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Article

Research on the Influence Mechanism of Environment Social Government Performance in State-Owned Enterprise Value: The Role of Digital Transformation

1
School of Management, Xi’an Jiaotong University City College, Xi’an 710018, China
2
Department of Accounting and Finance, School of Management, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(3), 928; https://doi.org/10.3390/su17030928
Submission received: 15 October 2024 / Revised: 9 December 2024 / Accepted: 30 December 2024 / Published: 23 January 2025
(This article belongs to the Special Issue Sustainable Digital Transformation and Corporate Practices)

Abstract

Under the background of sustainable development and high-quality development in China, it is of great significance to actively promote enterprises to practice ESG development concepts and digital transformation. This study uses a sample of state-owned companies in the Chinese capital market during the period from 2014 to 2020. The results of the research show that there is a U-shaped relationship between ESG performance and the value of state-owned enterprises. And digital transformation plays a positive moderating role in the relationship. The result of a further mechanism study indicates that ESG performance affects enterprise value by reducing the financing constraints. This study also examines the results using a dynamic panel model and endogeneity test method. Therefore, this study provides references to promote ESG practices and offers new insight into the role of digital transformation in state-owned companies. In order to pursue sustainable development and high-quality economic development, ESG practices and digital transformation should also be paid attention to in enterprises.

1. Introduction

ESG performance considers the environmental, social, and governance performance of companies. It follows a systematic and quantifiable implementation process and aligns with wider frameworks of economic, political, cultural, ecological, and social development. In recent years, in order to achieve sustainable and high-quality economic and social development, Chinese government departments have launched a series of policies aimed at practicing the concept of ESG development, hoping to enhance the economic value and social value of enterprises. The impact of ESG practices on creating value for companies is a highly debated topic among scholars. This is largely due to the rapid development of ESG practices, driven by increased attention from the Chinese government, capital markets, and society. The concept of ESG practices is widely used in enterprise management activities. ESG performance also aligns well with the objectives of achieving Carbon Neutrality and Carbon Emission Peak. The governments also make some policies to achieve sustainable and high-quality economic and social development.
Digital technologies such as big data, cloud computing, block chain, and artificial intelligence have promoted the digital economy. More and more companies are applying digital technologies to their daily operations. In order to obtain the goal of sustainable economic development, digital transformation also plays an important role in the process. And data have become a key factor of production for the new generation. Therefore, digital transformation is an important support for the high-quality development of enterprises.
State-owned enterprises are the most important part in promoting economic and societal progress. Compared with private enterprises, state-owned enterprises enjoy a dominant presence in many sectors. Because of China’s exceptional historical context, such entities are subject to more stringent governmental control than their privately owned counterparts [1]. The inefficiency of corporate governance in state-owned enterprises is attributed to political connections [2]. Following the enactment of the mixed-ownership reform, state-owned enterprises have integrated different forms of equity structure, including strategic investors, private capital, etc. The mixed ownership of state-owned enterprises can attain mutual balances among entities possessing diversified equity structures [3,4]. This has assisted in enhancing the level of corporate governance. Taking into consideration the above analysis, this paper examines the influence and mechanism of SOEs’ ESG performance on enterprise value. Further research examines the role of digital transformation in the relationship between ESG performance and enterprise value. Additionally, this study also tested the mechanism of ESG practices on enterprise value. Therefore, this research provides a theoretical basis about ESG practices and digital transformation in enterprises for the advancement of the economy under the background of high-quality development.
In recent years, many studies about ESG performance have been conducted. Some studies indicate that ESG performance has a positive effect on enterprise value. And some studies indicate that ESG performance has a negative effect on enterprise value. The studies have different results, especially about the role of digital transformation. Further studies have also indicated that property rights and enterprise size have important roles in the relationship. Most studies in the literature have found that ESG performance will promote the enterprise value. This paper believes that digital transformation plays a positive moderating role in the impact of ESG performance on enterprise value. The results indicate that enterprises with a high degree of digital transformation will have a stronger role in enhancing enterprise value. On the basis of examining the impact of ESG performance on enterprise value, this study further studied the moderating effect of digital transformation on the impact of ESG performance on enterprise value.
Previous research indicates that digital transformation has improved the enterprise value [5,6]. Recent studies have indicated that digital transformation can promote enterprise value through a more specialized division of labor, promoting business model innovation and some other aspects. Firstly, digital transformation has optimized the enterprise information acquisition channel through the dissemination of knowledge. Secondly, the introduction of digital equipment and technology has brought about the improvement of production efficiency and reductions in transaction costs. Digital transformation promotes the optimization of the company’s human capital structure. Finally, the availability of technology brought about by digital infrastructure has increased the innovation of enterprises’ business models and brought about subversive changes in production and operation models. Digital technology has combined enterprises and consumers together, which will be conducive to the innovative production and flexible customization of products. Therefore, based on the above analysis, it can be seen that the digital transformation of enterprises can improve the production scale and efficiency.
The research in this paper has made certain contributions in the following aspects: Firstly, compared with the existing literature, this paper constructs a theory analysis system of digital transformation, ESG performance, and enterprise value. Existing studies that have focused on ESG and the economic consequences of digital transformation have also found the relationship between them. However, this study comprehensively adopted the deduction and empirical research methods to explore how digital transformation plays a moderating role in the mechanism of ESG performance to enhance enterprise value. Therefore, this study conducted research on the impact of ESG performance on enterprise value from the new prospective of digital transformation. Secondly, this study enriches the discussion regarding the economic consequences of ESG practices and provides evidence to support a dynamic mechanism test of ESG practices for firms for an explanation of financing constraints.
The remainder of this paper is organized as follows: Section 2 reviews the prior literature; Section 3 introduces the methods and describes the data; and Section 4 discusses the results of the regression analysis and the moderating role of digital transformation in the impact of ESG performance on firm value. Section 5 discusses further mechanism tests and robustness checks. Section 6 discusses the results, and Section 7 concludes the paper. The technical roadmap for this article is shown in Figure 1.

