The Impact of ESG Ratings on Corporate Carbon Performance: From the Perspective of Internal and External Interaction

Abstract

Against the backdrop of global climate governance and evolving ESG (Environmental, Social, and Governance) infrastructure, this study examines how market-based ESG ratings drive corporate carbon performance. Employing a staggered difference-in-differences (DID) approach based on a quasi-natural experiment with the initial release of SynTao Green Finance’s ESG ratings as an exogenous shock, we analyze Chinese A-share listed firms (2014–2022) from an internal and external interaction perspective. The results show ESG ratings significantly enhance carbon performance by improving internal control quality and analyst attention. Equity balance degree and public environmental concern strengthen these effects internally and externally, respectively. The positive impact is more pronounced in non-polluting industries, firms with low customer concentration, and those undergoing digital transformation. This study reveals the dual transmission mechanisms and boundary conditions of ESG ratings, offering theoretical and policy insights for achieving “dual carbon” goals.

1. Introduction

One of the most pressing non-traditional security risks facing the global community today is climate change [1]. Against the backdrop of increasingly stringent ecological and environmental constraints and frequent extreme climate events, advancing the transformation of the economic and social development model from a high-emission, high-consumption pattern to a green and low-carbon one has become a global consensus and an era-defining imperative. As the core entities in resource allocation and carbon emissions, enterprises play a pivotal role in driving the low-carbon transition, and the improvement of their carbon performance is of irreplaceable significance for the implementation of national climate governance strategies [2]. Against the existing policy framework and market environment, the question of by what mechanisms enterprises can effectively enhance their carbon performance and achieve the synergistic development of economic benefits and environmental responsibilities has emerged as a core issue with both vital policy significance and pressing practical urgency.

Existing research on the influencing factors of corporate carbon performance has mainly been conducted from the perspectives of internal corporate management, technological innovation and government environmental regulation, with a particular emphasis on the restrictive role of command-and-control environmental policies. However, such “rigid” regulatory approaches often give rise to problems such as over-regulation or insufficient incentives due to neglect of corporate heterogeneity, resulting in obvious limitations in their governance effects. In recent years, academia has begun to pay attention to market-driven environmental governance tools represented by ESG ratings, but relevant research remains fragmented. On the one hand, there is still a lack of in-depth discussion on how ESG ratings act through the joint effect of internal corporate governance and external market supervision, and under what contextual conditions they exert stronger governance effects. On the other hand, most existing studies are based on correlation analysis between ESG scores and carbon performance, which makes it difficult to distinguish between enterprises’ inherent low-carbon foundation and the incremental effects brought by the introduction of ESG rating information, thus restricting the causal identification of the governance effects of ESG ratings to a certain extent. Therefore, from the novel perspective of “market-based soft regulation”, a systematic examination of the driving mechanism and boundary of the exogenous shock of ESG ratings on corporate carbon performance is of great value for filling the existing theoretical gaps and expanding the research perspective of environmental governance.

Based on the stakeholder theory, this paper constructs an integrated “dual-path and dual-context” analytical framework of internal governance and external supervision. Taking the first release of ESG ratings by SynTao Green Finance as an exogenous policy shock, we select samples of Chinese A-share listed companies from 2014 to 2022 to build a multi-period difference-in-differences model, so as to systematically test the causal effect, transmission mechanism and contextual conditions of ESG ratings on corporate carbon performance. The findings show that ESG ratings significantly promote corporate carbon performance through two paths: improving the quality of internal control and increasing analyst attention, and this effect is more pronounced in contexts with a high degree of equity checks and balances and strong public attention to the environment. Further analysis indicates that the above promotional effect is characterized by enhancement in the short and medium term and insufficient sustainability in the long run, and it is particularly prominent in enterprises in non-polluting industries, with low customer concentration and a high degree of digital transformation.

Compared with existing research, the marginal innovative contributions of this paper are mainly reflected in three aspects. First, from the research perspective, it reveals the governance function of ESG ratings as a third-party market signal in corporate carbon emission reduction from the perspective of “market-based soft regulation”, expanding the theoretical boundary of the driving mechanism of corporate environmental behavior. Second, from the research framework perspective, it constructs an internal and external collaborative “dual-path and dual-context” model, depicting the entire process of ESG pressure from signal transmission and organizational internalization to performance realization, which enriches the application of stakeholder theory in the field of environmental governance. Third, from the research method perspective, a quasi-natural experiment is constructed based on the institutional change in ESG ratings, which provides more rigorous empirical evidence for the causal relationship between ESG ratings and corporate carbon performance.

The remainder of this study is structured as follows: Section 2 presents a literature review. Section 3 outlines theoretical analysis and research hypotheses. Section 4 describes the data sources and model specification. Section 5 reports empirical analysis. Section 6 presents further analysis. Section 7 concludes the study.

2. Literature Review

2.1. Research on ESG Ratings

The primary objective of ESG ratings is to encourage companies to integrate both financial and non-financial factors, such as carbon emissions and employee welfare, into their operational decision-making processes [3]. By measuring and assessing a company’s initiatives and outcomes in environmental protection, social responsibility, and corporate governance, ESG ratings provide a comprehensive framework for evaluating sustainable practices. Beyond serving as a tool for external scrutiny and oversight, ESG ratings also act as a critical reference point for investors, consumers, and other stakeholders in their decision-making. This dual role not only promotes enterprises to assume responsibilities for social progress and environmental protection but also aligns corporate strategy with sustainable development goals.

The validity of ESG ratings remains a matter of debate, with most existing research focusing on their economic effects. On the one hand, some studies indicate that ESG ratings can help companies build a positive external image, alleviate financing constraints, and enhance enterprise value [4]. On the other hand, some scholars argue that ESG ratings may prompt companies to prioritize ESG scores over the substantive requirements of ESG, leading to phenomena such as “greenwashing” and “ESG rating divergence”, which ultimately undermine corporate financial performance and hinder sustainable development [5]. Furthermore, the causes and consequences of “greenwashing” and “ESG rating divergence” are increasingly central to debates over the validity of ESG ratings. These issues highlight the need for more rigorous standards and transparency in ESG assessment processes to ensure that ratings genuinely reflect corporate sustainability efforts.

With the deepening of the concept of green and sustainable development, it has become increasingly common for enterprises to voluntarily disclose ESG reports and relevant information, and ESG ratings conducted by third-party institutions based on such disclosures have gradually become an important basis for academia to study corporate environmental, social and governance (ESG) performance [6]. First, scholars have focused on the methodological and consistency issues of ESG ratings themselves. Owing to differences in data sources, evaluation models and weight settings across different rating agencies, there are often significant discrepancies in the rating results for the same enterprise. For example, Avramov et al. [7] found that the average correlation between ESG scores of six major US rating agencies was only 0.48. Such rating discrepancies may not only affect the capital market’s judgment of enterprises, but also exacerbate stock price volatility and push up external financing costs [8]. Second, researchers have examined the economic consequences of ESG rating results. Most studies show that there is a generally non-negative correlation between corporate ESG performance and their financial performance [9]. Specifically, a higher ESG rating helps alleviate information asymmetry between enterprises and their stakeholders, thereby garnering more external support and enhancing corporate financial performance and market value [10].

However, most existing literature treats ESG ratings as continuous variables, focusing on comparing differences in corporate performance across different score levels, yet few regard ESG ratings themselves as an exogenous information event that may reshape corporate behavioral expectations and the structure of market constraints. In fact, an ESG rating is not only an evaluation of an enterprise’s historical sustainable performance, but also an important information signal released by rating agencies to the capital market and stakeholders. Its initial release means that an enterprise is officially incorporated into the ESG evaluation system, which may in turn trigger changes in attention from external stakeholders such as investors and analysts, and prompt the enterprise to adjust its internal governance and environmental strategies [11]. The nature of this “rating event” as an information shock dictates that its governance effect should not be measured merely by the level of scores. Instead, it should be treated as a discrete exogenous policy shock to thoroughly identify its independent impact on corporate behavior and performance.

