The Impact of Corporate ESG Performance on Regional Energy Efficiency in China from the Perspective of Green Development

Abstract

In the context of pursuing green and low-carbon transformation, exploring how to improve regional energy efficiency in China is significant. This paper takes the Chinese A-share listed companies in Shanghai and Shenzhen from 2009 to 2023 as the research object to empirically test the relationship between corporate ESG performance and regional energy efficiency. The results show that the ESG performance of enterprises has significantly improved energy efficiency in China. A mechanism analysis reveals that corporate ESG practices help alleviate financing constraints, reduce agency costs, and enhance information transparency, promoting regional energy efficiency.

1. Introduction

In recent years, the frequent occurrence of extreme weather events worldwide has posed a significant threat to human social and economic development. Countries have formulated emission reduction plans and green development strategies to achieve the temperature control targets proposed in the Paris Agreement. Green development is the only way to combat climate change and an essential engine for promoting high-quality economic development. The concept of ESG (environmental, social, and governance) originated in the developed capital markets of Europe and the United States and has gradually been promoted and applied in China. According to the report in the “2022 China ESG Development White Paper” organized by New Think Tank and China ESG30 Forum, the ESG disclosure of A-share listed companies has increased year by year, and more than a quarter of A-share listed companies have released their ESG reports 2020. For listed companies, ESG performance reflects their non-financial performance and directly affects their market value, financing costs, and sustainability. More and more investors and consumers are beginning to take ESG performance as an important basis for investment decisions and consumption choices.

The report of the 19th National Congress of the Communist Party of China emphasized “promoting the revolution of energy production and consumption, building a clean, low-carbon, safe and efficient energy system” and “building an environmental governance system with government as the leading role, enterprises as the main body, and social organizations and the public as participants”. Improving energy efficiency has become one of the significant problems that need to be overcome to realize the reduction in energy consumption and green development of China’s economy from the high-speed growth stage to the high-quality development stage. Improving energy efficiency means reducing energy consumption and carbon emissions while maintaining economic growth, thus achieving resource conservation and environmental protection. Against continuous growth in global energy demand and tight energy supply, improving regional energy efficiency is significant in ensuring energy security and promoting sustainable economic development.

Naturally, corporates play an essential role in regional energy efficiency improvements. Through ESG behavioral practices, companies can reduce energy consumption and contribute to sustainable local economic growth and social well-being. Therefore, this paper takes the Chinese A-share listed companies in Shanghai and Shenzhen from 2009 to 2023 as the research object to empirically test the relationship between corporate ESG performance and regional energy efficiency, and based on the essential regression relationship between the two, introduces the tripartite perspectives of environmental regulation, industry competitiveness, and external attention to carry out the heterogeneity test. Further, the synergistic effect between corporate ESG performance and regional energy efficiency is analyzed by mechanism, and the subsequent economic effects test is used to fill the gaps in existing related research fields.

Many studies have been carried out in academia to explore ESG, and they mainly focus on ESG ratings themselves and the factors influencing ESG ratings, including macro policies, executive behavior, and internal corporate governance [1,2]. Typically, the innovation and application of green technologies are key drivers of regional energy efficiency improvements [3,4]. Enterprises with good ESG performance tend to focus more on green technology development and innovation. These innovations help enterprises reduce energy consumption and emissions and drive the energy efficiency of the entire industry and even the region through technology diffusion and demonstration effects.

Previous literature has primarily focused on the impact of corporate ESG practices on their subsequent economic outcomes, emphasizing the firm level while rarely integrating macroeconomic factors. This study adopts a macro-regional perspective to examine the impact of ESG on regional energy efficiency. The potential contributions of this study are mainly reflected in the following aspects: First, this study aims to explore the intrinsic connection and mutual influence mechanism between corporate ESG performance and regional energy efficiency from the perspective of green development. Through theoretical analysis and empirical research, it reveals the driving factors and paths of ESG performance on regional energy efficiency improvement. It provides new perspectives and evidence to enrich and improve the theory of green development and sustainable development. Second, this paper not only empirically tests the positive correlation between corporate ESG performance and regional energy efficiency but also further analyzes the differences in this facilitating effect under different environmental regulation intensities, industry competition levels, and differences under external attention. This multidimensional analysis reveals the heterogeneous impacts of corporate ESG behavior in other contexts, providing a more nuanced perspective for understanding how ESG performance specifically contributes to regional energy efficiency. Third, the impact mechanism test systematically explores how corporate ESG behavior indirectly enhances regional energy efficiency by mitigating financing constraints, lowering agency costs, and increasing information transparency. Fourth, in addition to focusing on the direct impact of corporate ESG behaviors on regional energy efficiency, it also provides an in-depth analysis of how such behaviors can further promote the enhancement of corporate value, risk-taking, and future development potential through the enhancement of regional energy efficiency, which reveals the critical role of corporate ESG behaviors in promoting sustainable development and realizing long-term economic benefits.

2. Theoretical Analysis and Research Hypothesis

The environmental dimension of a company’s ESG performance emphasizes compliance with environmental policies and implementing energy-saving and emission-reduction measures. Enterprises reduce energy consumption and carbon emissions and optimize production processes by adopting advanced energy-saving technologies and equipment [5].

From the perspective of stakeholders, ESG practices typically focus on the relationships between enterprises and various stakeholders, including investors, customers, suppliers, and communities [6,7,8]. Enterprises build solid stakeholder relationships and can win broader social support and resources, providing a strong impetus to promote regional energy efficiency [9]. In addition, enterprises also offer professional skills training for their employees, mainly focusing on energy saving, emission reduction, and energy management, so that employees can master the necessary professional knowledge and practical skills. This helps employees play a more significant role in the enterprise’s energy-saving and emission reduction work and effectively promotes the overall improvement of regional energy efficiency [10]. In summary, based on the stakeholder perspective, it can be found that ESG plays a good enhancement effect on the undertaking of corporate social responsibility.

The corporate governance perspective focuses more on the internal governance of enterprises, including risk management, incentive mechanisms, and risk-reduction aspects. On the one hand, enterprises focus on environmental, social, and governance risk management and identify, assess, and control risks related to energy efficiency through the establishment and improvement of a risk management system, which helps enterprises respond to the challenges brought by market changes and policy adjustments promptly, and ensures that the energy efficiency improvement work goes smoothly [11,12,13]. On the other hand, enterprises encourage employees to actively participate in energy-saving, emission reduction, and efficiency improvement by establishing incentive mechanisms. The energy saving and emission reduction reward system recognizes and rewards employees with outstanding performance in energy saving and emission reduction, which stimulates employees’ motivation and creativity and promotes the improvement of regional energy efficiency. Naturally, enterprises with good ESG performance tend to have a sound corporate governance structure and decision-making mechanism [14], which means that enterprises fully consider environmental, social, and governance factors when formulating strategies and making decisions to ensure sustainable development.

Based on signaling theory, enterprises convey their values, business philosophy, and sustainability capabilities to the market through ESG practices. This, in turn, influences market perceptions and evaluations of the enterprise, its external environment, and internal operations. Specifically, strong ESG performance enhances a company’s market image and reputation, which helps gain broader social recognition and support. This positive image facilitates access to more resources and opportunities for energy conservation, emission reduction, and technological innovation. Building on the above analysis, this study proposes the following hypothesis:

H1: 

Corporate ESG behavior significantly contributes to local regional energy efficiency.