2. Theoretical Analysis and Hypothesis Development

2.1. ESG Performance and Value Creation

Stakeholder theory asserts that a company’s activities have a strong link to its stakeholders, extending beyond shareholders and encompassing creditors, employees, customers, consumers, and government officials. The theory posits that the thriving of enterprises is unattainable without robust participation and support from stakeholders. Companies should place the maximization of shareholder interests on an equal footing with the overall interests of stakeholders. The pollution of the environment by companies, as well as their avoidance or inadequate corporate social responsibility and shortcomings in the corporate governance system have a direct impact on the interests of employees. These factors can lead to a decline in the value of the company, thereby hindering its long-term development. Furthermore, investors are increasingly focusing on the impact of environmental performance, social responsibility, and corporate governance on corporate value, going beyond traditional financial indicators. Broadly speaking, stakeholder theory also concludes that companies should make contributions to society and take social responsibility. The ESG system requires enterprises to rethink enterprise value. The ESG concept applied to corporate practices can expand the boundaries of financial reporting and re-examine the allocation of enterprise value.
Based on stakeholder theory, it can be challenging to consider all stakeholder groups when examining the company itself. Therefore, governmental oversight will offer important assurances for the implementation of the ESG system overall. In recent years, higher standards of green regulation have been formulated around the world to guide listed companies to take environmental sustainability and stakeholders into account. Among the G20 member countries, at least eight have incorporated ESG disclosure into their pension fund regulatory systems, and seven stock exchanges have successively issued listing guidelines for sustainable development. In addition, guidance at the organizational level of institutions such as industry associations also plays an important role. In this situation, a number of regulatory measures have also been formulated in China, and public attention to companies with excellent ESG performance is gradually increasing. Therefore, in order to be successful in the context of green and sustainable development, companies need to take all stakeholders into account.
The stakeholder theory also posits that the performance of a business is intricately linked to all stakeholders, encompassing lenders, proprietors, and government personnel. And the enterprise is the overall benefit maximized for all stakeholders. The external environment and corporate governance system will have an important impact on the long-term development, which will affect the overall interests of all stakeholders. Therefore, there is an increasing focus from stakeholders on the impact of ESG performance. Currently, the debate surrounding the function of ESG value creation remains inconclusive. And the research on their relationship is roughly divided into three competing perspectives: “positive”, “negative”, and “no correlation”.
The positive perspective suggests that ESG activities are closely related to corporate profitability [7,8,9,10]. They hold the view that there is a positive correlation between ESG ratings and corporate sustainability performance [11,12,13]. Optimizing management practices and enriching gender diversity on boards can increase corporate value [14,15]. First of all, the company’s information contains both financial and non-financial information. ESG refers to non-financial information that is difficult to disclose, which can not only alleviate the problem of information asymmetry between enterprises and investors but also transmit the signal of the good operation of enterprises to the outside world. Second, ESG performance can improve enterprise efficiency. To a large extent, human capital, management ability, and technical level can affect enterprise efficiency. Good ESG performance means that more attention is paid to the working environment of employees, which can bring employees a sense of belonging, improve employee enthusiasm, and improve overall production efficiency. For example, Giese et al. explored the relationship between ESG and short-term financial performance of companies using the standard discounted cash flow model, and the results showed that there was a positive correlation relationship. The study suggested that ESG should be included in policy benchmarking and financial analysis [16]. Dorfleitner et al. specifically divided the relevant activities of corporate social responsibility and matched them to the three dimensions of ESG in order to study the relationship between ESG performance and stock returns. The study found that the medium- and long-term returns with good ESG performance were significantly higher than those of other companies [17]. Ghoul S.E. et al. empirically analyzed the impact of ESG performance on firm value in 53 different countries. The study concluded that there was a significant positive impact, and the effect increased with the weakening of market mechanisms [9]. In addition, the results of Miralles Quirós et al. [18]., Yoon [19], and Wong et al. [20] also found that ESG performance had a significant positive impact on enterprise value. Friede et al. have shown that there is no negative correlation relationship between ESG and financial performance. The research indicated that higher ESG ratings are generally associated with higher operating performance and shareholder value [6]. Aouaid and Marsat conducted an empirical study using sample data from more than 4000 firms in 58 countries, and their findings also supported the conclusion that ESG performance is associated with greater enterprise value [21].
The negative perspective suggests that the value of enterprises with better ESG performance is lower [22,23]. Some scholars have found that the ultimate goal of an enterprise is to maximize the interests of its shareholders. Investing more toward environmental and social responsibility or other externalities may lead to more external costs, deplete corporate resources, impede the maximization of shareholder interests, and reduce the firm’s value [24,25]. Atan’s study selected listed companies in Malaysia as a sample for empirical analysis and found that there was no significant relationship between ESG performance and enterprise value [26]. In addition, Mardini provides international comprehensive empirical evidence by investigating the impact of ESG’s three factors separately as well as overall ESG disclosures’ effect on corporate financial performance [27].
Regarding the specific relationship between ESG and enterprise value, domestic and foreign experts have reached different research conclusions. Some scholars suggest that ESG and enterprise value are positively correlated, while others believe that there is no significant relationship between ESG components and enterprise value. According to the existing literature, we can include that scholars have reached inconsistent conclusions on the impact of environment, social responsibility, and corporate governance on corporate value within the ESG framework. Based on the above theoretical analysis, the following hypotheses are proposed:
Hypothesis 1.
There is a positive relationship between ESG performance and enterprise value.
Hypothesis 2.
There is a negative relationship between ESG performance and enterprise value.

