When Does ESG Performance Pay Off? Corporate Reputation and Firm Performance in Chinese State-Owned Enterprises

Abstract

Environmental, social, and governance (ESG) performance has become an important component of corporate sustainability and responsible governance, yet its economic implications remain contested, especially in state-owned enterprises (SOEs) that are expected to balance commercial goals with broader social responsibilities. This study examines the relationship between ESG performance and firm performance in Chinese listed SOEs, with particular attention to the mediating role of corporate reputation. The results show that ESG performance is positively associated with firm performance. Corporate reputation, risk-taking, and financial constraints are identified as important transmission channels through which ESG performance affects firm outcomes. Further analysis reveals a threshold effect in the ESG–performance relationship: when corporate reputation is relatively low, ESG investment may weaken firm performance; however, once reputation exceeds a critical threshold, ESG performance significantly improves firm performance. These findings enrich the literature on corporate sustainability and ESG value creation by showing that the performance effect of ESG is conditional on reputational capital. The study also provides practical implications for managers and policymakers seeking to promote sustainable corporate transformation in state-owned enterprises.

1. Introduction

Environmental, social, and governance (ESG) performance has become a central issue in corporate sustainability and responsible management practices. Prior studies generally suggest that firms can improve operational efficiency, strengthen stakeholder relationships, and mitigate various business risks by engaging in ESG activities, thereby enhancing firm performance [1,2,3]. However, although most recent studies show that ESG performance has a positive impact on firms’ financial and non-financial performance, some opposing findings have documented negative or U-shaped effects [4,5,6]. For example, Bénabou and Tirole (2010) argue that ESG may affect firms’ value-maximization objectives due to externalities [7]; Ammar et al. (2022) find that ESG investment may have a negative effect on ROA under certain conditions; and Umair and Weili (2023) show that the environmental and governance dimensions of ESG exert negative effects on financial performance, whereas non-financial performance is positively influenced by ESG [8,9]. These conflicting findings suggest that the economic consequences of ESG performance may be context-dependent rather than universally value-enhancing.

This issue is particularly salient in the context of state-owned enterprises (SOEs). Unlike private firms, SOEs operate within complex institutional environments where they are required to pursue both economic objectives and social and policy mandates [10]. Consequently, ESG investments in SOEs often involve trade-offs between social responsibility and financial performance. On the one hand, ESG initiatives can help SOEs meet regulatory requirements, enhance organizational legitimacy, and access critical resources. On the other hand, ESG engagement typically requires substantial resource commitments, and if external stakeholders question the authenticity and sustainability of these initiatives, the expected economic returns may not materialize, potentially crowding out resources for profit-generating activities.

Given the heterogeneous economic consequences of ESG performance, recent studies have begun to explore the mechanisms through which ESG engagement affects firm outcomes. Prior research suggests that ESG performance may alleviate financing constraints [11,12], promote innovation and corporate risk-taking [13], and enhance stakeholder trust. Among these mechanisms, corporate reputation has been increasingly recognized as a critical yet underexplored mediating factor. From a signaling perspective, ESG performance conveys information about firms’ long-term orientation, ethical standards, and governance quality to external stakeholders [14]. A strong corporate reputation can enhance the credibility of ESG signals, thereby facilitating the conversion of ESG investments into economic benefits [15,16].

However, the effectiveness of ESG signals may critically depend on firms’ existing reputational capital. When corporate reputation is weak, ESG initiatives are more likely to be interpreted as symbolic or opportunistic actions, leading stakeholders to discount their informational value. In such cases, ESG investments may fail to generate economic returns and may even suppress firm performance due to their associated costs. In contrast, when firms possess strong reputational capital, their ESG initiatives are more likely to be perceived as genuine and long-term oriented, allowing ESG performance to translate into significant performance gains. This suggests that the ESG–performance relationship may be nonlinear and subject to corporate reputation thresholds.

Although ESG and corporate sustainability have received increasing scholarly attention, empirical evidence on the conditional and nonlinear effects of ESG performance in SOEs remains limited. Most prior studies rely on linear models and focus on average effects, potentially obscuring important heterogeneity in the ESG–performance relationship [17,18]. Moreover, corporate reputation as a boundary condition shaping the economic consequences of ESG performance has not been systematically examined, particularly in emerging market contexts.

In this study, we use panel data of Chinese listed state-owned enterprises from 2018 to 2024 to systematically examine the impact of ESG performance on firm performance, with a particular focus on the mediating and threshold roles of corporate reputation. Since the China Securities Regulatory Commission issued the revised Code of Corporate Governance for Listed Companies in 2018, which established the basic framework for ESG information disclosure, a large number of firms have begun to disclose ESG-related information in a systematic manner. Therefore, this study selects the period from 2018 to 2024 as the research sample period to ensure data completeness and sample validity [19]. Employing two-way fixed effects and panel threshold models, we analyze both the underlying mechanisms and the nonlinear characteristics of the ESG–performance relationship, thereby revealing when ESG investments create value and when they may become a cost burden.

This study contributes to the literature in three important ways. First, as a theoretical contribution, by identifying corporate reputation as a critical boundary condition, this study reveals under what circumstances and why ESG investments can be transformed into economic benefits, thereby moving beyond the limitations of the traditional linear ESG–performance framework. Second, as a methodological contribution, we systematically examine the mechanisms through which ESG performance affects firm performance by using a triple-mediation model involving risk-taking, corporate reputation, and financial constraints, and further employ a panel threshold model to identify the nonlinear, reputation-contingent ESG–performance relationship. Third, as a contextual and policy contribution, evidence from Chinese listed state-owned enterprises enriches our understanding of ESG value creation in emerging markets and provides policy-relevant insights for designing ESG strategies that balance social responsibility and financial performance.

2. Theoretical Analysis and Hypothesis Development

2.1. ESG Performance and Firm Performance

ESG performance can be understood as an important signal through which firms convey their sustainability capabilities, level of responsibility fulfillment, and governance quality to external stakeholders. In an environment characterized by information asymmetry, higher ESG performance helps reduce the uncertainty of external actors, such as investors, creditors, government agencies, and consumers, regarding firms’ long-term development capabilities, while strengthening their trust and support. From the perspective of signaling theory, ESG performance reflects firms’ commitment to responsible management and sustainable development, thereby improving external stakeholders’ evaluation of firms and shaping their resource-allocation decisions. Meanwhile, the resource-based view and stakeholder theory provide complementary perspectives that further explain how ESG performance enhances firms’ long-term competitive advantage and sustainable value creation by accumulating strategic intangible resources, improving stakeholder relationships, and optimizing internal governance structures [4]. As a comprehensive manifestation of firms’ environmental responsibility, social responsibility, and corporate governance practices, ESG performance shapes firms’ relationships with stakeholders, influences their access to critical resources, and optimizes internal governance structures, thereby exerting an overall impact on firm performance.

