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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 5: 1306 - 1314

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.05.005

Resource Economics

Impact of ESG on the Total Factor Productivity of Energy Enterprises—From the Perspectives of Investor Preference and Financing Constraints

  • GAO Wenjing ,
  • SUN Jiayi , * ,
  • HAO Chunrui
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  • School of Economics, Shanxi University of Finance and Economics, Taiyuan 030006, China
* SUN Jiayi, E-mail:

GAO Wenjing, E-mail:

Received date: 2025-03-20

  Accepted date: 2025-06-17

  Online published: 2025-10-14

Supported by

The Humanities and Social Sciences Research Project of the Ministry of Education(22YJA790084)

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Abstract

In 2020, China clearly put forward the “double carbon” goal of striving to achieve its carbon peak by 2030 and carbon neutrality by 2060. Energy companies are the main source of carbon emissions and key players in reducing emissions, so they shoulder a pivotal responsibility in achieving this strategic goal. At the same time, Environmental, Social and Governance (ESG) concepts are highly compatible with the “dual-carbon” goal. Therefore, it is of great practical significance to explore the impact of ESG on energy companies to realize the “dual-carbon” goal. In this study, we analyzed the impact of ESG on the total factor productivity of energy companies and its mediating effect from the perspectives of investor preference and financing constraints. The results of this study show that good ESG performance of energy companies can enhance their total factor productivity through the positive effects of investor preference and financing constraints, and the extent of the effects was found to vary among companies with different equity natures, regional situations, and different backgrounds of the directors, supervisors, and senior executives. In view of these effects, energy companies should enhance their total factor productivity by strengthening investor preference and alleviating financing constraints; and they should also learn from each other’s ESG development models to promote the overall ESG level of the energy industry.

Key words： ESG; energy enterprise; total factor productivity; investor preference; financing constraints

Cite this article

GAO Wenjing , SUN Jiayi , HAO Chunrui . Impact of ESG on the Total Factor Productivity of Energy Enterprises—From the Perspectives of Investor Preference and Financing Constraints[J]. Journal of Resources and Ecology, 2025 , 16(5) : 1306 -1314 . DOI: 10.5814/j.issn.1674-764x.2025.05.005