Firms with good ESG performance are often perceived as having higher credit quality and lower default risk and thus have higher credit ratings [15,16]. Credit rating improvement can reduce enterprises’ financing costs in the capital market and make it easier to obtain support from bank loans, bond issuance, and other financing channels [17,18]. In recent years, with the improvement of social responsibility awareness and environmental protection awareness, investors have paid more and more attention to the ESG performance of enterprises. Companies with excellent ESG performance can attract more investors with a sense of social responsibility, thus increasing their financing sources and reducing costs. By easing financing constraints and lowering financing costs, companies can obtain more financial support for energy efficiency improvement programs. Based on the economic perspective, ESG-performing firms usually have higher asset operating efficiency, better financial quality, and stronger risk resistance [19,20,21]. These advantages make these firms more competitive in the market competition and thus likely to gain higher market share and profitability.

ESG standards require companies to establish sound internal control systems and governance structures to reduce information asymmetry and conflicts of interest between management and shareholders, thereby reducing agency costs. Corporate performance in ESG is often closely related to management’s sense of responsibility; that is, management’s attention and commitment to ESG can enhance their sense of responsibility and mission, fulfill their corporate responsibility more actively, and reduce moral hazard and agency costs.

Corporate disclosure of information on ESG will lead to increased transparency. Through open and transparent ESG disclosure, investors and stakeholders can better understand a firm’s operating conditions, risk management practices, and social impacts to make more informed decisions [22,23]. In addition, increased information transparency will enhance the firm’s market trust and recognition [24]. Investors and financial institutions will consider the ESG performance and disclosure quality of firms when assessing their value, giving higher valuations and lower financing costs, which will result in more surplus funds to be invested in green energy. Thus, the following hypothesis is formulated.

Based on asymmetric information theory, enterprises often face information asymmetry in financing, operations, and interactions with external stakeholders. This can lead to higher financing costs, increased agency costs, and reduced information transparency. However, ESG (environmental, social, and governance) practices can effectively mitigate these constraints. By actively fulfilling environmental and social responsibilities and strengthening corporate governance, enterprises send positive signals to the market regarding their stable operations and commitment to sustainable development. These actions enhance investor and creditor confidence and reduce risk assessments associated with information asymmetry, enabling firms to secure lower-cost financing. At the same time, strong ESG performance promotes greater transparency and standardization in internal management, mitigating agency conflicts between management and shareholders and thereby lowering agency costs. Furthermore, by improving ESG disclosure, enterprises enhance overall information transparency, allowing external stakeholders to assess their value and risk profile more accurately. This, in turn, fosters market trust. In summary, corporate ESG practices alleviate financing constraints, reduce agency costs, and enhance information transparency, optimizing resource allocation efficiency at the firm level while indirectly improving regional energy efficiency. This provides a solid foundation for green and sustainable regional economic development. Based on the above analysis, this study proposes the following hypothesis:

H2: 

Corporate ESG behavior can help alleviate financing constraints, reduce agency costs, and improve information transparency, promoting regional energy efficiency.

Environmental regulations provide companies with clear environmental objectives and standards. In regions with stronger environmental laws, enterprises face more significant environmental pressure, which prompts them to pay more attention to ESG management to improve their performance in environmental protection, and strict environmental regulations will prompt enterprises to increase their R&D investment in environmental protection technologies to cope with higher environmental protection requirements. These technological innovations help firms reduce pollution and emissions and improve their energy efficiency levels, thus promoting regional energy efficiency [25]. Based on the herd effect, enterprises will generally strengthen ESG management and enhance environmental protection when the whole industry faces strict environmental regulations. This collective action helps to form a benign competition and cooperative atmosphere within the industry, which promotes the improvement of energy efficiency in the whole industry. Therefore, this paper proposes the following hypothesis:

H3: 

The promotion effect of good corporate ESG performance on regional energy efficiency is more significant when environmental regulation is stronger.

With the rapid development of China’s economy and increasingly fierce competition in the industry, the ESG performance of enterprises has become one of their competitive advantages. By actively fulfilling their environmental, social, and corporate governance responsibilities, enterprises can establish a good brand image and enhance consumer trust and loyalty. By actively fulfilling their environmental, social, and corporate governance responsibilities, companies can build a good brand image and enhance consumer trust and loyalty. At the same time, companies with good ESG performance can also attract more investors and partners, thus gaining more financial support and market opportunities [26,27]. Therefore, to stand out from the competition, firms are more motivated to strengthen their ESG management and improve their ESG performance. This motivation drives firms to pay more attention to environmental protection measures such as energy conservation and reduction and energy efficiency, thus promoting regional energy efficiency. Therefore, this paper proposes the following hypothesis:

H4: 

The promotion effect of good corporate ESG performance on regional energy efficiency is more significant when there is greater competition in the industry.

External stakeholders often exert a certain amount of monitoring pressure on companies. This pressure prompts companies to pay more attention to their ESG performance, as any negative news on environmental, social, or corporate governance issues can cause significant damage to a company’s reputation and image. To maintain a good corporate image and reputation, companies will be more motivated to strengthen their ESG management and improve their ESG performance. Under the ESG framework, firms must balance economic and environmental benefits to achieve sustainable development [28]. When external concerns are high, enterprises will pay more attention to environmental benefits and invest more resources in energy saving and emission reduction, clean energy utilization, etc. This optimal allocation of resources not only helps enterprises to improve their ESG performance but also improves their energy efficiency level and reduces operating costs, thus enhancing their market competitiveness. At the same time, this positive behavior of enterprises will also have a demonstration effect on the whole industry, driving the entire industry to improve energy efficiency.

Therefore, this paper proposes the following hypothesis:

H5: 

The promotion effect of good corporate ESG performance on regional energy efficiency is more significant when external attention is higher.

3. Research Design

3.1. Data Source and Sample Selection

To exclude the impact of the 2008 financial crisis, this paper focuses on A-share listed companies in Shanghai and Shenzhen stock exchanges in China from 2009 to 2023, conducting an empirical analysis to examine the relationship between corporate ESG performance and regional energy efficiency. The data involved are sourced from the China Stock Market & Accounting Research Database (CSMAR), while ESG data are obtained from the Wind database (WIND). To ensure the validity of the data, the original data were filtered according to the following criteria: excluding samples from the financial industry, excluding listed companies with missing data, and excluding companies marked as ST or *ST during the sample period. Ultimately, 26,532 valid samples were obtained. This paper’s data organization, calculations, and regressions were performed using Stata 17.0. To avoid the influence of outliers, all continuous variables were winsorized at the 1% level at both ends.