Existing studies generally suggest that ESG performance can enhance firm performance by improving operational efficiency, reducing transaction costs, strengthening stakeholder trust, and lowering financing constraints. First, superior ESG performance improves operational efficiency and resource allocation efficiency. Continuous investments in environmental management, employee relations, and governance mechanisms help firms mitigate environmental and social risks, reduce operational uncertainty, and improve internal management processes, thereby enhancing productivity and investment efficiency [20,21,22]. Second, ESG practices strengthen trust between firms and external stakeholders, alleviate information asymmetry, and reduce transaction costs, thereby enhancing firms’ access to external financing and commercial resources [11,12,23]. Third, sound governance practices curb managerial opportunism, improve incentive mechanisms, and enhance decision-making quality and capital allocation efficiency, which promotes long-term firm performance [24,25].

However, the relationship between ESG performance and financial performance remains controversial. Some studies argue that ESG activities may impose additional costs on firms and crowd out resources that could otherwise be allocated to core business operations. Specifically, due to the strong externality attributes of corporate social responsibility activities, they may conflict with firms’ value-maximization objectives [7]. Other studies find that ESG performance has a significantly negative effect on corporate financial performance measured by ROA, supporting the trade-off view that ESG investment may increase operating costs [8]. Adding to this complexity, recent evidence shows that earnings management further obscures the ESG–performance link [26]. In addition, the environmental, social, and governance dimensions of ESG may negatively affect corporate financial performance, although ESG performance can improve non-financial performance [9]. Similarly, environmental disclosure may have a negative impact on corporate financial performance, while different ESG dimensions may also exert heterogeneous effects on firm performance [27]. Moreover, only moderate ESG levels are associated with reduced manipulation, whereas social and governance scores may coincide with heightened opportunistic reporting [28], which may contribute to lower financial performance. Additionally, earnings management significantly undermines corporate sustainability, directly eroding the basis for ESG value creation [29]. These findings suggest that ESG performance does not necessarily generate immediate financial benefits, and its economic consequences may depend on specific institutional, organizational, informational conditions and earnings management.

One important reason for the mixed findings lies in ESG measurement and signal credibility. ESG ratings may vary across rating agencies because of differences in evaluation criteria, indicator weights, and data sources [30]. Such ESG rating discrepancies may weaken the credibility of ESG signals and make it more difficult for external stakeholders to accurately evaluate firms’ true sustainability performance. Moreover, firms may engage in strategic ESG presentation, such as greenwashing or cross-washing, by selectively emphasizing certain ESG dimensions while concealing weaknesses in others. Recent empirical evidence indicates that corporate ESG greenwashing significantly undermines financial sustainable development performance [31]. Firms with higher customer concentration are more likely to engage in ESG greenwashing to cater to major customers’ ESG preferences, particularly when facing financial constraints [32]. Furthermore, there exists a significant positive correlation between corporate ESG performance and greenwashing behavior, suggesting that high ESG scores do not necessarily guarantee authentic sustainability commitment [33]. Such behavior may reduce the credibility of ESG signals and weaken the positive relationship between ESG performance and firm performance [34].

In the context of state-owned enterprises (SOEs), the signals of responsibility fulfillment, governance quality, and sustainable development conveyed by ESG performance carry stronger institutional implications. SOEs not only pursue economic objectives but also social responsibility, policy responsiveness, and public missions; therefore, their ESG practices are more likely to be shaped by institutional policies and governance mechanisms. Recent studies show that ESG-related policies can significantly promote SOEs’ ESG development and enhance their sustainable development capability by reducing financing costs and other mechanisms [35]. Dividend-right incentive reform in central SOEs can also improve SOEs’ ESG performance by enhancing employee value creation and increasing public green attention [36]. Therefore, ESG practices in SOEs not only strengthen organizational legitimacy and policy responsiveness but also affect firm performance by improving information transparency, governance quality, and access to critical resources [37,38]. Meanwhile, whether ESG performance can be transformed into financial benefits may depend on the credibility of ESG signals, corporate reputational capital, and external stakeholder recognition. This makes Chinese listed SOEs a suitable context for examining the performance consequences of ESG performance and its boundary conditions.

In sum, ESG performance, as an important manifestation of firms’ sustainable development practices, is expected to positively affect firm performance. Accordingly, we propose the following hypothesis:

H1. 

ESG performance is positively associated with firm performance in state-owned enterprises.

2.2. ESG Performance, Risk-Taking, and Firm Performance

Corporate risk-taking refers to the degree to which firms are willing to bear uncertainty in pursuit of returns and is a key determinant of long-term firm performance [24]. Prior research suggests that superior ESG performance is associated with higher corporate risk-taking [37].

First, superior ESG performance can signal to the market, government agencies, and other stakeholders that firms possess strong capabilities in compliant operations, responsibility fulfillment, and risk management. This enhances corporate legitimacy, reduces the likelihood of environmental, social, and regulatory risks, and mitigates potential losses [3,39]. Corporate investments in social responsibility activities (e.g., philanthropy and environmental initiatives) generate social capital and create an “insurance-like” effect. When facing market volatility or policy uncertainty, firms can obtain stakeholder support and critical resources, which enhances their risk-taking capacity [40]. For SOEs, ESG performance is particularly important, as it helps firms balance economic objectives with social and policy mandates while coping with market risks [41].

Second, from the perspective of internal governance mechanisms, ESG practices help strengthen monitoring and constraints, curb managerial risk aversion and opportunistic behavior, and thereby increase firms’ overall risk-taking levels [42]. When implementing ESG strategies, managers tend to adopt a long-term orientation and allocate more resources to projects with long-term return potential, such as technological innovation and green investments, which further enhances firms’ profitability and performance [43].

Therefore, we propose:

H2. 

ESG performance improves firm performance by increasing corporate risk-taking.

2.3. ESG Performance, Financial Constraints, and Firm Performance

Financial constraints are a critical factor restricting firms’ sustainable development and performance improvement [44]. Prior studies generally argue that financial constraints weaken firm performance by increasing operating costs [45] and restricting external financing channels [46]. However, some studies suggest that, under certain conditions, superior ESG performance can alleviate financial constraints and thereby improve firm performance [47,48].

The fundamental cause of financial constraints lies in information asymmetry between firms and external capital providers. ESG practices have a significant signaling function, conveying positive information about firms’ operational stability, long-term strategic orientation, and risk management capabilities to external capital providers, such as investors, creditors, and suppliers, thereby reducing information asymmetry [49]. At the same time, ESG practices can strengthen internal governance mechanisms, constrain managerial opportunism, and improve disclosure transparency, which further enhances the credibility of ESG signals, improves firms’ information environment, and increases the trust of external capital providers [11]. As information asymmetry decreases, firms can more easily obtain funding from investors, creditors, and suppliers, thereby alleviating financial constraints, expanding investment, and improving firm performance.