2.2. Research on Corporate Carbon Performance

To date, there is no consensus on the evaluation of corporate carbon performance, and data measurement also presents challenges. Academic discourse on carbon performance primarily focuses on two areas: the evaluation criteria for corporate carbon performance and the factors influencing it. From an external perspective, the development of digital technology has a positive impact on corporate carbon performance [12]. Similarly, provincial-level data show that carbon trading policies have a significant positive relationship with the carbon performance of pilot provinces [5]. Pressure from external stakeholders can also enhance corporate carbon performance [13]. Additionally, carbon performance among enterprises included in government green procurement lists has improved markedly. Government green procurement can enhance the environmental consciousness of corporate leaders, stimulate green technology innovation, and encourage enterprises to invest in pollution control [14]. From an internal perspective, corporate carbon performance is influenced by enterprises’ recognition of carbon risk [15].

Against the macro backdrop of the global response to climate change, corporate carbon performance has become a core indicator for measuring the substantive contributions of enterprises to their environmental responsibilities [16]. Regarding the driving mechanisms of corporate carbon performance, existing research has mainly developed three relatively clear explanatory pathways. First, the internal governance-dominant pathway holds that optimizing the board structure, strengthening incentive and restraint mechanisms oriented toward environmental responsibilities, and embedding carbon emission reduction targets into executive compensation contracts can effectively mitigate agency conflicts and short-termism in enterprises’ emission reduction actions, thereby improving carbon performance [3,17]. Second, the technological innovation-driven pathway emphasizes the direct role of digital transformation and intelligent manufacturing in enhancing energy utilization efficiency and optimizing production processes, which in turn reduces corporate carbon emission intensity at the operational level [4,5]. Third, the external policy regulation pathway focuses on how rigid constraints, such as government environmental regulatory tools (e.g., the carbon emission trading system), force enterprises to adjust production decisions and reduce carbon emissions by increasing compliance costs and non-compliance risks [12,13].

Although the above studies have deepened the understanding of the formation mechanism of corporate carbon performance from different dimensions, their analytical perspectives remain mainly focused on internal corporate decision-making or government-led mandatory institutional arrangements, with insufficient attention paid to market-oriented governance mechanisms that lie between the two. In particular, these studies often regard enterprises as subjects that passively respond to policy or technological constraints and rarely explore how non-mandatory market signals form supervision and incentives through stakeholder networks to guide enterprises to proactively improve their carbon performance. As a comprehensive evaluation tool provided by independent third parties, ESG ratings integrate the expectations of multiple stakeholders for enterprises in the environmental, social and governance (ESG) dimensions. They not only transmit information on enterprises’ environmental performance but also are likely to form soft constraints on enterprises’ emission reduction behaviors by influencing capital market perceptions, financing conditions and the intensity of external supervision. However, existing research has not yet systematically explained how ESG ratings function as a market-oriented governance tool, and the transmission mechanisms and boundary conditions of their impact on carbon performance remain to be further examined.

3. Theoretical Analysis and Research Hypothesis

Stakeholder theory points out that enterprises are not isolated economic entities, but are embedded in a social relational network composed of multiple stakeholders, including shareholders, creditors, employees, customers, suppliers, governments, communities and the natural environment [15]. The survival and development of enterprises fundamentally rely on continuous relational coordination and value exchange with various stakeholders. From this perspective, the “rational” decisions of enterprises are those that can maintain organizational legitimacy, accumulate trust capital and promote long-term cooperation; corporate “success” is redefined as achieving sustainable economic prosperity while meeting diverse social expectations [18].

As a comprehensive evaluation tool from independent third parties that integrates environmental, social and governance (ESG) dimensions, ESG ratings essentially convey to the market the expectations and assessments of multiple stakeholders regarding enterprises’ non-financial performance. Therefore, enterprises’ response to ESG ratings and their carbon emission reduction actions are, in essence, strategic responses to the demands of different stakeholders under specific institutional and market environments [19].

3.1. The Impact of ESG Ratings on Corporate Carbon Performance

Based on stakeholder theory, the improvement of corporate carbon performance is not merely a technical environmental issue, but a dynamic process of interest gaming and co-evolution among multiple stakeholders. Specifically, when promoting low-carbon strategies, managers often face a trade-off dilemma between “long-term value creation” and “short-term performance realization” due to the long-term, asset-specific, and uncertain return characteristics of carbon emission reduction investments. Shareholders tend to support carbon reduction initiatives because they help reduce future compliance costs, environmental litigation risks, and reputational damage, thereby safeguarding the firm’s long-term cash flows and shareholder wealth [20]. Analysts regard a firm’s carbon emission management capability as a key factor in evaluating its risk exposure and value stability, which further influences earnings forecasts, valuation models, and investment ratings. The public, through brand perception and consumption choices, views carbon reduction as the enterprise’s commitment to public interests and ecological sustainability, which is directly linked to its social legitimacy and brand image [21]. Thus, the improvement of corporate carbon performance essentially depends on whether the firm can build a stable and sustainable incentive structure at the intersection of pressures and expectations from multiple stakeholders, enabling low-carbon strategies to possess both internal implementation motivation and external supervision and resource support.

Unlike voluntary sustainability reports disclosed by enterprises themselves, third-party ESG ratings conduct systematic assessments of corporate environmental, social, and governance performance through unified standards and evaluation systems, and transmit standardized signals to the capital market and the public. Its core function is to transform dispersed and heterogeneous stakeholder demands into quantifiable and comparable market information, thereby reshaping the external pressure structure and resource constraints faced by enterprises [22].

Specifically, according to institutional theory, organizations need to obtain legitimacy from the institutional environment to maintain their survival [23]. Against the background that climate change has become a global consensus, active emission reduction has become crucial for enterprises to achieve social legitimacy [24]. As institutional gatekeepers, ESG rating agencies establish widely recognized evaluation standards for corporate environmental practices through their rating activities. Once included in the rating system, enterprises’ environmental performance is subject to systematic and regular external supervision. To maintain and enhance the social legitimacy conferred by ratings, and to avoid regulatory, reputational, and public trust crises caused by inadequate low-carbon performance, corporate managers have incentives to elevate carbon emission reduction from a peripheral social responsibility to a core strategy. This drives enterprises to shift from passive compliance to strategic carbon management that actively pursues social recognition.

According to resource dependence theory, enterprises rely on external sources for critical resources. In a market environment where green finance and sustainable investment are mainstream, a favorable ESG rating has become a scarce intangible strategic resource that can directly improve the exchange relationship between enterprises and resource providers [25]. For investors, a high ESG rating signals an enterprise’s strong long-term risk control and sustainable development potential, helping attract low-cost green capital to support emission reduction investments [26]. For customers, ESG performance influences purchasing decisions and brand loyalty; an excellent rating helps build a responsible image, capture market returns, and secure supply chain opportunities [27]. Therefore, by transmitting credible signals to key resource providers, ESG ratings create favorable resource and incentive conditions for enterprises to improve carbon performance, forming a virtuous cycle of “rating improvement, resource favoritism, performance enhancement”.

In summary, ESG ratings form a dual-driving mechanism for improving corporate carbon performance by responding to both the legitimacy expectations of social stakeholders and the pursuit of sustainable value by resource-providing stakeholders. Accordingly, this paper proposes Hypothesis H1.

H1: 

ESG ratings have a positive impact on corporate carbon performance.

3.2. Meditation of ESG Ratings to Improve Corporate Carbon Performance

The external pressures and expectations conveyed by ESG ratings can only be translated into substantive carbon reduction actions through effective transmission via corporate internal governance structures and external market monitoring. This corresponds to the two primary response logics of enterprises toward internal stakeholders and external market stakeholders.

3.2.1. The Meditation of Internal Control Quality

The external stakeholder pressure reshaped by ESG ratings does not automatically translate into corporate behavioral outcomes. Instead, it must undergo a process of cognitive construction and institutional translation within the organization. The environmental responsibility demands from shareholders, analysts, and the public are essentially external normative and market signals. Whether they evolve into substantive carbon reduction actions depends on how managers interpret, prioritize, and allocate resources in response to these signals. Managers are not only resource allocators but also “sense-givers” of external institutional pressures and architects of strategic direction. Within the modern corporate governance structure, managers hold authority over strategy formulation, investment decision-making, and organizational process design. Therefore, their cognitive understanding and implementation willingness toward sustainable development determine whether carbon reduction is integrated into core strategic agendas, rather than remaining at the level of symbolic responses.