1 Introduction

With the rapid economic and social development in China, enterprise development is facing increasingly severe constraints on environmental and social issues, and a growing number of enterprises have begun to pay attention to Environmental, Social and Governance (ESG) disclosure. As of June 2024, about 38.8% of A-share listed companies have disclosed ESG reports, which have received extensive attention from many scholars. The focus of their research mainly centers on the impacts of ESG on enterprise value (Bai et al., 2022; Wang et al., 2022), enterprise risk (Li et al., 2022), employment level (Mao and Wang, 2023), and outbound investment (Xie and Lü, 2022).
As large energy consumers, energy enterprises face more prominent environmental problems in the development process. The large amount of pollutants produced by energy enterprises in production may seriously damage the environment as well as human health. Besides, measures such as enterprise restructuring may lead to the loss of some jobs, triggering employment pressure; and problems in internal management, decision-making, and regulation may also adversely affect the development of enterprises. Therefore, environmental, social, and governance (ESG) issues are extremely important for energy companies. Some scholars believe that ESG management can significantly improve the performance of traditional energy enterprises and help the energy industry to achieve the “double carbon” goal (Xie and Yu, 2023). In addition, higher ESG rating levels will help to improve the enterprise’s green technological innovation (Zhou et al., 2023; Li et al., 2024) and green total factor productivity (Ding and Bai, 2024).
For companies, investors are an indispensable factor in driving corporate development. Investors usually focus on the long-term sustainable value creation of companies rather than just short-term profits, and investors are beginning to recognize that supporting companies with good ESG performance can help enhance the likelihood of obtaining long-term returns (Tang and Jin, 2023). The continuous push by regulatory bodies in some countries and regions to adopt ESG standards has also led more investors to take ESG factors into account. Therefore, ESG ratings are not only important for firms, but also for investors (Zumente and Lāce, 2021). Firms with good ESG performance can encourage investors to invest by conveying positive signals (Liu et al., 2024; Xie and Lü, 2024). At the same time, the level of investor shareholding has a positive effect on ESG (Deng et al., 2024) or a U-shaped nonlinear relationship (Martínez-Ferrero and Lozano, 2021), and the increase in corporate investment in turn helps to improve corporate trust, alleviate corporate financing constraints (Zhang, 2024), improve corporate financing difficulties overall (Liu and Lu, 2024), and contribute to the formation of a good interactive situation between corporate development and the capital market (Cui et al., 2024). Investor selection is also conducive to increasing green corporate’s market value and can help enterprises to further realize sustainable development (Kräussl et al., 2023).
However, the implementation of existing ESG may have a negative impact on corporate growth (Li and Zheng, 2022). The important position of energy companies in the low-carbon market and the lack of uniform disclosure standards for ESG increase the likelihood of “greenwashing” risks. “Greenwashing” refers to the practice some enterprises use to exaggerate and distort the green attributes of their products or services or falsely advertise them in order to improve their competitive advantage in the market. The essence of this is that, in the case of asymmetric information, companies want to obtain higher returns at lower costs by taking advantage of investors’ preferences for sustainable attributes and government support for green economy development. The existence of the “greenwashing” phenomenon may mislead investors’ decision-making and make them invest in enterprises that do not really have a high level of ESG, which in turn undermines investor confidence. However, some scholars have argued that greater investor attention to corporate ESG can also effectively mitigate the likelihood of corporate “greenwashing” (Jin et al., 2024; Wang and Chen, 2025; Zhao et al., 2025), and that increased technological innovation in renewable energy (Huang et al., 2024) and regulation by relevant authorities (Keresztúri et al., 2024) can also inhibit ESG “greenwashing” phenomenon to a certain extent.
The above discussion shows that scholars have conducted multi-dimensional studies on ESG, but fewer have focused on the energy industry, which is closely related to the core elements of ESG. Therefore, in order to understand the impact of the ESG performance level of energy enterprises on their total factor productivity, as well as the role of investor preferences and financing constraints, this study selected a sample of energy-related A-share listed companies in Shanghai and Shenzhen from 2009 to 2022, and applied a two-way fixed-effects model to test these relationships. It also further analyzed the differences in the impact of ESG performance level on the total factor productivity of energy enterprises with different equity natures, regional situations, and backgrounds of the directors, supervisors, and senior executives. Based on the findings of this study, we propose some targeted recommendations for energy companies to further realize green and low-carbon transformation and accelerate the realization of China’s “dual-carbon” goal.

2 Theoretical analysis and research hypotheses

2.1 ESG performance and total factor productivity of energy enterprises

The core idea of ESG is rooted in an in-depth study of sustainability theory. When a company performs well in ESG aspects, it may implement efficient initiatives in the environmental, social, and governance (ESG) domains. In terms of the environment, enterprises may phase out production tools with high energy consumption and low output, actively introduce advanced technologies, reduce production costs, minimize potential harm to the environment, and further achieve a balance between economic and ecological development. In fulfilling their social responsibilities, enterprises may place more emphasis on employee welfare and actively engage in public welfare activities. This can not only enhance their employees’ sense of belonging and loyalty and attract high-quality talents but also help to create a favorable corporate image. In corporate governance, enterprises may opt to enhance information transparency and adopt other measures to reduce decision-making risks, accurately identify market demands, and optimize resource allocation. Therefore, when an enterprise performs well in ESG, this will facilitate its full realization of the coordinated development among the environment, society, and governance, promote the high-quality development of the enterprise, and boost its total factor productivity. Based on these considerations, this study proposes the first hypothesis:
Hypothesis 1: Good ESG performance of energy companies is conducive to improving their total factor productivity.

