Enhancing Environmental, Social, and Governance Performance through New Quality Productivity and Green Innovation

Abstract

Sustainability practices are increasingly significant in the current business environment, much more so in China with its rapid economic growth. What drives environmental, social, and governance (ESG) performance, especially regarding the impacts of new quality productivity and green innovation, is thus of importance. The study further assesses the impact of new quality productivity and green innovation on ESG performance with listed companies in China. The research is empirical and regresses the data of the Chinese listed companies from 2011 to 2022. The important findings indicate that new quality productivity significantly improves ESG performance. Efficient innovation practice plays a key role in the promotion of corporate sustainability. Green innovation contributes directly to ESG improvement and enhances the effects of new quality productivity on ESG. This moderating effect has proven to be the more important reason why innovation should be well-infused into core business strategies for sustainable optimization. In addition, this study examines the differential impact of new quality productivity across regions, firm types, and development stages, showing that its effectiveness in driving ESG performance is constrained by the geographic location, firm development stage, and industry characteristics. These findings emphasize the importance of incorporating new quality productivity enhancement and green innovation into corporate strategies, providing valuable insights for managers, policymakers, and investors.

1. Introduction

Over the past few years, in order to tackle with the intensified market competition, many companies have attached importance to R&D investment and the use of advanced technologies to propel innovation [1]. This strategy drives the listed companies’ profits growth, strengthens their competitive edge, and thus contributes to sustainable development [2]. Additionally, increasing numbers of companies start to incorporate sustainability principles into their everyday operation to fulfill increasingly rigorous environmental standards, so as to further improve their attractiveness to consumers and investors with environmental awareness [3].

As environmental, social, and governance (ESG) standards increasingly become the core to assess corporate performance and risk resilience, the modern business environment is transiting towards a sustainability pattern [4]. Against this backdrop of dynamic change, new quality productivity has emerged as a key force driving innovation-driven, efficiency-enhancing, and technological advances for Chinese listed companies. Throughout the development and changes of recent years, most of the driving forces for global economic growth have been the changes and developments brought about by new technologies, which have resulted in the formation of new productive forces. Further, the emancipation and development of social productive forces require the promotion of structural adjustment through technological reforms, the continuous improvement of productive forces, and, ultimately, the realization of the goal of high-quality economic development. In September 2023, President Xi introduced the concept of new quality productivity. He defined this concept as innovation playing a leading role, getting rid of the traditional economic growth mode and productivity development path with high-tech, high-efficiency, high-quality characteristics, in line with the new development concept of the advanced-productivity qualitative state [5]. There is a significant difference between new quality productivity and traditional productivity. Traditional productivity is mainly driven by factors, such as capital and labor. It will encounter a development bottleneck when it develops to a certain stage. New quality productivity is led by technological innovation, innovation, emancipation, and development as the driving force of productivity.

New quality productivity is a strategic concept. For enterprises, it can be viewed as all economic activities based on technological progress that can contribute to increasing the scientific and technological content and added value of a unit of product [6]. This modern productivity concept breaks through the traditional productivity framework and focuses on improving the quality and efficiency of the production process with innovation at its core [7]. It represents a leap in productivity, characterized by disruptive innovation, new industrial chains, and development quality improvements [8]. New quality productivity exhibits the basic features of innovation-driven and advanced industrial processes and the modern features of digitalization and sustainability. This concept covers production goals, entities, objects, and environmental factors and attaches importance to integrating technology, knowledge, information, and data as new production factors [9]. New quality productivity is at the heart of achieving quality development and modernization, focusing on optimizing the use of resources and automating production processes to respond to the progressive needs of contemporary economies and societies.

New quality productivity for listed companies in China means producing more with less, maximizing resource utilization, reducing waste, lowering costs, and increasing profitability [10]. This concept represents a strategic shift towards an innovation-driven development model for companies, which improves efficiency and quality and enhances competitiveness in the global marketplace by utilizing modern technologies and adopting sustainable practices [11]. New quality productivity is closely linked to China’s broader goal of economic transformation, which aims to transition from traditional labor-intensive manufacturing to high-value, knowledge-intensive, and technologically advanced production methods [12]. In essence, new quality productivity is the key to the economic success of Chinese listed companies. It embodies a profound commitment to international competitiveness, innovation, and sustainability [13]. As a foundation in propelling the nation’s transition to a greener economy, this strategy is in line with China’s goal to realize high-quality development.

Apart from that, there is a reinforced focus on Chinese-listed companies’ green innovation. Through the development of new commodities, procedures, and practices targeted at lowering environmental impacts, green innovation promotes the positive influence of new quality productivity on ESG performance [14]. By integrating green innovation into the corporate strategic framework, Chinese listed companies can enhance productivity via technology progress, decrease environmental risks, boost sustainable resource usage, and drive international sustainable development.

In essence, this study sits at the intersection of innovation, sustainability, and corporate governance, providing detailed insights into how Chinese listed companies drive the sustainability agenda. This study comprehensively analyzes the impact of new quality productivity and green innovation on the performance of Chinese listed companies in terms of environmental, social, and governance (ESG). The study empirically examines the direct impacts, moderating effects, and the impacts of various firm characteristics of new quality productivity and green innovation. Further analysis considers the heterogeneity of firms’ life-cycle stages, regional differences, and pollution intensity. Moreover, robustness tests are conducted using other ESG data sources and different data processing methods. This study pioneered the quantitative assessment of the role of new quality productivity in firms’ ESG performance. Furthermore, by considering green innovation not only as a dependent variable but also as a moderating variable of the impact of enhanced productivity on ESG outcomes, the study provides deeper insights into how firms can strategically utilize their innovation efforts to improve sustainability performance.

This study supports and extends sustainability theory by directly linking productivity and innovation to ESG outcomes. Unlike previous studies that have typically examined these factors individually, this study demonstrates their role when integrated. This finding enriches the theoretical discussion around the compound benefits of combining technological advances with sustainable practices in business strategy. On the other hand, this study extends and deepens the research on new quality productivity by expanding the concept to cover the quality and impact of outputs for a broader understanding of productivity in modern economies. It also broadens the study of the consequences of new quality productivity. It contributes to the theoretical understanding of the importance of integrating innovation into business strategy, provides evidence to support the synergies between new quality productivity and green innovation in improving corporate sustainability, and has implications for policy development to encourage sustainable business practices. It also contributes to the debate on sustainable business practices through this study and helps listed companies portray a more responsible, innovative, and sustainable image in the market.

2. Literature Review

The topic of environmental, social, and corporate governance (ESG) has attracted increasing attention over the past two decades, reflecting the growing sensitivity of investors and businesses to sustainability issues [15]. ESG performance relates to a company’s performance and achievements in environmental protection, social responsibility, and governance practices [16]. This includes a wide range of measures, such as reducing carbon emissions, improving resource efficiency, promoting diversity and inclusion, and maintaining transparent and ethical governance. ESG is a key indicator of how well a company integrates sustainability into its business model and decision-making processes [17]. This study examines the impact of new quality productivity on ESG performance, revealing how innovative and sustainable business practices can contribute to excellent environmental, social, and governance practices, which in turn enhance a company’s overall sustainability.

