Corporate ESG Performance, Green Innovation, and Green New Quality Productivity: Evidence from China

Abstract

In recent years, China has placed significant emphasis on sustainable economic and social development, actively implementing the concept of green development. In 2023, General Secretary Xi Jinping proposed that all regions should actively develop new-quality productivity, signifying a deepening of green and sustainable development principles. As an internationally recognized indicator for measuring corporate sustainability, Environmental, Social, and Governance (ESG) criteria may influence the enhancement of new-quality productivity across regions, particularly in relation to green new quality productivity. This paper investigates the effects of corporate ESG performance on the levels of green and new-quality productivity using data from China’s A-share listed companies from 2013 to 2022. The findings reveal the following: (1) corporate ESG performance significantly enhances the level of green new quality productivity; (2) mechanism tests indicate that corporate ESG performance enhances green new quality productivity by promoting firms’ green innovation; and (3) further analysis shows that the effect of ESG performance on green new quality productivity is more pronounced in firms with low levels of financing constraints, high media attention, and elevated green awareness among executives. These findings provide empirical evidence for strengthening corporate ESG performance, promoting green innovation, elevating regional levels of green and new-quality productivity, and advancing sustainable development, thereby offering valuable insights for developing countries.

1. Introduction

In recent years, China has attached great importance to sustainable development, and in order to promote the process of sustainable development in China and realize the goal of green development, the Chinese Government has formulated a number of policies, which are mainly reflected in the following: First, in the energy sector, it has introduced a subsidy policy to encourage the construction of renewable-energy projects, and has formulated energy-efficiency standards that require energy-consuming enterprises to implement energy-saving reforms. Second, in the area of industrial development, it has actively supported the development of green industries, giving tax incentives, financial subsidies, and other support to promote the green upgrading of traditional industries and reduce pollution emissions. Third, in terms of ecological environmental protection, the Action Plan for Prevention and Control of Air Pollution has been implemented, the Action Plan for Prevention and Control of Water Pollution has been formulated, and the Action Plan for Prevention and Control of Soil Pollution has been introduced. Fourthly, in terms of resource utilization and the circular economy, tax breaks and other incentives will be given to enterprises that use waste to produce products, advocate the circular economy, and promote the construction of circular-economy demonstration cities and parks. Fifth, green finance initiatives have become prominent, advocating green credit, guiding banks and other financial institutions to increase credit support for green projects, promoting green industry financing, and issuing green bonds.

In recent years, ESG (i.e., Environmental, Social, and Governance) has emerged as a focal point for both the international community and China, reflecting increased attention to environmental issues [1], corporate social responsibility, and governance matters [2]. Sustainable development is closely related to ESG. The ESG performance of Chinese enterprises is closely linked to the UN Sustainable Development Goals (SDGs). In terms of the environment, Chinese companies’ initiatives aiming to improve energy efficiency and reduce emissions correspond to the SDGs of combating climate change and the sustainable use of resources. On the social front, companies’ efforts to protect employees’ rights and interests and participate in community building are in line with the SDGs’ goals on education, employment, reducing inequality, and building sustainable communities. In terms of governance, enterprises should strengthen internal management and enhance transparency, which will help realize the SDG goals of good governance and global partnership.

To align with international ESG concepts, the China Securities Regulatory Commission (CSRC) and Chinese stock exchanges have issued a series of regulations aimed at steering enterprises and society toward a greener and more sustainable trajectory [3]. It is widely recognized that China should actively integrate foreign ESG governance frameworks, enhance corporate ESG practices, and promote transparency in ESG information disclosure [4]. Corporate ESG performance, as a critical indicator of a company’s commitment to sustainability, represents a positive developmental trajectory and signifies a company’s dedication to the principles of green and sustainable development [5]. Superior corporate ESG performance is likely to enhance a company’s reputation, thereby attracting greater investor interest and alleviating capital constraints, which in turn provides firms with the necessary resources to pursue green innovation [6]. Existing studies indicate that green innovation is a pivotal response to China’s “dual-carbon” policy, with a strong correlation found between ESG performance and green innovation [7].

The concept of “new quality productivity” was highlighted during China’s 2024 “Two Sessions”, emphasizing that green new quality productivity, driven by green innovation [8], represents an evolution of the traditional productive forces and is essential for fostering high-quality economic development. In contrast to conventional productivity, this approach signifies a departure from traditional economic growth models, focusing instead on high technology, efficiency, and quality [9]. Green innovation integrates the principles of sustainable development across all facets of research and development, production, and marketing, facilitating the efficient use of resources while ensuring environmentally friendly practices through technological and model innovations. Some scholars have suggested that green innovation contributes to the formation of new-quality productivity at various levels—micro, meso, and macro—with breakthrough technological advancements playing a crucial role in this process [10]. Currently, China is undergoing a transformation and upgrading of its economic structure amid intensifying global competition [11], necessitating an urgent acceleration of green innovation to drive industrial advancement and sustainable development, as well as enhance local levels of green new quality productivity [12]. In this context, the ESG performance of enterprises, as key market participants, is directly linked to the promotion of green innovation and the development of green new quality productivity [13]. To address these issues, this paper analyzes a dataset comprising 11,470 samples from A-share listed companies in China, spanning the years from 2013 to 2022. It examines the impact of corporate ESG performance on the levels of green and new-quality productivity across various regions of China, while also analyzing the mediating role of green innovation in this process.

Compared to the existing literature, this paper contributes in several ways: First, in terms of the research framework, it integrates enterprise-level ESG and green new quality productivity into a comprehensive model, thereby enriching the understanding of the economic implications of corporate ESG performance and expanding the exploration of factors influencing green new quality productivity. Second, regarding research content, this study examines the role of green innovation in conjunction with the characteristics of new-quality productivity. Specifically, it investigates how ESG performance affects green innovation and how green innovation subsequently enhances green new quality productivity, elucidating the pathways through which corporate ESG performance influences productivity. This enriches the relevant literature on the subject. Third, in terms of methodology, this paper elaborates on the moderating effects of financing constraints and media attention within the context of corporate ESG performance and the latter’s impact on regional green new quality productivity. This clarification of the intrinsic connections between corporate ESG performance and green new quality productivity provides intellectual support for regions striving to accelerate the formation of green new quality productivity, improve their sustainable development levels, and enhance corporate ESG practices.

