Corporate ESG Performance and New Quality Productive Forces: Based on Signaling Theory

Abstract

Amid the new wave of technological revolution and industrial transformation, new quality productive forces (NQPFs) have become the key to a firm’s sustainable development. To help enterprises accelerate the improvement of their NQPFs, grounded in signaling theory, this paper takes data of China’s A-share listed companies from 2015 to 2024 as the research sample and uses a two-way fixed effects model to empirically examine whether and how superior ESG performance, serving as a high-quality signal, fosters NQPFs. The results show the following. There is a significant positive relationship between corporate ESG performance and NQPFs. This finding remains robust across a series of checks, including replacing the key explanatory variable, removing outlier years and cities, and addressing endogenous problems through instrumental-variable estimation. Heterogeneity tests reveal that the effect is more pronounced among non-state-owned firms and those located in Northeast China, whereas it is statistically insignificant for firms in Western China. Mechanism analysis indicates that ESG performance boosts NQPFs indirectly by raising analyst attention and investor confidence. Overall, this paper not only enriches research perspectives on the relationship between corporate ESG performance and NQPFs, but also offers theoretical support and practical reference for the formulation of corporate ESG strategies and the precise policy-making of governments.

1. Introduction

In the contemporary era, the new wave of scientific, technological revolution, and industrial transformation is unfolding at an accelerated pace, which has led to profound alterations in social productive forces and their constituent factors. During an inspection of Heilongjiang in September 2023, General Secretary Xi Jinping proposed the important concept of new quality productive forces (NQPFs), a notion that aptly encapsulates the tremendous shifts in productive forces in the new era. With innovation-driven development at its core and high technology, efficiency, and quality as its defining characteristics, NQPFs embody an advanced manifestation of productive forces in line with the new development philosophy [1]. The Fourth Plenary Session of the 20th Central Committee of the Communist Party of China, convened on 23 October 2025, adopted the Central Committee’s proposals for drawing up the 15th Five-Year Plan for National Economic and Social Development. Chapter Four emphasizes “accelerating high-level self-reliance and self-strengthening in science and technology to lead the development of NQPFs,” and Point 12 stresses “strengthening the principal role of enterprises in scientific and technological innovation, driving innovation resources to cluster in enterprises, supporting enterprises to lead the establishment of innovation consortia and undertake more national scientific and technological research tasks, encouraging enterprises to increase investment in basic research, and promoting the deep integration of the innovation, industrial, capital, and talent chains”.

For enterprises, elevating their level of NQPFs has become pivotal to their survival and sustainable development. The essence of corporate NQPFs lies in the systemic enhancement of innovation capabilities, which hinges on the sustained support of critical scarce resources such as capital, talent, and partners. However, the inherent information asymmetry between enterprises and external stakeholders may induce adverse selection, impede the optimal allocation of resources [2,3], and thereby constrain the cultivation and upgrading of enterprises’ NQPFs. To address this dilemma, the signaling theory proposed by Michael Spence provides a crucial analytical perspective [4]. Strong ESG performance among enterprises represents a desirable high-quality signal capable of alleviating information asymmetry and supporting the advancement of NQPFs. Through consistent ESG disclosure and performance improvement, enterprises transmit favorable signals to the capital market about robust corporate governance, sophisticated management systems, and commitment to long-term value. In doing so, they effectively mitigate information asymmetry, draw in scarce resources, and support the fostering and advancement of NQPFs. Accordingly, examining how ESG performance affects NQPFs and its intrinsic mechanism based on signaling theory holds considerable practical significance.

An examination of prior relevant research indicates that, while existing studies have laid a robust groundwork for analyzing the association between ESG performance and NQPFs, several distinct research gaps remain unexplored. First, studies investigating the association between ESG performance and NQPFs are still limited in number. While prior studies have established the positive economic effects of ESG performance and the multiple driving forces behind NQPFs, the direct effect of ESG performance on NQPFs remains to be empirically tested. Second, the differential effects of ESG performance on NQPFs under varied institutional contexts remain underexplored. Although prior research has recognized the heterogeneous traits in the economic outcomes of ESG performance, studies investigating the differential effects of ESG performance on NQPFs from the dimensions of firm ownership types and regional location are still relatively inadequate. Such insufficient empirical evidence cannot offer targeted theoretical support for enterprises with different ownership types and from various regions to conduct ESG practices and foster the development of NQPFs. Third, the internal transmission mechanism through which ESG performance influences NQPFs remains unclear. Prior studies have established that analyst attention and investor confidence play mediating roles when ESG performance influences firms’ financial outcomes and innovation capacity. However, whether these two factors act as transmission channels between ESG performance and NQPFs is yet to be determined, and the specific mediating pathways require further investigation.

In response to the above research gaps, this study proposes the following research questions:

(1) Based on signaling theory, is it possible for ESG performance to serve as a high-quality signal that reduces information asymmetry between firms and external stakeholders, directs premium resources toward enterprises with strong ESG performance, and consequently improves their NQPFs level?

(2) Is the positive effect of ESG performance on NQPFs heterogeneous across various ownership types and geographic regions?

(3) Can ESG performance indirectly promote the development of corporate NQPFs by increasing analyst attention and strengthening investor confidence?

To address these questions, this study constructs an analytical framework—“ESG Signaling—Alleviation of Information Asymmetry—Resource Inflow—Enhancement of NQPFs”—grounded in signaling theory. Specifically, strong ESG performance can effectively reduce information asymmetry between enterprises and external stakeholders via signaling mechanisms. This, in turn, can draw increased analyst attention, enhance investor confidence, promote the inflow of scarce resources including capital and talent, and ultimately drive the advancement of firms’ NQPFs. Moreover, this impact shows significant differences among enterprises with different ownership types and regions. Based on a dataset of Chinese A-share listed firms over the 2015–2024 period, this study utilizes a two-way fixed-effects specification to empirically test the promoting effect of ESG performance on NQPFs development, the heterogeneous impacts across different ownership types and regions, as well as the partial mediating roles of analyst attention and investor confidence.

This paper clarifies its potential marginal contributions from three perspectives: theoretical, empirical, and policy. From a theoretical perspective, this research expands the application of signaling theory to the investigation of the relationship between ESG performance and NQPFs, which further diversifies its micro-level application contexts within firms. It also improves the research framework of micro-level influencing factors of NQPFs, fills the theoretical gap regarding the intrinsic connection between ESG and NQPFs, and provides a theoretical perspective for subsequent related studies. Empirically, relying on a comprehensive dataset and stringent empirical tests, this study validates the influence, mediating mechanism, and heterogeneous traits of corporate ESG performance on NQPFs, offering credible empirical support for the causal link between the two variables. By subdividing dimensions such as ownership type and regional distribution, it systematically reveals the differential effects of ESG performance across contexts, rendering conclusions more targeted and persuasive, and better guiding the practical operations of diverse enterprises. Policy-wise, this study offers a theoretical basis for policymakers to guide enterprises in optimizing ESG performance and cultivating NQPFs. It supports the design of differentiated policy support systems that accommodate the development needs of firms with different ownership structures and across different regions.

To clearly present the research framework, the outline of each chapter is summarized as follows:

Section 1: Introduction. This chapter elaborates on the research background and motivation, clarifies the research questions and research content, details the potential contributions at the theoretical, empirical, and policy levels, and concludes with an overall research framework.

Section 2: Theoretical Foundation and Literature Review. The theoretical foundation section introduces the connotation of NQPFs, the definition and signaling characteristics of ESG performance, as well as the core logic of signaling theory. The literature review section summarizes the economic impacts of corporate ESG performance, along with the measurement methods and influencing factors of firm-level NQPFs.

Section 3: Theoretical Analysis and Hypothesis. This chapter selects and compares theoretical perspectives, explores the direct effect of corporate ESG performance on NQPFs and the mediating roles of analyst attention and investor confidence based on signaling theory, and formulates corresponding research hypotheses.

Section 4: Research Design. This chapter details sample selection and data sources; defines and measures the dependent variable, independent variable, mediating variables, and control variables; and constructs the empirical models.

Section 5: Empirical Results. This chapter presents descriptive statistics, baseline regression, robustness tests, endogeneity test, heterogeneity tests, and mediating mechanism tests.

Section 6: Discussion. Integrating empirical results, this chapter conducts an in-depth discussion on the economic significance and magnitude of effects, as well as practical application scenarios of multi-dimensional findings, clarifying the theoretical extension and practical implications of the results.