On the other hand, new quality productivity is not only a collection of emerging technologies, but also a new way of production and organization, covering a wide range of socio-economic activities and industrial development. New quality productivity embodies an innovation-driven approach to modern productivity, focusing on the integration of advanced technologies, sustainable practices, and high-value production processes [7]. It goes beyond traditional productivity measures by emphasizing quality, efficiency, and the adoption of cutting-edge technologies. In this study, new quality productivity serves as a foundational concept that influences firms’ sustainability practices. It represents a firm’s ability to innovate and adapt to changing business environments, which is critical for sustaining growth and long-term success [18].

At present, scholars have launched a multidimensional discussion on new quality productivity. They analyze the connotation of new quality productivity from spatiotemporal [19], structural [20], and other dimensions, define the conceptual scope of new quality productivity [6], and make theoretical interpretations of new quality productivity [21]. Some scholars believe that new quality productivity contributes to improving total factor productivity [22] and continuously promotes the development of a green economy [23]. Through new quality productivity, the talent demand of the innovation-driven development strategy can be met [24]. In addition, new quality productivity, as an important driving force to achieve Chinese-style modernization, is a driving force to promote high-quality development by advancing the modern industrial system and clarifying strategic choices in the context of global competition [25].

Further, innovation and new quality productivity are strategic economic development concepts [6]. New quality productivity can be understood as all economic activities that can assist the country in promoting industrial upgrading based on technological upgrading. Although innovation covers many aspects, such as systems and technology, the core is technological innovation. The birth of a new technology spawns new industries and leads to institutional innovation in all other aspects. Green innovation, on the other hand, refers to the process by which companies develop new products, processes, technologies, or practices [26]. These innovative activities include environmental goals, such as resource conservation, pollution reduction, and energy efficiency [27,28]. Green innovation can significantly affect environmental and organizational performance [29] and contribute to achieving sustainability performance. In this study, green innovation is not only an influencing factor on firms’ ESG performance but also serves as a moderating variable that can enhance the positive impact of new quality productivity on ESG performance, thus contributing to the overall performance of firms in terms of environmental, social, and governance.

3. Hypothesis Development

New quality productivity signifies an innovation-driven productivity enhancement that helps listed companies make significant progress in effective governance, social welfare, and environmental management [10]. With high-quality and effective production processes, such types of productivity revolve around innovation and play a vital role in strengthening a company’s sustainability and governance practice. Apart from that, new quality productivity integrates cutting-edge technology, sustainable practices, and reinforced operation efficiency, jointly contributing to better environmental management and social responsibility. As crucial indicators of a company’s overall sustainability and responsibility practices, these achievements establish the foundation for ESG performance.

From a standpoint of the environment, companies that make investments in new quality productivity tend to minimize wastes and decrease energy consumption through advanced technologies [18]. While alleviating the adverse impacts of business operations on the natural context, this proactive environmental management tactic reveals a company’s commitment to environmental responsibility [30]. In terms of social responsibility, companies focusing on new quality productivity often serve society via innovations (e.g., the development of securer goods and promotion of workplace diversity). With these activities, companies can not only establish strong social capital but also reinforce their gross ESG performance [31]. From a perspective of corporate governance, companies need to establish a strong governance structure to achieve new quality productivity. This is the foundation to oversee innovative practices and ensure their consistency with the corporate sustainability goals. This includes implementing transparent reporting mechanisms, following ethical business practices, and actively engaging stakeholders [32]. New quality productivity can strengthen a company’s governance capabilities through these measures, thereby enhancing its ESG performance.

The following hypothesis is established from the preceding discussion:

H1. 

The new quality productivity of listed companies in China significantly affects corporate ESG performance.

Green innovation involves the process of developing and implementing environmentally friendly technologies, products, processes, and practices, and it is a key factor in enhancing a company’s ESG performance [3]. By introducing innovative solutions to environmental challenges, green innovation demonstrates a company’s commitment to sustainable development but also significantly improves a company’s environmental management and positively influences social and governance outcomes [33].

The core strength of green innovation lies in its direct contribution to a company’s environmental performance [34]. By adopting innovative products or processes, companies can reduce resource consumption and waste generation, improve energy efficiency, and demonstrate their leadership in sustainability by producing healthier and safer products and services. This enhances the reputation of a firm in the marketplace and helps build stronger trust and cooperation with all stakeholders [35]. In addition, companies implementing green innovation need strong governance structures to ensure that sustainable practices are effectively integrated into the company’s core strategy and daily operations. Such governance mechanisms are an integral part of optimizing ESG performance. For listed companies in China, prioritizing green innovation provides a differentiating advantage in the market and attracts investors, customers, and talent who are increasingly environmentally conscious [36].

The following hypothesis is established from the preceding discussion:

H2. 

For listed companies in China, green innovation substantially enhances ESG performance among companies.

New quality productivity inherently enhances firms’ capacity with regard to ESG, while also incorporating green innovation in firms’ strategies and operations significantly enhances this positive impact. In essence, green innovation acts as a catalyst to amplify the positive effects of new quality productivity on ESG performance, creating synergies that lead to better sustainability performance from the combination of the two. It is due to the relationship between green innovation and ESG activities that companies must balance their resource allocation [37]. By adopting eco-friendly technologies and processes, green innovation enhances the efficiency and impact of new quality productivity. Further, it reduces waste, emissions, and resource consumption, thereby enhancing the environmental benefits of new quality productivity [38].

When companies invest in green innovation, they improve operational efficiency and reduce waste and energy consumption in an environmentally friendly manner. This synergy can significantly improve environmental performance and become an important component of ESG [39]. As China’s environmental regulations become increasingly stringent, green innovation helps companies stay ahead of regulatory requirements, alleviate noncompliance risks, and enhance firm reputation for governance compliance [40]. Green innovation allows firms to stand out market-wise by providing efficient and environmentally friendly products and services, a differentiation that helps to increase brand value and customer loyalty, thereby positively influencing ESG’s social arm [30]. Likewise, it may significantly reduce operational costs over time [41]. These cost savings, combined with efficiency gains from new quality productivity, amplified by green innovation, can lead to better financial performance and further investment in sustainability practices. Meanwhile, companies actively involved in green innovation are more attractive to the growing number of investors interested in sustainable investments, improving green financing options, and providing the resources needed to increase productivity and implement sustainable development initiatives [42]. Thus, the role of green innovation is not limited to directly improving ESG performance; it can also amplify its contribution to ESG performance through complementarities with new quality productivity, enhanced stakeholder perceptions, strategic regulatory compliance, and financial benefits from operational efficiency.

The following hypothesis is established from the preceding discussion:

H3. 

The presence of green innovation within publicly traded Chinese companies may moderate, in a positive way, the association between corporate ESG performance and new quality productivity.

4. Methodology

4.1. Sample Selection and Data Source

The financial reports for this study were selected from A-share listed companies in China and the ESG data provided by Bloomberg from 2011 to 2022 as the initial data. To ensure the quality and reliability of the data, the following processing steps were carried out in this study: first, ST and *ST listed firms’ data with poor operations were excluded; second, insufficient samples were purged; and third, information from financial industry were excluded. Through these steps, a total of 11,701 valid observations were screened. In addition, continuous variables were winsorized at the 1% and 99% levels to reduce the extreme values’ impact on the analysis results. This study used STATA 17.0 to analyze the data.

