Environmental,Social,and Governance (ESG) Performance and New Quality Productivity in China: The Mediating Role of Patient Capital

Abstract

This study addresses a critical gap in the literature by examining the mediating role of patient capital in the relationship between Environmental, Social, and Governance (ESG) performance and New Quality Productivity (NQP) among Chinese enterprises, a strategic priority of national economic policy. Using data from 5,323 Chinese listed companies between 2015 and 2023, the analysis reveals that ESG performance is positively associated with NQP. More specifically, patient capital serves as a key mediator that connects ESG performance and NQP. The analysis reveals a dual mediation mechanism: strategic equity enhances the ESG–NQP link, while relational debt unexpectedly weakens it, offering new insights into the complex pathways through which ESG influences NQP. Heterogeneity analysis shows that Non-State-Owned enterprises (Non-SOEs) and Specialized, Refined, Distinctive, and Innovative (SRDI) firms exhibit greater NQP gains from ESG adoption, indicating the need for tailored sustainability policies for these enterprise types. This study advances understanding of how patient capital conditions the ESG–NQP nexus, offering novel insights into the financial foundations of sustainable innovation in China.

Keywords

new quality productivity ESG patient capital sustainable growth innovation operational efficiency

Introduction

Chinese firms increasingly face “involution,” where intense competition yields diminishing returns, while the Sino-US tariff conflict heightens external uncertainty (Cai et al., 2016; Zeng et al., 2023). In response, the government has elevated new quality productivity (NQP) as a strategic priority. At the December 2023 Central Economic Work Conference, leaders stressed the need for disruptive technologies to drive industrial innovation and sustainable growth, positioning NQP as central to building a modern economic system (Shao et al., 2024).

China’s innovation-driven transformation, supported by a robust industrial base, skilled research talent, and its low-carbon transition, has created fertile ground for advancing high-tech industries (Ali & Ali, 2021; X. Liu, 2023). Within this context, ESG performance serves as a key indicator of firms’ sustainable development capacity, with growing evidence linking ESG practices to improvements in NQP (J. Li et al., 2024; Y. Ma et al., 2024). However, the mechanisms underlying this relationship remain insufficiently theorized and empirically tested. In particular, patient capital—recognized as a crucial enabler of long-term value creation—has not yet been systematically examined as a moderating or mediating factor. Addressing this gap is essential for understanding how capital structures shape the realization of NQP in Chinese firms.

Patient capital refers to long-term investment that prioritizes sustained value creation over short-term returns, often linked to government funds, pensions, and insurance capital (Dafe & Upadhyaya, 2024; Deeg et al., 2016; Kahane, 2020). While coordinated market economies have traditionally fostered such capital, even liberal economies now see the rise of “universal owners” like BlackRock and Vanguard (Fichtner & Heemskerk, 2020).

China has recently emphasized patient capital as a driver of new-quality productivity (NQP). Policies since 2024 aim to expand the role of social security, insurance, and wealth management funds in equity markets, enhance countercyclical investment, and direct more long-term capital—particularly insurance and pension funds—into domestic equities (Government of China, 2024; State Council of the People’s Republic of China, 2025). The 2025 Implementation Plan requires major insurers to allocate 30% of new premiums into A-shares, injecting hundreds of billions of RMB annually (Reuters, 2025).

Despite progress, China’s equity market remains retail-dominated and speculative (J. Y. Lin & Fu, 2017). Its institutional investor base is still small compared to the U.S., where pension and insurance assets exceed $40 trillion, nearly 100 times the size of China’s National Social Security Fund ($415 billion) (Global SWF, 2023; Investment Company Institute, 2024). Building a stronger institutional investor framework is key to China’s capital market development.

Given this context, this paper seeks to contribute to the ongoing debates by examining how patient capital influences the relationship between ESG and NQP. Understanding this dynamic is crucial for developing targeted policies that foster innovation and enhance firm performance (Ammer et al., 2020; Branstetter et al., 2023; Eccles et al., 2014). Using a proprietary NQP index calibrated with firm-level data (2015–2023), we identify patient capital as the mediating channel through which ESG initiatives elevate NQP. We also performed multiple robustness checks, including propensity score matching, lagging the explained variable, the instrumental variable method, generalized method of moments, excluding abnormal years and cities, and bootstrap resampling, all of which confirmed the consistency of our findings. Heterogeneity tests revealed amplified effects among non-SOEs and SRDI firms.

Most studies on patient capital are situated within the comparative political economy literature, particularly in the “varieties of capitalism” framework (Dafe & Upadhyaya, 2024; Fichtner & Heemskerk, 2020; Harrison et al., 2016; Klingler-Vidra, 2016; McCarthy et al., 2016; Thatcher & Vlandas, 2016). Empirical studies have shown that patient capital is positively associated with corporate innovation output (Asker et al., 2015; He & Tian, 2013), long-term financial and social performance (Flammer & Bansal, 2017), and corporate ESG and sustainability initiatives (Amore & Bennedsen, 2016; Dyck et al., 2019). These findings consistently suggest that patient capital plays a central role in shaping firms’ strategic orientation and value creation.

This study makes three key contributions. It first introduces patient capital as a mediating variable within the ESG–NQP framework, thereby uncovering the mechanisms through which ESG performance translates into innovation-driven productivity gains. It further enriches the research context of patient capital by moving beyond the Western corporate governance literature to examine its role in the Chinese institutional environment and emerging industrial sectors. This research provides a foundation for integrating patient capital into China’s economic transformation to advance NQP sustainably and efficiently. Finally, it advances the understanding of patient capital’s heterogeneity by distinguishing between strategic equity and relational debt as two distinct forms of long-term capital. The results indicate that strategic equity-based patient capital is more effective in promoting the development of NQP within firms, whereas relational debt-based patient capital tends to suppress such development.

The paper is organized as follows. Section 2 reviews relevant literature and develops hypotheses. Section 3 describes the research design and data. Section 4 reports the main results, with robustness checks in Section 5. Section 6 examines heterogeneity by ownership and innovation profile. Section 7 concludes with implications and limitations.

Literature Review and Hypotheses Development

Overview of NQP in China

New-quality productivity (NQP) is defined by its innovation-driven growth model, emphasizing high-technology intensity, operational efficiency, and quality excellence in line with the new development paradigm (Chu et al., 2025; W. Liu, 2024; W. Wang, 2024; Ye & Fang, 2025). Originating from Marxist political economy, the concept highlights productivity enhancement through technological revolution, factor innovation, and industrial transformation (Xue & Chen, 2025; H. L. Zhang & Zhang, 2024; Zhao-zi, 2020). NQP serves as a core driver of high-quality development and Chinese-style modernization (Ren, 2024; W. Wang, 2024). In contemporary China, NQP has become a central strategy for sustainable, efficient, and high-quality development, as reflected in initiatives such as Made in China 2025 (L. Li, 2018; State Council of the People’s Republic of China, 2015) and the State Council’s policy on strategic emerging industries (Y. Zhou & Lazonick, 2021). These programs aim to cultivate new growth drivers, including advanced technology, infrastructure, and financial systems (Jinping, 2024).

While NQP is primarily framed within China’s national policy discourse, it is conceptually aligned with broader international debates on innovation-driven productivity and sustainable growth. Extending beyond the domestic policy perspective, NQP can be understood through the lens of absorptive capacity (Cohen & Levinthal, 1990), innovation-led productivity improvements (Aghion et al., 2019), and the role of technological advancement in sustaining long-term economic growth (Bloom et al., 2020).

