The Impact of ESG on Corporate Value Under the ‘Dual Carbon’ Goals: Empirical Evidence from Chinese Energy Listed Companies

Abstract

As China pursues its dual carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the energy sector is central to the country’s climate strategy. This study investigates the impact of Environmental, Social, and Governance (ESG) performance on firm value in China’s energy sector, an industry critical to national carbon emissions and energy consumption. Using a panel dataset of 20,225 firm-year observations from A-share listed firms between 2016 and 2023, we apply regression models to assess how ESG performance affects firm value, with controls for industry characteristics and policy effects. The results show that ESG performance significantly enhances firm value, especially among non-state-owned firms and those in high-pollution industries. ESG performance also facilitates access to green bond financing, providing firms with enhanced capital for green investments, thereby boosting market value. Furthermore, we find that firms in regions with higher green development attention benefit more from ESG practices, with local carbon trading policies playing a key role in improving firm competitiveness and market performance. This study provides critical insights into how ESG strategies and carbon governance policies influence firm performance in the energy sector. The findings offer practical implications for policymakers aiming to support low-carbon industrial transformation and for firms seeking to integrate sustainability into their long-term strategic planning. These insights are crucial for driving the successful implementation of China’s dual carbon strategy.

1. Introduction

In recent years, China’s pursuit of its dual-carbon targets—peaking carbon emissions and achieving carbon neutrality—has become central to national environmental policy. To advance these objectives, the Chinese government has introduced a series of strategic policies aimed at ensuring their gradual realization. In September 2020, President Xi Jinping announced at the UN General Assembly that China would aim to peak emissions by 2030 and reach carbon neutrality by 2060. Subsequently, on 24 October 2021, two landmark policy documents were released: The Opinions of the CPC Central Committee and the State Council on Fully and Accurately Implementing the New Development Philosophy and Achieving Carbon Peak and Carbon Neutrality, and The Action Plan for Carbon Peaking Before 2030, issued by the State Council [1,2]. These documents provided a top-level strategic design for the realization of dual carbon goals. In May 2022, the Ministry of Finance issued the Opinions on Financial Support for Carbon Peaking and Carbon Neutrality [3,4], reflecting robust fiscal support at the national level. In 2023, the Ministry of Ecology and Environment, together with the State Administration for Market Regulation, promulgated the Administrative Measures for Voluntary Greenhouse Gas Emission Reduction Trading (Trial), formalizing the voluntary carbon-trading framework [5,6], thereby formalizing the framework for emission trading. Most recently, in November 2024, multiple government agencies jointly released the Corporate Sustainability Disclosure Standards—Basic Guidelines (Trial), emphasizing the importance of corporate responsibility and ESG development while calling for their integration into national governance mechanisms. These guidelines also outlined expectations for upgrading the secondary sector into a more advanced, technology-driven energy industry.

Amid rapid economic and social development, the principles of sustainable, green growth have gained broad recognition. Using a Nature-Based Solutions (NBS) framework, Gutberlet et al. (2023) [7] identify barriers to circular green economies across market contexts and underscore firms’ central role in green transitions. In response to mounting environmental pressures, capital markets have increasingly integrated ESG—an important non-financial performance indicator—into corporate evaluation systems. ESG encompasses environmental protection, social responsibility, and corporate governance [8,9]. Strong ESG performance mitigates operational risk, enhances reputation and market recognition, expands access to trade credit, and supports long-term growth [10,11,12]. Effective environmental strategies—particularly those focused on emission reduction and energy efficiency—have been shown to attract both investors and consumers [13,14]. In this context, green finance has emerged as a pivotal enabler of environmental and sustainable development. Financial institutions now play a crucial role in ecological governance [15,16]. As green finance evolves, ESG performance is critical to attracting green investment and lowering firms’ financing costs [17,18]. Consistent with carbon-trading mechanisms, firms with strong ESG are better positioned to issue green bonds and secure green loans to fund low-carbon innovation and meet emissions-reduction targets [19]. These financial tools not only reduce financing costs but also bolster firm competitiveness in capital markets, thereby mitigating financial risk [20,21]. However, although green-credit policies significantly improve environmental and social performance, their effects on financial performance may materialize with a lag [22]. Against the backdrop of the dual carbon agenda, the Chinese government has launched a variety of initiatives to promote green finance and incentivize green innovation, thereby accelerating corporate green transformation. These policies help firms reduce emissions and diversify financing channels [17]. Shi et al. (2023) [23] show that adoption of the dual-carbon targets heightened investors’ awareness of ESG-related risks, raising valuations for firms with stronger ESG performance. In response to the green development agenda, China’s carbon trading market has rapidly expanded since the early 2000s. Participation in carbon markets has become a vital mechanism for firms to reduce emissions, optimize environmental strategies, and support sustainable transitions [10,24]. A growing literature finds that carbon trading enhances long-term firm value, particularly in jurisdictions with stringent emissions regulations [25,26].

