Does Corporate ESG Performance Influence Carbon Emissions?

Abstract

Against the backdrop of increasingly severe global carbon emissions and China’s commitment to achieving carbon peaking by 2030, accelerating the transition to a low-carbon economy has become an urgent priority. As fundamental microeconomic entities, enterprises play a crucial role in the national governance of carbon emissions. This study uses panel data on Chinese A share listed companies from 2019 to 2023 and employs fixed effects models that control for firm, year, and industry effect to analyze how ESG performance influences carbon emissions and through which mechanism. The findings indicate that improvements in ESG ratings significantly reduce firms’ carbon emissions. This effect operates primarily through the following two channels: (1) promoting green technological innovation, thereby enhancing environmental performance, and (2) increasing the attention of financial analysts, which strengthens external monitoring. The heterogeneity analysis further reveals that the mitigating effect of ESG improvement on carbon emissions is more pronounced in firms with a lower proportion of institutional ownership, while this effect is relatively weaker in firms with higher institutional ownership. This suggests that in contexts where institutional investors hold a smaller share, firms may place greater emphasis on the policy pressure and social responsibility expectations associated with ESG performance, thereby exhibiting stronger commitment to emission reduction actions. In contrast, in firms dominated by institutional investors, the implementation of ESG policy objectives may be partially compromised due to the investors’ short-term profit orientation. This study provides empirical evidence for firms to fulfill their environmental and social responsibilities and offers actionable insights for investors aiming to promote sustainable development. From a policy perspective, the findings also offer theoretical support for developing differentiated regulatory strategies based on variations in ownership and shareholding structures.

1. Introduction

The increase in greenhouse gas (GHG) emissions poses a growing threat to existing forms of life. Since 2014, the global annual average growth rate of GHG emissions has reached 1.4%, with a sharp increase of 2.6% recorded in 2019 [1,2]. In response, international consensus on controlling GHG emissions has emerged through the successive adoption of the Kyoto Protocol and the Paris Agreement [3]. The Kyoto Protocol, adopted in 1997, was the first treaty to set binding emission targets for developed countries and introduced mechanisms like carbon trading to support global cooperation [4]. Building upon this foundation, the Paris Agreement marked a shift toward a more inclusive and flexible approach to climate governance. It encourages all countries, regardless of development level, to participate in global climate action through nationally determined contributions (NDCs), thereby promoting a global transition toward greener and more sustainable growth models. This shift reflects a growing recognition of the need to reduce long-standing dependence on fossil fuels, which continues to threaten natural ecosystems. Under the Paris Agreement framework, investment flows are expected to increasingly favor green energy, low-carbon development, and enhanced environmental governance [5]. However, interim results reveal substantial regional disparities, which may be attributed to variations in resource endowments and the stringency of policy enforcement across regions [6]. In response, from the perspective of informal institutions, sustainable finance policies have emerged as an effective mechanism to curb carbon emissions and steer economic development toward low carbon and development paths [7]. Against this backdrop, both firms and investors have become increasingly aware of the importance of environmental issues and have begun to integrate sustainability into corporate governance and investment decision making. The concept of environmental, social, and governance (ESG) has gradually gained prominence within this trend [8].

In emerging markets such as China, ESG remains a relatively novel concept. In 2018, the China Securities Regulatory Commission (CSRC) revised the Code of Corporate Governance for Listed Companies, incorporating environmental and ecological considerations into the corporate governance evaluation framework, thereby partially institutionalizing ESG information disclosure. According to the 2023 China ESG White Paper, the ESG disclosure rate among A-share listed companies increased to 34.85% in 2022, a notable improvement compared to 26% in 2021. [9,10].

As shown in Figure 1, developed economies such as the European Union, the United States, and Japan have already reached their emission peaks and have entered a phase of continuous decline. In contrast, the timing of China’s carbon emissions peak remains uncertain, and the transition toward a more sustainable economic framework continues to face significant challenges [11].

As one of the world’s largest carbon emitters, China faces growing pressure to advance its decarbonization agenda. In this context, informal institutions, including social norms, reputational concerns, and stakeholder expectations, have become increasingly important in guiding corporate behavior, especially when formal regulations are limited or lag behind [12]. ESG performance can be understood as a reflection of these informal institutional pressures, capturing how firms respond to societal and reputational expectations in the low carbon transition. Firms, as the most fundamental microeconomic agents within the economic system, play a central role in the transmission and redistribution of social resources, while also accounting for a significant share of total carbon emissions. [13]. With the growing emphasis on sustainable development, firms are increasingly expected not only to fulfill their economic functions but also to take on greater environmental and social responsibilities. Within this context, ESG performance has emerged as a critical indicator of corporate sustainability [14]. ESG ratings and disclosures help reduce information asymmetry between managers and investors, mitigate operational and environmental risks, attract long-term investors, lower financing costs, and promote green transformation [15].

Building on existing literature, this study primarily investigates whether ESG, as a form of informal institution, exerts an influence on corporate carbon emission reduction. The main contributions of this paper are as follows:

First, existing research on carbon emissions has primarily concentrated on the regulatory role of formal institutions, while relatively little attention has been given to the influence of informal institutions. By incorporating the perspective of informal institutions, this study investigates the impact of the ESG framework on corporate carbon emissions, thereby contributing to a more comprehensive understanding of the factors influencing carbon emissions and enriching the existing analytical framework.

Second, this study adopts a micro-level perspective by utilizing firm-level data on carbon emissions and ESG performance. It is grounded in institutional theory, information asymmetry theory, and signaling theory. The study provides empirical evidence on the determinants of carbon emissions at the firm level and contributes to the literature by deepening the understanding of carbon reduction mechanisms from a micro level institutional and behavioral perspective.

Third, building on the finding that ESG performance can influence corporate carbon emissions, this study further explores the mechanisms through which ESG affects emissions at the firm level. In particular, it identifies analyst attention and the heterogeneity of institutional ownership as key channels. The findings provide a set of empirical insights that support the development of informal institutions and offer evidence for improving and standardizing ESG rating systems.

The remainder of this paper is structured as follows. Section 2 discusses the current trends in corporate carbon emissions and ESG performance. Section 3 presents the theoretical framework and develops the research hypotheses. Section 4 describes the data sources and research design. Section 5 conducts the empirical analysis, including baseline regressions, mechanism testing, and heterogeneity analysis. Section 6 concludes the paper, provides policy recommendations, and outlines the study’s limitations.

2. Literature Review

2.1. Carbon Emission

The rapid increase in atmospheric carbon dioxide emissions has significantly exacerbated global climate issues and has emerged as one of the most pressing challenges to sustainable development in the 21st century. As the world’s largest emitter of carbon dioxide [16], China bears substantial responsibility in global decarbonization efforts.

Enterprises, as key economic actors, are both major contributors to carbon emissions and crucial agents in emission reduction. In this context, understanding how Chinese firms respond to carbon constraints and environmental expectations has become essential for achieving the country’s “dual carbon” goals.