Recent scholarship has primarily examined NQP’s impact on carbon emissions (Han et al., 2025; H. Wang et al., 2025; X. Zhang & Shen, 2025; Zhao et al., 2025), economic development (Gu, 2024), industrial structure (Shao et al., 2024), and ESG performance (Chu et al., 2025; J. Li et al., 2024). Prior research has also identified key drivers of NQP, such as intellectual property protection (Ruanzhou & Guo, 2025), green finance (Fu & Zhao, 2025), big data–driven tax collection (Sun et al., 2025), and green innovation (J. Li & Ibrahim, 2024).

Yet, the role of patient capital—particularly strategic equity and relational debt—in shaping NQP remains underexplored. This omission is critical because patient capital can support long-term innovation, sustainability, and efficiency, which are essential to NQP. Addressing this gap, the present study investigates how patient capital mediates the ESG–NQP relationship. In doing so, it contributes to the literature by uncovering a financial mechanism that channels ESG performance into innovation-driven productivity gains, while also offering actionable insights for policymakers and enterprises seeking to advance China’s new-quality economic transformation.

ESG Performance and NQP

Research has consistently highlighted the positive relationship between ESG practices and firm-level NQP (Ammer et al., 2020; Y. Ma et al., 2024). ESG-focused firms benefit from improved internal processes, enhanced brand reputation (D. Ma et al., 2023), and stronger stakeholder engagement (Kumar et al., 2020), which collectively contribute to higher productivity (Guo & Hong, 2023; J. Ma et al., 2022). For instance, firms emphasizing environmental sustainability adopt efficient production methods (X. Zhou et al., 2020), while those advancing workforce diversity and community engagement foster innovation and employee commitment, thereby facilitating the development of NQP (Kumar et al., 2020).

From a stakeholder theory perspective, ESG performance reduces information asymmetry between firms and the public, thereby strengthening human capital, enhancing consumer trust, and improving relationships with employees, investors, and clients (Ruan & Liu, 2021; R. Wang, 2022). Skilled and innovative employees, as critical stakeholders, are particularly important in driving NQP through their expertise and creativity.

Signaling theory further suggests that strong ESG performance communicates a firm’s commitment to long-term sustainable growth. High ESG ratings send positive signals to investors, helping firms broaden financing channels, lower financing costs, and attract more capital (Y. Ma et al., 2024; M. Zhang & Chang, 2024).

While ESG performance is widely associated with enhanced innovation capacity, long-term growth, and overall corporate sustainability, the linkage with New Quality Productivity (NQP) is not unambiguously positive. First, some studies emphasize that ESG initiatives may primarily reflect external signaling rather than substantive operational improvements (E. H. Kim & Lyon, 2015; Marquis & Qian, 2014). In such cases, firms engage in symbolic compliance or “greenwashing,” where reported ESG efforts do not translate into actual productivity gains. Second, Agency-based perspectives warn that ESG investment may be driven by managerial self-interest or reputational motives rather than efficiency enhancement (Barnea & Rubin, 2010; Krueger, 2015), leading to resource misallocation or overinvestment. Likewise, compliance-oriented theories suggest that ESG engagement could impose additional regulatory and operational burdens that reduce firms’ flexibility and innovation capacity, particularly in capital-intensive sectors (Ambec & Lanoie, 2008; Porter & van der Linde, 1995). Therefore, whether ESG performance truly enhances New Quality Productivity remains an empirical question rather than a theoretical given.

Taken together, these perspectives suggest that the ESG–NQP linkage should be critically assessed. Our study hypothesizes that ESG performance may promote the formation of NQP by enhancing internal efficiency, strengthening stakeholder relationships, and facilitating resource acquisition. Accordingly, we propose:

Hypothesis 1: ESG performance is positively associated with new quality productivity.

The Mediating Role of Patient Capital

Prior research indicates that many Confucian-influenced Asian economies exhibit a long-term orientation (Hofstede, 1991; Hofstede et al., 2010). With high domestic savings rates, these nations have accumulated substantial “patient capital,” a resource particularly suitable for long-horizon investment (J. Y. Lin & Wang, 2017).

While existing research often assumes that ESG-oriented firms naturally attract long-term investors, it is crucial to distinguish patient capital from adjacent constructs such as generic long-term holdings, passive index-based ownership, or institutional equity. Patient capital, as defined in sustainable finance literature (Clark & Monk, 2017), is characterized not only by extended investment horizons but also by tolerance for short-term volatility, relational engagement, and support for strategic renewal. ESG performance can foster such capital through at least three mechanisms.

First, from a risk mitigation perspective, credible ESG performance lowers firms’ exposure to regulatory, reputational, and environmental shocks, thereby appealing to investors with downside-protection motivations rather than speculative trading incentives (Albuquerque et al., 2020; Brandon et al., 2021; Krueger, 2015).

Second, from a relational governance perspective, ESG performance strengthens trust-based relationships with long-horizon institutional investors (e.g., pension funds or sovereign wealth funds), who value stewardship and are more willing to exercise “voice” rather than “exit” during periods of underperformance (Dyck et al., 2019; Edmans, 2011).

Third, from a strategic alignment perspective, ESG initiatives often coincide with innovation-driven transformation—such as decarbonization, digitalization, or inclusive employment—which requires investors willing to forgo immediate returns in favor of long-term value co-creation (Flammer, 2021; Heinkel et al., 2001).

Together, these mechanisms suggest that ESG performance does not merely attract capital that stays longer, but capital that behaves differently—actively patient rather than passively persistent.

Patient capital has two defining features: strategic orientation and relational embeddedness. Strategically, it emphasizes future returns, risk tolerance, and long-term governance engagement (Cremers & Pareek, 2016; S. B. Kaplan, 2021). Relationally, it relies on enduring high-trust partnerships, where investors and firms exchange proprietary information, diversify financial services, and sustain implicit long-term contracts (Boot, 2000).

Building on these features, this study distinguishes strategic equity and relational debt as two forms of patient capital. Strategic equity, primarily from institutional investors such as government funds, pensions, and insurers, is characterized by long-term holdings and active monitoring (Appel et al., 2016). Government-backed equity addresses market failures in financing innovative firms, supplying capital for high-risk projects that generate broader economic benefits (Lerner, 2002; Munari & Toschi, 2015). Social sources, such as pension funds and insurance capital, pursue intergenerational wealth preservation, prioritizing stable returns and corporate engagement to support long-term strategies (Andrews & Criscuolo, 2013; McCarthy et al., 2016).

Relational debt originates from long-standing bank–firm ties, where lenders provide not only credit but also governance support through screening, monitoring, and multi-faceted interactions (Boot, 2000; David et al., 2008). This structure fosters innovation by ensuring stable financing, enhancing board independence, and offering flexibility for strategic adjustment (Armstrong et al., 2010; Mou, 2024).

Together, strategic equity and relational debt strengthen firms’ innovation capabilities by combining sustained financial commitment with active governance. Importantly, patient capital’s dual functionality extends beyond traditional financing: it supports ESG implementation, mitigates information asymmetry, and reinforces long-term organizational sustainability (Ahmad et al., 2021; J. Kim et al., 2022).

In summary, by linking strategic orientation and relational embeddedness to ESG practices, patient capital provides a governance and financing framework that promotes new quality productivity (NQP). This theoretical nexus motivates our next proposition:

Hypothesis 2a: Strategic equity positively mediates the relationship between ESG performance and NQP.

Hypothesis 2b: Relational debt positively mediates the relationship between ESG performance and NQP.