Although the positive association between ESG performance and firm value is well documented, it is neither linear nor uniform across contexts. ESG outcomes are shaped by industry characteristics, regional development levels, and policy environments [19]. Short-term economic pressures and market competition further moderate this relationship across sectors and regions [27,28]. In high-pollution sectors, green innovation is essential; many energy firms have advanced energy-saving and emissions-reduction technologies [29,30,31], which support environmental sustainability and reduce operating costs [11,14]. In carbon-intensive industries, ESG efforts tend to center on emissions management and green production, highlighting the need for ESG strategies that are both policy-driven and tailored to specific industrial conditions [32,33]. Accordingly, enhancing ESG performance to sustain long-term value creation remains a critical priority for both firms and policymakers. Despite comprehensive policy efforts, recent studies reveal disparities in implementation and firm-level responsiveness. Many energy enterprises face significant innovation pressures under the dual carbon mandate [8,34]. From 2000 to 2017, the energy sector accounted for an average of 41.8% of China’s total carbon emissions and exceeded 38% in every year [35]. Furthermore, carbon tax policies have had substantial impacts on industrial supply chains, reshaping firm operations [2].

In the global literature, the link between ESG and firm value has mostly been studied in general institutional settings and broad cross-industry samples, leaving little evidence on contexts where strong policy constraints and high emissions coexist. We address this gap by bringing together two forces that are usually examined in isolation: the soft constraint of local governmental attention to green development and the hard constraint of market-based carbon trading. Focusing on China’s dual-carbon agenda and the energy sector, we show how the joint presence of these forces reshapes the pricing of ESG in capital markets. Our design identifies two transmission routes through green finance and carbon trading and reports effect sizes that quantify both the economic magnitude and the share of the total impact that operates through these channels. We further document heterogeneous returns to ESG across ownership types and pollution intensities, revealing why value creation is stronger for non-state firms and for high-emission businesses. By integrating institutional attention with market instruments within a single empirical framework and by reporting transparent effect sizes, the study contributes evidence with both contextual relevance and international salience.

2. Research Hypotheses

This study conducts an empirical analysis to investigate how ESG performance affects firm value in China’s energy sector. Based on relevant theoretical and policy frameworks, the following hypotheses are proposed:

Hypothesis 1.

Strong ESG performance significantly enhances the firm value of Chinese energy enterprises.

Hypothesis 2.

Strong ESG performance facilitates the issuance of green bonds, and the utilization of such financing instruments further enhances a firm’s market value.

Hypothesis 3.

Strong ESG performance supports the implementation of carbon trading policies in the firm’s local region and enhances competitiveness in carbon trading pilot cities, thereby significantly improving firm value.

Hypothesis 4.

Green development attention positively moderates the relationship between ESG performance and firm value.

3. Research Methodology

3.1. Sample Selection and Data Sources

To enhance transparency, we detail the sample construction. The study covers A-share energy firms from 2016 to 2023. The start year reflects the maturation of green-bond regulation and trading rules, while 2024 data are not yet complete. Sources include Wind, CSMAR, firms’ annual reports, publicly disclosed indicators from commercial banks, and local government data. We exclude firms under ST treatment and winsorize continuous variables at the first and ninety-ninth percentiles to mitigate outliers, yielding 20,225 firm-year observations. The sample comprises 3647 distinct firms. The panel is unbalanced, with an average time coverage of about 5.55 years per firm. Industry classification follows the 2012 CSRC scheme, and we include two-digit industry and year fixed effects to absorb sector and time heterogeneity. To mitigate the influence of outliers, we winsorize all continuous variables at the 1% and 99% percentiles, replacing values below 1% and above 99% with the respective cutoffs. The treated variables include Tobin’s Q, ROE, leverage, firm size, fixed asset ratio, and listing age. The choice of the 1% threshold follows [36], and the results remain unchanged when using a 5% threshold.

3.2. Model Design

3.2.1. Model Specification

To examine whether ESG performance significantly enhances firm value, this study draws on the methodology employed by Xue et al. (2022) [36]. Specifically, to test the hypotheses outlined above, the following firm value model is constructed:

$$Tobin{Q}_{it}={\beta }_0+{\beta }_{1}hzesg{1}_{it}+{\beta }_{2}Siz{e}_{it}+{\beta }_{3}employe{e}_{it}+{\beta }_{4}ag{e}_{it}+{\beta }_{5}RO{E}_{it}+{\beta }_{6}Fixe{d}_{it}+{\beta }_{7}Le{v}_{it}+{\epsilon }_{it}$$

(1)

$$\begin{array}{l}Tobin{Q}_{it}={\beta }_0+{\beta }_{1}hzesg{1}_{it}+{\beta }_{2}GreenBond{s}_{it}+{\beta }_{3}Siz{e}_{it}+{\beta }_{4}employe{e}_{it}+{\beta }_{5}ag{e}_{it}+{\beta }_{6}RO{E}_{it}\\ +{\beta }_{7}Fixe{d}_{it}+{\beta }_{8}Le{v}_{it}+{\epsilon }_{it}\end{array}$$