Existing research on corporate carbon emissions has predominantly focused on two dimensions, internal firm characteristics and external influencing factors. On the internal side, prior studies highlight the roles of corporate governance, technological innovation, and managerial attributes. For instance, [17] find that green finance policies can reduce emissions by easing financing constraints and enhancing ESG performance. [18] show that supplier concentration may inhibit green innovation, thereby increasing firms’ emission intensity. [19] demonstrate that green innovation can effectively reduce emissions, with digital transformation acting as a positive moderator. [20] shows that managers with overseas experience are more likely to promote emission reductions, particularly in state-owned enterprises (SOE). [21] finds that corporate managers’ understanding of environmental issues plays an important role during firms’ low-carbon transition. From a governance perspective, [22,23] emphasize the influence of board composition such as new appointments and gender diversity on firms’ sustainability outcomes.

External factors have also received growing attention. [24] identify social performance and sustainable investment as key drivers of voluntary carbon disclosure, indicating the importance of reputational concerns and stakeholder expectations. [25] find that the presence of green investors can improve ESG disclosure and accelerate decarbonization through both external pressure and market mechanisms. Additionally, political connections, firm size, industry type, and geographic location have all been shown to influence carbon outcomes, while foreign investment and policy regulation are increasingly recognized as shaping firms’ environmental strategies [26,27,28,29].

Overall, the existing literature on corporate carbon emissions underscores the significance of both internal management and the external environment, suggesting that these two dimensions play complementary roles in enhancing environmental sustainability and reducing emissions. These studies, from multiple perspectives, reveal how internal reforms and external measures can work together to promote an effective decarbonization process. While these studies provide valuable insights, several limitations remain. First, much of the existing literature examines internal and external factors in isolation, with limited attention to their potential interaction or joint influence. Second, although ESG is emerging as a comprehensive framework that integrates both internal practices and external accountability, few studies explore how ESG performance functions as a mechanism linking these two dimensions to carbon outcomes. Third, the applicability of findings from developed economies to transitional contexts like China remains underexplored, particularly given differences in institutional maturity, regulatory enforcement, and information transparency.

Therefore, there is a pressing need to investigate how ESG performance—an integrative indicator of environmental, social, and governance behavior—affects firms’ carbon emissions in China and to uncover the mechanisms through which ESG exerts its influence. By focusing on firm-level ESG and identifying its transmission pathways, this study aims to fill this gap and provide new evidence on how corporate sustainability practices can contribute to national and global decarbonization goals.

Importantly, ESG also functions as a form of informal institution that extends from internal governance structures to external stakeholder engagement, bridging corporate strategy with regulatory expectations and societal pressures. Section 2.2 reviews the concept of ESG and its role in shaping firm behavior in the context of sustainability and emission reduction.

2.2. Environmental, Social, and Governance (ESG)

Since 2006, ESG has gradually gained attention in both practice and academia. While focusing on their core business operations, firms can leverage ESG practices to enhance long-term value and support sustainable growth. For investors, understanding a company’s ESG performance helps improve risk assessment and optimize investment decisions, while also incentivizing firms to strengthen their ESG practices [30]. In recent years, the relationship between carbon emissions and ESG performance has attracted growing interest across various disciplines, including environmental science, economics, and political governance. However, due to the relatively recent implementation of ESG policies and the limited availability of firm-level carbon emission data, existing empirical research on this topic remains scarce. The available studies suggest that ESG policy implementation or corporate ESG disclosure tends to have a negative effect on regional or firm-level carbon emissions.

At the firm level, the relationship between ESG and carbon emissions has received increasing attention. [31] find that the implementation of ESG disclosure reduces carbon emissions among Chinese listed companies and highlight the critical role of alleviating financing constraints in enhancing the emission-reducing effect of ESG, particularly in highly polluting industries. [32] find that in countries with higher risk tolerance, firms are more enthusiastic about engaging in ESG practices and committed to carbon emission reduction. Existing research suggests that ESG performance positively influences corporate behavior and the achievement of sustainability goals in several ways. First, strong ESG performance can help firms reduce financing costs and alleviate financial constraints [33]. Unlike traditional financial disclosures, ESG-conscious firms disclose non-financial information related to environmental protection, social responsibility, and corporate governance. This allows investors to evaluate both financial and non-financial aspects of the firm more comprehensively, thereby helping to reduce perceived risk [34]. Such firms convey their proactive commitments to environmental protection, social responsibility, and sustainable development through ESG practices, thereby establishing a responsible corporate image [35]. In the context of rising pressures related to carbon emissions, ESG performance can help ease the financial burden faced by firms during their transition toward low-carbon operations [36]. In addition, a firm’s ESG performance may have a direct positive effect on access to commercial credit, thereby enhancing its competitiveness in the product market [37].

Firms with strong ESG performance tend to demonstrate a higher sense of social responsibility. A sound governance structure enables more effective supervision of management, curbs short-term managerial behavior, and enhances corporate competitiveness, thereby promoting sustainable development [38]. At the same time, ESG functions as a form of informal institution that complements traditional regulatory frameworks by imposing additional constraints, increasing the level of external oversight, and attracting sustained media attention [39,40]. In addition, rational business decision making and robust internal controls within the organizational structure provide a solid foundation for the firm’s long-term and stable development [41].

Meanwhile, existing studies have shown that firms with strong ESG performance typically demonstrate higher value creation efficiency, improved internal operational effectiveness, reduced resource waste, and optimized resource allocation [42]. Strong ESG performance also helps alleviate financing constraints, thereby facilitating corporate green innovation [43]. At the practical level, firms gradually establish a synergistic mechanism to promote green innovation through ESG practices such as information disclosure, external monitoring, and internal incentives, thereby facilitating the transition toward green and low-carbon development [44,45,46,47].

Overall, existing studies have, to some extent, revealed the positive role of ESG in corporate carbon reduction. However, most of these studies focus primarily on its favorable effects, while paying limited attention to the specific transmission mechanisms through which ESG exerts its influence. Moreover, they often overlook the heterogeneity of institutional environments. In particular, in transitional economies, ESG should not only be viewed as a performance indicator but also as an informal institutional arrangement that links internal corporate governance with external regulatory pressure. Its influence on firm behavior is complex and deeply embedded within the institutional context. Nevertheless, current research remains insufficient in exploring how ESG fulfills this bridging role under the Chinese institutional setting.

3. Theoretical Framework and Research Hypotheses

3.1. The Impact of ESG Performance on Corporate Carbon Emissions

According to institutional theory [48], corporate behavior is strongly influenced by institutional factors in the external environment. These factors include formal regulations (such as carbon emission policies) as well as informal social norms (such as public opinion). To ensure survival and gain legitimacy, firms must respond to and adapt to these external institutional pressures [49].