However, from the perspective of principal-agent theory, managerial decision preferences are often shaped by short-term performance evaluation, career risk aversion, and incentive contract structures. Carbon reduction investments are characterized by long-term horizons and uncertain returns, with weak short-term financial gains that may even reduce current profits [28]. In the absence of effective institutional constraints, managers may choose to delay or weaken low-carbon strategies to avoid short-term performance pressure. This implies that even if ESG ratings send strong signals of sustainable development to the market, external signals may still be “strategically absorbed” rather than “substantively implemented” without a governance structure that can institutionalize and formalize external pressures.

Against this background, internal control quality serves as a critical institutional carrier connecting external ESG pressure and internal strategy execution. The internal control system is not merely a set of compliance rules but an institutional arrangement that embeds abstract strategic goals into daily organizational decision-making and operational processes [29]. High-quality internal control incorporates carbon emission-related risks into the management framework through risk identification mechanisms, directs resource allocation via budgeting and approval procedures, and strengthens implementation accountability through information communication and monitoring mechanisms, thereby translating managerial strategic commitments into sustained organizational actions.

Accordingly, internal control quality theoretically functions as an “institutional translation mechanism”, enabling the external pressure generated by ESG ratings to be internalized into stable behavioral norms within the organization and ultimately reflected in observable improvements in carbon performance. Therefore, the external pressure and market attention induced by ESG ratings do not directly affect carbon performance. Instead, they push enterprises to improve their internal governance structure and enhance internal control quality, thereby strengthening managers’ implementation of low-carbon strategies. In other words, internal control quality plays a key mediating role between ESG ratings and corporate carbon performance.

Thus, as an institutionalized translation mechanism, internal control quality can embed stakeholder pressure triggered by ESG ratings into daily operations and strategy execution, thereby mediating the relationship between ESG ratings and corporate carbon performance. Based on this, this paper proposes Hypothesis H2a.

H2a: 

ESG ratings improve corporate carbon performance by increasing internal control quality.

3.2.2. The Meditation of Analyst Attention

If internal control quality represents the organizational mechanism for the institutional absorption of external pressures, the information structure of the external capital market determines whether such pressures can exert a sustained and dynamic influence on corporate behavior. Within the framework of stakeholder theory, the capital market is not merely a venue for resource allocation, but an important institutional platform where the expectations of diverse stakeholders are amplified and redistributed. Only when ESG pressures faced by enterprises are identified, interpreted, and continuously amplified in the information structure of the capital market can they be transformed into binding external governance forces [30].

Against this background, securities analysts serve as critical information intermediaries and market interpreters. According to information asymmetry theory, corporate carbon emission management capabilities and actual emission reduction effects are highly specialized and complex, making them difficult for external investors to directly observe and accurately evaluate [31]. Although ESG ratings, as a third-party evaluation mechanism, provide standardized signals, their implications and economic consequences still need to be reinterpreted and retransmitted by market participants to be embedded in the capital pricing system. By issuing research reports, earnings forecasts, and investment ratings, analysts integrate the sustainability information contained in ESG ratings into risk assessment and valuation models, thereby completing the translation from “rating signals” to “market constraints” [32].

More importantly, analyst coverage is not a one-off media exposure, but a sustained market monitoring mechanism [33]. Regular tracking, inquiries, and information updates by analysts keep corporate management under a constant framework of external evaluation and comparison. If enterprises fail to achieve substantive improvements in carbon performance after obtaining ESG ratings, their market valuation, financing costs, and reputational capital may suffer adverse consequences. Therefore, analyst coverage strengthens the link between corporate carbon performance and capital costs, incorporates environmental performance into the function of corporate economic consequences, and thus raises the opportunity cost for management to neglect carbon reduction responsibilities. In this sense, analyst coverage acts as a “market amplification mechanism”, enabling stakeholder pressure generated by ESG ratings to be continuously transmitted through the capital market and form dynamic constraints on enterprises. Without sufficient analyst coverage, ESG ratings may remain at the level of formal information disclosure. In contrast, under intensive analyst coverage, ESG signals are more easily embedded in market pricing and reputation evaluation systems, thereby incentivizing enterprises to improve carbon performance.

Therefore, the impact of ESG ratings on corporate carbon performance is not direct. Instead, it enhances analyst coverage, strengthens external market supervision and pricing constraints, and ultimately drives enterprises to implement more substantive carbon reduction actions. In other words, analyst coverage plays a key mediating role between ESG ratings and corporate carbon performance. Based on this, this study proposes Hypothesis H2b.

H2b: 

ESG ratings improve corporate carbon performance by enhancing analyst attention.

3.3. Moderation of ESG Ratings to Improve Corporate Carbon Performance

The strength of the governance effect of ESG ratings depends not only on the direct transmission paths but also is profoundly shaped by the structural characteristics of the internal and external stakeholder networks in which firms are embedded.

3.3.1. The Moderation of Equity Balance Degree

Whether enterprises truly translate ESG pressure into long-term low-carbon investment decisions fundamentally depends on whether their power structure supports long-termism and risk-sharing. Within the framework of stakeholder theory, shareholders are among the most important resource providers of enterprises, and their governance structure directly determines the firm’s response mode and strategic orientation toward external pressure.

From the perspective of principal-agent theory, in firms with highly concentrated ownership and ineffective checks and balances, controlling shareholders may use their control rights to distort resource allocation toward areas with more certain short-term returns to satisfy personal or group interests. Carbon emission reduction investments are characterized by long duration, asset specificity, and uncertain returns, which are difficult to significantly improve financial performance in the short run and may even reduce profit margins. Therefore, under a governance structure of “dominant single shareholder”, the long-term sustainable development signals conveyed by ESG ratings may be weakened or strategically addressed due to the short-term preferences of controlling shareholders, thereby undermining the actual promotion effect on carbon performance [34].

In contrast, firms with high equity balance, a relatively balanced power structure and mutual monitoring mechanism form among the top several large shareholders. Such a checks-and-balances pattern strengthens the governance effect of ESG ratings through two channels [35]. The multi-shareholder gaming and negotiation mechanism formed by equity balance can effectively restrain the opportunistic tendency of a single shareholder to crowd out long-term resources for short-term private gains, thus providing sustained institutional space for low-carbon investment. Meanwhile, mutual supervision among shareholders strengthens accountability over management, making it difficult for managers to ignore market signals and reputational pressure from ESG ratings.

In this context, external pressure is more easily internalized into strategic commitments and ultimately reflected in continuous improvement of carbon performance. Based on this, this paper proposes Hypothesis H3a:

H3a: 

Equity balance degree positively moderates the relationship between ESG ratings and corporate carbon performance.

3.3.2. The Moderation of Public Environmental Concern

Within the framework of stakeholder theory, corporate environmental behavior is not only evaluated by capital market participants but also must respond to the expectations and value judgments of broader social groups. As an important external stakeholder, the public’s focus and issue salience directly shape the external pressure structure faced by enterprises. When the public pays close attention to climate change and carbon emissions, corporate environmental performance is more likely to become a key topic in social evaluation and public discussion, thereby strengthening enterprises’ strategic response to environmental responsibility.

From the perspective of institutional theory, public environmental concern amplifies the governance effect of ESG ratings by strengthening normative pressure and legitimacy constraints [36]. When environmental issues become part of mainstream social values and public norms, enterprises that ignore the environmental signals conveyed by ESG ratings may face legitimacy risks such as reputational damage, declining social trust, and even intensified regulatory scrutiny [37]. In this case, environmental responsibility is no longer merely an optional strategic direction but an institutional expectation that requires continuous response.

Conversely, in contexts where public attention is low, the normative pressure of environmental issues is relatively limited, and the external constraint formed by ESG ratings is weakened accordingly. Therefore, public environmental concern moderates the impact of ESG ratings on corporate carbon performance by altering the institutional context and the intensity of legitimacy pressure faced by enterprises. Based on this, this paper proposes Hypothesis H3b:

H3b: 

Public environmental concern positively moderates the relationship between ESG ratings and corporate carbon performance.

Figure 1, shown below, illustrates the conceptual framework of this study, which highlights how ESG ratings relate to corporate carbon performance.