(2)

$$\begin{array}{l}Tobin{Q}_{it}={\beta }_0+{\beta }_{1}hzesg{1}_{it}+{\beta }_{2}CarbonTradin{g}_{it}+{\beta }_{3}Siz{e}_{it}+{\beta }_{4}employe{e}_{it}+{\beta }_{5}ag{e}_{it}+{\beta }_{6}RO{E}_{it}\\ +{\beta }_{7}Fixe{d}_{it}+{\beta }_{8}Le{v}_{it}+{\epsilon }_{it}\end{array}$$

(3)

$$Tobin{Q}_{it}={\alpha }_0+{\alpha }_{1}ES{G}_{it}+{\alpha }_{2}GD{A}_{it}+{\alpha }_3(ES{G}_{it}\times GD{A}_{it})+\beta X_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(4)

In the above model, TobinQ is the dependent variable representing firm value, while hzesg1 is the key explanatory variable measuring ESG performance. The control variables include Size (firm size), Employee (number of employees), Age (firm age), ROE (return on equity as a proxy for profitability), Fixed (fixed assets), and LEV (leverage ratio). ε denotes the error term. Additionally, the model includes industry fixed effects at the two-digit level and year fixed effects to control for unobserved heterogeneity across sectors and time.

3.2.2. Variable Definition

The main explanatory variables, explained variables, and control variables used in this study are presented below. A complete description of the variables can be found in

Table 1. Variable Definition.

Category	Abbreviation	Variable Name	Definition
Dependent Variable	GDA	Green Development Attention	Analyzing the frequency of green-related keywords in local government work reports using a predefined keyword dictionary. And the detailed measurement methodology for the GDA indicators is provided in Supplementary Materials.
Dependent Variable	Green Bonds	Green Bond Issuance	A dummy variable measuring whether the firm issued green bonds in the current year, taking a value of 1 if issued and 0 otherwise.
Dependent Variable	Carbon trading cities	Implementation of Carbon Trading Policies and Competitiveness of Pilot Carbon Trading Cities	Using a dummy variable representing whether the firm is located in a pilot carbon trading city and reflecting the progress of policy implementation through the timing of carbon trading policy enactment.
Control Variable	employee	Number of employees	The natural logarithm of the number of company employees, used to measure the size of the workforce.
Control Variable	age	Firm Age	The number of years since the company’s listing, used to measure the firm’s market experience and stability.
Control Variable	Fixed	Fixed Assets	Measured by the natural logarithm of the firm’s fixed asset investments in the current year.
Control Variable	_cons	Capital Structure	Controlling for the impact of the company’s capital structure on firm performance.
Control Variable	Ind	Industry Dummy Variable	Based on the industry classification guidelines revised by the China Securities Regulatory Commission in 2012, used to control for industry fixed effects.
Control Variable	Year	Year Dummy Variable	Used to control for year fixed effects.

.

• Dependent Variable:

Firm Value: The measurement of firm value follows the methodology employed by Gonenc et al. (2017) [37] in their relevant research, with Tobin’s Q used as the indicator.

• Explanatory Variable:

ESG Performance (hzesg): ESG performance is measured using the Huazheng ESG rating, which evaluates a firm’s performance in environmental, social, and governance dimensions. The rating scale ranges from A to AAA, with AAA representing the best performance [36]. This rating enjoys high recognition within China and is considered authentic and reliable. Values are assigned on a 1–8 scale from low to high, and the detailed measurement methodology for the Huazheng ESG indicators is provided in Supplementary Materials.

• Control Variable:

Firm Size (Size): Measured by the natural logarithm of the firm’s total assets.

Profitability (ROE): Measured by the ratio of net profit to total assets.

Leverage (LEV): Measured by the ratio of a firm’s total liabilities to its total assets, reflecting the company’s financial leverage.

3.2.3. Estimation and Diagnostics

All regressions employ firm and year fixed effects, and we report firm-clustered robust standard errors, which are valid under heteroskedasticity and within-firm serial correlation. In

Table 2. Pairwise correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) TobinQ	1.000
(2) esg	0.025 ***	1.000
(3) Size	−0.231 ***	0.184 ***	1.000
(4) Lev	−0.243 ***	−0.070 ***	0.530 ***	1.000
(5) employee	−0.212 ***	0.187 ***	0.849 ***	0.516 ***	1.000
(6) ROE	0.280 ***	0.150 ***	0.125 ***	−0.013	0.137 ***	1.000
(7) FIXED	−0.105 ***	−0.024 ***	0.150 ***	0.152 ***	0.232 ***	−0.056 ***	1.000
(8) ListAge	−0.014	−0.071 ***	0.527 ***	0.363 ***	0.469 ***	−0.143 ***	0.168 ***	1.000

*** p < 0.01.

Pairwise correlations show no abnormal concentration or extreme high correlations. And in

Table 3. Variance inflation factor.

	VIF	1/VIF
Size	4.158	0.241
employee	3.875	0.258
ListAge	1.542	0.649
Lev	1.505	0.665
esg	1.133	0.883
ROE	1.104	0.906
FIXED	1.084	0.922
Mean VIF	2.057

Multicollinearity diagnostics are well within conventional thresholds: the maximum variance inflation factor is 4.158, the variance inflation factor for ESG is 1.133, the mean variance inflation factor is 2.057, and all tolerances exceed 0.24. These diagnostics indicate no material multicollinearity. The positive link between ESG and firm value is modest in size but statistically reliable, and the sign and magnitude of the key coefficients remain stable under the robust error specification.