In this context, a firm’s ESG performance represents its response to external institutional pressures. Specifically, ESG performance can be viewed as a key indicator of a firm’s adaptation to formal institutions such as environmental regulations. By improving their ESG performance, firms demonstrate responsiveness to these external pressures, which not only enables compliance with increasingly stringent environmental policies but also helps them gain a competitive advantage and broader societal recognition and support. ESG performance is particularly important in the process of coping with external pressures, especially in enhancing environmental management standards. From an internal perspective, firms with strong ESG performance, in order to meet higher environmental management standards, tend to allocate more financial and managerial resources toward improving energy efficiency and promoting green innovation. This, in turn, contributes to a reduction in corporate carbon emissions [43]. From an external perspective, improved ESG performance is more likely to attract analysts’ attention. Such attention functions as an effective external monitoring mechanism. In order to maintain favorable analyst evaluations, firms are incentivized to continuously uphold their environmental management standards. This forms a virtuous cycle that further promotes the firm’s low-carbon transition [50].

In summary, under the influence of external institutional pressures, firms improve their ESG performance to enhance legitimacy and competitiveness. This is reflected not only in internal resource reallocation and the promotion of green innovation but also in the strengthening of external oversight mechanisms, thereby facilitating a gradual transition toward low-carbon development. Therefore, this study proposes the following hypothesis:

Hypothesis 1. 

Corporate carbon emissions are negatively affected by ESG performance.

3.2. Corporate Green Innovation

In contexts where environmental resource constraints are relatively weak and the costs of pollution abatement are low, firms tend to adopt high-emission and extensive growth models in pursuit of economic competitiveness. While such production strategies may reduce operational costs in the short term, they often lead to the overexploitation of resources and environmental degradation in the long run, ultimately compromising firms’ overall performance. Consequently, firms lack sufficient incentives to pursue sustainable development through technological innovation [51]. Within this context, the Porter Hypothesis offers a novel perspective; well-designed environmental regulations are not merely external constraints but may also function as effective incentives for innovation and improved performance [52].

The ESG framework exerts a significant influence on corporate carbon emissions through multiple mechanisms. According to the Porter Hypothesis, stringent environmental regulations do not hinder corporate development; rather, they can serve as external pressures that stimulate technological innovation and improvements in environmental performance. Such incentive mechanisms encourage firms to shift away from extensive, energy-intensive, and high-emission development paths toward models of high-quality and sustainable growth [53].

Behavioral decision theory [54], originating from interdisciplinary research between psychology and economics, primarily explores the impact of non-rational factors on individual decision-making processes. It posits that in complex, uncertain, or high-risk situations, decision makers are often constrained by cognitive biases, bounded rationality, and psychological factors. These influences are particularly salient in managerial decisions regarding green technological innovation [55]. Corporate managers often exhibit low enthusiasm in promoting green innovation activities, a tendency closely linked to their preference for stability and risk aversion in decision making. Given the high uncertainty associated with the success of green technological innovation and the fact that managers generally possess a lower risk tolerance than shareholders, they are more inclined to subjectively avoid high-risk projects [56]. Moreover, green innovation typically involves long R&D cycles, making it difficult to assess its value and realize economic returns in the short term [57].

Due to the difficulty of accurately assessing the potential value of green innovation projects, external markets may respond negatively, leading to short-term declines in stock prices and weakening firms’ performance in capital markets. As a result, out of concern for adverse market reactions, firms may reduce their investment in green innovation and instead prioritize short-term, high-return projects that lack long-term sustainability [8].

In contrast, higher ESG performance helps reduce information asymmetry and enhances the overall transparency of corporate information disclosure, thereby encouraging managers to engage more proactively in green innovation and influencing their decision-making behavior [41,58]. ESG ratings provide the market with professional and credible information regarding firms’ environmental responsibilities, enabling investors to better understand short-term performance fluctuations during the low-carbon transition and to exhibit greater tolerance toward early-stage failures of green innovation projects. This plays a critical role in supporting managers’ long-term commitment to green innovation initiatives aimed at sustainable value creation [59].

In summary, the above discussion suggests that ESG, as a form of environmental regulation, can guide firms toward a path of sustainable development. At the same time, ESG helps to mitigate information asymmetry between corporate managers and shareholders, enabling the management to look beyond short-term gains and make decisions that are more aligned with long-term sustainability goals, thereby further promoting green technological innovation within firms. These mechanisms ultimately contribute to reducing corporate carbon emissions. Accordingly, this paper proposes the following research hypothesis.

Hypothesis 2. 

Firms’ ESG performance reduces corporate carbon emissions through the mechanism of green innovation.

3.3. Analyst Attention

Due to various constraints, investors are unable to process all available information and must selectively focus on certain parts. Psychological research suggests that individuals can only process a limited amount of information at any given time. As a result, when confronted with information overload, investors tend to rely more heavily on information that is more salient and pre-processed [60].

In capital markets, information asymmetry often hinders market participants from accurately assessing a firm’s true value. According to signaling theory [61], security analysts play a crucial role as information intermediaries. Through in-depth research and analysis, they uncover firm-specific information relevant to valuation, thereby reducing information asymmetry among market participants and providing more transparent data and insights [62]. Analyst attention is typically measured by the number or frequency of analysts covering a firm’s stock, serving as a key indicator of market attention and the level of external monitoring. A higher level of analyst attention indicates greater allocation of professional perspectives and analytical resources to the firm, which not only improves the accessibility of information but also enhances its accuracy and credibility [63]. Firms with better ESG performance are generally more likely to attract analyst attention. This is because strong ESG performance not only reflects a firm’s excellence in environmental protection, social responsibility, and corporate governance but also signals its forward-looking capabilities in managing potential risks and identifying long-term growth opportunities. Analyst attention, in turn, contributes to more comprehensive information disclosure, thereby helping to alleviate information asymmetry [39]. Analyst attention can also promote improvements in environmental information disclosure, as firms with higher analyst coverage tend to provide more comprehensive and higher-quality disclosures [50]. This phenomenon highlights the important role of market analysts, who not only enhance information transparency but identify and emphasize potential environmental issues and risks through continuous monitoring and in-depth analysis [64]. This external monitoring function substantially contributes to the improvement of corporate environmental responsibility. For example, analyst scrutiny may prompt firms to comply more actively with environmental regulations, report pollutant emissions more accurately, or implement sustainability initiatives in a more systematic manner. Accordingly, it can be inferred that analysts’ professional evaluation plays a significant role in enhancing firms’ environmental accountability [65]. Therefore, this study proposes the following hypothesis.

Hypothesis 3. 

Firms’ ESG performance reduce corporate carbon emissions through the mechanism of attracting analyst attention.