2.2 ESG performance, investor preference, and total factor productivity of energy enterprises

Stakeholder theory posits that the long-term sustainable development of an enterprise requires the support of all stakeholders, such as investors, employees, and consumers. At the investor level, when enterprises actively fulfill their environmental and social responsibilities, this will, to a certain extent, leave a favorable impression of the enterprises on investors. They may believe not only that such enterprises can effectively address the risks posed by increasingly stringent environmental regulations and gain an edge in the emerging green economy market but also that they are more likely to win the trust of consumers, possess strong market competitiveness, and be worthy of the investors’ long-term shareholding. Investors may even make additional investments to enhance their long-term returns. Therefore, enterprises with excellent ESG performance can help to enhance the trust of stakeholders, win the favor of investors, and boost investor confidence. Based on this scenario, this study proposes the second hypothesis:
Hypothesis 2: Good ESG performance of energy companies can positively influence corporate total factor productivity by increasing investor preference.

2.3 ESG performance, financing constraints, and total factor productivity of energy enterprises

The disclosure of an ESG report allows investors, regulatory authorities, the public, and other stakeholders to clearly understand the key measures taken by enterprises to address environmental deterioration, their fulfillment of social responsibilities, and corporate governance approaches. This improves the transparency of enterprises and largely mitigates the potential drawbacks caused by information asymmetry. When an enterprise demonstrates excellent ESG performance, it usually sets higher standards for its own environmental protection, social responsibility fulfillment, and corporate governance. Compared with enterprises that lack information or have poor ESG performance, it is more likely to attract the attention of financing institutions. These institutions may be more willing to provide financial support due to the lower financing risks associated with such enterprises. Meanwhile, the timely disclosure of an ESG report also helps to compel enterprises to regulate their pollution-related behaviors, reducing the likelihood of negative external impacts on society. This allows enterprises to build a positive corporate image and gain the trust of financiers, thereby alleviating financing constraints. Based on these issues, this study proposes the third hypothesis:
Hypothesis 3: Good ESG performance of energy companies can positively influence corporate total factor productivity by alleviating financing constraints.

2.4 ESG performance and total factor productivity of different types of energy enterprises

The growth and development of enterprises are influenced by various internal and external factors, and energy enterprises are no exception. The property rights theory posits that due to differences in the nature of enterprise equity, enterprises are subject to the influences of various macro and micro factors. State-owned enterprises are under more intense supervision from relevant departments and society compared to non-state-owned enterprises. As a result, the requirements for environmental protection, social responsibility, and corporate governance (ESG) may be more stringent, thereby impacting the total factor productivity of these enterprises. Regions vary in terms of economic development levels, resource endowments, and policy environments, and these differences can lead to variations in the ESG development of energy companies. The backgrounds of directors, supervisors, and senior executives reflect the level of internal control within an enterprise. Given that China’s development in the ESG context started relatively late, those with overseas backgrounds may possess a deeper understanding of ESG principles and practices. Based on these factors, this study proposes the fourth hypothesis:
Hypothesis 4: The level of ESG performance of different types of energy enterprises affects their total factor productivity differently.

3 Study design

3.1 Data sources

According to the industry classification of the Chinese national economy, this study selected Shanghai and Shenzhen A-share listed companies in coal, power, heat, gas, and other energy-related industries from 2009 to 2022 for analysis. The data were sourced from the National Taian Database. SynTao Green Finance was the first to release ESG rating data in China, and it started releasing such data in 2015. Therefore, this study excluded companies that went public after 2015 and those with missing ESG scores. To ensure the consistency of financial data, companies that were designated as ST, *ST, or PT in that year were also excluded. To eliminate the impact of extreme values, a winsorization treatment of 1% and 99% was carried out on the data.