4. Empirical Results

4.1. Descriptive Statistics

Table 4. Descriptive Statistics.

Variable	Obs	Mean	SD	Min	Median	Max
TobinQ	20,225	2.198	1.801	0.000	1.665	9.637
hzesg1	20,225	4.109	0.879	1.000	4.000	8.000
Size	20,225	22.084	1.178	18.608	21.933	25.505
employee	20,225	7.584	1.158	4.554	7.504	10.550
age	20,225	2.969	0.306	1.099	2.996	3.611
ROE	20,225	0.064	0.143	−0.657	0.073	0.579
Fixed	20,225	0.210	0.128	0.006	0.188	0.597
LEV	20,225	0.382	0.190	0.059	0.371	0.897

presents the descriptive statistics of the variables used in this study. The mean value of firm value (TobinQ) is 2.198, with a maximum of 9.639 and a standard deviation of 1.801, indicating substantial variation in firm value across the sample firms. The ESG rating variable (hzesg1) ranges from 1 to 8, suggesting that no firm in the sample received the highest ESG rating of AAA. The mean ESG rating is 4.109, implying that the average ESG performance among the sample firms is at a moderate level. The descriptive statistics of the remaining control variables are generally consistent with those reported in previous studies [38,39,40,41,42,43] and are therefore not discussed in detail here.

4.2. Analysis of Baseline Regression Results

Table 5. Basic Regression.

	(1)	(2)
	TobinQ	TobinQ
hzesg1	0.096 ***	0.072 ***
	(3.550)	(4.071)
Size		−0.381 ***
		(−10.380)
employee		−0.144 ***
		(−4.080)
age		−0.379 ***
		(−4.903)
ROE		2.054 ***
		(12.519)
Fixed		−1.064 ***
		(−6.887)
LEV		−1.715 ***
		(−11.199)
_cons	2.811 ***	14.279 ***
	(19.297)	(23.448)
year	Yes	Yes
ind	Yes	Yes
N	20,225	20,225
R2	0.062	0.321

Note: Values in parentheses are t-values. *** denote significance levels at 0.01.

reports the results of the baseline regression analysis. The findings indicate a significant and positive relationship between ESG performance and firm value. In Model 1, the coefficient of hzesg1 is 0.096 and statistically significant at the 1% level, suggesting that improvements in ESG performance are associated with increases in firm value. This result provides empirical support for Hypothesis 1, which posits that stronger ESG performance significantly enhances the market value of Chinese energy firms.

The R² values for the regression models are 0.062 and 0.321, respectively, indicating that while the model captures part of the variation in firm value, other unobserved factors—such as industry-specific characteristics or broader macroeconomic influences—may also play a role. Overall, the baseline regression results demonstrate that ESG performance is an important driver of firm value in China’s energy sector. Strong ESG practices can enhance corporate reputation and investor confidence, thereby boosting market valuation. These findings offer robust empirical support for Hypothesis 1.

In firm-level panel regressions, relatively modest R² values are common and acceptable in social science research, if coefficients are directionally consistent, statistically significant, economically meaningful, and robust [44]. In this study, the consistency of results across instrumental variable estimations, alternative ESG measures, bootstrap mediation tests, and heterogeneity analyses demonstrates robustness. Moreover, while the relatively low R² indicates limited explanatory power at the firm level, the inclusion of multiple validation strategies ensures the reliability of the findings and supports their interpretability (1).

4.3. Robustness Tests

4.3.1. Endogeneity Analysis

To address potential endogeneity between ESG ratings and firm value, this study employs an instrumental variable approach. Following prior research [45,46], we use an indicator of whether a firm is held by an ESG fund (ESG-Holding) as the instrument for ESG performance. This choice is supported by both theoretical reasoning and empirical precedent. As influential market participants, ESG funds often engage in corporate governance through continuous monitoring and “voting with their feet” [47]. As green investment vehicles, ESG funds naturally incorporate sustainability objectives into the firms they invest in, frequently engaging in top-level interactions to improve ESG practices (see China Responsible Investment Annual Report 2020). These characteristics satisfy the relevance condition for a valid instrument. Moreover, as external institutional investors, ESG funds operate independently of the firm’s internal governance structures and valuation mechanisms. Their investment decisions are primarily driven by fund managers’ strategies and are unlikely to directly affect firm value, thus meeting the exogeneity assumption.

Table 6. Instrumental Variable—ESG_Holding.