2.2. Mechanism Analysis: A Moderating Role of Digital Transformation

Sustainable development theory holds that the sustainable development goals revolve around the realization of a harmonious relationship between man and nature, encompassing economic and social development as well as the allocation of environmental resources [28,29]. Governments are striving to achieve green sustainability goals both domestically and globally, while also ensuring rapid economic development. However, it is equally important to protect the limited resources and environment necessary for human survival. The pursuit of harmony between man and nature therefore requires the establishment of multi-level and multi-dimensional sustainable development goals that promote harmonious coexistence. In the ESG reporting framework, the ‘G’ (governance) does not refer to corporate governance in the traditional sense. Rather, it emphasizes the integration of environmental and social issues into governance systems, mechanisms, and decisions to avoid an excessive focus on economic issues at the expense of environmental and social concerns. Sustainability theories generally do not directly address specific corporate governance issues, but their policy recommendations typically call for governments or other institutions to pay attention to changes and innovations in institutional arrangements to ensure that governance addresses social, economic, and environmental sustainability issues through appropriate procedures and methods. In this sense, the G in the ESG report can be seen as a mechanism for implementing the policy recommendations of sustainability theory.
Based on the theory of sustainable development, ESG performance will convey the company’s superior ESG performance to stakeholders and provide references for stakeholders’ ESG investments [30]. Based on signal transmission theory, the good ESG performance of an enterprise conveys a positive signal of the enterprise’s sustainable development ability and sustainable operation. This also reduces the degree of information asymmetry between enterprises and stakeholders and helps stakeholders to have a more comprehensive understanding of an enterprise’s operating conditions. Therefore, the good ESG performance of enterprises will ease financing constraints and reduce financing costs. Additionally, digital transformation improves the quality, authenticity, and timeliness of ESG information disclosure, which helps stakeholders to grasp corporate information in a timely and comprehensive manner and ease corporate financing constraints. The digital transformation of enterprises improves the way investors obtain corporate information and promotes the transparency of ESG performance information disclosure. The use of ESG concepts in enterprises retains more long-term investors. Digital transformation strengthens the authenticity of enterprise ESG information and further enhances the non-financial information and financial information of enterprises, reducing the financing cost of enterprises and improving the financing ability of enterprises.
Firstly, the positive demonstration of superior ESG performance can effectively convey the company’s commitment to stable operations and long-term growth, thereby enhancing transparency and ultimately elevating the value of state-owned enterprises [31]. Secondly, the disclosure of ESG performance can attract the attention of institutional investors, consequently leading to an increase in their shareholdings. Institutional investors as market vane can convey positive signals to the capital market, helping to build the company’s reputation and increasing the capital market’s confidence in the company. Not only can this improve the risk resilience of companies, but it can also have a positive impact on enterprise value. Therefore, the following are assumed:
Hypothesis 3.
Digital transformation has a positive moderating effect on corporate ESG performance to mitigate financing constraints.
Hypothesis 4.
Digital transformation has a positive moderating effect on the ESG performance of enterprises to increase the shareholding of institutional investors.

3. Research Design

3.1. Sample and Data

The selection of the sample in this study was based on the following reasons. In 2013, the mixed-ownership economy was highlighted and attracted widespread attention. Subsequently, various provinces and regions across the country issued a series of relevant policies to further promote the continuous development of mixed-ownership reform. Therefore, this paper mainly selects the non-financial listed companies’ data of A-share capital market from 2014 to 2020 in the CSMAR database as a sample. Based on the above sample data, the following processing was carried out: (1) the data of the financial industry and the real estate industry were excluded; (2) companies that were treated by ST, *ST, and PT during the sample period were excluded; and (3) samples with missing data were removed. The ESG rating data in this article came from the Wind HuaZheng ESG database, which was developed by the HuaZheng Index Information Service Company. The digital transformation data draw on the research methods of previous scholars. This study used Excel and Stata 15.0 to complete empirical tests.

3.2. Main Variables

3.2.1. Dependent Variable

The dependent variable in this research was enterprise value, which is measured using Tobin’s Q. Tobin’s Q is calculated by dividing the market value of the total assets of the enterprise by the book value of the total assets of the enterprise. The market value of total assets is determined by the sum of the market value shares and the market value of liabilities. Tobin’s Q is used to comprehensively evaluate various factors such as the company’s operations, investments, and profitability in order to reflect investors’ forecasts of the company’s expected profitability in the future. This comprehensive valuation index takes into account both non-financial and financial factors and not only includes the performance generated by the enterprise in the past but also reflects the performance in the future.