Section 7: Conclusions. This chapter summarizes the main findings, proposes targeted practical implications for firms and policymakers, acknowledges research limitations, and outlines future research directions to inform subsequent studies.

2. Theoretical Foundation and Literature Review

2.1. Theoretical Background

2.1.1. Connotation of NQPFs

As an emerging core concept in economics, NQPFs maintain theoretical connections with traditional productive forces and innovation theory while embodying fundamental differences. Its fundamental innovation resides in transcending the factor-driven developmental paradigm inherent in conventional productive forces. Centered on innovation-fueled expansion, it is distinguished by prominent attributes of advanced technology, enhanced efficiency, and superior quality, thereby embodying a sophisticated manifestation of productive forces aligned with the new development philosophy [1]. In terms of developmental factors, NQPFs are fundamentally defined by the upgrading and optimal combination of laborers, means of labor, and objects of labor [5]. Unlike traditional productive forces, which rely on quantitative factor inputs, it emphasizes the improvement of factor quality and collaborative optimization. From the perspective of integration into the existing system of economic literature, NQPFs do not negate traditional productivity theory; instead, based on Marxist productivity theory, they deeply integrate modern theoretical achievements such as innovation economics, sustainable development economics, and digital economics [6]. They address the insufficient adaptability of traditional productivity theory within the digital and green economy era by deeply integrating technological innovation, institutional innovation, ecological constraints, and productivity advancement. In doing so, they establish a distinctive theoretical implication characterized by “innovation-oriented, multi-dimensional coordination, quality-centered, and green sustainable development.” At the firm level, NQPFs refer to an advanced form of productivity guided by scientific and technological innovation, with enterprises as the main actors of innovation. They emerge from the deep integration of intelligent equipment, high-end talent, and cutting-edge technologies, reconstructing production, management, and business models in line with the new development philosophy.

2.1.2. Connotation and Signaling Attributes of ESG Performance

ESG performance is a comprehensive evaluation of a firm’s environmental protection, social responsibility fulfillment, and corporate governance capabilities, transcending the limitations of traditional financial performance evaluation and reflecting a firm’s long-term development potential and sustainable operation capacity. Specifically, the environmental dimension reflects a firm’s ability to identify and address ecological risks; the social dimension reflects its cooperative relationships with stakeholders such as employees, customers, and communities; and the governance dimension reflects the effectiveness of internal governance structures and incentive mechanisms [7].

As critical non-financial information, ESG performance inherently possesses strong signaling attributes: First, observability: firms disclose detailed ESG information through sustainability reports, regulatory filings, official websites, and other channels, which can be easily accessed by external stakeholders. Second, verifiability: third-party auditors and rating agencies can verify the authenticity and reliability of ESG performance, enhancing the credibility of the signal. Third, high cost: improving and maintaining ESG performance requires long-term and continuous investment in environmental equipment, employee benefits, governance system optimization, and other areas, making it difficult for low-quality firms to imitate [8]. Fourth, quality relevance: ESG performance is closely associated with internal management, risk control, and long-term value creation, effectively conveying a firm’s unobservable core quality. These attributes make ESG performance an ideal high-quality signal for firms to communicate their intrinsic quality to the outside world.

2.1.3. Core Logic of Signaling Theory

Signaling theory was first proposed by Spence to explain information asymmetry in the labor market [4], and has since been widely applied in capital markets, product markets, and other fields. The theory holds that, in markets with severe information asymmetry, an information gap exists between informed parties (signal senders) and uninformed parties (signal receivers). Signal senders can transmit credible information about their quality by adopting costly and hard-to-imitate signaling behaviors, thereby reducing information asymmetry and influencing the decisions of signal receivers. In corporate finance and management research, firms act as signal senders, while external stakeholders such as shareholders and creditors are signal receivers. Information asymmetry between firms and external stakeholders tends to induce adverse selection, impeding the efficient flow of resources to firms with high development potential. ESG performance can effectively alleviate this problem. For firms, as a comprehensive signal reflecting long-term development potential, ESG performance can gain recognition from external stakeholders through effective disclosure and continuous improvement, thereby helping firms access scarce resources such as capital and talent to support the development of core competencies.

2.2. Literature Review

2.2.1. Research on the Economic Consequences of Corporate ESG Performance

Research on the economic consequences of ESG performance has formed a relatively rich research system, with scholars conducting in-depth studies from the perspectives of corporate value, financial performance, financing constraints, human resources, and innovation capacity. In terms of corporate value, Li and You found that ESG performance has a significant positive impact on the corporate value of Chinese A-share listed companies, particularly for non-state-owned and polluting enterprises [9]. In terms of financial performance, Xie et al. pointed out that ESG promotes financial performance by strengthening product competitive advantages [10]. In terms of financing constraints, Song and Deng stated that financing constraints are negatively correlated with the improvement in corporate ESG performance [11]. In terms of human resources, Indra Firdiyansyah et al. found that enterprises with strong ESG performance can achieve higher job satisfaction, higher participation, and better retention rates [12]. In terms of innovation capacity, Lu et al. argued that ESG performance promotes green technological innovation of listed companies by encouraging R&D investment [13].

2.2.2. Research on NQPFs

Research on NQPFs mainly involves their connotation, measurement, and influencing factors. The connotation of NQPFs has been briefly summarized earlier, and here we briefly introduce the measurement and influencing factors of firm-level NQPFs.

In China, scholars mainly adopt two types of methods to measure firm-level NQPFs. The first is the NQPFs indicator system constructed by Song et al. [14] based on the two-factor theory of productivity. Productivity includes two factors: labor and means of production. Among them, labor consists of two sub-factors: living labor and materialized labor (objects of labor); means of production consist of two sub-factors: hard technology and soft technology. The second is the NQPFs indicator system constructed by Zhang et al. [15] based on new-quality laborers, new-quality objects of labor, and new-quality means of labor. The new-quality laborers dimension is based on two levels: employees and managers. The new-quality objects of labor dimension is based on two levels: ecological environment and future development. The new-quality means of labor dimension is based on three levels: technological means of labor, green means of labor, and digital means of labor.

This study adopts the NQPFs indicator system proposed by Song et al., mainly for the following two reasons. First, the basic connotation of NQPFs lies in the upgrading of laborers, means of labor, objects of labor, and their optimal combination. In the indicator system proposed by Song et al., living labor corresponds to laborers, materialized labor corresponds to objects of labor, hard technology corresponds to means of labor, and soft technology can represent the optimal combination of the three. Second, the NQPFs indicator system proposed by Zhang et al. includes the environmental score in the Huazheng ESG rating system, which overlaps with the independent variable (ESG performance) selected in this study, potentially leading to significant endogeneity issues.

The influencing factors of firm-level NQPFs involve artificial intelligence, financial technology, patient capital, data elements, digital intelligence transformation, and government subsidies. In terms of artificial intelligence, Li et al. pointed out that artificial intelligence promotes the development of corporate NQPFs [16]. In terms of financial technology, Shen and Zhang found that financial technology promotes the improvement in corporate NQPFs by alleviating financing constraints [17]. In terms of patient capital, Yang et al. stated that patient capital, with its long-term value orientation and tolerance for short-term fluctuations, can significantly improve the level of NQPFs in manufacturing enterprises [18]. In terms of data elements, Li et al. found that data elements have a significant positive impact on enterprises’ improvement in NQPFs [19]. In terms of digital intelligence transformation, Feng et al. argued that digital intelligence transformation combines digitalization and intelligence, reshapes the corporate growth model and governance structure, and becomes a key lever for cultivating corporate NQPFs [20]. In terms of government subsidies, Wang and Zhang pointed out that government subsidies can improve the level of corporate NQPFs by alleviating financing constraints, enhancing the digital level, and stimulating corporate innovation [21].

3. Theoretical Analysis and Hypothesis

3.1. Selection and Comparison of Theoretical Perspectives

Stakeholder theory, resource-based view, institutional theory, and signaling theory are the four mainstream theories in ESG research, each with distinct focuses in explaining corporate ESG performance and its economic consequences. This paper compares the above four theories to clarify why signaling theory is the most appropriate analytical perspective for studying the relationship between ESG performance and NQPFs.