The alleviation of financial constraints creates favorable conditions for firms to acquire critical resources, increase investment, and enhance performance. Thus, we propose:

H3. 

ESG performance improves firm performance by alleviating financial constraints.

2.4. Corporate Reputation as a Mediator Between ESG Performance and Firm Performance

Corporate reputation is an accumulative evaluation formed by external stakeholders based on firms’ long-term behavior and continuous information disclosure, reflecting the market’s overall judgment of firms’ reliability, responsibility, and governance quality. Prior research shows that ESG practices contribute to reputation building [50,51], and higher corporate reputation further enhances firm performance [52].

ESG performance conveys key information about firms’ long-term orientation, ethical standards, and governance quality to external stakeholders [14,53]. When such information is continuously received and recognized by stakeholders such as investors, consumers, suppliers, and government agencies, corporate reputation is enhanced. Investments in employee welfare, social initiatives, and consumer protection enhance societal trust and corporate reputation. Firms with strong reputations can attract more investors, business partners, and consumers, thereby improving firm performance.

Moreover, government agencies are also important recipients of ESG information from SOEs. Improved ESG performance sends positive signals to government agencies regarding firms’ active fulfillment of environmental and social responsibilities, thereby enhancing their reputation within governmental systems [16]. High corporate reputation not only facilitates access to policy support and financing opportunities but also enables firms to obtain more resources, thereby promoting firm performance [54].

Accordingly, we propose:

H4. 

ESG performance improves firm performance through the mediating role of corporate reputation.

2.5. Threshold Effect of Corporate Reputation: A Nonlinear Relationship Between ESG Performance and Firm Performance

Prior studies suggest that the relationship between ESG performance and firm performance may be nonlinear across different contexts [5,6,17]. Some studies argue that ESG investments may suppress firm performance due to increased costs and resource crowding-out effects [8,9], whereas others find that ESG practices reduce transaction costs and facilitate cooperation, thereby enhancing firm performance [55]. These mixed findings indicate that the economic consequences of ESG performance may depend on firms’ reputational conditions.

Corporate reputation influences how external stakeholders interpret and evaluate ESG signals. When firms have low reputational standing, their ESG disclosures and practices may be perceived as symbolic actions or “greenwashing”, leading stakeholders to question their authenticity and value [56]. In such cases, the costs associated with ESG investments (e.g., environmental management, social responsibility, and governance improvements) may not translate into immediate economic benefits [57]. Moreover, low-reputation firms face greater barriers in resource acquisition, and ESG practices may crowd out productive resources, thereby suppressing firm performance [58].

As corporate reputation improves, the positive effects of ESG practices gradually emerge. Once corporate reputation reaches a certain level, ESG performance significantly enhances firm performance, indicating that corporate reputation plays a critical role in strengthening the credibility of ESG signals [59,60,61]. High-reputation firms can enhance stakeholder trust through ESG practices related to environmental protection, employee rights protection, investor value creation, and agency problem mitigation [62]. This enhanced trust facilitates access to financing, market expansion, and high-quality development resources [63]. Therefore, the impact of ESG performance on firm performance exhibits a nonlinear pattern contingent on corporate reputation, with a stronger positive effect beyond a reputational threshold.

Based on the above analysis, we propose:

H5. 

The relationship between ESG performance and firm performance is nonlinear, with a significant threshold effect contingent on corporate reputation.

3. Research Design

3.1. Data Sources and Sample Selection

This study focuses on Chinese A-share listed state-owned enterprises (SOEs) from 2018 to 2024. Firm-level financial data are obtained from the Shanghai, Shenzhen, and Beijing Stock Exchange database in the Xinhua Finance Professional Terminal V2.0, while ESG performance data are sourced from the Wind Financial Terminal’s Wind ESG ratings, which cover environmental, social, and governance dimensions and are widely used in related research.

Empirical analyses are conducted using Stata 18.0. Following common practices in the literature, we apply the following sample screening procedures:

(1) firms in the financial industry are excluded;

(2) firms designated as ST and *ST are excluded;

(3) observations with missing key variables are removed;

(4) All variables are bilaterally winsorized at the 1% level on an annual basis to alleviate the influence of extreme values.

After these procedures, the final sample consists of 1219 Chinese A-share listed SOEs, yielding 8533 firm-year observations in an unbalanced panel dataset from 2018 to 2024.

3.2. Variable Definitions

3.2.1. Dependent Variable

Firm Performance (ROA). Following Duque-Grisales et al. (2021) [64], we measure firm performance using return on assets (ROA), defined as net income divided by total assets. This indicator captures firms’ operating efficiency and has been widely used in studies on ESG and firm performance.

3.2.2. Independent Variable

ESG Performance (ESG). Firm-level ESG performance is measured using the annual composite ESG scores provided by the Wind database. The Wind ESG rating system covers Chinese A-share listed firms and incorporates environmental, social, and governance dimensions. Due to its high credibility and academic recognition, it is employed as the proxy for firms’ ESG performance.

3.2.3. Mediating Variables

Corporate Reputation (Rep). Following Li Lizhuo and Cui Linhao (2023) [14], we construct a corporate reputation evaluation system based on twelve indicators: asset size, operating revenue, net profit, earnings per share, dividends per share, industry ranking, asset–liability ratio, current ratio, long-term debt ratio, whether audited by an international Big Four accounting firm, sustainable growth rate, and the proportion of independent directors.

First, all indicators are standardized. Then, factor analysis is conducted to extract four principal factors with factor rotation, with weights of 3.16559, 2.02869, 1.13452, and 1.01870, respectively. Finally, a weighted composite method is used to calculate corporate reputation scores, and firms are assigned reputation values ranging from 1 to 10 based on their scores.

Furthermore, in addition to factor analysis, we employed two additional news-based reputation measurement indicators following Pan Wanbin (2021) [65]. Specifically, Rep2 is calculated as the ratio of positive news to total news volume, capturing the proportion of favorable media coverage. Rep3 is calculated as the difference between positive and negative news divided by total news volume, capturing the net balance of media sentiment. Together, these two indicators provide alternative measures of corporate reputation from the perspective of media information and public perception.

Risk Taking (RiskTake). According to the study by Fang Xianming and Hu Ding (2023) [13], the degree of corporate risk-taking is measured by the year-on-year growth rate of R&D expenditure as a percentage of total assets (RiskTake1) and the proportion of R&D personnel to the total workforce (RiskTake2).