3.2 Variable selection

(1) Explained variable
Total factor productivity (TFP) is an important indicator of the quality and efficiency of the economic growth of enterprises, and it reflects the portion of output growth in production that exceeds the increase in factor inputs. Referring to the research of Lu and Lian (2012), this study chose the LP method to measure the total factor productivity (TFP) of energy enterprises. The LP method is an econometric method that addresses endogeneity bias through intermediate input proxy variables in order to estimate the coefficients of factor elasticities (α, β, γ) of the production function. Total factor productivity (TFP) based on this estimation can be calculated as:
$TFP=\frac{Y}{{{L}^{\alpha }}{{K}^{\beta }}{{M}^{\gamma }}}$
In formula (1), Y refers to actual output, which indicates the amount of final output produced in a specific period; L refers to labor input, which indicates the amount of labor invested in the production process (man-hours or number of people); K refers to capital input, which indicates the sum of net fixed assets used in production and working capital (working capital = current assets - current liabilities); M refers to intermediate inputs, which indicates the total value of raw materials, energy, and other auxiliary materials consumed by production; and the indices α, β and γ are the output elasticity coefficients of labor, capital and intermediate inputs, respectively.
(2) Core explanatory variables
The ESG concept was first proposed by the United Nations Global Compact in 2004, and it serves as a comprehensive indicator evaluated from three dimensions: environment, society, and governance. By referencing the approach of Hu et al. (2023), this study constructed an ESG rating dummy variable: if SynTao Green Finance published the ESG score data of an enterprise in a given year, the variable was assigned a value of 1, otherwise it was 0. The interaction term between this dummy variable and the ESG scores released by SynTao Green Finance constitutes the core explanatory variable of this study, which is denoted by ESG×lnESGS.
(3) Intermediary variables
Drawing on the research of Xie and Lü (2024), this study selected the proportion of institutional investor shareholding (the total amount of institutional investor shareholding divided by the total amount of enterprise equity) to measure investor preference, which is denoted by IP, and a larger IP indicates stronger investor preference for the enterprise. At the same time, with reference to the existing research, the KZ index was selected to measure the level of financing constraints of energy enterprises, with a larger KZ index indicating a higher level of financing constraints.
(4) Control variables
This study referred to the research of Deng and Du (2023) and selected indicators related to the long-term development of energy enterprises as control variables. The specific definitions of these variables are presented in Table 1.
Table 1 Variable definitions
Variable type Variable name Variable symbol Variable definition
Explained
variable		 Total factor productivity of energy enterprises ln TFP Estimate the total factor productivity using the LP method and take the natural logarithm
Core explanatory
variable		 ESG scores ESG × ln ESGS The interaction term between the ESG dummy variable and the ESG scores released by SynTao Green Finance
Control variables Enterprise age ln Age Subtract the year of enterprise establishment from the research year, and take the natural logarithm
Enterprise size ln Size The natural logarithm of the total assets of the enterprise at the end of the year
Debt-to-asset ratio ln LEV The debt-to-asset ratio = Total liabilities ÷ Total assets, and take the natural logarithm
Return on assets ln ROA The return on assets = Net profit ÷ Total assets, and take the natural logarithm
Total leverage ln DTL The total leverage coefficient = The operating leverage coefficient × The financial leverage coefficient, and take the natural logarithm
Return on investment ln ROI The return on investment = [(Earnings ÷ Cost) - 1] × 100%, and take the natural logarithm
Equity multiplier EM The equity multiplier = Total assets ÷ Total shareholder equity
Sustainable growth rate SGR The sustainable growth rate = Return on equity × Retention rate
Total number of shareholders ln SH Sum the number of shareholders, and take the natural logarithm
Intermediary
variables		 Investor preference IP Measured by the institutional investor shareholding
Financing constraint KZ Measured by the KZ index