3.2.2. Independent Variable

ESG performance is measured as an independent variable, which is very different from the traditional financial index system. ESG mainly considers the impact of non-financial index factors on corporate value, mainly manifested in the company’s environmental, social responsibility performance, etc. Since these indicators cannot provide a clear and intuitive assessment of company-like financial indicators, it is easy for both investors and the company to ignore the effects of these factors. The index data were taken from the ESG index system of the HuaZheng database. The index scores were assigned from 1 to 9 according to the nine ratings from C to AAA. The ESG data of C-rated companies were assigned 1 point, CC-rated companies were assigned 2 points, CCC-rated companies were assigned 3 points, and so on. In total, 1–9 ESG indicators were generated.

3.2.3. Moderating Variable

Digital transformation is the moderating variable. Based on the practice of Wu Fei et al. [32], this study took the natural logarithm of the sum of five dimensions of word frequency in the text information of the company’s annual report as the digital transformation of the enterprise.

3.2.4. Control Variables

The control variables in the model included Size, Boardsize, Lev, and ROA. Size represents the size of the company, while Leverage represents the degree of leverage in the company’s capital structure. The Boardsize variable represents the size of the board. ROA represents the total return on assets of the enterprise. The research model sets control variables for years and industries. The indicators and measurements adopted are shown in Table 1.

3.3. Empirical Models

Based on the above analysis, a benchmark model was constructed to examine the impact of listed companies’ ESG performance on corporate value. The model is based on the research of Bebchuk and Cohen et al. [33].
Tobin’s Qi,t = α0 + α1ESGi,t + α2ESG2i,t + α3Controlsi,t + ∑Industry + ∑Year + εi,t
In the equation above, i represents the number of enterprises, t represents the year, and controls represents control variables.
This article draws on the practice of Liu Jingjun [34] to measure the proportion of institutional investors’ shareholding with the ratio of institutional investors’ shareholding to the total value of shares in circulation and adopts the SA index as an indicator to measure the degree of financial constraint of firms.
In order to investigate the relationship between ESG, digital transformation, and enterprise value, this study constructed model (2) to test the moderating effect of digital transformation on the relationship between ESG and enterprise value.
Tobin’s Qi,t = α0 + α1ESGi,t + α2DT + α3ESGi,t × DT + α4Controlsi,t + ∑Industry + ∑Year + εi,t

4. Empirical Results

4.1. Descriptive Statistics

Table 2 provides the descriptive statistics for the variables. The maximum enterprise value observed is 56.66, while the minimum enterprise value is 0.68. On average, the enterprise value is 1.87. The standard deviation, which is 1.82, indicates a significant variation in the sample data of the selected state-owned enterprise-listed companies. This variation reflects a significant difference in the level of development among the SOEs. The average level of ESG performance is 4.236, with a standard deviation of 1.139. The results indicate that there are significant differences in ESG performance levels among companies. The differences can be attributed to differences in the awareness and understanding of ESG performance among these companies. Among control variables such as board size, company size, ownership concentration, and profitability, the standard deviation of ownership structure is the largest, followed by company size. The remaining control variables have relatively small differences across the sample.

4.2. Correlation Analysis

To examine the correlation between the variables selected in the model, this study conducted Pearson correlation and Spearman correlation tests on the relevant variables in the model. The test results are presented in Table 3 and Table 4 below.
Table 3 reports the cross-correlations of the variables. The results show a significant correlation between the enterprise value and the ESG performance at the level of 1%. The correlation test results indicate that ESG performance affects the value of state-owned enterprises. In addition, the remaining control variables of equity concentration, board size, company size, and financial leverage all pass the statistical test of significance at the 1% level. The profitability index passes the statistical test at the 5% level.
Table 4 reveals a relationship between enterprise value and ESG performance. The result is significant at the 1% level. The results of the Spearman correlation test also show that ESG performance affects the enterprise value of state-owned enterprises. In addition, the remaining control variables of equity concentration, board size, company size, and financial leverage all pass the statistical test of significance at the 1% level. And the profitability index passes the statistical test at the 5% level. Additionally, the coefficient level between the individual variables is below 0.4. Therefore, the model does not suffer from serious multi-collinearity problems.