First, comparison with stakeholder theory. Stakeholder theory holds that enterprises should balance the interests of all stakeholders, including shareholders, employees, customers, and the government, and fulfilling ESG responsibilities is an inevitable requirement for enterprises to coordinate the interests of all parties [22]. This theory can well explain the motivation for enterprises to carry out ESG practices, but it fails to reveal how enterprises transmit information on fulfilling stakeholder responsibilities to the outside world, nor can it explain the process by which external stakeholders respond to corporate ESG performance and provide resource support. Signaling theory can make up for this deficiency: it clarifies the information transmission path of ESG performance as a high-quality signal, and reveals how enterprises alleviate information asymmetry through ESG performance to obtain resource support from external stakeholders, which is a strong support for enterprises to cultivate NQPFs.

Second, comparison with the resource-based view. The resource-based view holds that the heterogeneous resources owned by enterprises are the source of their sustainable competitive advantages, and ESG performance, as an intangible resource of enterprises, can help enterprises form unique competitive advantages [23]. This theory highlights the resource attribute of ESG performance, but it does not explain how this intangible resource is transformed into the enterprise’s actual resource acquisition capacity, nor does it reveal the interaction mechanism between corporate ESG performance and external resource providers. Signaling theory can explain the transformation process of ESG performance from intangible resources to actual resource acquisition capacity, clarifying that enterprises can transmit quality signals to external resource providers through ESG performance, thereby promoting the effective flow of external resources to enterprises.

Third, comparison with institutional theory. Institutional theory holds that corporate ESG behaviors are affected by formal and informal institutional environments such as national policies, industry norms, and social culture, and enterprises carry out ESG practices to obtain organizational legitimacy and meet the institutional requirements of the external environment [24]. This theory can explain the differences in corporate ESG practices under different institutional environments, but it cannot explain the obvious differences in the cultivation of NQPFs among enterprises with different ESG performance under the same institutional environment. Signaling theory can make up for this deficiency: from the perspective of enterprises actively sending signals, it explains that enterprises with excellent ESG performance can obtain more scarce resources through active signal transmission, thereby promoting the development of NQPFs, while for enterprises with poor ESG performance it is difficult to obtain external resource support due to the lack of effective signal transmission.

In summary, compared with other mainstream theories, signaling theory can not only explain the motivation for enterprises to improve ESG performance, but also reveal the internal logic and specific path of ESG performance affecting the cultivation of NQPFs from the perspective of information transmission. It is more consistent with the research context of this paper—“ESG Performance—Alleviation of Information Asymmetry—Resource Inflow—Enhancement of NQPF”—and can better explain the micro-mechanism by which ESG performance promotes the development of NQPFs. Therefore, this paper conducts in-depth theoretical analysis with signaling theory as the core analytical perspective.

3.2. Direct Effects of Corporate ESG Performance on NQPFs

The essence of corporate NQPFs lies in the systemic enhancement of innovation capabilities, which hinges on the sustained support of critical scarce resources such as capital, talent, and partners. However, the inherent information asymmetry between enterprises and external stakeholders may induce adverse selection, impede the optimal allocation of resources, and thereby constrain the cultivation and upgrading of enterprises’ NQPFs. Grounded in signaling theory, superior ESG performance functions as a credible signal of firm quality, thereby enabling enterprises to more efficiently secure capital, attract talent, and establish strategic partnerships.

(1) Alleviating Financing Constraints and Channeling “Patient Capital”. The credit market has long been plagued by severe information asymmetry. Zhou and Chen argue that the greatest impediment to the development of green credit is the heightened screening cost and uncertainty risk for banks arising from this asymmetry [25]. Grounded in signaling theory, when a firm voluntarily discloses and continuously improves its ESG performance, it transmits a verifiable, high-quality signal: environmental risks are controllable, social obligations are fulfilled, and corporate governance is transparent and effective. This signal reduces banks’ ex-ante screening and ex-post monitoring expenses, markedly mitigating adverse selection and moral hazard. Wang, Xie and Guo demonstrate that superior ESG performance lowers banks’ assessment of a firm’ s future risk and the difficulty of subsequent monitoring, inducing banks to relax credit constraints and offer larger loans at lower interest rates, with longer maturities and less stringent collateral requirements [26]. Such financing constitutes “patient capital”, indispensable for sustaining long-cycle, high-risk basic research and frontier technological breakthroughs that fuel the development of NQPFs.

(2) Attracting and Retaining High-End Talent: Building a “Talent Magnet”. In labor markets characterized by incomplete information and the dispersion of both talent and employers, high-level human capital faces severe adverse-selection risk. According to signaling theory, when internal firm quality is unobservable, credible information can be transmitted through costly, verifiable signals. ESG performance—especially leading practices in the “S (Social)” dimension—serves as such a signal, materially reducing information asymmetry between firms and elite talent, thereby enabling firms to attract and retain high-end personnel. Guan, Guo and Mei find that superior ESG performance upgrades a firm’s human-capital structure by drawing in high-quality talent [27]. Likewise, Li, Gao and Chen show that ESG performance enhances corporate reputation, which in turn accelerates the upgrading of human capital [28]. Because top-tier talent is the core carrier of NQPFs, only firms that assemble a cadre of innovative, globally minded professionals can repeatedly break technological bottlenecks and achieve disruptive innovation in products and services, thereby propelling the development of NQPFs.

(3) Winning the trust of customers and supply chain partners to form a “value alliance”. Because of information asymmetry between upstream and downstream supply chain actors, downstream customers cannot accurately assess a supplier’s true operational quality, compliance level, or long-term fulfillment capability, resulting in adverse selection. Grounded in signaling theory, a firm’s ESG performance serves as a high-quality signal that conveys private information to downstream customers—namely, that the firm possesses robust operations, continuous innovation, and compliant management—thereby mitigating customers’ concerns about hidden risks and averting adverse selection. Chen, Ding and Zhang demonstrate that superior ESG performance strengthens customer relationship stability by reducing information asymmetry, intensifying product differentiation, and improving corporate performance [29]. Zhang, Pan and Wu further show that ESG performance advances high-quality development in the manufacturing sector through two channels: supply chain discourse power and supply chain relationship stability [30]. Consequently, downstream customers adopt ESG performance as a key criterion for supplier selection, adverse selection is curbed, high-quality firms secure stable orders, and—via upstream–downstream knowledge sharing and collaborative innovation—the overall efficiency and resilience of the industrial chain are enhanced, raising the firm’s level of NQPFs.

Based on the above analysis, this study proposes the following hypothesis:

H1: 

Firms’ ESG performance can foster corporate NQPFs.

3.3. The Mediating Role of Analyst Attention

In capital markets characterized by severe information asymmetry, superior ESG performance transmits private information to external stakeholders: governance is sound and future risk exposure is limited. As a key intermediary in the reception and retransmission of signals, analysts’ attention and recommendation behaviors can be decomposed into two complementary ‘signal-response’ paths. (1) Information cost path. Highly ESG companies voluntarily disclose environmental, social, and governance data that are more systematic and verifiable, significantly reducing analysts’ information search, processing, and verification costs [31], and enhancing their willingness to cover. (2) Reputation insurance path. In the context of increasingly stringent regulatory enforcement and increased volatility in capital markets, high ESG performance serves as a ‘low-risk label’, implying lower probabilities of future litigation, regulatory penalties, and valuation downgrades. It has become a safe choice for analysts to avoid reputational losses [32]. Guided by prudence preferences, analysts seeking to avoid tail risk to their careers issue more optimistic recommendations for high-ESG firms.

Furthermore, through in-depth interpretation of corporate ESG performance, analysts effectively convey the innovation potential and long-term value embedded in firms’ ESG practices to capital markets, thereby mitigating information asymmetry between enterprises and external investors [33], and steering capital toward high-ESG performers. Simultaneously, the sustained external scrutiny generated by analyst attention creates reputational pressure that restrains managerial opportunism and short-termism [34], prompting greater investment in technology R&D and human capital upgrading and, in turn, fostering the development of firms’ NQPFs.

Based on the above analysis, this study proposes the following hypothesis:

H2: 

Firms’ ESG performance fosters NQPFs by intensifying analyst attention.

3.4. The Mediating Role of Investor Confidence

Grounded in signaling theory, ESG performance serves as a composite signal of a firm’s sustainable development capacity and can strengthen investor confidence and thus influence investment decisions through three channels. First, superior ESG performance indicates that the firm prioritizes long-term value creation, implying more stable expected future cash flows and lowering investors’ assessment of its systematic risk [35]. Second, high-level ESG practices confer organizational legitimacy, providing a buffering effect in the wake of negative events and mitigating shocks to investor sentiment [36]. Third, ESG disclosure attenuates information asymmetry between the firm and its investors, enhancing market trust [37].