Research Design

Data

The sample of this study includes all A-share listed companies, excluding those under Special Treatment (ST) and firms in the financial industry. The classification of the financial industry is based on the 2012 Guidelines for the Industry Classification of Listed Companies issued by the China Securities Regulatory Commission (CSRC). The study uses data from 2015 (the inception year of the database) to 2023, ultimately selecting 5,323 listed companies, forming a panel dataset with 31,902 firm-year observations. The sample selection process is detailed in Table 1. All continuous variables were winsorized at the 1st and 99th percentiles.

Table 1.

Sample Selection Process.

Panel A: Sample selection process		Total
All companies listed in A-share from 2015 to 2023		5,689
Excluding ST companies		(239)
Excluding financial industry companies		(127)
Total companies in the final sample		5,323
Total firm-year observations in the period 2015–2023		33,710
Removing missing values for the dependent variable		(2393)
Removing missing values for the independent variable		(98)
Removing missing values for control variables		(486)
Winsorized at the 1st and 99th percentiles		31,902
Final observation		31,902
Panel B: Distribution sample by year	N	%
2015	2,332	7.31
2016	2,560	8.02
2017	2,977	9.33
2018	3,159	9.9
2019	3,375	10.58
2020	3,687	11.56
2021	4,261	13.36
2022	4,622	14.49
2023	4,929	15.45
Total	31,902	100

Source. Authors’ work.

To ensure data integrity, any observations with missing values on critical variables—including ESG scores, innovation measures, or financial indicators—were removed (Petersen, 2009; Wooldridge, 2010). Newly listed and delisted firms were included, resulting in an unbalanced panel that reflects the dynamic market environment (Hsiao, 2014). Additionally, robustness checks were conducted to assess the impact of potential survivorship bias by excluding firms with extreme operational or financial conditions, confirming that the main findings remain consistent (S. N. Kaplan & Schoar, 2005).

We acknowledge that the period 2015 to 2023 coincides with pronounced policy or economic disruptions, such as 2020 to 2022 during the COVID-19 pandemic in China. To account for these exogenous shocks, our analysis incorporates year fixed effects to control for macro-level changes affecting all firms simultaneously. Additionally, we conduct robustness checks by excluding years from 2020 to 2022, and re-estimate the models.

We use the Sino-Securities (Huazheng) ESG Index as our primary ESG performance data source, which is widely recognized in Chinese academic research for its rigor and comprehensiveness (e.g., Li and Li, 2024; Y. Ma et al., 2024). Data on NQP and other variables were sourced from the China Stock Market & Accounting Research (CSMAR) Database.

Measurement of Variables

Dependent Variable

Consistent with prior research (Chu et al., 2025; Jia et al., 2024; J. Li et al., 2024; Y. Ma et al., 2024), our study utilized the New Quality Productivity (NQP) index available from the CSMAR database. The NQP index based on the two-factor productivity theory, incorporating the roles of labor and production tools. Strategic emerging and future industries are selected as samples, and the index system is built with labor divided into living and materialized labor, and production tools into hard and soft technology. Various indicators under each sub-factor are weighted using the entropy method to generate the enterprise-level NQP. Appendix 1, Table A1, contains details about the NQP index. Following the approach of a prior study (Y. Ma et al., 2024), the index values are scaled by multiplying them by 10,000. This adjustment enhances the magnitude of the regression coefficients, thereby improving their interpretability and providing clearer insights into the relationships being examined.

Independent Variable

ESG scores serve as a widely used proxy for ESG performance (Ge et al., 2022; S. Li et al., 2022; D. Zhang & Liu, 2022). The more comprehensive ESG indicator systems available are primarily those from Huazheng, Wind, Shangdao Ronglv, Menglang, Hexun, Jiasi, and FTSE Russell (J. Li et al., 2024; Y. Ma et al., 2024). Among these, Huazheng offers the most complete set of ESG indicators, while the other providers offer fewer ESG metrics. Therefore, this paper uses the Huazheng ESG Index as the source for the independent variable. Higher scores and better ESG ratings reflect the superior ESG performance of a firm.

Mediating Variable

Patient capital is defined by two key characteristics: a strategic orientation, reflecting long-term investment in value-creating initiatives, and relational embeddedness, capturing stable, trust-based connections between capital providers and firms. This distinguishes patient capital from conventional long-term investments or generic institutional holdings. In this study, we operationalize patient capital along two dimensions: strategic equity (PatCap1) and relational debt (PatCap2), with all data sourced from the CSMAR database.

The variable PatCap1 is constructed to capture the presence of strategic equity, building upon the work of previous studies (Hebb, 2008; Tang et al., 2022). Its value represents the aggregate ownership stake held by insurance institutions and the Social Security Fund (SSF) in a given listed company. This operationalization stems from the observation that both insurance funds and SSF investments typically display long-term holding intentions and enhanced risk-bearing capacity, consistent with the defining features of patient capital. The intrinsic nature of these institutions' capital compels an investment focus on long-term developmental value, superseding concerns over short-term fluctuations (H. Kim et al., 2018). Consequently, their shareholding is marked by increased stability and a supportive orientation, establishing them as valid empirical proxies for patient capital. The definition of PatCap1 is shown below:

PatCap 1_{i, t} = (Ownershi {p_{i, t}}^{Insurance} + Ownershi {p_{i, t}}^{SSF}) \times 100

Ownership_i,t^Insurance denotes the proportion of shares in firm i at time t held by insurance institutions. Ownership_i,t^SSF denotes the proportion of shares in firm i at time t held by the Social Security Fund.

Following the established approach to relational debt (David et al., 2008; X. Wu et al., 2002), we treat all long-term bank loans as relational debt (PatCap2). This perspective views the lending bank as leading a syndicate that provides substantive monitoring on behalf of all participating small and medium-sized lenders. Given the inherent long-term nature of relational debt, this study defines the relational debt ratio as the ratio of all long-term bank loans to total debt. Total debt comprises bank loans, corporate bonds, and notes payable. Thus, the ratio is total long-term bank loans divided by the sum of these three components. Given relational deb’s constraint to 0 and 1, we transformed it into an unbounded log-odds space using the natural logarithm of its odds ratio. We construct the variable PatCap2 by referring to the following calculation formula:

PatCap 2_{i, t} = \ln (\frac{RelDeb {t^{*}}_{i, t}}{1 - RelDeb {t^{*}}_{i, t}})

RelDebt_i,t denotes the relational debt ratio of firm i in year t, measured as the proportion of total long-term bank loans to the total debt, where total debt comprises bank loans, corporate bonds, and notes payable. RelDebt^*_i,t = min {max (RelDebt_i,t,ε),1 − ε} is the adjusted ratio for boundary cases where RelDebt_i,t ∈ {0,1}, and ε = 10⁻⁶.

Control Variables

This study rigorously adheres to the empirical research paradigms established in corporate finance and governance literature (e.g., Y. Ma et al., 2024). Seven key firm-specific characteristic variables are incorporated as controls to mitigate potential confounding effects on the core explanatory variables. Firm size (Size) is measured in log-linear form to adjust for scale-related productivity biases. Firm age (Age) is calculated from the founding year, reflecting organizational learning curves and institutional inertia. Financial leverage (Leverage) is the total liabilities-to-total assets ratio, controlling for transmission effects of capital structure decisions. Board size (Board) is a proxy for balancing executive and supervisory functions. The proportion of Independent Directors (Indep) is an institutional representation of corporate governance quality. CEO Duality (Duality) is a dummy variable (equal to 1 if the CEO chairs the board), capturing decision-making concentration risks. Ownership balance degree (Balance) is the sum of ownership of the second to fifth largest shareholders divided by the primary shareholder's holding, reflecting power-check mechanisms. The first three variables (Size, Age, Leverage) control for fundamental differences in resource endowments and financial policies. The latter four variables (Board, Indep, Duality, and Balance) address endogenous effects of corporate governance structures.