	(1)	(2)
	hzesg1	TobinQ
hzesg1		6.042 ***
		(9.010)
ESG_Holding	0.171 ***
	(9.411)
Size	0.009	−0.551 ***
	(0.541)	(−4.875)
employee	0.198 ***	−1.366 ***
	(11.263)	(−7.793)
age	−0.167 ***	0.741 ***
	(−4.952)	(2.846)
ROE	0.509 ***	−0.875
	(7.560)	(−1.513)
Fixed	−0.475 ***	1.878 ***
	(−5.625)	(2.957)
LEV	−1.152 ***	5.113 ***
	(−16.791)	(5.485)
_cons	2.975 ***	−1.366
	(10.027)	(−0.530)
year	Yes	Yes
ind	Yes	Yes
N	15,849	15,849
R2	0.143	−6.301

Note: Values in parentheses are t-values. ***, denote significance levels at 0.01.

reports the results of the two-stage least squares regression. In column (1), the coefficient on the instrumental variable—whether the firm is held by an ESG fund is significantly positive at the 1% level, indicating that such firms are more likely to achieve stronger ESG performance. In column (2), the coefficient on hzesg1 remains significantly positive at the 1% level, suggesting that ESG performance continues to exert a significant positive effect on firm value even after accounting for potential endogeneity. These findings reinforce the robustness of the baseline results.

4.3.2. Alternative Measurement of Explanatory Variable

In the baseline regression, we use the HuaZheng ESG rating (hzesg1) as the primary explanatory variable. To further test the robustness of our findings, we replace it with the Wind ESG rating (windesg1) as an alternative measure of ESG performance. The regression results, presented in

Table 7. Robustness Test—Changing the Explanatory Variable.

	(1)
	TobinQ
windesg1	0.101 ***
	(4.357)
Size	−0.287 ***
	(−7.482)
employee	−0.136 ***
	(−3.664)
age	−0.425 ***
	(−5.113)
ROE	1.878 ***
	(11.440)
Fixed	−1.117 ***
	(−6.802)
LEV	−1.667 ***
	(−10.677)
_cons	10.532 ***
	(16.895)
year	Yes
ind	Yes
N	15,879
R2	0.266

Note: Values in parentheses are t-values. *** denote significance levels at 0.01.

, show that the coefficient of windesg1 remains significantly positive at the 1% level. This indicates that the positive relationship between ESG performance and firm value holds even when using a different ESG metric, thereby reinforcing the robustness and consistency of the baseline conclusion.

4.4. Mechanism Analysis

Building upon the theoretical foundation discussed earlier, this study proposes that ESG performance may enhance firm value through two key mechanisms: the Carbon Emission Trading Pilot and alleviating financing constraints. To test these mechanisms, we adopt a mediation analysis approach based on the procedures developed by Mackinnon et al. (1995) [48] and Wen et al. (2004) [49], employing a stepwise regression framework with the proposed mediating variables.

4.4.1. Green Bonds

Extensive prior research suggests that strong ESG performance can attract socially responsible investors and ease corporate financing constraints by improving access to capital markets, particularly for environmentally oriented projects [50]. In this context, we investigate whether ESG performance facilitates the issuance of green bonds and whether this financing channel contributes to enhanced firm value.

To test this mechanism, we construct a binary variable indicating whether a firm issued a green bond in a given year (1 = yes, 0 = no). Columns (1) and (2) of

Table 8. Mechanism Analysis—Green Bonds.

	(1) Green Bonds	(2) TobinQ
ESG	0.171 ***	0.060 **
	(0.008)	(0.027)
Size	0.856 ***	1.232 ***
	(0.040)	(0.039)
Lev	−0.226	0.034
	(0.191)	(0.032)
Employee	0.002 **	−0.001 **
	(0.001)	(0.001)
Roe	−0.143 **	−0.264 **
	(0.072)	(0.107)
Fixed	−0.108	−0.046
	(0.169)	(0.253)
Age	0.026 ***	0.023 ***
	(0.004)	(0.004)
year	YES	YES
ind	YES	YES
N	20225	20225
R2	0.286	0.406

Note: Values in parentheses are t-values. *** and **, denote significance levels at 0.01, 0.05, respectively.

present the results of the mediation analysis. In column (1), the coefficient on hzesg1 is significantly positive at the 1% level, suggesting that firms with higher ESG performance are more likely to issue green bonds. In column (2), green bond issuance is shown to have a significantly positive effect on firm value at the 5% significance level, confirming that ESG performance contributes to firm value through this financing mechanism. These findings provide empirical support for Hypothesis 3.

Further analysis reveals that firm size (Size) also significantly influences the likelihood of green bond issuance (coefficient = 0.856), indicating that smaller firms may be more cautious in accessing green bond markets due to perceived risks and financing barriers. In contrast, larger firms are more proactive in issuing green bonds, likely owing to their greater financial capacity and stronger risk-bearing ability.

4.4.2. Carbon Emission Trading Pilot

This section investigates whether ESG performance enhances firm value by strengthening a firm’s alignment with regional carbon trading policy implementation. Specifically, we examine whether being in a carbon trading pilot city mediates the relationship between ESG performance and firm value. A binary variable is constructed to indicate whether a firm is situated in a carbon trading pilot city (1 = yes, 0 = no).

Column (1) of

Table 9. Mechanism Analysis—Carbon Trading Pilot Cities.