3.2. Model Construction and Variable Description

To test the hypotheses, models (1)–(2) are constructed. Among them, model (1) is used to test the relationship between corporates’ ESG performance and regional energy efficiency, the core concerns at this time are the sign and significance of a1. Model (2) focuses on testing the mechanism variable effects. Controls is the control variable, i is each enterprise, and t is each year. Year is the year fixed effect, u is the individual enterprise fixed effect, and ε is the residual term.

$${SBM}_{i,t}=a_0+a_1{ESG}_{i,t}+a_i{Controls}_{i,t}+\sum Year+u_{i,t}+{\epsilon }_{i,t}$$

(1)

$$Z_{i,t}=b_0+b_1{ESG}_{i,t}+b_i{Controls}_{i,t}+\sum Year+u_{i,t}+{\epsilon }_{i,t}$$

(2)

The dependent variable is regional energy efficiency (SBM). Based on the study of Stan et al. (2020) [29], regional labor, capital, and energy were selected as inputs, regional GDP as desired output, and emissions of industrial sulfur dioxide, industrial soot and dust, and industrial wastewater (effluents) as undesired output, and the SBM–Malmquist–Luenberger index method was used to measure the green total factor energy efficiency of each prefecture-level city. In the robustness test, CCR was used as a proxy variable for regional energy efficiency to enhance the objectivity of the benchmark regression. For the measurement of SBM, capital inputs, labor, and resource inputs (mainly natural gas and liquefied petroleum gas) are used to represent the input variables of production, while GDP represents the final desired output, and the pollution emissions in the production process are used to describe the non-desired output. At the same time, the SBM model of non-desired output is chosen to measure the above input and output variables comprehensively, and the final result can be expressed as green economic efficiency. In the SBM model, there are n decision-making units, and the above input variables (x ∈ R^m), desired output variables (y^b ∈ R^s2), and non-desired output variables (y^b ∈ R^s2) are all contained in each decision-making unit of the model. x reflects the matrix of the above input variables (X > 0), X = [x₁, x₂, ⋯, x_n] ∈ R^m×n, and yg reflects the matrix of the above-desired output variables (X > 0), X = [x₁, x₂, ⋯, x_n] ∈ R^m×n, and Yg reflects the matrix of the above-desired output variables (X > 0). reflects the matrix of desired outputs GDP (Y^g > 0), Y^g = [y^g1, y^g2, ⋯, y^gn] ∈ R^s1×n, and Y^b reflects the matrix of undesired outputs such as pollution emissions (Y^b > 0), Y^b = [y^1b, y^2b, ⋯, y^nb] ∈ R^s2×n, where m, s1, and s2 denote the number of inputs, desired outputs, and undesired outputs, respectively. The energy development efficiency measurement model is as follows:

$$\begin{gathered} {SBM}_{i,t}={\displaystyle \frac{1-1/m{\sum }_{i=1}^m\frac{S_i^{-}}{x_{i0}}}{1+(1/s_1+s_2({\sum }_{r=1}^{s_2}\frac{s_r^g}{y_{r0}^g}+{\sum }_{r=1}^{s_2}\frac{s_r^b}{y_{r0}^b}))}} \\ {s}_{.}{t}_{.}{x}_0=X\lambda +s^{-} \\ {y}_{r0}^g=Y_g\lambda -s^g \\ {y}_{r0}^b=Y_b+s^b \\ {\lambda \ge 0,s^{-}\ge 0,s}^g\ge 0,s^b\ge 0 \end{gathered}$$

(3)

The SBM-ML index integrates the advantages of Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist–Luenberger (ML) index, allowing for a comprehensive assessment of the relationship between energy inputs, desirable outputs (such as GDP and industrial added value), and undesirable outputs (such as carbon emissions). This enables a more accurate evaluation of energy efficiency. Moreover, the SBM-ML index not only assesses energy efficiency at a specific time but also captures changes in productivity and technological efficiency across different periods or organizations. This dynamic capability provides a quantitative evaluation of efficiency improvements and resource utilization efficiency. However, the accuracy and reliability of the SBM-ML index largely depend on the completeness and accuracy of input and output data. Errors or missing data may distort the evaluation results. Additionally, constructing the SBM-DEA model requires assigning weights to different input and output indicators. Since these weight assignments involve a degree of subjectivity, they may impact the objectivity and accuracy of the evaluation results.

The independent variable is corporate ESG performance (ESG), a comprehensive evaluation of the corporate fulfillment of environmental, social, and governance responsibilities. The current academic measurement of corporate ESG performance is mainly based on the evaluation results released by third-party institutions. This paper adopts the CSI ESG ratings as the measurement standard for the following reasons: The CSI ESG ratings have a wide coverage, encompassing not only listed companies but also bond issuers, non-listed companies, and overseas enterprises. Moreover, it offers a more extended historical coverage, tracing back to 2009, after the financial crisis. The ESG indicators are more precise, as the CSI ESG rating system sets key issues for approximately 70 tertiary industries and assigns different weights to the key problems of various sectors. This industry-specific approach helps more accurately reflect the characteristics and differences in ESG performance across industries, thereby enhancing the relevance and scientific rigor of the evaluation. Drawing on Zhang, et al. (2024) [30], this paper measures corporate ESG performance by assigning a value of 1~9 to the CSI ESG ratings, from low to high C~AAA correspondingly.

The mechanism variable Z mainly includes three types: financing constraint indicators, measured by the absolute value of the SA index and the KZ index, with larger values indicating more substantial financing constraints; information transparency (Opacity), using the Shenzhen Stock Exchange and Shanghai Stock Exchange disclosure shall prevail, the distribution of the dummy variable in the range of 1 to 4, with larger values indicating more opaque information; and agency costs (Mfee), measured using “Administrative Expenses/Operating Revenue”. Control variables include firm size, gearing ratio, return on net assets, cash flow, etc. Detailed variable definitions are shown in

Table 1. Description of variables.

Variable Type	Symbol	Variable Description
Dependent variable	SBM	Corporate energy efficiency, as defined in the text
Independent variable	ESG	Corporate ESG performance assigned a value of 1 to 9 based on performance
Control variables	size	Size of the corporate, natural logarithm of total assets
	lev	Gearing ratio, total liabilities/total assets
	roe	Return on equity, net profit/total assets
	cashflow	Cash flow, net cash flow from operating activities/total assets
	top1	Shareholding concentration, the proportion of shares held by the largest shareholder
	dual	The general manager and the chairman of the board of directors are the same person, if so, assigned a value of 1, otherwise assigned a value of 0
	board	Size of the board of directors, natural logarithm of the number of board members
	soe	Property rights, state-owned enterprises are assigned a value of 1; otherwise, 0
	big4	Audit institutions, the international Big Four audit is assigned a value of 1 otherwise 0
	age	Age of business establishment in natural logarithms

.

4. Empirical Results

4.1. Descriptive Statistics

The descriptive statistics of each variable are given in

Table 2. Descriptive statistics.