4. Research Design

4.1. Empirical Model

To investigate the impact of corporate ESG performance on carbon emissions, this paper takes firms’ ESG performance as the core explanatory variable. On this basis, a multi-dimensional fixed effects model (1) is constructed to empirically test the research hypotheses proposed earlier.

$$\begin{array}{l}C{E}_{i,t+1}={\alpha }_0+{\alpha }_{1}ES{G}_{i,t}+{\alpha }_{2}Leverag{e}_{i,t}+{\alpha }_{3}RO{A}_{i,t}+{\alpha }_{4}S{A}_{i,t}+{\alpha }_{5}Growt{h}_{i,t}\\ +{\alpha }_{6}Siz{e}_{i,t}+{\alpha }_{7}Dua{l}_{i,t}+{\alpha }_{8}Top{1}_{i,t}+{\beta }_{\mathit{year}}+{\delta }_{\mathit{company}\_ID}+{\phi }_{\mathit{industry}}+{\epsilon }_{\mathrm{it}}\end{array}$$

(1)

The dependent variable, ${\mathit{CE}}_{i,t+1}$, denotes the CO₂ emissions of firm i in year t + 1. The explanatory variable, ${\mathit{ESG}}_{i,t}$, represents the ESG rating of firm i in year t. The terms ${\beta }_{\mathit{year}}$, ${\delta }_{\mathit{company}\mathit{\text{\_}}\mathit{ID}}$, ${\phi }_{\mathit{industry}}$ represent year, firm, and industry fixed effects, respectively. Year fixed effects control for time-specific shocks such as macroeconomic conditions or regulatory changes that affect all firms in a given year. Firm fixed effects account for unobserved, time-invariant firm characteristics, including corporate culture or governance structure. Industry fixed effects help capture systematic differences across sectors, such as baseline emission intensity or ESG disclosure norms. The inclusion of these fixed effects helps isolate the effect of ESG performance on carbon emissions by removing potential confounding factors. The error term, denoted as ${\epsilon }_{i,t}$ captures random disturbances. A significantly negative (or positive) regression coefficient suggests that, ceteris paribus, an improvement in a firm’s ESG performance leads to a significant reduction (or increase) in its carbon emissions. Considering the potential lagged effect of ESG performance on corporate carbon emissions, the regression model specifies the firm’s carbon emissions in the subsequent period (t + 1) as the dependent variable. Descriptions of the remaining control variables are provided below.

4.2. Data and Variable Selection

In light of the current limitations in corporate carbon emission disclosures, this study selects A-share listed companies on the Shanghai and Shenzhen stock exchanges in China from 2019 to 2023 as the initial research sample. ESG rating data are obtained from the ESG module of the Wind database, a widely used financial and economic information platform in China that provides comprehensive data on listed companies. Corporate carbon emission data are sourced from the China Stock Market and Accounting Research Database (CSMAR) and firms’ sustainability or ESG reports. The process is detailed as follows:

(1)

Corporate carbon emission data for the period 2019–2023 were downloaded from the CSMAR database.

(2)

Exclusion of companies designated as ST or ST* during the sample period.

(3)

Missing carbon emission data were supplemented by manually collecting information from firms’ corporate social responsibility (CSR) reports and ESG reports.

(4)

Observations with severely missing ESG data were removed from the sample.

(5)

The final research sample consists of 2536 firm-year observations.

4.2.1. Enterprise Carbon Emission

Corporate carbon emissions serve as the dependent variable in this study. The relevant data are primarily obtained from the following two sources: (1) disclosed information in the CSMAR database and (2) manually collected carbon emission data disclosed in firms’ ESG reports or sustainability reports. The types of carbon emissions covered in this study include the following two categories:

(1)

Direct carbon emissions: refers to emissions generated from a firm’s direct use of fossil fuels (such as natural gas, gasoline, and diesel).

(2)

Indirect carbon emissions: refers to emissions indirectly generated during the electricity production process when a firm purchases electricity from external suppliers.

4.2.2. Explanatory Variable

The explanatory variable in this study is corporate ESG performance, measured using ESG ratings published by HuaZheng Index Information Co., Ltd. As one of the earliest authoritative institutions in China to establish an ESG rating system, HuaZheng’s ESG ratings have been widely applied in areas such as green financial product design and investment evaluation. The system has gained broad recognition from regulatory authorities, financial institutions, and the academic community. To enhance the robustness of the empirical results, this study further introduces the ESG ratings provided by the Wind database as an alternative explanatory variable. Although the two ratings originate from different sources, the Wind ESG rating is likewise based on a comprehensive assessment of firms’ performance in environmental, social responsibility, and corporate governance dimensions. Given its strong representativeness and comparability, it serves as a valid instrument to verify the main empirical findings.

4.2.3. Control Variables

To address potential endogeneity issues arising from omitted variables and to improve the accuracy of the empirical results, this study introduces a set of control variables. Prior research has demonstrated that corporate carbon emissions are closely related to firms’ financial performance and financing constraints. Accordingly, the following variables are controlled for: leverage (measured by the debt-to-asset ratio) to capture the firm’s financial structure; return on assets (ROA) to reflect profitability; firm size, revenue growth rate (Growth), and the SA index, which proxies for financing constraints. In addition, governance-related studies [22,23] suggest that managerial structure may also influence a firm’s carbon emission level. In light of this, the model further controls for Top1 (the shareholding ratio of the largest shareholder) and a state-owned enterprise (SOE) dummy variable to capture the ownership nature of the firm. The definitions of the above variables are provided in

Table 1. Description of variables.

Variable Symbol	Description
CE	Log of total corporate carbon emissions in the current period.
ESG Wind ESG	Corporate ESG rating from Huazheng Corporate ESG rating from Wind
Leverage	Total liabilities/total assets
ROA	Net profit/average total assets
SOE	A dummy variable indicating the ownership type of the firm, equal to 1 if the firm is state-owned, and 0 otherwise.
Size	Natural log of total assets
Growth	Revenue growth rate
SA	SA index (used to measure financing constraints)
Top1	Ownership share of the largest shareholder
MO	Proportion of shares held by company management during the period
IS	Natural log of (1 + institutional ownership proportion)

Note: Carbon emissions and other control variables are obtained from the CSMAR database. ESG data are sourced from HuaZheng ESG and Wind ESG databases.

.

5. Empirical Results

5.1. Descriptive Statistics and Baseline Results

The descriptive statistics for the variables employed in the baseline regression are presented in

Table 2. Descriptive statistics.

Variables	Observation	Mean	S.D	Min	Max
CE	2536	12.075	2.82	0.391	19.624
ESG	2536	4.864	0.970	2	8
Wind ESG	2536	6.780	0.499	1.9	9.62
Leverage	2536	0.511	0.211	0.022	1.956
ROA	2536	0.472	0.728	−1.395	0.542
SA	2536	−3.757	0.728	−5.45	−0.518
Growth	2536	0.129	3.444	−1.344	171.745
Size	2536	24.029	1.942	19.749	31.310
SOE	2536	0.520	0.4996	0	1
Top1	2536	37.004	16.451	3.91	100
Mo	2536	7.441	15.031	0	74.619
IS	2536	0.667	0.465	0.001	3.793

. Building on the theoretical analysis presented earlier, this study constructs regression model (1) and conducts empirical testing. The regression results are reported in

Table 3. Baseline regression results.