Model

Given the longitudinal structure of the data, we first compared pooled OLS, random-effects, and fixed-effects specifications. As firm-level unobservables (e.g., governance style, strategic orientation) are likely correlated with ESG and NQP, a fixed-effects model was theoretically preferred. This was empirically confirmed by a Hausman test (χ² = 5,878.52, df = 8, p < .001), leading us to adopt a two-way fixed-effects specification with firm and year controls (plus industry dummies).

To empirically examine Hypothesis 1, we estimate the fixed-effects model (1). The regression specification models New Quality Productivity (NQP) as a function of Corporate ESG Performance (ESG), a set of control variables (firm size, age, financial leverage; refer to Table 2), year fixed effects, and industry fixed effects. The error term (ε) is estimated with firm-clustered robust standard errors. This model is designed to empirically investigate how ESG is linked to higher NQP.

Table 2.

Variable Definition and Measurement.

Variables	Symbol	Measurement	Source
Dependent variable
New quality productivity	NQP	The index calculated on the basis of the weights × 10,000	CSMAR
Independent variable
ESG performance	ESG	ESG score	The Sino-Securities (Huazheng) ESG Index
Mediating variable
Patient capital	PatCap1	Ownership held by insurance institutions and the Social Security Fund (SSF) × 100	CSMAR
	PatCap2	Relational debt ratio = Bank loans/total debt	CSMAR
Control variables
Asset size	Asset	Ln (total asset + 1)	CSMAR
Firm age	Age	Years since IPO = Observation year (current statistical cutoff date) − IPO year	CSMAR
Financial leverage	Leverage	Total liabilities/total assets	CSMAR
Board size	Board	The number of board members.	CSMAR
Ownership balance	Balance	The second to fifth shareholder/the first largest shareholder	CSMAR
Independent director ratio	Indep	The ratio of independent directors to overall board size.	CSMAR
CEO duality	Duality	If the chairman and general manager are the same person, take 1, otherwise take 0	CSMAR

Source. Authors’ work.

The models are shown below:

\begin{matrix} NQ P_{i, t} = α 0 + α 1 ES G_{i, t} + \sum Control s_{i, t} + Industr y_{i, t} \\ + Yea r_{i, t} + \in_{i, t} \end{matrix}

(1)

\begin{matrix} MVit = β 0 + β 1 ES G_{i, t} + \sum Control s_{i, t} + Industr y_{i, t} \\ + Yea r_{i, t} + \in_{i, t} \end{matrix}

(2)

\begin{matrix} NQPit = γ 0 + γ 1 ES G_{i, t} + γ 2 M V_{i, t} + \sum Control s_{i, t} \\ + Industr y_{i, t} + Yea r_{i, t} + \in_{i, t} \end{matrix}

(3)

To test Hypothesis 2, we set up Model (2) and Model (3) (Baron & Kenny, 1986). MV represents the mediating variable. The subscript i denotes the enterprise, t is the year, and ε represents the random perturbation term.

Results

Descriptive Results

Table 3 presents descriptive statistics for our sample data. The dependent variable (NQP) exhibits significant variation, ranging from 4.51 to 516.49, with a mean of 122.98 and a standard deviation of 8.17, indicating a high degree of variability within the sample. Furthermore, the general level of new quality productivity in Chinese firms is relatively low, indicating that there is considerable room for improvement.

Table 3.

Descriptive Analysis.

Variable	Observation	Mean	SD	Minimum	Maximum
NQP	31,902	122.98	8.17	4.51	516.49
ESG	31,902	69.36	9.52	42.06	87.57
Size	31,902	22.09	1.34	19.52	26.22
Age	31,902	10.01	8.31	0	29
Leverage	31,902	0.41	0.20	0.058	0.93
Balance	31,902	0.81	0.62	0.03	2.83
Board	31,902	8.31	1.55	5	13
Indep	31,902	37.78	5.27	33.33	57.14
Duality	31,902	0.33	0.47	0	1

Source. Authors’ work.

Note. NQP = new quality productivity; ESG = ESG performance; Size = firm size; Age = firm age; Leverage = financial leverage; Balance = ownership balance; Board = board size; Indep = independent director; Duality = CEO duality.

The independent variable (ESG) also shows a significant difference between the minimum value (42.06) and the maximum value (87.57), indicating that the sample chosen for this study has broad coverage, which contributes to the representativeness of the research findings. There are also notable differences between the minimum and maximum values of a series of control variables, which help ensure effective control in this study. Diagnostic tests demonstrate tolerance levels within acceptable limits (VIF: 1.04–1.67), effectively ruling out multicollinearity issues (Table 4).

Table 4.

Variance Inflation Factor (VIF) Scores.

Variable	VIF	1/VIF
Board	1.67	0.598
Size	1.55	0.646
Indep	1.52	0.658
Leverage	1.33	0.751
Age	1.31	0.762
Duality	1.1	0.908
ESG	1.07	0.934
Balance	1.04	0.965
Mean VIF	1.32

Source. Authors’ work.

Correlation Analysis

Table 5 presents a significant positive correlation between ESG performance and NQP (β = 0.329), which supports Hypothesis 1. Additionally, NQP is positively correlated with financial leverage (β = 0.027), ownership balance (β = 0.044), and CEO duality (β = 0.05). In contrast, firm size (β = −0.118), firm age (β = −0.235), and independent directors (β = −0.014) exhibit a significant negative relationship with NQP. Smaller firms, younger firms, and those with a better ownership balance, as well as higher financial leverage, tend to exhibit higher NQP. Diagnostic checks reveal that all bivariate correlations remain under 0.6, supporting model stability and mitigating concerns about multicollinearity. However, further regression analysis is required to validate the findings from the correlation analysis.

Table 5.

Pearson Correlation Analysis of Main Variables.

Variables	NQP	ESG	Size	Age	Leverage	Balance	Board	Indep	Duality
NQP	1.000
ESG	0.329***	1.000
	0.000
Size	−0.188***	−0.011**	1.000
	0.000	0.041
Age	−0.235***	−0.280***	0.468***	1.000
	0.000	0.000	0.000
Leverage	0.027***	−0.152***	0.472***	0.334***	1.000
	0.000	0.000	0.000	0.000
Balance	0.044***	0.053***	−0.128***	−0.173***	−0.088***	1.000
	0.000	0.000	0.000	0.000	0.000
Board	−0.019***	−0.026***	0.287***	0.188***	0.138***	0.022***	1.000
	0.001	0.000	0.000	0.000	0.000	0.000
Indep	−0.014**	0.052***	−0.020***	−0.029***	−0.014**	−0.027***	−0.562***	1.000
	0.010	0.000	0.001	0.000	0.014	0.000	0.000
Duality	0.050***	0.081***	−0.214***	−0.260***	−0.139***	0.061***	−0.189***	0.115***	1.000
	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

p < .05. ***p < .01. p-Values in parentheses.

Benchmark Regression

Table 6 presents the fixed-effects regression (Model 1) results supporting Hypothesis 1. The ESG coefficients across columns (1) to (3), 5.800, 5.663, and 5.516, respectively, are all statistically significant at the 1% level, indicating a robust positive relationship between ESG performance and new quality productivity (NQP). Additionally, the R² value of 0.452 in column (3) suggests that the model explains approximately 45.2% of the variance in NQP, highlighting the explanatory strength of the regression model.

