Does Digital Finance Improve Corporate ESG Performance? An Intermediary Role Based on Financing Constraints (2024)

1. Introduction

In a macro context, in which the economy and society are gradually moving towards high-quality development, the fulfilment of social responsibility is an important strategic objective at the enterprise level in order to achieve high-quality development and meet the requirements of sustainable development. Meanwhile, a good commitment to social responsibility is also an important way to achieve sustainable development goals at the corporate level. Scholars have also pointed out that a greater degree of corporate social responsibility would be beneficial for green development (Xue, Y. et al., 2022; Mbanyele et al., 2022) [1,2]. On a practical level, ESG is an important non-financial evaluation indicator reflecting the fulfilment of corporate social responsibility, which aims to facilitate the transition from the single objective of profit maximization to multiple objectives of environmental protection and social responsibility; thus, it aims to achieve sustainable development practices at the corporate level (Shalhoob, H. et al., 2023) [3]. Because green investment has received widespread attention from the capital markets, it is becoming increasingly common for investment institutions to start looking at ways to better assess corporate ESG profiles and integrate ESG information into investment decisions.

At the same time, in the process of the development of emerging technologies such as big data, blockchain and the Internet of Things, various types of financial services have been given wider access, traditional financial services have been deeply integrated with digital technologies, and new models of digital finance have emerged, which provides an essential vehicle for the development of individual subjects. Digital finance covers almost all traditional types of financial business in China and enables accurate access to and analysis of market behavior at a lower cost while reducing information asymmetry to a certain extent (Zhang, L. et al., 2023) [4]. From the available studies, research on digital finance has now covered technological innovation (Ma, F. et al., 2023) [5] and firm growth, and the relationship with financing constraints (Dai, D. et al., 2023) [6], risk-taking and other related behaviors has been partly discussed in academia; thus, the impact of digital finance development on the behavior of some firms is supported by a considerable amount empirical evidence. From the relevant studies on corporate ESG performance, scholars have mainly focused on the study of the economic consequences of corporate ESG performance, such as corporate performance (Aqabna, S.M et al., 2023) [7] and managerial behavior (Jia, F. et al., 2022) [8], while few scholars have studied the factors influencing corporate ESG performance, such as the degree of corporate competition (Chang, Y.J. et al., 2023) [9] and CEO traits (Zhao, Y. et al., 2023) [10]. Thus, it is easy to see from the reality and research results that there is still some need for research on the impact mechanisms of digital finance development on CSR performance.

In view of this, in order to deeply explore the impact and mechanism of the level of digital finance development in China on corporate ESG performance, this paper establishes an econometric research model using data from Chinese A-share listed companies from 2011–2021 to empirically test the positive effect of digital finance development on corporate ESG performance in China. We further delve into the mediating effect played by financing constraints and analyze the heterogeneity of corporate performance under different property rights’ nature and regional economic development levels. Results show that the level of digital finance development in China has a significant positive effect on corporate ESG performance, while financing constraints play a mediating role in this path, i.e., the development of digital finance induces companies to better fulfill their social responsibility by alleviating their financing constraints, which can lead to better ESG performance. Additionally, in the heterogeneity analysis, we found that the effect of the level of digital finance development on corporate ESG performance is more significant and heterogeneous among non-state enterprises and enterprises in the central and western regions. Therefore, in practice, it is necessary to implement differentiated policies for enterprises in different regions and the nature of property rights in order to maximize the role of digital finance in promoting corporate ESG performance.

The possible marginal contributions of this paper are: (1) most of the existing academic studies on corporate ESG performance focus on its economic consequences, while there is still much room for research on the antecedent factors affecting corporate ESG performance; this paper aims to improve the research on the antecedent factors affecting corporate ESG performance in the process of empirical testing and mechanism analysis at the micro level; (2) exploring the impact of China’s digital finance development level on CSR performance, especially ESG performance, which can enrich the existing literature on digital finance and ESG; (3) based on a series of theoretical analyses and empirical studies, this paper provides a more comprehensive theoretical basis and more prudent suggestions at the practical level for the three major stakeholders.

In summary, this paper not only enriches the theoretical research related to digital finance and corporate ESG performance to a certain extent, but also provides some empirical support for digital finance to play a role in promoting the sustainable development of the real economy.

The rest of the paper is structured as follows. The second part is a review of the literature on digital finance and corporate ESG performance; additionally, we include related theoretical analysis, upon which the basis of the research hypothesis of this paper is presented. The third part presents the research design. The fourth part addresses the endogeneity issues and focuses on the analysis of the empirical results, as well as the robustness tests. The fifth part presents the heterogeneity analysis. Finally, on the basis of the conclusions drawn from this study, relevant policy recommendations are made, while the remaining limitations of this study and the outlook for future research are described.

