ESG Performance Drives Enterprise High-Quality Development Through Financing Constraints: Based on the Background of China’s Digital Transformation

Abstract

Under the current digital transformation landscape, environmental, social, and governance (ESG) performance and financing constraints exert non-negligible impacts on corporate high-quality development. Building on this foundation, the present study seeks to elucidate these critical relationships. The methodology involves constructing an evaluation system for firms’ high-quality development, followed by revealing the impact of firms’ ESG performance on their high-quality development and the mediating effect of financing constraints in the process of digital transformation. The study finds that corporate ESG performance significantly mitigates financing constraints, and that higher ESG levels help to promote corporate high-quality development. In addition, digital transformation significantly moderates the mediating effect of financing constraints on the ESG performance and high-quality development of enterprises. The findings suggest that enterprises are actively committed to practicing ESG principles and optimize financing constraints to promote high-quality development with the help of digital transformation. Accordingly, we calls on the government to motivate enterprises to pay comprehensive attention to improving ESG and digitalization levels to promote the sustainability of the national economy.

1. Introduction

With the deepening of economic development, China’s economy is gradually shifting to high-quality development and positioning itself as a core task in the comprehensive construction of a modern socialist country. At the same time, the widespread application of information technology has fueled the rise of the digital economy, in which the data element has become a key driving force in transforming the mode of economic development. This data-centric economic model can not only improve the operational efficiency of the economy but also promote the optimization and upgrading of the economic structure, injecting new vitality into China’s economic development. From a micro perspective, enterprises are the basic cells of the economy, and when enterprises prosper, the economy prospers, and exploring feasible paths to achieve high-quality development of enterprises is the key to promoting high-quality economic development. Luo et al. Ref. [1] constructed a measurement system of high-quality development of enterprises from six dimensions, which provides reference and inspiration for subsequent research, only that it has not been empirically tested and does not provide relevant empirical evidence.

In order to resolve the contradiction between economic development and environmental protection, the United Nations released Who Cares Wins in 2004, which proposed the concept of environmental, social, and corporate governance (ESG) for the first time, and is regarded by the industry as an advanced concept of corporate social responsibility (CSR) that promotes all-round development of enterprises. In their early studies of CSR, scholars argued that in addition to focusing on economic benefits, companies should also assume responsibility for stakeholders, pay attention to environmental sustainability, and focus on the joint progress of social and financial performance [2].

However, with the increasing emphasis on the construction of ecological civilization since Chinese ‘dual-carbon’ goal was proposed, the ESG concept has received more and more attention as an expanded version of CSR, with the core view that enterprises should take into account social responsibility and focus on the governance of the environment in which they are located while improving their economic efficiency in order to facilitate the development of the enterprise and achieve social, environmental and environmental benefits. The core idea is that enterprises should improve economic efficiency while taking into account social responsibility and focusing on the management of the environment. So that they can facilitate the development of enterprises, and achieve a win–win situation for society, environment, and enterprises. At present, a unified evaluation system for high-quality development of enterprises has not yet been formed, but enterprises have achieved both their own value and social value, and the practice of the ESG concept is an inherent requirement and a powerful impetus for high-quality development of enterprises, and seizing the ESG opportunity is an important path for enterprises to achieve high-quality development.

A large number of studies have shown that ESG performance can effectively enhance enterprise value, promote enterprise green technology innovation, improve enterprise investment efficiency, etc., and effectively alleviate the financing constraints faced by enterprises [3]. This raises the question of whether ESG performance can alleviate financing constraints to foster high-quality enterprise development. Since China first listed data as a production factor for the first time in 2019, digital transformation centered on data factors has had a certain effect on improving enterprise performance [4], digital technology has become the core means to promote economic development at present, and it is both an opportunity and a challenge for enterprises. Building upon the tripartite framework of ESG performance, financing constraints, and high-quality development developed earlier, this section further incorporates digital transformation as a critical moderating variable. This study thus integrates digital transformation into its theoretical framework to systematically investigate its multifaceted influences on high-quality enterprise development, significantly improving the research’s relevance to contemporary corporate operational realities.

The possible contributions of this paper are as follows. Firstly, this paper tries to construct a set of measurement system for high-quality development of enterprises, which provides empirical data and practical suggestions for enterprises to practice high-quality development, which not only helps enterprises to understand their own development status but also provides specific directions for improvement and enhancement. Second, using financing constraints as the mediating variable, this paper designs and tests the facilitating role and transmission mechanism of ESG performance on the high-quality development of enterprises, which enriches the empirical research on the drivers of high-quality development, and provides new perspectives and evidence for understanding how ESG performance affects the high-quality development of enterprises. Finally, considering the importance of digital transformation in the current era context, this paper incorporates digital transformation as a moderating variable into the analysis framework and explores the boundaries of the impact of ESG performance on enterprise high-quality development. This study empirically examines the impact of ESG performance on enterprise high-quality development in the context of digitization, and puts forward suggestions for achieving enterprise high-quality development in line with the context of the era.

The remainder of this paper is organized as follows. The second part conducts a systematic literature review of existing Chinese and international studies, establishing the theoretical foundation while identifying gaps for our research contribution; the third part conducts a theoretical analysis of the content of the research to be conducted and puts forward research hypotheses; the fourth part carries out the design of the research, which firstly elaborates on the selection and source of variables, and then carries out the construction of the model; the fifth part analyses the empirical results and conducts the robustness test; the sixth part focuses on the reality, applies the empirical results to practice, and puts forward countermeasures and suggestions.

2. Literature Review and Commentary

2.1. Conceptual Definition

The ESG concept has evolved from Corporate Social Responsibility (CSR), emphasizing that enterprises should integrate environmental, social, and governance considerations into their core operations [5]. Financing constraints generally refer to a situation where a company cannot raise necessary funds for development at a reasonable cost. High-quality enterprise development can be essentially understood as a dynamic state that satisfies all stakeholders, thereby achieving sustainable development. Digital transformation requires enterprises to thoroughly integrate digital technologies into every aspect and process of their operations, innovating or even reinventing business models to enhance market competitiveness and achieve significant efficiency gains [6,7].