4.3. Regression Analysis

4.3.1. Baseline Regression Analysis

The results of the regression analysis are shown in Table 5.
Table 5 presents the results of the regression analysis, examining the relationship between ESG performance and enterprise value. In column (1), the regression coefficient between ESG performance and enterprise value is significantly negative (t = −3.48) at the 1% significance level without incorporating additional control variables. Moreover, the coefficient on the quadratic term of enterprise value is significantly positive (t = 1.65) at the 10% level significance, suggesting a U-shaped relationship between the two variables. Column (2) presents the regression results after incorporating other control variables. The results indicate that the primary term of ESG on enterprise value is significantly negative at the 1% significance level (t = −5.12), while the quadratic term coefficients on enterprise value are significantly positive (t = 5.01) at the 1% level of significance. This confirms the existence of a U-shaped relationship between ESG performance and enterprise value.
To test the relationship between the independent variable and the dependent variable, most studies usually incorporate the quadratic of the independent variable in the model. According to the research of relevant scholars, there are still many loopholes in the testing method, and there may be a U-shaped relationship between the misestimated variables [35]. To further validate the conclusions, this study checked whether the U-shaped relationship satisfies the following two points. Firstly, the left endpoint of the independent variable has a negative slope, while the right endpoint has a positive slope. Secondly, it is important to determine whether the extreme point of the U-shaped relationship falls within the range of the independent variable in the confidence interval. The results are shown in Table 6 below.
The results show that the model demonstrates a negative slope of −0.3976 at the left endpoint and a positive slope of 0.4613 at the right endpoint. Both slopes are statistically significant at the 1% level. This is consistent with the change in the endpoints from negative to positive within the U-shaped relationship interval. Additionally, the extreme value point of the ‘U’ curve is 4.2404, with a 95% confidence interval of 3.8886 to 4.6607. The extreme point falls within the confidence interval and therefore satisfies both of the above conditions. Therefore, the existence of a U-shaped relationship between the ESG performance and enterprise value of state-owned companies is further confirmed. The result of the relationship is shown below.
Figure 2 illustrates a U-shaped relationship between ESG performance and enterprise value. The results indicate that the impact of ESG performance on enterprise value can be divided into two stages: short-term and long-term. In the short term, the value of state-owned enterprises tends to decline as the level of ESG performance increases. However, when the ESG performance level reaches its lowest point of 4.2404, the value of state-owned enterprises gradually starts to increase with the rise in the ESG performance level. The relationship between ESG performance and enterprise value is generally characterized by a non-linear trend. It starts with a downward trend and then transitions to an upward trend, rather than a simple linear positive or negative correlation. Based on the previous descriptive statistical results, the average ESG performance of China’s state-owned enterprises is 4.246, which is higher than the lowest point of 4.2404. These results indicate that the enterprise value of sample companies with ESG performance exceeding the average will continue to rise as their ESG performance levels increase. Hypotheses H1 and H2 are established based on different time frame structures.

4.3.2. Regression Analysis of Regulatory Effect

The previous literature studies have shown that digital transformation can enable enterprises to make ESG-related products more intelligent and improve the quality of corporate information disclosure. Therefore, this paper believes that digital transformation will have a positive moderating role in the relationship between ESG performance and enterprise value. The results are shown in Table 7 below.
In Table 7, the interaction item of ESG and digital transformation is introduced into Equation (1) to investigate the relationship between digital transformation and ESG performance and enterprise value. The results show that the coefficient of ESG × DT is significant and positive, indicating that digital transformation positively regulates the relationship between ESG performance and enterprise value.

5. Additional Analysis and Robustness Checks

5.1. Further Mechanism Test

In order to further analyze the mechanism of ESG performance on the value of state-owned enterprises, this paper proposes a mediation effect test model. As shown below, model (3) is used to test the effect of ESG performance on enterprise value, model (4) is used to test the impact of ESG performance on the intermediary variable, and model (5) incorporates the core independent variable and the intermediary variable into the model for analysis. At the same time, according to the previous study of Wen Zhonglin et al. [36], the three-step method is used to test the mediation effect, in which MV represents the mediating variable. If coefficient β3 is significant and β2 is insignificant, it is a complete mediation effect. However, if coefficients β2 and β3 are significant, it is a partial mediation effect. If coefficient β3 is not significant, the mediation effect is not established. The models for testing the mediation effect are as follows:
Tobin’s Qi,t = α0 + α1ESGi,t + α2ESG2i,t + ∑Industry + ∑Year + εi,t
MVi,t = γ0 + γ1ESGi,t + γ2 ESG2i,t + γ3 Controls + ∑Industry + ∑Year + εi,t
Tobin’s Qi,t = β0 + β1ESGi,t + β2ESG2i,t + β3MVi,t + ∑Industry + ∑Year + εi,t

5.1.1. Financing Constraints

In order to investigate the impact of ESG performance on enterprise value through the financing constraint effect, this study adopted research methods used by previous scholars to avoid endogenous issues. The SA index is used as an intermediate variable to assess the extent of corporate financing constraints. The SA index is based on the financial report of the enterprise, and only uses the enterprise age variable and enterprise size variable. Given that the two variables are strongly exogenous, most scholars use this index as an indicator to measure the degree of financing constraints. Table 8 below presents the results of the mechanism test using the mediation effect. The SA indicator indicates the degree of financing constraints for the company. A lower SA indicator and a higher absolute value indicate a higher level of constraint. Conversely, if the SA indicator is the same, it means that the level of the financing constraint is similar.
As can be seen from Table 8 above, column (1) shows that the estimation coefficient of ESG performance is significantly positive at the 1% level, and column (2) shows that the coefficient is significantly positive at the 1% level, indicating that enterprises with superior ESG performance are more likely to reduce the level of corporate financing constraints on enterprises. Column (3) shows that the estimated coefficient of ESG performance is significantly positive at the 1% level, and the estimated coefficient of the SA index is significantly positive at the 1% level. Therefore, the mediation effect is significant, and the impact plays a partial mediating role.