3.3 Model setting

To analyze the impact of ESG performance on the total factor productivity of energy enterprises, the benchmark model (2) was established:
$\ln TF{{P}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}ES{{G}_{it}}\times \ln ESG{{S}_{it}}+{{\alpha }_{2}}{{Z}_{it}}+{{\delta }_{i}}+{{\eta }_{t}}+{{\varepsilon }_{it}}$
In model (2), $\ln TF{{P}_{it}}$ is the explained variable, represented by the natural logarithm of the total factor productivity of energy enterprises; $ESG{{S}_{it}}$ is a dummy variable, which indicates whether SynTao Green Finance published the rating data of enterprise i in year t; $\ln ESG{{S}_{it}}$ is the natural logarithm of the ESG score, and its interactive term $ES{{G}_{it}}\times \ln ESG{{S}_{it}}$ is the core explanatory variable, which indicates the level of ESG development of enterprise i in year t; Zit is a set of control variables; δi is an individual fixed effect; ηt is the time fixed effect; and Ɛit represents the robust standard error clustered at the firm’s individual level.
The causal stepwise regression test is an important tool for exploring the causal relationships and action paths among variables. Its core lies in disassembling the mechanism by which independent variables influence dependent variables through stepwise regression, and identifying the transmission role of the mediating variables therein. To explore whether the ESG performance level of energy enterprises can improve total factor productivity by enhancing investor preferences and alleviating financing constraints, this study established models (3)-(6):
$I{{P}_{it}}={{n}_{0}}+{{n}_{1}}ES{{G}_{it}}\times \ln ESG{{S}_{it}}+{{n}_{2}}{{\text{Z}}_{it}}+{{\delta }_{i}}+{{\eta }_{t}}+{{\varepsilon }_{it}}$
$K{{Z}_{it}}={{m}_{0}}+{{m}_{1}}ES{{G}_{it}}\times \ln ESG{{S}_{it}}+{{m}_{2}}{{\text{Z}}_{it}}+{{\delta }_{i}}+{{\eta }_{t}}+{{\varepsilon }_{it}}$
$\begin{align} & \ln TF{{P}_{it}}={{\gamma }_{0}}+{{\gamma }_{1}}ES{{G}_{it}}\times \ln ESG{{S}_{it}}+ \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ {{\gamma }_{2}}I{{P}_{it}}+{{\gamma }_{3}}{{Z}_{it}}+{{\delta }_{i}}+{{\eta }_{t}}+{{\varepsilon }_{it}} \\ \end{align}$
$\begin{align} & \ln TF{{P}_{it}}={{\beta }_{0}}+{{\beta }_{1}}ES{{G}_{it}}\times \ln ESG{{S}_{it}}+ \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ {{\beta }_{2}}K{{Z}_{it}}+{{\beta }_{3}}{{Z}_{it}}+{{\delta }_{i}}+{{\eta }_{t}}+{{\varepsilon }_{it}} \\ \end{align}$
In models (3)–(6), $\ln TF{{P}_{it}}$, $ES{{G}_{it}}\times \ln ESG{{S}_{it}},$${{Z}_{it}}$, ${{\delta }_{i}}$, ${{\eta }_{t}}$, ${{\varepsilon }_{it}}$ were explained in model (2). Furthermore, $I{{P}_{it}}$ represents the degree of investor preference; and $K{{Z}_{it}}$ represents the level of financing constraints.

4 Results and analysis

4.1 Descriptive statistics

Table 2 reports the descriptive statistical results of the main variables in this study. All continuous variables have been winsorized at the 1% level. Note that the standard error of the total factor productivity of energy enterprises is 1.033, and those of investor preference and financing constraints are 18.058 and 1.827, respectively. This indicates that there are significant differences in the total factor productivity, investor preference, and financing constraints among different energy enterprises, which may be attributed to factors such as technology, management, and scale differences. The standard error of the ESG score is 0.29. Overall, the differences in the ESG development levels among energy enterprises are relatively small, probably because energy enter prises attach greater importance to their own sustainable development.
Table 2 Descriptive statistics
Variable Sample number Average value Standard error Minimum value Maximum value
lnTFP 1096 8.695 1.033 6.548 11.023
ESG×lnESGS 348 1.413 0.29 0.693 1.946
lnAge 1171 2.601 0.565 0 3.367
lnSize 1179 23.649 1.664 20.081 28.635
lnLEV 1179 -0.64 0.328 -2.025 -0.157
lnROA 1082 -3.542 1.018 -7.504 -1.809
lnDTL 1092 0.956 0.67 0.12 3.454
lnROI 939 -2.55 1.361 -10.232 1.199
EM 1179 2.533 1.021 1.152 6.884
SGR 1179 0.062 0.08 -0.191 0.332
lnSH 681 11.272 0.939 9.156 13.678
IP 1165 64.264 18.058 22.441 97.133
KZ 1178 1.541 1.827 -4.216 5.302