2.2. Literature Review

Research results on the measurement of high-quality development are broadly differentiated into two categories: the first measurement that arose was a single indicator typified by total factor productivity (TFP), with the unique value implication of TFP as a proxy variable for the high-quality development of an enterprise [8]. However, because the use of a single TFP to measure the quality of economic growth does not conform to the complex and changeable real economic situation [9], two forms of measurement of multidimensional indicators are gradually evolved: one is to select a number of indicators to use the principal component analysis method, entropy method, etc. to reduce the dimensionality, and to obtain a corresponding composite indicator [10]; and the other is to select different indicators based on the high-quality development of the enterprises, selecting different indicators (such as environmental performance and total factor productivity) as the proxy variables constituting different dimensions of high-quality development of enterprises, and constructing a model to complete the relevant empirical analysis.

Currently, there are fewer research results on the drivers of high-quality enterprise development [11]. CSR performance related to corporate governance and the informal hierarchy of the board of directors can promote the development quality of enterprises [12], and the regulation of the external environment of enterprises, the quality of auditing, and the reduction in taxes and fees can also effectively promote the high-quality development of enterprises [1,13], but the promotion effect of technological innovation on the high-quality development of enterprises is affected by financing constraints [8].

In recent years, under the guidance of the dual-carbon goal, ESG concepts and research have gradually heated up. Extensive empirical research demonstrates that strong ESG performance mitigates agency conflicts and financing constraints while stimulating corporate innovation. These effects, in turn, enhance market valuation, operational efficiency, and foreign investment inflows, while also improving corporate transparency [14]. Hence increasing the quantity of corporate green innovation, and then improving the quality of green innovation [15,16].

Digital transformation centered on data elements has a significant contribution to TFP and positively enhances the efficiency of firms’ technological innovations [17], but due to the multiple economic consequences of digital transformation on firms, the results of the interactions may not always significantly enhance firm performance [18].

2.3. Literature Commentary

In summary, the extant literature on measurement methodologies and driving factors of high-quality enterprise development predominantly adopts a macro-level perspective. Concurrently, research from a micro-level perspective has yet to reach scholarly consensus. And most of the existing literature ignores the fact that the high-quality development of enterprises is systematically influenced by a variety of factors, and should seek to construct a comprehensive and integrated measurement and evaluation methodology. Moreover, while existing literature has established that ESG performance enhances corporate performance, the transmission mechanisms between ESG performance and high-quality enterprise development remain underexplored and warrant further investigation. As well, the relationship between the digital transformation triggered by the data factor as a new production factor and the high-quality development of enterprises is also of academic value for in-depth analysis.

Therefore, based on the micro-level, this paper constructs a multi-dimensional high-quality development measurement system for listed companies to explores the effect of ESG performance on high-quality development of enterprises. Furthermore, we introduce financing constraints as a mediating variable and digital transformation as a moderating variable under the background of data factors to analyze the mechanism of ESG performance on the high-quality development of micro-enterprises, which explores effective paths to promote the high-quality development of enterprises, and the boundary of the influence of ESG performance on the high-quality development of enterprises. All these have certain research value and practical significance.

3. Theoretical Analysis and Hypothesis Formulation

3.1. ESG Performance and High-Quality Enterprise Development

According to stakeholder theory and signaling theory, companies with excellent ESG performance have incentives to transmit information related to corporate ESG ratings to the outside world, thus attracting the attention of various stakeholder groups in order to obtain more support [19], providing various competitive resources for the enterprise, which is conducive to the enterprise being in a favorable business environment, and equipping the enterprise with the competitive advantage and the driving force for sustainable development, greatly reducing the multiple risks and uncertainties faced by enterprises, and promoting high-quality development of enterprises [20]. According to Ruf et al.’s [20] thinking, ESG performance affects enterprise development in the following four dimensions: (1) Monetary capital. Good ESG performance of enterprises indicates excellent corporate governance structure, alleviates information asymmetry and principal-agent problems, and is conducive to the interest group of monetary capital tilting monetary resources to companies with excellent ESG performance, reducing corporate financing constraints and financial risks, and enhancing corporate financial performance, thus maximizing the value of this interest group [21,22]. (2) Human capital. When enterprises adequately address employee needs regarding compensation, benefits, professional training, and career advancement opportunities, this reflects strong ESG performance in fulfilling social responsibilities toward employees. In return, the enterprises can obtain strong support from the human capital interest group which means realizing both qualitative and quantitative improvements in employee loyalty, customer satisfaction and corporate performance. This accelerates the turnover rate of accounts receivable, reduces the risk of bad debts, optimizes the allocation of human resources, and is beneficial to the high-quality development of enterprises [23]. (3) Social capital. ESG as a non-market means can assist enterprises to establish a good government-enterprise relationship with the government, which can help enterprises to obtain important subsidies or competitive resources, enhance the legitimacy of business operations, reduce the risk of being subjected to litigation, and contribute to the high-quality development of the enterprise [24]. (4) Ecological capital. If the enterprise has a good ESG performance, it means that the enterprise has a higher level of environmental protection responsibility fulfillment and obtains a higher degree of recognition from the ecological capital interest group, thus reducing the negative evaluation of the outside world on the enterprise’s pollution of the environment and waste of resources, which has an unignorable role in the development of the enterprise [25].

Therefore, the practice of ESG by enterprises not only wins the support of various stakeholder groups but also helps managers to avoid short-sighted behavior and focus on the comprehensive and sustainable development of enterprises. Based on the above analysis, this paper proposes the following hypothesis:

Hypothesis 1.

Corporate ESG performance can significantly promote high-quality corporate development.