	Sample	Mean	Standard Deviation	Minimum	Maximum
SBM	26,532	0.451	0.216	0.178	1.053
ESG	26,532	4.113	1.050	1.000	8.000
size	26,532	22.420	1.393	19.774	26.762
lev	26,532	0.450	0.203	0.064	0.907
roe	26,532	0.067	0.130	−0.632	0.387
cashflow	26,532	0.050	0.069	−0.157	0.243
top1	26,532	0.370	0.155	0.079	0.757
dual	26,532	0.232	0.422	0.000	1.000
board	26,532	2.146	0.202	1.609	2.708
soe	26,532	0.476	0.499	0.000	1.000
big4	26,532	0.078	0.269	0.000	1.000
age	26,532	18.803	6.140	5.000	34.000

. Among them, the dependent variable corporate energy efficiency SBM has a minimum value of 0.178 and a maximum value of 1.053; the independent variable ESG is distributed between 1 and 8, with a mean value of 4.113. For the control variables, size is naturally logarithmized, with a minimum value of 19.774 and a maximum value of 26.762. The mean value of lev is 0.450, which indicates that an average level of debt of about 45% existed among the research object during the study period. The dual is a dummy variable with a 0–1 distribution, and the mean value is 0.232; the age is distributed between 5 and 34, and the mean value is 18.803. In summary, the dependent, independent, and control variables selected in this paper are distributed in a reasonable range.

4.2. Regression Results

Benchmark regression results of this paper are given in

Table 3. Benchmark regression results.

	(1)	(2)	(3)	(4)
	SBM	SBM	SBM	SBM
ESG	0.0044 ***	0.0050 ***	0.0050 ***	0.0050 ***
	(5.1565)	(5.7922)	(5.8169)	(5.7613)
size		−0.0004	−0.0003	−0.0001
		(−0.2712)	(−0.1789)	(−0.0940)
lev		0.0270 ***	0.0267 ***	0.0250 ***
		(3.9708)	(3.9280)	(3.6663)
roe		−0.0292 ***	−0.0287 ***	−0.0280 ***
		(−4.5616)	(−4.4717)	(−4.3693)
cashflow		−0.0112	−0.0114	−0.0124
		(−0.9230)	(−0.9351)	(−1.0203)
top1			−0.0126	−0.0097
			(−1.3036)	(−1.0066)
dual			−0.0000	0.0002
			(−0.0093)	(0.0775)
board			−0.0033	−0.0036
			(−0.5235)	(−0.5681)
soe				0.0085 *
				(1.8138)
big4				−0.0038
				(−0.6337)
age				−0.0106 ***
				(−4.7833)
_cons	0.3022 ***	0.2998 ***	0.3090 ***	0.4207 ***
	(63.9028)	(9.1418)	(8.9671)	(9.9240)
Corporate	Control	Control	Control	Control
Year	Control	Control	Control	Control
Observations	26,532	26,532	26,532	26,532
R-squared	0.4253	0.4266	0.4266	0.4273

*** and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

. Among them, column (1) shows the univariate regression results of ESG and SBM without considering control variables, with a coefficient value of 0.0044, which is significant at the 1% level, while columns (2)–(4) add the control variables step by step. Under the full-variable regression, the ESG coefficient value is 0.0050, which is significant at the 1% level.

The above results suggest that corporate ESG performance significantly contributes to regional energy efficiency. Energy efficiency can be effectively promoted through positive ESG practices of enterprises, which can bring multiple benefits to society, the economy, and the environment. To improve ESG performance, companies often adopt strategies to utilize resources more efficiently, including energy use. By promoting technological innovation and process optimization, companies can reduce energy consumption and resource waste, boosting energy efficiency. This effect may spread to the firm’s supply chain and local industries.

Companies concerned with ESG issues focus more on long-term sustainability than short-term profits. As a result, they tend to make long-term investments and improvements in energy efficiency to reduce energy price volatility and supply risk, thereby improving overall efficiency and stability. In recent years, as environmental regulations have tightened and the marketplace has become more focused on sustainability, companies have become pressured to adopt more environmentally friendly and efficient energy management practices. This pressure is driving companies to take more proactive measures in energy efficiency, such as investing in energy-efficient technologies and renewable energy sources, which indirectly improves energy efficiency in the local region. Based on signaling theory, companies with good ESG performance usually have strong social influence and reputation, and they are more likely to adopt positive social responsibility initiatives in the local area, including energy efficiency improvement programs. These programs can not only directly improve the energy efficiency level of the local region but also inspire other companies and residents to follow suit, creating a positive effect.

4.3. Robustness Test

4.3.1. Robustness Test

To enhance the objectivity of the basic conclusions, this paper conducts a robustness test, as follows:

(1)

In the baseline regression, super-efficiency SBM is the core dependent variable. In this section, super-efficiency CCR is a proxy for regional energy efficiency. The results are reported in Column (1) of

Table 4. Robustness test.

	Replacement of Dependent Variable	Replacement of Independent Variables	Excluding the Epidemic Factor	Exclusion of First-Tier Cities	Adding Control Variables
	(1)	(2)	(3)	(4)	(5)
	CCR	SBM	SBM	SBM	SBM
ESG	0.0037 ***		0.0018 **	0.0015 **	0.0048 ***
	(5.0185)		(2.4683)	(1.9632)	(5.6325)
ESG_1		0.1135 ***
		(6.3340)
size	−0.0045 ***	−0.0003	−0.0016	0.0040 ***	0.0003
	(−3.2592)	(−0.1864)	(−1.1666)	(2.7475)	(0.1868)
lev	0.0195 ***	0.0258 ***	0.0144 **	0.0102 *	0.0242 ***
	(3.3352)	(3.7790)	(2.4670)	(1.7062)	(3.5475)
roe	−0.0105 *	−0.0282 ***	−0.0040	−0.0097 *	−0.0284 ***
	(−1.9067)	(−4.3919)	(−0.7466)	(−1.7551)	(−4.4347)
cashflow	−0.0330 ***	−0.0126	−0.0053	−0.0048	−0.0130
	(−3.1554)	(−1.0351)	(−0.5055)	(−0.4691)	(−1.0687)
top1	0.0164 **	−0.0099	0.0302 ***	0.0115	−0.0098
	(1.9769)	(−1.0263)	(3.7126)	(1.3106)	(−1.0112)
dual	0.0026	0.0002	−0.0022	−0.0011	0.0002
	(1.2767)	(0.0684)	(−1.0844)	(−0.5058)	(0.0625)
board	0.0067	−0.0036	−0.0019	−0.0058	−0.0009
	(1.2225)	(−0.5689)	(−0.3455)	(−1.0194)	(−0.1448)
soe	0.0035	0.0084 *	0.0063	0.0121 ***	0.0080 *
	(0.8787)	(1.7943)	(1.6358)	(2.6392)	(1.7019)
big4	0.0050	−0.0037	−0.0138 **	−0.0133 **	−0.0037
	(0.9898)	(−0.6323)	(−2.4417)	(−2.3972)	(−0.6200)
age	−0.0030	−0.0105 ***	0.0054 **	−0.0128 ***	−0.0104 ***
	(−1.5819)	(−4.7535)	(2.2978)	(−5.2405)	(−4.7210)
financialback					−0.0019
					(−1.0279)
overseaback					−0.0076 ***
					(−3.9633)
eduback					−0.0102 ***
					(−3.0928)
_cons	0.6299 ***	0.3602 ***	0.2428 ***	0.3740 ***	0.4189 ***
	(17.2973)	(8.3032)	(6.1533)	(8.8637)	(9.8694)
Corporate	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control
Observations	26,532	26,532	19,364	18,184	26,532
R-squared	0.4655	0.4275	0.3341	0.3377	0.4280

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

, where the coefficient is 0.0037 and is significant at the 1% level.