2. Literature Review and Hypotheses

Environmental, social and governance (ESG) issues are the cornerstone of sustainable development, a concept developed from ethical and responsible investment and enriched and extended by corporate social responsibility (CSR), which is now a core framework for corporate sustainability that has not yet been defined in academic terms. The concept of ESG was first introduced by the United Nations Global Compact in 2004 by integrating the three dimensions of ESG. At the practical level, listed companies publish ESG reports based on their regulatory framework, and professional third-party assessment agencies carry out ESG ratings through ESG performance. Meanwhile, investment institutions in the capital market complete their ESG investment decisions based on ESG ratings. ESG is not only a new path for sustainable human development, but also a new perspective for understanding the opportunities and risks faced by companies. In terms of literature studies, academics have also analyzed them from different perspectives. Much of the existing research has focused on the ESG performance of economic consequences of ESG performance, often focusing on the micro level of the firm. Generally, much of the literature has examined the impact of ESG performance on corporate financial performance and firm value, with Mackey et al., (2007) [11] using the KLD database and focusing on environmental (E) and social (S) dimensions, arguing that good ESG performance can significantly enhance firm value. Similarly, Flammer et al., (2019) [12] examined the effectiveness and impact of incorporating environmental and social performance criteria into executive compensation. The study found that an increased focus on ESG performance not only mitigated short-sightedness but also significantly increased firm performance and firm value. In addition, there is a relationship between ESG performance and corporate financing. Lavin et al., (2022) [13] found that firms with superior ESG performance had lower bond financing and were more likely to have access to equity financing in the capital markets. R.M. et.al (2023) [14] extended the exploration of the relationship between ESG and dividend payments and found that good ESG performance is conducive to boosting firms’ dividend payments while slowing dividend growth. In addition to the positive effect, the existing literature also finds a negative effect of ESG performance on certain corporate behaviors. For example, Chen et al., (2018) [15] found that mandatory CSR disclosure not only reduces performance but also increases the cost of social responsibility, thereby creating a positive externality at the expense of shareholders. For the antecedent influences on corporate ESG performance, scholars’ main studies have found influences such as market competition and environmental regulation. For example, from the external environment, market competition (as measured by the HHI index and the number of participants in each industry) is conducive to CSR performance; Vallentin, S. et al., (2015) found that government policies such as environmental regulation and high-tech support are important factors influencing CSR [16]. In terms of the internal environment, factors such as CEO characteristics and controlling shareholder pledges may have an impact on corporate social responsibility commitment and ESG performance. What can be seen is that the existing research on corporate ESG performance has mainly focused on the micro level of corporate behavior. Most of the literature has examined the economic consequences of corporate ESG performance, while the antecedent factors affecting corporate ESG performance have been very poorly explored. We know that whether or not companies engage in ESG practices depends largely on policy directions and various macro contexts, with emerging technologies and new market policies having an impact on companies’ ESG practices. Therefore, it is essential to study the relationship between the current development of the macro context and the ESG performance of companies.

As the world’s second largest economy, China has now entered an important stage of economic transformation and development. The upgrading and rise of various new technologies have provided an important vehicle for enterprises to achieve high-quality development. In this process, the development of digital technology has given tremendous vitality to the real economy and has not only brought about disruptive improvements to traditional financial services, but has also been transmitted to the enterprise level, influencing various market behaviors of enterprises to a large extent. At the research literature level, the economic impact of digital finance has been extensively explored in the existing literature. Scholars have mainly achieved some results in their research on the effects of digital finance on improving financial performance [17], enhancing enterprise risk resistance [18], reducing enterprise bankruptcy risk [19], and promoting enterprise risk-taking [20]. Abbasi et al., (2021) find that the use of digital finance is beneficial in improving business efficiency through an empirical analysis of data from SMEs in 22 countries [21,22]. A further study by Xie, W. et al., (2021), based on data related to Chinese listed companies, confirmed that the development of digital finance can reduce the financing constraints on enterprises to a certain extent because it can reduce transaction costs [23,24]. In terms of the performance of green innovation, Zhang and Liu (2022) [25] argue that the synergistic effect of digital finance and green technology innovation plays an important role in promoting local carbon efficiency, and that digital finance will promote green technology innovation, which plays an important role in companies’ efforts to fulfil their social responsibility and thus contribute to high-quality development. In order to better measure the level of development of digital finance, Guo Feng et al., (2020), together with Ant Financial Services, pioneered the first indicator system to measure the level of development of digital finance in China, which also provides a good tool for scholars’ research [26]. In terms of negative consequences, some scholars have also found that service target drift and financial exclusion may occur during the development of digital finance [27], but overall it still shows a positive effect on business behavior.

To sum up, this paper put the macro-level digital finance development and micro-level corporate ESG performance in the same area to explore the impact of China’s digital finance development on CSR performance and the mechanism of its role. From a review of the existing literature, it is easy to see that business performance cannot be separated from the influence of the external economic environment. As the lubricant of the real economy, the role of finance on business performance is self-evident (Du, M. et al., 2021) [28]. With the use of digital technology in finance, corporate finance has been greatly facilitated (Liu, Y. et al., 2023) [29]. The direct impact of the development of digital finance on companies is that it can effectively alleviate their financing constraints, and companies can use the savings in financing costs to make ESG investments.