4.2 Regression analysis

To validate the relationship between the ESG performance level of energy enterprises and their total factor productivity (TFP), this study employed the Stata 18.0 software to conduct tests using a two-way fixed-effects model. Table 3 presents the regression results of this effect. Note that the level of ESG performance of energy enterprises is positively correlated with their TFP either with or without the inclusion of control variables, but it becomes significant with the inclusion of control variables. As shown in column 2, a 1% increase in the ESG performance level of energy enterprises is associated with a 0.1749% improvement in TFP. This indicates that better ESG performance of energy enterprises contributes significantly to enhancing their TFP, thus confirming the validity of Hypothesis 1. These results also strongly confirm the fact that ESG is highly consistent with the requirements of high-quality economic development in China.
Table 3 Benchmark regression
Variable lnTFP
(1) (2)
ESG×lnESGS 0.0895 0.1749***
(1.317) (3.002)
lnAge 0.2166*
(1.958)
lnSize 0.4167***
(3.127)
lnLEV -0.0430
(-0.226)
lnROA 0.2037
(1.526)
lnDTL 0.1129
(0.608)
lnROI 0.0396
(1.288)
EM -0.0990
(-1.412)
SGR -0.3398
(-0.536)
lnSH 0.0402
(0.869)
Constant 9.3936*** -1.3906
(98.436) (-0.409)
Individual fixed effects yes yes
Time fixed effects yes yes
Observed values 278 172
Adjusted R2 0.954 0.969

Note: *** and * indicate significance levels of P<0.01, and P<0.1, respectively. Column (1) does not include control variables, while column (2) includes control variables.

4.3 Mechanistic analysis

To clarify the specific mechanisms through which ESG enhances total factor productivity (TFP) in energy enterprises, this study employed a two-step mediation effect testing approach. This methodology involves first analyzing the impact of the core explanatory variable on the mediating variable, followed by examining the effect of the mediating variable on the explained variable, which will identify the transmission role of mediating variables in the baseline effect. The findings are presented in Table 4.
Table 4 Mechanistic analysis
Variable (1) (2) (3) (4)
IP lnTFP KZ lnTFP
ESG×lnESGS 4.6340* 0.0174 -0.6476** 0.1311*
(1.723) (0.180) (-2.136) (1.704)
IP 0.0056
(0.833)
KZ -0.0527*
(-1.954)
Controlled variable yes yes yes yes
Individual fixed effects yes yes yes yes
Time fixed effects yes yes yes yes
Observed values 87 112 157 146
Adjusted R2 0.930 0.975 0.872 0.981

Note: **, and * indicate significance levels of P<0.05, and P<0.1, respectively. Column (2) is the mediating effect of investor preference in the impact of ESG on the total factor productivity of energy firms; while column (4) is the mediating effect of financing constraints in the impact of ESG on the total factor productivity of energy firms.

First, column (1) shows the results of the impact of energy enterprise ESG performance on investor preference. The results show that excellent ESG performance significantly strengthens investor preference, providing preliminary validation for Hypothesis 2. Based on the mediation effect model, column (2) indicates that enhanced investor preference contributes significantly to improving corporate TFP, verifying the hypothesis that good ESG performance of energy enterprises can enhance TFP by strengthening investor preference. The insignificance of certain coefficients may be attributed to the current lack of unified ESG disclosure standards, leading to the risk of “greenwashing”. Some investors may choose to wait and see or refrain from investing due to concerns about potential losses from such risks.
Column (3) presents the impact of energy enterprise ESG performance on financing constraints. The results show that good ESG performance helps alleviate financing constraints, providing preliminary support for Hypothesis 3. Column (4) demonstrates that alleviating financing constraints has a significant positive effect on corporate TFP, explicitly verifying the hypothesis that good ESG performance of energy enterprises can positively influence TFP by relieving financing constraints. Hypothesis 3 is thereby validated. This may be because energy enterprises with better ESG performance are less likely to experience negative events, which helps improve their corporate image. This can help financing institutions to identify and reduce financing risks to a certain extent, thereby expanding financing channels and sources of funds for energy enterprises and alleviating their financing pressure.