	(1)	(2)	(3)
Variables	CE	CE	CE
ESG	−0.222 ***	−0.802 ***	−0.215 ***
	(0.0624)	(0.0596)	(0.06231)
leverage		−2.964 ***	−1.918 ***
		(0.365)	(0.617)
ROA		−1.337	−2.031 ***
		(0.877)	(0.682)
SA		−1.023 ***	5.121 ***
		(0.153)	(1.056)
Growth		0.546 ***	0.0422
		(0.168)	(0.106)
Size		1.008 ***	−0.294 *
		(0.0547)	(0.173)
SOE		−0.209 *	−0.424
		(0.117)	(0.401)
Top1		0.0239 ***	0.0107
		(0.00338)	(0.0103)
Constant	13.18 ***	−11.25 ***	40.40 ***
	(0.0307)	(1.633)	(6.777)
Observations	2081	2081	2081
R-squared	0.871	0.215	0.875
Firm FE	Y	N	Y
Year FE	Y	N	Y
Industry FE	Y	N	Y

Note: The values in parentheses are standard errors. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

. Specifically, Column (1) presents the regression results including only the ESG variable and fixed effects, without any control variables. Column (2) adds control variables but does not include fixed effects. Column (3) incorporates both control variables and firm, year, and industry fixed effects, forming the full baseline regression model.

The results indicate that ESG performance consistently exhibits a significant negative effect on corporate carbon emissions, with coefficients of −0.222, −0.802, and −0.215 in Columns (1), (2), and (3), respectively, all significant at the 1% level. This suggests that, regardless of whether control variables or fixed effects are included, improved ESG performance is significantly associated with lower carbon emissions. These findings highlight the positive role of ESG performance in promoting firms’ low-carbon transition and demonstrate the robustness of the regression results.

This finding is consistent with institutional theory; under increasing institutional pressure, firms face external constraints from government regulation, social expectations, and industry norms. ESG performance reflects a proactive corporate response to these institutional pressures. By enhancing ESG practices, firms can strengthen their legitimacy and reputation, thereby gaining greater motivation to implement green transition strategies and reduce carbon emissions on a sustained basis. Thus, Hypothesis 1 is verified.

5.2. Robustness Check

5.2.1. Changing Independent Variable

Given the differences across ESG rating agencies in terms of evaluation methodologies, weighting schemes, and data sources, relying on a single provider may introduce bias and limit the generalizability of the findings. To address this issue and test the robustness of the results, this study employs ESG ratings from Wind Information as an alternative explanatory variable. Developed by Wind, one of China’s leading financial data providers, the Wind ESG rating system builds upon internationally accepted ESG standards while incorporating the unique characteristics of the Chinese market. It offers a more targeted and explanatory assessment of firms across the environmental, social, and corporate governance dimensions.

Column (1) of

Table 4. Robustness check result.

Variables	(1) Wind ESG Rating	(2) Adding Control Variable	(3) Excluding Specific Year
	CE	CE	CE
Wind ESG	−0.106 ** (0.0450)
ESG		−0.216 *** (0.0631)	−0.371 *** (0.0779)
leverage	−1.999 *** (0.645)	−1.920 *** (0.630)	−0.827 (1.161)
ROA	−2.034 *** (0.696)	−2.052 *** (0.683)	2.107 (1.415)
SA	4.962 *** (1.078)	5.139 *** (1.069)	1.182 (2.666)
Growth	0.0120 (0.107)	0.0411 (0.106)	−0.299 * (0.160)
Size	−0.248 (0.170)	−0.293 (0.180)	0.326 (0.268)
SOE	−0.403 (0.411)	−0.426 (0.402)	0.391 (0.359)
Top1	1.118 (1.062)	1.032 (1.025)	2.393 (1.876)
MO		0.00419 (0.0100)
Constant	38.41 *** (6.905)	40.39 *** (6.988)	9.693 (12.94)
Controls	YES	YES	YES
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Industry FE	YES	YES	YES
Observations	2081	2081	1068
R-squared	0.876	0.875	0.960

Note: Wind ESG is sourced from Wind Information Co., Ltd., Shanghai, China. and is used as an alternative explanatory variable for robustness checks. The model specification remains consistent with the main regression. The values in parentheses are standard errors. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

presents the regression results using Wind ESG ratings as an alternative explanatory variable. The coefficient on ESG improvement remains negative (−0.106) and statistically significant at the 5% level, consistent with the baseline results. Although the magnitude is smaller, the direction and significance of the relationship persist, suggesting that the negative effect of ESG performance on corporate carbon emissions is not sensitive to the choice of ESG metric. This consistency helps mitigate concerns about model specification and measurement heterogeneity and further reinforces the robustness and reliability of the core findings.

5.2.2. Adding Control Variable

According to stakeholder theory, firms are expected to balance the interests of various stakeholders, including shareholders, employees, and broader society. Managerial ownership, as a key internal governance mechanism, can influence corporate environmental behavior by aligning managers’ incentives with long-term organizational sustainability goals [66]. Existing studies suggest that managerial ownership may influence corporate carbon emissions [67]. Given the critical role of managerial ownership in promoting corporate sustainability initiatives, this study includes the proportion of managerial shareholding as an additional control variable in the baseline regression model to enhance the robustness of the results. Column (2) of Table 4 shows that the coefficient on ESG improvement is −0.216 and remains significantly negative at the 1% level. This indicates that even after controlling for managerial ownership, the negative effect of ESG performance on carbon emissions still holds, thereby further confirming the robustness of the main findings.

5.2.3. Excluding Specific Years

To further test the robustness of the empirical results, this study excludes data from 2020 to 2021 to mitigate the potential short-term shocks of the COVID-19 pandemic on corporate operations and carbon emissions. These two years represent the peak of the pandemic’s impact, during which firms faced abnormal external shocks that may reduce the representativeness of the data. Excluding this period helps to better identify the structural relationships among the variables. In contrast, the macroeconomic environment in 2022 showed signs of recovery. According to data from the National Bureau of Statistics of China, China’s GDP growth rate in 2022 reached 3.0%, significantly higher than the 2.2% recorded in 2020. Industrial output and total exports also exhibited a rebound trend. Therefore, it is reasonable to retain the 2022 data, which is unlikely to distort the results of the robustness check.

Column (3) of Table 4 presents the regression results after excluding data from 2020–2021. The coefficient on ESG improvement is −0.371 and remains significantly negative at the 1% level, consistent in direction with the baseline regression results. This finding indicates that even after removing observations from the pandemic-affected period, the negative impact of ESG performance on corporate carbon emissions remains robust, further supporting the reliability of the study’s conclusions.

Although the coefficient becomes larger in magnitude compared to the baseline model, the direction remains negative and statistically significant. This may reflect that the pandemic period masked the full extent of firms’ ESG efforts on emissions reduction, and its exclusion helps reveal a clearer structural relationship.