4.2. Description of Variables

4.2.1. Explained Variables

This research’s explained variables are environmental, social, and governance performance (ESG). China’s ESG construction started late, and the main ESG indicator systems that are more complete at present are the ESG indicators of Bloomberg, Sino-Securities, Wind, SynTao Green Finance, RKS, and FTSE Russell. Comparing the ESG data of the above companies, the ESG of Bloomberg and Sino-Securities is more complete. The ESG indicators of other companies are less, so this paper adopts the Bloomberg ESG indicators as the explanatory variables for the study. At the same time, the ESG indicators of Sino-Securities ESG are used as the alternative explanatory variables for ascertaining robustness.

4.2.2. Explanatory Variables

This study’s explanatory variable is enterprises’ new quality productivity (NPro). The core of new quality productivity is innovation, so this study refers to Song et al. and adopts the entropy weight method to measure new quality productivity based on the two-factor productivity theory while also taking into consideration the function and value of labor objects in the production process [10,22]. The entropy weight method is a method to assess and determine the importance of each indicator in a multi-indicator decision-making problem based on the concept of information entropy in information theory. The core of the entropy weight method is to measure the degree of dispersion of the indicators by calculating the information entropy and then determining their weights. There are two main reasons for choosing the entropy weight method to calculate new quality productivity in this study. The first is to reduce subjectivity. Instead of expert scoring or a subjective assignment, the weights are determined based on the characteristics of the data themselves, minimizing the influence of subjective bias. Second, information utilization is maximized. The entropy weighting method evaluates the amount of information that each indicator contributes to the overall variability in the dataset, and indicators that can show more variability and thus provide unique information are given higher weights. Multiple indicators can be considered simultaneously without the limitations of a single-indicator evaluation, and a more comprehensive and integrated evaluation result can be obtained.

In the first step, strategic emerging and future industries closely related to new quality productivity are selected as samples for the new quality productivity calculation. This is because both industries are significantly associated with new quality productivity.

In the second step, the new quality productivity indicator system is constructed based on the two-factor productivity theory. This study selects four major indicators, namely live labor, materialized labor, hard technology, and soft technology, to construct the new quality productivity indicator system. The basis for selecting the above indicators is based on the definition of new quality productivity given by President Xi, which is a new product of the development of traditional productivity. It is a leap based on traditional productivity with the addition of new technology to traditional productivity. While the main components of traditional productivity are living labor and materialized labor, new technology is a product of both hard and soft technology [43]. Productivity consists of two elements: the labor force and production tools. Among them, the labor force consists of two sub-factors: live labor and materialized labor (labor object); production tools consist of two sub-factors, hard and soft technology. Considering the connotation of innovation in new quality productivity, the indicators of the live labor sub-factor are measured via the R&D staff’s salary and proportion, as well as the percentage of highly educated personnel, respectively; the indicators of the object labor sub-factor are expressed by the percentage of fixed assets, respectively. Considering that the enterprises of the new quality productivity are mainly concentrated in the high-precision science and technology field of equipment manufacturing, most of these enterprises have to rely on the production of high-end machines and instruments, and because the production of machines replaces human beings, the manufacturing costs of these enterprises have a higher manufacturing cost ratio than other enterprises, so the manufacturing cost ratio is also included in the indicator selection. Hard technology sub-factors are mainly related to the hardware equipment invested in R&D, so they are determined through the ratio of direct R&D investment, depreciation, and amortization, as well as that of leasing expenses, respectively, and considering the role of intangible assets, such as software, they are also measured by the ratio of intangible assets; soft technology sub-factors are mainly measured by total asset turnover and the equity multiplier, and considering that the higher the equity multiplier is, a negative indicator signifies greater risks among firms finance-wise, different from the other indicators. Negative indicators are inconsistent with other indicators, so the inverse of the equity multiplier signifies how as the inverse increases, the risk decreases, indicating that the enterprise productivity level is better. The values of the above indicators are shown in

Table 1. Indicators of new quality productivity of companies.

Factor	Sub-Factor	Indicator	Description of Indicator Value	Features	Weight
Labor	Active labor	R&D Personnel Salary Ratio	R&D Expenses-Wages and Salaries/Operating Revenue	+	28
		R&D Personnel Ratio	Number of R&D Personnel/Number of Employees	+	4
		Proportion of Highly Educated Personnel	Number of Personnel with Bachelor’s Degree or Above/Number of Employees	+	3
	Materialized labor (Object of labor)	Fixed Assets Ratio	Fixed Assets/Total Assets	+	2
		Manufacturing Cost Ratio	(Total Cash Outflow from Operating Activities + Depreciation of Fixed Assets + Amortization of Intangible Assets + Provision for Impairment Losses-Cash Paid for Goods and Labor Services-Cash Paid to Employees and for Employees)/(Total Cash Outflow from Operating Activities + Depreciation of Fixed Assets + Amortization of Intangible Assets + Provision for Impairment Losses)	+	1
Means of Production	Hard technology	R&D Depreciation and Amortization Ratio	R&D Expenses-Depreciation and Amortization/Operating Revenue	+	27
		R&D Lease Expense Ratio	R&D Expenses-Leasing Fees/Operating Revenue	+	2
		R&D Direct Input Ratio	R&D Expenses-Direct Input/Operating Revenue	+	28
		Intangible Assets Ratio	Intangible Assets/Total Assets	+	3
	Soft technology	Total Asset Turnover Rate	Operating Revenue/Average Total Assets	+	1
		Equity Multiplier Reciprocal	Owner’s Equity/Total Assets	+	1
	New Quality Productivity				100

“+” indicates a positive relationship between the variable and the outcome.

.

In the third step, the entropy weighting method is used to calculate the weights of each indicator further, which are summed up to obtain the new quality productivity. Since the units of measurement of different indicators may be different, the influence of the units should be removed, that is, eliminating the dimension. Before the elimination of the dimension, it is necessary to distinguish between positive and negative indicators. The larger the value of the positive indicator, the better the evaluation, and the larger the value of the negative indicator, the worse the evaluation. Since all the indicators have been adjusted to the positive indicators above, it is only necessary to standardize the positive indicators. After the indicators’ standardization, each indicator’s objective weight is calculated according to the steps of the entropy weighting method. The specific results are shown in Table 1.

4.2.3. Moderating Variables

In this study, the green patents of enterprises were selected for evaluating the green innovation degree among firms based on previous studies [44,45]. Patents, especially green patents, provide tangible, quantifiable evidence of a company’s innovation efforts. Unlike other, more subjective measures of innovation, patents are concrete and legally based and provide clear evidence of a company’s commitment to developing a new technology or process. Patent records are standardized and publicly verifiable, making them a reliable data source. Green patents are specifically associated with innovations that reduce environmental impacts or contribute to the more efficient use of resources [46]. Applying for a green patent often indicates a positive stance by a company in securing a competitive advantage in sustainable technologies. This can indicate to investors and stakeholders that the firm is forward-thinking and ready to capitalize on or lead the emerging green market. Using green patents as a metric allows for consistent and transparent measurements across companies and industries. This specificity makes them an ideal metric for measuring green innovation, as they directly reflect a company’s contribution to environmental sustainability.