Table 6.

Benchmark Regression Results.

Variables	NQP
Variables	(1)	(2)	(3)
ESG	5.800*** (62.27)	5.663*** (69.15)	5.516*** (68.26)
Size			−32.873*** (−46.70)
Age			−3.150*** (−29.43)
Leverage			141.596*** (33.93)
Balance			5.411*** (4.65)
Duality			−0.251 (−0.16)
Board			2.705*** (4.57)
Indep			−0.517*** (−3.15)
_cons	−279.304*** (−42.83)	−320.506*** (−52.53)	387.005*** (23.96)
Year fixed effect	No	Yes	Yes
Industry fixed effect	No	Yes	Yes
N	31,902	31,902	31,902
R ²	0.108	0.368	0.452
Adjusted R²	0.108	0.366	0.451

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < .01.

Critically, our results indicate that financial leverage (Leverage), ownership balance (Balance), and board size (Board) have significant positive effects on NQP. This suggests that firms with higher leverage can access more financial resources to support innovative and high-quality productive activities (Berger & Udell, 2006; Simerly & Li, 2000), more balanced ownership structures enhance monitoring and decision-making (Jensen & Meckling, 1976; McConnell & Servaes, 1990), and larger boards provide diverse expertise (Dalton et al., 1999; Pfeffer & Salancik, 1978), all of which facilitate the development of NQP. In contrast, firm size (Size), firm age (Age), and the proportion of independent directors (Indep) exhibit negative impacts. Larger and more mature firms may face bureaucratic inefficiencies and organizational inertia that hinder innovation (Benner & Tushman, 2003; Cohen & Levinthal, 1990), while higher board independence could reduce the alignment between management and innovation-focused governance (Baysinger & Butler, 1985; He & Tian, 2013), thereby potentially constraining the advancement of NQP.

Mediating Effect Test

Table 7 illustrates the results of Model 2 and Model 3, indicating the robust mediating mechanisms that link ESG performance to NQP. Columns (1) and (2) reveal that strategic equity (PatCap1) is a significant mediator. Superior ESG performance certifies sustainable development capacity (Zhan, 2023), attracting long-term institutional capital. This triggers three synergistic effects: capital injection alleviates financing constraints (K. Wang, 2023), governance enhancement improves decision quality (Ammer et al., 2020), and knowledge spillovers transfer industry best practices (Zhu & Yang, 2024), collectively driving productivity through capital upgrading and leaps in managerial efficiency. These parallel mediation pathways empirically confirm that ESG elevates NQP through a strategic equity mechanism. Hypothesis 2a is supported.

Table 7.

Mediating Effect Test Results.

Variables	(1)	(2)	(3)	(4)
Variables	PatCap1	NQP	PatCap2	NQP
ESG	0.016*** (8.67)	5.53*** (67.22)	−0.015*** (−14.18)	4.646*** (52.95)
PatCap1		1.866*** (7.19)
PatCap2				−2.928*** (−5.51)
_cons	−6.206*** (−14.15)	570.372*** (28.38)	−1.177*** (−5.75)	331.519*** (19.22)
Control variables	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes	Yes
N	31,053	31,053	25,190	25,190
R ²	0.050	0.451	0.153	0.454
Adjusted R²	0.050	0.449	0.150	0.452
Sobel test	p = 0		p = 0

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < 0.01.

Columns (3) and (4) reveal that relational debt (PatCap2), unlike strategic equity, exhibits a suppression effect—namely, it dampens the development of corporate NQP. This finding is consistent with prior research suggesting that bank-centered financing often prioritizes risk control and short-term solvency, thereby constraining innovation-driven investment (Boot, 2000; David et al., 2008). In particular, relational debt tends to be concentrated in capital-intensive and low-risk sectors, which are less aligned with ESG-oriented innovation (Jang et al., 2020). Consequently, such financing may crowd out credit for firms pursuing sustainable and productive strategies, exacerbating capital misallocation and inhibiting growth in new quality productivity (Schoenmaker & Schramade, 2019). Therefore, contrary to Hypothesis 2b, the findings reveal that relational debt significantly mediates the ESG–NQP relationship, but in a negative direction. This suggests that while ESG performance does influence NQP through relational debt, the mechanism may impose additional constraints or inefficiencies, thereby weakening rather than enhancing NQP.

Moreover, the partial mediation observed—where ESG performance promotes NQP in part by reducing reliance on relational debt—echoes prior findings that ESG engagement can reshape financing structures by lowering dependence on risk-averse funding channels (B. Cheng et al., 2014; Krüger, 2015). This suggests that ESG may not only directly enhance innovation but also indirectly do so by mitigating the constraining role of relational debt.

The Sobel test shows that the p-values are well below the .05 threshold. This suggests that the mediating effect of patient capital is highly significant, which provides strong support for Hypotheses 2a and 2b.

Robustness Checks

Propensity Score Matching (PSM)

In line with prior studies (Atif & Ali, 2021; Z. Cheng & Su, 2024), this study utilizes the PSM method to process and analyze the data, aiming to eliminate the potential bias that could arise from self-selection in the ESG-NQP relationship. We created a dummy variable based on the median ESG rating of all companies in the sample. Companies with an ESG score higher than the median in a given year were assigned a value of 1 and grouped as the treatment group. Companies with an ESG score lower than the median were assigned a value of 0 and grouped as the control group. Using a 1:1 ratio, we matched the treatment and control groups employing the nearest neighbor method and re-ran the benchmark regression. Table 8 presents the balance test results following matching, with standardized errors for all variables falling below 10%, indicating that the propensity score matching (PSM) effectively mitigates bias arising from sample self-selection. As demonstrated by the post-matching regression results in Table 9, the impact of ESG on new quality productivity (NQP) remains robust. These findings confirm the reliability and robustness of the study’s conclusions.

Table 8.

The Balance Test Results.

	Mean
Variable	Treated	Control	%bias
Size	22.072	22.01	4.6
Age	9.0673	9.0567	0.1
Leverage	0.38034	0.37447	2.9
Balance	0.83259	0.8411	−1.4
Board	8.2734	8.2206	3.4
Indep	38.035	38.15	−2.2
Duality	0.35901	0.37869	−4.2

Table 9.

Regression Results After Matching.

Variables	NQP
Variables	(1)	(2)
ESG	5.817*** (43.3)	5.473*** (44.21)
_cons	−332.433*** (−33.08)	358.740*** (15.89)
Control variables	No	Yes
Fixed year effect	Yes	Yes
Industry fixed effect	Yes	Yes
N	24,358	24,358
R ²	0.350	0.440

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < .01.

Lagging Explained Variable

Benchmark regressions indicate a positive relationship between ESG and NQP, although concerns about reverse causality persist (i.e., high-NQP firms may exhibit better ESG performance). Table 10 Column (1) shows that when NQP is lagged by one period, the ESG coefficient remains significantly positive at the 1% level, confirming robust short-term effects. Column (2) indicates that extending the lag to 2 years significantly attenuates ESG’s impact, indicating diminishing marginal returns. These findings demonstrate immediate yet non-persistent effects of ESG on productivity, aligning with dynamic capabilities theory (Teece et al., 1997), which suggests that continuous ESG renewal is essential for sustained gains. We thus recommend that firms implement annual ESG reinvestment cycles and policymakers design persistent incentive schemes rather than one-off subsidies.

Table 10.

Lagging Explained Variables Regression Results.