4. Research Design

4.1. Data and Sampling

Given that SynTao Green Finance rating data have been available since 2015, this paper selects Chinese A-share listed companies over the period 2014—2022 as the sample and employs a staggered DID approach to investigate the impact of third-party ESG ratings on corporate carbon performance. The sample is processed as follows: (1) Companies in the financial sector and those classified as ST (Special Treatment) or ST* (Delisting Risk Warning) during the year are excluded; (2) Companies with missing data for key variables are removed; (3) To mitigate the impact of extreme values, the main variables are winsorised at the 1% and 99% levels. After these adjustments, the final sample comprises 20,222 observations from 3073 listed companies. The data sources include following components: ESG rating data are obtained from the Wind Database; data related to carbon emissions are sourced from corporate annual reports, social responsibility reports, environmental reports and sustainable development reports; data on the quality of internal control is derived from the DIB Internal Control and Risk Management Database; data on public environmental concern are derived from the search index disclosed on Baidu’s website; the remaining data are sourced from the CSMAR Database. Additionally, the statistical analyses in this report were conducted using STATA 18.0.

4.2. Model Construction

The primary objective of this paper is to examine the impact of third-party ESG ratings on corporate carbon performance. Leveraging the exogenous shock of SynTao Green Finance’s initial release of ESG ratings for Chinese listed companies, the following model is constructed using a staggered DID approach. Staggered DID is a form of multi-period DID that allows treatment groups to enter the “treated state” at different points in time. Compared to traditional DID, which treats all treated groups as receiving treatment simultaneously, staggered DID makes fuller use of the temporal variation in the data, thereby more accurately estimating the average treatment effect under heterogeneous treatment timing. This approach compares changes in outcomes between a treatment group and a control group before and after an exogenous event, thereby controlling for unobserved time-invariant firm characteristics and common time trends.

$$C{P}_{it}=\alpha +\beta ES{G}_{it}+\gamma X_{it}+{\mu }_i+{\eta }_t+{\epsilon }_{it}$$

(1)

where the explanatory variable $C{P}_{it}$ is the carbon performance of firm $i$ in year $t$; $ES{G}_{it}$ is the core explanatory variable, $ES{G}_{it}=1$ if Business SynTao Green Finance publishes data on firm $i$’s ratings for year $t$, and otherwise $ES{G}_{it}=0$; $X_{it}$ is a series of control variables; ${\mu }_i$ is an individual fixed effect; ${\eta }_t$ is a time fixed effect; and ${\epsilon }_{it}$ is a random error term.

4.3. Variable Setting

4.3.1. Explained Variable

Corporate Carbon Performance (CP). Following Clarkson et al. [38], carbon performance is measured as the reciprocal of total carbon emissions per 100 yuan of net sales. It is mainly based on carbon emission data from listed companies’ annual social responsibility reports, sustainability reports, or environmental reports. A higher value indicates stronger corporate carbon performance, reflecting better environmental performance. Among them, carbon emissions are equal to the sum of the emissions from combustion and escape, production process emissions, waste emissions, and emissions resulting from changes in land use patterns (such as converting forests into industrial land) [33].

When direct emission data are unavailable, estimates based on energy consumption may introduce non-classical measurement errors. Such errors mainly stem from two aspects: (1) Differences exist between the actual energy quality, combustion efficiency, and measured emission factors of different enterprises and the default values in guidelines; (2) The energy consumption data disclosed by enterprises themselves may have measurement or reporting errors. However, this study argues that such measurement errors are more likely to cause “attenuation bias” in the core estimation results. This is because measurement errors lead to random disturbances between the constructed corporate carbon performance indicator (CP) and its “true value”. Total emissions are summed across energy types. While this approach may introduce measurement error due to estimated factors, we mitigate this by using standardized sources and cross-checking with reported data. Results are robust to alternative factors.

4.3.2. Explanatory Variable

ESG Ratings (ESG). As one of China’s early local ESG rating agencies, SynTao Green Finance has demonstrated an obvious, gradual and systematic path in expanding its rating coverage, which provides a quasi-natural experimental setting for this study. Specifically, its rating coverage follows the following non-random but relatively exogenous rules: first covering large-market-cap, actively traded companies that are components of major indices, and then gradually extending to other listed companies. This expansion strategy is mainly based on data availability, market influence, and the agency’s own capacity building, rather than conducting selective ratings targeting the environmental performance or potential carbon performance of specific companies.

Although this “size-and-visibility-based” selection mechanism leads to differences in characteristics such as firm size between the treatment group (large and well-known enterprises) and the control group, its selection criteria have no direct correlation with unobservable factors affecting corporate carbon performance (e.g., management’s environmental awareness, non-public emission reduction plans). More importantly, the staggered difference-in-differences (DID) design adopted in this study allows each company to be compared with itself before and after its “treatment period,” which can effectively control the impact of inherent firm characteristics that do not change over time. To further mitigate systematic differences in observable characteristics between the treatment and control groups, we will preprocess the sample using propensity score matching (PSM) in the robustness test. Thus, referring to Tan and Zhu [23], $ES{G}_{it}=1$ if SynTao Green Finance disseminates ESG rating data for a firm $i$ in year $t$ (treatment group), and 0 otherwise (control group).

This study operationalizes third-party ESG ratings as a binary variable with this specification based on the following considerations: First, from a theoretical mechanism perspective, this study aims to capture the institutional signals and legitimacy shocks emanating from the event of “obtaining an ESG rating” itself. According to institutional theory, a firm’s initial inclusion in an authoritative third-party ESG evaluation system signifies that its environmental, social, and governance (ESG) practices have begun to be subject to systematic external scrutiny and market supervision. This “entry” event constitutes a clear legitimacy threshold—regardless of the initial score level—it exerts significant normative pressure on the firm, prompting it to adjust its behaviors to meet external expectations. Therefore, the binary variable can more purely identify the net effect of this exogenous institutional shock.

Second, from a research design perspective, binary treatment is an appropriate choice for applying the staggered difference-in-differences (DID) method. Our identification strategy relies on variations in the timing of firms’ first acquisition of ESG ratings. If a continuous score were used, it would be difficult to distinguish whether the effect stems from the event of “obtaining a rating” or the outcome of “obtaining a high score.” The latter is more likely to be influenced by unobservable inherent characteristics of the firm (such as long-accumulated environmental reputation or management quality), thereby giving rise to severe endogeneity issues. Focusing on the event shock, the binary variable can more clearly satisfy the DID definition of “treatment.”

From a time-series perspective, the coverage of ESG shows a distinct upward trend. In 2014, the ESG value of all companies was 0, indicating that no company entered the ESG-rated status that year. Over time, the proportion of companies with ESG = 1 gradually increased: in 2015, approximately 20% of companies (those with a treatment year of 2015) had their ESG value turn to 1; from 2016 to 2017, the proportion stabilized at around 22.5% (including companies with treatment years of 2015 and 2016); in 2018, the proportion jumped to 42.5% (with the addition of companies whose treatment year was 2018); from 2019 to 2021, the proportion remained at around 45% (covering companies with treatment years of 2015, 2016, 2018, and 2019); by 2022, the ESG value of all companies had turned to 1, achieving full coverage. This distribution reflects the gradual implementation and diffusion process of ESG policies or disclosure requirements, with significant growth mainly concentrated in key years such as 2015 and 2018, demonstrating the popularization trend of ESG practices among the sample companies.

4.3.3. Control Variables

This report accounts for some variables that may affect corporate carbon performance, as indicated by prior research. These control variables include: the enterprise’s size (Size), which is the natural logarithm of its total assets of the company; Return on total assets (ROA), which calculates the profitability of a company by dividing its net profit by its total assets; The growth rate of corporate revenue (Growth), which is used to measure the future growth opportunities of the company; Management compensation (Pay), which is employed to evaluate how effectively the company’s internal management is performing; Corporate gearing ratio (Lev), which is employed to evaluate the company’s level of debt; The proportion of the first largest shareholders (TOP1), which is used to measure the company’s concentration of shareholding; Accounts receivable turnover rate (Trac), which is used to evaluate the company’s operational capacity.

4.3.4. Mechanism Variables

Internal control quality (IC). Following the methodology of Zhang et al. [28], the DiBo Internal Control Comprehensive Index is based on five elements: control environment, risk assessment, control activities, information communication, and internal supervision. It quantitatively reflects the effectiveness of the design and implementation of internal control in listed companies through a multi-dimensional indicator system and a scientific scoring method.

Analyst attention (Attention). Referring to the methodology of Li and Qi [39], select the number of analysts who pay attention to the listed company and perform a logarithmic transformation after adding 1.