Enhanced investor confidence directly reduces the firm’s cost of equity capital [38], enabling it to secure long-term financing at a lower cost and supplying stable, patient capital for high-risk, long-horizon R&D and innovation activities. Moreover, the stock price stability and market capitalization appreciation induced by stronger investor confidence [39] alleviate managerial short-term earnings pressure, encouraging greater focus on breakthrough technological innovation and investment in high-end productive factors.

Based on the above analysis, this study proposes the following hypothesis:

H3: 

Firms’ ESG performance fosters NQPFs by strengthening investor confidence.

4. Research Design

4.1. Sample Selection and Data Sources

Drawing on the research content of this paper, we chose Chinese A-share listed firms over the period 2015–2024 as our research sample, gathered corresponding data, and conducted the below screening procedures: (1) We exclude enterprises operating in the financial and real estate industries. (2) Observations labeled ST or *ST at any point within the sample period are removed. (3) Firm-year observations with unavailable ESG are removed. (4) We further drop firm-year observations with missing values in other core variables. The final sample consists of 28,197 firm-year observations. Furthermore, all continuous variables are winsorized at the 1st and 99th percentiles annually. Data are obtained from the Wind Financial Terminal and the China Stock Market & Accounting Research (CSMAR) database.

4.2. Variable Definitions

(1) Dependent variable. The dependent variable in this study is firms’ NQPFs. Following Song, Zhang and Pan [14], this study reconstruct the NQPFs score for each firm-year observation using their pre-defined indicator system. The detailed indicators are reported in Table 1.

Table 1. Indicator system for evaluating the level of firms’ NQPFs.

Factor	Sub-Factor	Indicator	Indicator Description	Direction
Labor Force	Living Labor	R&D personnel salary ratio	R&D Expense—Wages and Salaries/Operating Revenue	+
		R&D personnel ratio	Number of R&D personnel/Number of employees	+
		Higher education personnel ratio	Number of employees with a bachelor’s degree or above/Number of employees	+
	Materialized Labor	Fixed Asset Ratio	Fixed Assets/Total Assets	+
		Manufacturing Overhead Ratio	(Total cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Allowance for impairment—Cash paid for purchasing goods and services—Cash paid to employees and for employee benefits)/(Total cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Allowance for impairment)	+
Production Tools	Hard Technology	R&D Depreciation and Amortization Ratio	R&D Expense—Depreciation and Amortization/Operating Revenue	+
		R&D leasing fee ratio	R&D Expense—Lease Expense/Operating Revenue	+
		R&D Direct Investment Ratio	R&D Expense—Direct Investment/Operating Revenue	+
		Intangible Asset Ratio	Intangible assets/total assets	+
	Soft Technology	Total Asset Turnover Ratio	Operating Revenue/Average Total Assets	+
		Inverse of the equity multiplier	Owner’s Equity/Total Assets	+

Note: + represents positive indicators.

(2) Explanatory variable. The core explanatory variable is corporate ESG performance. Among the few ESG that publicly cover most Chinese A-share listed firms, two data vendors dominate: Huazheng ESG and Wind ESG. Compared with Wind ESG, Huazheng ESG started earlier and provides broader coverage of A-share companies. Therefore, we employ Huazheng ESG (ESG) as the key explanatory variable in the empirical analysis. Huazheng ESG assigns nine ascending grades—C, CC, CCC, B, BB, BBB, A, AA, AAA—which are numerically coded 1 to 9.

(3) Mediating variables. Consistent with the research hypotheses, we select analyst attention (Analysis) and investor confidence (IC) as mediating variables. Analyst attention is measured as the weighted average (0.5/0.5) of two standardized components: (1) Analyst-team coverage = ln(1 + number of distinct analyst teams following the firm in that year); (2) Report coverage = ln(1 + number of research reports released on the firm in that year). Investor confidence (IC) is constructed following Lei, Wang and Jin [40] and Du, Liu and Du [41]. We use the growth rate of main-business revenue, the market-to-book ratio, and the proportion of shares held by institutional investors as proxies. A composite IC score is then derived via the entropy-weighting method.

(4) Control Variables. Drawing on the existing literature, we include the following firm-level controls: firm age (Age), firm size (Size), asset-liability ratio (Lev), return on net assets (Roe), cash flow ratio (Cash), equity balance degree (Bala), independent director ratio (Idr), and CEO duality (Dual). Detailed definitions for each control variable are provided in Table 2.

Table 2. Variable Definition Table.

Variable Type	Variable Name	Variable Symbol	Variable Value
Dependent variable	New Quality Productive Forces	NQPFs	please refer to the text above.
Explanatory variable	ESG performance	ESG	please refer to the text above.
Mediating variable	Analyst attention	Analysis	please refer to the text above.
	Investor confidence	IC	please refer to the text above.
Control variable	firm age	Age	Ln(current year-company establishment year)
	firm size	Size	Ln (Total Assets)
	asset-liability ratio	Lev	Total Liabilities/Total Assets
	return on net assets	Roe	Net Profit/Net Assets
	cash flow ratio	Cash	Net Cash Flow from Operating Activities/Current Liabilities
	equity balance degree	Bala	Shareholding ratio of shareholders 2–5/Shareholding ratio of the largest shareholder
	independent director ratio	Idr	Number of independent directors/Number of board members
	CEO duality	Dual	Chairman and General Manager is coded as 1, otherwise 0.

4.3. Model Design

To test Hypothesis H1, this paper employs a two-way fixed-effects model controlling for industry and year fixed effects, thereby accounting for time-invariant industry-specific characteristics such as inherent technology, regulation, and ESG endowments, as well as industry-invariant macroeconomic factors including macro policies, economic cycles, and technological shocks. This specification helps alleviate omitted variable bias and improve the precision of causal identification regarding the impact of ESG performance on NQPFs.

$$\begin{array}{c}{\mathit{NQPFs}}_{i,t}={\alpha }_0+{\alpha }_1{\mathit{ESG}}_{i,t}+{\displaystyle \sum }{\gamma }_j{\mathit{Control}}_{j,i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(1)

In this model, ${\mathit{NQPFs}}_{i,t}$ denotes firms’ new quality productive forces; ${\mathit{ESG}}_{i,t}$ indicates corporate ESG performance; ${\mathit{Control}}_{j,i,t}$ stands for the set of control variables; ${\mu }_i$ and ${\lambda }_t$ capture industry-fixed and year-fixed effects, respectively; and ${\epsilon }_{i,t}$ is the stochastic error term.

To examine the mechanisms underlying Hypotheses H2 and H3, mediating-effect test models are further constructed:

$$\begin{array}{c}{\mathit{MV}}_{i,t}={\beta }_0+{\beta }_1{\mathit{ESG}}_{i,t}+{\displaystyle \sum }{\delta }_j{\mathit{Control}}_{j,i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(2)

$$\begin{array}{c}{\mathit{NQPF}}_{i,t}={\theta }_0+{\theta }_1{\mathit{ESG}}_{i,t}+{\theta }_2{\mathit{MV}}_{i,t}+{\displaystyle \sum }{\mathsf{\varphi }}_j{\mathit{Control}}_{j,i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(3)

Here, ${\mathit{MV}}_{i,t}$ denotes the mediating variable, which is alternately replaced in the empirical analysis by Analysis (analyst attention) and IC (investor confidence).

5. Empirical Results

5.1. Statistical Description

Table 3 presents the descriptive statistics of all variables. For China’s A-share listed firms, the NQPFs index peaks at around 65.97 and bottoms out at 0.66, with a mean value of 7.98, a standard deviation of 6.88, and a median of 5.56. These statistics reveal that the overall level of NQPFs among China’s listed firms is relatively low and varies considerably across enterprises. Turning to ESG performance, the ESG rating ranges from 1 to 8, averaging 4.1841 with a standard deviation of 1.0055 and a median of 4, suggesting an approximately normal distribution. This distributional property facilitates a reliable investigation of how corporate ESG performance affects NQPFs. In addition, the control variables selected in this study exhibit wide ranges, enhancing the robustness and explanatory power of the subsequent econometric estimations.

Table 3. Descriptive statistics for variables.