4.4 Robustness test

To ensure the reliability of the baseline regression results presented above, this study conducted robustness tests by changing the core explanatory variables and explained variables, while incorporating time fixed effects and individual fixed effects. The results are as follows.
(1) Alteration of the core explanatory variable
This study selected the ESG rating data from Huazheng (other than SynTao Green Finance) for robustness testing. Referencing the approach of Hu et al. (2023), the Huazheng ESG ratings were assigned values where a larger numerical value indicates a higher ESG level and better ESG performance of the enterprise. As shown in column (1) of Table 5, the regression coefficient of Huazheng ESG is 0.2181, which is significant at the 1% level, demonstrating the robustness of the above conclusions.
Table 5 Robustness test results
Variable (1) (2) (3) (4) (5)
lnTFP lnTFP-OP lnTFP-OLS lnTFP-FE lnTFP-GMM
lnHZ 0.2181***
(3.260)
ESG×lnESGS 0.1350** 0.1580*** 0.1592*** 0.1524**
(2.155) (2.663) (2.662) (2.446)
Controlled variable yes yes yes yes yes
Individual fixed effects yes yes yes yes yes
Time fixed effects yes yes yes yes yes
Observed values 484 172 172 172 172
Adjusted R2 0.915 0.940 0.977 0.979 0.933

Note: ***, ** indicate significance levels of P<0.01, P<0.05, respectively.

(2) Alteration of the explained variable
This study employed total factor productivity (TFP) values estimated by four alternative methods other than the LP method (i.e., the OP method, OLS method, FE method, and GMM method) as the new explained variables. As shown in columns (2)-(5) of Table 5, the coefficients of ESG*ln (ESGS) are all significantly positive, indicating that good ESG performance helps to improve the TFP of energy enterprises, which is consistent with the baseline regression results.

4.5 Heterogeneity analysis

To further analyze the impact of ESG development levels in energy enterprises on their total factor productivity (TFP) under different equity natures, regional situations, and backgrounds of the directors, supervisors and senior executives, this study re-employed the two-way fixed-effects model to conduct separate tests. The conclusions are presented in Table 6, with a detailed analysis as follows.
Table 6 Analysis of heterogeneity
Variable (1) (2) (3)
State-owned Non-state-owned Developed areas Less-developed areas With overseas
background		 Without overseas
background
lnTFP lnTFP lnTFP lnTFP lnTFP lnTFP
ESG×ln(ESGS) 0.1648*** -0.2129 0.2213*** -0.0049 0.2749** 0.0057
(3.036) (-0.250) (3.359) (-0.026) (2.563) (0.063)
Controlled variable yes yes yes yes yes yes
Individual fixed effects 148 23 110 62 98 71
Time fixed effects yes yes yes yes yes yes
Observed values yes yes yes yes yes yes
Adjusted R2 0.979 0.763 0.972 0.935 0.948 0.985

Note: ***, ** indicate significance levels of P<0.01, P<0.05, respectively.