4.3.5. Moderation Variables

Equity balance degree (Share). Following the methodology of Chen et al. [34], the Z-indicator is used to measure the degree of equity balance, calculated as the ratio of the shareholding of the largest shareholder to the combined shareholdings of the second to fifth largest shareholders.

Public environmental concern (Index). Referring to the methodology of Zheng et al. [40] and other domestic and international studies, the public environmental concern index is constructed using the Baidu Index. Specifically, the index is based on the search volume for keywords such as “haze” and “environmental pollution” in specific regions. The Baidu search index service calculates the weighted sum of search frequencies for these keywords, providing a quantitative measure of public attention to environmental issues. The definitions, symbols, and measurements of all the main variables used in this study are summarized in

Table 1. Main variables and their definitions.

Variable Type	Variable Name	Variable Symbol	Variable Definition and Computation
Explained variable	Corporate carbon performance	CP	Ratio of business income to carbon emissions
Explanatory variable	ESG ratings	ESG	0–1 variable, defined according to whether SynTao Green Finance publishes ESG rating data of the company.
Mechanism variables	Internal control quality	IC	The internal control score of DiBo is increased by 1, and then the natural logarithm is taken.
	Analyst attention	Attention	The number of analysts focusing on the enterprise is taken as the natural logarithm of the sum, incremented by 1.
Moderation variables	Equity balance degree	Share	The total of the shares held by the first largest shareholder divided by the number of shares held by the second to fifth shareholders.
	Public environmental concern	Index	Total search index of Baidu smog
Control variables	Scale of enterprise	Size	The natural logarithm of an organization’s t assets
	Return on total assets	ROA	Net profit/total assets balance
	Growth ability	Growth	Growth rate of enterprise operating income
	Management compensation	Pay	Total compensation of enterprise management
	Corporate gearing ratio	Lev	Total liabilities divided by total assets
	Equity concentration	TOP1	The largest shareholder’s shareholding ratio
	Operational capability	Trac	Accounts receivable turnover rate

.

5. Empirical Analysis

5.1. Descriptive Statistics

Table 2. Descriptive statistics of variables.

Variables	Obs	Mean	SD	Min	Median	Max
CP	20,222	0.489	0.452	0.243	0.318	2.321
ESG	20,222	0.279	0.448	0.000	0.000	1.000
Growth	20,222	0.163	0.365	−0.555	0.106	2.287
Pay	20,222	6,520,000	5,480,000	708,000	4,910,000	33,100,000
Size	20,222	22.300	1.309	19.675	22.084	26.207
ROA	20,222	0.041	0.061	−0.251	0.039	0.205
Lev	20,222	0.409	0.199	0.051	0.401	0.895
Trac	20,222	29.309	119.842	0.847	4.904	1077.928
TOP1	20,222	34.370	14.619	8.720	32.280	74.870
Index	20,222	232.870	214.664	0.000	195.033	1273.994
Share	20,222	0.760	0.620	0.003	0.596	4.000
IC	20,222	6.468	0.144	4.830	6.492	6.831
Attention	20,222	1.305	1.183	0.000	1.099	4.331

presents the descriptive statistics for the sample. Corporate carbon performance (CP) varies significantly, with a maximum value of 2.321, a minimum value of 0.243, a mean of 0.489, and a standard deviation of 0.452—providing a favorable context for the study. The mean value of the explanatory variable ESG is 0.279, indicating that 27.9% of the companies in the sample received ESG ratings during the study period. For the control variables, the size of the companies (measured as the natural logarithm of total assets) ranges from 19.675 to 26.207, with an average of 22.300, reflecting a diverse mix of small and large firms. The average asset-liability ratio is 41%, suggesting that liabilities account for nearly half of the assets of most firms. All other variables fall within reasonable ranges, further supporting the robustness of the sample.

5.2. Correlation Analysis

Table 3. Correlation matrix.

Variables	CP	ESG	Growth	Pay	Size	ROA	Lev	Trac	TOP1
CP	1
ESG	0.621 ***	1
Growth	−0.027 ***	−0.053 ***	1
Pay	0.106 ***	0.348 ***	0.072 ***	1
Size	0.037 ***	0.426 ***	0.044 ***	0.476 ***	1
ROA	−0.037 ***	0.028 ***	0.259 ***	0.167 ***	−0.028 ***	1
Lev	0.018 ***	0.116 ***	0.023 ***	0.145 ***	0.542 ***	−0.392 ***	1
Trac	−0.016 **	0.058 ***	0.018 ***	0.053 ***	0.132 ***	0.055 ***	0.062 ***	1
TOP1	−0.042 ***	0.074 ***	−0.012 *	−0.014 **	0.204 ***	0.126 ***	0.054 ***	0.063 ***	1

*** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of the correlation analysis. The explanatory variable ESG exhibits a significant positive correlation with corporate carbon performance at the 1% significance level, preliminarily validating the primary hypothesis that third-party ESG ratings positively influence corporate carbon performance. The variance inflation factor (VIF) test shows no significant multicollinearity issues in the model: the maximum VIF is 2.23, and the average VIF is 1.43—both well below the threshold of 10.

5.3. Benchmark Regression Analysis

This study employs a stepwise regression approach to examine the relationship between third-party ESG ratings and corporate carbon performance. Column (1) of

Table 4. Results of baseline regression.

	(1)	(2)
	CP	CP
ESG	0.0222 *** (3.5401)	0.0257 *** (4.0105)
Growth		0.0454 *** (9.4869)
Pay		−0.0000 (−1.0383)
Size		−0.0010 (−0.1940)
ROA		0.0067 (0.1889)
Lev		0.0845 *** (4.4292)
Trac		−0.0000 ** (−2.4647)
TOP1		−0.0003 (−0.8815)
Id	Yes	Yes
Year	Yes	Yes
_cons	0.3166 *** (163.1851)	0.3138 *** (2.7507)
N	20,222	20,222
adj. R2	0.8760	0.8775

*** p < 0.01, ** p < 0.05.

presents the regression results controlling only for individual and time fixed effects: the coefficient of ESG is 0.0222, significant at the 1% level. Column (2) adds all control variables while retaining individual and time fixed effects: the coefficient of ESG remains significantly positive at 0.0257 (p < 0.01). These results confirm Hypothesis H1, indicating that ESG ratings have a significant positive impact on corporate carbon performance—specifically, companies affected by ESG rating shocks exhibit stronger carbon performance.

Among the control variables, the coefficients of revenue growth rate (Growth) and gearing ratio (Lev) are significantly positive. This suggests that larger firms and those with greater growth capacity are more likely to engage in environmentally responsible practices. Additionally, these firms may finance their carbon abatement activities through higher levels of debt, supporting their carbon reduction efforts and ultimately enhancing their carbon performance.

5.4. Mechanism Analysis

To examine the mediating effects of internal control quality and analyst attention on the relationship between ESG ratings and corporate carbon performance, the regression results are reported in Columns (1) and (2) of

Table 5. Mechanism Analysis and Moderation Analysis.

Variable	IC	Attention	CP
	(1)	(2)	(3)	(4)
ESG	0.0109 *** (2.8933)	0.2026 *** (7.7843)	0.0265 *** (4.1115)	0.0252 *** (3.9011)
ESG*Share			0.0233 ** (2.2769)
ESG*Index				0.0001 *** (5.2182)
Controls	Yes	Yes	Yes	Yes
Id	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
_cons	6.3557 *** (77.2605)	−9.1648 *** (−15.4114)	0.3194 *** (2.7987)	0.3274 *** (2.8849)
N	20,222	20,222	20,222	20,222
adj. R2	0.0896	0.1900	0.8776	0.8779

*** p < 0.01, ** p < 0.05.

. The regression coefficients of ESG ratings on internal control quality and analyst attention are 0.0109 and 0.2026, respectively, both significantly positive at the 1% level, indicating that ESG ratings can improve internal control quality and increase analyst attention. Existing studies have shown that both internal control quality and analyst attention are conducive to enhancing corporate carbon performance [28,39]. Therefore, the mediating effects of internal control quality and analyst attention are confirmed, and Hypotheses H2a and H2b are supported.