5.3. Endogeneity Test

While this study assumes that ESG performance affects carbon emissions, there is a possibility that firms with higher emission levels may proactively improve their ESG disclosure quality in response to regulatory pressure or stakeholder concerns. To address this issue, the study employs a propensity score matching (PSM) approach to alleviate potential sample selection bias. In addition, province-by-year fixed effects are introduced to control for time trends and macro-level regional influences. These measures help mitigate the potential biases arising from reverse causality and omitted variables.

5.3.1. PSM

To alleviate potential omitted variable bias, this study adopts the propensity score matching (PSM) method for robustness testing, following the approach of [68]. Specifically, firms are divided into a high-ESG group (treatment group) and a low-ESG group (control group) based on the median ESG score within the sample. Firms with ESG scores above the sample median are classified into the high-ESG group (treatment group), while the remaining firms constitute the control group. The matching procedure is based on a set of firm-level characteristics that may influence ESG performance, including leverage, return on investment (ROI), the SA index (as a proxy for financing constraints), revenue growth rate, firm size, ownership type (state-owned or not), and the shareholding ratio of the largest shareholder (Top1). To improve matching precision, a 1:1 nearest-neighbor matching algorithm with replacement is employed.

Column (1) of

Table 5. Exogenous test result.

	(1) PSM	(2) Multiple Fixed Effects
Variables	CE	CE
PSM ESG	−0.435 *** (0.135)
ESG		−0.215 *** (0.0643)
Leverage	−1.998 ** (0.776))	−1.860 *** (0.643)
ROA	−2.099 ** (1.054)	−2.106 *** (0.714)
SA	5.167 *** (1.154)	4.667 *** (1.112)
Growth	0.119 (0.121)	0.0712 (0.109)
Size	−0.383 * (0.220)	−0.258 (0.187)
SOE	−0.756 (0.537)	−0.415 (0.379)
Top1	0.491 (1.225)	0.963 (1.048)
Constant	42.32 *** (8.246)	37.83 *** (7.231)
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES
Industry FE	YES	YES
Province year FE	NO	YES
Observations	1801	2081
R-squared	0.890	0.879

Note: Column (1) uses the propensity score matching (PSM) method to address potential endogeneity concerns. Firms with high ESG performance (treatment group) are matched with firms with similar observable characteristics (control group) based on estimated propensity scores. This helps reduce selection bias and isolate the effect of ESG performance on carbon emissions (CE). Column (2) includes firm, year, industry, and province-year fixed effects. The values in parentheses are standard errors. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

reports the regression results based on the PSM method. The findings show that firms with high ESG performance have significantly lower carbon emissions compared to those with low ESG performance, with a coefficient of −0.435 that is statistically significant at the 1% level. This result provides empirical support for Hypothesis 1 and further reinforces the main conclusions of this study.

5.3.2. Multi-Dimensional Fixed Effects

Although the baseline regressions have controlled for key variables that may affect corporate carbon emissions, the risk of omitted variable bias may still exist due to unobservable factors. To further mitigate this issue, this study follows the approach of [69] by incorporating province-by-year interaction fixed effects. This allows for the simultaneous control of regional heterogeneity as well as time-varying macroeconomic conditions and policy changes across different regions that may influence corporate behavior.

In Column (2), province-by-year interaction fixed effects are included. The coefficient on ESG performance is −0.215 and remains statistically significant at the 1% level, further confirming the reliability of the study’s conclusions. The model with added province-by-year interaction fixed effects is specified as follows:

$$C{E}_{i,t+1}={\alpha }_0+{\alpha }_1{\mathit{ESG}}_{i,t}+{\alpha }_{2}Controls+{\beta }_{\mathit{year}}+{\delta }_{\mathit{company}\mathit{\text{\_}}\mathit{ID}}+{\phi }_{\mathit{industry}}+{\lambda }_{province}+{\lambda }_{province} · {\beta }_{\mathit{year}}+{\epsilon }_{\mathit{it}}$$

(2)

5.4. Mechanism Analysis

In the preceding analysis, the baseline regressions have provided preliminary evidence that corporate ESG performance has a negative impact on carbon emissions. However, the underlying mechanisms through which this effect occurs have not yet been fully explored or rigorously validated. To further investigate the internal mechanisms by which ESG performance influences carbon emissions, this study follows the approach of [70] and, based on the previously proposed hypotheses, constructs a regression framework incorporating corporate green innovation and analyst attention. An empirical strategy of subgroup regressions is employed to test the effectiveness of these potential transmission channels.

$${\mathit{GI}}_{i,t}={\delta }_0+{\delta }_1{\mathit{ESG}}_{i,t}+{\delta }_2{\mathit{Controls}}_{i,t}+{\beta }_{\mathit{year}}+{\delta }_{\mathit{company}\mathit{\text{\_}}\mathit{ID}}+{\phi }_{\mathit{industry}}+{\epsilon }_{i,t}$$

(3)

$${\mathit{AA}}_{i,t}={\phi }_0+{\phi }_{1}ES{G}_{i,t}+{\phi }_{2}Control{s}_{i,t}+{\beta }_{\mathit{year}}+{\delta }_{\mathit{company}\mathit{\text{\_}}\mathit{ID}}+{\phi }_{\mathit{industry}}+{\epsilon }_{i,t}$$

(4)

${\mathit{GI}}_{i,t}$ represents the mechanism variable of corporate green innovation, measured as the natural logarithm of the number of patents granted to the firm in a given year. ${\mathit{AA}}_{i,t}$ represents the mechanism variable of analyst attention, measured as the natural logarithm of one plus the number of times a firm is mentioned by analysts within a given year. All other variables are consistent with those in model (1).

5.4.1. Testing H2: The Mechanism Role of Green Innovation

To examine whether corporate ESG performance can reduce carbon emissions through green innovation, this study follows the approach of [19] and uses the natural logarithm of the number of green patents plus one as a proxy for green innovation (GI).

Table 6. The test of green innovation mechanism.

	(1)	(2)	(3)
Variables	GI	CE (High Patent Group)	CE (Low Patent Group)
ESG	0.0583 ** (0.0252)	−0.294 *** (0.0851)	−0.277 *** (0.0982)
Leverage	0.654 *** (0.244)	−1.461 * (0.861))	−4.110 *** (1.429)
ROA	−0.102 (0.303)	−1.137 (1.083)	−2.835 (1.935)
SA	−0.986 ** (0.419)	4.158 ** (2.081))	7.717 *** (1.609)
Growth	0.0601 (0.0519)	−0.0153 (0.133)	−0.0796 (0.170)
Size	0.291 *** (0.0656)	0.232 (0.230)	−0.977 *** (0.309)
SOE	−0.114 (0.138)	−0.158 (0.371)	−0.0549 (0.410)
Top1	0.140 (0.522)	1.146 (1.066)	0.761 (1.442)
Constant	−9.853 *** (2.698)	23.64 ** (10.20)	69.13 *** (12.49)
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes
Observations	2536	969	864
R2	0.880	0.856	0.893

Note: Column (1) presents the regression results of ESG performance on green innovation (GI). Columns (2) and (3) show the heterogeneous effects of ESG on carbon emissions (CE) across firms with high and low levels of patent activity, respectively. Standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

reports the results of the mechanism analysis, examining how ESG performance influences corporate carbon emissions through green technological innovation. Specifically, Column (1) of Table 6 uses green innovation as the mechanism variable. The regression coefficient is 0.0583 and is significantly positive at the 5% level, indicating that improved ESG performance significantly promotes green innovation. This finding is consistent with existing studies, which suggest that green innovation contributes to enhancing firms’ carbon emission efficiency [71].