Research on corporate ESG has also been analyzed and discussed from different perspectives in academia. Much of the existing research has focused on exploring the consequences of ESG, and has typically focused on the micro level of the firm: there is a significant positive relationship between ESG performance and abnormal stock returns [30]; corporate ESG disclosure can promote corporate performance [31], discourage managerial misconduct [32] and slow down stock market crashes. The existing literature exploring the antecedent influences on corporate ESG performance is inadequate. In terms of the external environment, market competition (as measured by the HHI index and the number of players in each industry) favors CSR performance [33]; Vallentin, S. et al., (2015) found that government policies such as environmental regulation and high-tech support are important factors influencing CSR [16]. In terms of internal environment, factors such as CEO characteristics and controlling shareholder pledges [34] may have an impact on corporate social responsibility commitment and ESG performance. Firstly, digital finance can alleviate the information asymmetry between banks and companies. According to credit rationing theory, information asymmetry can limit corporate finance. In other words, digital finance can help banks obtain accurate information about firms by making full use of technologies such as blockchain, machine learning and big data to reduce the cost of financing for the banking sector and the financial sector through digital channels [35], and can act as a transmission to firms [36]. Second, the development of digital finance can diversify the financing channels for enterprises. Enterprises can obtain financing not only from the banking sector, but also from other financing channels from fintech companies. In short, the development of digital finance can expand sources of financing, improve information asymmetry between borrowers and lenders, reduce the cost of financing for enterprises and improve their financing capacity. In addition, alleviating the financing constraints of enterprises can, to a certain extent, promote corporate social responsibility and enhance ESG performance. Based on resource dependency theory, companies always want to reduce their dependence on external resources; in reality, companies need external resources to support any investment and financing behavior, which also includes social responsibility and ESG practices. Therefore, if a company faces severe financing constraints, its investment behavior is bound to be affected. Corporate ESG practices involve items such as environmental investments, which increase the company’s operating costs and are detrimental to its earnings performance. Therefore, financing constraints can discourage firms from engaging in ESG practices, as confirmed by Chan et al., (2017) in their study [37]. In view of this, alleviating corporate financing constraints or increasing cash flow can significantly promote corporate social responsibility commitment and enhance their ESG performance. In contrast, easing financing constraints can help improve corporate performance and will also lead to more investments in social responsibility activities and conduct more ESG activities to attract stakeholders. In addition, several studies have empirically confirmed the contribution of digital finance to environmental improvements at the city and regional levels. Yu, Y. (2023) found in his study that financial system reforms in a digital context contributed to a reduction in carbon intensity [38]. Yang, C. et al., (2022) confirm the role of the digital reform of the financial structure for sustainable development, especially for energy efficiency, and confirm that this role has some transmission effects to firms [39].

Based on this, the study makes the following suggestions in light of the preceding analysis.

Hypothesis1(H1).

Digital finance facilitates the improvement of corporate ESG performance.

Hypothesis2(H2).

Digital finance can enhance the ESG performance of firms by mitigating financing constraints, and financing constraints play a mediating role in this impact path, acting as a mediating variable.

3. Research Design

3.1. Modeling Variable Definitions and Data Sources

The following regression models have been constructed as part of this study in order to investigate the influence that China’s current digital finance development level has on companies’ ESG performance:

$e s g_{i, t} = α_{0} + α_{1} I n d e x_{i, t} + α_{2} C o n t r o l_{i, t} + y e a r_{t} + i n d_{i} + ε_{i, t}$

(1)

i and t represent the company and the year; $e s g_{i, t}$ indicates the ESG rating result of the i company in year t; $I n d e x_{i, t}$ is a province-level digital financial indicator of the province in year t; $C o n t r o l_{i, t}$ is a set of control variable groups containing multiple variables; $ε_{i, t}$ is a random interference item. Under the combined effects of macroeconomic factors such as macroeconomics, industry and city, this study deals with the fixed effects of the year (year) and industry (ind).

To verify the aforementioned H2, this study draws on the test for the relevant mediating effect proposed by Zhonglin Wen and Baojuan Ye (2014) [40] and constructs the mediating effect model as follows:

$SA = β_{0} + β_{1} I n d e x_{i, t} + β_{2} C o n t r o l_{i, t} + y e a r_{t} + i n d_{i} + ε_{i, t}$

(2)

$e s g_{i, t} = γ_{0} + γ_{1} I n d e x_{i, t} + γ_{2} S A + γ_{3} C o n t r o l_{i, t} + y e a r_{t} + i n d_{i} + ε_{i, t}$

(3)

On the basis of the baseline regression model, if the test result shows that α_1 is positively significant, it can indicate that there is some direct effect between the level of digital financial development and the ESG performance of enterprises. Based on this, in order to further explore the influence path between the two main variables, we construct a mediating effect regression model with financing constraints as the mediating variable model. Firstly, we test the influence effect of the level of digital financial development on financing constraints. If the regression result shows that $β_{1}$ is positively significant, then it indicates that the level of digital financial development can; if the regression results show that the coefficient of $γ_{2}$ is positively significant and the value of $γ_{1}$ is lower than that of $α_{1}$ , then it indicates that the level of digital finance development can alleviate and reduce the financing constraint faced by enterprises, which in turn can improve the ESG performance of firms. Thus, H2 is verified.

3.2. Variable Definitions and Data Sources

3.2.1. Core Explanatory Variable—Level of Digital Financial Development

In this study, we choose to use the province-level digital inclusive finance index that was compiled by the Digital Finance Research Center of Peking University [26]. This index was developed by Guo Feng and his colleagues in 2020. It is the most important explanatory variable that is utilized in the process of measuring the level of development of digital finance. These numbers provide an analysis of the progression of the digital finance industry in China from 2011 to 2021. Coverage, depth of use, and digitalization are the three primary components that make up the Digital Inclusive Finance Index. To reduce the fluctuation range of variables, prevent the generation of heteroskedasticity, and improve the operation efficiency, this paper normalizes the digital financial index by taking the logarithm. The resilience test makes use of the natural logarithm of the city’s overall digital financial development index.