Variables	NQP
Variables	(3) Lag1	(4) Lag2
ESG	0.540*** (4.7)	0.196 (1.52)
_cons	315.973*** (15.93)	314.551*** (14.49)
Control variables	Yes	Yes
Fixed year effect	Yes	Yes
Industry fixed effect	Yes	Yes
N	27,409	22,743
R ²	0.352	0.354
Adjusted R²	0.349	0.352

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < .01.

Instrument Variable Method

Following prior studies (Gao et al., 2022; Y. Ma et al., 2024), we employ the average ESG score of same-province firms as an instrumental variable to address endogeneity. Table 11 shows the two-stage regression results. In the first stage, the instrument (Mean ESG) exhibits significantly positive coefficients, with the F-statistic exceeding the critical threshold of 10, confirming the instrument’s exogeneity and resolving the weak instrument problem. Stage two results confirm the robust positive effect of ESG on NQP (β significant at 1%), validating ESG’s material contribution to productivity gains after addressing endogeneity.

Table 11.

Instrumental Variables Two-Stage Regression.

Variables	(1) First stage	(2) Second stage
Variables	ESG	NQP
ESG		3.127*** (7.45)
Mean ESG	0.627*** (24.46)
_cons	−29.301*** (−14.55)	8.538 (0.71)
Control variables	Yes	Yes
Fixed year effect	Yes	Yes
Industry fixed effect	Yes	Yes
N	28,283	28,283
R ²	0.243	0.375
Adjusted R²	0.241
F-statistic	99.39***

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < .01.

Excluding Abnormal Years and Abnormal Cities

To mitigate confounding effects from the COVID-19 pandemic (2020–2022), we exclude this exceptional period for robustness checks. Table 12, Column (1) shows a statistically significant positive ESG coefficient (p < .05), confirming the pandemic shock did not alter ESG’s productivity-enhancing mechanism. This yields a critical policy implication that ESG should serve as a core policy lever for post-pandemic recovery, with sustained investments buffering extreme external disruptions.

Table 12.

Regression Results After Excluding Abnormal Years and Abnormal Cities.

Variables	Excluding abnormal years	Excluding abnormal cities
Variables	(1) NQP	(2) NQP
ESG	4.281*** (43.98)	0.221*** (2.86)
_cons	372.685*** (18.40)	73.689 (5.12)
Control variables	Yes	Yes
Fixed year effect	Yes	Yes
Industry fixed effect	Yes	Yes
N	19,332	16,154
R ²	0.461	0.373
Adjusted R²	0.459	0.369

Note. Robust t-statistics shown in parentheses.

Statistical significance denoted as follows: ***p < .01.

In China, municipalities and provincial capitals have distinct economic characteristics. To minimize their influence on the results, this study excluded enterprises from Beijing, Shanghai, Tianjin, Chongqing, and 27 provincial capitals before conducting the regression. Per Column (2) of Table 12, the coefficient of ESG maintains statistical significance at the 1% level, confirming ESG advancement as a structural driver of new-quality productivity enhancement across China’s heterogeneous regional development paradigms.

Bootstrap Resampling

To examine the mediating role of patient capital in the ESG–NQP relationship, we conducted bootstrap analyses. For strategic equity (PatCap1), based on 4,244 replications, the indirect effect of ESG on NQP is 0.0291 (z = 4.60, p < .001), with a 95% bootstrap confidence interval of [0.0167, 0.0415], indicating a statistically significant and robust mediating effect. Although 756 replications failed due to estimation issues, the results based on complete replications remain reliable.

Similarly, for relational debt (PatCap2), with 4,169 replications, the indirect effect is 0.0431 (z = 4.93, p < .001), with a 95% bootstrap confidence interval of [0.02596, 0.06027], also indicating a statistically significant and robust mediating effect despite 831 failed replications.

Both the regression-based Sobel test (Table 7) and bootstrap analyses (Table 14) confirm that patient capital (strategic equity and relational debt) significantly mediates the ESG–NQP relationship. The bootstrap results further provide robust confidence intervals, supporting the stability of the mediating effect.

Differential Generalized Method of Moments

To further address potential dynamic endogeneity concerns, we re-estimate the baseline model using the two-step difference Generalized Method of Moments (GMM) estimator proposed by Arellano and Bond. We use lagged levels of the dependent variable NQP_i,t−3 and NQP_i,t−4 as internal instruments (collapsed), while meanESG and other controls as strictly exogenous in first differences. The provincial-level mean ESG (meanESG) is used as an external instrument for firm-level ESG, following the rationale that regional ESG norms and regulatory pressure shape individual firms’ ESG practices but do not directly affect firm-level NQP beyond their influence through ESG behavior. This exclusion restriction is consistent with prior literature using regional averages as external shifters of firm-level strategic or disclosure choices (Gao et al., 2022; Y. Ma et al., 2024).

The model successfully passes all key diagnostic tests. The AR(2) test fails to detect second-order serial correlation (p = .789), confirming the validity of moment conditions. Both the Sargan test (p = .496) and Hansen test (p = .200) fail to reject the null of instrument validity, suggesting that the instrument set is neither overfitted nor weak. The estimated coefficient of ESG remains statistically insignificant, and the sign is consistent with our baseline IV-FE results, reinforcing our main conclusion (shown in Table 13).

Table 13.

Dynamic Panel Difference GMM Estimation Results.

Variables	NQP
L.NQP	0.1742*** (4.99)
MeanESG	−3.0426*** (−4.60)
ESG	0.0540 (0.48)
Size	−24.8987*** (−6.37)
Age	−1.5957*** (−4.13)
Leverage	63.3585*** (6.26)
Balance	−7.3555*** (−2.68)
Board	0.1691 (0.17)
Indep	−0.1125 (−0.50)
Duality	−3.3100 (−1.56)
AR(1)test (p value)	0.000
AR(2)test (p value)	0.789
Sargan test (p value)	0.496
Hansen test (p value)	0.200
Observations	21,285
Number of firms	4,306
Number of instruments	11

Note. Two-step difference GMM estimator with Windmeijer finite-sample correction for standard errors. Lagged dependent variables (L3–L4.NQP) are employed as collapsed GMM-style instruments. All other regressors are treated as first-differenced exogenous instruments. The provincial-level mean ESG (meanESG) is used as an external instrument for firm-level ESG. Robust t-statistics shown in parentheses.

***

Statistical significance at the 1% level.

Heterogeneity Analysis

To further explore potential heterogeneity in the relationship between ESG performance and new quality productivity, sub-sample sensitivity analyses were employed. Specifically, the sample was stratified by ownership type (SOEs vs. non-SOEs) and industry sector (SRDIs vs. non-SRDIs). These specifications allow for a rigorous examination of whether the ESG-NQP linkage differs across institutional contexts, firm scale, and industrial characteristics. The results reveal that the positive effects of ESG performance are more pronounced in non-SOEs, smaller firms, and technology-intensive sectors, highlighting the importance of considering firm heterogeneity in interpreting the impact of ESG initiatives.

Analysis of Ownership

Due to variations in ownership structure, firms exhibit distinct business models and objectives. State-owned enterprises (SOEs) often focus on fulfilling government-set political and social goals, operating in high-energy, high-pollution industries (K. Zhang et al., 2022). They require significant investment to meet ESG standards (W. Zhang & Wang, 2023). In contrast, private enterprises, more aligned with innovation-driven goals, can implement ESG performance with lower investment and greater impact on new quality productivity. To assess the influence of ownership structure on the research findings, the study categorizes central and local state-owned enterprises as state-owned enterprises (SOEs) and designates all remaining ownership forms as non-state-owned enterprises (non-SOEs). Columns (1) and (2) of Table 15 reveal that the ESG coefficients for both SOEs and non-SOEs are significantly positive at the 1% level. This suggests that better ESG performance leads to higher NQP levels across ownership types, with non-SOEs exhibiting a more pronounced effect. This finding validates the theoretical proposition advanced in foundational work (Kweh et al., 2017; J. Ma et al., 2022; X. Zhou et al., 2020) that SOEs’ ESG efforts tend to be more policy-driven, making their impact on productivity weaker compared to non-SOEs (Table 15).