Var	Obs	Mean	Std. Dev.	Min	Med	Max
NQPFs	28,197	7.978341	6.883544	0.6622052	5.56423	65.97105
ESG	28,197	4.184062	1.00553	1	4	8
Analysis	28,197	0.338044	0.3049004	0	0.3186935	1
IC	28,197	0.2367895	0.1069552	0.0127191	0.2349783	0.8552267
Age	28,197	2.984142	0.2963789	1.94591	2.995732	3.688879
Size	28,197	22.3531	1.248133	20.08206	22.1673	26.41544
Lev	28,197	0.4036824	0.1864951	0.0521241	0.398304	0.8890919
Roe	28,197	0.054066	0.1213485	−0.991924	0.065404	0.34211
Cash	28,197	0.259655	0.3712161	−0.606158	0.17067	2.036798
Bala	28,197	0.7813431	0.6117511	0.0267	0.6172	2.9695
Idr	28,197	37.84496	5.357481	28.57	36.36	60
Dual	28,197	0.3169131	0.4652815	0	0	1

5.2. Basic Regression

Table 4 reports the baseline regression outcomes. In all four models, the regression coefficient of ESG remains positively significant at the 1% level, which verifies the promoting effect of corporate ESG performance on NQPFs. Accordingly, Hypothesis H1 is preliminarily validated.

Table 4. Baseline regression.

Variables	(1)	(2)	(3)	(4)
	NQPFs	NQPFs	NQPFs	NQPFs
ESG	0.885 ***	0.979 ***	0.492 ***	0.388 ***
	(0.140)	(0.123)	(0.0761)	(0.0518)
Age		−0.538 *	0.0690	−1.890 ***
		(0.287)	(0.295)	(0.220)
Size		−0.291 ***	0.0714	−0.00159
		(0.0632)	(0.0729)	(0.0701)
Lev		−9.245 ***	−7.273 ***	−7.262 ***
		(1.185)	(0.935)	(0.894)
Roe		−9.573 ***	−8.424 ***	−6.897 ***
		(1.410)	(0.960)	(0.809)
Cash		−1.007 **	−0.170	−0.405
		(0.396)	(0.262)	(0.254)
Bala		1.100 ***	0.623 ***	0.505 ***
		(0.100)	(0.0631)	(0.0635)
Ind		0.0378 ***	0.0244 ***	0.0134 *
		(0.00947)	(0.00753)	(0.00769)
Dual		1.395 ***	1.017 ***	0.623 ***
		(0.141)	(0.104)	(0.0924)
Constant	4.276 ***	13.77 ***	5.824 ***	14.34 ***
	(0.446)	(1.575)	(1.317)	(0.907)
industry fixed	NO	NO	YES	YES
year fixed	NO	NO	NO	YES
Observations	28,197	28,197	28,197	28,197
Adj R-squared	0.017	0.141	0.306	0.364

Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

5.3. Robustness Tests

(1) Replace the explanatory variable. To further validate the relationship between ESG performance and NQPFs, we substitute the Huazheng ESG rating with the Wind ESG rating (WESG), which has been released publicly starting from 2018. By re-estimating the baseline regression using the 2018–2024 sub-sample, Column (1) of Table 5 shows that the coefficient for WESG is 0.927 and statistically significant at the 1% level. The positive influence of ESG on NQPFs is thus reconfirmed, providing additional support for Hypothesis H1.

Table 5. Robustness tests.

	(1)	(2)	(3)
	Replace the Explanatory Variable	Exclude Abnormal Years	Exclude Abnormal Cities
Variables	NQPFs	NQPFs	NQPFs
ESG		0.376 ***	0.386 ***
		(0.0532)	(0.0575)
WESG	0.927 ***
	(0.0989)
L.ESG
Age	−2.255 ***	−1.752 ***	−1.275 ***
	(0.257)	(0.290)	(0.170)
Size	−0.0172	−0.0484	−0.0782
	(0.0675)	(0.0835)	(0.0659)
Lev	−8.781 ***	−6.548 ***	−5.800 ***
	(0.995)	(1.087)	(0.744)
Roe	−7.292 ***	−6.859 ***	−5.447 ***
	(0.863)	(1.087)	(0.802)
Cash	−0.660 **	−0.210	−0.103
	(0.290)	(0.248)	(0.225)
Bala	0.556 ***	0.444 ***	0.326 ***
	(0.0734)	(0.0797)	(0.0541)
Ind	0.0158 *	0.00850	0.0154 *
	(0.00941)	(0.00937)	(0.00885)
Dual	0.749 ***	0.643 ***	0.527 ***
	(0.110)	(0.111)	(0.101)
Constant	13.65 ***	14.52 ***	13.29 ***
	(1.092)	(1.052)	(1.071)
industry fixed	YES	YES	YES
year fixed	YES	YES	YES
Observations	22,440	18,382	22,655
Adj R-squared	0.361	0.366	0.329

Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

(2) Exclude abnormal years. To mitigate the interference brought about by the COVID-19 pandemic, we exclude observations from the lockdown years 2020–2022. As illustrated in Column (2) of Table 5, the coefficient of ESG remains positively significant at the 1% level (0.376), which confirms the results of the baseline regression.

(3) Exclude abnormal cities. Because Beijing, Tianjin, Shanghai, and Chongqing enjoy distinctive economic and policy environments, we exclude firms registered in these four cities. Column (3) of Table 5 shows that the ESG coefficient is virtually unchanged (0.386) and still significant at the 1% level, underscoring the robustness of H1.

5.4. Endogeneity Tests

To mitigate endogeneity bias caused by omitted variables and reverse causality, this paper employs the two-stage least squares (2SLS) method by using two instrumental variables: the mean ESG score of the same province and same industry (excluding the firm itself), and the one-period lagged ESG. The mean ESG of the same province and same industry reflects exogenous environmental pressure at the regional and industry levels, with a significant peer effect. It is not driven by individual firm behavior, thus satisfying the exogeneity requirement and effectively alleviating omitted variable bias. Corporate ESG performance exhibits path dependence and intertemporal persistence; the one-period lagged ESG is highly correlated with current-period ESG and unaffected by current NQPFs, which can effectively mitigate reverse causality.

Columns (1) and (3) of Table 6 present the results of the first-stage regression. The coefficients of ESG_mean_excl and L.ESG are significantly positive, which suggests that the instrumental variables have a strong correlation with ESG, thus meeting the relevance requirement. The p-values of the Kleibergen–Paap LM test are all below 1%, which rejects the null hypothesis of under-identification. The Kleibergen–Paap F-statistics are 55.3 and 558.14, respectively, both of which surpass the critical value of 16.38, thereby rejecting the weak instrument hypothesis. Columns (2) and (4) of Table 6 demonstrate that the second-stage coefficients of ESG are significantly positive. This reveals that corporate ESG performance still exerts a significant positive effect on NQPFs. Consequently, Hypothesis H1 remains valid even after addressing endogeneity issues caused by omitted variables and reverse causality.

Table 6. Endogeneity tests.

	(1)	(2)	(3)	(4)
Variables	ESG	NQPFs	ESG	NQPFs
ESG		2.804 ***		0.725 ***
		(0.550)		(0.104)
ESG_mean	0.195 ***
	(0.0262)
L.ESG			−0.0616 ***
			(0.0203)
Age	−0.0925 ***	−1.813 ***	0.176 ***	−2.065 ***
	(0.0218)	(0.233)	(0.0131)	(0.243)
Size	0.288 ***	−0.693 ***	−0.567 ***	−0.149 **
	(0.0189)	(0.156)	(0.0400)	(0.0702)
Lev	−1.024 ***	−5.430 ***	0.497 ***	−7.266 ***
	(0.0529)	(0.974)	(0.0506)	(0.929)
Roe	0.824 ***	−9.512 ***	0.0288	−6.917 ***
	(0.0599)	(1.130)	(0.0177)	(0.870)
Cash	0.0861 ***	−0.689 **	−0.00642	−0.503 *
	(0.0198)	(0.286)	(0.00866)	(0.278)
Bala	0.00471	0.506 ***	0.256 **	0.527 ***
	(0.0109)	(0.0768)	(0.0993)	(0.0715)
Ind	0.754 ***	−0.457	−0.00498	1.434
	(0.115)	(1.077)	(0.0131)	(0.888)
Dual	0.0124	0.567 ***	−0.0616 ***	0.640 ***
	(0.0135)	(0.108)	(0.0203)	(0.0963)
Constant	−2.701 ***	14.52 ***	−2.109 ***
	(0.387)	(1.052)	(0.301)
industry fixed	YES	YES	YES
year fixed	YES	YES	YES
Observations	24,479	24,479	24,450	24,450
Adj R-squared	0.194	−0.047	0.425	0.076
Kleibergen–Paap LM	30.617		151.936
	[0.0000]		[0.0000]
Kleibergen–Paap	55.300		558.14
F-statistics	{16.38}		{16.38}

Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

5.5. Heterogeneity Tests

(1) Ownership heterogeneity. Given that the ownership structure determines corporate objectives and operational modes, we divide the research sample into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Columns (1) and (2) of Table 7 indicate that the ESG coefficient is positively significant at the 1% level in both sub-samples, verifying that ESG promotes NQPFs irrespective of the type of ownership. However, compared with the baseline regression result (0.388), the marginal effect is significantly stronger for non-SOEs (0.529) and notably weaker for SOEs (0.107). Two plausible explanations can be put forward. First, SOEs bear multiple responsibilities, including economic (maintaining the value of state-owned assets), social (stabilizing employment and people’s livelihoods), and political (implementing national policies) obligations, which means that ESG initiatives may not be given top priority in resource allocation. Second, non-SOEs operate in a more competitive market environment; as a result, they have stronger incentives to utilize better ESG performance to improve their reputation, attract capital, and appeal to customers, thereby promoting the upgrading of NQPFs. In contrast, SOEs have a relatively stable market status and may lack adequate incentive mechanisms to drive ESG-related innovation.

Table 7. Heterogeneity tests.

	(1)	(2)	(3)	(4)	(5)	(6)
	SOEs	Non-SOEs	Eastern	Northeastern	Central	Western
Variables	NQPFs	NQPFs	NQPFs	NQPFs	NQPFs	NQPFs
ESG	0.107 ***	0.529 ***	0.395 ***	0.780 ***	0.479 ***	0.0851
	(0.0398)	(0.0691)	(0.0577)	(0.194)	(0.0869)	(0.0939)
Age	−1.275 ***	−1.988 ***	−1.952 ***	−0.403	−1.955 ***	−1.558 ***
	(0.190)	(0.263)	(0.243)	(0.620)	(0.335)	(0.426)
Size	−0.118 **	0.132	−0.0190	0.0486	0.00741	0.149 **
	(0.0493)	(0.0946)	(0.0724)	(0.124)	(0.108)	(0.0695)
Lev	−3.507 ***	−8.947 ***	−8.130 ***	−6.852 ***	−6.509 ***	−3.811 ***
	(0.415)	(1.085)	(1.016)	(1.350)	(1.155)	(0.597)
Roe	−2.725 ***	−8.362 ***	−7.782 ***	−3.887 ***	−3.720 ***	−3.917 ***
	(0.523)	(1.002)	(0.927)	(1.097)	(0.799)	(0.845)
Cash	0.429 **	−0.744 **	−0.538 *	0.767	−0.891 **	−0.0502
	(0.193)	(0.288)	(0.285)	(0.558)	(0.444)	(0.233)
Bala	0.735 ***	0.381 ***	0.532 ***	0.329	0.412 ***	0.464 ***
	(0.0966)	(0.0678)	(0.0746)	(0.298)	(0.121)	(0.126)
Ind	−0.0118 *	0.0218 **	0.0116	−0.0772 ***	0.0416 **	0.0147
	(0.00667)	(0.0102)	(0.00985)	(0.0247)	(0.0182)	(0.0115)
Dual	−0.122	0.559 ***	0.581 ***	1.123 ***	0.597 ***	0.476 **
	(0.140)	(0.109)	(0.101)	(0.324)	(0.202)	(0.189)
Constant	13.79 ***	12.25 ***	15.79 ***	8.933 ***	11.88 ***	7.973 ***
	(1.308)	(1.383)	(0.965)	(3.001)	(2.161)	(1.697)
industry fixed	YES	YES	YES	YES	YES	YES
year fixed	YES	YES	YES	YES	YES	YES
Observations	8472	19,725	20,170	908	3785	3332
Adj R-squared	0.350	0.355	0.367	0.518	0.337	0.394

Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

(2) Regional heterogeneity. China’s extensive territory is associated with notable discrepancies in economic development and policy contexts, which might distort the research findings. We thus divide the sample into four economic regions—eastern, northeastern, central, and western—and re-estimate the model for each sub-sample. Columns (3)–(6) of Table 7 demonstrate that the ESG coefficient is positively significant at the 1% level in the eastern, northeastern, and central regions, while it is statistically insignificant in the western region. This phenomenon may be attributed to the lack of sufficient market competition pressure for enterprises in the western region, making it difficult for ESG performance to become a key factor in securing resources, market share, or financing cost advantages. Furthermore, the northeastern region exhibits the largest coefficient (0.780), which is far higher than those of the other three regions. This suggests that ESG exerts the most prominent driving effect on the NQPFs of enterprises in the northeastern region. Two possible reasons can be offered: First, the northeastern region has traditionally been dominated by heavy industry, and enterprises there face substantial environmental regulatory pressure. They must comply with policy requirements through ESG practices, thereby promoting technological upgrading and productivity improvement. Second, the northeastern region possesses a relatively solid industrial foundation and technical accumulation, which allows enterprises to advance technological innovation through ESG initiatives.

5.6. Impact Mechanism Analysis

(1) Mediating role of analyst attention. Columns (1)–(2) of Table 8 report the mediation analysis with respect to analyst attention. Column (1) indicates that the coefficient of ESG is significantly positive, suggesting that improved corporate ESG performance helps attract greater analyst attention. Column (2) shows that the coefficient for analyst attention is also significantly positive, implying that heightened analyst attention contributes to higher NQPFs. Meanwhile, the ESG coefficient decreases from 0.388 in the baseline regression to 0.305 in Column (2), representing a notable decline. These results confirm that analyst attention partially transmits the effect of ESG on NQPFs, supporting Hypothesis H2.

Table 8. Intermediary mechanism test.

	(1)	(2)	(3)	(4)
Variables	Analysis	NQPFs	IC	NQPFs
ESG	0.0296 ***	0.305 ***	0.00148 **	0.382 ***
	(0.00184)	(0.0514)	(0.000744)	(0.0514)
analysis		2.795 ***
		(0.311)
IC				4.154 ***
				(0.617)
Age	−0.114 ***	−1.572 ***	−0.0231 ***	−1.794 ***
	(0.00639)	(0.197)	(0.00302)	(0.211)
Size	0.128 ***	−0.359 ***	0.0119 ***	−0.0511
	(0.00233)	(0.0535)	(0.000932)	(0.0664)
Lev	−0.0736 ***	−7.056 ***	0.0858 ***	−7.618 ***
	(0.0135)	(0.870)	(0.00547)	(0.925)
Roe	0.574 ***	−8.502 ***	0.123 ***	−7.408 ***
	(0.0250)	(0.934)	(0.0114)	(0.848)
Cash	0.0681 ***	−0.595 **	0.0344 ***	−0.548 **
	(0.00673)	(0.258)	(0.00260)	(0.260)
Bala	0.0291 ***	0.423 ***	−0.0184 ***	0.581 ***
	(0.00244)	(0.0580)	(0.00105)	(0.0687)
Ind	−0.000239	0.0141 *	−0.000508 ***	0.0155 **
	(0.000261)	(0.00768)	(0.000115)	(0.00779)
Dual	0.0447 ***	0.498 ***	−0.0110 ***	0.669 ***
	(0.00393)	(0.0867)	(0.00115)	(0.0962)
Constant	−2.351 ***	20.91 ***	0.0201	14.25 ***
	(0.0525)	(1.024)	(0.0200)	(0.928)
industry fixed	YES	YES	YES	YES
year fixed	YES	YES	YES	YES
Observations	28,197	28,197	28,197	28,197
Adj R-squared	0.427	0.373	0.199	0.367

Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

To further validate the mediating effect of analyst attention, this study adopts the Bootstrap approach with 1000 resamplings and a random seed of 123, with results presented in Table 9. The estimated indirect effect is 0.08286, accompanied by a Bootstrap standard error of 0.00621 and a z-statistic of 13.34, which is significant at the 1% level (p < 0.001). The corresponding 95% confidence interval is [0.07068, 0.09503], which excludes zero. This confirms the presence of a statistically significant partial mediating effect of analyst attention between ESG performance and NQPFs.