3.2.2. Core Interpreted Variable—Enterprise ESG Performance

The ESG general rating of listed companies in China is used as the explanatory variable in this study. The ESG general rating is a rating system that is adopted to measure the ESG performance of businesses. The data that Huazheng provides for its ESG ratings are intimately connected to the Chinese market, and its scope is both extensive and timely. The overall evaluation index of the enterprise’s performance with regard to the environment, social responsibility and corporate governance are computed using three primary indicators, as well as fourteen secondary indicators and twenty-six tertiary indicators. According to the most recent rating, there are eight levels of ESG performance (AA, A, BBB, BB, B, CCC, CC, and C respectively). These scores, which are used as the scores of ESG performance and the interpreted variable data of this study, range from the lowest possible ESG score, C, which is quantified as 1, to the highest possible ESG score, AA, which is assigned as 1–8, respectively.

3.2.3. Intermediary Variable—Financing Constraints

The five main types of existing methods for measuring corporate financing constraints are single indicator discriminant, multivariate composite index, sensitivity coefficient, exogenous event shock and textual analysis. In this paper, the SA index of the multivariate composite index is chosen as the measure of corporate financing constraints. Drawing on Hadlock and Pierce’s (2010) [41] study, the more exogenous variables (firm size, age) are used to regress the judged level of corporate financing constraints, and the estimated coefficients are used to construct the indicator financing constraint, namely SA index (SA = −0.737 × Size + 0.043 × Size2 − 0.04 × Age), which has the advantage of being able to overcome certain endogeneity problems and does not show large changes over time.

3.2.4. Control Variables

To improve the estimation accuracy, we also introduced the following control variables: (1) enterprise size (Size); (2) asset–liability ratio (Lev); (3) corporate profitability (ROA); (4) Tobin Q (TobinQ); (5) firm age (FirmAge); (6) Institutional Investor Shareholding Ratio (INST); (7) Top1; (8) gdp. The specific variables are shown in Table 1.

3.2.5. Data Sources

The research object of this study is A-share listed companies in China, and the research interval spans from 2011 to 2021. In this article, the data come from two different sources: first, the rating for ESG is determined by the Chinese securities ESG Index, which is compiled and maintained by Wind Financial Terminal; second, all of the financial data and information regarding the corporate governance of the company is obtained from the CSMAR. Based on existing data processing methods, the following data cleaning processes are carried out on the sample data: (1) removing the companies in finance and insurance; (2) excluding companies with large data gaps and abnormal data values; (3) clear the companies with three stages of PT, ST and *ST; (4) each variable is tail-shrunk between 1% before and 1% after operation to eliminate the influence of extreme value. Finally, 32,818 observations covering 2011–2021 are obtained.

3.3. Descriptive Statistical Analysis

Table 2 summarizes the important variables’ descriptive statistical results.

The table shows descriptive summary statistics for the variables. Specifically, it includes average, standard deviation, minimum and maximum distribution. The benchmark regression sample includes observations from 28,510 listed companies in 2011–2021.

3.4. Relevance Analysis

Table 3 shows the Pearson correlation analysis findings for the variables considered in this article. The correlation analysis results show that the correlation coefficient between the level of digital finance development (Index) and corporate ESG performance (esg) is 0.02 and is significant at the 1% level, indicating a possible positive relationship between the level of digital finance development and corporate ESG performance, which is consistent with the aforementioned Hypothesis 1 expectations, and allows for further investigation. Aside from that, the correlation coefficients between the variables are all within 0.5, showing that the variables are not multicollinear.

4. Empirical Results

4.1. Basic Regression Results

We used the above model (1) to conduct a benchmark regression on the data set in order to explore the connections between the business environment, society and governance (ESG) and digital finance. The results of the benchmark regression are presented in Table 4. In particular, the results of the province-level digital finance index’s natural logarithm that was used in the regression process are displayed in the first and second columns of the table. In Column (1), the model is predetermined according to the industry and the year without control variables added. In Column (2), we added all the control variables to the regression and fixed the effect of year and industry. According to the findings of the regression analysis, the regression coefficients of each province’s digital financial index are positive, and there is a statistically significant difference at the 1% level. This indicates that the level of digital financial development has a positive impact on the ESG performance of businesses. It appears that the advancement of digital finance has the potential to greatly encourage businesses to participate more in ESG practice activities, which provides a preliminary confirmation of Hypothesis 1.

4.2. Intermediary Effects Regression Results Robustness Test

Table 5 presents the regression results of the mechanisms in this paper under the stepwise regression method. Column (1) shows that the regression coefficient of the level of digital finance development (Index) is 0.319 and is positively significant at the 1% level, indicating that the development of digital finance can enhance the ESG performance of enterprises, and then proceed to the next step of testing the intrinsic mechanism; from the results in Column (2), the regression coefficient of the level of digital finance development (Index) on the level of financing constraints (SA) of enterprises is 0.0216, which is significant at the 1% level. Since the larger the SA index is, the smaller the financing constraint a firm is subjected to, digital financial development can reduce the level of financing constraint of a firm to a certain extent; Column (3) shows that the coefficient of the impact of the financing constraint (SA) on the ESG performance (esg) of a firm is 0.305 and is significant at the 1% level. In addition, the regression coefficient is reduced compared to that in Column (1), indicating that the development of digital finance can enhance the ESG performance of firms through the path of alleviating their financing constraints, as verified by the aforementioned Hypothesis 2.

4.3. Robustness Test

This paper tests the robustness of the model from four perspectives to further verify the robustness of the model: first, the city-level digital financial development index is used to replace the province-level digital financial development index with the core explanatory variable; second, the original samples are grouped and the sub-samples are regressed to illustrate the robustness of the benchmark results; the third is to replace the core interpreted variables with Hexun ESG rating data; the fourth is to examine whether cash flow has the same mediating effect as financing constraints between digital finance and corporate ESG.