2. Literature Review

2.1. Origin, Connotation, Characteristics, and Realization of New-Quality Productivity

As a prominent topic in 2024, new-quality productivity has garnered extensive attention from Chinese scholars, particularly in the context of developing green new quality productivity under the guidance of sustainable development principles. Marx posited that scientific progress leads to the continual development of productive forces. Chinese leaders have built upon Marx’s productivity theory, with General Secretary Xi Jinping introducing the concept of “new quality productivity” as an extension of Marxist theory [14]. New-quality productivity necessitates advancement in the three components of traditional productivity: the enhancement of worker quality, increased technological sophistication of labor materials, and an expanded scope of labor [15]. The core of this concept lies in the significant augmentation of total factor productivity, one wherein the essence of “quality” is paramount. The advancement of new-quality productivity is inextricably linked to industrial support and requires improvements in the modern industrial system, particularly through the development of strategic emerging industries [16]. What characterizes new-quality productivity? Scholars have identified several essential attributes, including “driven by scientific and technological innovation”, “efficiency and low consumption in industry and sustainable environmental practices”, “enhanced quality of life and equal access to social services”, and “digital empowerment” alongside “modernization of national governance capacity [17]”. The development of new productivity necessitates prioritizing innovation, actively pursuing scientific and technological advancements [18], and ensuring that workforce development keeps pace. This includes addressing critical technological challenges and fostering talent in innovative and technical fields to promote scientific progress. Additionally, enhancing the modern industrial system and optimizing the structures of traditional, emerging, and future industries are imperative [19]. Upholding the basic socialist economic system and accelerating the evolution of systems compatible with this new quality of productive forces are also vital. The rationale for advancing new-quality productive forces lies in their potential to drive comprehensive economic progress and sustainable development. Scholars generally concur that the advancement of new-quality productive forces can address prevailing major social contradictions and facilitate the transition to high-quality economic development [20]. The critical question arises: how can new-quality productivity be cultivated? The cultivation of new-quality productivity necessitates a collaborative effort among the government, market forces, and talent [21]. The government must encourage innovation, enterprises should recruit high-level talents to enhance their innovative capabilities, workers must foster collaboration and elevate their awareness of self-innovation, and the nation should effectively implement industrial layouts to improve the level of new-quality productivity within regions [22]. Furthermore, as an extension of new-quality productivity, green new quality productivity emphasizes the importance of green and sustainable development within enterprises, which is particularly significant for developing countries seeking to harmonize economic growth with environmental protection [23].

2.2. Economic Consequences of ESG Performance

Previous studies have demonstrated that superior ESG performance positively influences investor behavior; investors are more inclined to allocate resources to firms with robust ESG practices [24]. This tendency alleviates financial pressures on enterprises, improves corporate financing conditions, and assists in overcoming financing challenges [25]. Additionally, a higher ESG score correlates with a reduced likelihood of default on corporate debts [26]. Good ESG performance also serves to enhance enterprise value through three primary mechanisms: first, improved ESG performance fosters corporate green innovation, subsequently leading to enhanced operational performance [27]; second, effective ESG practices can improve investment outcomes, mitigate capital constraints, and reduce financial risks, thereby augmenting enterprise value [28]; third, strong ESG performance bolsters investor trust, attracts new consumer demographics, and ultimately contributes to an increase in enterprise value [29]. It is important to acknowledge that the impact of ESG performance on enterprise value is a gradual and sustained process [30], one which is crucial for firms aiming to achieve green sustainable development and cultivate green new quality productivity.

2.3. The Important Role of Green Innovation in Fostering Green and New-Quality Productivity

Cultivating new productivity necessitates a departure from traditional economic growth models and conventional productivity development pathways, positioning innovation as the primary driving force. The cultivation of green new quality productivity is intrinsically linked to enterprise-level green innovation. The role of green innovation in fostering green new quality productivity operates through several mechanisms: it facilitates industrial transformation and upgrading via green technologies [31], enhances various components of productivity, and supports the green development of enterprises. Green innovation operates at the micro, meso, and macro levels to promote the formation of new-quality productivity, with breakthrough technological innovations playing pivotal roles [32]. To nurture green new quality productivity, it is essential to enhance the innovation system and bolster talent support; innovation must be central to development, driving industrial advancements through enterprise-level innovations to achieve an overall enhancement in regional innovation capacities across China and expedite the modernization process [33]. Additionally, actively promoting the adoption of energy-saving and clean technologies within enterprises is vital, alongside establishing platforms for deep integration of industry, academia, and research, and thereby incentivizing enterprises to leverage these resources for the introduction of green technologies and realization of sustainable development [34]. From a long-term strategic perspective, the positive impact of improved ESG performance on future corporate assets is evident [35], as ESG performance enhances an enterprise’s capacity for high-quality development, leading to speedier and higher-quality growth [36].

Building upon existing studies, several observations emerge: First, the current research in China manifests a deficiency in the investigation of the impact of corporate ESG performance on green new quality productivity. Both corporate ESG performance and green new quality productivity are critical factors in achieving high-quality development within China’s economy, underscoring their significance. Second, the majority of studies relating to green new quality productivity predominantly feature qualitative analyses, with a scarcity of quantitative approaches apparent, indicating an opportunity to enrich the discourse on this topic. The research presented in this paper aims to fill this gap by contributing to the literature of quantitative analyses of green new quality productivity. Third, this paper will integrate China’s green development concept with productivity-related theories to empirically assess the influence of corporate ESG performance on the level of green new quality productivity across Chinese provinces, further analyzing the impact mechanism of green innovation, a practice which holds significant implications for the vigorous advancement of Chinese-style modernization.