Table 9. The Bootstrap results of the mediating effect test—analyst attention.

Type of Effect	Coefficient	Std. Error	z-Value	p > |z|	95% Confidence Interval
Indirect Effect	0.0828559	0.0062125	13.34	0.000	[0.0706795, 0.0950322]
Direct Effect	0.3054032	0.0340052	8.98	0.000	[0.2387543, 0.3720522]
Total Effect	0.3882591	0.0341507	11.37	0.000	[0.3213249, 0.4551933]

(2) Mediating role of investor confidence. Columns (3)–(4) of Table 8 display the mediation test for investor confidence. Column (3) reveals a significantly positive coefficient for ESG, demonstrating that stronger ESG performance boosts investor confidence. Column (4) further shows that the coefficient of investor confidence is significantly positive, indicating that improved investor confidence promotes NQPFs. Although the ESG coefficient only falls slightly from 0.388 to 0.382, the reduction still aligns with the pattern of partial mediation. Accordingly, investor confidence acts as a partial transmission channel through which ESG affects NQPFs, supporting Hypothesis H3.

To robustly confirm the mediating role of investor confidence, we also perform the Bootstrap test with 1000 iterations and a random seed set at 123, with results reported in Table 10. The indirect effect is estimated at 0.00616, with a Bootstrap standard error of 0.00265 and a z-statistic of 2.33, significant at the 5% level (p = 0.020). The 95% confidence interval is [0.00098, 0.01135], which does not include zero. This provides consistent evidence for a significant partial mediating effect of investor confidence in the ESG-NQPFs linkage.

Table 10. The Bootstrap results of the mediating effect test—investor confidence.

Type of Effect	Coefficient	Std. Error	z-Value	p > |z|	95% Confidence Interval
Indirect Effect	0.0061635	0.0026458	2.33	0.020	[0.0009777, 0.0113492]
Direct Effect	0.3820956	0.034098	11.21	0.000	[0.3152648, 0.4489264]
Total Effect	0.3882591	0.0341507	11.37	0.000	[0.3213249, 0.4551933]

6. Discussion

Integrating the empirical results, this section conducts an in-depth discussion from two aspects: the economic significance and magnitude of the effects, as well as the practical application scenarios of the multi-dimensional results, elaborating on the theoretical extensions and practical implications of the empirical findings.

6.1. Interpretation of the Economic Significance and Magnitude of Research Results

The descriptive statistics intuitively reflect the overall development features of NQPFs and ESG performance among Chinese A-share listed companies. In terms of NQPFs, the overall level is relatively modest, and the imbalance across enterprises is striking. This pattern is consistent with China’s current economic transition phase: some enterprises have accomplished the transformation from factor-driven to innovation-driven growth, attaining high NQPFs through advanced technology and improved efficiency, while most remain restricted by insufficient R&D investment, irrational talent structures, and backward upgrades of production tools. Their slow progress in cultivating NQPFs has become a micro-level constraint hindering high-quality economic development. With respect to ESG performance, A-share listed companies generally exhibit a moderate level with a relatively rational distribution. This suggests that corporate ESG practices in China have entered a popularization stage but have not yet formed a universal high-quality development model. Most enterprises still confine ESG performance to basic compliance, and only a small number have deeply integrated ESG into their corporate strategies. This provides a solid sample basis for exploring the marginal impact of ESG performance on NQPFs: the normal distribution effectively avoids sample bias, rendering the positive effect of ESG performance on NQPFs more statistically reliable and practically interpretable.

The baseline regression results indicate that, after controlling for industry and year fixed effects, the coefficient of ESG performance is 0.388 and statistically significant at the 1% level. This result is largely consistent with the findings of Song and Yang [5]. This coefficient embodies a clear marginal effect: under the condition that other factors remain unchanged, a one-level improvement in a firm’s ESG rating (for instance, from B to BB) will significantly increase NQPFs by 0.388 units. Given that the mean value of NQPFs is 7.98, this improvement is equivalent to an approximate 4.86% increase in NQPFs. This magnitude fully illustrates that ESG performance provides substantial practical value for enhancing NQPFs, representing a quantifiable positive contribution rather than just a statistical correlation. Robustness tests further verify the stability of the core effect: fluctuations in coefficients across different model specifications do not change the conclusion that the effect is significantly positive. Endogeneity tests show that the coefficient becomes larger after addressing endogeneity issues, suggesting that the positive impact of ESG performance on NQPFs is somewhat underestimated when endogeneity is not corrected. The actual effect of improving ESG performance in promoting NQPFs may be stronger than what the baseline results imply. From an economic perspective, ESG performance is not merely a “image project” for enterprises; instead, it can continuously and stably drive the cultivation of NQPFs by mitigating information asymmetry and attracting scarce resources.

6.2. Multi-Dimensional Application Scenarios of Research Results

Integrating the results of the heterogeneity tests and mechanism tests, the impact of corporate ESG performance on NQPFs is subject to boundary conditions associated with ownership structure and regional distribution, and it functions through two transmission channels: analyst attention and investor confidence. These findings can be applied to three core scenarios: the formulation of differentiated corporate ESG strategies, the promotion of regional industrial transformation and upgrading, and the optimization of capital market resource allocation. The specific application logic is elaborated as follows.

6.2.1. Formulating Differentiated Corporate ESG Strategies

The results of ownership heterogeneity indicate that the coefficient of ESG performance on NQPFs is considerably higher for non-state-owned enterprises (0.529) than for state-owned enterprises (0.107), offering targeted guidance for formulating ESG strategies based on different ownership types. As key participants in market competition, non-state-owned enterprises face tighter resource constraints and stronger incentives for innovation, enabling the signaling effect of ESG performance to be more effectively transformed into resource inflows and NQPFs enhancement. Consequently, non-SOEs should prioritize ESG strategy as a core component of their corporate development, continuously improve ESG performance (for example, increasing environmental investment, enhancing employee welfare, and optimizing corporate governance), strengthen signaling to analysts and investors, attract scarce capital and talents, and thus accelerate the cultivation of NQPFs. In contrast, state-owned enterprises bear economic, social, and political responsibilities, and ESG initiatives often receive lower priority in resource allocation; additionally, SOEs have relatively stable access to resources. Therefore, their ESG strategies should be aligned with national policy directions—such as improving environmental performance to meet the “dual carbon” goals and fulfilling social responsibilities in the context of rural revitalization. By integrating ESG practices with the appreciation of state-owned assets and the implementation of national strategies, SOEs can optimize internal management efficiency and indirectly promote the improvement in NQPFs.

6.2.2. Regional Industrial Transformation and Upgrading

Regional heterogeneity results indicate that the coefficient of ESG performance on NQPFs in Northeast China (0.780) ranks highest among the four regions, followed by the Central (0.479) and Eastern regions (0.395), while the coefficient in the Western region is insignificant. These results offer differentiated ESG-oriented approaches for regional industrial upgrading. As China’s old industrial base, Northeast China features a heavy industry-dominated structure under strict environmental regulation, alongside solid industrial foundations and technological accumulation. Improving ESG performance can precisely match environmental requirements and industrial upgrading needs. Thus, Northeast China can take ESG as a core engine for the green transformation of traditional industries, promoting technological upgrading and efficiency improvements in steel, coal, equipment manufacturing, and other sectors through ESG practices, thereby driving NQPFs and supporting the revitalization of old industrial bases. This resonates with the emphasis by Nurcan Kilinc-Ata and Serhat Camkaya on how green energy infrastructure, clean technologies, and targeted policy interventions can reduce carbon dioxide emissions and support sustainable development at the regional level [42]. The Eastern and Central regions enjoy relatively high economic development and intense market competition, where the signaling effect of ESG performance effectively mitigates information asymmetry. These regions can leverage sound market mechanisms to deeply integrate corporate ESG performance with capital and product markets, enhance market competitiveness and resource acquisition capacity via ESG rating improvements, and foster cluster effects for regional NQPFs by integrating innovation, industrial, capital, and talent chains around ESG. In Western China, the insignificant impact of ESG performance mainly stems from insufficient market competition and low awareness of ESG value. The region should prioritize ESG as a preliminary step in industrial cultivation: popularize ESG concepts, build corporate ESG capabilities, and then promote ESG practices in characteristic industries (e.g., new energy, special agriculture), gradually unleashing ESG’s driving effect on NQPFs.