Table 14.

Bootstrap Results for the Mediating Effect.

Mediator	Bootstrap reps (N)	Indirect effect (a × b)	SE	z	p Value	95% CI (bootstrap)	Failures
PatCap1 (strategic equity)	4,244	0.0291	0.00632	4.60	<.001	[0.0167, 0.0415]	756
PatCap2 (relational debt)	4,169	0.0431	0.00875	4.93	<.001	[0.02596, 0.06027]	831

Note. Bootstrap confidence intervals are based on complete replications. “Failures” indicates the number of bootstrap replications where parameters could not be estimated.

Table 15.

Heterogeneity Analysis Results.

Variables	(1)	(2)	(3)	(4)
Variables	Non-SOE	SOE	SRDI enterprises	Non-SRDI enterprises
ESG	6.311*** (46.59)	2.437*** (19.29)	9.594*** (24.74)	5.119*** (62.47)
_cons	529.185*** (23.00)	169.806*** (8.10)	902.470*** (8.71)	358.911 (22.11)
Control variables	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Industry fixed effect	Yes	Yes	Yes	Yes
N	22,650	9,252	2,094	29,808
R ²	0.436	0.587	0.530	0.458
Adjusted R²	0.434	0.583	0.513	0.456

Note. This table reports the regression results for the effect of ESG performance on firm outcomes across different subsamples. Non-SOE refers to non–state-owned enterprises, and SOE refers to state-owned enterprises. SRDI enterprises are “specialized, refined, differentiated, and innovative” firms, while Non-SRDI enterprises represent other firms. All regressions control for firm-level characteristics, as well as year and industry fixed effects. Robust t-statistics are reported in parentheses.

***

Statistical significance at the 1% level.

From an institutional perspective, SOEs operate under dual mandates of political legitimacy and economic performance (W. Li & Xia, 2020; Musacchio & Lazzarini, 2014), which may weaken the efficiency motive of ESG engagement. In contrast, non-SOEs are more market-disciplined and therefore treat ESG as a strategic tool for signaling credibility and accessing patient capital (Dyck et al., 2019; Lins et al., 2017).

Analysis of Specialized Refined Distinctive Innovative Enterprises (SRDI)

SRDI enterprises are a category of exceptional small and medium-sized enterprises (SMEs) in China known for their focus on specialization, refinement, uniqueness, and innovation. These businesses play a crucial role in China’s national innovation system, acting as key players in efforts to strengthen supply chains and address critical technological challenges (D. Liu et al., 2024). The heterogeneity estimates in Columns (3) and (4) of Table 15 reveal a significantly amplified ESG-NQP elasticity coefficient within SRDI enterprises (β = 9.594, p < .01), contrasting with statistically weaker associations observed in non-SRDI counterparts (β = 5.119, p < .01). This may be attributed to the financial assistance provided by the Chinese government to SRDI firms, which enhances their innovation capabilities and drives their productivity to new levels (Branstetter et al., 2023; Xianju et al., 2024).

Firms in technology-intensive or high-competition sectors are more likely to internalize ESG practices as innovation enablers (Dean & McMullen, 2007; Porter & van der Linde, 1995), whereas firms in resource-heavy or heavily regulated industries may adopt ESG symbolically to satisfy compliance requirements rather than pursue productivity enhancement (E. H. Kim & Lyon, 2015; Marquis & Qian, 2014). Therefore, the heterogeneity observed in our results reflects underlying variations in incentive structures, institutional dependence, and capability readiness rather than random variation.

Conclusions

Building on previous empirical studies that show that ESG performance improves firms’ new quality productivity (NQP), our study goes further into the underlying function of patient capital. Evidence from 5,323 Chinese-listed firms (2015–2023) confirms that ESG performance is linked to higher NQP, a relationship crucially facilitated by patient capital. Our examination of differential effects reveals that the relationship between ESG performance and NQP is particularly robust among non-state-owned enterprises and firms classified as SRDI. These firms, often more innovation-driven, stand to benefit significantly from government support, which could further enhance their contribution to economic growth, technological advancement, and sustainability goals.

The mediating role of patient capital highlights a concrete pathway for promoting sustainable business practices. Strong institutional backing not only provides financial resources but also governance expertise, creating continuous pressure for ESG improvement and long-term productivity gains (Aldridge & Martin, 2022). Expanding equity financing and channeling investment into key sectors with high ESG potential can further accelerate productivity growth (McCahery et al., 2022; Zhan, 2023).

Governments, in turn, could refine strategic equity funds to support ESG-leading firms, thereby aligning corporate development with national objectives such as sustainability and innovation (Xie & Zhang, 2021; Yao, 2019; Y. Zhang & Zhang, 2021).Tax incentives or interest subsidies can reward long-term shareholders who maintain equity positions in high-ESG firms. Public–private co-investment funds or government-backed “green guidance funds” can crowd in private patient capital while signaling long-term policy support. At the firm level, mandatory disclosure of investor holding horizons would improve transparency and discourage speculative trading.

The suppressing role of relational debt suggests that overreliance on bank-centered, relationship-based financing may undermine firms’ ability to translate ESG performance into new quality productivity. For managers, this highlights the importance of diversifying financing channels toward more strategic, equity-based, or innovation-friendly capital that aligns with long-term sustainability goals. For policymakers, it underscores the need to mitigate capital misallocation by incentivizing financial institutions to incorporate ESG considerations into credit allocation. By fostering financing environments that reward sustainable innovation rather than merely prioritizing short-term risk control, firms and regulators can jointly enhance the productivity gains associated with ESG initiatives.

Although this study advances understanding of how ESG performance and patient capital influence NQP, several limitations warrant consideration. First, the sample is limited to Chinese A-share listed firms, which may constrain the generalizability of findings to other countries. Cross-country differences in regulatory frameworks, capital markets, and corporate governance could produce different ESG–productivity dynamics, and future research could adopt a comparative perspective to validate the broader applicability of these results.

Second, the positive effects observed are most pronounced among Non-SOEs and SRDI firms, which benefit from stronger market discipline and higher innovation capacity. Caution is therefore needed in generalizing to large SOEs or resource-intensive firms, where ESG adoption may be compliance-driven rather than strategically embedded. Moreover, ESG implementation in China is influenced by institutional factors, such as government subsidies, policy mandates, and local signaling (W. Li & Lu, 2021; M. Zhang et al., 2023), suggesting that some productivity gains may reflect policy-induced upgrading rather than voluntary firm transformation. Future studies should disentangle market-driven versus policy-driven ESG engagement and explore whether the ESG–NQP relationship persists in highly regulated or low-competition sectors.

Finally, potential short-term trade-offs should be acknowledged, including higher costs, reduced immediate profits, or diverted managerial attention (Barnett & Salomon, 2012; Christensen et al., 2022). Firms with limited resources may experience financing pressures, and excessive reliance on ESG incentives could create strategic dependency. Future research should examine the conditions under which ESG serves as a long-term enabler versus a short-term burden.

Footnotes

Appendix 1

Table A1.

The Index System of New-Quality Productivity (NQP).