(2)

For corporate ESG performance, a composite score (ESG_1) is used as an alternative indicator to measure the dependent variable, regional energy efficiency. The regression results are reported in Column (2) of Table 4. The coefficient for ESG_1 is 0.1135, which is significant at the 1% level, further validating the baseline regression results.

(3)

The COVID-19 pandemic in 2020 had a significant impact on China’s economy [31,32]. Lockdowns and social distancing measures led to a sharp decline in global traffic, including aviation, public transportation, and personal vehicle use. Column (3) reports the regression results after excluding the impact of the pandemic.

(4)

First-tier cities, such as Beijing, Shanghai, Guangzhou, and Shenzhen, typically possess advanced technology and well-developed infrastructure, including efficient building designs, modern transportation systems, and intelligent energy management systems. To reduce the influence of complex development factors in these cities on the conclusions, the sample data are re-regressed after excluding Beijing, Shanghai, Guangzhou, and Shenzhen. The results are reported in Column (4) of Table 4, where the ESG core coefficient is 0.0015 and significant at the 5% level.

(5)

Corporate managers play a critical role in shaping development strategies [33]. Managerial decisions regarding the allocation of funds and resources directly influence corporate actions in the ESG domain, such as investments in environmental technologies, improvements in labor conditions, and reforms in governance structures. This section incorporates managerial financial background (financialback), overseas experience (overseaback), and educational background (eduback) into the regression. The results are reported in the final column of Table 4, where the coefficient is 0.0048 and passes the significance test.

4.3.2. Endogeneity Test

It has been demonstrated above that corporate ESG performance can promote regional energy efficiency; however, only the relationship between the independent variable in the current period and the independent variable in the current period was tested. Naturally, better corporate ESG performance is more likely to promote local energy efficiency. Conversely, local firms are also expected to encourage greater attention to ESG behaviors for regions with higher energy use. To avoid the problem of endogeneity where the independent and dependent variables are mutually dependent, regressions are conducted using lagged effects with a two-stage instrumental variable approach. In column (1) of

Table 5. Endogeneity test.

	Lag Effect	Two-Stage Least Squares, 2SLS
	(1)	(2)	(3)
	SBM	ESG	SBM
ESGt−1	0.0048 ***
	(5.2538)
iv		0.3424 ***
		(6.4356)
Fitted Value			0.0692 ***
			(13.1108)
size	−0.0004	0.2361 ***	−0.0158 ***
	(−0.2498)	(20.1593)	(−7.7779)
lev	0.0275 ***	−0.9684 ***	0.0877 ***
	(3.6979)	(−19.2979)	(10.3289)
roe	−0.0269 ***	0.1302 ***	−0.0350 ***
	(−3.9136)	(2.7387)	(−5.4449)
cashflow	−0.0156	−0.3138 ***	0.0083
	(−1.1704)	(−3.4787)	(0.6773)
top1	−0.0191 *	0.3256 ***	−0.0293 ***
	(−1.8252)	(4.5460)	(−2.9973)
dual	0.0008	0.0064	−0.0006
	(0.3131)	(0.3603)	(−0.2685)
board	−0.0036	−0.0940 **	0.0030
	(−0.5288)	(−1.9932)	(0.4684)
soe	0.0049	0.0471	0.0044
	(0.9656)	(1.3555)	(0.9422)
big4	−0.0039	0.0800 *	−0.0095
	(−0.6072)	(1.8250)	(−1.6030)
age	−0.0093 ***	−0.0081	−0.0091 ***
	(−3.5591)	(−0.4966)	(−4.1118)
_cons	0.4462 ***	−4.2851 ***	0.4414 ***
	(8.9672)	(−12.2279)	(10.4353)
Corporate	Control	Control	Control
Year	Control	Control	Control
Observations	23,919	26,532	26,532
R-squared	0.4183	0.0672	0.4306

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

, lagging ESG by one period, the ESG_t−1 coefficient value is 0.0048, which is significant at the 1% level, indicating that ESG performance in the current period promotes regional energy efficiency in the future period; using the mean value of the ESG performance of other firms in the province where the firms are located as an instrumental variable, ESG is regressed, and the first stage coefficient value of 0.3424 is significantly positive, the fitted values generated by the first-stage regression were regressed on the dependent variable SBM, the regression coefficient value of the fitted value is 0.0692, which is significant at 1% level. The above results indicate no endogeneity of the independent and dependent variables that are causal to each other.

4.4. Heterogeneity Test

4.4.1. Environmental Regulation Heterogeneity

From the perspective of pressure and incentives, strengthening environmental regulations typically increases the external pressure and incentives for enterprises to improve energy efficiency [34,35,36,37]. To comply with laws and regulations, enterprises must seek technologies and strategies to reduce energy consumption and emissions. This section uses the natural logarithm of the frequency of green and environmental protection terms in provincial government work reports as the measurement indicator. The ecological regulation data are divided into quartiles: regions below the 25th percentile are categorized as having weak environmental regulation, those above the 75th percentile as having strong environmental regulation, and those in the middle range as having moderate regulation. According to

Table 6. Heterogeneity test: environmental regulation heterogeneity.

	(1)	(2)	(3)
	SBM	SBM	SBM
	Strong	Medium	Weak
ESG	0.0046 ***	0.0042 ***	0.0025
	(2.6344)	(4.5814)	(1.2373)
size	−0.0016	−0.0032 *	0.0116 ***
	(−0.4668)	(−1.8720)	(2.8359)
lev	−0.0053	0.0272 ***	0.0466 ***
	(−0.3761)	(3.7216)	(2.8052)
roe	−0.0309 **	−0.0102	−0.0219
	(−2.3843)	(−1.5146)	(−1.5190)
cashflow	0.0206	−0.0041	0.0050
	(0.8685)	(−0.3186)	(0.1792)
top1	0.0158	−0.0024	0.0129
	(0.7612)	(−0.2347)	(0.5750)
dual	0.0000	−0.0032	0.0001
	(0.0001)	(−1.2044)	(0.0105)
board	−0.0133	0.0076	−0.0276 *
	(−1.0143)	(1.1211)	(−1.8285)
soe	0.0045	0.0187 ***	−0.0098
	(0.4468)	(3.8017)	(−0.8572)
big4	0.0275 **	−0.0125 **	−0.0173
	(2.0647)	(−2.0043)	(−1.2143)
age	−0.0141 **	−0.0031	−0.0113 ***
	(−2.2641)	(−1.0515)	(−3.1935)
_cons	0.5452 ***	0.3455 ***	0.2948 ***
	(5.4201)	(7.1376)	(2.9437)
Corporate	Control	Control	Control
Year	Control	Control	Control
Observations	6641	13,118	6773
R-squared	0.5174	0.4611	0.4389

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

, in regions with strong environmental regulation, the ESG coefficient is 0.0046, significant at the 1% level; in areas with moderate environmental regulation, the ESG coefficient is 0.0042, also important at the 1% level; in regions with weak environmental regulation, the ESG coefficient is 0.0025 and not significant. These results indicate that the positive impact of corporate ESG performance on regional energy efficiency is more important in regions with stronger environmental regulation.