Financial Constraints. Financial constraints are measured using the SA index, FC index, and KZ index. The SA index is calculated based on the SA index proposed by Gu Leilie et al. (2020) [66]. Additionally, the FC index is derived from the regression analysis conducted by Lamont et al. (2001) [67]. The KZ index is calculated according to Wei Zhihua et al. (2014) [68].

3.2.4. Control Variables

Following Sun Hui et al. (2023) [53], Wang et al. (2023) [69] and Wu Xun and Yang Meiyi (2024) [18], we include inventory ratio (INV), proportion of independent directors (Indep), firm age (Age), sustainable growth rate (Growth), and current rate (CR) as control variables. In addition, industry fixed effects and year fixed effects are included in all regression models. Detailed variable definitions are presented in

Table 1. Variable Definitions.

Variable Type	Variable Name	Symbol	Definition
Dependent Variable	Firm Performance	ROA	Return on assets (net income divided by total assets)
Independent Variable	ESG Performance	ESG	ESG composite score from the Wind database
Mediating Variables	Corporate Reputation	Rep1	Factor score derived from the corporate reputation evaluation system
		Rep2	The ratio of positive news to total news volume
		Rep3	The difference between positive and negative news divided by the total news volume
	Risk-Taking	RiskTake1	Year-on-year growth rate of R&D expenditures scaled by total assets.
		RiskTake2	The proportion of R&D personnel to the total workforce
	Financial Constraints	SA	Financial constraints measured using the SA index
		FZ	Financial constraints measured using the FZ index
		KZ	Financial constraints measured using the KZ index
Threshold Variable	Corporate Reputation	Rep	Factor score derived from the corporate reputation evaluation system
Control Variables	Inventory Ratio	INV	Inventory divided by current assets
	Independent Director Ratio	Indep	Proportion of independent directors on the board
	Firm Age	Age	Number of years since listing
	Sustainable Growth	Growth	Growth rate of net profit or operating revenue
	current rate	CR	The ratio of current assets to current liabilities

.

3.3. Model Specification

To empirically examine the impact of ESG performance on firm performance and explore the underlying mechanisms and nonlinear effects, we employ panel regression models with industry and year fixed effects. All regressions are estimated using Stata 18.0, and standard errors are clustered at the firm level to account for within-firm serial correlation and heteroskedasticity.

3.3.1. Baseline Regression Model

To test the baseline relationship between ESG performance and firm performance in state-owned enterprises (SOEs), we estimate the following model:

$$RO{A}_{i,t}={\alpha }_0+{\alpha }_{1}ES{G}_{i,t}+{\alpha }_2\mathrm{Ʃ}Contro{l}_{i,t}+{\eta }_j+{\lambda }_t+{\epsilon }_{i,t}$$

(1)

where $i$ denotes the firm and $t$ denotes the year. $ES{G}_{i,t}$ represents firm-level ESG performance, and ${Controls}_{i,t}$ is a vector of firm-level control variables. ${\eta }_j$ and ${\lambda }_t$ denote industry fixed effects (based on industry classification) and year fixed effects, respectively. Industry fixed effects control for time-invariant heterogeneity across industries, while year fixed effects capture macroeconomic shocks and regulatory changes common to all firms. ${\epsilon }_{i,t}$ is the error term. Standard errors are clustered at the firm level.

3.3.2. Mediation Effect Models

Following the two-step approach proposed by Jiang Ting (2022) [70], we construct the mediation models as follows:

$${\mathrm{M}\mathrm{V}}_{i,t}={\beta }_0+{\beta }_{1}ES{G}_{i,t}+{\beta }_2\mathrm{Ʃ}Contro{l}_{i,t}+{\eta }_j+{\lambda }_t+{\epsilon }_{i,t}$$

(2)

where $\mathrm{M}{\mathrm{V}}_{i,t}$ denotes corporate risk-taking (RiskTake), corporate reputation (Rep), and financial constraints (SA), respectively. ${\eta }_j$ and ${\lambda }_t$ represent industry and year fixed effects, respectively. Standard errors are clustered at the firm level.

3.3.3. Threshold Regression Model

To investigate the potential nonlinear effects of ESG performance on firm performance, we employ a panel threshold regression model. Following Chen et al. (2019) [10], threshold values are identified using a bootstrap procedure.

Using corporate reputation (Rep) as the threshold variable, we estimate the following multiple-threshold panel model:

$$\begin{array}{c}RO{A}_{i,t}={\beta }_0+{\beta }_{1}ES{G}_{i,t}I({\mathrm{R}\mathrm{e}\mathrm{p}}_{i,t}\le {\phi }_1)+{\beta }_{2}ES{G}_{i,t}I({\phi }_1<{\mathrm{R}\mathrm{e}\mathrm{p}}_{i,t}\le {\mathrm{\phi }}_2)\\ +{\beta }_{3}ES{G}_{i,t}I({\mathrm{R}\mathrm{e}\mathrm{p}}_{i,t}>{\mathrm{\phi }}_2)+{\beta }_4ƩContro{l}_{i,t}+{\eta }_j+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(3)

where $I(\cdot )$ is an indicator function, and ${\varphi }_1$ and ${\varphi }_2$ denote the estimated threshold values. When ${\varphi }_1={\varphi }_2$, the model degenerates into a single-threshold specification. ${\eta }_j$ and ${\lambda }_t$ represent industry and year fixed effects, respectively. This model allows the marginal effect of ESG performance on firm performance to vary across different corporate reputation regimes.

4. Empirical Analysis

4.1. Descriptive Statistics

Table 2. Descriptive Statistics.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
ROA	8533	4.244	6.241	−18.752	23.552
ESG	8533	6.071	0.900	4.060	8.73
INV	8533	0.128	0.13	0	0.654
Indep	8533	0.503	0.275	0	1
Age	8533	27.477	5.878	8	67
Growth	8533	0.085	0.315	−0.654	2.032
CR	8533	1.820	1.961	−20	37
Rep	8533	5.500	2.630	1	10
Rep2	8533	0.659	0.514	−3	4
Rep3	8533	0.435	0.620	−7	5
RiskTake1	8533	−0.021	6.483	−33	23
RiskTake2	8533	0.140	0.118	0	0.786
SA	8533	−0.089	0.430	−3	0
FC	8533	0.281	0.449	0	1
KZ	8533	1.839	1.933	−2	5

reports descriptive statistics for the main variables. The mean value of firm performance (ROA) is 4.244, with a standard deviation of 6.241, a minimum of −18.752, and a maximum of 23.552, indicating substantial heterogeneity in operating performance among Chinese state-owned enterprises (SOEs). The mean ESG score is 6.071 (standard deviation = 0.900), suggesting considerable variation in firms’ ESG performance.