3.2. The Mediating Role of Financing Constraints on Enterprise ESG Performance and High-Quality Development

ESG performance represents an integration of both non-financial and financial corporate information. Disclosing ESG-related data enhances internal information transparency within enterprises, which is conducive to the monitoring role of stakeholder groups, provide more comprehensive decision-making information for information users, alleviate the information asymmetry between enterprises and stakeholder groups, and contribute to the creation of enterprise value [3]. At the same time, the disclosure of ESG information is a positive signal to the outside world to establish a good corporate reputation, but also easy to obtain preferential credit from banks to enhance the availability of funds by virtue of good government-enterprise relations, to achieve better prospects for sustainable development, to protect the future earnings of investors, and thus win the recognition and trust of investors to obtain a wider degree of social acceptance by investors and the government [20]. Therefore, good ESG performance helps enterprises to obtain financing from internal and external investors and banks, eases financing constraints, accelerates the transformation of innovation results, expands market share, enhances enterprise value, and promotes the quality of enterprise development [8]. In summary, this paper proposes the following hypothesis:

Hypothesis 2.

Corporate ESG performance can alleviate corporate financing constraints and thus promote high-quality corporate development.

3.3. The Moderating Role of Digital Transformation in the Relationship Among ESG Performance, Financing Constraints, and High-Quality Development

Data elements guide the digital transformation of China’s economic structure and promote the digital transformation of micro-enterprises, so as to embed digital science and technology, such as artificial intelligence, blockchain, cloud computing, and big data, in production, management, and other activities, and achieve corresponding operational outcomes. Firstly, digital resources enrich internal corporate information and optimize resource allocation, inhibit inefficient investment, and rapidly coordinate to reduce corporate governance costs [7]. Secondly, digital technology can better handle enterprise information, improve the efficiency of capital operation, reduce the pressure of enterprise capital while improving the financial stability of the enterprise, and apply more resources to the main business so as to improve the performance of the enterprise [26]. Finally, in enterprises facing financing constraints, the management usually has a risk aversion preference and a low level of risk-taking. Digital transformation improves the transparency of information among enterprises, which helps various stakeholder groups to strengthen the supervision of enterprises and prompts enterprises to improve the level of risk-taking, which not only improves the enthusiasm for innovation and the formation of a competitive advantage, but also enables enterprises to choose relatively high-risk and high-return projects in their investment decision-making, which rapidly increase enterprise value in the short term and contribute to the long-term development of the enterprise [27]. Based on the above analysis, this paper puts forward the following hypotheses:

Hypothesis 3.

Digital transformation acts as a positive moderator, mitigating the adverse effects of financial constraints on ESG performance and high-quality development.

In summary, this paper introduces financing constraints, digital transformation, and constructs a structural framework for the model of ESG performance and high-quality corporate development, as shown in Figure 1.

4. Research Methodology

4.1. Construction of the Evaluation System for High-Quality Enterprise Development

4.1.1. Theoretical Basis

Enterprises can achieve value growth and high-quality development by improving the quality of fulfilling their responsibilities to stakeholder groups. From an external perspective, enterprises’ meeting the needs of stakeholders has a positive impact on social development and contributes to the high-quality development of the economy. The fulfillment of this social responsibility helps build a harmonious social environment and promotes the coordinated development of the economy and society.

From an internal perspective, enterprises that proactively fulfill their responsibilities to various stakeholders are conducive to sustainable and high-quality development. Stakeholders can be categorized into four distinct groups (monetary, human, social, ecological), with each category’s demands subjected to systematic analysis [28]. Specifically, by prioritizing employee welfare, providing high-quality products and services, and protecting the environment, enterprises can cultivate a positive social image, enhance their market competitiveness, and secure the trust and support of consumers and investors. This responsibility-oriented development model assists enterprises in maintaining stable and healthy growth in the long term.

4.1.2. Selection and Testing of Evaluation Indicators

Following the approach of Ruf et al. [20], this paper combines stakeholder groups categorized by the form of input capital with the high-quality development of enterprises and selects the relevant evaluation indicators for the following four categories:

1. The monetary capital stakeholder group is represented by shareholders and creditors. Financial indicators such as earnings before interest and tax (EBIT), earnings per share (EPS), and return on total assets (ROA) are selected to reflect the short-term needs of shareholders and creditors, and the resource allocation efficiency (TFP) of the enterprise is selected to reflect the long-term needs of this group, reflecting the competitiveness of the enterprise’s market and its potential for future development [8]. In this paper, TFP is calculated based on the LP method proposed by Li and Lv [29], and the model is as follows:

$$ln{Y}_{i,t}={\beta }_0+{\beta }_{1}ln{L}_{i,t}+{\beta }_{2}ln{K}_{i,t}+{\beta }_{3}ln{M}_{i,t}+{\epsilon }_{i,t},$$

(1)

Among them, Y represents the total output of the enterprise, L represents labor, K represents capital, and M represents intermediate inputs.

2. The human capital stakeholder group is represented by enterprise employees. The employee education expenses can measure the fulfillment of the enterprise’s responsibility to employees and observe the employees’ satisfaction with the enterprise and their own work [30]. In addition, if the enterprise’s level of fulfillment of the employees’ needs such as salary and promotion is poor, it will trigger lower employee loyalty, which will lead to more turnover, so this paper selects the employee education expenses, employee retention level, and turnover rate as three indicators to reflect the quality of enterprise development.

3. The social capital stakeholder group is represented by the government and the public. The responsibility to the government is mainly reflected through active tax payment. The responsibility to the public includes the aspects of valuing reputation, performing public welfare, and abiding by morality, and the enterprises with a high level of morality have a relatively low degree of surplus management [31]. Therefore, with reference to the existing literature, this paper chooses eight indicators to reflect the fulfillment of corporate responsibility to social capital stakeholders, such as tax planning, surplus management, and donation amount.