4.3.1. Replace Digital Finance Development City-Level Indicators

This paper uses the Peking University Digital Financial Development Index as an index to conduct quantitative analysis on the core explanatory variables of the benchmark regression model. It then obtains a preliminary conclusion from the results of the benchmark regression that show that the degree of digital financial development plays a positive role in promoting ESG performance of businesses. In this section, the province-level indicators of digital financial development are swapped out for the city-level indicators, which are then inserted into the model and regressed in order to determine whether the significant positive relationship between digital financial development and the environmental, social, and governance performance of businesses holds up when the core explanatory variables are changed. The findings are detailed in Column (3) of Table 4.

From the regression results, it can be seen that the results of the re-regression of the benchmark model with the city-level digital financial development index instead of the core explanatory variables indicate that the provincial digital financial development index has a regression coefficient greater than zero and has a significant correlation above 1%, which further illustrates the robustness of the regression results of the benchmark model.

4.3.2. Subsample Regression

In 2015, the central bank focused on: putting forward a series of behaviors and standards to regulate the development of internet finance; issued policy measures to encourage innovation and support the stable development of internet finance; promoted the establishment of the Chinese Internet Finance Association; strengthened the self-discipline mechanism of internet finance; promoted the healthy development of the internet finance industry; and promoted the establishment of the Chinese Internet Finance Association. Considering the objective factors of the macro-policy background and possible impact on this study, the growth of digital financial services will unquestionably be influenced in some way as a result of this. Therefore, in this section, the initial sample is split into two sub-samples based on the cut-off point of 2015, one before and one after 2015, and then the aforementioned two samples are regression tested separately in order to verify the robustness of the benchmark regression results of the model. The results of the regression can be found in Table 6.

The findings indicate that the sub-sample after 2015 demonstrates a substantial positive relationship between digital financial development and ESG performance in the regression results, and this correlation is significant at 1%. The findings also show that this correlation is significant in the after-2015 sub-sample. The subsamples that were collected before 2015, on the other hand, did not demonstrate a significant linear correlation. This implies that the increased internet financial supervision in China since 2015 has led to the growth of digital finance playing a more important and advantageous role in improving enterprise ESG performance. It also indicates that the standard regression is robust.

4.3.3. Replace ESG Rating Indicator

Due to the different ESG ratings given by different organizations to the enterprises, the ESG rating of Huazheng Company is selected in the main regression in this paper. In this part, the ESG rating of Hexun Company from 2011 to 2021 is re-selected for regression to test the robustness of the main regression. The regression results are shown in Column (4) of Table 4.

From the regression results, the model is regressed when the core explained variables are replaced, and the Index has a significant positive correlation coefficient, indicating that the results of benchmark regression are still robust.

4.3.4. Intermediary Role of Cash Flow

In the section on testing the mediating effect of financing constraints, we have demonstrated that digital finance development can improve corporate ESG performance by alleviating corporate financing constraints. In this section, we introduce the variable of corporate cash flow, expecting to test whether cash flow plays a mediating role, similar to that of financing constraints in the effect of digital finance on corporate ESG performance, as a way to further illustrate the robustness of the model. From the test results in Table 7, the 95% confidence intervals for both the indirect and direct effects do not contain 0, it is clear that digital finance can enhance ESG performance by increasing corporate cash inflows with a mediating role similar to that of financing constraints, and the robustness of the model is further verified.

4.4. Endogeneity Test

In the process of empirical research, it is very important to solve the endogenous problem between the set explanatory variables and the explained variables. Beginning with the primary focus of this piece of writing, this section focuses primarily on the idea that many different factors influence ESG performance in the real world, and that there may be endogenous problems due to the absence of certain variables. As a result, the authors of this study opt to use the instrumental variable approach and system GMM method to test the hypothesized pure effect of digital financial development on ESG performance while minimizing the impact of any potential endogenous issues.

4.4.1. Instrumental Variables Method

Since the development of digital finance has been progressing to a specific extent depending on the Internet’s growth in order to meet the relevant requirements of the instrumental variables, we introduce the Internet penetration rate (unit: millions of people) of each province in China from 2011 to 2021 as the instrumental variables. This is because the development of digital finance has been progressing to a certain extent and relying on the development of the Internet. In addition, the historical impact of the Internet penetration rate on the ESG performance of enterprises can be ignored, which meets the exogenous requirements of the instrumental variables. Therefore, this part chooses the Internet penetration rate (IP) as a tool variable and uses a two-stage least square method for endogeneity treatment to alleviate the endogeneity problem of the main regression model. The regression results are shown in Table 8.

From Table 8, we can see that in the first stage regression of two-stage least squares estimation (Column (1)), the instrumental variable IP presents a positive significant coefficient, which proves that the selection of instrumental variables satisfies the condition of correlation. In the second-stage regression of two-stage least squares estimation (Column (2)), the significance of the explanatory variable Index is also positive and significant at the 1% statistical level, indicating that the development of digital finance does help to improve the ESG performance of enterprises. Furthermore, the p value of the Hausman test is 0.000, and the exogenous hypothesis is rejected at the 1% level, indicating that digital finance is an endogenous variable. The results presented above demonstrate that the fundamental regression findings of this study are robust.

4.4.2. System GMM

We used the core explanatory variable lagged one period as the explanatory variable and used a systematic GMM for estimation of the results. Although the individual fixed effects used in the baseline regression control for some influences eliminate the endogeneity problem that may result from omitted variables, the endogeneity problem caused by two-way causality needs to be addressed. Given this, a systematic GMM model is used to control the endogeneity problem.