(1) Differences in equity natures
By 2024, the disclosure rate of ESG reports by central enterprises had reached as high as 94.4%, which was better than those of other types of enterprises. As shown in column (1) of Table 6, the ESG development level of state-owned enterprises has a positive impact on the total factor productivity of energy enterprises, and this impact is significant at the 1% level. In contrast, the impact on non-state-owned enterprises is negative and not significant. Thus, Hypothesis 4 is verified. The possible reason is that, compared with non-state-owned enterprises, state-owned enterprises have better ESG performance and a more complete system, which promotes improvement in the total factor productivity of these enterprises.
(2) Differences among regions
In 2022, the relevant departments in China defined seven provinces and municipalities, namely Guangdong, Jiangsu, Shandong, Zhejiang, Fujian, Shanghai, and Beijing, as major economic regions in a strict sense. In this study, these seven regions were defined as economically developed areas, while the other provinces and regions were regarded as economically underdeveloped areas. As shown in column (2) of Table 6, the ESG performance level of energy enterprises in economically developed areas has a positive impact on the total factor productivity of the enterprises, which is significant at the 1% level. In contrast, the impact in economically underdeveloped areas is negative and not significant. Thus, Hypothesis 4 is verified. The possible reason for this is that economically developed areas possess more capital, technology, and talent. Their high-quality infrastructure and advanced governance systems contribute to better ESG performance levels. Moreover, the public in these areas pays more attention to environmental issues, compelling the enterprises there to adopt more proactive environmental protection measures which enhance their ESG development level.
(3) Differences in the backgrounds of directors, supervisors and senior executives
Directors, supervisors, and senior executives are an important part of any enterprise. As discussed above, the development of ESG in China has a relatively short history. Directors, supervisors, and senior executives with overseas backgrounds may have a better understanding of ESG, thereby influencing the ESG development level of their enterprises. As shown in column (3) of Table 6, the ESG performance level of energy enterprises whose directors, supervisors, and senior executives have overseas backgrounds has a positive impact on their total factor productivity, and this impact is significant at the 5% level. In contrast, the impact of energy enterprises with directors, supervisors, and senior executives who do not have overseas backgrounds is positive but not significant. Therefore, the overseas background of directors, supervisors, and senior executives is conducive to the development of ESG, which in turn promotes the improvement of the total factor productivity of enterprises. Thus, Hypothesis 4 is verified.

5 Conclusions and recommendations

Taking the samples of energy-related A-share listed companies in Shanghai and Shenzhen from 2009 to 2022, this study explored the influence of ESG on the total factor productivity of energy enterprises and analyzed the intermediary effect of investor preference and financing constraints in the mechanism. It further studied the heterogeneity of the influencing mechanism. This study found that: 1) A higher ESG level of energy enterprises is more conducive to improving the total factor productivity; 2) The ESG performance of energy enterprises can promote the development of total factor productivity by strengthening investor preference and alleviating financing constraints; 3) The ESG performance of energy enterprises with different equity natures, regional situations and backgrounds of directors, supervisors and senior executives affect total factor productivity to different extents. Specifically, the effects of energy enterprise ESG on total factor productivity are more significant in state-owned enterprises, economically developed areas, and enterprises with directors, supervisors, and senior executives who have overseas backgrounds.
The findings of this study have certain reference significance for the improvement and development of China’s energy industry ESG system. Based on this, several suggestions can be put forward.
First, energy enterprises should enhance investor preference to improve total factor productivity. They should formulate clear ESG strategies and objectives, conduct regular evaluations, and disclose ESG development progress in a standardized manner. Strengthening communication with stakeholders, soliciting investor opinions, and reducing information asymmetry with potential investors can enhance investor confidence. Meanwhile, investors should actively pay attention to corporate ESG-related information releases, reduce the “greenwashing” behavior of energy enterprises, increase their investments in energy enterprises with high real ESG levels, and jointly promote the sound development of China’s energy industry.
Second, energy enterprises should reduce their financing constraints to improve total factor productivity. Actively publishing ESG reports through online media and other channels to improve information transparency and enhance corporate credibility can attract more funds and alleviate financing constraints. Energy enterprises should also actively strive for eligible government subsidies, loan interest subsidies, and other policy support, reasonably plan fund use, improve fund turnover, and reduce financing pressure. Regulatory authorities should implement strong supervision to ensure that supporting funds truly flow to energy enterprises that are committed to green development, thereby maintaining market order.
Third, energy enterprises should learn from each other’s ESG development models. Carrying out ESG exchange activities and establishing professional and standardized ESG development systems can allow energy enterprises with slow and poor ESG development to learn from the experiences of those with fast and good ESG development, thereby promoting the overall improvement of the ESG level of the energy industry. Relevant departments should also commit to unifying ESG information disclosure and rating standards, and formulate relevant policies to provide some level of encouragement and financial support to non-state- owned enterprises, enterprises in less economically developed regions, and enterprises with directors, supervisors and senior executives who do not have an overseas background. These efforts would jointly promote the further high-quality development of China’s energy industry and accelerate the realization of the “double carbon” goal.

References

Publishing order | Descend order by publishing year | Descend order by cited within
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