This paper draws on the approach of Tsai et al. [32] and calculates the degree of corporate tax planning using the indirect method. The model is as follows:

$${\displaystyle \frac{AT{P}_{i,t}}{EBI{T}_{i,t}}}={\partial }_0+{\partial }_1\sum Control{s}_{i,t}+{\epsilon }_{i,t},$$

(2)

Among them, ${ATP}_{i,t}$ represents the actual tax paid; ${EBIT}_{i,t}$ represents earnings before interest and tax. The degree of tax planning is the part that cannot be explained by control variables, which is measured by the regression residual.

Accrual-based earnings management is calculated using the modified Jones model. Due to limited space, the calculation process is not presented here. Drawing on the approach of Kim and Sohn [33], the real earnings management (${DREM}_{i,t}$) is obtained using Model (3):

$$DRE{M}_{i,t}=DPRO{D}_{i,t}-DCF{O}_{i,t}-DDISEX{P}_{i,t},$$

(3)

Among them, ${DPROD}_{i,t}$ represents abnormal production costs; ${DCFO}_{i,t}$ represents abnormal cash flows; and ${DDISEXP}_{i,t}$ represents abnormal discretionary expenses.

4. Ecological capital stakeholder groups are typically represented by environmental protection organizations, which are mainly concerned with the sustainable development of the ecological environment. In other words, they focus on the environmental performance of the firm. On the ground of the above, this paper chooses enterprise environmental protection tax and environmental protection investment to reflect the environmental performance of high-quality development of enterprises [34].

In summary, 19 indicators are selected and listed in

Table 1. Selection of indicators of high-quality development of enterprises.

Indicator Dimension	Code	Indicator Meaning	Indicator Calculation Methodology
Monetary Capital Stakeholders	x1	Total factor productivity	Measured by LP method (see Equation (1))
	x2	Earnings Before Interest and Tax	ln(net profit + income tax expense + financial expense)
	x3	Earnings per share	Current value of net profit/Current ending value of paid-in capital
	x4	Economic value added	ln(net operating profit after tax − cost of capital)
	x5	Return on gross assets	Net profit/Total assets
	x6	Return on net assets	Net profit/Net assets
Human Capital Stakeholders	x7	Employee retention level	(Number of employees)t/(Number of employees)t-1
	x8	Employee turnover rate	((Number of employees)t−1 − Number of employees)t)/(Number of employees)t−1
	x9	Employee education expenses	ln(Employee Education Expenses)
Social Capital Stakeholders	x10	Tax contribution	Actual tax paid/total assets
	x11	Tax payable	ln(tax payable + 1)
	x12	Tax planning	See Equation (2)
	x13	Corporate intangible assets	ln(Net intangible assets + 1)
	x14	Intangible assets ratio	Net intangible assets/Total assets
	x15	Donations	ln(amount of social donations made by the enterprise in the year)
	x16	Real surplus management	See Equation (3)
	x17	Accrued surplus management	Modify the residuals of Jones model
Ecological Capital Stakeholders	x18	Corporate environmental tax	ln(enterprise’s environmental protection tax + 1)
	x19	Corporate environmental investment	ln(Enterprise’s environmental protection expenditure + 1)

. The KMO value is 0.653, and the significance level of the Bartlett test is 0.000, indicating that the research data are suitable for factor analysis.

4.2. Sample Selection and Data Sources

In this paper, Chinese listed companies from 2015–2023 were selected as the research sample, and the ESG data were obtained from Index of Wind database; digital transformation data used the frequency of occurrence of the corresponding keywords in the annual reports of listed companies as a proxy variable, and the word frequency data were obtained by using Python 3.8 crawling technology; the rest of the data were obtained from the Economic and Financial Sectors in CSMAR database. In order to enhance the generalizability of the regression results, the samples were screened as follows: (1) excluding financial and insurance listed companies; (2) excluding ST, *ST, and PT-treated listed companies; (3) excluding listed companies that were delisted and IPO during the study period; and (4) excluding samples with missing data. A total of 8032 sample observations for 1004 sample companies were finally obtained. To reduce the adverse effects of outliers, all continuous variables were winsorized at the 1% level at both ends. The data processing and analysis in this paper were carried out using SPSS 25 and Stata 17.

4.3. Variable Definitions

4.3.1. Dependent Variable: High-Quality Development of Enterprises (HQD)

This paper derived the comprehensive score of enterprise high-quality development by constructing a multi-dimensional evaluation index system of enterprise high-quality development.

4.3.2. Independent Variable: ESG Performance (ESG)

Considering that China Securities Index ESG ratings are more in line with the current situation and characteristics of Chinese companies and cover all A-share listed companies. In short, the data volume is large and easy to obtain. In this paper, we adopt China Securities Index ESG ratings to evaluate the ESG level of enterprises, which are categorized into nine levels from C to AAA, with C representing the most basic and AAA the highest. The scores are labeled from 1 to 9 in descending order.

4.3.3. Mediating Variable: Financing Constraints (KZ)

Referring to the notion of Zhao and Wang [35], the KZ index is used as a proxy for corporate financing constraints, and the degree of corporate financing constraints is proportional to the KZ index.

4.3.4. Moderating Variable: Digital Transformation (DT)

Based on the research results of Zhai et al. [26], the keyword word frequency analysis of listed companies’ annual reports is carried out to portray the level of enterprises’ digital transformation, i.e., a high frequency of a keyword means that the enterprise pays more attention to and invests more in it, and it can reflect the enterprise’s outlook for future development. In this paper, the quantitative steps of digital transformation are as follows: (1) keywords were identified from the five dimensions of digital transformation, namely, ‘ABCD’, four underlying technologies and technology application; (2) Python crawling skills were used to extract the textual content of annual reports and analyze the number of times that the core vocabulary words appear; (3) in order to solve the problem of right skewness. In order to solve the right-skewed problem, logarithmic processing was implemented on the word frequency data to obtain the specific strength value of digital transformation.