3. Theoretical Analysis and Research Hypotheses

3.1. ESG Performance and Green New Quality Productivity

According to signaling theory, ESG performance serves as an indicator of an enterprise’s sustainability and investment-related desirability. As information-superior parties, enterprises actively communicate their developmental prospects and investment value to investors, who represent information-inferior parties, to attract investment. Internally, the standardization of ESG management allows enterprises to focus on environmental protection, engage in green innovation, and enhance research and development of green products, thereby improving the innovative capabilities and sustainable development levels of these enterprises [37,38]. This, in turn, elevates the quality of labor resources. Furthermore, ESG initiatives assist enterprises in strengthening internal management, enhancing corporate value, garnering social recognition, building a positive corporate image, and attracting high-quality talent, thus improving labor quality and contributing to the advancement of green and new productivity [39]. Externally, ESG practices enable investors to shift their focus from traditional financial indicators to a broader spectrum of sustainable development factors, including corporate strategy, positioning, governance mechanisms, and social responsibility [40,41]. Consequently, strong ESG performance signals to external investors that an enterprise prioritizes the durability and stability of its growth; higher ratings indicate a greater emphasis on sustainability. Such credibility reduces corporate financing challenges and associated costs, facilitating access to development resources and enhancing labor quality. In essence, improved ESG performance correlates with increased transparency as to company information, effectively mitigating investor risk and aligning the enterprise with more suitable investors [42,43]. This alignment also enhances the company’s attractiveness in the talent market, allowing for the recruitment of a higher-quality workforce that can drive innovation and improve the level of green new quality productivity [44]. Based on this analysis, we propose the following hypothesis:

Hypothesis 1:

Corporate ESG performance contributes to the improvement of regional green new quality productivity levels.

3.2. Mediating Effects of Green Innovation

The impact of ESG performance on green innovation is predominantly manifested through three key aspects: first, the implementation of ESG practices effectively stimulates the market incentive mechanism. Creditors and financial institutions exhibit a greater willingness to provide preferential green credit to enterprises demonstrating strong Environmental, Social, and Governance (ESG) performance [45]. This trend incentivizes enterprises to adopt green technologies more actively, engage in research and development relating to green products, and enhance their corporate environmental awareness and green innovation initiatives [46]. Concurrently, investors tend to favor companies with high ESG ratings as they pursue sustainable development goals [47,48]. Additionally, transparent ESG information disclosure compels enterprises to undergo green transformation. Such disclosure serves as an effective mechanism for alleviating information asymmetry. With the ongoing improvement of ESG governance, corporate ESG information disclosure is becoming increasingly transparent [49]. When enterprises exhibit environmental pollution or other negative behaviors, stakeholders are likely to pressure these enterprises to enhance their levels of green innovation and transformation. Furthermore, higher ESG ratings and scores contribute to enhancing a company’s visibility and establishing a positive public image regarding its commitment to social responsibility [50,51]. This increased visibility can attract business collaborations and facilitate the influx of external capital, technology, and talent—crucial elements for fostering innovation [52]. The integration of external resources is likely to improve the success rate of the enterprise’s green research and development (R&D) efforts, thereby achieving effective green innovation.

Green innovation is shaped by various components contributing to new-quality productivity. This is primarily manifested in two ways: First, the training of innovative talent can significantly enhance the skills and capabilities of workers, enabling them to become highly qualified personnel suited for green quality productivity. Second, the adoption of green innovation practices can render production and service processes cleaner and more efficient, leading to an overall upgrade in labor means and objectives [53,54]. Furthermore, green innovation can influence the pathways through which new-quality productivity is formed [55,56]. By transforming traditional production methods and business models, green innovation fosters the emergence of new business forms and models, thereby driving improvements in worker quality [57]. It also alters the socio-economic structure, optimizes the allocation of capital, talent, technology, and other resources, and reshapes the workforce and consumption structures [58]. These changes collectively enhance societal effectiveness and promote the development of new-quality productivity. In summary, we propose the following hypothesis:

Hypothesis 2:

Green innovation serves a positive mediating role in the relationship between corporate ESG performance and new-quality productivity.

4. Research Design

4.1. Sample Selection and Data Source

In order to study whether the ESG performance of Chinese companies affects the development of green new quality productivity, this paper has selected Chinese A-share listed companies for its study. As to why Chinese A-share companies are selected for the study, the authors mainly consider that A-share companies cover a wide range of industries, have more standardized management, and have easier access to data, as well as the fact that China’s economy has a significant impact on the global economy. This paper considers the data of Chinese A-share listed companies during the period of 2013–2022, during which the relevant data are more complete and there is sufficient data to support the research on new-quality productivity-related issues. During the research process, financial-industry companies, poorly managed ST, *ST enterprises, and enterprises with missing data were excluded, and the data was reduced by Winsor2 tailing. Winsor2 tail reduction was applied to the data, and 11,470 sample observations were ultimately obtained. The ESG performance data are sourced from the CSI database, while the data necessary for measuring green new quality productivity are obtained from the China Statistical Yearbook, the China Science and Technology Statistical Yearbook, the China Energy Statistical Yearbook, the China Urban Statistical Yearbook, and the CNRDS database for the corresponding years. Due to significant missing data relating to Tibet, this study focuses on the panel data of 30 provinces in mainland China (excluding Tibet) from 2013 to 2022. For a limited number of outliers and missing values, interpolation and index prediction methods are employed for data treatment. The initial data screening was conducted using Excel, while empirical analysis was performed using Stata 16.0 software.

4.2. Definition and Selection of Variables

4.2.1. Explained Variable: Green New Quality Productivity (Npro)

This paper constructs a system to measure the level of green new quality productivity, drawing on the existing definition and the research results of Ren and Wu (2024) [59]. The measurement framework encompasses three perspectives: laborers, labor materials, and labor objects. Details regarding the breakdown, measurement methods, and impacts of the green new quality productivity index are presented in

Table 1. Green new quality productivity measures.