The mean corporate reputation (Rep) is 5.500 with a standard deviation of 2.599, ranging from 1 to 10, indicating a stratified distribution of reputational standing across firms. The mean value of RiskTake is −0.021 with a standard deviation of 6.458, reflecting substantial dispersion in firms’ innovation investment growth and heterogeneous exposure to innovation-related risk. The mean value of financial constraints (SA) is 0.043 with a standard deviation of 0.470, with values ranging from −3.248 to 0.335, indicating wide variation in financing frictions across firms.

Multicollinearity diagnostics indicate that the mean variance inflation factor (VIF) is 1.06, well below conventional thresholds, suggesting that multicollinearity is unlikely to be a concern in the regression models.

4.2. Baseline Regression Results

Table 3. Baseline Regression Results.

Variable	ROA
ESG	0.416 ***
	(0.092)
INV	−1.966
	(1.248)
Indep	0.173
	(0.295)
Age	−2.581 ***
	(0.980)
Growth	4.110 ***
	(0.212)
CR	0.211 ***
	(0.058)
_cons	4.928 ***
	(1.544)
Firm FE	Yes
Year FE	Yes
N	8533
r2	0.578
r2_a	0.506

Note: Robust standard errors are reported in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The same convention applies to all subsequent tables.

reports the baseline regression results on the relationship between ESG performance and firm performance. After controlling for firm characteristics, firm fixed effects, and year fixed effects, the coefficient on ESG is 0.416 and statistically significant at the 1% level, indicating that ESG performance significantly enhances firm performance in SOEs. This finding provides support for Hypothesis H1.

Regarding control variables, firm age is negatively associated with firm performance, whereas firm growth is positively associated with performance. Firm age (Age) is negatively associated with firm performance, as mature firms often face organizational rigidity, bureaucratic inefficiencies, and reduced adaptability to market changes. The current ratio (CR) is positively related to firm performance, suggesting that higher liquidity helps enhance firm performance, possibly by ensuring sufficient working capital to support operations and reduce short-term financial risk.

4.3. Mechanism Analysis

To uncover the underlying mechanisms through which ESG performance affects firm performance, we examine three potential channels: corporate risk-taking, corporate reputation, and financial constraints.

4.3.1. Risk-Taking Channel

Columns (1) and (2) of

Table 4. Mechanism Test Results (RiskTake).

Variable	RiskTake1	RiskTake2
ESG	0.226 ***	0.003 ***
	(0.083)	(0.001)
Control	Yes	Yes
Firm FE	Yes	Yes
Year FE	Yes	Yes
_cons	−3.019 *	0.108 ***
	(1.652)	(0.024)
N	8533	8533
r2	0.587	0.715
r2_a	0.518	0.667

report the mechanism test results using two proxies for corporate risk-taking as dependent variables. Specifically, RiskTake1 is measured by the year-on-year growth rate of R&D expenditures scaled by total assets, while RiskTake2 is measured by the proportion of R&D personnel in the total number of employees. Prior studies have documented a significant positive relationship between corporate risk-taking and firm performance (Fang, 2023) [13]. We therefore further examine whether ESG performance affects firm performance through the risk-taking channel.

The results show that the coefficient of ESG is positive and statistically significant in both specifications. In Column (1), the coefficient of ESG on RiskTake1 is 0.226 and significant at the 1% level, indicating that firms with better ESG performance tend to increase innovation-related investment intensity. In Column (2), the coefficient of ESG on RiskTake2 is 0.003 and also significant at the 1% level, suggesting that ESG performance is positively associated with a higher proportion of R&D personnel. These findings remain robust after controlling for firm-level control variables, firm fixed effects, and year fixed effects. Overall, the evidence suggests that ESG performance promotes corporate risk-taking by encouraging greater innovation investment and human capital allocation toward R&D activities.

A plausible explanation is that SOEs often face socio-political responsibilities and may therefore adopt relatively conservative managerial strategies. Strong ESG performance can send positive signals to governments, regulators, investors, and the public regarding a firm’s long-term sustainability orientation. This may enhance institutional trust and increase tolerance for long-term, high-risk innovation activities. As a result, managers are more likely to allocate resources to R&D investment and innovation-related personnel, thereby increasing corporate risk-taking. These results provide empirical support for the view that ESG performance improves firm performance through the risk-taking channel, consistent with Hypothesis H2.

4.3.2. Financial Constraints Channel

Columns (1)–(4) of

Table 5. Mechanism Test Results (Financial Constraints).

Variable	SA	FC	KZ
ESG	−0.005 ***	−0.008 ***	−0.092 ***
	(0.002)	(0.002)	(0.023)
Control	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
_cons	1.118 ***	0.414 ***	1.533 ***
	(0.029)	(0.035)	(0.389)
N	8533	8533	8533
r2	0.983	0.886	0.750
r2_a	0.980	0.867	0.708

report the regression results with financial constraints as the dependent variable. Prior literature suggests that financial constraints have a significantly negative effect on firm performance. We therefore test the effect of ESG performance on financial constraints.

The results show that ESG performance consistently alleviates financial constraints across all specifications. In Column (1), the coefficient on ESG is −0.030 and statistically significant at the 5% level. In Column (2), the coefficient is −0.005 and significant at the 1% level. Columns (3) and (4) further confirm this effect with coefficients of −0.008 and −0.092, both significant at the 1% level, suggesting that superior ESG performance effectively alleviates financial constraints for SOEs. This finding provides empirical support for the financing constraints mechanism proposed in Hypothesis H3.

A possible explanation is that superior ESG performance conveys positive signals to external stakeholders, improves the firm’s information environment, and strengthens investors’ and creditors’ trust in the firm, thereby reducing financing frictions caused by information asymmetry. The alleviation of financial constraints facilitates firms’ investment expansion and improves resource allocation efficiency, ultimately enhancing firm performance.

4.3.3. Corporate Reputation Channel

Columns (1)–(4) of

Table 6. Mechanism Test Results (Rep).

Variable	Rep1	Rep2	Rep3
ESG	0.062 ***	0.011 *	0.020 **
	(0.023)	(0.006)	(0.009)
Control	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
_cons	3.393 ***	0.494 ***	0.318 **
	(0.366)	(0.089)	(0.131)
N	8533	8533	8533
r2	0.872	0.370	0.368
r2_a	0.850	0.264	0.262

report the regression results with corporate reputation (Rep) as the dependent variable. Prior research has shown that corporate reputation significantly promotes firm performance (Yang, 2020) [71]. We therefore test the effect of ESG performance on corporate reputation.