These findings are consistent with the Porter Hypothesis, which posits that well-designed environmental and social responsibilities reflected in ESG frameworks can act as drivers of innovation rather than constraints on firm performance. In this context, a strong commitment to ESG encourages firms to invest in green technologies, which help meet external expectations while simultaneously improving resource efficiency and operational effectiveness, ultimately contributing to the reduction in carbon emissions.

To further explore the role of green innovation in shaping the ESG–carbon emissions relationship, the sample firms are divided into high and low green innovation groups based on the median level of green innovation. The effect of ESG performance on corporate carbon emissions is then examined separately within each group. The empirical results show a significantly negative relationship between ESG performance and carbon emissions in both subgroups. In the high green innovation group, the coefficient on ESG performance is −0.294 and significant at the 1% level; in the low green innovation group, the coefficient is −0.277, also significant at the 1% level.

These results suggest that ESG performance contributes to carbon emission reduction regardless of a firm’s level of green innovation, but the effect is more pronounced among firms with higher levels of green innovation. This indicates that green innovation may serve as an effective mechanism that strengthens the environmental impact of ESG performance.

In other words, when firms invest more in green technologies and possess stronger innovation capabilities, their ESG strategies are more likely to translate into substantive environmental performance improvements, thereby achieving greater reductions in carbon emissions. Thus, Hypothesis 2 is verified.

5.4.2. Testing H3: The Mechanism Role of Analyst Attention Mechanism

In addition, this study explores the potential role of analyst attention as a mechanism linking ESG performance to corporate carbon emissions, drawing on signaling theory. According to this theory, firms with superior ESG performance send credible signals to the market about their long-term value and commitment to sustainability. Analysts, as informed external stakeholders, respond to these signals by increasing their coverage and monitoring activities. This heightened attention functions as an external governance mechanism that enhances corporate transparency and exerts greater market pressure on firms. As a result, firms are more likely to fulfill their environmental responsibilities and adopt proactive emission reduction strategies [65]. To examine whether corporate ESG performance reduces carbon emissions through analyst attention, this study adopts the method proposed by [39] to measure analyst attention (AA), using the natural logarithm of one plus the number of times a firm is mentioned by analysts in a given year.

The empirical results of this study support the proposed mechanism. As shown in Column (1) of

Table 7. The test of analyst attention mechanism.

	(1)	(2)	(3)
Variables	AA	CE (High Analyst Attention)	CE (Low Analyst Attention)
ESG	0.0555 *** (0.0213)	−0.340 *** (0.0905)	−0.0408 (0.0894)
leverage	0.303 (0.236)	−1.257 (0.996)	−2.415 *** (0.905)
ROA	3.109 *** (0.409)	−0.474 (1.248)	−2.677 *** (0.990)
SA	−0.0980 (0.308)	4.832 * (2.500)	5.233 *** (1.296)
Growth	−0.00215 (0.00150)	0.00314 (0.00236)	0.0579 (0.142)
Size	0.315 *** (0.0526)	−0.0774 (0.212)	−0.188 (0.212)
SOE	−0.176 (0.150)	−0.775 ** (0.333)	−0.943 (0.827)
Top1	−0.0121 *** (0.00433)	0.0142 (0.0133)	−0.00603 (0.0129)
Constant	−5.580 *** (2.035)	16.12 *** (2.828)	38.19 *** (8.150)
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes
Observations	2152	876	1050
R2	0.880	0.896	0.900

Note: Column (1) reports the effect of ESG performance on analyst attention (AA), aiming to identify a potential mechanism. Columns (2) and (3) present the results of grouped regressions, where the sample is divided into high and low analyst attention groups to examine the relationship between ESG performance and carbon emissions (CE). The values in parentheses are standard errors. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

, the regression coefficient of ESG performance on analyst attention is 0.0555, which is significantly positive at the 1% level. This indicates that better ESG performance significantly increases the level of attention a firm receives from analysts. This finding suggests that improvements in ESG performance effectively attract analyst attention, thereby reinforcing external monitoring and encouraging firms to proactively adopt emission reduction measures to enhance their environmental performance. The study further validates the effectiveness and plausibility of analyst attention as a mediating channel. To further examine this mechanism, we divide the sample based on the average level of analyst attention and conduct subgroup regressions. As presented in Columns (2) and (3) of Table 7, the negative effect of ESG performance on carbon emissions is statistically significant only among firms with higher analyst attention. In this group, the estimated coefficient is −0.316 and is significant at the 1% level. By contrast, the relationship is not statistically significant in the low-attention subgroup. These findings provide additional evidence for the role of analyst attention as an amplifying pathway through which ESG performance affects corporate environmental outcomes. Thus, Hypothesis 3 is supported.

5.5. Heterogeneity Analysis

Based on the above findings, corporate ESG performance is shown to reduce firms’ carbon emissions. Considering that different levels of institutional ownership may influence corporate decision making, this study further explores potential heterogeneity from this perspective. Specifically, the sample is divided into high and low institutional ownership groups based on the 75th percentile of institutional ownership. The following analysis examines whether the impact of ESG performance on carbon emissions varies across these two groups.

The regression results show that, among firms with low institutional ownership in Column 1 of

Table 8. ESG effects on carbon emissions: evidence from groups with high vs. low institutional ownership.

	(1)	(2)
Variables	CE (Low Institutional Group)	CE (High Institutional Group)
ESG	−0.252 *** (0.0854)	−0.132 (0.0974)
Leverage	−1.940 *** (0.739)	−1.745 (1.065)
ROA	−2.009 *** (0.733)	−3.947 * (2.131)
SA	3.576 ** (1.402)	6.817 *** (2.131)
Growth	0.00645 *** (0.00160)	0.270 (0.194)
Size	−0.258 (0.232)	−0.471 * (0.245)
SOE	−0.731 (0.497)	0.302 (0.284)
Top1	0.0202 (0.0130)	−0.00363 (0.0154)
Constant	32.47 *** (9.208)	49.82 *** (9.446)
Firm FE	Yes	Yes
Year FE	Yes	Yes
Industry FE	Yes	Yes
Observations	1401	635
R2	0.838	0.932

Note: Columns (1) and (2) present regression results for subsamples grouped by the level of institutional ownership. The grouping is based on the median institutional shareholding ratio. This analysis explores whether the effect of ESG performance on carbon emissions (CE) varies under different levels of institutional monitoring. Standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

, ESG performance is significantly negatively associated with corporate carbon emissions. This relationship remains robust after controlling for firm-level characteristics, with a regression coefficient of −0.252 that is significant at the 1% level. This finding suggests that firms with lower levels of institutional ownership are more effective in translating improvements in ESG performance into actual emission reductions. In contrast, for firms with high institutional ownership in Column 2 of Table 8, the effect of ESG performance on carbon emissions is relatively weaker. The regression coefficient is −0.132, but it does not reach statistical significance. This indicates that the impact of ESG performance on carbon reduction is relatively limited among firms with higher levels of institutional ownership.