Target Level	Standardized Layer	Program Level (Computing)	Composition of Indicators	Standard Measure	Formula	Affect
Green and New-Quality Productivity data	Green Workers	Green Labor Productivity	Economic output	GDP per capita	GDP/total population	+
			Economic income	Wages per capita	Average wage of employed workers	+
			Employment structure	Share of tertiary employment	Tertiary employment/total employment	+
		Green Worker Quality	Educational attainment	Percentage of population in tertiary education	Average years of schooling per capita	+
			Funds for incubation	Intensity of funding for education	Expenditure on education/total fiscal expenditure	+
			Knowledge accumulation potential	Structure of enrolled students	Number of students in school/total population	+
		Green Worker Spirit	Creativity	Innovative human inputs	Full-time equivalent of R&D personnel	+
			Enterprising spirit	Entrepreneurial activity	Number of new start-ups per 100 people	+
	Green labor targets	Level of Green Industry Development	Informatization level	Enterprise informatization level	Number of enterprises with e-commerce trading activities/total number of enterprises	+
			Percentage of strategic industries	Percentage of emerging strategic industries	Value added by emerging strategic industries/GDP	+
			Future industries	Robot mounting density	Number of industrial robots installed in the region × (regional industrial employment/national total employment)	+
		Ecological Environment	Green ecology	Green resource	Forest cover	+
				Environmental protection efforts	Expenditure on environmental protection/government expenditure on public finance	+
			Green production	Pollution prevention and control quality	COD emissions/GDP	-
					Sulfur dioxide emissions/GDP	-
				Green inventions results	Number of green patent applications/number of patent applications	+
	Green labor information	Green Material Labor Materials	Infrastructure	Traditional infrastructure	Road mileage	+
					Railroad mileage	+
				Digital infrastructure	Fiber length	+
					Internet broadband access ports per capita	+
			Level of energy utilization	Energy intensity	Energy consumption/CDP	-
				Level of green energy consumption	Decarbonization index of energy consumption structure	+
			Energy utilization potential	Pollution prevention and control potential	Treatment capacity of waste gas treatment facilities	+
		Green Intangible Labor Materials	Level of science, technology and innovation	Patents per capita	Number of patents granted/total population	+
				Economic inputs for new products	New product development expenditure/GDP	+
			Level of digitization	Digital economy	Digital Economy Index	+
				Enterprise digitization	Enterprise digitization level	+

. To address the limited scale of the measurement index, we follow the methodology proposed by Zhang and Shi by multiplying the index by 100 to enhance the regression coefficient’s order of magnitude [60].

4.2.2. Explanatory Variables: ESG Performance (ESG)

Numerous ESG rating systems exist, among them those of influential international agencies including FTSE Russell, Thomson Reuters, Dow Jones, Mingsheng, Morningstar, and Luft. In China, notable agencies include CSI, Shangdao Ronglv, Chidian, Jiasi Fund, Hexun, and Wande. Considering the comprehensive nature of the ESG data provided by CSI [61], this paper utilizes the CSI ESG ratings as the explanatory variable.

4.2.3. Mediating Variable: Green Innovation (GPI)

To analyze the mediating effect of green innovation, this paper measures it by the number of green patents granted [62]. The calculation involves two steps: first, summing the total number of three types of green patents authorized in the given year; second, applying the natural logarithm (Ln) to the total number of authorized green patents plus one.

4.2.4. Control Variables

To accurately assess the impact of corporate ESG performance on green new quality productivity, this study incorporates control variables. Based on prior research, seven indicators are selected: enterprise size (Size), enterprise age (Age), gearing ratio (FL), board size (Boa), percentage of independent directors (BI), duality of positions (Dua), and shareholding concentration (Top 10). The definitions of each variable are detailed in

Table 2. Variable definition table.

Name	Symbol	Description	Source
Level of green and new-quality productivity in the registry	Npro	Calculated on the basis of the weights in Table 1 × 100	China Statistical Yearbook, Provincial Statistical Yearbook, Provincial Statistical Bulletin
ESG performance	ESG	CSI ESG Score	Wind database
Green innovation	GPI	Ln (total number of three types of green patents authorized in the year + 1)	CSMAR database
Enterprise size	Size	Ln (total assets)	CSMAR database
Age of business	Age	Ln (current year - year of establishment of the enterprise)	CSMAR database
gearing	FL	Total liabilities/total assets	CSMAR database
Board size	Boa	Ln (number of board members)	CSMAR database
Percentage of sole director	BI	Number of independent directors/total number of corporate directors	CSMAR database
Two jobs in one	Dua	If the chairman and general manager are the same person, take 1, otherwise take 0	CSMAR database
Shareholding concentration	Top 10	Shareholding ratio of top ten shareholders	CSMAR database
Year dummy variable	Year	Controlling for year fixed effects	CSMAR database
Industry dummy variables	Ind	Controlling for industry fixed effects	CSMAR database

.

4.3. Model Construction

To validate the hypotheses of this study, regression models (1), (2), and (3) are constructed for the analysis.

$${Npro}_{i,t}={\alpha }_0+{\alpha }_1{ESG}_{i,t}+\sum {control}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t}$$

(1)

In model (1), Npro represents the explained variable, indicating the level of green new quality productivity; ESG denotes the explanatory variable, reflecting corporate ESG performance; the set of control variables is denoted by Controls. Time and industry fixed effects are indicated by $\sum Year$ and $\sum Ind$, respectively; ε represents the residual term; and α₀ denotes the constant term. According to model (1), Hypothesis 1 is supported if the coefficient of ESG is significantly positive.

$${GPI}_{i,t}={\beta }_0+{\beta }_1{ESG}_{i,t}+\sum {control}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t}$$

(2)

In model (2), GPI serves as the mediating variable, representing the level of green innovation among enterprises; ε remains the residual term.

$${Npro}_{i,t}={\gamma }_0+{\gamma }_1{ESG}_{i,t}+{\gamma }_2{GPI}_{i,t}+\sum {control}_{i,t}+\sum Year+\sum Ind+{\epsilon }_{i,t}$$

(3)

Model (3) continues to utilize ε as the residual term. Following the mediation effect testing procedures established by Wen and Ye [63], if the coefficients α₁, β₁, γ₁, and γ₂ are significant and positive, it indicates that green innovation plays a positive mediating role in the influence of ESG performance on green new quality productivity, thereby confirming Hypothesis 2.