The regression results show that in Column (1), which controls for industry and year fixed effects, the coefficient on ESG is 0.610 and statistically significant at the 1% level. In Column (2), which further controls for firm fixed effects, the coefficient on ESG is 0.062 and remains significant at the 1% level. For the alternative measures, the coefficient on ESG regarding Rep2 is 0.011 and significant at the 10% level in Column (3), while the coefficient regarding Rep3 is 0.020 and significant at the 5% level in Column (4). These results provide empirical evidence that ESG performance affects firm performance through the corporate reputation channel, consistent with the mechanism proposed in Hypothesis H4.

A plausible explanation is that SOEs face high societal expectations, and their proactive social responsibility practices directly signal a commitment to long-term sustainable value creation to governments and the public. Such visible and perceivable social contributions are more likely to be recognized by external stakeholders, thereby translating into improved corporate reputation and ultimately enhancing firm performance.

4.4. Threshold Effect Analysis

Based on the preceding analysis, corporate reputation represents an important channel through which ESG performance affects firm performance, as it conveys key signals regarding firms’ long-term orientation and responsibility commitments to external stakeholders. However, this signaling mechanism may not follow a simple linear process. Prior studies suggest that when corporate reputation is at a low level, reputation itself may transmit negative signals to the market, thereby suppressing firm value (Aouad et al., 2018) [72]. This implies that the economic consequences of corporate reputation may exhibit significant nonlinear characteristics.

To further examine the conditional role of corporate reputation in the relationship between ESG performance and firm performance, we treat corporate reputation as a threshold variable and construct a panel threshold model to identify whether the impact of ESG performance on firm performance undergoes structural changes across different reputation levels. Threshold values are identified using Hansen’s method, and their statistical significance is tested using 1000 grid searches and 500 bootstrap replications.

Table 7. Threshold Effect Tests.

Variable	Model	F-Statistic	1%	5%	10%	Threshold Value	95% Confidence Interval
ESG	Single threshold	929.91	22.1842	16.7974	14.1398	3.3000	[3.2400, 3.3100]
	Double threshold	537.86	20.9598	17.4097	14.3487	2.4000; 5.8200	[2.3900, 2.4100]; [5.8050, 5.8300]

reports the results of the threshold effect tests.

The results in Table 6 indicate the presence of a significant double-threshold effect in the relationship between ESG performance and firm performance, which is statistically significant at the 1% level. This suggests that corporate reputation plays a significant threshold moderating role in the ESG–performance relationship.

After confirming the existence of a double-threshold effect of corporate reputation, we further estimate a double-threshold panel model using the threshold values identified in Table 7 to examine the marginal impact of ESG performance on firm performance across different reputation regimes.

Table 8. Double-Threshold Regression Results.

Variable	ROA
ESG (Rep < 2.4000)	−0.636 ***
	(0.087)
ESG (2.4000 < Rep < 5.8200)	0.250 ***
	(0.083)
ESG (Rep > 5.8200)	0.888 ***
	(0.082)
Control	Yes
Firm FE	Yes
Year FE	Yes
Constant	7.998 ***
	(1.493)
Observations	8533
R-squared	0.249
Number of ids	1219

reports the double-threshold regression results.

Figure 1 visually presents the marginal effects of ESG performance on firm performance across different levels of corporate reputation. The shaded areas and capped bars in the figure denote 95% confidence intervals, the vertical dashed lines mark the estimated thresholds (rep = 2.400 and rep = 5.820), and the horizontal dashed line at zero serves as the benchmark for statistical significance. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

The results in Table 8 show that when corporate reputation is at a low level, namely Rep < 2.4000, the coefficient of ESG is −0.636 and statistically significant at the 1% level. This indicates that ESG performance significantly suppresses firm performance in the early stage of reputation accumulation. When corporate reputation falls within the intermediate range, namely 2.4000 < Rep < 5.8200, the coefficient of ESG is 0.250 and statistically significant at the 1% level, suggesting that ESG performance begins to positively influence firm performance as reputation accumulates. When corporate reputation is at a high level, namely Rep > 5.8200, the coefficient of ESG turns positive at 0.888 and is statistically significant at the 1% level, indicating that ESG performance strongly enhances firm performance in firms with high reputational standing.

These findings suggest that the effect of ESG performance on firm performance is strongly reputation-dependent. ESG investments translate into performance gains progressively as corporate reputation increases, thereby supporting Hypothesis H5.

From a mechanism perspective, in the early stage of reputation accumulation, ESG investments primarily exhibit cost attributes. Compliance expenditures related to environmental governance, social responsibility investments, and corporate governance optimization costs are difficult for external stakeholders to recognize and trust in the absence of reputational endorsement and may even be perceived as strategic “greenwashing” As a result, these investments fail to translate into market recognition and economic returns. Moreover, ESG investments may crowd out other corporate resources, negatively affecting short-term financial performance, thereby leading to a suppressing effect of ESG on firm performance.

Once corporate reputation surpasses the first threshold, firms’ governance structures gradually improve, and ESG investments begin to be increasingly recognized by external stakeholders. This recognition starts translating into positive performance outcomes, as evidenced by the significant coefficient in the intermediate range.

After corporate reputation further increases and surpasses the second threshold, high reputation provides strong credibility endorsement for firms’ ESG practices. At this stage, environmental governance improves resource utilization efficiency, social responsibility practices help build extensive stakeholder networks, and corporate governance mechanisms effectively reduce agency costs and enhance internal decision-making efficiency. Consequently, ESG investments are converted into significant improvements in firm performance.

Overall, as corporate reputation increases, the impact of ESG performance on firm performance shifts from a suppressing effect to a moderate positive effect and eventually to a strong promoting effect, exhibiting a pronounced nonlinear pattern. These results provide empirical evidence on the economic consequences of ESG investments and demonstrate that corporate reputation plays a critical conditional role in determining when ESG investments translate into performance returns, thereby addressing the core research question of this study.

4.5. Robustness Checks

4.5.1. Instrumental Variable Approach

To mitigate potential endogeneity concerns, we employ a two-stage least squares (2SLS) strategy using the industry–year average ESG score excluding the focal firm itself as an instrument for firm-level ESG performance. This instrument captures peer pressure and industry-level ESG norms while avoiding reverse causality, as it is constructed by excluding the firm’s own ESG score from the industry–year average. The instrument is plausibly exogenous conditional on firm fixed effects year dummies and the included controls.

Table 9. Instrumental Variable Regression Results.