5.5. Moderation Analysis

To examine the moderation effects of equity balance degree and public environmental concern on the relationship between ESG ratings and corporate carbon performance, the regression results are shown in columns (3) and (4) of Table 5. The coefficient of the interaction term ESG*Share and ESG*Index is 0.0233 and 0.0001, significant at the 5% and 1% level, indicating that the degree of equity balance and public environmental concern significantly and positively moderate the relationship between ESG ratings and corporate carbon performance. This confirms Hypothesis H3a and H3b.

5.6. Robustness Test

(1)

Placebo test

To determine whether the positive effect of third-party ESG ratings on corporate carbon performance is driven by random factors, this paper conducts a placebo test using a simulated dummy variable for ESG rating shock events. Referring to the methodology of La Ferrara et al. [41], we randomly sample 1000 times and regress the constructed dummy independent variable under the benchmark regression model to test the distribution of its coefficient and p-value. The results show that the estimated coefficients of the dummy independent variable cluster are around 0, significantly lower than the benchmark regression coefficient—indicating that the impact of ESG rating events on carbon performance is not attributable to random factors, thus confirming the robustness of the core findings. The results of this placebo test are shown in Figure 2.

(2)

Parallel Trend Test

The validity of the DID method relies on the parallel trend assumption: the carbon performance of the treatment and control groups should follow the same trend before the third-party ESG rating event. However, due to the staggered release of ESG ratings, it is not feasible to designate a single year as a policy benchmark. To address this, we follow the approaches of Li et al. [42] to conduct a parallel trend test and construct the following econometric model:

$$C{P}_{it}=\alpha +{\displaystyle \sum _{k=-5}^{-2}{\beta }_k{B}_k}+{\displaystyle \sum _{k=0}^5{\rho }_k{A}_k}+\gamma X_{it}+{\mu }_i+{\eta }_t+{\epsilon }_{it}$$

(2)

Among them, $B_k$ and $A_k$ denote the virtual variables for the $k$ years preceding and succeeding the initial announcement of the enterprise’s ESG rating, respectively. Their estimation coefficients, ${\beta }_k$ and ${\rho }_k$, signify the disparities in carbon performance levels between the treatment and control groups during the $k$ years before and after the first announcement of the enterprise’s ESG rating. The other symbolic representations are consistent with those in Formula (1).

The test results show that the coefficients of the relative time dummy variables are statistically insignificant and economically close to zero before the release of ESG ratings (from period k = −5 to k = −2). This formally confirms that there is no systematic difference in carbon performance between the treatment and control groups prior to the ESG rating shock, satisfying the core prerequisite for applying the difference-in-differences model—the parallel trend assumption. The visualization of this test is shown in Figure 3. After the release of third-party ESG ratings, the gap in carbon performance between the treatment and control groups widens, and the coefficient of ESG is significantly positive, confirming that third-party ESG ratings positively influence corporate carbon performance. In addition, ESG ratings exhibit a continuous and progressively strengthening dynamic treatment effect. In the year of obtaining the rating (k = 0), the coefficient turns ${\beta }_k$ positive and becomes statistically significant, indicating that market-oriented ESG supervision exerts a relatively immediate impact. More importantly, in the first to third years after the event (k = 1, 2, 3), the coefficients ${\beta }_k$ not only remain highly significant but also show a clear monotonically increasing trend in their point estimates over time. This dynamic pattern strongly reveals that the promoting effect of ESG ratings on corporate carbon performance is not one-off, but a long-term process with continuously deepening impacts and accumulating effects.

(3)

PSM-DID method

This study employs PSM-DID to rigorously assess the impact of policy shocks while minimizing the influence of confounding factors. First, using nearest-neighbor matching and radius matching, control variables such as ownership concentration and management compensation are matched as covariates to eliminate systematic differences between the treatment and control groups. Specifically, the matching covariates we selected include financial leverage (Lev), ownership concentration (TOP1), and management compensation (Pay). These variables are believed to potentially influence both the probability of a firm being covered by SynTao Green Finance’s ESG ratings and its carbon performance level. Among them, management compensation (Pay) is included because, as a core proxy variable for corporate governance incentives, it is not only related to a firm’s information transparency and market visibility (thereby affecting the probability of rating coverage) but may also impact carbon performance by influencing managers’ long-term investment decisions. As a key indicator of corporate governance structure, ownership concentration (TOP1) may, on the one hand, affect a firm’s information transparency and decision-making efficiency, thereby changing the probability of it being noticed by rating agencies; on the other hand, it may constrain or support a firm’s long-term environmental investments through the supervisory willingness or resource control capabilities of major shareholders. Financial leverage (Lev) reflects a firm’s financial risks and resource constraints. Highly leveraged firms may reduce long-term investments in environmental performance due to debt repayment pressure, and their risk characteristics may also affect rating agencies’ coverage tendencies. Controlling these variables helps eliminate systematic differences in key economic and governance characteristics between the treatment group and the control group after matching. After removing unmatched samples, the regression test is re-conducted using the same model. The results (

Table 6. Results of robustness tests.

Variable	CP
	(1)	(2)
	Nearest Neighbor Matching	Radius Matching
ESG	0.0233 *** (3.5323)	0.0231 *** (3.5027)
Controls	Yes	Yes
Id	Yes	Yes
Year	Yes	Yes
_cons	0.3228 *** (2.8009)	0.3216 *** (2.7896)
N	19,564	19,559
r2_a	0.8781	0.8781

*** p < 0.01.

) show that the coefficient of ESG on corporate carbon performance remains positive and significant at the 1% level—further, validating the robustness of the findings. The matching quality tests results are presented in Figure 4.

(4)

Changing the sample interval

The COVID-19 pandemic had a profound impact on global economies, significantly disrupting market-based business operations. To avoid the confounding effects of the pandemic, the 2020 sample is excluded when re-examining the impact of ESG ratings on corporate carbon performance. After removing the 2020 sample, the regression coefficient of ESG remains positive at 0.024 (p < 0.01), demonstrating that ESG ratings exert a positive and significant influence on corporate carbon performance.

6. Further Analysis

6.1. Dynamic Effect

To capture the potential time-varying effects of ESG ratings on corporate carbon performance, recognizing that market reactions and subsequent strategic adjustments are not instantaneous, this study employs a dynamic difference-in-differences specification as follows:

$$C{P}_{it}=\alpha +{\delta }_{0}I(t=T_i)+{\delta }_{1}I(t=T_i+1)+\dots {\delta }_{\Lambda }I(t=T_{\Lambda }+1)+\gamma X_{it}+{\mu }_i+{\eta }_t+{\epsilon }_{it}$$

(3)

where T_i denotes the first year in which firm $i$ is covered by the SynTao Green Finance ESG ratings(if never covered, T_i = ∞). I(·) is an indicator function that equals one if the condition in parentheses holds and zero otherwise represents the maximum number of years since initial coverage observed in the sample. The coefficients δ₀~δ_Λ capture the effect in the year of coverage and in each subsequent year, allowing us to trace the short-, medium-, and long-term dynamics of the rating’s impact.

Table 7. Dynamic effect test.

	CP
	(1)	(2)
I(t = Ti)	0.0249 *** (3.1689)	0.0280 *** (3.5125)
I(t = Ti + 1)	0.0129 *** (2.8940)	0.0184 *** (3.9525)
I(t = Ti + 2)	0.0144 * (1.8869)	0.0207 *** (2.7153)
I(t = Ti + 3)	0.0073 (1.3493)	0.0130 ** (2.3446)
I(t = Ti + 4)	0.0134 (0.5368)	0.0219 (0.8727)
I(t = Ti + 5)	0.0040 (0.5624)	0.0140 * (1.8831)
_cons	0.3156 *** (172.4738)	0.3033 *** (2.7788)
Controls	No	Yes
Id	No	Yes
Year	No	Yes
N	21,854	21,720
r2_a	0.8759	0.8771

*** p < 0.01, ** p < 0.05, * p < 0.1.

reports the estimation results of Equation (3). Relative to firms never rated by SynTao, the adoption of an ESG rating leads to a statistically significant increase in carbon performance by 2.8% in the coverage year, 1.84% in the first year after coverage, 2.07% in the second year, and 1.3% in the third year (all significant at least at the 5% level). The effect becomes statistically indistinguishable from zero in the fourth and fifth years, indicating that the ESG rating exerts a meaningful influence on carbon performance primarily in the short to medium term, with no persistent long-term impact detected within this horizon.