5. Analysis of Empirical Results

5.1. Descriptive Statistics

The variables outlined in Table 1 underwent statistical analysis, with the results as presented in

Table 3. Descriptive statistics of main variables.

Variables	N	Mean	SD	Min	P50	Max
Npro	11,470	30.953	13.063	12.900	27.900	71.100
ESG	11,470	74.069	5.031	59.560	74.290	85.100
GPI	11,470	0.938	1.181	0.000	0.693	4.898
Size	11,470	22.697	1.328	20.320	22.495	26.951
Age	11,470	2.963	0.302	2.079	2.996	3.526
FL	11,470	0.416	0.186	0.063	0.414	0.823
Boa	11,470	2.143	0.191	1.609	2.197	2.639
BI	11,470	37.463	5.365	33.330	35.290	57.140
Dua	11,470	0.235	0.424	0.000	0.000	1.000
Top 10	11,470	56.310	14.791	24.366	56.482	91.311

. Among the 11,470 samples selected, the maximum value of the green new quality productivity index is 71.100, the minimum value is 12.900, the mean is 30.953, and the median is 27.900. This distribution suggests that green new quality productivity levels are generally normally distributed, though there exists a significant regional disparity, given the research conditions. The results for ESG performance reveal a maximum of 85.100, a minimum of 59.560, a standard deviation of 5.031, and a mean of 74.069, indicating minimal overall variance, with most enterprises exhibiting strong performance. Conversely, the overall changes in enterprise size, age, and gearing ratio are not statistically significant. However, the disparity between the maximum and minimum values of equity concentration is considerable, indicating substantial variance in equity concentration among different enterprises. To mitigate covariance issues among variables, we calculate the Variance Inflation Factor (VIF), with values ranging between 1.05 and 1.87, all below the threshold of 10, confirming the absence of multicollinearity.

5.2. Benchmark Regression Results

The regression results of Model 1 are summarized in

Table 4. Benchmark regression results.

Variables	(1)	(2)	(3)
	Npro	Npro	Npro
ESG	0.224 ***	0.111 ***	0.062 ***
	(9.31)	(5.69)	(3.13)
Size			0.805 ***
			(7.78)
Age			−1.844 ***
			(−5.52)
FL			−4.105 ***
			(−6.42)
Boa			−3.537 ***
			(−5.69)
BI			−0.029
			(−1.35)
Dua			1.887 ***
			(8.93)
Top 10			0.049 ***
			(7.20)
Constant	14.365 ***	1.322	−0.543
	(8.09)	(0.80)	(−0.19)
Observations	11,470	11,470	11,470
R-squared	0.007	0.464	0.478
Ind	NO	YES	YES
Year	NO	YES	YES

Note: *** denotes significant within 1 percent.

. Column (1) presents a regression analysis utilizing only the explanatory and interpreted variables without fixed effects, revealing an ESG coefficient of 0.224, which is significantly correlated at the 1% level. This implies that a one-unit increase in ESG results in a 0.224 rise in the level of green new quality productivity. Column (2) incorporates industry and time fixed effects into the analysis based on column (1), resulting in an ESG coefficient of 0.111. Column (3) builds upon column (2) by including control variables, with the ESG coefficient remaining at 0.062. In all three cases, the ESG coefficients are positive and significantly correlated at the 1% level, thereby validating Hypothesis 1. The R² value in column (3) is 0.478, indicating that 47.8% of the sample data support the model, confirming that ESG performance significantly and positively influences the level of regional green new quality productivity. In model (3), the coefficients for enterprise size (Size), dual employment (Dua), and equity concentration (Top 10) are all positive, and significant at the 1% level. This indicates that larger enterprises, those with dual employment, and a higher concentration of equity positively influence the level of regional green new-quality productivity. Moreover, better-developed enterprises can significantly drive the enhancement of regional green new-quality productivity, providing empirical support for strengthening regional enterprise management and fostering growth. Conversely, the coefficients for Age, FL, and Bua are negative and significant, suggesting that an older enterprise and an increased number of directors can restrict the development of green new-quality productivity to some extent.

5.3. Mediating Effect Test

To further investigate the influence mechanism of Environmental, Social, and Governance (ESG) factors on green new-quality productivity, this paper introduces the variable of green innovation (GPI) to analyze how ESG performance affects regional green new-quality productivity. The regression results for models (1), (2), and (3) are presented in

Table 5. Intermediation-effect regression results.

Variables	(1)	(2)	(3)
	Npro	GPI	Npro
ESG	0.062 ***	0.018 ***	0.054 ***
	(3.13)	(9.94)	(2.74)
GPI			0.433 ***
			(3.87)
Size	0.805 ***	0.432 ***	0.618 ***
	(7.78)	(40.09)	(5.71)
Age	−1.844 ***	−0.152 ***	−1.779 ***
	(−5.52)	(−4.36)	(−5.34)
FL	−4.105 ***	0.236 ***	−4.207 ***
	(−6.42)	(4.04)	(−6.57)
Boa	−3.537 ***	0.033	−3.552 ***
	(−5.69)	(0.57)	(−5.72)
BI	−0.029	0.001	−0.029
	(−1.35)	(0.76)	(−1.38)
Dua	1.887 ***	0.011	1.882 ***
	(8.93)	(0.55)	(8.91)
Top 10	0.049 ***	−0.001	0.049 ***
	(7.20)	(−1.57)	(7.28)
Constant	−0.543	−10.473 ***	3.992
	(−0.19)	(−36.56)	(1.32)
Observations	11,470	11,470	11,470
R-squared	0.478	0.489	0.479
Ind	YES	YES	YES
Year	YES	YES	YES

Note: *** denotes significant within 1 percent.

.