The State Intellectual Property Office (SIPO) website was queried and crawled utilizing Python 3.8 software and the IPC classification number from the World Intellectual Property Organization’s (WIPO) International Patent Classification Green Inventory (IPC Green Inventory) in conjunction with the business name (including previous names). Then, the IPC classification numbers of green patents in the WIPO’s IPC Green Inventory are compared with those from listed companies’ patents searched from the SIPO database. Finally, the number of green patent applications filed by listed companies each year is compiled. Considering that the number of green patent applications of listed companies may be zero, the number of green patent applications of listed companies is added by one. Then, the natural logarithm is taken as a measure of green innovation (GI).

4.2.4. Control Variables

This study’s control variable inclusion helps to separate the effects of new quality productivity, green innovation, and their interactions on firms’ ESG performance from other influencing factors. Based on previous studies [3,10,44], each control variable was selected based on its potential impact on firms’ ESG performance or its ability to influence the main variables of interest. Control variables include Firm size (Size), financial leverage (Lev), cash flow (Cashflow), fixed assets (FIXED), size of independent directors (Indep), sales revenue growth (Growth), Dual (Dual), and whether they are state-owned enterprises (SOE).

Table 2. Variable definition.

Type	Name	Symbols	Definition
Independent variable	New quality productivity	Npro	In the text
Dependent variable	Environmental, social, and governance	ESG	In the text
Moderating variable	Green innovation	GI	In the text
Control variable	Firm size	Size	Natural logarithm of total assets for the year
	Leverage	Lev	Total liabilities at year-end/total assets at year-end
	Cash flow	Cashflow	Net cash flows from operating activities/total assets
	Fixed assets	FIXED	Net fixed assets/total assets
	Independent directors	Indep	Proportion of independent directors to the number of directors
	Revenue growth	Growth	Current year’s operating income/previous year’s operating income-1
	Enterprise property	SOE	State-owned enterprises take the value of 1, others 0
	Dual	Dual	Whether the chairman of the board of directors is also the general manager, yes is 1, no is 0
	Industry	Ind	Classified according to the Guidelines for Industry Classification of Listed Companies.
	Year	Year	1 for the year t and 0 otherwise.

discusses all such variables.

4.3. Model Design

Below is established in this study for assessing H1:

$${ESG}_{it}={\beta }_0+{\beta }_1{Npro}_{it}+\sum {Control}_{it}+\sum Ind+\sum Year+{\epsilon }_{it}$$

(1)

In the model, Npro is an explanatory variable representing firms’ new quality productivity; ESG is an explanatory variable representing firms’ ESG performance; Control is a set of control variables; Ind and Year are fixed effects, representing industry fixed and year fixed, respectively; ε is a random disturbance term; the i and t represent individual firms and time, respectively.

Below is established in this study for assessing H2:

$${ESG}_{it}={\beta }_0+{\beta }_1{GI}_{it}+\sum {Control}_{it}+\sum Ind+\sum Year+{\epsilon }_{it}$$

(2)

Below is established in this study for assessing H3:

$${ESG}_{it}={\beta }_0+{\beta }_1{Npro}_{it}+{\beta }_2{Npro}_{it}\times {GI}_{it}+\sum {Control}_{it}+\sum Ind+\sum Year+{\epsilon }_{it}$$

(3)

5. Empirical Analysis

The p-value obtained from the Hausman test was nearly zero (p < 0.0001), suggesting the suitability of the Fixed Effect (FE) model for analyzing these data. In addition, the Variance Inflation Factor (VIF) validated way less than 10 for all VIFs, signifying no multicollinearity problem in the regression analysis. This enhances the model results’ reliability and validity.

5.1. Descriptive Statistics

From

Table 3. Descriptive statistics.

Variable Name	Observation	Mean	SD	Min	Median	Max
ESG	11,701	29.627	9.439	9.909	28.518	63.213
Npro	11,701	5.282	2.717	0.455	4.935	19.711
GI	11,701	1.222	1.479	0.000	0.693	7.437
Size	11,701	23.221	1.316	20.109	23.108	27.326
Lev	11,701	0.477	0.199	0.045	0.489	0.927
Cashflow	11,701	0.060	0.069	−0.199	0.056	0.328
FIXED	11,701	0.227	0.177	0.001	0.184	0.768
Growth	11,701	0.172	0.408	−0.593	0.110	4.712
Indep	11,701	0.376	0.056	0.300	0.364	0.600
Dual	11,701	0.213	0.409	0.000	0.000	1.000
SOE	11,701	0.499	0.500	0.000	0.000	1.000

, ESG performance (ESG) values range from 9.909 to 63.213, showing significant differences in ESG performance across companies. The majority of businesses exhibit ESG performance that falls within the moderate range, as mean and median values are close to each other, indicating more needed enhancement within this area for all the companies. New quality productivity (Npro) has a mean of 5.282 and a standard deviation of 2.717, reflecting how companies have mixed levels of new quality productivity, but the data distribution is relatively symmetrical. The distribution of green innovation (GI) ranges from 0.000 (no innovation) to 7.437 (highly innovative) and the right-skewed distribution with a mean higher than the median reveals that while some firms excel in green innovation, the majority of firms continue to underperform, signaling a potential growth area for environmental sustainability efforts.

5.2. Benchmark Regression

Table 4. Benchmark regressions.

	(1)	(2)	(3)	(4)
	ESG	ESG	ESG	ESG
Npro	0.787 ***	0.180 ***	0.690 ***	0.123 ***
	(25.15)	(6.91)	(24.08)	(5.09)
Size			3.932 ***	2.649 ***
			(59.04)	(48.71)
Lev			−6.937 ***	−3.166 ***
			(−15.42)	(−8.71)
Cashflow			12.010 ***	6.254 ***
			(10.47)	(7.14)
FIXED			−4.211 ***	−1.376 ***
			(−9.22)	(−3.33)
Growth			−0.384 **	0.071
			(−2.11)	(0.51)
Indep			4.636 ***	3.324 ***
			(3.50)	(3.31)
Dual			0.657 ***	0.245 *
			(3.48)	(1.72)
SOE			−1.166 ***	0.510 ***
			(−7.19)	(4.03)
Year	No Control	Control	No Control	Control
Industry	No Control	Control	No Control	Control
_cons	25.470 ***	16.917 ***	−63.010 ***	−41.184 ***
	(137.06)	(29.88)	(−42.73)	(−32.65)
N	11,701	11,701	11,701	11,701
R2	0.051	0.507	0.297	0.605
adj. R2	0.051	0.505	0.297	0.604
F	632.768	374.733	549.774	446.772

t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the results of the benchmark regressions. In particular, column (1) shows the direct regression outcomes between new quality productivity and ESG, column (2) displays the regression outcomes controlling for industry and year-fixed effects, column (3) shows regression outcomes once control variables are introduced, and column (4) shows regression outcomes with control variables introduced and year and industry fixed effects controlled. These benchmark regression results illustrate the relationship between new quality productivity (Npro) and ESG performance (ESG) under different models. These results show that new quality productivity is consistently significant across all models, confirming the robust positive relationship between new quality productivity and ESG performance. Before and after fixed effects have been controlled and control variables have been introduced, new quality productivity significantly positively affects the ESG performance. Hypothesis H1 of this paper holds.