4.4.2. External Attention Heterogeneity

When external attention is high, consumers pay more attention to enterprises’ environmental performance and social responsibility [38,39]. Research reports attention and the number of analysts are used as measurement indicators for external attention. Samples with values greater than or equal to the median are considered to have high external attention, while those below the median are classified as having low external attention. According to

Table 7. Heterogeneity test: external attention heterogeneity.

	(1)	(2)	(3)	(4)
	SBM	SBM	SBM	SBM
	Higher Analyst Focus	Lower Analyst Focus	Higher Attention of Research Reports	Lower Attention of Research Reports
ESG	0.0049 ***	0.0039 ***	0.0045 ***	0.0041 ***
	(3.7317)	(3.3756)	(3.7036)	(3.4747)
size	−0.0126 ***	0.0024	−0.0095 ***	0.0025
	(−3.9135)	(1.1398)	(−3.0450)	(1.1634)
lev	0.0411 ***	0.0098	0.0408 ***	0.0148 *
	(3.3104)	(1.1244)	(3.3560)	(1.6709)
roe	−0.0674 ***	−0.0153 **	−0.0728 ***	−0.0112
	(−5.0592)	(−2.0746)	(−5.6030)	(−1.4920)
cashflow	−0.0361 *	0.0147	−0.0300	0.0034
	(−1.8156)	(0.9352)	(−1.5288)	(0.2115)
top1	−0.0316 *	0.0207	−0.0280 *	0.0196
	(−1.9195)	(1.5815)	(−1.7399)	(1.4745)
dual	−0.0076 **	−0.0002	−0.0069 *	−0.0001
	(−1.9672)	(−0.0567)	(−1.8138)	(−0.0260)
board	−0.0154	−0.0100	−0.0141	−0.0057
	(−1.5348)	(−1.1682)	(−1.4209)	(−0.6487)
soe	−0.0168 *	0.0190 ***	−0.0154 *	0.0171 ***
	(−1.7870)	(3.3218)	(−1.6893)	(2.9175)
big4	−0.0067	−0.0086	−0.0094	0.0012
	(−0.8659)	(−0.8454)	(−1.2359)	(0.1128)
age	−0.0077 **	−0.0095 ***	−0.0089 ***	−0.0098 ***
	(−2.2201)	(−3.0393)	(−2.6266)	(−3.1423)
_cons	0.7103 ***	0.3582 ***	0.6540 ***	0.3519 ***
	(8.8763)	(6.1437)	(8.3972)	(5.9715)
Corporate	Control	Control	Control	Control
Year	Control	Control	Control	Control
Observations	12,577	13,955	12,823	13,709
R-squared	0.4393	0.4068	0.4348	0.4081

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

, in regions with high external attention, the ESG coefficients are 0.0049 and 0.0045, both significant at the 1% level. For enterprises with low external attention, the ESG coefficients are 0.0039 and 0.0041, which are lower than those in the high external attention group. These results indicate that the positive impact of corporate ESG performance on regional energy efficiency is more significant when external attention is higher.

4.4.3. Industry Competition Heterogeneity

Stricter environmental regulations and higher social pressures often accompany industries with intense competition. Enterprises tend to take proactive measures to improve energy efficiency to comply with laws, reduce environmental impact, and respond to societal expectations.

Table 8. Industry competition heterogeneity.

	(1)	(2)	(3)	(4)
	SBM	SBM	SBM	SBM
	Low	High	Low	High
ESG	0.0043 ***	0.0065 ***	0.0031 ***	0.0051 ***
	(3.5441)	(5.3279)	(2.6539)	(4.2323)
size	−0.0028	0.0053 **	0.0014	0.0014
	(−1.1808)	(2.1826)	(0.5959)	(0.6147)
lev	0.0445 ***	0.0186 *	0.0364 ***	0.0108
	(4.4454)	(1.8522)	(3.6760)	(1.1103)
roe	−0.0204 **	−0.0286 ***	−0.0198 **	−0.0311 ***
	(−2.2215)	(−3.1342)	(−2.2082)	(−3.4919)
cashflow	−0.0036	−0.0339 *	0.0040	−0.0340 **
	(−0.2109)	(−1.9123)	(0.2381)	(−1.9924)
top1	0.0087	−0.0349 **	−0.0106	−0.0156
	(0.6005)	(−2.4357)	(−0.7513)	(−1.0989)
dual	−0.0004	−0.0030	−0.0022	−0.0001
	(−0.1047)	(−0.8919)	(−0.6149)	(−0.0203)
board	−0.0142	0.0117	−0.0208 **	−0.0014
	(−1.5759)	(1.2617)	(−2.3505)	(−0.1478)
soe	0.0367 ***	−0.0151 **	0.0353 ***	−0.0141 **
	(5.1052)	(−2.2546)	(4.8674)	(−2.1763)
big4	0.0149 *	−0.0182 **	0.0123	−0.0151 *
	(1.8612)	(−1.9841)	(1.5022)	(−1.6682)
age	−0.0146 ***	−0.0096 ***	−0.0161 ***	−0.0070 **
	(−4.2761)	(−3.2789)	(−4.7398)	(−2.4252)
_cons	0.5069 ***	0.2910 ***	0.4573 ***	0.3716 ***
	(7.8943)	(4.6794)	(7.0901)	(6.2220)
Corporate	Control	Control	Control	Control
Year	Control	Control	Control	Control
Observations	13,492	13,040	13,432	13,100
R-squared	0.4398	0.4007	0.4242	0.4034

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

presents the comparative regression results based on the heterogeneity of industry competition. For measuring industry competition, the total asset Herfindahl index (Columns 1–2 of Table 8) and the revenue Herfindahl index (Columns 3–4 of Table 8) are used as indicators. Samples with values greater than or equal to the median have lower industry competition, while those below the median have higher industry competition. In industries with higher competition, the ESG coefficients are 0.0065 and 0.0051, both significant at the 1% level. In contrast, for sectors with lower competition, the ESG coefficients are 0.0043 and 0.0031. These results indicate that the positive impact of corporate ESG performance on regional energy efficiency is more significant in industries with intense competition.

4.5. Mechanism Test

Table 9. Results of the mechanism test.