As shown in Table 5, after introducing GPI as a mediating variable, the coefficient of ESG in column (2) is 0.018. This indicates that for every 1 unit increase in ESG performance, the green innovation level of the enterprise increases by 0.018 units, which is significant at the 1% level. Thus, ESG performance positively impacts the green innovation of enterprises. In column (3), the coefficient of ESG is 0.054, and the coefficient of GPI is 0.433, both significant at the 1% level. This demonstrates that enterprise green innovation can enhance the level of regional green new-quality productivity, thereby validating Hypothesis 2. In summary, green innovation serves as a positive mediating factor in the relationship between ESG and green new-quality productivity, one in which ESG influences enterprise green innovation, which in turn affects regional green new-quality productivity.

5.4. Robustness Test

To assess the robustness of the model examining the impact of ESG on green new-quality productivity, this study employs several methods for its robustness test.

5.4.1. Instrumental Variable Method

Corporate ESG performance enhances corporate green innovation, qualitatively shapes the corporate image, secures abundant resources, and promotes an increase in green new-quality productivity. Conversely, improved green new-quality productivity can foster a better business environment, thereby enhancing corporate vitality and increasing investments in ESG performance. Therefore, drawing on the methodology of Gao and Chu (2024) [64] and Nie and Song (2023) [65], a 2SLS regression is performed using the lagged-one period of ESG (IV1) and the mean ESG of other firms in the same province (IV2) as instrumental variables for ESG; the results are shown in column 6 of

Table 6. Instrumental-variables-based approach.

Variables	(1)	(2)	(3)	(4)
	ESG	Npro	ESG	Npro
ESG		0.093 ***		7.124 ***
		(0.033)		(0.844)
IV1	0.638 ***
	(0.007)
IV2			2.078 ***
			(0.238)
Constant	6.778 ***	23.164 ***	20.557 ***	204.648 ***
	(1.212)	(3.467)	(1.704)	(26.514)
Observations	10,323	10,323	11,470	11,470
R-squared	0.542	0.443	0.215	11,470
Control Variable	YES	YES	YES	YES
Ind	YES	YES	YES	YES
Year	YES	YES	YES	YES

Note: *** denotes significant within 1 percent.

. In the first stage, the regression coefficients of both instrumental variables on ESG are significantly positive at the 1% level, and the F-value of the first-stage regression is much larger than 10, indicating that the instrumental variables satisfy the correlation requirement. In addition, the test for weak instrumental variables and the test for over-identification, which are not presented, also indicate that there is no weak-instrumental-variables problem and no over-identification problem. In the second stage, ESG performance still significantly enhances firms’ green new quality productivity level at the 1% level. This indicates that the main findings of this study still hold after controlling for potential endogeneity problems.

5.4.2. Replacement of Explanatory Variables

Following the study by Wang et al. (2024) [66], the green new-quality productivity index system they constructed is utilized to measure green new-quality productivity levels (Npro1) for each province in China, through the entropy method. This index is then matched with the listed companies selected for this analysis to assess enterprise dimensionality. As evidenced by the regression results in columns (1)–(3) of

Table 7. Results of regression with replacement of explanatory variables and exclusion of anomalous years.

Variables	Alternative Explanatory Variables			Excluding Anomalous Years (2020–2022)
	(1)	(2)	(3)	(4)	(5)	(6)
	Npro1	GPI	Npro1	Npro	GPI	Npro
ESG	0.055 ***	0.017 ***	0.050 ***	0.066 ***	0.017 ***	0.057 ***
	(3.75)	(9.75)	(3.45)	(3.33)	(8.18)	(2.91)
GPI			0.243 ***			0.502 ***
			(3.02)			(4.48)
Constant	7.718 ***	−10.206 ***	10.197 ***	3.737	−9.399 ***	8.458 ***
	(3.50)	(−36.90)	(4.43)	(1.35)	(−29.51)	(2.91)
Observations	11,470	11,470	11,470	8029	8029	8029
R-squared	0.467	0.485	0.468	0.400	0.440	0.401
Control variable	YES	YES	YES	YES	YES	YES
Ind	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES

Note: *** denotes significant within 1 percent.

, the coefficients of ESG in models (1), (2), and (3) are 0.055, 0.017, and 0.050, respectively. Furthermore, the coefficient of GPI in the model (3) is 0.243, with all results showing a significant positive correlation at the 1% level. Thus, even after replacing the explanatory variables, ESG performance continues to positively affect regional green new-quality productivity, and green innovation maintains its positive mediating effect, confirming the validity of Hypotheses 1 and 2.

5.4.3. Excluding Abnormal Years

Drawing upon the research of Song, Zhang, and Pan (2024) [67], this paper excludes data from the years 2020–2022, during which some regions experienced economic decline due to the impact of the pandemic, in order to conduct further robustness testing. The regression results are shown in columns (4)–(6) of Table 7. Following the exclusion of data from 2020–2022, the ESG coefficients in columns (4), (5), and (6) are 0.066, 0.017, and 0.057, respectively, while the GPI coefficient is 0.502, with all results significantly correlated at the 1% level. These findings suggest that excluding abnormal years does not diminish the positive influence of corporate ESG performance on regional green new-quality productivity, and green innovation continues to play a positive mediating role in the relationship between corporate ESG performance and regional green new-quality productivity, thereby corroborating Hypotheses 1 and 2.

5.4.4. Tests of Different Dimensions of ESG

Building on the work of Zhang, Qin, and Liu (2020) [68], this study further examines the impact of the three dimensions of ESG on green new-quality productivity. The regression results presented in

Table 8. ESG regression results for different dimensions of the test.

Variables	Npro
	(1)	(2)	(3)
E	0.027 *
	(1.96)
S		0.003
		(0.28)
G			0.046 ***
			(3.01)
Constant	0.744	1.181	−0.600
	(0.26)	(0.42)	(−0.21)
Observations	11,470	11,470	11,470
R-squared	0.478	0.478	0.478
Control variable	YES	YES	YES
Ind	YES	YES	YES
Year	YES	YES	YES

Note: *** denotes significant within 1 percent, and * denotes significant within 10 percent.

reveal that the coefficients for the Environment (E) and Governance (G) dimensions are both significantly positive, underscoring their substantial contributions to enhancing green new-quality productivity. Conversely, the coefficient for Social Responsibility (S) is 0.003, which does not achieve significance, suggesting that the impact of corporate social responsibility on improving green new-quality productivity is not yet apparent. Possible explanations for this include the following: (1) Some enterprises may perceive social responsibility as a moral obligation rather than a core component of corporate strategy, leading to a lack of systematic and long-term commitment to fulfilling CSR, and thus fail to effectively enhance green new-quality productivity. (2) Some enterprises might prioritize short-term profits, overlooking the potential benefits of fulfilling social responsibility on green new-quality productivity enhancement. In the future, enterprises can bolster green new-quality productivity by actively engaging in their social responsibilities.