	(1)	(2)
	Carbon trading cities	TobinQ
hzesg1	0.031 ***	0.065 ***
	(4.311)	(3.679)
Carbon trading cities		0.231 ***
		(5.056)
Size	−0.028 **	−0.375 ***
	(−2.052)	(−10.265)
employee	0.052 ***	−0.156 ***
	(3.700)	(−4.391)
age	0.041	−0.388 ***
	(1.431)	(−5.072)
ROE	−0.200 ***	2.101 ***
	(−4.798)	(12.814)
Fixed	−0.612 ***	−0.922 ***
	(−9.474)	(−5.862)
LEV	−0.112 **	−1.689 ***
	(−2.243)	(−11.044)
_cons	0.467 **	14.171 ***
	(2.046)	(23.361)
year	Yes	Yes
ind	Yes	Yes
N	20,225	20,225
R2	0.040	0.324

Note: Values in parentheses are t-values. *** and ** denote significance levels at 0.01, 0.05, respectively.

reports the regression of the carbon trading pilot city dummy on hzesg1. The coefficient is significantly positive at the 1% level, indicating that firms with higher ESG performance are more likely to be in—or to play a role in shaping—these policy-advanced regions. In column (2), we include the carbon trading pilot city variable in the baseline regression. The results show that the coefficient on this variable is significantly positive at the 1% level, and hzesg1 remains positively significant as well.

These findings suggest that better ESG performance increases the likelihood of a firm operating in a region with more advanced carbon trading infrastructure, thereby enhancing its adaptability to environmental regulations and contributing to higher firm value. This provides empirical support for Hypothesis 4. After in-depth analysis and scenario analysis, we conclude that: Carbon emissions trading, a price-based market instrument, translates corporate ESG management into firm value through two channels. First, the compliance cost and efficiency channel: binding allowances and explicit carbon prices internalize emission costs, so firms with stronger ESG practices in energy efficiency, process upgrades, abatement technologies, and supply-chain coordination reach targets at lower marginal abatement cost, preserve cash flows, reduce expected penalties and policy uncertainty, and raise valuation. Second, the information and risk-pricing channel: required participation, disclosure, and auditable transaction records increase the observability of environmental performance, ease investor concerns about regulatory failure and earnings volatility, compress risk premia and the cost of equity, and make ESG more readily priced by capital markets. These effects are context dependent; tighter caps, deeper trading, and stricter verification strengthen price signals and credibility, thereby amplifying the valuation impact of ESG, consistent with our mediation and interaction evidence.

4.4.3. Mediation Robustness: Bootstrap Evidence

Based on robustness checks using bootstrap resampling with 500 draws, the mediation mechanism through which ESG affects firm value is statistically reliable. Specifically, we estimate a structural equation framework with Tobin’s Q as the dependent variable and the ESG score as the focal regressor, controlling for firm size, leverage, number of employees, ROE, fixed asset ratio, and listing age. Using a bias-corrected percentile bootstrap with 500 resamples, the indirect effects are significant: at the 95% confidence level, the confidence intervals for the two mediators do not include zero, with intervals of 0.011 to 0.026 and 0.0007 to 0.004. This indicates that the mediation paths do not attenuate the positive impact of ESG on firm value; rather, they transmit additional value through the mediators. The Baron–Kenny stepwise criteria are satisfied, and the Sobel test yields Z equals 4.87 with p less than 0.001.

4.5. Moderating Effect—Green Development Attention

To reflect regional differences in institutional emphasis on sustainability, we use green development attention as the moderating variable. Following Yang et al. 2024 [30], GDA is constructed by counting the frequency of green-related keywords in local government work reports based on a predefined dictionary, which captures the intensity of local governments’ policy attention to green development and serves as a soft indicator of the environmental governance context in which firms operate. We adopt the conventional method of moderation analysis to test its role [51].

To assess the boundary role of GDA, we estimate a moderation model in the same firm and year fixed-effects framework with firm-clustered robust errors.

Table 10. Moderating Effect-Green Development Attention.

	(1)
	TobinQ
hzesg1	0.2178 ***
	(5.0062)
GDI	1.8630 ***
	(5.0682)
c.esg#c.gdi	0.0247 ***
	(4.9602)
Size	−0.2638 ***
	(−14.8805)
Lev	−0.8697 ***
	(−12.8714)
employee	−0.0979 ***
	(−5.5362)
ROE	6.0748 ***
	(30.6050)
FIXED	−0.1549 *
	(−1.8795)
ListAge	0.3497 ***
	(27.6897)
_cons	−8.6858 ***
	(−2.7079)
Year	yes
Ind	yes
N	20225
R2	0.300
F	245.197

Note: Values in parentheses are t-values. ***, * denote significance levels at 0.01, 0.1, respectively.

, column (1), shows a positive association between ESG and firm value that is significant at the 1% level: beta equals 0.2178, and t equals 5.006. The ESG by GDA interaction is also positive and significant at the 1% level: beta equals 0.0247, and t equals 4.960. These results indicate that stronger local green development attention strengthens the conversion of ESG into reputation, legitimacy, and market advantage, thereby amplifying its value effect. As a concise textual substitute for a simple-slopes figure, the marginal effect of ESG on Tobin’s Q increases monotonically as GDA moves from one standard deviation below its mean to the mean and then to one standard deviation above its mean, consistent with the interaction term.