Moreover, the effect of control variables on ESG emphasizes the multifaceted nature of ESG performance, which is influenced by financial indicators, firm characteristics, and governance factors. Including fixed effects and control variables still exhibits a significant positive impact, highlighting the complexity of ESG performance determinants. In the regression analyses, the relationship between new quality productivity (Npro), being the independent variable, and ESG performance (ESG), being the dependent one, is consistently positive and statistically significant across different model specifications. This positive relationship suggests that an increase in new quality productivity is associated with a higher ESG capacity among companies.

Accordingly, ESG performance among firms may be developed via a concentration on innovations that sustainably increase productivity. This result is valuable for firms aiming to improve their sustainability, suggesting that investments in innovation and efficient practices lead to better ESG outcomes. From a policy and strategy perspective, the findings favor encouraging and promoting new quality productivity to achieve higher ESG performance. Focusing on new quality productivity is a strategic lever that improves operational efficiency and promotes environmental stewardship, social welfare, and governance quality for firms, especially listed companies in China. In conclusion, the relationship between new quality productivity and ESG performance is positive and substantial. This suggests that innovations leading to new quality productivity are key drivers for firms to improve ESG outcomes.

5.3. Heterogeneity Analysis

When analyzing Chinese listed companies and their ESG performance, various regions’ social, environmental, and economic perspectives are vital. Regression analysis outcomes by Eastern (East), Central (Mid), and Western (West) regions demonstrate how the relationship between new quality productivity (Npro) and ESG performance (ESG), as well as other variables, varies across different geographic regions in China.

Based on

Table 5. Heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variables	East	Mid	West	High-Tech	Non-High-Tech	High- Pollution	Non- High- Pollution
Npro	1.0489 ***	2.0532 ***	1.4330 ***	1.398 ***	1.106 ***	2.8909 ***	1.0151 ***
	(16.26)	(7.02)	(10.88)	(13.47)	(10.98)	(11.55)	(14.22)
Size	8.3319 ***	8.2554 ***	7.9532 ***	7.842 ***	9.365 ***	9.5130 ***	7.8661 ***
	(11.65)	(19.31)	(15.29)	(9.91)	(15.90)	(17.52)	(11.53)
Lev	−10.3925 ***	−16.2972 ***	−11.6300 ***	−10.127 ***	−13.815 ***	−22.0032 ***	−7.0556 ***
	(−6.87)	(−9.45)	(−10.38)	(−8.28)	(−11.57)	(−8.09)	(−4.52)
Cashflow	4.3722 ***	14.2343 ***	7.7336 ***	7.447***	5.406 ***	3.6102 *	5.6493 ***
	(3.27)	(11.76)	(4.50)	(11.13)	(3.18)	(2.19)	(3.47)
FIXED	−12.7997 ***	−8.0554 ***	−15.9169 ***	−12.509 ***	−10.643 ***	−23.3599 ***	−11.4150 ***
	(−7.59)	(−3.40)	(−5.23)	(−8.09)	(−4.96)	(−10.19)	(−7.03)
Growth	−0.8649 ***	−0.4366	−1.0874 ***	−1.044 ***	−0.558 ***	−0.5995	−1.0144 ***
	(−6.99)	(−1.15)	(−7.12)	(−5.44)	(−4.18)	(−1.50)	(−9.22)
Indep	14.6312 ***	1.6098	12.2595 ***	13.769 ***	10.948 ***	17.3165 ***	9.8879 ***
	(9.13)	(1.02)	(3.66)	(6.09)	(7.05)	(12.24)	(6.85)
Dual	−0.0493	0.3819	−0.3049	0.066	−0.030	1.3517 ***	−0.6118 ***
	(−0.47)	(1.02)	(−1.03)	(0.75)	(−0.18)	(6.18)	(−6.93)
SOE	0.4579 *	−1.5490	−1.4274 **	−0.911	−0.030	0.4957	−0.4895 *
	(1.81)	(−1.52)	(−2.50)	(−1.77)	(−0.11)	(0.44)	(−1.86)
Constant	−167.5889 ***	−163.2272 ***	−157.1707 ***	−156.316 ***	−190.391 ***	−196.0556 ***	−156.2316 ***
	(−10.45)	(−17.56)	(−13.32)	(−8.74)	(−14.80)	(−16.80)	(−9.98)
Observations	7979	1703	2019	6091	5610	3086	8615
R-squared	0.453	0.416	0.440	0.452	0.441	0.476	0.450
Number of groups	974	191	238	811	666	388	1067

t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

, a high association (1.0489 ***) exists between new quality productivity and ESG performance among listed firms from the eastern region (East). In the economically developed eastern region, innovations in new quality productivity can be effectively transformed into better ESG outcomes. Since the eastern region is China’s most economically developed region with many high-tech industries, these firms tend to have more resources and incentives to invest in new quality productivity and related ESG measures. The coefficient for the central region (Mid) (2.0532 ***) is higher than that of the eastern region, indicating a greater positive impact of new quality productivity on ESG performance. This may be due to the recent years’ substantial investment in economic development and sustainable development practices in the central region. As a region that is a key focus of the Chinese government’s economic development, the Central Region demonstrates its unique development potential through the co-existence of industry and agriculture. The western region (West) also exhibits a significant positive effect (1.4330 ***), suggesting that new quality productivity also contributes to ESG performance here. The west is rich in natural resources, and firms can rely on these abundant resources when innovating to enhance new quality productivity.

Overall, the relationship between new quality productivity and ESG performance varies by region, reflecting differences in the economic, environmental, and social challenges and opportunities different regions face. This regional variation underscores the importance of developing region-specific strategies to enhance ESG performance through new quality productivity while addressing the unique challenges of each region. Insights into regional differences can help policymakers assess the effectiveness of current policies and identify areas that may require region-specific interventions to support sustainable development and innovation in different regions. In addition, understanding these heterogeneities is critical for investors and development agencies to help them identify regions with the greatest potential for sustainable growth. The need to consider regional specificities when developing strategies and policies to promote sustainable development and innovation was highlighted.

The next section explores new quality productivity’s differential effect on high-tech and non-high-tech firms’ ESG in the context of China. This distinction is critical because high-tech firms often face unique challenges and opportunities in sustainability and innovation compared to non-high-tech firms. New quality productivity regression results regarding the ESG performance of these two kinds in China are presented in columns 4 and 5, respectively, of the table.

The new quality productivity of high-tech firms has a significant positive impact on ESG performance (1.398 ***), suggesting that new quality productivity is a strong driver of ESG in high-tech firms, possibly due to the greater focus on innovation and sustainable technologies in these firms. New quality productivity remains positive for non-high-tech firms, but the coefficient (1.106 ***) is slightly lower than that of high-tech firms, suggesting that new quality productivity also positively affects the ESG performance of non-high-tech firms, albeit less strongly than that of high-tech firms.

New quality productivity has a consistent positive effect in both high-tech and non-high-tech firms, reinforcing its importance as a driver of ESG performance. However, the extent of the effect differs between high-tech and non-high-tech firms, reflecting the industry-specific dynamics of firm characteristics affecting ESG performance. This result highlights the subtle influence of industry context high-tech versus non-high-tech. For policymakers, investors, and corporate strategists, understanding these differences is critical to tailoring approaches to improving sustainability in different sectors of the economy.

Heterogeneity analyses of heavily polluting firms (column 6) and non-heavily polluting firms (column 7) show that the relationship between new quality productivity and ESG performance is similarly significantly different in both types of firms.