Variable	ESG	ROA
ESG		1.830 **
		(0.861)
IV	0.676 ***
	(0.081)
Control	Yes
Firm FE	Yes
Year FE	Yes
Cragg-Donald Wald F statistic	83.68
Observations	8533	8533

reports the instrumental variable regression results. In the first-stage regression, the coefficient of the instrumental variable (IV) is 0.676 and statistically significant at the 1% level, indicating a strong correlation between the instrument and firm ESG performance. The Cragg–Donald Wald F statistic is 83.68, which is higher than the relevant weak-instrument critical value of 65.86. This result suggests that weak instrument concerns are unlikely.

In the second-stage regression, the coefficient on ESG is 1.830 and statistically significant at the 5% level, indicating that ESG performance continues to significantly enhance firm performance after accounting for potential endogeneity. These findings suggest that our main conclusions are unlikely to be driven by endogeneity bias and provide robust empirical support for the causal interpretation of the results.

4.5.2. Additional Control Variables

To further mitigate concerns about omitted variable bias, we augment the baseline model by including additional firm-level control variables, namely asset growth (Assetgrowth), operating cash flow (Cashflow), board size (Board), fixed asset intensity (FIXED), and ownership concentration (TOP1).

Table 10. Regression Results with Additional Control Variables.

Variable	ROA
ESG	0.341 ***
	(0.089)
Assetgrowth	4.686 ***
	(0.375)
Indep	0.071
	(0.283)
CR	0.176 ***
	(0.050)
Cashflow	21.954 ***
	(1.332)
INV	−0.007
	(1.264)
Board	−0.095
	(0.163)
FIXED	−3.872 ***
	(1.075)
Growth	2.406 ***
	(0.217)
Top1	1.232
	(0.984)
Age	−2.181 **
	(0.935)
_cons	3.956 **
	(1.578)
Firm FE	Yes
Year FE	Yes
N	8533
r2	0.620
r2_a	0.555

reports the regression results. The coefficient on ESG remains positive (0.341) and statistically significant at the 1% level, indicating that the positive effect of ESG performance on firm performance remains robust after controlling for a richer set of firm characteristics. These results suggest that the baseline findings are unlikely to be driven by omitted variable bias.

4.5.3. Alternative Dependent Variable

To examine the sensitivity of our results to the choice of performance measure, we replace return on assets (ROA) with return on equity (ROE) as an alternative proxy for firm performance and re-estimate the regression models.

Table 11. Regression Results with Alternative Dependent Variable.

Variable	ROE
ESG	1.178 ***
	(0.273)
Control	Yes
_cons	−1.436
	(4.449)
Firm FE	Yes
Year FE	Yes
N	8533
r2	0.489
r2_a	0.403

reports the regression results. The coefficient on ESG is 1.178 and statistically significant at the 1% level, which is consistent with the baseline findings. This result further reinforces the robustness of our conclusions.

4.5.4. Winsorize Robustness Check

To address the potential influence of outliers on the regression results, we winsorize all continuous variables at the 5th and 95th percentiles and re-estimate the baseline model.

The results in

Table 12. Regression Results with Winsorization.

Variable	ROA
ESG	0.550 ***
	(0.116)
Control	Yes
_cons	2.218 *
	(1.204)
Firm FE	Yes
Year FE	Yes
N	8533
r2	0.479
r2_a	0.391

show that the coefficient of the core explanatory variable ESG is 0.550, which is positive and statistically significant at the 1% level. This finding is consistent with the baseline regression results, further confirming the robustness of our conclusions.

4.5.5. Propensity Score Matching

To mitigate the self-selection bias in the sample, this study divides the treatment and control groups based on the median of ESG scores, generates a dummy variable as the treatment indicator, and uses all control variables from the baseline regression as covariates to estimate propensity scores, followed by 1:1 nearest neighbor matching. After matching, the regression is re-estimated using the matched sample.

The results in

Table 13. Regression Results with Propensity Score Matching.

Variable	ROA
ESG	0.406 *
	(0.219)
Control	Yes
_cons	6.651 ***
	(1.209)
Firm FE	Yes
Year FE	Yes
N	4656
r2	0.640
r2_a	0.518

show that the coefficient of the core explanatory variable ESG is 0.406, which is positive and statistically significant at the 10% level, indicating that ESG has a significant positive impact on ROA. This finding further strengthens the robustness of the core conclusions of this paper.

4.5.6. Adding Province–Year Fixed Effects

To examine the robustness of the main regression results, this study further incorporates province fixed effects and year fixed effects into the baseline model to control for unobservable factors such as regional industrial policies, local regulatory intensity, and macroeconomic fluctuations, thereby mitigating endogeneity issues arising from inter-provincial differences or temporal trends. The results are reported in

Table 14. Regression Results with Province–Year fixed effects.

Variable	ROA
ESG	0.801 ***
	(0.075)
Control	Yes
_cons	5.923 ***
	(1.769)
Province FE	Yes
Year FE	Yes
N	8533
r2	0.130
r2_a	0.126

.

4.5.7. Alternative Independent Variable

To test the sensitivity of our results to the choice of ESG measurement, we replace the ESG score from the Wind database with the Bloomberg ESG score as an alternative proxy for the core explanatory variable and re-estimate the baseline regression model.

The regression results are presented in

Table 15. Regression Results with Alternative Independent Variable.

Variable	ROA
Bloomberg_ESG	0.026 ***
	(0.009)
Control	Yes
_cons	4.618 ***
	(0.883)
Firm FE	Yes
Year FE	Yes
N	8533
r2	0.577
r2_a	0.505

. The coefficient of the Bloomberg ESG score is 0.026 and is statistically significant at the 1% level, which is consistent with the findings of the baseline regression. This result further confirms the robustness of our core conclusions.

4.5.8. Driscoll–Kraay Method

To further control for the interference of heteroskedasticity, serial correlation, and cross-sectional dependence in panel data on the regression results, this study re-estimates the baseline model using the Driscoll–Kraay standard error method to test the robustness of the core conclusions. This method can simultaneously address the above issues and provide more reliable standard error estimates for the regression results.

As shown in

Table 16. Regression Results with Driscoll–Kraay.

Variable	ROA
ESG	0.416 **
	(0.119)
Control	Yes
_cons	4.196 ***
	(0.320)
Firm FE	Yes
Year FE	Yes
N	8533
r2
r2_a

, the coefficient of the core explanatory variable ESG is 0.416 and statistically significant at the 5% level. This indicates that, after adopting a more rigorous standard error estimation method, the core conclusion of this study that ESG performance has a significant positive impact on corporate ROA still holds, further enhancing the robustness and reliability of the research findings.

4.6. Heterogeneity Analysis

To further examine the contextual heterogeneity in the impact of ESG performance on firm performance, we conduct regional heterogeneity analyses. Given the substantial regional disparities in institutional environments, industrial structures, and resource endowments across China, following Shen et al. (2021) [73], we divide the sample into Central, Western, and Eastern regions and perform subsample regressions.