As can be seen from Table 9, the p-values of the statistics of Hansen’s test are all greater than 10%, the original hypothesis cannot be rejected, and the instrumental variables are exogenous; the instrumental variables used are valid. Meanwhile, the p-values of both the AR(1) statistic and the AR(2) statistic are likewise greater than 10%, indicating that the original hypothesis cannot be rejected at the 10% level for the different term of the disturbance term, thus the setting of the system GMM model is reasonable.

5. Heterogeneity Analysis

Based on the research presented in this paper, we can conclude that the advancement of digital finance can encourage enterprises to engage in more ESG practice activities, but does this impact have different effects in different degrees among enterprises with different property rights and different regions? This part will analyze heterogeneity from these two aspects. First of all, the sample is divided into two sub-samples according to the nature of enterprise property rights and the region in which the enterprise is located (the classification is divided into three types of regions according to the economic development level of the region: the east, the middle and the west). Set the dummy variable Dum, which is equal to 1 if the companies in the sample are non-state-owned enterprises and enterprises in the central and western regions, or 0 if they are not (if the ownership of the enterprises is non-state-owned, it is defined as non-state-owned enterprises; based on the economic development level of the provinces and municipalities directly under the central government where the cities are located, the eastern, central and western regions are further divided into regions, among which the eastern regions are Beijing, Hebei, Liaoning, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan, and the enterprises in the central and western regions are enterprises from other regions except the above regions). If the interaction term Index × Dum is positively significant, it can be proved that the development of digital finance has different degrees of impact on ESG performance in different enterprise property rights and different regions where the enterprise belongs. Table 10 shows the results of the heterogeneity analysis.

The results of a heterogeneity analysis based on the characteristics of corporate property rights and the degree of economic advancement in the regions to which they belong are presented in the table above. According to the findings, we can see that when the enterprise belongs to non-state-owned enterprises and enterprises in the central and western regions, and controls the annual fixed effect and the industry fixed effect, the regression results of the interaction terms and the core explanatory variables are significant. This suggests that when the enterprise belongs to non-state-owned enterprises and enterprises in the central and western regions, the influence of digital finance development is greater. State-owned businesses, on the other hand, have a better chance of obtaining credit support, whereas non-state-owned businesses are typically hampered by a lack of financing [21], demonstrating that the advancement of digital finance has played an important role in improving the ESG performance of non-state-owned enterprises, implying that digital finance can promote the performance of corporate social responsibility and ESG performance by easing financial constraints.

6. Conclusions

This paper investigates the impact of China’s digital finance development on corporate ESG performance using the 2011–2021 Peking University Digital Inclusive Finance City-level and Provincial indices and ESG rating data of China’s listed companies by China Securities Rating Corporation, combined with relevant financial data of Guotaian listed companies. The findings are as follows: first, from the results of the benchmark regression and robustness tests, the development of digital finance in China has a significantly positive and positive contribution to corporate ESG performance and practice, helping enterprises to participate more in ESG practice activities and enhancing their willingness to further assume social responsibility; second, from the results of the mechanism analysis, the level of development of digital finance in China is mainly through the alleviation of corporations; third, from the results of the heterogeneity analysis, the impact of China’s digital finance development on corporate ESG performance shows different degrees of impact effects under different property rights’ nature and different economic levels of the regions to which the enterprises belong: when the enterprises belong to non-state enterprises and enterprises in the central and western regions, the impact is digital. The effect of financial development on corporate ESG performance is more significant when the enterprises belong to non-state enterprises and enterprises in the central and western regions, and the willingness of enterprises to practice ESG activities is more sensitive to the influence of the level of digital financial development.

Combined with the above empirical results, this paper verifies the veracity of the proposed hypothesis and provides rich empirical support for this purpose. It can be seen that digital finance development can enhance the ESG performance of enterprises by alleviating their financing constraints, and presents different effects under different types of property rights and regional divisions. To this end, this paper makes the following policy recommendations based on the findings of the study: First, as the most important link in the process of conducting economic policies at the macro level to the micro-enterprise level, financial institutions such as the banking industry should continuously enhance their digitalization, attach importance to the application of emerging technologies in traditional financial business, enable technology to be used in the services of financial institutions, promote banks to allocate funds and scientific and technological resources rationally and utilize the marginal cost of digital finance and the advantages of economies of scale to achieve more perfect and effective services for enterprises and other non-financial institutions. Intensify the advancement of digital financial inclusion, realize improvements in both quality and effectiveness in financial inclusion and actively instruct the public in understanding the development of digital finance, as well as unblock the channels for delivering diverse financial services, in order to alleviate the degree of information imbalance with customers, to achieve more reasonable and perfect policies and requirements embodied by digital financial platforms in enterprise-oriented financing and other businesses and open up the transmission path of macro policies to the micro-enterprise level.

Second, at present our country attaches great importance to high-quality development in the process of economic development, advocating that all levels and fields should unify green, innovation, openness, sharing and coordination. As the most important implementation level of policies, businesses should focus on the demands of high-quality growth, pay attention to corporate social responsibility, improve the quality of ESG information disclosure and participate in more social responsibility practice activities. While at the same time, it is necessary to actively respond to the demands of the country for the development of digital finance, enhance the understanding and grasp of digital finance in promoting the high-quality development of enterprises, fortify the application of digital technology, strengthen the correlation with the digital financial services of financial institutions, actively use digital financial systems in order to improve the accuracy and comprehensiveness of disclosure of information, reduce the information asymmetry with capital providers, strive for more relaxed and better capital support services for enterprises’ financing activities and further help enterprises to invest more resources in social responsibility to meet the requirements of high-quality development.