The effect of new quality productivity on ESG performance is significantly higher for heavily polluting firms (2.8909 ***) than for non-heavily polluting firms. This suggests that for heavily polluting firms, the increase in new quality productivity significantly enhances ESG performance, which may be attributed to the direct impact of increased productivity on environmental efficiency and sustainability measures. The effect of new quality productivity on ESG performance for non-heavily polluting firms is still positive (1.0151 ***) but smaller than that for heavily polluting firms, suggesting that although new quality productivity improves ESG performance for non-heavily polluting firms, such an effect is less compared to the opposite. This result highlights the key role of new quality productivity in enhancing ESG performance, which is more pronounced in heavily polluting firms, suggesting that productivity improvements in these firms are likely to be closely associated with improved environmental performance.

This heterogeneity analysis highlights the subtle role of industry characteristics (especially the technology level and pollution intensity) regarding new quality productivity’s association with performance in ESG, providing valuable insights for policymakers, investors, and corporate managers aiming to improve sustainability performance in different industries.

Heterogeneity analyses of firms at different life-cycle stages (growth, maturity, and decline) reveal that the impact of new quality productivity on ESG performance varies depending on the stage of the firm, shown as

Table 6. Analysis of life-cycle heterogeneity.

	(1)	(2)	(3)
Variables	Growth	Maturity	Decline
Npro	0.7067 ***	1.3100 ***	1.4970 ***
	(9.64)	(12.36)	(17.53)
Size	8.1236 ***	8.4423 ***	8.4494 ***
	(12.76)	(16.03)	(19.58)
Lev	−8.1759 ***	−12.4184 ***	−14.0568 ***
	(−6.80)	(−10.81)	(−11.97)
Cashflow	6.5924 ***	7.4475 ***	6.5258 ***
	(6.93)	(9.07)	(8.81)
FIXED	−2.1192	−11.7321 ***	−13.8791 ***
	(−1.43)	(−8.73)	(−4.48)
Growth	−0.2976 ***	−0.4342	0.7193
	(−3.11)	(−1.74)	(1.24)
Indep	7.9149 ***	9.7634 ***	13.8550 ***
	(3.13)	(7.43)	(12.97)
Dual	−1.1904 ***	−0.2755	0.4512 ***
	(−5.52)	(−1.72)	(3.68)
SOE	−1.6575	0.1239	−0.7526 **
	(−1.70)	(0.15)	(−2.66)
Constant	−160.8046 ***	−169.3232 ***	−169.2108 ***
	(−11.07)	(−14.16)	(−18.52)
Observations	2789	4109	4803
Number of groups	923	1187	1017
R-squared	0.504	0.458	0.397

t statistics in parentheses; ** p < 0.05, *** p < 0.01.

.

The new quality productivity of firms in the Growth Period substantially influences ESG (0.7067 ***), signifying how new quality productivity is critical for improving ESG performance in the Growth Period, albeit to a lesser extent compared to the later stages. New quality productivity of firms in the Maturity Period (Maturity Period) shows a stronger positive impact on ESG (1.3100 ***), suggesting that the benefits of new quality productivity on sustainability become more evident as firms mature. The Decline Period Firms’ new quality productivity shows the strongest positive impact on ESG across all stages (1.4970 ***), suggesting that focusing on new quality productivity may be a key strategy for firms in decline to maintain or improve ESG performance.

The importance of new quality productivity is highlighted by the consistent and increasingly positive impact of new quality productivity on ESG performance across all life-cycle stages. The results provide valuable insights into the strategic importance of new quality productivity in improving the ESG performance of firms at different life-cycle stages, providing a nuanced understanding that can inform targeting strategies for sustainability efforts.

5.4. Endogeneity Test

To address the possible endogeneity problem in the regression model, the 2SLS model was used for regression in this study. The endogenous variable first-order lag was chosen as the instrumental variable. There is a significant correlation between instrumental and endogenous variables in regression 1. In addition, the unidentifiable test and the weak instrumental variable test results significantly reject the original hypothesis, indicating that the selection of instrumental variables is valid and that the instrumental variables satisfy exogeneity. The results of correcting the bias of endogenous variables using instrumental variables are shown in column 2 of

Table 7. Endogeneity test.

	First Stage	2SLS
Variables	Npro	BloombergESG
L.Npro	0.904 ***
	(202.48)
Npro		0.119 ***
		(4.19)
Size	−0.003	2.751 ***
	(−0.25)	(46.65)
Lev	−0.197 ***	−3.133 ***
	(−2.86)	(−7.88)
Cashflow	−0.362 **	6.702 ***
	(−2.27)	(7.27)
FIXED	0.781 ***	−1.602 ***
	(10.11)	(−3.56)
Growth	0.000	−0.004
	(0.10)	(−1.10)
Indep	0.082	3.213 ***
	(0.44)	(3.02)
Dual	0.041	0.171
	(1.48)	(1.07)
SOE	0.031	0.585 ***
	(1.27)	(4.20)
Constant	0.587 **
	(2.56)
Underidentification test Weak identification test		8177.339 *** 4.1 × 104 ***
Observations	10,205	10,205
R-squared	0.856	0.210

t statistics in parentheses; ** p < 0.05, *** p < 0.01.

, where the core independent variable Npro remains significantly and positively correlated with the dependent variable at the 1% significance level, consistent with the previous findings. The 2SLS results validate the positive impact of new quality productivity on ESG performance, while potential endogeneity issues are addressed using lagged Npro as an instrumental variable.

5.5. Robustness Tests

In order to assess the outcomes’ dependability and stability, robustness tests examined the relationship between new quality productivity (Npro) and ESG performance under different conditions, shown as

Table 8. Robustness tests.

	(1)	(2)	(3)
Variables	SESG	Delete after 2020	Non-Winsorized
Npro	0.015 ***	0.080 ***	0.117 ***
	(3.59)	(2.92)	(5.05)
Size	0.245 ***	2.243 ***	2.658 ***
	(26.09)	(39.11)	(49.20)
Lev	−1.158 ***	−2.716 ***	−3.123 ***
	(−18.43)	(−7.10)	(−8.59)
Cashflow	0.642 ***	5.045 ***	5.398 ***
	(4.24)	(5.50)	(6.51)
FIXED	−0.339 ***	−0.568	−1.444 ***
	(−4.74)	(−1.31)	(−3.50)
Growth	−0.104 ***	−0.299 **	−0.001
	(−4.31)	(−2.18)	(−1.29)
Indep	1.673 ***	2.314 **	3.234 ***
	(9.65)	(2.19)	(3.31)
Dual	−0.035	0.153	0.247 *
	(−1.40)	(1.02)	(1.69)
SOE	0.229 ***	0.517 ***	0.483 ***
	(10.48)	(3.92)	(3.75)
_cons	−1.483 ***	−31.382 ***	−41.183 ***
	(−6.80)	(−23.72)	(−32.76)
N	11,701	8743	11,701
R2	0.145	0.545	0.597
adj. R2	0.142	0.543	0.595
F	49.322	282.151	431.078

t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

. For the benchmark regression, Bloomberg ESG was used for the study, and to further test ESG’s effects among firms’ new quality productivity, the paper uses the Sino-Securities ESG indicator to replace the explanatory variable (SESG). Column 1 shows the regression results with Sino-Securities ESG as the source of the explanatory variables. New quality productivity shows a significant effect on ESG performance (0.015 ***). The positive correlation persists, suggesting that new quality productivity affects ESG performance even when different sources of ESG are used.