5.1.2. Institutional Investors

In this study, we adopted the approach used by Liu Jingjun et al. (2012) [34] to measure the shareholding ratio of institutional investors for the intermediation effect test analysis. This is carried out by calculating the sum of the outstanding shares held by all institutional investors.
According to Table 8, column (4) reveals that the estimation coefficient of ESG performance is significantly positive at the 1% level. Additionally, column (5) examines the impact of ESG performance on the shareholding ratio of institutional investors using an intermediate variable and shows that the estimation coefficient variable is not statistically significant, indicating that enterprises with superior ESG performance do not affect the shareholding ratio of institutional investors. Column (6) shows that the estimation coefficient of ESG performance is significantly positive at the 1% level, and the estimated coefficient of the institutional investor shareholding variable is significantly positive at the 1% level. Therefore, the mediation effect is not present.
In order to further verify the mechanism of the influence of institutional investors’ shareholding, the bootstrap test is used to test the intermediary effect. The test results are shown in Table 9.
Table 9 reveals that the bootstrap test results indicate no empirical recognition of the mediation effect. And the results fails to pass the significance statistical test, indicating that the increase in ESG performance has not increased the value of state-owned enterprises by affecting the shareholding ratio of external institutional investors. In summary, the mechanism of ESG performance on enterprise value is the mitigation of financing constraints on state-owned enterprises, rather than the shareholding ratio of external institutional investors. And the test of this mechanism further confirms the implementation path of ESG performance.
Based on the above theoretical analysis, digital transformation may positively regulate the two mechanisms of enterprise ESG performance to enhance enterprise value. The moderating effect of digital transformation is mainly reflected in the impact of ESG performance on enterprise value, which is specifically manifested in mitigating financing constraints. The empirical results obtained using model (2) are shown below.
Table 10 shows that digital transformation improves the quality, authenticity, and timeliness of ESG information disclosure and helps stakeholders to grasp corporate information in a timely and comprehensive manner, thus alleviating corporate financing constraints. In conclusion, hypothesis H3 proposed in this paper is verified.

5.2. Robustness Checks

  • Instrumental variable test. In order to address potential endogenous issues such as reverse causation and enhance the reliability of the study’s findings, this study used the lagged ESG performance data as an instrumental variable to re-test endogenous problems. The results are presented in Table 11 below.
Table 11 shows that the results show that the coefficients of the instrumental variable are significant at the 1% significance level in columns (1) and (2). The regression results in both cases are consistent with those in Table 5 of the main regression results. Hence, the outcomes of the robustness tests yet again display a “U”-shaped correlation, and the regression outcomes are relatively consistent.
  • Replacing the dependent variable. According to the above analysis, Tobin’s Q is analyzed as the dependent variable. In order to re-verify the results of the empirical test and to make the results more robust, the price-to-book ratio (P/B) is chosen to replace the former dependent variables for the re-analysis. The results are shown in Table 12.
Table 12 illustrates that the quadratic term of the estimation coefficient of the ESG performance remains significantly positive at a level of 1% in column (1) before including other control variables. After incorporating the control variable in column (2), the ESG performance quadratic term estimation variable coefficient remains significantly positive at a level of 1%, which is consistent with the results of the regression analysis discussed above. Thus, by updating the model, the test generates more robust outcomes.

6. Discussion

This paper objectively analyses the impact and mechanism of ESG performance on enterprise value. The enterprise digital transformation represented by artificial intelligence and big data provides technical support for enhancing enterprise ESG performance and enhancing enterprise value. The findings demonstrate a U-shaped correlation between ESG performance and the enterprise value of SOEs. Specifically, a negative relationship is evident when ESG performance falls below the extreme point, while positive correlation is observed above it. The results also show that digital transformation has a positive moderating effect on ESG performance to mitigate financing constraints rather than institutional investors.
The research results show that ESG performance has a positive effect on the value of the firm. Based on these findings, this study makes the following theoretical contributions. Firstly, this study contributes to the research on how ESG performance influences the value of state-owned enterprises. This study demonstrates that digital transformation has a positive moderating effect on ESG performance by mitigating financing constraints. This study presents empirical evidence on the consequences and mechanism of corporate ESG performance, offering valuable insights for companies and investors to focus on ESG performance. Secondly, the discussion based on the empirical research results can provide a theoretical basis for the high-quality development of state-owned enterprises. At present, the reform of state-owned enterprises is in a critical period. The research can further guide the practical work of the mixed ownership reform of state-owned enterprises and contribute to the high-quality economic development of state-owned enterprises. Thirdly, the results of this study support the need for enterprises to implement ESG practices and digital transformation. The findings can contribute to the theoretical basis for sustainable economic and social development. Under the background of Carbon Neutrality and Carbon Emission Peak in China, ESG practices and ESG investment have been a major part in global market. This research also provides some experiences and evidence for international ESG scholars and prompts global sustainable development.
Despite the theoretical and practical contributions of this paper, it has certain limitations. Firstly, the sample selection was limited to state-owned enterprises of A-share listed companies in China’s capital market, ignoring unlisted companies and excluding incomplete data samples, which could potentially impact the results. In light of ongoing research on sustainable development, the ESG research in China is currently in its developmental stage and can be further enhanced in both range and scope in the years to come. Despite these constraints, this research significantly contributes to the body of knowledge exploring the link between ESG performance and enterprise value. Taking state-owned enterprises as samples, this study discusses the regulatory role of state-owned enterprises’ digital transformation in ESG economic consequences, which can provide certain theoretical guidance for the digital transformation of state-owned enterprises. Future research can be further extended to non-state-owned enterprises to provide recommendations for digital transformation and ESG practices in non-state-owned enterprises. We hope this research will encourage further investigation into the ESG performance and digital transformation of unlisted, non-state-owned enterprises, etc. Such exploration will promote enterprise ESG improvement and the achievement of sustainable development.