Building on the empirical results and existing literature, ESG performance significantly promotes carbon reduction among firms with low institutional ownership, suggesting that in contexts with limited institutional investor involvement, firms are more likely to view ESG as an internal driving force for sustainable development. This finding reflects that some institutional investors may prioritize short-term financial returns while paying insufficient attention to firms’ long-term environmental responsibilities and sustainability goals, which is consistent with the existing literature [72,73]. In contrast, the effect of ESG performance on carbon emissions is not significant among firms with high institutional ownership, indicating that institutional investors may, to some extent, weaken the carbon reduction effect of ESG performance. To enhance the practical role of ESG in promoting corporate green transition, future efforts should focus on optimizing the structure of the capital market by attracting more institutional investors with long-term value orientation and sustainability awareness. These investors should be encouraged to play a more active role in resource allocation, information monitoring, and responsible investment, thereby facilitating the coordinated advancement of corporate development and environmental governance [74].

6. Conclusions and Implications

6.1. Main Conclusions

Achieving low-carbon development is a necessary requirement for China’s transition toward high-quality economic growth. In recent years, corporate ESG ratings and management mechanisms have become essential components in driving green transformation. Enhancing ESG performance not only helps mitigate the environmental externalities of traditional finance-driven growth models but also provides strategic guidance and momentum for carbon emission management, green technological innovation, and sustainable corporate development. These efforts lay a solid foundation for building a resource-efficient and environmentally friendly society.

This study investigates how improvements in corporate ESG performance affect carbon emissions. Using firm-level panel data and a multi-dimensional fixed effects regression model, the empirical analysis effectively controls for unobservable heterogeneity at the firm, year, and industry levels, thereby mitigating potential estimation bias due to omitted variables. The results show that higher ESG ratings are significantly associated with lower corporate carbon emissions, indicating that firms with better ESG performance are more likely to achieve superior environmental outcomes and fulfill their social responsibilities more effectively.

In addition, this study identifies two key transmission mechanisms, green technological innovation and analyst attention, through which ESG performance affects carbon emissions. The empirical results suggest that improved ESG performance indirectly reduces emissions by promoting corporate green innovation and strengthening external oversight from financial analysts. This finding highlights that good ESG practices not only encourage firms to increase investments in environmentally friendly technologies but also enhance the monitoring function of analysts regarding firms’ environmental responsibilities.

Furthermore, the heterogeneity analysis reveals that the carbon-reducing effect of ESG performance is more pronounced among firms with lower levels of institutional ownership. This suggests that in contexts with less institutional investor participation, firms may be more responsive to ESG policy signals and more proactive in implementing emission reduction measures. By contrast, among firms with high institutional ownership, the effect of ESG performance on carbon emissions appears weaker, possibly due to institutional investors’ greater focus on short-term financial returns.

In conclusion, this study provides empirical support for promoting corporate environmental and social responsibility and guiding capital market participants to pay greater attention to environmental performance. The findings demonstrate that under the influence of informal institutional factors, ESG performance has a significant impact on corporate carbon emissions. This offers theoretical insights into the role of soft institutional environments in green transition and provides policy implications for improving the environmental governance system.

6.2. Policy Implications

Given the international community’s growing emphasis on sustainable development and environmental governance, ESG initiatives have become an important guiding force in capital markets. In China, the era of green transition and low-carbon development has prompted both the government and enterprises to actively explore sustainable pathways to support high-quality economic growth. In this context, advancing systematic policy arrangements centered around the strategic value of ESG is of great practical importance for promoting corporate sustainability. Based on the findings of this study, the following policy recommendations are proposed:

First, it is necessary to improve the ESG rating system to guide high-quality corporate development. As the global economy accelerates toward green and low-carbon growth, ESG has increasingly become a key reference for capital allocation. As one of the major carbon-emitting countries, China urgently needs to establish an authoritative ESG evaluation framework that aligns with international standards while reflecting domestic realities. Particular emphasis should be placed on the environmental dimension, with clear evaluation criteria for corporate environmental responsibility. In addition, China should actively participate in the formulation and alignment of international ESG standards to enhance the transparency and credibility of its ESG system and steer enterprises from traditional growth models toward sustainable development strategies.

Second, it is necessary to strengthen policy support for green innovation through fiscal incentives and technological assistance to stimulate the internal drivers of corporate sustainability. This study finds that green innovation plays a critical mediating role in the relationship between ESG performance and carbon emissions at the micro level. Therefore, the government should introduce more targeted incentive policies, including tax relief, R&D subsidies, and green finance support, to encourage firms to invest in environmental technologies and cleaner production processes, thereby promoting synergy between environmental performance and economic benefits.

Third, it is necessary to enhance mandatory environmental information disclosure to improve the supervisory effectiveness of analysts and capital markets. The study reveals that increased analyst attention is associated with lower corporate carbon emissions, highlighting the positive role of market oversight in corporate environmental governance. It is thus necessary to further strengthen mandatory environmental disclosure requirements for listed companies, improving both the transparency and completeness of ESG information. This would provide analysts and investors with sufficient data to better exercise external monitoring functions in the capital market.

Fourth, it is necessary to optimize capital market mechanisms and encourage institutional investors to adopt a long-term investment orientation. The findings show that high institutional ownership tends to weaken the negative relationship between ESG performance and carbon emissions, especially in high-polluting industries. Policymakers should, therefore, refine incentive mechanisms in capital markets to reinforce the role of institutional investors in supervising firms’ long-term environmental performance. Investors should be guided to focus more on long-term ESG outcomes in their decision making, avoiding the environmental risks associated with short-term profit-seeking behavior.

6.3. Limitations and Prospects

This study selects A-share listed companies in China that publicly disclose carbon emission data as the research sample. Such a sample selection may introduce a degree of selection bias, as firms that disclose carbon emissions are typically more sensitive to environmental regulations or subject to stricter oversight. Therefore, the findings of this study primarily reflect the behaviors and practices of firms that are relatively advanced in terms of environmental transparency and social responsibility.

Future research should broaden the scope to include a wider variety of firms in terms of type and size, especially those that have not yet disclosed carbon emission data. This would allow for a more comprehensive industry-level perspective to better understand the relationship between ESG performance and corporate emission reduction behavior.