During the sample period, the post-2020 neo-crest epidemic has a very strong impact on firms’ performance, and to reduce the uncertainty of the results of the study due to the outlier years, the regressions are run after excluding the 2020–2022 data. Column 2 shows regression outcomes following the purge of the 2020–2022 data. The coefficient increases (0.080 ***), indicating that the effect of new quality productivity on ESG becomes more pronounced when excluding pandemic years and years shortly after the pandemic.

The third column provides the regression outcomes between new quality productivity and ESG performance using non-winsorized data to verify outcome stability in the presence of outliers. This positive and significant effect of new quality productivity on ESG performance increases further (0.117 ***), determining how the new quality productivity effect on ESG performance is greater when extreme values are included, which suggests robustness.

Robustness tests under different scenarios—changing the ESG data source, excluding specific years, and including outliers—all suggest that new quality productivity has a consistent positive impact on ESG performance, albeit with varying degrees of this impact. These robustness tests affirm the reliability of the main findings, suggesting that the positive correlation between new quality productivity and ESG performance is stable across a range of analytical conditions and data treatments.

5.6. Impact of Green Innovation

Table 9. Impacts of green innovation.

	(1)	(2)	(3)
Variables	ESG	ESG	SESG
Npro	−0.016		−0.017 ***
	(−0.59)		(−3.59)
Npro × GI	0.071 ***		0.016 ***
	(10.01)		(13.42)
GI		0.454 ***
		(9.28)
Size	2.435 ***	2.414 ***	0.196 ***
	(41.83)	(40.15)	(19.54)
Lev	−3.234 ***	−3.341 ***	−1.174 ***
	(−8.93)	(−9.22)	(−18.82)
Cashflow	6.296 ***	6.234 ***	0.651 ***
	(7.22)	(7.13)	(4.34)
FIXED	−0.758 *	−0.563	−0.196 ***
	(−1.82)	(−1.40)	(−2.73)
Growth	0.100	0.126	−0.097 ***
	(0.72)	(0.91)	(−4.07)
Indep	3.410 ***	3.313 ***	1.693 ***
	(3.41)	(3.31)	(9.84)
Dual	0.227	0.221	−0.039
	(1.60)	(1.55)	(−1.58)
SOE	0.474 ***	0.534 ***	0.221 ***
	(3.76)	(4.24)	(10.17)
_cons	−36.005 ***	−35.687 ***	−0.288
	(−26.51)	(−25.88)	(−1.23)
N	11,701	11,701	11,701
R2	0.609	0.607	0.158
adj. R2	0.607	0.606	0.155
F	442.028	450.574	53.249

t statistics in parentheses; * p < 0.1, *** p < 0.01.

demonstrates the moderating effect of green innovation (GI) on the relationship between new quality productivity (Npro) and ESG performance and the direct effect of green innovation on ESG. In addition, the robustness of these effects is tested using an alternative source of ESG (Sino-Securities ESG).

In column 1, the interaction term (Npro × GI) coefficient between green innovation and new quality productivity is 0.071 ***, showing a significant positive moderating effect. This suggests that the presence of green innovation significantly enhances the effect of new quality productivity in enhancing ESG performance. The higher R² value of the model (0.609) indicates that the model, including green innovation as a moderator, explains most of the variance in ESG performance. The results in column 2 show a strong direct positive effect of green innovation on ESG performance (0.454 ***), highlighting green innovation as an important driver of ESG improvement. This finding suggests that firms focusing on sustainable innovation typically exhibit higher ESG performance. Column 3, which is robustly tested with Sino-Securities ESG data, shows similar results to column 1, displaying a positive and significant moderating effect (0.016 ***), albeit the magnitude of the moderating effect is smaller. This further confirms the robustness of green innovation in moderating the relationship between new quality productivity and ESG performance across ESG measures. The relatively low R2 value (0.158) may indicate that the data source of Sino-Securities captures different aspects of ESG performance or is less sensitive to model variables.

These results highlight the dual key role of green innovation in directly improving firms’ ESG performance and enhancing the effects of new quality productivity. Using green innovation as a moderating variable reveals more precisely how firms utilize productivity gains to improve ESG performance, especially in the context of sustainable innovation. Consistent moderating effects across different ESG data sources emphasize the robustness of the finding that green innovation enhances the positive impact of new quality productivity on ESG performance.

Overall, green innovation as a strategic productivity-enhancing tool is important significantly improving firms’ ESG performance. At the same time, examining the interactions between different sustainability drivers is valuable to understand how they affect ESG performance fully.

6. Discussion

This study comprehensively analyzes the interactions between firms’ new quality productivity (Npro) and green innovation (GI) and their impacts on firms’ environmental, social, and governance (ESG) performance using data from listed companies in China over the period 2011–2022. The empirical results show that new quality productivity has a sustained positive impact on ESG performance, emphasizing the importance of innovation-driven productivity enhancement in promoting excellence in sustainable development. New quality productivity enhances productivity through innovative technologies and efficient processes [7]. It involves not only improvements in products and services but also the implementation of environmental and resource-saving measures in production and operations that can contribute to the efficiency of a firm [47]. At the heart of new quality productivity lies promoting sustainable and responsible production methods [20]. Such production methods help reduce firms’ environmental impacts while increasing their adaptability to changing market conditions and long-term sustainability. On the other hand, digital transformation, as a key component of new quality productivity [18], can help firms optimize energy efficiency and resources and achieve greater resource efficiency. At the same time, digital technological innovations can improve the working environment [48] and help to promote corporate social responsibility further. In high-tech enterprises, new quality production has been especially critical. Digital transformation can significantly improve operational efficiency and market responsiveness for high-tech enterprises, leading to better ESG performance [49].

Furthermore, the impact of new quality productivity on ESG performance shows significant differences across regions and stages of firm development. This reveals the impact of geographical and developmental differences on corporate sustainability. The eastern region is China’s most economically developed region with a more mature economic and technological infrastructure [50]. Firms here were the first to implement ESG-related measures, so the direct impact of new quality productivity improvements on ESG performance may be relatively small, making the effect of increased new quality productivity on ESG less pronounced than in other regions. The central region has experienced rapid economic development in recent years, and the government and enterprises may be more actively seeking to accelerate regional development and improve competitiveness through new quality productivity improvements [51], thus improving ESG performance via new quality productivity more significantly. Although the western region is rich in natural resources, its economic development and technological application are relatively late compared to the eastern and central parts of the country, and infrastructure development is still in progress. Therefore, new quality productivity gains may translate more directly into improved ESG performance here, with more room for improvement.

The study also found that the extent to which new quality productivity and green innovation affect ESG performance varies across firms, especially between high-tech and non-high-tech firms and between heavily polluting and non-heavily polluting firms. The differences in the impact of new quality productivity on ESG performance across different types of firms reflect the essentially different nature of these firms in terms of technological innovation, resource allocation, market demand, and industry characteristics. High-tech firms are more effective and significant in utilizing new quality productivity to enhance ESG performance due to their intrinsic innovation orientation and the requirements of the external environment. High-tech firms usually invest heavily in technological development and innovation and possess advanced technological foundations and R&D capabilities [52,53]. This makes it easier for high-tech firms to integrate and apply new technologies to improve the efficiency and environmental friendliness of the production process. Non-high-tech firms are relatively slower in technological innovation and application, and their innovations are mainly focused on traditional production methods or rarely involve core technological innovations. As a result, the positive impact of new quality productivity on their ESG performance may be less pronounced than for high-tech firms.