5.5. Further Analysis

5.5.1. Heterogeneity Analysis of the Level of Financing Constraints

Financing constraints directly impact an enterprise’s capital liquidity and investment decisions [69]. When capital pressure rises, enterprises facing financing constraints may lack sufficient funds for green innovation, forcing them to allocate the existing funds primarily to their day-to-day operations. Consequently, this situation may slow down green innovation, especially for projects that require significant initial investments and have long payback periods, potentially suppressing the level of green new quality productivity.

To assess the intensity of corporate financing constraints, this paper introduces the KZ index, as proposed by Hadlock and Pierce (2010) [70]. A higher KZ index indicates more stringent financing conditions. This analysis utilizes the average KZ index to categorize financing constraints: values greater than the median indicate high financing constraints, while values below the median indicate low levels of financing constraints. Column (1) of

Table 9. Heterogeneity analysis.

Variables	Npro
	Low Financing Constraints	High Financing Constraints	Low Media Attention	High Media Attention	Low Green Awareness Among Executives	High Green Awareness Among Executives
	(1)	(2)	(3)	(4)	(5)	(6)
ESG	0.095 ***	0.034	0.048 *	0.071 **	0.040	0.090 ***
	(3.35)	(1.23)	(1.72)	(2.51)	(1.55)	(2.87)
Constant	−9.833 **	9.318 **	−4.013	2.626	−0.225	−5.968
	(−2.22)	(2.43)	(−0.92)	(0.63)	(−0.06)	(−1.18)
Observations	5132	6338	5775	5695	7319	4151
R-squared	0.489	0.479	0.498	0.469	0.490	0.473
Control variable	YES	YES	YES	YES	YES	YES
Ind	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES

Note: *** denotes significant within 1 percent, ** denotes significant within 5 percent, and * denotes significant within 10 percent.

presents the regression results for enterprises with low financing constraints, revealing that the coefficient of corporate ESG performance on green new-quality productivity is 0.095, significant at the 1% level. In contrast, Column (2) shows the results for enterprises with high financing constraints, with a coefficient of 0.034 that does not pass the significance test. These findings suggest that ESG performance exerts a more significant effect on the level of green new-quality productivity for enterprises with low financing constraints, aligning with expectations.

5.5.2. Analysis of Media Attention Heterogeneity

Currently, ESG performance is receiving increasing attention on social media, a forum which plays a crucial role in the external evaluation of enterprises. When media scrutiny of ESG performance is heightened, enterprises face external pressures and incentives to enhance their ESG performance. This dynamic, in turn, creates conditions favorable for improving the level of regional green new-quality productivity.

Referring to the study by Zhang et al. (2014) [71], the number of media tracking analysis studies was utilized to measure the media attention received by enterprises; a higher number of studies correlates with increased media attention. The results, presented in column (3) of Table 9, indicate the regression outcomes for low levels of media attention, revealing a regression coefficient of 0.048 with a significant correlation at the 1% level. Conversely, column (4) displays the regression results for high levels of media attention, yielding a coefficient of 0.071, which demonstrates a significant correlation at the 5% level. This suggests that the Environmental, Social, and Governance (ESG) performance of firms receiving high degree of media attention significantly enhances their levels of green and new-quality productivity, aligning with our expectations.

5.5.3. Analysis of Heterogeneity of Executives’ Green Perceptions

The level of executives’ “green perceptions” profoundly influences corporate business strategies. In terms of corporate ESG performance, a higher level of green perception among executives tends to lead to the integration of ESG considerations into long-term development strategies and day-to-day operational practices. This implies that companies are better equipped to respond to and acknowledge both external environmental factors and internal management processes. Furthermore, these executives actively disclose relevant information and incorporate it into their daily decision-making, thereby significantly fostering improvements in green and new-quality productivity levels. Drawing on the work of Wang, Tian, and Jiang. (2023) [72], we measured executives’ green perceptions through the frequency of keywords in corporate annual reports that highlight the significance of environmental protection. A higher frequency of such keywords indicates elevated levels of executives’ green perceptions. The results in column (5) of Table 9 present the regression outcomes for low executive “green cognition”, yielding a regression coefficient of 0.040, which does not meet the significance threshold. In contrast, column (6) illustrates the regression outcomes for high executive green cognition, reporting a coefficient of 0.090, with a significant correlation at the 1% level. This indicates that the ESG performance of enterprises led by executives with heightened green cognition significantly enhances the levels of green new-quality productivity, which aligns with our expectations.

5.6. Conclusions and Implications

5.6.1. Conclusion

This paper investigates the impact of corporate ESG performance on the level of green new quality productivity across Chinese provinces. The findings of the study are as follows:

First, corporate ESG performance positively contributes to the enhancement of green new quality productivity levels in all provinces. Enterprises have strengthened ESG management and raised their awareness of ESG disclosure, which helps standardize corporate management. Compared with the previous management model, which focused solely on the economic benefits of enterprises, ESG management places more emphasis on the comprehensive performance of enterprises in their environmental, social, and governance aspects. Improved ESG performance helps to attract quality talent and promotes active corporate social responsibility as to environmental protection. In contrast with existing studies that do not pay attention to ESG in technology introduction, this paper finds that the improvement of ESG performance helps enterprises to introduce more advanced technology, which leads to the improvement of laborers and the means of labor, and leads to the green and sustainable development of the region, thus contributing to the enhancement of the level of green and new-quality productivity in the region.