Third, the government, as a macro-policy maker, should strengthen its support for digital finance, intensify financial reform and innovation, speed up the digital transformation of financial institutions—especially traditional financial institutions—and improve the allocation efficiency of financial resources. For example, it should use blockchain and other technologies to build decentralized supply chain finance, expand the scale of loans and enhance credit trust. In addition, we should also pay attention to the feedback content of the policy transmission, such as setting a more relaxed access environment for financial institutions and enterprises, to ensure the substantive role of digital finance in the market, and to a certain extent to avoid unreasonable monopoly. Establishing a balanced supervision mechanism between the digital finance function and the supervision function can effectively guide digital finance to play its role, establish a sound financial market system, strengthen the rationality of the matching of investment and financing terms, and prevent the systematic risks brought by “turning the reality into the reality”. In addition, as digital finance has different effects on the ESG performance of enterprises in different regions and with different property rights, it is necessary for the relevant government departments to provide differentiated policies, such as giving certain policy tilts to non-state enterprises in the central and western regions, and accelerate the full coverage of digital finance in enterprises in such regions, so as to better alleviate the financing difficulties of such enterprises.

However, there are certain limitations to the research in this paper. Firstly, as digital finance is a macro-level policy performance, the depth and breadth of its coverage varies across provinces and cities; this paper only uses the total indicators of the Digital Finance Development Index in the empirical test to conduct the analysis. The different roles of the depth and breadth of digital finance development on the ESG performance of enterprises can be discussed separately to obtain more informative research findings. In addition, this study needs to further expand on the channels through which digital finance contributes to corporate ESG performance. This paper is guided by the theoretical basis to illustrate the role of digital finance on corporate ESG performance only in terms of the mediating role of financing constraints, and we will consider exploring more possible mechanisms such as green innovation and market regulation in our future research.

Author Contributions

Conceptualization, Y.N. and Y.Z.; methodology, Y.N. and Y.Z.; software, Y.Z.; formal analysis, Y.N. and Y.Z.; resources, Y.N.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.N.; supervision, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Social Science Fund of China (Funding No. 15XJY022).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1.Definition of major variables.

Variable Type	Variable Name	Variable Symbol	Explanation
Interpreted variable	Enterprise ESG performance	esg	According to the ESG rating of Hua Zheng, it is divided into nine grades of C–AA and is given a score of 1–8
Explanatory variable	Digital finance	Index	Peking University Digital inclusive finance Index takes logarithm (province level)
Control variable	Scale	Size	Natural logarithm of total assets at the end of the year
	Asset–liability ratio	Lev	Total liabilities/total assets
	Corporate profitability	ROA	Net profit/total assets
	Tobin Q	TobinQ	The ratio of the market value of an enterprise’s shares to the replacement cost of the asset represented by the shares
	Firm age	FirmAge	The difference between the year of observation and the year of establishment
	Institutional investors’ shareholding ratio	INST	Total institutional investor holdings/circulating share capital
	Percentage of shareholding of the largest shareholder	Top1	Number of shares held by the largest shareholder/total number of shares
	Gross domestic product per city	gdp	Logarithm of the gross domestic product per city

Table 2.Descriptive statistical results.

Variable	Obs	Mean	Std. Dev.	Min	Max
Index	32,818	5.518	0.543	3.487	6.129
esg	32,818	4.158	1.101	1	8
SA	32,818	−3.8	0.251	−4.42	−3.152
Size	32,818	22.154	1.287	19.951	25.994
Lev	32,818	0.416	0.208	0.056	0.889
ROA	32,818	0.043	0.064	−0.22	0.208
TobinQ	32,818	2.031	1.284	0.865	8.171
FirmAge	32,818	2.014	0.952	0	3.332
INST	32,818	0.372	0.239	0	0.87
Top1	32,818	0.342	0.148	0.09	0.729
gdp	32,818	6.687	1.082	3.973	8.371

Table 3.Relevance Analysis.

	esg	Index	SA	Size	Lev	ROA	TobinQ	FirmAge	INST	Top1	gdp
esg	1
Index	0.020 ***	1
SA	0.081 ***	−0.316 ***	1
Size	0.182 ***	0.103 ***	−0.082 ***	1
Lev	−0.065 ***	−0.041 ***	−0.125 ***	0.518 ***	1
ROA	0.231 ***	−0.022 ***	0.097 ***	−0.041 ***	−0.401 ***	1
TobinQ	−0.106 ***	0.071 ***	0.043 ***	−0.371 ***	−0.250 ***	0.141 ***	1
FirmAge	−0.113 ***	0.028 ***	−0.446 ***	0.426 ***	0.401 ***	−0.299 ***	−0.085 ***	1
INST	0.071 ***	−0.035 ***	−0.077 ***	0.469 ***	0.245 ***	0.025 ***	0.330 ***	0.050 ***	1
Top1	0.100 ***	−0.082 ***	0.126 ***	0.187 ***	0.044 ***	0.141 ***	−0.127 ***	−0.085 ***	0.330 ***	1
gdp	0.049 ***	0.378 ***	−0.032 ***	0.058 ***	−0.019 ***	0.017 ***	0.013 **	0.026 ***	−0.073 ***	0.003	1

***, **, represent significance at the 1%, 5% levels, respectively.

Table 4.Basic Regression Results.