Taken together, the institutional context in which firms operate plays a critical role in shaping the effectiveness of ESG.

4.6. Heterogeneity Analysis

4.6.1. Firm Ownership

It is widely recognized that substantial differences exist in operational and managerial practices between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). To assess whether the effect of ESG performance on firm value varies by ownership structure, the sample is divided into two groups: SOEs and non-SOEs, with separate regressions conducted for each.

As shown in

Table 11. Variables Heterogeneity Analysis—Nature of the Firm.

	(1)	(2)
	Nationalized	Non-State
hzesg1	−0.011	0.095 ***
	(−0.369)	(4.408)
Size	−0.335 ***	−0.379 ***
	(−4.957)	(−8.498)
employee	−0.111 *	−0.164 ***
	(−1.762)	(−3.886)
age	−0.282 *	−0.380 ***
	(−1.826)	(−4.260)
ROE	1.433 ***	2.274 ***
	(6.256)	(10.841)
Fixed	−1.426 ***	−0.822 ***
	(−5.041)	(−4.304)
LEV	−1.898 ***	−1.641 ***
	(−7.764)	(−8.520)
_cons	13.260 ***	14.249 ***
	(12.007)	(18.564)
year	Yes	Yes
ind	Yes	Yes
N	4959	15,266
R2	0.381	0.290
Chow Test	0.106 **

Note: Values in parentheses are t-values. ***, **, and * denote significance levels at 0.01, 0.05, and 0.1, respectively.

, the regression results for SOEs indicate that ESG performance (hzesg1) does not have a statistically significant impact on firm value, with a coefficient of –0.011. In contrast, for non-SOEs, ESG performance exhibits a significantly positive association with firm value, with a coefficient of 0.095, significant at the 1% level. This suggests that, among non-state-owned enterprises, stronger ESG performance meaningfully enhances market valuation.

These findings highlight notable heterogeneity in the ESG–value relationship across ownership types. Non-SOEs appear more responsive to ESG signals, likely due to their greater exposure to market forces, competitive pressures, and investor scrutiny. Additionally, in the ownership heterogeneity analysis, the ESG–value slope is significantly positive for non-state-owned enterprises, whereas it is insignificant for state-owned enterprises. This divergence reflects three differences. First, incentives and constraints: state-owned enterprises face more administrative performance assessments and task-oriented policy mandates, so ESG spending is primarily compliance-driven with lower incremental information content; markets treat it as a necessary cost rather than a new governance signal, yielding a muted valuation response. Second, financing and risk absorption: state-owned enterprises typically enjoy easier financing access and implicit guarantees, leaving less room for ESG to reduce the cost of capital, reputation risk, or supply-chain bargaining frictions; non-state firms reap larger gains along these margins. Third, policy exposure and cost pass-through: under the dual-carbon agenda and tightening environmental governance, state-owned enterprises often shoulder policy roles such as supply security and price stabilization, so allowance constraints and abatement costs may transmit to valuation more slowly; by contrast, non-state firms are more flexible, allowing ESG investment to translate more readily into efficiency improvements and demand-side gains.

4.6.2. Industry Heterogeneity

Due to the nature of energy industries, there are inevitably substantial differences in pollution levels and carbon emissions across firms. Prior studies, such as those by Gonenc et al. [37], have examined the link between environmental and financial performance in fossil-fuel-intensive industries.

To further examine whether the impact of ESG performance on firm value varies by industry pollution level, we categorize the sample into high-pollution and non-high-pollution industries and conduct separate regressions.

Table 12. Variables Heterogeneity Analysis—High Pollution Industries.

	(1)	(2)
	High pollution	Non-high pollution
hzesg1	0.109 ***	0.042 *
	(3.790)	(1.931)
Size	−0.523 ***	−0.322 ***
	(−8.797)	(−6.660)
employee	−0.037	−0.177 ***
	(−0.561)	(−4.196)
age	−0.243 *	−0.439 ***
	(−1.653)	(−4.897)
ROE	1.579 ***	2.335 ***
	(5.969)	(11.284)
Fixed	−1.253 ***	−0.757 ***
	(−4.852)	(−3.663)
LEV	−1.396 ***	−1.856 ***
	(−5.581)	(−9.634)
_cons	14.966 ***	13.536 ***
	(15.605)	(16.392)
year	Yes	Yes
ind	Yes	Yes
N	6830	13,395
R2	0.330	0.323
Chow Test	0.067 ***

Note: Values in parentheses are t-values. ***, * denote significance levels at 0.01, 0.1, respectively.

reports the results. In high-pollution industries, ESG performance (hzesg1) has a significantly positive effect on firm value, with a coefficient of 0.109, significant at the 1% level. This indicates that firms in environmentally intensive sectors receive greater market recognition and valuation benefits from strong ESG performance. In contrast, for non-high-pollution industries, the coefficient is 0.042 and only marginally significant, suggesting a weaker influence of ESG performance on firm value in cleaner sectors.

