting regulatory landscapes, technological advancements, and societal expectations. Yet research tracking these temporal dynamics remains sparse, limiting our understanding of how divergence patterns adapt to external shocks or institutional reforms. These gaps underscore the need for future studies to develop prescriptive frameworks for divergence mitigation, conduct cross-industry comparative analyses, and employ longitudinal designs to capture the co-evolution of ESG assessments and sustainability ecosystems.

In response to the shortcomings of current research, future research can be deepened and expanded in the following directions: Firstly, in terms of research methodology, institutions have different ESG rating results for enterprises, and robustness and effectiveness tests should be conducted on data sources from different rating agencies [100,101]. Secondly, in terms of exploring mechanisms and heterogeneity, future research should go beyond individual phenomenon descriptions and delve deeper into macro heterogeneity factors such as industry attributes and regional differences that cause differences in ESG ratings and their impact effects [102]. The interaction between these factors and micro characteristics of enterprises can also be examined. Again, at the level of research dimensions, future research urgently needs to move from “synthesis” to “decomposition”. Not only should we examine the independent impact effects of rating differences in dimensions E, S, and G in more detail, but we should also pay attention to the interrelationships between different dimensions of differences (such as whether governance differences amplify the impact of environmental or social differences), as well as their contribution weights to the overall ESG rating differences. Finally, it is crucial to strengthen research on the dynamic evolution of ESG ratings. More longitudinal research should be conducted to track the trend of rating divergence over time, the impact of key turning points (such as before and after major regulatory policies are introduced), and the interactive process between corporate ESG strategy adjustments and rating dynamic feedback.