	(1)	(2)	(3)	(4)
Variables	esg	esg	esg	esg
Index	0.281 ***	0.117 ***	0.200 ***	0.0568 **
	(0.0621)	(0.0591)	(0.117)	(0.0342)
Size		0.275 ***	0.276***	0.0941 ***
		(0.00993)	(0.00994)	(0.00552)
Lev		−0.821 ***	−0.825 ***	−0.108 ***
		(0.0481)	(0.0481)	(0.0299)
ROA		1.324 ***	1.317 ***	7.630 ***
		(0.109)	(0.109)	(0.0892)
TobinQ		−0.0154 **	−0.0160 ***	−0.0531 ***
		(0.0063)	(0.00603)	(0.00418)
FirmAge		−0.245 ***	−0.245 ***	0.0139 **
		(0.0111)	(0.0111)	(0.00628)
INST		0.127 ***	0.124 ***	0.0957 ***
		(0.0341)	(0.0341)	(0.0218)
Top1		0.235 ***	0.232 ***	0.0455
		(0.0630)	(0.0630)	(0.0342)
gdp		−0.0125	−0.0167	0.0200 ***
		(0.0119)	(0.0137)	(0.00578)
Constant	2.625 ***	−2.206 ***	−2.583 ***	0.360 **
	(0.334)	(0.337)	(0.531)	(0.182)
year	YES	YES	YES	YES
industry	YES	YES	YES	YES
N	32818	32,818	32,818	32,818
R²	0.0838	0.2775	0.2775	0.4962

***, **, represent significant at the 1%, 5% and levels, respectively.

Table 5.Intermediary Effects Regression Results.

	(1)	(2)	(3)
	esg	SA	esg
Index	0.319 ***	0.0216 ***	0.305 ***
	(6.22)	(7.06)	(6.07)
SA			0.265 ***
			(1.39)
Size	0.0978 ***	0.0587 ***	0.283 ***
	(22.66)	(41.16)	(37.42)
Lev	−0.910 ***	−0.0665 ***	−0.797 ***
	−0.102 ***	(−8.44)	(−19.46)
ROA	7.504 ***	−0.473 ***	2.556 ***
	(87.46)	(−21.40)	(21.99)
TobinQ	−0.0549 ***	0.0362 ***	−0.0348 ***
	(−14.23)	(31.46)	(−5.70)
FirmAge	0.0173 ***	−0.147 ***	−0.200 ***
	(3.37)	(−90.14)	(−21.06)
INST	−0.027	0.0160 *	0.163 ***
	(−0.83)	(2.49)	(5.23)
Constant	0.194	−4.650 ***	−2.995 ***
	(1.31)	(−95.18)	(−10.38)
year	YES	YES	YES
industry	YES	YES	YES
N	32,818	32,818	32,818
R²	0.1994	0.4060	0.2

***, * represent significant at the 1%, 10% levels respectively.

Table 6.Regression Results of Sub-samples.

	(1)	(2)
Variables	Subsample1_esg	Subsample2_esg
Index	0.142	0.699 ***
	(−0.0612)	(0.178)
Constant	−1.680 ***	−7.225 ***
	(0.417)	(0.972)
Controls	YES	YES
year	YES	YES
industry	YES	YES
N	14,676	18,142
R²	0.2816	0.2743

*** represent significant at the 1% level.

Table 7.Bootstrap test.

	Lower Limit of Confidence Interval	Upper Limit of Confidence Interval
_bs_1	−0.0065479	−0.0022115
_bs_2	−0.2368439	−0.2055353

Table 8.Endogeneity Test.

	(1)	(2)
Index	-	0.053 ** (0.021)
IP	0.027 *** (0.0002)	-
Size	0.062 *** (0.003)	0.229 *** (0.007)
Lev	−0.219 *** (0.015)	−0.565 *** (0.041)
ROA	−0.524 *** (0.045)	2.932 *** (0.119)
TobinQ	0.039 *** (0.002)	−0.067 *** (0.006)
FirmAge	0.035 *** (0.003)	−0.186 *** (0.009)
INST	−0.153 *** (0.013)	0.161 *** (0.034)
Top1	−0.157 *** (0.018)	0.017 (0.047)
gdp	−0.018 *** (0.003)	0.010 (0.007)
Constant	2.64 *** (0.056)	−0.733 *** (0.153)
year	YES	YES
industry	YES	YES
N	27,915	27,915
R²	0.48	0.121

***, **, represent significant at the 1%, 5% levels, respectively.

Table 9.System GMM.

	(1)
Variables	SYS-GMM
Index	-
LIndex	0.614 *** (8.51)
Controls	YES
Constant	4.275 ** (2.445)
N	18,618
AR(1)	0.367
AR(2)	0.596
Hansen	0.117

***, **, represent significant at the 1%, 5% levels, respectively.

Table 10.Regression results of heterogeneity analysis.

	(1)	(2)
Variables	Firmownership	Regioneconomy
Index × Dum	0.432 ***	0.06 ***
	(−0.28)	(−0.025)
Index	−0.375 *	0.014 *
	(−0.33)	(−0.257)
Constant	−0.69	−2.336
	(−2.77)	(−0.578)
Controls	YES	YES
year	YES	YES
industry	YES	YES
N	32,818	32,818
R²	0.4102	0.2960

***, * represent significant at the 1%, 10% levels, respectively.

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