4.3.5. Control Variables

Referring to previous research findings, this paper used profitability (ROE), firm size (SIZE), firm age (AGE), growth (GROWTH), equity concentration (TOP1), financial risk (LEV), and fixed asset ratio (FAR) as control variables. To further control for certain factors that do not vary with industry and time, time fixed effects (YEAR) and industry fixed effects (IND) were controlled. The specific definitions of the variables are shown in

Table 2. Variable definitions.

	Code	Definition
Dependent Variable	HQD	High-Quality Development Level = Comprehensive score
Independent variables	ESG	ESG Rating
Mediating variable	KZ	Constructing the financing constraint index
Moderating variable	DT	Digital Transformation = ln(word frequency + 1)
Control variables	ROE	Profitability = net profit/net assets
	SIZE	Enterprise scale = ln(number of employees + 1)
	AGE	Enterprise life = ln(years of business establishment + 1)
	GROWTH	Growth = Operating income growth rate
	TOP1	Equity concentration = proportion of shares held by the first largest shareholder
	LEV	Financial risk = total liabilities/total assets
	FAR	Fixed assets ratio = net fixed assets/total assets
	IND	Industry dummy variable
	YEAR	Year dummy variable

.

4.4. Model Construction

Drawing on the approach of Morgan-Lopez and MacKinnon [36], combining methods of the mediating effect test and the moderating effect test, four equations were constructed to test the moderated mediating effect. To test Hypothesis 1, Equation (4) is constructed as follows:

$$HQ{D}_{i,t}={\alpha }_0+{\alpha }_{1}ES{G}_{i,t-1}+{\alpha }_{2}D{T}_{i,t}+\sum Control{s}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t},$$

(4)

As there is a lag in the impact of ESG performance on firm development [20], Equation (4) has a lagged one-period ESG performance, and there is a bidirectional impact between firm development and ESG performance, the lagged treatment can also alleviate the endogeneity problem.

In order to test Hypothesis 2, this paper constructs Equations (5) and (6) as follows:

$$K{Z}_{i,t}={\beta }_0+{\beta }_{1}ES{G}_{i,t-1}+{\beta }_{2}D{T}_{i,t}+{\sum Controls}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t},$$

(5)

$${HQD}_{i,t}={\gamma }_0+{\gamma }_{1}ES{G}_{i,t-1}+{\gamma }_{2}K{Z}_{i,t}+{\gamma }_3{DT}_{i,t}+{\sum Controls}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t},$$

(6)

To test Hypothesis 3, this paper constructs Equation (7):

$${HQD}_{i,t}={\delta }_0+{\delta }_{1}ES{G}_{i,t-1}+{\delta }_{2}K{Z}_{i,t}+{\delta }_3{DT}_{i,t}+{\delta }_4{KZ}_{i,t}\times {DT}_{i,t}+{\sum Controls}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t},$$

(7)

5. Empirical Results and Analysis

5.1. Comprehensive Evaluation of High-Quality Enterprise Development

5.1.1. Extraction of Principal Components

In factor analysis, the scree plot (Figure 2) graphically displays eigenvalues (y-axis) against factor numbers in descending order (x-axis). The optimal factor retention is typically determined at the elbow point where the eigenvalue slope transitions from steep to flat, indicating diminishing returns in variance explanation by subsequent factors. Nevertheless, researchers should acknowledge the visual interpretation subjectivity of this method. Thus, in this paper, we used factor analysis to reduce the dimensionality of the selected 19 indicators and adopted the Kaiser criterion (factor eigenvalue ≥ 1) to determine the number of principle factors. Observing the scree plot (Figure 2), it can be seen that the extraction of nine principal factors F1–F9 is more appropriate; at this time, the extraction degree of each indicator is above 50%, which indicates that the degree of information loss is low, the indicators are appropriately selected, and it is possible to carry out the calculation of the comprehensive score of enterprises.

5.1.2. Score of High-Quality Enterprise Development

The variance contribution rates and cumulative contribution rates of the rotated principal factors F1–F9 are shown in

Table 3. Rotated variance contribution of each factor.

Principle Factor	Percentage of Variance Contribution	Percentage of Cumulative Variance Contribution
F1	11.016	11.016
F2	10.591	21.607
F3	10.479	32.086
F4	8.329	40.415
F5	8.141	48.556
F6	7.868	56.424
F7	7.397	63.822
F8	6.781	70.603
F9	5.637	76.240

. According to the matrix of component score coefficients, the scores S1–S9 of the nine principal factors are calculated sequentially, and the variance contribution rate corresponding to each rotated principal factor is used as the weight to calculate F, which serves as a comprehensive measure of the high-quality development of enterprises.

$$F=11.016\%\times S1+10.591\%\times S2+10.479\%\times S3+8.329\%\times S4+8.141\%\times S5+7.868\%\times S6+7.397\%\times S7+6.781\%\times S8+5.637\%\times S9,$$

(8)

5.2. Descriptive Statistical Analysis

Table 4. Descriptive statistical results for key variables.

	Number	Men	S.D.	Min	Max
HQD	8032	0.001	0.295	−0.677	0.837
ESG	8032	4.217	1.086	1	6
KZ	8032	0.804	2.12	−5.537	5.105
DT	8032	1.462	1.309	0	5.106
ROE	8032	0.075	0.096	−0.328	0.363
SIZE	8032	8.078	1.280	5.231	11.669
AGE	8032	2.985	0.291	2.079	3.555
GROWTH	8032	0.101	0.250	−0.678	1.017
TOP1	8032	0.343	0.148	0.091	0.761
LEV	8032	0.432	0.195	0.067	0.865
FAR	8032	0.226	0.163	0.002	0.701

presents the descriptive statistics of the main variables. The mean value of enterprise high-quality development (HQD) is 0.001, with a standard deviation of 0.295, indicating that there is uneven high-quality development among the sample enterprises. And some enterprises are still in the initial stages of high-quality development and require focused attention. The mean value of enterprise ESG rating is 4.217, with a standard deviation of 1.086, indicating that there is a significant difference in ESG performance among the sample enterprises. This shows that some enterprises are performing well in terms of social responsibility, while others need to improve their level of fulfillment. The mean and standard deviation of the enterprise financing constraint index (KZ) are 0.804 and 2.12, respectively. This indicates that the degree of financing constraints faced by sample enterprises is uneven. It also means that some firms are no longer constrained by financing issues, but there are still firms that face serious financing constraints that may affect operations and growth. The mean value of the enterprise digital transformation index (DT) is 1.462, with a standard deviation of 1.309, which indicates that the progress of the digital transformation of the sample enterprises is inconsistent, and there is still substantial room for development of digital transformation led by digital elements.