6.2.3. Optimizing Capital Market Resource Allocation

The results of regional heterogeneity show that the coefficient of ESG performance on NQPFs in Northeast China (0.780) is the highest among the four regions, followed by the Central (0.479) and Eastern regions (0.395), while the coefficient in the Western region is statistically insignificant. These findings provide differentiated ESG-focused strategies for regional industrial upgrading. As China’s old industrial base, Northeast China has an industrial structure dominated by heavy industry, which is subject to strict environmental regulations, along with a solid industrial foundation and technological accumulation. Improving ESG performance can accurately align with environmental requirements and the needs of industrial upgrading. Therefore, Northeast China can take ESG as a core driver for the green transformation of traditional industries, promote technological upgrading and efficiency improvement in sectors such as steel, coal, and equipment manufacturing through ESG practices, thereby boosting NQPFs and supporting the revitalization of old industrial bases. This is consistent with the view of Kilinc-Ata and Camkaya, who emphasize that green energy infrastructure, clean technologies, and targeted policy interventions can reduce carbon dioxide emissions and support regional sustainable development [42]. The Eastern and Central regions boast relatively high economic development levels and fierce market competition, where the signaling effect of ESG performance can effectively alleviate information asymmetry. These regions can leverage sound market mechanisms to deeply integrate corporate ESG performance with capital and product markets, enhance market competitiveness and resource acquisition capabilities by improving ESG, and foster cluster effects for regional NQPFs by integrating innovation, industrial, capital, and talent chains around ESG. In Western China, the insignificant impact of ESG performance is mainly due to insufficient market competition and low awareness of ESG value. This region should prioritize ESG promotion as a preliminary step in industrial development: popularize ESG concepts, build corporate ESG capabilities, and then promote ESG practices in characteristic industries (such as new energy and special agriculture), gradually unlocking the driving effect of ESG on NQPFs.

7. Conclusions

Grounded in signaling theory, this study conducts a systematic investigation into how corporate ESG performance influences NQPFs and the underlying mechanisms of this relationship. Utilizing a panel dataset consisting of 28,197 firm-year observations of Chinese A-share listed companies spanning the period 2015–2024, we arrive at three key findings. (1) ESG performance significantly promotes NQPFs. The baseline regression reveals a stably positive effect that remains intact after a series of robustness tests—including replacing the explanatory variable, excluding outlier year samples, eliminating outlier city samples—and addressing endogeneity issues through the instrumental variable method, thus verifying Hypothesis H1. (2) The impact of ESG performance on NQPFs is subject to property types and regional heterogeneity. From the perspective of property rights, ESG performance exerts a more prominent role in boosting NQPFs for non-state-owned enterprises; from a regional standpoint, the positive impact of corporate ESG performance on NQPFs is more pronounced in the Northeast region, whereas it is statistically insignificant in the Western region. (3) Analyst attention and investor confidence exert significant mediating effects. Mechanism analysis demonstrates that a firm’s strong ESG performance indirectly facilitates the development of NQPFs by enhancing analyst attention and investor confidence.

Integrating the empirical results, this paper proposes targeted and operable practical recommendations for enterprises and policymakers, transforming research findings into specific action plans for nurturing NQPFs.

Recommendations for enterprises: Develop customized ESG strategies based on their unique characteristics.

First, optimize the allocation of ESG resources according to the type of ownership. Non-state-owned enterprises should focus their ESG resources on areas that enhance signaling effects, such as improving ESG disclosure quality and upgrading third-party ESG. By adopting high-cost and hard-to-imitate ESG practices, they can send high-quality signals to the market and attract high-quality external resources. State-owned enterprises should prioritize the allocation of ESG resources in line with national strategies and social missions, including green R&D, rural revitalization, and employment stability. Through standardized ESG practices, they can improve internal management efficiency and guide state-owned capital to focus on the cultivation of NQPFs.

Second, align the focus of ESG practices with regional characteristics. Enterprises in Northeast China should prioritize the environmental dimension of ESG, increase investment in environmental protection equipment, promote clean production, achieve technological upgrading by complying with environmental regulations, and drive the improvement of NQPFs through enhanced environmental performance. Enterprises in Eastern and Central China should integrate the governance and social dimensions of ESG, optimize corporate governance structures, improve employee welfare, strengthen ESG management of the supply chain, and enhance market competitiveness and resource acquisition capacity through comprehensive ESG performance. Enterprises in Western China should first implement basic ESG practices, such as improving ESG disclosure standards and establishing standardized ESG management systems, to gradually elevate their ESG performance. Once the market competition mechanism matures, they can further integrate ESG practices with the cultivation of NQPFs.

Third, strengthen ESG signaling through mediating channels. Enterprises should proactively engage with securities analysts, disclose ESG information in a timely and accurate manner, reduce the cost of information collection and processing for analysts, and attract more attention from analysts. At the same time, through sustained and stable ESG practices, enterprises can send signals of long-term value creation to investors, boost investor confidence, reduce financing costs, and provide financial support for key links in NQPFs cultivation, such as R&D investment and talent introduction.

Recommendations for policymakers: Improve the differentiated policy system based on empirical findings.

First, improve fundamental institutions based on the distribution characteristics of variables. In response to the generally low level and uneven development of NQPFs across enterprises, introduce targeted supportive policies for NQPFs cultivation, focusing on enterprises with insufficient R&D investment and irrational talent structures to promote technological upgrading and talent development. Given the moderate overall level of ESG performance, accelerate the establishment of a mandatory ESG information disclosure system, formulate unified ESG rating standards, and elevate the overall level of corporate ESG performance.

Second, strengthen policy guidance based on the characteristics of core effects. Introduce incentive policies that link ESG performance with NQPFs cultivation, such as providing financing preferences, R&D subsidies, and tax reductions for enterprises with excellent ESG performance and significant improvements in NQPFs. These policies will guide enterprises to integrate ESG practices with NQPFs development and unlock the sustained driving role of ESG.

Third, implement differentiated regulation and support in line with heterogeneous characteristics. For non-state-owned enterprises, adopt market-oriented ESG incentive policies, encouraging them to improve ESG performance through pricing mechanisms in the capital and credit markets. For state-owned enterprises, incorporate ESG performance into the performance appraisal system and clarify the connection between ESG indicators and NQPF cultivation. For Northeast China, launch special policies to promote the ESG-driven green transformation of traditional industries, and increase support for environmental technology R&D and clean production. For Western China, implement a special ESG capacity-building program, and provide ESG training and consulting services through government procurement and cooperation with universities and think tanks.

Fourth, optimize market guidance policies based on mediating mechanisms. Introduce policies to encourage securities analysts to expand research coverage of high-ESG enterprises, such as integrating ESG research into analyst evaluation systems. Guide institutional investors to increase the proportion of ESG investments, and launch financial products including ESG index funds and green funds, so as to strengthen the positive feedback effect of investor confidence on corporate NQPFs cultivation.

The findings of this study further enrich the application of signaling theory at the micro-firm level, verifying the core role of corporate ESG performance as a “high-quality signal” in mitigating information asymmetry, attracting resource inflows, and promoting the cultivation of NQPFs. We construct an analytical framework of “ESG signal–alleviation of information asymmetry–resource inflow–improvement of NQPF”, which fills the gaps in the existing literature regarding the relationship between ESG performance and NQPFs. However, this study has several limitations.

First, the measurement of NQPFs adopts the index system proposed by Song et al. Although this system effectively captures the core characteristics of corporate NQPFs, it excludes emerging indicators such as digital assets and green patents. Future research can refine and optimize the measurement system of NQPFs to enhance their comprehensiveness.

Second, this study does not take into account the heterogeneous impacts of the three disaggregated ESG dimensions (environmental, social, and governance) on NQPFs. Future research could conduct in-depth analysis on how different ESG dimensions drive NQPFs and explore the differences in the underlying mechanisms.

Third, industry heterogeneity is not examined in this research. Future studies can carry out heterogeneity tests between heavily polluting and lightly polluting industries, as well as between strategic emerging industries and other industries, to explore industry-specific characteristics.

Fourth, potential interactions and chain mediation effects between analyst attention and investor confidence are not considered in this study, which can be verified and explored in future research.

Fifth, the moderating effects of external institutional environments (e.g., marketization level, legal system development) on the relationship between ESG performance and NQPFs are not incorporated. Future research may conduct in-depth moderating effect analyses by combining with the institutional environment context to further enrich the research conclusions.