These findings confirm that the value-enhancing effect of ESG performance is more pronounced in high-pollution industries.

5. Conclusions and Implications

As a key driver of China’s carbon peak and neutrality goals, the energy sector plays a vital role in advancing ESG adoption and shaping related policy frameworks. Strengthening ESG practices is essential not only for the sustainable growth of Chinese manufacturers but also for achieving national climate targets. Drawing on existing literature, this study examines the impact of ESG performance on firm value in China’s energy sector, investigates the underlying mechanisms, and incorporates recent policy developments to enrich the analytical framework.

The main findings are as follows: First, ESG performance significantly enhances firm value. Heterogeneity analysis shows this effect is stronger among non-state-owned enterprises and in high-pollution industries. Second, firms with stronger ESG performance are more likely to issue green bonds, which positively affect firm value. Third, ESG supports the implementation of local carbon trading policies, enabling firms to access government incentives, participate in emissions trading, and attract green investment—ultimately improving market performance. Fourth, the relationship between ESG performance and firm value is significantly moderated by the degree of local green development attention. For interpretability we report standardized effect sizes. Using the formula coefficient times the standard deviation of ESG divided by the standard deviation of Tobin’s Q, a one standard deviation increase in ESG is associated with a 0.035 standard deviation increase in Tobin’s Q. The effect is stronger for non-SOEs and high-emission industries at 0.046 and 0.053 and weaker for non-high-emission industries at 0.021. These magnitudes are computed under the same fixed-effects and firm-clustered settings as the baseline and map directly to the reported tables. Based on these findings, the following policy implications are proposed:

First, treat ESG as a strategic asset, not a compliance burden. Regulators can drive this by redesigning disclosure around measurable efficiency gains, emissions reductions, and verifiability; issuing a unified, transparent taxonomy; and enforcing anti-greenwashing via public blacklists and targeted penalties. Coordination between green finance and carbon markets is essential: greater ETS transparency and liquidity make the carbon price a binding investment signal, accept qualified allowances as collateral, and publish regional pipelines of eligible projects to guide capital. Local institutions matter. Embedding green-development attention in budgeting and cadre evaluations creates a closed loop from attention to resources, projects, and outcomes. Strengthening soft institutional signals in public communication and government work reports amplifies firms’ responses to ESG incentives, while cross-regional benchmarking diffuses best practices and narrows policy dispersion. Mutual-recognition pilots for international standards (e.g., TCFD, ISSB) in free-trade zones further enhance comparability for global investors. On financing, streamline green-bond approvals, standardize use-of-proceeds and impact metrics, and build liquidity facilities for green securitization and sustainability-linked instruments to lower issuance frictions and channel funds to credible transitions.

Additionally, enterprises can operationalize the agenda by embedding ESG in strategy and capital budgeting. Boards can tie incentives to verifiable ESG results, introduce carbon pricing, and price carbon in hurdle rates so low-carbon retrofits and process upgrades clear investment-committee approval on financial and environmental grounds. Energy firms can align transition plans with international guidance, use green bonds and sustainability-linked loans to lower funding costs and payback, and build pipelines linking inputs, processes, outputs, and outcomes with third-party assurance to raise information value and reduce risk premia. Execution should reflect heterogeneity: high-emission subsectors focus on equipment/process upgrades and supply-chain co-abatement; non-state firms use ESG to improve financing access and bargaining power; state-owned firms convert compliance spending into priced performance via higher-quality disclosure, performance contracts, and allowance planning/trading in the carbon market.

Lastly, investors and intermediaries can make valuation explicitly context-sensitive by conditioning on regional green attention and the intensity of carbon constraints, assigning greater weight to high-quality ESG where institutional support is strongest. Monitoring should move from composite scores to mechanism-based indicators that are decision-useful, including green financing costs, delivery of retrofit investments, allowance gaps and trading activity, and the mapping of these metrics to risk premia and growth assumptions. Stewardship can prioritize assured disclosures, internal carbon pricing, and credible transition milestones, while analysts and rating agencies promote outcome-centric, comparable benchmarks to reduce reliance on unverifiable narrative claims and improve the pricing of the ESG-to-value transmission.

The findings are situated within clear institutional and temporal bounds. The study centers on China’s dual-carbon agenda and the energy sector during a period of accelerated policy rollout, reflecting strong regulation, high emissions, and active capital-market response. Implications apply to this setting; broader generalizations should be interpreted relative to institutional comparability, sectoral emissions, and market maturity. Cross-country and cross-industry designs can reveal common patterns amid contextual differences. Measurement follows mainstream ratings and text-based constructs, aligning policy language with capital-market interpretation. As digital governance and data availability advance, indicator systems and data sources evolve. Cross-rating mapping, transaction-level carbon-market records, Scope-3 supply-chain coverage, and multi-source text and satellite data can capture complementary dimensions and enhance observability and comparability of the ESG–mechanism–value chain. Overall, within defined bounds, the paper provides robust evidence on how ESG affects firm value under strong policy constraints and high emissions, supported by mechanism analysis. Further work on institutional comparability, data granularity, and identification strategies will broaden external validity and deepen conclusions.