7. Conclusions

Developing a sustainable economy and society is an inevitable way for China to achieve economic and social transformation, as well as an inevitable requirement for promoting high-quality development. State-owned enterprises are the lifeblood of the national economy and play an important role in achieving sustainable economic and social development. This paper examines the impact and mechanism of ESG performance on enterprise value and provides a theoretical basis for the role of digital transformation in ESG performance in enhancing enterprise value. Therefore, the following suggestions are put forward.
To achieve sustainable economic and social development, it is essential that relevant government departments at the national level provide policy support. The ESG system’s impact shall broaden through the introduction of preferential policies for enterprises with superior ESG performance in terms of taxation and credit. This will be complemented by incentives such as recognizing or rewarding superior ESG performance and implementing corresponding penalty systems. To promote the transition of enterprises toward green and sustainable development, pertinent government regulators may reinforce oversight and the promotion of corporations’ ESG systems, steadily escalate information dissemination, and bolster the ESG system’s influence.
As the external policy and regulatory environment changes, it is important for companies to proactively respond to the impact of legislation and regulation in line with the concept of sustainable development. A company’s overall value is closely tied to its performance in ESG factors. Thus, companies must implement ESG strategies from the beginning, fulfill ESG responsibilities and obligations, take responsibility for relevant social matters, and integrate sustainable green development theory into daily operations to achieve green and sustainable development.
At the same time, enterprises should accelerate the process of digital transformation in marketing, management, production, operation, and other aspects and strengthen the construction of information infrastructure, to achieve efficient transmission and communication of information. On the basis of digital transformation, future studies should fully explore the innovation of various production processes and business methods, thereby increasing the value of enterprises.