5.3. Analysis of Regression Results

5.3.1. Model Selection Test

To test Hypotheses 1–3, the Breusch–Pagan (B-P) test and Hausman test were conducted first. The test results (see

Table 5. Results of the B-P and Hausman tests.

	B-P Test	Hausman Test
Statistic	12,418.88	113.14
p Value	0.00	0.00

) indicate that the fixed effects model is applicable in this paper. Therefore, industry and year fixed effects are controlled in this paper, and regressions are carried out on Equations (4)–(7).

5.3.2. Regression Test of ESG and High-Quality Enterprise Development (HQD)

From the regression results of Equation (4) in

Table 6. Primary regression results.

	Equation (4) HQD	Equation (5) KZ	Equation (6) HQD	Equation (7) HQD
ESG	0.012 *** (5.44)	−0.074 *** (−4.77)	0.010 *** (4.59)	0.010 *** (4.62)
KZ			−0.028 *** (−16.33)	−0.028 *** (−16.31)
DT	−0.026 *** (−12.03)	0.028 * (1.89)	−0.025 *** (−11.82)	−0.025 *** (−11.78)
KZ × DT				0.002 ** (2.05)
ROE	1.288 *** (39.80)	−6.812 *** (−29.78)	1.097 *** (32.45)	1.095 *** (32.42)
Size	0.094 *** (42.71)	−0.255 *** (−16.56)	0.087 *** (39.59)	0.086 *** (39.32)
Age	0.090 *** (10.05)	0.019 (0.29)	0.090 *** (10.31)	0.090 *** (10.29)
Growth	−0.044 *** (−3.67)	0.028 (0.35)	−0.043 *** (−3.64)	−0.043 *** (−3.61)
TOP1	0.130 *** (7.79)	−0.636 *** (-5.43)	0.112 *** (6.83)	0.113 *** (6.87)
Lev	0.027 * (1.83)	7.270 *** (70.15)	0.231 *** (12.00)	0.232 *** (12.04)
FAR	0.068 *** (3.62)	0.549 *** (5.16)	0.083 *** (4.53)	0.084 *** (4.56)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Cons	−1.200 *** (−38.29)	0.537 ** (2.37)	−1.185 *** (−38.48)	−1.185 *** (−38.48)
F value	683.43 ***	782.40 ***	686.91 ***	626.82 ***
N	8032	8032	8032	8032
Adj R2	0.5352	0.5614	0.5530	0.5531

Note: *, **, *** denote significance at the 0.10, 0.05, and 0.01 level, respectively. Standard errors are presented in parentheses. The variables are defined as indicated in Table 2.

, it can be seen that the correlation coefficient of ESG and HQD is 0.012, and there is a positive correlation between the ESG performance of sample enterprises and their high-quality development, which supports Hypothesis 1. This is in line with the expected results. ESG performance guides enterprises onto the path of sustainable development, and with the increasing importance of ecological civilization construction, enterprises with excellent ESG performance have more chances to seize development opportunities and achieve comprehensive development of enterprises. In conclusion, Table 6 can show that ESG performance is positively correlated with HQD, but causality requires further validation.

5.3.3. Test of the Mediating Effect of Financing Constraints (KZ)

The mediating effect analysis was applied to test whether financing constraints play a mediating role. The test results of Equation (5) in Table 6 show that the coefficient of ESG’s impact on KZ is −0.074 and is significant at the 1% level, which indicates that ESG performance may contribute to alleviate the dilemma of financing constraints faced by enterprises. This is mainly due to the recognition of ESG performance by financial institutions and investors, which makes it easier for ESG-enhanced enterprises to obtain financing. In the regression results of Equation (6) in Table 6, the marginal contribution of KZ on HQD is −0.028, which means that the introduction of KZ improves the adjusted goodness-of-fit of Equation (6) in Table 6 when significantly compared with Equation (4) ($∆adj{R}^2$= 1.78%), suggesting that financing constraints may hinder the high-quality development of enterprises. This is consistent with existing research findings [3] that financing constraints restrict the technological progress and scale expansion of enterprises, which is one of the obstacles to enterprise progress. Moreover, in Equation (6) in Table 6, the coefficient of ESG performance is significantly positive at the 1% level, and the regression results of Equation (4) to Equation (6) in Table 6 are combined, and it is known that KZ may play a partly mediating role between ESG and HQD, indicating that alleviating financing constraints is one of the paths for ESG performance to promote high-quality development of enterprises. Thus, Hypothesis 2 is verified.

5.3.4. Test of the Moderating Effect of Digital Transformation (DT)

According to the related study of Morgan-Lopez and MacKinnon [36], on the basis of the above mediating effect being verified, Equation (7) in Table 6 introduces the interaction term of KZ and DT (KZ $\times$ DT). If the coefficient of KZ $\times$ DT is significant, it implies that the moderated mediating effect is verified. The test results of Equation (7) presented in Table 6 indicate that the regression coefficient of KZ $\times$ DT is significantly positive, and the explanatory degree of Equation (7) in Table 6 increases less than that of Equation (6) ($∆adj{R}^2$= 0.01%), which is consistent with the conclusions of the existing studies [37]. This explains that the mediating effect of KZ on ESG and HQD is moderated by the moderating effect of DT, and Hypothesis 3 is verified. It can be seen that data, as a core element in the new era, play a complementary role to traditional elements, and the digital transformation centered on data is greatly beneficial to the development of enterprises.