Table 17. Regional Heterogeneity Regression Results.

	Central	Western	Eastern
Variable	ROA	ROA	ROA
ESG	1.216 ***	0.951 ***	0.736 ***
	(0.189)	(0.202)	(0.090)
Control	Yes	Yes	Yes
_cons	2.461	5.009	5.565 ***
	(3.976)	(4.519)	(2.131)
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	1666	1547	5320
r2	0.185	0.171	0.135
r2_a	0.175	0.160	0.132

reports the regional subsample regression results. The coefficients of ESG performance on firm performance are significantly positive across the Central, Western, and Eastern regions, but the magnitudes differ substantially across regions. When controlling for firm and year fixed effects, the ESG coefficient is 1.216 in the Central region, 0.951 in the Western region, and 0.736 in the Eastern region. All estimates are statistically significant at the 1% level.

These findings indicate pronounced regional heterogeneity in the ESG–performance relationship. Regional differences may be associated with variations in industrial structures, resource endowments, and the mechanisms through which ESG signals are perceived and translated into economic value by stakeholders. Firms in the Central region are more concentrated in manufacturing and labor-intensive industries, where ESG practices may directly improve operational efficiency and resource utilization, thereby exerting a stronger impact on firm performance. Firms in the Western region are more concentrated in resource extraction and primary processing industries, where ESG practices play an important role in promoting resource efficiency and environmental risk management, but the marginal performance gains are relatively limited. Firms in the Eastern region have a higher share of service and high-technology industries and often adopt asset-light business models; thus, ESG practices are more likely to affect firm performance indirectly through reputation building and enhanced stakeholder trust, with economic returns potentially materializing with a lag.

Overall, the regional estimates suggest that the performance-enhancing effect of ESG performance is strongest for SOEs in the Central region, followed by the Western region, and is relatively weaker in the Eastern region. These results further reveal the context-dependent nature of the ESG–performance relationship.

5. Discussion

This study systematically examines the mechanisms through which ESG performance affects firm performance, with a particular focus on the conditional role of corporate reputation. The results show that ESG performance is positively associated with SOE performance, and that corporate reputation, risk-taking, and financial constraints constitute important transmission mechanisms. More importantly, this study documents a significant threshold effect of corporate reputation in the ESG–performance relationship: when corporate reputation is low, ESG performance may suppress firm performance, whereas once corporate reputation exceeds a certain threshold, ESG performance significantly promotes firm performance. This finding provides new empirical evidence on when ESG performance translates into economic returns.

5.1. Theoretical Implications

This study advances the theoretical understanding of the economic consequences of ESG performance in several ways. First, we provide empirical evidence of a positive relationship between ESG performance and firm performance, consistent with prior studies (Huarng, 2024) [4], and further support the view that ESG represents a strategic intangible resource, consistent with the resource-based view of the firm. Second, we identify corporate reputation, risk-taking, and financial constraints as key mechanisms linking ESG performance to firm performance, thereby extending the application of the resource-based view and stakeholder theory in the context of state-owned enterprises.

More importantly, this study reveals a threshold-based conditional role of corporate reputation in the ESG–performance relationship. We find that ESG investments may initially exhibit cost-like characteristics and suppress firm performance when corporate reputation is low, whereas ESG investments translate into performance gains only after corporate reputation reaches a sufficiently high level. This result resonates with Aouad et al. (2018) [72] and highlights corporate reputation as a critical boundary condition that determines the credibility and value relevance of ESG signals in capital markets. By documenting the conditional nature of ESG value creation, this study enriches the literature on ESG and firm performance and provides new empirical evidence on the context-dependent mechanisms underlying ESG value creation.

5.2. Managerial and Policy Implications

Our findings offer important implications for SOE managers and policymakers. For high-reputation SOEs, proactively strengthening ESG practices can leverage reputational advantages, enhance stakeholder trust, alleviate financing constraints, improve corporate governance efficiency, and stimulate innovation activities, thereby promoting firm performance. In contrast, low-reputation firms may benefit from strengthening operational fundamentals and gradually accumulating reputational capital before undertaking intensive ESG investments. Gradually deepening ESG practices after credibility is established can help avoid situations in which ESG investments fail to generate economic returns. These findings are consistent with Li (2023) [14].

From a policy perspective, the results provide empirical evidence for designing differentiated ESG regulatory and incentive mechanisms. For high-reputation SOEs, policymakers may encourage high-quality ESG disclosure aligned with international standards and provide institutional support and incentives aligned with ESG disclosure quality to generate demonstration effects. For low-reputation SOEs, regulators may strengthen basic ESG compliance requirements and provide capacity-building and disclosure support to help firms gradually establish credible sustainability profiles. Such measures can facilitate the formation of long-term, self-enforcing ESG governance frameworks and promote high-quality development of SOEs, consistent with institutional theory emphasizing the role of regulatory frameworks in shaping corporate sustainability practices.

5.3. Limitations and Future Research

Despite its theoretical and practical contributions, this study has several limitations. First, the sample focuses on Chinese A-share listed SOEs during 2018–2024, which may limit the external validity of the findings. Future research could extend the sample period and incorporate longer time-series data to examine the dynamic evolution of the economic consequences of ESG performance.

Second, this study does not include private firms, and the findings primarily apply to SOEs. Future research could compare ESG implementation pathways and performance outcomes across ownership types to further explore how ownership structures shape ESG value creation mechanisms.

Third, ESG performance and corporate reputation are measured using secondary data sources and constructed indices, which may introduce measurement error and subjectivity. Future research could employ alternative ESG ratings and reputation proxies to validate the robustness of our findings.

In addition, future studies could extend the analysis to cross-country samples to enhance international comparability and generalizability. Employing dynamic models to capture lagged effects of ESG performance on firm performance and exploring the moderating roles of corporate culture, institutional environments, and investor structures would further deepen our understanding of ESG value creation mechanisms.

6. Conclusions

Based on a sample of Chinese A-share listed SOEs, this study systematically investigates the relationships among ESG performance, corporate reputation, and firm performance. The results indicate that ESG performance is positively associated with firm performance, but its economic consequences are highly context-dependent. Specifically, when corporate reputation is below a certain threshold, ESG investments may suppress firm performance; once corporate reputation exceeds the threshold, ESG performance significantly translates into firm performance improvements.

A key finding of this study is that corporate reputation plays a critical conditional role in ESG value creation, determining when ESG performance generates economic returns. This result extends the contextual dependence perspective in the ESG–performance literature and provides important insights for SOEs in designing differentiated ESG strategies.

Future research could expand the sample scope, incorporate cross-country contexts, and adopt dynamic analytical frameworks to further deepen our understanding of ESG value creation mechanisms.