Author Contributions

Conceptualization, J.Z.; Methodology, J.J.; Investigation, J.J.; Resources, J.J.; Writing—original draft, J.J.; Writing—review & editing, W.Q.; Supervision, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 72072143). This research was funded by the Major Projects of the National Social Science Fund in 2023 (Grant No. 23&ZD092).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Technology roadmap.
Figure 1. Technology roadmap.
Sustainability 17 00928 g001
Figure 2. ESG performance and enterprise value relationship map.
Figure 2. ESG performance and enterprise value relationship map.
Sustainability 17 00928 g002
Table 1. Variables.
Table 1. Variables.
VariablesMeasurement
Dependent VariableTobin’s QIt represents the corporation’s value.
Independent VariableESGIt represents the corporation’s ESG performance.
Mechanism VariablesDTIt is reflected by the digital transformation index.
SA 1It represents the corporation’s financing constraint level.
Control VariablesIOIt uses the shareholding ratio of institutional investors.
OwnerstateIt represents the corporation’s ownership concentration.
BoardsizeLogarithm of the total number of board members.
SizeLogarithm of the total number of total assets.
LevThe ratio of total liabilities to total assets.
ROAThe ratio of total profits to total assets.
IndustryIn this year, the value is 1; otherwise, it is 0.
YearIf the firm is in this industry, the value is 1; otherwise, it is 0.
1 SA Index = −0.737 × Size + 0.043 × Size2 − 0.040 × Age. Note: ESG performance is from the HuaZheng ESG Composite Index of Listed Companies from China.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariablesTobin’s QESGBoardsizeDTSizeOwner StateROALev
mean1.8714.2462.1907.04023.00338.7540.0440.508
max56.6648.0002.9969828.54389.091.6131.108
min0.6851.0001.099018.3703.622−7.2200.063
median4.00012.197222.86937.7520.0600.510
SD1.8281.1390.19815.3251.42014.7820.2380.197
Table 3. Pearson correlation analysis.
Table 3. Pearson correlation analysis.
VariablesTobin’s QESGOwner State Boardsize SizeLevROA
Tobin’s Q1
ESG−0.127 ***1
Owner state−0.095 ***0.119 ***1
Boardsize−0.072 ***−0.002−0.031 ***1
Size−0.392 ***0.311 ***0.276 ***0.169 ***1
Lev−0.180 ***−0.009−0.0160.042 ***0.322 ***1
ROA−0.033 **0.145 ***0.069 ***0.030 **0.107 ***−0.064 ***1
Note: ** p < 0.05, *** p < 0.01.
Table 4. Spearman correlation analysis.
Table 4. Spearman correlation analysis.
VariablesTobin’s QESGOwner State Boardsize SizeLevROA
Tobin’s Q1
ESG−0.1427 ***1
Owner state−0.1348 ***0.1235 ***1
Boardsize−0.0665 ***0.0058−0.0280 **1
Size−0.6337 ***0.2958 ***0.2612 ***0.1806 ***1
Lev−0.3281 ***−0.007−0.00720.0415 ***0.3403 ***1
ROA0.0444 **0.2362 ***0.1724 ***0.0464 ***0.2263 ***−0.0248 *1
Note: * p < 0.1, ** p < 0.05, *** p < 0.01.
Table 5. Regression analysis results.
Table 5. Regression analysis results.
Variables(1) Tobin’s Q(2) Tobin’s Q
ESG−0.5116 ***
(−4.73)
−0.6126 ***
(−6.07)
ESG20.0415 ***
(3.16)
0.0757 ***
(6.17)
Owner state 0.0002
(0.14)
Boardsize 0.0044
(0.04)
Size −0.4778 ***
(−23.71)
Lev −0.7590 ***
(−5.82)
ROA 0.0735
(0.76)
_cons3.071 ***
(13.75)
14.1472 ***
(27.63)
Industry FEYesYes
Year FEYesYes
F20.2645.82
Observations52695269
Note: *** p < 0.01. The numbers in parentheses represent T-values. The larger the T-values, the more significant it is.
Table 6. U-shaped relationship test results.
Table 6. U-shaped relationship test results.
Lower BoundUpper Bound
Interval18
Slope−0.39760.4613
t-Value−5.11054.6546
p > |t|00
Extreme Point4.2404
Bound Point3.88864.6607
Observations5269
Table 7. Moderating effect test results.
Table 7. Moderating effect test results.
(1) Tobin’s Q (2) Tobin’s Q
ESG−0.2006 ***
(−8.18)
−0.0278
(−1.14)
DT−0.0088
(−1.32)
−0.0034
(−0.55)
ESG × DT 0.0022 *
(1.75)
Control Variables Yes
F19.4243.59
Industry FEYesYes
Year FEYesYes
Observations52695269
Note: * p < 0.1, *** p < 0.01. The numbers in parentheses represent T-values. The larger the T-values, the more significant it is.
Table 8. Mechanism test results.
Table 8. Mechanism test results.
VariablesFinancing Constraint Institutional Investors
(1) Tobin’s Q(2) SA(3) Tobin’s Q(4) Tobin’s Q(5) IO(6) Tobin’s Q
ESG−0.6126 ***
(−6.07)
−0.0578 ***
(−4.19)
−0.5242 ***
(−5.31)
−0.6126 ***
(−6.07)
−0.1088
(−0.15)
−0.6038 ***
(−6.03)
ESG20.0757 ***
(6.17)
0.0087 ***
(5.24)
0.0623 ***
(5.17)
0.0757 ***
(6.17)
0.0614
(−0.71)
0.0743 ***
(6.10)
Med 1.5290 ***
(15.47)
0.0192 ***
(9.85)
_cons14.1472 ***
(27.63)
−6.058 ***
(−86.52)
23.4102 ***
(30.00)
14.1472 ***
(27.63)
−73.873 ***
(−20.42)
15.4814 ***
(29.31)
F45.82100.88 45.82207.5948.28
Industry FEYesYes YesYesYes
Year FEYesYes YesYesYes
Observations526952695269526952085208
Note: *** p < 0.01, t statistics in parentheses.
Table 9. Bootstrap test results.
Table 9. Bootstrap test results.
VariablesnBootstrap Std. Err.p > |z|Normal-Based [95% Conf. Interval]
_bs_152690.00280.613−0.00414570.0070309
_bs_252690.01960.339 −0.05722810.0196769
Table 10. Regression analysis of regulatory mechanism results.
Table 10. Regression analysis of regulatory mechanism results.
(1) SA
ESG0.0089 ***
(2.71)
DT−0.0038 ***
(−4.51)
ESG × DT0.0006 ***
(3.56)
Control variablesYes
F97.69
Industry FEYes
Year FEYes
Observations5269
Note: *** p < 0.01, t statistics in parentheses.
Table 11. Endogeneity test.
Table 11. Endogeneity test.
(1) Tobin’s Q(2) Tobin’s Q
IV−0.725 ***
(−5.46)
−0.820 ***
(−6.59)
IV20.067 ***
(4.15)
0.100 ***
(6.58)
Control variables Yes
_cons3.649 ***
(10.80)
14.207
(23.36)
F16.4134.94
Industry FEYesYes
Year FEYesYes
Note: *** p < 0.01, t statistics in parentheses.
Table 12. Robustness test.
Table 12. Robustness test.
Variables(1) P/B(2) P/B
ESG−2.481 ***
(−4.76)
−2.340 ***
(−4.63)
ESG20.201 ***
(3.17)
0.268 ***
(4.35)
Control variables Yes
Industry FEYesYes
Industry FEYesYes
_cons8.566
(1.44)
34.316 ***
(5.56)
n52695269
Note: *** p < 0.01, t statistics in parentheses.
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Ji, J.; Zhang, J.; Qu, W. Research on the Influence Mechanism of Environment Social Government Performance in State-Owned Enterprise Value: The Role of Digital Transformation. Sustainability 2025, 17, 928. https://doi.org/10.3390/su17030928

AMA Style

Ji J, Zhang J, Qu W. Research on the Influence Mechanism of Environment Social Government Performance in State-Owned Enterprise Value: The Role of Digital Transformation. Sustainability. 2025; 17(3):928. https://doi.org/10.3390/su17030928

Chicago/Turabian Style

Ji, Jiunan, Junrui Zhang, and Wen Qu. 2025. "Research on the Influence Mechanism of Environment Social Government Performance in State-Owned Enterprise Value: The Role of Digital Transformation" Sustainability 17, no. 3: 928. https://doi.org/10.3390/su17030928

APA Style

Ji, J., Zhang, J., & Qu, W. (2025). Research on the Influence Mechanism of Environment Social Government Performance in State-Owned Enterprise Value: The Role of Digital Transformation. Sustainability, 17(3), 928. https://doi.org/10.3390/su17030928

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