5.4. Robustness Tests

5.4.1. Replace the Dependent Variable

To verify the stability of the obtained conclusions, the measure method of the dependent variable is changed. According to Wu et al. [8], the LP method is used to measure the total factor productivity (TFP) of enterprises as a proxy variable for HQD, and Hypotheses 1–3 are tested. Equations (4)–(7) are still used above, and the regression results are shown in

Table 7. Replacement of dependent variables test results.

	Equation (4) TFP	Equation (5) KZ	Equation (6) TFP	Equation (7) TFP
ESG	0.079 *** (11.84)	−0.074 *** (−4.77)	0.077 *** (11.47)	0.077 *** (11.52)
KZ			−0.032 *** (−6.27)	−0.032 *** (−6.35)
DT	−0.008 (−1.27)	0.028 * (1.89)	−0.007 (−1.13)	−0.006 (−0.99)
KZ × DT				0.009 *** (3.39)
Controls	Yes	Yes	Yes	Yes
Cons	12.289 *** (127.73)	0.537 ** (2.37)	12.306 *** (127.92)	12.306 *** (127.82)
F value	301.17 ***	782.40 ***	281.35 ***	256.85 ***
N	8032	8032	8032	8032
Adj R2	0.4366	0.5614	0.4394	0.4402

Note: *, **, *** denote significance at the 0.10, 0.05, and 0.01 level, respectively. Standard errors are presented in parentheses. The variables are defined as indicated in Table 2.

.

The regression results of Equation (4) in Table 7 indicate that ESG performance is significantly positively correlated with HQD, and Hypothesis 1 is verified; the regression results of Equations (5) and (6) show that ESG performance is significantly negatively correlated with KZ, and KZ is significantly negatively correlated with HQD, indicating the existence of a mediating effect and verifying Hypothesis 2; the regression coefficient of KZ $\times$ DT in Equation (7) is significant, verifying Hypothesis 3.

5.4.2. Shortening the Research Period

In 2017, China first put forward the national economic development concept of high-quality development, indicating that China’s economy had shifted from the stage of high-speed growth to the stage of high-quality development. Therefore, shortening the sample time and retaining only the data from 2018 to 2023 help to verify the stability of the conclusions of this paper under the influence of policies (the regression results are presented in

Table 8. Shortening the study period to test results.

	Equation (4) HQD	Equation (5) KZ	Equation (6) HQD	Equation (7) HQD
ESG	0.012 *** (4.40)	−0.059 *** (−3.00)	0.010 *** (3.85)	0.010 *** (3.95)
KZ			−0.029 *** (−14.19)	−0.029 *** (−14.20)
DT	−0.026 *** (−10.11)	0.034 * (1.80)	−0.025 *** (−9.89)	−0.025 *** (−9.94)
KZ × DT				0.003 *** (2.94)
Controls	Yes	Yes	Yes	Yes
Cons	−1.207 *** (−28.73)	0.223 (0.70)	−1.200 *** (−29.19)	−1.200 *** (−29.15)
F value	481.82 ***	503.14 ***	489.31 ***	447.83 ***
N	5020	5020	5020	5020
Adj R2	0.5664	0.5691	0.5861	0.5867

Note: *, *** denote significance at the 0.10, and 0.01 level, respectively. Standard errors are presented in parentheses. The variables are defined as indicated in Table 2.

). It can be observed that the coefficient of ESG on HQD is significantly positive, thus Hypothesis 1 is verified. The coefficient of ESG on KZ is significantly negative, and the coefficient of KZ on HQD is also significantly negative, so Hypothesis 2 is verified. The coefficient of KZ $\times$ DT is significantly positive, supporting Hypothesis 3. The above findings indicate that the findings of this paper have strong stability.

5.5. Endogeneity Test

5.5.1. Instrumental Variables (IV) Test

Following established empirical approaches in the literature, we constructed instrumental variables using data on firms’ investments from broad ESG-themed funds [38]. Due to data availability constraints, we ultimately employed the number of ESG investment funds holding shares in each firm (FNUM) as our primary instrumental variable. To maintain consistency with our treatment of the core explanatory variable (ESG ratings), which was lagged by one period in preceding analyses, we similarly applied a one-period lag to FNUM. The results from the two-stage least squares (2SLS) estimation are reported in columns (1) and (2) of

Table 9. Endogeneity Test results.

	Instrumental Variables (IV) Test		Heckman Two-Stage Procedure
	First-Stage Regressions ESG	Second-Stage Regressions HQD	Second-Stage HQD
FNUM	0.019 *** (14.74)
ESG		0.285 ** (2.51)	0.062 *** (4.54)
IMR			−0.002 (−0.98)
Controls	Yes	Yes	Yes
Cons	−3.474 *** (−12.45)	0.228 *** (3.61)	−2.525 *** (−5.99)
F value	217.362 ***	304.81 ***	209.21 ***
N	8032	8032	8032
Adj R2	0.2290	0.2446	0.2900

Note: **, *** denote significance at the 0.05, and 0.01 level, respectively. Standard errors are presented in parentheses. The variables are defined as indicated in Table 2.

.

The first column of Table 9 shows that first-stage regression results demonstrate a statistically significant coefficient of 0.019 (p < 0.01) for the instrumental variable, indicating a strong positive association with the endogenous variable, which aligns with theoretical expectations. The Kleibergen–Paap rk Wald F-statistic of 217.362 substantially exceeds the conventional threshold of 10, allowing us to confidently reject the null hypothesis of weak instruments. Based on the second column of Table 9, in the second-stage estimation, the ESG rating shows a significant positive coefficient of 0.285 (p < 0.05), providing robust evidence that superior ESG performance enhances corporate high-quality development. This finding strongly supports Hypothesis 1.