	Financing Constraints		Agency Cost	Information Transparency
	(1)	(2)	(3)	(4)
	SA	KZ	Mfee	Opacity
ESG	−0.0042 ***	−0.0543 ***	−0.0025 ***	−0.0636 ***
	(−6.9566)	(−5.8157)	(−7.2819)	(−10.8197)
size	0.0061 ***	−0.6606 ***	−0.0129 ***	−0.1356 ***
	(5.3514)	(−36.7808)	(−20.1086)	(−12.4482)
lev	0.0101 **	7.2621 ***	−0.0203 ***	−0.1463 ***
	(2.0805)	(95.5645)	(−7.4632)	(−3.1364)
roe	0.0150 ***	−1.4668 ***	−0.0872 ***	−0.3857 ***
	(3.2826)	(−20.3909)	(−34.1659)	(−8.7941)
cashflow	0.0029	−14.4327 ***	−0.0574 ***	−0.0585
	(0.3332)	(−107.7777)	(−11.6710)	(−0.7030)
top1	−0.0073	−1.3115 ***	−0.0131 ***	0.3069 ***
	(−1.0600)	(−12.5429)	(−3.4040)	(4.6435)
dual	−0.0032 *	−0.0643 **	−0.0009	0.0612 ***
	(−1.8492)	(−2.4805)	(−0.9256)	(3.7274)
board	0.0203 ***	−0.2372 ***	0.0040	−0.0589
	(4.4804)	(−3.4694)	(1.5672)	(−1.3547)
soe	0.0201 ***	0.1916 ***	−0.0040 **	−0.2175 ***
	(6.0472)	(3.7379)	(−2.1388)	(−6.7877)
big4	−0.0370 ***	0.2070 ***	0.0022	0.0032
	(−8.7896)	(3.2109)	(0.9129)	(0.0782)
age	0.0041 ***	−0.1429 ***	0.0004	0.0441 ***
	(2.6231)	(−5.7969)	(0.4880)	(2.9216)
_cons	3.3027 ***	16.6055 ***	0.3893 ***	3.9149 ***
	(109.6291)	(34.6438)	(22.9610)	(13.5092)
Corporate	Control	Control	Control	Control
Year	Control	Control	Control	Control
Observations	26,532	24,068	26,134	26,532
R-squared	0.8034	0.6097	0.1555	0.2905

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

gives the results of the mechanism test.

In columns (1)–(2), the regression coefficient values of ESG for the financing constraint indicator are −0.0042 and −0.0543, respectively, which are both significant at the 1% level, indicating that corporate ESG behaviors help to alleviate their financing constraints, and thus promote regional energy efficiency. More and more investors consider ESG factors when choosing investment targets, and these investors are usually more inclined to support long-term sustainable development. Hence, companies with good ESG performance are more likely to attract funds from environmentally and socially responsible investors.

In column (3), the regression coefficient value of ESG for the agency cost indicator is −0.0025, which is significant at the 1% level. This suggests that corporate ESG behaviors help to reduce agency costs and thus promote regional energy efficiency. Good ESG practices help to increase the alignment of interests between corporate managers and shareholders. Managers tend to adopt long-term sustainable business strategies that include improving energy efficiency and reducing environmental impacts, and this alignment minimizes the likelihood of agency problems.

In column (4), the regression coefficient value of ESG for the information transparency indicator is −0.0636, which is significant at the 1% level, suggesting that corporate ESG behaviors will increase information transparency, promoting regional energy efficiency. Firms can improve their transparency in the market and society by openly and transparently reporting their ESG performance, including data on energy use, carbon emissions, waste management, etc. Investors, consumers, and other stakeholders can better understand a company’s environmental impacts and sustainability practices. This transparency makes it easier for companies to assess their energy efficiency performance, incentivizing them to take more effective measures to reduce energy consumption and environmental impact.

The public and the market tend to have more trust and goodwill towards companies with good ESG performance. This trust can help companies attract more consumers and investors, leading to more support and resources for energy efficiency. Good ESG performance can be an essential part of a company’s brand image and competitiveness in the marketplace and thus can drive positive action on energy management and efficiency improvements.

4.6. Economic Effect Analysis

Corporations can promote regional energy efficiency through ESG behaviors. How will it affect subsequent behaviors? This section further analyzes the economic effects based on the three perspectives of enterprise value, growth potential, and risk-taking. Among them, enterprise value is measured by Tobin’s Q (tobinq), growth potential is the growth rate of enterprise total assets (agrowth), and risk-taking is the volatility of enterprise return on assets (sd). As can be seen in

Table 10. Economic effect analysis.

	Enterprise Value	Growth Potential	Risk-Taking
	(1)	(2)	(3)
	tobinq	agrowth	sd
SBM	2.0999 **	0.1072 **	0.0148 ***
	(2.1132)	(2.0767)	(3.1066)
ESG	−0.1871 *	0.0037	−0.0028 ***
	(−1.7330)	(0.6680)	(−5.2827)
jc	0.4565 **	−0.0115	−0.0040 ***
	(2.1271)	(−1.0307)	(−3.6149)
size	−1.2514 ***	0.1186 ***	−0.0034 ***
	(−13.3038)	(24.6059)	(−13.8103)
lev	1.5975 ***	0.0228	−0.0078 ***
	(3.9840)	(1.1072)	(−5.3749)
roe	1.1276 ***	0.7186 ***	−0.0513 ***
	(3.0005)	(37.0318)	(−24.9493)
cashflow	−0.9599	−0.4407 ***	0.0196 ***
	(−1.3495)	(−11.9720)	(5.2370)
top1	−0.6694	0.2889 ***	−0.0155 ***
	(−1.1806)	(9.8790)	(−9.5905)
dual	−0.2657 *	0.0220 ***	0.0008
	(−1.8962)	(3.0310)	(1.3586)
board	−0.2198	0.0006	−0.0109 ***
	(−0.5924)	(0.0308)	(−8.5248)
soe	−0.2406	−0.0528 ***	−0.0097 ***
	(−0.8768)	(−3.7269)	(−17.4862)
big4	0.5031	−0.0945 ***	0.0023 **
	(1.4612)	(−5.2693)	(2.3445)
age	−0.0719	0.0410 ***	0.0004 ***
	(−0.5636)	(6.1195)	(8.8032)
_cons	31.1492 ***	−2.9236 ***	0.1689 ***
	(12.3618)	(−22.5163)	(30.2726)
Corporate	Control	Control	Control
Year	Control	Control	Control
Observations	26,134	26,527	26,466
R-squared	0.0145	0.1407	0.1190

***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively. The t value is in parentheses.

, the regression coefficient values of SBM for tobinq and sd are 2.0999 and 0.0148, respectively, which indicate that ESG behaviors of firms that promote regional energy efficiency in the current period help to increase the overall value of firms but also increase their risk-taking. Good ESG performance can enhance a firm’s brand reputation and increase the trust and goodwill of consumers and investors towards the firm. This positive image helps increase market share and the attractiveness of products and services, which in turn indirectly increases the company’s value. ESG practices often involve long-term strategic planning and investment, particularly in energy efficiency, which, by reducing energy costs and resource wastage, improves operational efficiency and long-term sustainability, thereby enhancing long-term stability and value. However, as regulations and standards for environmental and social responsibility continue to tighten, companies may face increased regulatory and legal risks. Failure to comply with ESG standards can result in fines, lawsuits, and other legal consequences, increasing company risk-taking.

The regression coefficient value of ESG on agrowth is 0.1072, which is significant at the 5% level, suggesting that corporate ESG behaviors that promote energy efficiency in the current region will also further enhance the future growth potential of the firm in the current period. Focusing on energy efficiency and environmental protection needs usually prompts firms to engage in technological innovation and develop more energy-efficient and environmentally friendly products and solutions. This technological leadership can help firms stay ahead of the market in the face of competition in the future. As consumers and investors pay more attention to environmental protection and sustainability, companies with good ESG performance are more prevalent in the market, leading to more market opportunities and asset growth potential.

5. Conclusions and Recommendations

5.1. Conclusions

Focusing on a sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2009 to 2023, this paper empirically analyzes the intrinsic relationship between corporate environmental, social, and governance performance (ESG) and regional energy efficiency. The results show that corporate ESG performance significantly impacts regional energy efficiency, especially in the context of strengthened environmental regulation, intensified competition in the industry, and increased external public attention.