This dynamic pattern carries clear economic implications. The strengthening phase of the effect (from the year of coverage to the third year) reflects that ESG ratings, as an exogenous market monitoring signal, trigger a series of strategic adjustments and resource reallocation processes within enterprises. The initial release of ratings imposes immediate legitimacy pressure and market attention (heightened analyst focus), prompting enterprises to respond in the short term (e.g., strengthening internal controls). In the following one to three years, to maintain or improve their ratings, firms further institutionalize ESG pressures, embed them into long-term strategies, and undertake substantial emission reduction investments, leading to continuous improvement in carbon performance, with the effect peaking in the second year.

The attenuation of the effect (insignificant after the fourth year) may stem from several mechanisms: first, the habituation effect, meaning that the external pressure brought by ESG ratings becomes normalized over time, weakening its marginal incentive effect; second, the rapid adjustments made by enterprises in response to ratings are largely completed in the medium term, leaving limited room for further improvement; third, enterprises may face new and more pressing competitive or operational pressures, shifting the priority of resource allocation and reducing sustained investment in carbon performance. This indicates that the promotional effect of ESG ratings is mainly evident in the medium and short term, and the sustainability of their long-term impact may depend on continuous reinforcement of external monitoring or the deepening of internal governance mechanisms.

6.2. Heterogeneity Analysis

6.2.1. Pollution Intensity

This paper classifies the sample into heavily polluting and non-heavily polluting industries for regression analysis, based on the China Securities Regulatory Commission’s Guidelines for Industry Classification of Listed Companies and the Ministry of Ecology and Environment’s Catalogue for Classification Management of Environmental Verification Industries. The results are presented in Columns (1) and (2) of

Table 8. Results of heterogeneity analysis.

Variables	CP
	Pollution Intensity		Customer Concentration		Digital Transformation Degree
	(1)	(2)	(3)	(4)	(5)	(6)
ESG	0.0159 (1.3851)	0.0242 *** (3.0153)	0.0272 *** (3.0255)	0.0088 (0.8251)	0.0032 (0.3193)	0.0392 *** (3.9230)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Id	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
_cons	0.2527 (1.1445)	0.3478 ** (2.3588)	0.4425 *** (2.7339)	0.1641 (0.8389)	0.5229 *** (3.1465)	0.2815 (1.5189)
N	5218	14,346	9753	9811	8122	11,442
r2_a	0.8815	0.8785	0.8808	0.8761	0.8649	0.8827

*** p < 0.01, ** p < 0.05.

. For non-heavily polluting industries, the coefficient of ESG is 0.0242 (p < 0.01), while for heavily polluting industries, the coefficient of ESG is insignificant. This indicates that third-party ESG ratings have a stronger positive effect on corporate carbon performance in non-heavily polluting industries. ESG ratings and societal expectations require substantial financial and time investments in environmental, social, and governance management, particularly in heavily polluting industries, which may impact operating profits. In contrast, non-heavily polluting industries face relatively lower barriers to green and low-carbon transformation and thus exhibit greater willingness and internal motivation to implement such changes.

The stronger effect in non-heavily polluting industries may be attributed to several factors: first, these industries typically have lower baseline emissions and greater technological flexibility, making it easier and less costly to adopt green practices; second, they may face stronger stakeholder pressure to maintain a “green” image, as consumers and investors often associate them with environmental friendliness; third, heavily polluting industries are already subject to stringent environmental regulations, which may diminish the marginal effect of ESG ratings. In contrast, non-heavily polluting industries may respond more strongly to ESG ratings as a complementary governance mechanism.

6.2.2. Customer Concentration

Testing the heterogeneity of customer concentration is essentially an examination of how the power structure in a firm’s value chain moderates the efficiency of converting external governance pressures (ESG) into internal strategic actions. It reveals a critical yet often overlooked boundary condition in “collaborative governance”: a firm’s structural dependence on key external trading partners constitutes a strong constraint on its ability to respond to the pressures from a broad range of stakeholders. A high level of customer concentration often means that a firm’s R&D directions, production rhythms, and even profit margins are “locked in” by the demands of major customers. This causes the firm’s internal resource allocation decisions—including whether to invest in green innovation—to be largely subordinated to the short-term goal of maintaining customer relationships, eroding the autonomy and strategic flexibility of internal governance. In such cases, even if shareholders or management have long-term environmental intentions (internal motivation), they may compromise for fear of losing core customers. When external pressures such as ESG ratings arise, firms with high customer concentration exhibit a fragmented response logic. They are forced to make difficult trade-offs between “meeting external green compliance requirements” and “fulfilling the specific cost and delivery schedule demands of core customers”. If core customers have no explicit requirements for green supply chains or focus solely on costs, ESG pressures are likely to be filtered out or diluted in the transmission process.

Customers are a crucial component of the corporate value chain. In companies with high customer concentration, large customers often dominate transaction processes, wielding significant bargaining power [43]. In contrast, companies with low customer concentration are less reliant on any single customer, granting them greater flexibility and influence in transactions. This flexibility enables them to respond more swiftly to customer needs and market changes, helping to attract new potential customers, reduce business risks, and alleviate financing constraints. Operating in a lower-risk and well-capitalized environment further motivates companies to pursue green and low-carbon emission reduction initiatives. Based on the research of Crawford et al. [44], customer concentration is defined as the ratio of the total sales of the top five customers to the annual total sales. Using the median of this value as the criterion, the sample is divided into the high customer concentration group and the low customer concentration group. As shown in Column (3) and (4) of Table 8, the coefficient of ESG is 0.0272 (p < 0.01), indicating that the positive impact of ESG ratings on carbon performance is stronger in companies with low customer concentration compared to those with high customer concentration.

6.2.3. Digital Transformation Degree

Digital transformation is far more than an isolated technological application; it is a systemic reform that drives enterprises to restructure the cognitive foundation and implementation capabilities of their environmental governance. From a theoretical mechanism perspective, this reform precisely acts on the transmission path through which ESG ratings influence carbon performance: First, technologies such as big data and the Internet of Things (IoT) greatly enhance enterprises’ ability to perceive and decode external ESG pressures and internal carbon emissions, converting abstract ratings into quantifiable and manageable operational data. Second, cloud computing and collaborative platforms break down departmental barriers, enabling cross-functional integration of environmental goals with production, R&D, and supply chain management, thereby addressing the key challenge of internal collaborative governance. Finally, artificial intelligence (AI) and algorithmic optimization can be directly embedded in and reshape the core processes of production and operations, transforming carbon emission reduction from a cost center into an endogenous outcome driven by efficiency. Therefore, digital transformation constructs a “digital empowerment hub” that efficiently and accurately converts external institutional pressures (ESG ratings) into internal substantive actions (improvements in carbon performance). Testing its heterogeneous effects aims to reveal how enterprises’ “digital capability gap” systematically determines their ability to transform external challenges into long-term competitive advantages amid sustainable transformation, thereby providing a profound and era-relevant micro footnote to the core argument of “internal-external collaborative governance.”

Digital transformation enables organizations to achieve intelligent, data-driven, and automated operation, monitoring, and management of production processes through digital technologies such as big data, the Internet of Things, and artificial intelligence [45]. For example, companies can use big data twins and similar technologies to monitor their carbon footprints and analyze carbon emissions data, enabling managers and employees to better understand carbon generation and emissions, identify and address process inefficiencies, and improve corporate carbon performance. Moreover, digital transformation often goes hand-in-hand with organizational innovation and cultural shifts towards sustainability, further amplifying the impact of ESG initiatives. Digital transformation fosters the development of green technologies, enhancing its impact on corporate carbon performance. Enterprises with a high degree of digital transformation, substantial technological upgrading, and process optimization provide strong technical support and viable pathways for fulfilling the environmental and social responsibilities outlined in ESG ratings. Based on the research of Yue and Lv [46], the level of enterprise digital transformation was measured by the natural logarithm of the frequency of keywords related to five core digital technology domains (Artificial Intelligence, Big Data, Cloud Computing, Blockchain, and Digital Technology Application) identified in the firms’ annual reports. Likewise, to control for time trends and industry differences, we group the samples based on the year-industry median of this index: samples above the median are defined as the high digital transformation group, and the rest as the low digital transformation group. As shown in Column (5) and (6) of Table 8, the coefficient of ESG is 0.0392 (p < 0.01), demonstrating that the positive impact of ESG ratings on corporate carbon performance is more pronounced in firms with a high degree of digitalization.