Secondly, corporate green innovation serves as a positive intermediary in the relationship between ESG performance and the enhancement of green new quality productivity. Unlike traditional corporate innovation models, ESG-based green innovation is more focused on sustainability goals. Improved ESG performance propels advancements in green innovation, prompting enterprises to prioritize the adoption of green technologies. This, in turn, enhances the reputation of these enterprises, optimizes resource allocation through green innovation, and boosts levels of green new quality productivity while driving the high-quality development of the regional economy. The discovery of this mediating effect remedies the inadequacy of previous studies in analyzing the direct correlation between firms’ ESG performance and green new-quality productivity and reveals in greater depth the intrinsic influence mechanism of ESG performance as to green new-quality productivity.

Thirdly, for enterprises facing low levels of financing constraints, coupled with high media attention and elevated executive green cognition, ESG performance demonstrates a more pronounced effect on enhancing green new quality productivity levels. Financing constraints exacerbate financial pressures on enterprises, limiting their ability to allocate funds for green innovation and thus hindering improvements in green new quality productivity. Enterprises with superior ESG performance are better-positioned to attract external financing, alleviating financial pressure and enabling greater investment in green innovation, thereby enhancing local green new quality productivity levels. Increased media attention compels enterprises to prioritize their ESG performance, actively fulfilling the social responsibilities of these enterprises and promoting environmental protection while bolstering green innovation and productivity. The green awareness of executives significantly influences corporate business strategies, as enterprises with executives possessing heightened environmental awareness are more likely to consider the long-term sustainability of the operations of the enterprise, implementing sustainable development strategies that foster improvements in green and new productivity levels.

5.6.2. Implications

The level of green and new-quality productivity cannot be improved without the joint efforts of enterprises, government, and society.

In light of the conclusions of this study, enterprises should improve their ESG levels and increase R&D investment. They can start with the following three aspects: First, ESG management and information disclosure could be strengthened. Enterprises need to actively practice ESG management in accordance with China’s relevant policy requirements. Enterprises can set up a special department to be fully responsible for ESG management, study ESG-related standards and requirements, and formulate ESG strategies and implementation plans, in line with their own characteristics. By organizing internal training and conducting seminars and other activities, all employees can be fully made aware that ESG management is not only related to the social image of the enterprise, but also an inevitable choice for the long-term development of the enterprise. Enterprises should proactively, timely, and accurately publicize their environmental, social, and governance performance to society, so that investors, consumers, and other stakeholders can fully understand the operations of the enterprise. The introduction of third-party assurance can further improve the reliability of ESG information. Professional third-party organizations can endorse the authenticity of corporate ESG information with their independent and objective assessments, enhancing the market’s trust in the company. Second, investment in green technology R&D could be increased. Enterprises should firmly increase R&D investment, especially in green technology and product development. Green technology research and development can help enterprises reduce energy consumption and pollutant emissions in the production process, in order to achieve a win–win situation in terms of the economic and environmental benefits. For example, the enterprise-level research and development of efficient renewable-energy utilization technology can not only reduce the dependence on traditional fossil energy, but also ensure that the enterprise takes the lead as to the market opportunities in energy transformation, better adapts to future market development trends, and enhances its core competitiveness, contributing to the development of green new quality productivity. Third, auditors need to act actively. Auditors should actively pay attention to and utilize ESG information disclosed by listed companies as a means of identifying and assessing the risk of material misstatements. With the growing popularity of ESG concepts in the corporate world, ESG information has become one of the important bases on which investors can make decisions. Through in-depth analysis of such information, auditors can identify possible risks of financial-statement misstatement and potential problems in ESG management, which can help to reduce information asymmetry and safeguard the legitimate rights and interests of investors, and at the same time prompt enterprises to be more standardized in ESG management and information disclosure, encouraging them to move steadily forward on the road towards enhancing the development of green and new-quality productivity.

From the government’s point of view, the government should improve the supervision of ESG activities and create a good ESG implementation environment for enterprises. First, create a favorable implementation environment and strengthen supervision. The government should create a good implementation environment for ESG management by formulating clear and detailed ESG-related laws and regulations to regulate the ESG behavior of enterprises and provide them with a clear code of conduct. Set up a special department responsible for ESG regulation, or clarify the specific responsibilities of existing regulatory agencies (e.g., environmental protection, labor inspection, market supervision, etc.) in ESG regulation, so as to avoid inefficiencies caused by regulatory gaps or overlapping responsibilities. Actively realizing convergence with international standards will enable Chinese enterprises to better compete and cooperate with their international counterparts in the global economic arena. Strengthen the supervision of ESG management to ensure that enterprises effectively fulfill their ESG responsibilities. At the same time, focus on incentives for enterprises, encourage them to actively practice ESG through tax incentives and financial subsidies, and establish a regularized system for monitoring and evaluating their performance, so as to increase their enthusiasm and initiative as to participating in ESG management. Second, provide policy support to help enterprises improve their ESG performance. The enhancement of green and new-quality productivity requires systematic investment and planned development by micro-entrepreneurs, and it is a long-term and continuous process. In this process, the government can point out the development direction for enterprises through policy guidance, such as by formulating green industry development plans, guiding enterprises to enter the field of green technology research and development, green product production, etc., and also give enterprises policy concessions, such as research and development subsidies, loan subsidies, etc., to reduce the pressure on enterprise funds. Third, improve the modern industrial system. In the process of the transformation and upgrading of traditional industries, the government should focus on guiding enterprises to adopt new technologies and processes, and improve the greening and “intelligentization” of industries. At the same time, focusing on cultivating new industries and developing future industries, the government can promote the vigorous development of new industries through the introduction of supportive policies, the construction of industrial parks, etc., to provide a broad space for the development of new-quality productivity. Fourthly, the media can be used to promote the development of green and new-quality productivity. The media can publicize and report on the ESG practices of excellent enterprises, set up industry benchmarks, and guide other enterprises to learn from them, as well as expose undesirable ESG behaviors to form public-opinion pressure and prompt enterprises to improve. The media can also organize forums and seminars to promote exchanges and cooperation among enterprises and between enterprises and the government, so as to jointly promote the development of green and new-quality productivity.