Through the impact mechanism test, this paper reveals the specific paths through which corporate ESG practices promote regional energy efficiency improvement through multiple channels. Firstly, corporates with excellent ESG performance can alleviate their financing constraints and provide better funding for their investments in energy efficiency improvement programs. Secondly, these enterprises’ ESG practices help reduce internal agency costs and enhance the consistency between management and shareholders regarding green transformation and energy efficiency improvement. Finally, improving ESG performance also significantly increases enterprises’ information transparency. It enhances market recognition of their environmental and social responsibility behaviors, thus indirectly promoting the optimization of regional energy efficiency.

At the level of economic effect analysis, when the corporate ESG behavior effectively promotes the improvement of local energy efficiency, this positive effect will be further fed back to the corporates themselves, which will increase their market value, enhance their risk-taking ability, and lay a more solid foundation for their long-term development. This finding strengthens the practical value of ESG investment concepts and provides an essential reference for policymakers, corporate management, and investors.

5.2. Recommendations

Based on the conclusions above, this paper puts forward relevant suggestions as follows. First, the synergy between corporate ESG strategies and regional green development planning must be strengthened. The government can formulate clear green development goals and targets and incorporate them into regional development plans, guiding enterprises to formulate ESG strategic plans according to their business characteristics. For example, relevant government agencies may plan to increase urban green coverage to 45% and achieve more than 300 days of good air quality by 2025. To this end, the government has set specific targets, such as the annual increase in green space and the reduction in industrial wastewater discharge. Additionally, supporting policies have been introduced to encourage enterprises to adopt clean energy and reduce carbon emissions. These goals and indicators clarify the direction of green development and provide a quantifiable action guide for the government, businesses, and citizens. Second, it enhances the quality and transparency of corporate ESG information disclosure. Establish a unified ESG information disclosure standard and evaluation system, standardize the preparation and release of corporate ESG reports, and ensure the comparability and credibility of the information. Meanwhile, corporate ESG information disclosure supervision and enforcement should be strengthened, and severe penalties should be imposed on violations to safeguard the market order and investors’ interests. Third, investors, as essential participants in the capital market, impact resource allocation, and corporate development through their investment behavior. Therefore, ESG education for investors should be strengthened by organizing ESG investment forums, seminars, and other activities to enhance investors’ knowledge and understanding of ESG concepts, guide them to pay attention to the environmental, social, and governance performance of enterprises, and incorporate ESG factors into investment decisions, which can effectively promote the improvement of ESG performance of enterprises, and thus promote the optimization of regional energy efficiency and the realization of sustainable development goals. Fourth, ESG policy guidance should be strengthened, and ESG incentive policies should be established. The government should introduce relevant policies to incentivize enterprises with outstanding ESG performance through tax reductions, subsidies, and other rewards, encouraging them to enhance their ESG practices. A dedicated ESG fund could also be established to support corporate initiatives related to energy conservation, emission reduction, and environmental protection. Fifth, improving the ESG regulatory framework. A comprehensive legal framework for ESG should be developed to clearly define corporate responsibilities in environmental protection, social responsibility, and corporate governance. Moreover, stricter penalties for non-compliant enterprises should be enforced to increase the cost of violations, thereby promoting corporate compliance and responsible business practices.

5.3. Limitations and Future Directions

5.3.1. Limitations

This study has certain limitations, firstly, data acquisition and accuracy. Corporate ESG (environmental, social, and governance) data collection faces numerous challenges globally. Geographic, industrial, and regulatory differences limit data collection, as there are significant disparities in the formulation of ESG standards, the construction of policy environments, and the level of economic development across countries and regions. This diversity makes it challenging to establish unified standards and methods for data collection and analysis, affecting the data’s comparability and accuracy. Particularly in China, although ESG concepts have gained increasing attention in recent years, the collection, organization, and disclosure of relevant data are still in their early stages. Compared with Western countries, China has considerable room for improvement in the standardization, transparency, and accessibility of ESG data. Therefore, when examining the relationship between corporate ESG performance and regional energy efficiency, this study primarily focuses on China, and the conclusions drawn are more applicable to the specific context of China.

Second is the complexity of the relationship between corporate ESG performance and regional energy efficiency. The relationship between corporate ESG performance and regional energy efficiency is not isolated but is influenced by various interconnected factors. These factors include, but are not limited to, technological advancements and policy orientation. Technological progress is one of the key drivers of energy efficiency improvement. As technology evolves, enterprises can adopt more efficient, energy-saving production technologies and equipment, thereby reducing energy consumption and emissions. This technological progress is closely related to a company’s research and development capabilities and innovation levels and is influenced by external technology transfer and diffusion. Policy orientation is also crucial in enhancing corporate ESG performance and energy efficiency. Governments can encourage companies to strengthen ESG management and improve energy utilization efficiency by enacting relevant laws, regulations, financial subsidies, and tax incentives. However, the effectiveness of policies is often influenced by factors such as the strength of policy enforcement, regulatory mechanisms, and the willingness of enterprises to comply.

5.3.2. Future Directions

The future research directions are as follows.

First, future research on the relationship between ESG performance and regional energy efficiency should deepen comparative studies across countries and regions. Given the significant differences in economic development, resource availability, policy environments, and cultural backgrounds worldwide, the relationship between ESG performance and energy efficiency may exhibit distinct characteristics in different regions. By conducting comparative analyses of various countries and regions, researchers can identify common patterns and uncover unique regional characteristics. For instance, in some countries where strict environmental regulations are enforced, ESG performance may have a stronger positive correlation with energy efficiency, as companies are incentivized to adopt sustainable practices. Conversely, in countries with pressing economic development needs, businesses may prioritize economic gains over ESG factors, potentially negatively affecting energy efficiency.

Second, future research should explore the impact of technological advancements, policy incentives, and other multidimensional factors on the relationship between ESG performance and regional energy efficiency. Technological advancements are a critical driver of energy efficiency improvements. With rapid technological progress, various high-efficiency and energy-saving production technologies and equipment continue to emerge. Adopting these innovations significantly reduces energy consumption and emissions during production, thereby enhancing energy efficiency. For example, advanced manufacturing processes and energy management systems can substantially lower corporate energy consumption while reducing greenhouse gas and pollutant emissions. This strengthens corporate ESG performance and contributes positively to regional energy efficiency. At the same time, policy guidance is crucial in shaping the relationship between ESG performance and energy efficiency. Governments implement environmental regulations, energy conservation policies, and tax incentives to encourage companies to improve ESG management and enhance energy utilization efficiency. These policies provide clear guidelines and regulatory frameworks, while economic incentives and penalty mechanisms drive companies to prioritize environmental sustainability. For example, governments can offer tax benefits and subsidies to incentivize businesses to adopt clean energy and energy-efficient technologies. Simultaneously, imposing fines or restrictions on high-energy-consuming industries helps curb energy waste and environmental pollution. By analyzing these multidimensional influencing factors, future research can provide a more comprehensive understanding of the relationship between ESG performance and regional energy efficiency, revealing the interaction mechanisms and impact magnitudes of different contributing factors.