5.5.2. Heckman Two-Stage Procedure

This study employs environmental disclosure in corporate annual reports as our exclusion restriction variable [39]. In the first stage, we estimated a Probit model incorporating this instrument alongside all control variables from the baseline specification to derive the inverse Mills ratio (IMR). The second-stage regression introduces the IMR as an additional control variable in our baseline model. As presented in third column of Table 9, the ESG coefficient remains statistically significant at the 1% level (β = 0.062), confirming a robust positive association between ESG ratings and corporate high-quality development after controlling for potential sample selection biases. This result substantiates the reliability of our baseline estimates.

6. Research Conclusions and Policy Implications

6.1. Research Conclusions

The goal of high-quality development of the national economy and the concept of ESG are rich in connotation and far-reaching in meaning, and they influence and promote each other. Accordingly, this study constructs a comprehensive evaluation system for enterprise high-quality development. Theoretically, this study elucidates the feasibility and mechanisms through which ESG performance contributes to high-quality development, as well as the moderating role of digital transformation in this relationship. Subsequently, an empirical examination is designed and conducted to validate these propositions. The main conclusions are as follows: (1) There exists a statistically significant relationship between ESG performance and corporate high-quality development. Empirical evidence suggests that superior ESG performance generally exerts a positive and statistically significant influence on corporate high-quality development; (2) Financing constraints function as a mediating mechanism in the observed relationship. Financial constraints exert detrimental effects on both routine business operations and long-term sustainable development of enterprises. ESG performance promotes enterprise high-quality development by alleviating the financing constraints faced by enterprises, which is one of the influential paths for ESG performance in promoting high-quality development; (3) Digital transformation serves as a moderating factor in the observed relationship. The digital transformation of enterprises triggered by data elements can inhibit the negative impact of financing constraints on high-quality development and contribute enterprise to achieving high-quality development.

6.2. Policy Implications

Based on the findings of the study, the following recommendations are made.

6.2.1. The Government Level

Firstly, promoting ESG disclosure. The government should play a proactive role in guiding enterprises to adopt ESG principles and mandating comprehensive public disclosure of ESG-related information. Coupled with the systematic development of a localized ESG evaluation and disclosure framework tailored to China’s socioeconomic context, these measures will establish a robust foundation for facilitating high-quality development. Specifically, the government could establish a centralized ESG disclosure platform to standardize corporate reporting and deploy regulatory technology solutions to ensure data integrity and real-time monitoring. In addition, penalties such as reduction of credit limits can be applied if necessary

Secondly, tilting the capital allocation. Government subsidies and financing policies of financial institutions should be tilted towards ESG-related projects, and investors should be guided to make investments in line with the ESG concept. For example, companies are graded according to their ESG performance, with higher grades having priority in loan applications.

Finally, supporting digital transformation. Governments should intensify investments in digital infrastructure while implementing fiscal and tax incentives to stimulate corporate digital transformation. Concurrently, strengthening patent protection for digital innovations and optimizing talent cultivation ecosystems are critical complementary measures. For instance, deploying specialized teams to engage directly with enterprises enables in-depth understanding of firm-specific characteristics and transition challenges, which could help to provide of tailored assistance to enterprises.

6.2.2. The Enterprise Level

Firstly, shifting the perspectives on ESG performance. The enterprises could evolve their operational paradigms by systematically integrating ESG principles and high-quality development objectives into core business processes. They should realize that optimizing ESG performance is not in conflict with obtaining economic benefits, and that good ESG performance can signal to the outside world that the enterprise is well-managed, is socially responsible, and focuses on sustainable development, and that the various stakeholders will in turn give more resources to support the enterprises with good ESG performance. Specifically, enterprises can integrate their corporate culture with the ESG concepts to raise the importance of ESG. As well as they could select the person in charge of target implementation and the supervisor of the plan execution by setting the ESG targets.

Additionally, seizing opportunities. The enterprises should seize the good opportunities of digital transformation which is reflected in the vigorous application of digital technology to improve resource allocation efficiency and risk-bearing capacity. As well as actively cultivating digital talents, researching and developing advanced digital technologies to enhance the core competitiveness of enterprises. For example, the enterprises could analyze customer preferences and potential needs through historical data, and mine customer opinions by intelligently identifying and analyzing internet social media statements.

6.3. Limitations and Prospects

Firstly, the indicators of the evaluation system for high-quality enterprise development are not exhaustive, and further exploration is required in subsequent research. In addition, the impact of the industry should be taken into account when constructing an evaluation system for the high-quality development of enterprises. Such an evaluation system would be better able to reflect the real-world performance and development potential of enterprises in different industries, providing more targeted guidance for enterprises to achieve high-quality development. Furthermore, it can assist investors, regulators, and other stakeholders in making more informed decisions based on the unique characteristics of each industry.

Additionally, the intricacies of the interplay between ESG performance and the advancement of high-quality enterprises are profoundly multifaceted. Financing constraints are only one of the reasons that hinder business development, implying that alleviating them is also only one of the paths. Future research should explore the multidimensional transmission mechanisms linking ESG performance to high-quality enterprise development. For example, an environmental perspective on how the implementation of renewable energy and energy efficiency measures in the production workflow of an enterprise affects its sustainable development. In the field of corporate governance, this study intends to explore how transparent decision-making, effective risk management, and accountable leadership influence the stability and growth of enterprises.

Finally, the impact of firm heterogeneity, such as the nature of the firm’s property rights and industry, which can affect the development of the firm, is not considered in this study. In other words, some key variables were omitted from this study. Therefore, more targeted conclusions could not be drawn. As for the omission of key variables, future studies should incorporate more firm-level variables, such as industry, region, and ownership type. In other words, tailored studies should be conducted for different types of enterprises to enhance the credibility of the conclusions. These in-depth studies can assist enterprises in integrating ESG concepts into their business strategies and enable governments to formulate more effective policies for promoting the high-quality development of enterprises.