7. Conclusions

7.1. Conclusions of This Study

This study investigates the relationship between third-party ESG ratings and corporate carbon performance using a staggered DID model, leveraging the exogenous shock of SynTao Green Finance’s initial ESG rating release and a sample of Chinese A-share listed companies over the period 2014–2022. The findings reveal three key insights: First, as a form of market-based soft regulation, third-party ESG ratings have a significant positive impact on corporate carbon performance. Second, mediation effect analysis shows that ESG ratings drive the improvement of corporate carbon performance by enhancing internal control quality and increasing analyst attention, which means the governance effects of ESG ratings exert a synergistic role through two dimensions: internal governance and the external market. Moderation effect analysis indicates that both the degree of equity checks and balances and public environmental concern positively moderate the relationship between ESG ratings and corporate carbon performance, namely that the governance effects of ESG ratings rely on the synergistic support of internal governance structures and the external social environment. Third, the impact of third-party ESG ratings on corporate carbon performance exhibits significant heterogeneity: it is particularly pronounced in non-heavily polluting industries, firms with low customer concentration, and organizations undergoing a high degree of digital transformation.

7.2. Theoretical Contributions

The above findings not only address the empirical debate regarding whether ESG ratings can improve corporate carbon performance; more importantly, based on stakeholder theory and centering on the core logic of “multiple actors–heterogeneous functions–collaborative governance”, this study makes the following three theoretical contributions.

First, this study breaks through the assumption of “homogeneous pressure” and reveals the functional differentiation of stakeholder roles. Most existing studies treat stakeholder pressure as a unified, aggregate concept, implying an underlying assumption of homogeneous pressure and a single transmission channel. In contrast, this study clearly proposes and verifies that internal and external stakeholders play fundamentally distinct roles in improving corporate carbon performance: Internal stakeholders (the board of directors, non-controlling shareholders, and management) dominate strategic choices and resource allocation, with ex ante and implicit influence. External stakeholders (analysts, investors, and the public) exert effects through information transmission and reputation evaluation, whose effectiveness depends on market feedback efficiency. This distinction advances stakeholder theory from static actor identification to dynamic functional analysis.

Second, this study constructs a dual internal–external collaborative transmission path and opens the black box of the governance effect of ESG ratings. Focusing on the core question of how external pressure is transformed into internal action, we identify and verify two heterogeneous mechanisms: internal control quality and analyst attention. Internal control quality serves as the institutional carrier for internal stakeholders to integrate external expectations. Analyst attention acts as the information channel for external stakeholders to intervene in corporate value assessment. By incorporating both mechanisms into a unified mediation framework, this study is the first to systematically reveal the complete transmission chain from ESG signals to carbon performance from an internal–external collaborative perspective.

Third, this study identifies the context-dependent conditions of internal–external synergy and expands the theoretical boundary. We find that the governance effectiveness of the above transmission paths is constrained by the firm’s internal power structure and external social norms: Equity checks and balances determine whether internal stakeholders can effectively restrain managerial myopia. Public environmental concern determines whether external normative pressure can elevate ESG ratings to a legitimacy threshold. These findings provide a new interpretive dimension for understanding the contingency of ESG ratings’ governance effects and offer a replicable analytical paradigm for future research on multi-stakeholder collaboration.

7.3. Practical Implications

(1)

Clarify the Position of ESG Ratings as Market-Based Soft Regulation and Integrate Them into the Climate Governance Policy System

ESG ratings should not be regarded as a one-off information disclosure tool, but rather institutionalized as a continuous governance instrument. To this end, policymakers may take authoritative ESG ratings as an important reference for differentiated policy support in institutional arrangements such as green finance, government-guided funds, industrial support and credit support, guiding the sustained tilt of financial and policy resources toward enterprises with remarkable achievements in carbon performance improvement. Meanwhile, it is imperative to strengthen the requirements for dynamic updating and continuous disclosure of ESG ratings to enhance their temporal continuity and binding stability, forging a long-term interactive relationship between ESG ratings and enterprises’ carbon emission reduction behaviors, thereby making up for the deficiencies of traditional command-and-control environmental regulation in terms of long-term incentives.

Policymakers should move beyond the perception of ESG ratings as a mere information disclosure tool and clearly define them as a market-oriented governance instrument. Authoritative ESG rating results can be embedded in the construction of systems such as green finance standards, government green procurement and industrial support catalogs, making them the basis for resource allocation and policy inclination. At the same time, by strengthening the requirements for dynamic updating and continuous disclosure of ESG ratings to improve their temporal continuity and binding stability, a long-term interactive relationship between ESG ratings and enterprises’ carbon emission reduction behaviors can be established, which offsets the shortcomings of traditional command-and-control environmental regulation in long-term incentives.

(2)

Promote the Reform of Internal Governance Structure and Consolidate the institutional Foundation for Translating ESG Pressures into Emission Reduction Actions

In the process of advancing carbon emission reduction practices, enterprises should attach importance to the coordinated optimization of corporate governance structures. On the one hand, when using ESG ratings for decision-making, enterprises should make differentiated judgments in light of their own equity structures and internal control quality to avoid institutional mismatches of “high ratings but low implementation” in enterprises with weak governance foundations. On the other hand, enterprises need to improve their capacity to bear long-term carbon strategies by perfecting the protection mechanisms for minority shareholders and strengthening the requirements for internal control information disclosure and accountability, so that the external pressures conveyed by ESG ratings can be effectively absorbed and transformed into sustained emission reduction actions.

(3)

Strengthening the Synergy of Market Information Intermediaries and Social Public Opinion Supervision to Amplify the External Governance Effect of ESG Ratings

Regulators and investors should establish a coordinated system of “professional supervision and social supervision”. On the one hand, the standardization, comparability and readability of ESG and carbon information disclosure can be improved to reduce analysts’ information processing costs and enhance their integration of ESG factors into valuation analysis, earnings forecasts and investment decisions. On the other hand, the public’s ability to be informed of and supervise enterprises’ carbon behaviors should be enhanced through information disclosure, media guidance and the dissemination of environmental issues. By strengthening the linkage between capital market supervision and social public opinion pressure, the reputational and legitimacy costs borne by enterprises that disregard ESG ratings can be raised, thereby boosting their motivation for carbon emission reduction.

(4)

Implement Differentiated ESG Governance Strategies to Improve the Precision and Adaptability of Policy Tools

Differentiated ESG guidance and support policies should be formulated in accordance with the industrial characteristics, market structures and digitalization levels of enterprises: for enterprises with sound technical foundations and favorable market conditions, greater reliance can be placed on ESG ratings and market mechanisms to exert governance effects; for enterprises with relatively weak governance foundations and technical capabilities, it is necessary to combine ESG ratings with more targeted institutional constraints and capacity-building measures, so as to improve the effectiveness and inclusiveness of the overall ESG governance system.

7.4. Limitations and Prospects of This Study

This study has several limitations that provide avenues for future research: First, ESG ratings are still in the early stages of development in China. Currently, third-party rating agencies only provide comprehensive ESG scores, with no disaggregated sub-dimension data for Environment (E), Social (S), and Governance (G). This study only examines the overall impact of ESG ratings on corporate carbon performance. In the future, with the further improvement of ESG legislation and the refinement of rating data, researchers can explore the impact of individual sub-dimensions on corporate carbon performance.

Second, the sample data of this study is limited to Chinese A-share listed companies, exploring the impact of ESG ratings on corporate carbon performance during the early stages of ESG development. Whether this conclusion is applicable to other countries with different ESG rating standards and at different stages of development remains to be verified. Future research can explore corporate economic and environmental performance under different ESG development stages and standards. Third, the measure of carbon performance used in this study—while widely adopted—may not fully capture the multi-dimensional nature of corporate carbon activities. Future research could develop more nuanced metrics that simultaneously account for carbon efficiency, reduction efforts, and innovation.

Additionally, future research may build on the integrated framework of this study to further explore other internal governance mechanisms that have not yet been thoroughly analyzed (such as board structure and executive incentive mechanisms) and external contextual factors (such as media supervision and institutional pressure), investigating how they interact with ESG ratings to jointly influence corporate carbon performance.