The potential for new quality productivity and green innovation to contribute to ESG performance is particularly significant in industries with a high environmental impact. Highly polluting companies typically have a higher environmental impact due to the nature of their business. As a result, these firms face stricter environmental regulatory requirements and social responsibility pressures, forcing them to take more proactive measures to improve their ESG performance [54]. Improvements in new quality productivity have a more pronounced positive impact on the ESG performance of these firms, as they start from a lower baseline with more room for improvement and potential impact. Consumer and market demand for sustainable products is growing, and highly polluting firms will likely gain greater market acceptance for their products and services as they transition to more environmentally friendly operations, enhancing their sustainability performance. At the same time, investors are increasingly inclined to support firms demonstrating a strong ESG commitment [15]. For highly polluting firms, boosting new quality productivity and significantly improving their ESG performance can attract more green and socially responsible investments and improve the efficiency of their capital acquisition. Technological advances provide high-polluting industries with concrete means to improve environmental performance [55]. Therefore, the significant impact of new quality productivity in highly polluting firms reflects the unique challenges and improvement needs of these firms regarding environmental and social responsibility. By effectively enhancing new quality productivity, these firms can better address environmental challenges, meet regulatory requirements, and improve their overall ESG performance. These findings highlight the importance of incorporating green innovation into firms’ strategies to improve productivity, demonstrating the value of studying the interactions between sustainability drivers to understand their combined impact on ESG performance fully.

As a key moderator, green innovation not only directly contributes to outcomes in terms of ESG but also amplifies the positive impact of new quality productivity on ESG performance, highlighting the synergies between sustainability-focused innovation and productivity improvements. This is even though some studies have argued that green innovation requires more costs from daily business operations [56], which implies that high investment in green innovation increases the financial burden of firms and reduces their profits [57]. Therefore, green innovation is a long-term activity for firms. Although green innovation may require a large investment at the initial stage, in the long term, by saving energy and optimizing the use of resources, firms can achieve a reduction in operating costs and an increase in economic efficiency. Green innovation can differentiate products and services, thereby enhancing the company’s reputation, and these benefits can offset the costs of implementing these green innovation activities [58]. New quality productivity represents a leap in productivity. On the other hand, green innovation usually involves adopting environmentally friendly technologies and processes that increase energy and resource use efficiency while reducing waste and emissions from the production process. Thus, when green innovation exists in a firm, the new quality productivity gains are economically efficient and efficient in terms of environmental protection, thus positively impacting the environmental dimension of ESG. Firms implementing green innovation are usually required to follow stricter environmental regulations and standards, which prompts firms to strengthen their internal compliance mechanisms and governance structures and increase the transparency of their operations [59,60]. As a result, green innovation enables firms to integrate and optimize their ESG performance, which meets regulatory requirements and market expectations and helps firms stay ahead of the curve in a competitive market. Therefore, green innovation has become an important driver of corporate ESG performance.

These findings help firms’ stakeholders to understand how innovation practices, particularly those focused on new quality productivity and green innovation, play a key role in improving firms’ ESG performance. It highlights the multifaceted nature of this relationship, which is influenced by factors, such as the regional context, firm life-cycle stage, and industry characteristics. By identifying the moderating role of green innovation, this study highlights the importance of integrating sustainability-focused innovation into broader corporate strategies to achieve ESG excellence. All in all, the findings offer policymakers, corporate leaders, and investors valuable insights to deal with the sustainability intricacy of corporate industry.

Corporate leaders need to incorporate green innovation into their commercial strategies, so as to enhance their ESG performance and fortify the positive influence of new quality productivity improvements on sustainability results. According to the requirement of this method, companies need to invest in sustainable techniques and procedures that propel efficiency and environmental management. In view of varying impacts in different business life-cycles and areas, managers should customize sustainability and innovation tactics to the specific context.

Policymakers should implement policies and incentives to bolster companies’ endeavors in new quality productivity and green innovation for improving ESG performance. This is likely to encompass tax incentives for sustainable investment, grants for green technology R&D, etc. Given the regional differences in the relationship among ESG, productivity, and innovation, policymakers need to take account of companies’ particular needs and potential in different areas in the design of regional development policies, such as providing targeted support for green innovation in more polluting sectors.

Based on the research results, investors can understand the significance of considering companies’ commitment to green innovation, as well as its influence on ESG performance, in investment decision-making. Companies with strong interplay among ESG excellence, green innovation, and new quality productivity may provide more profitable investment chances. Investors can incorporate different companies with strong sustainability practices to mitigate the risks related to sustainability.

7. Conclusions

This study comprehensively examines the joint impact of new quality productivity (Npro) and green innovation (GI) on environmental, social, and governance (ESG) performance among Chinese listed companies. The findings suggest that new quality productivity has a direct positive impact on ESG performance. In addition, the study highlights the key role of green innovation as both a direct contributor to ESG performance and a moderator that amplifies the positive impact of new quality productivity on ESG outcomes. The study reveals the key role of green innovation and new quality productivity in driving ESG performance and provides nuanced insights into how these relationships change in different contexts. The research implications for corporate strategy, policymaking, and investment practices highlight the importance of adopting a multidimensional approach to sustainability that considers the interplay among innovation, productivity, and ESG. By utilizing these insights, stakeholders can better navigate the complexities of corporate sustainability.

While this study provides valuable insights into the relationship among the new quality productivity, green innovation, and ESG performance of listed companies in China, it still has limitations. First, this study focuses only on Chinese listed companies, which limits the applicability of the findings to other geographies, as well as to unlisted or small firms operating under different regulatory and market dynamics. China’s economic, cultural, and regulatory environment is unique, and its impact on firm behavior and outcomes may not fully apply to firms in other regions. Second, as a new and complex concept, the measurement of a firm’s new quality productivity is relatively homogeneous for the time being. Indicators used to measure new quality productivity may not fully reflect its multidimensional nature, thus affecting the accuracy and completeness of the analysis. Finally, ESG performance indicators include a range of factors, from environmental impact to social responsibility and governance. New quality productivity and green innovation may affect each component of ESG differently, and the study’s methodology may mask these nuances.

Based on this, future research could explore the following areas to expand the understanding of the dynamic relationship among firms’ productivity, innovation, and sustainable development. First, expanding the geographic and industry scope improves the generalizability of the findings. Follow-up studies should include a wider range of firms, covering different countries, geographic regions, industries, and unlisted and small firms. Comparative studies in different regulatory and cultural contexts can also provide a deeper understanding of how the external environment affects corporate sustainability practices. Second, refining measurement techniques to develop more comprehensive measures of firms’ new quality productivity will improve the precision of future research. Utilizing more advanced analytical methods and perhaps integrating qualitative data would provide a richer and more comprehensive understanding of the concept. Finally, specific analyses are conducted for specific components of ESG. Profiling the components of ESG performance (environmental, social, and governance) examines how new high-quality productivity and green innovation uniquely impact each. This will address the complexity of ESG and provide more targeted insights to improve specific outcomes.