First level indicator	Second level indicator	Third level indicator	Measurement	Weight
Labor	Live labor	R&D personnel salary ratio	R&D salary expenses/operating revenue	28
		R&D personnel ratio	Number of R&D personnel/total number of employees	4
		High-education personnel ratio	Number of employees with a bachelor’s degree or higher/total number of employees	3
	Materialized labor (object of labor)	Fixed asset ratio:	Fixed assets/total assets	2
	Materialized labor (object of labor)	Manufacturing cost ratio	(Operating cash outflows + depreciation of fixed assets + amortization of intangible assets + impairment provisions − cash paid for purchasing goods and services − cash paid to employees and for employee wages)/(Operating cash outflows + depreciation of fixed assets + amortization of intangible assets + impairment provisions)	1
Production tools	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 − lease expenses)/operating revenue	2
		R&D direct investment ratio	(R&D expenses − direct investment)/operating revenue	28
		Intangible asset ratio	Intangible assets/total assets	3
	Soft technology	Total asset turnover	Operating revenue/average total assets	1
	Soft technology	The inverse of equity multiplier	Owner’s equity/total assets	1
New quality productivity				100

Source. Authors’ work.

Acknowledgements

We would like to express our sincere gratitude to the academics at Universiti Malaya, Kuala Lumpur, and Beihua University, China, for their valuable feedback and insightful suggestions, which significantly contributed to the improvement of this paper.

ORCID iDs

Yuan Zheng

Mazni Abdullah

Nurliana Md Rahin

Ethical Considerations

There are no human participants in this article, and informed consent is not required.

Author Contributions

Yuan Zheng conducted the research, analyzed the data, and drafted the manuscript. Mazni Abdullah, as the corresponding author, contributed to the research design, methodology, and interpretation of results. Nurliana Md Rahin contributed to the review and refinement of the manuscript, offering valuable insights and suggestions. All authors read and approve the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the China Stock Market Accounting Research (CSMAR) repository, CSMAR.

References

Aghion

Akcigit

Howitt

(2019). Innovation and growth: Theory and evidence. American Economic Review, 109(11), 4085–4127. https://doi.org/10.1257/aer.20180251

Ahmad

Mobarek

Roni

N. N.

(2021). Revisiting the impact of ESG on financial performance of FTSE350 UK firms: Static and dynamic panel data analysis. Cogent Business & Management, 8(1). https://doi.org/10.1080/23311975.2021.1900500

Albuquerque

Koskinen

Zhang

(2020). Resiliency of environmental and social stocks: An analysis of the exogenous COVID-19 market crash. The Review of Corporate Finance Studies, 9(3), 593–621. https://doi.org/10.1093/rcfs/cfaa011

Aldridge

Martin

(2022). ESG in corporate filings: An AI perspective. Cornell University.

Ali

(2021). Rise of China: Growing strategic competition between the US and China. Margalla Papers, 25(1), 103. https://doi.org/10.54690/margallapapers.25.1.55

Ambec

Lanoie

(2008). Does it pay to be green? A systematic overview. Academy of Management Perspectives, 22(4), 45–62. https://doi.org/10.5465/amp.2008.35590353

Ammer

M. A.

Aliedan

Alyahya

(2020). Do corporate environmental sustainability practices influence firm value? The role of independent directors: Evidence from Saudi Arabia. Sustainability, 12(22), 9768. https://doi.org/10.3390/su12229768

Amore

M. D.

Bennedsen

(2016). Corporate governance and green innovation. Journal of Environmental Economics and Management, 75, 54–72. https://doi.org/10.1016/j.jeem.2015.11.003

Andrews

Criscuolo

(2013). Knowledge-based capital, innovation and resource allocation: A going for growth report (OECD Economic Policy Papers). OECD Publishing. https://ideas.repec.org/p/oec/ecoaab/4-en.html

10.

Appel

I. R.

Gormley

T. A.

Keim

D. B.

(2016). Passive investors, not passive owners. Journal of Financial Economics, 121(1), 111–141. https://doi.org/10.1016/j.jfineco.2016.03.003

11.

Armstrong

Guay

W. R.

Weber

(2010). The role of information and financial reporting in corporate governance and debt contracting. Journal of Accounting and Economics, 50, 179–234. https://doi.org/10.1016/j.jacceco.2010.10.001

12.

Asker

Farre-Mensa

Ljungqvist

(2015). Corporate investment and stock market listing: A puzzle? Review of Financial Studies, 28(2), 342–390. https://doi.org/10.1093/rfs/hhu077

13.

Atif

Ali

(2021). Environmental, social and governance disclosure and default risk. Business Strategy and the Environment, 30(8), 3937–3959.

14.

Barnea

Rubin

(2010). Corporate social responsibility as a conflict between shareholders. Journal of Business Ethics, 97(1), 71–86. https://doi.org/10.1007/s10551-010-0496-z

15.

Barnett

M. L.

Salomon

R. M.

(2012). Does it pay to be really good? Addressing the shape of the relationship between social and financial performance. Strategic Management Journal, 33(11), 1304–1320. https://doi.org/10.1002/smj.1980

16.

Baron

R. M.

Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

17.

Baysinger

B. D.

Butler

H. D.

(1985). Corporate governance and the board of directors: Performance effects of changes in board composition. Journal of Law, Economics, & Organization, 1(1), 101–124.

18.

Benner

M. J.

Tushman

M. L.

(2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238–256. https://doi.org/10.5465/amr.2003.9416096

19.

Berger

A. N.

Udell

G. F.

(2006). A more complete conceptual framework for SME finance. Journal of Banking & Finance, 30(11), 2945–2966. https://doi.org/10.1016/j.jbankfin.2006.05.008

20.

Bloom

Jones

C. I.

Van Reenen

Webb

M..

(2020). Innovation and productivity in firms: Evidence from the world. Journal of Economic Literature, 58(3), 721–800. https://doi.org/10.1257/jel.20191450

21.

Boot

A. W. A.

(2000). Relationship banking: What do we know? Journal of Financial Intermediation, 9(1), 7–25. https://doi.org/10.1006/jfin.2000.0282

22.

Brandon

R. G.

Krueger

Schmidt

P. S.

(2021). ESG rating disagreement and stock returns. The Review of Financial Studies, 34(8), 3469–3519. https://doi.org/10.1093/rfs/hhab022

23.

Branstetter

Ren

(2023). Picking winners? Government subsidies and firm productivity in China. Journal of Comparative Economics, 51(4), 1186. https://doi.org/10.1016/j.jce.2023.06.004

24.

Cai

(2016). Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China. Journal of Development Economics, 123, 73. https://doi.org/10.1016/j.jdeveco.2016.08.003

25.

Cheng

Ioannou

Serafeim

(2014). Corporate social responsibility and access to finance. Strategic Management Journal, 35(1), 1–23. https://doi.org/10.1002/smj.3738

26.

Cheng

(2024). ESG and Chinese corporate OFDI. Research in International Business and Finance, 72, 102522. https://doi.org/10.1016/j.ribaf.2024.102522

27.

Christensen

D. M.

Serafeim

Sikochi

(2022). Why is corporate virtue in the eye of the beholder? The case of ESG ratings. The Accounting Review, 97(1), 147–175. https://doi.org/10.2308/TAR-2019-0506

28.

Chu

Niu

Wei

(2025). Research on the impact of new quality productivity on enterprise ESG performance. International Review of Economics & Finance, 99, 104009. https://doi.org/10.1016/j.iref.2025.104009

29.

Clark

G. L.

Monk

A. H. B.

(2017). Patient capital: The role of long-term finance in sustainable economic development. Journal of Sustainable Finance & Investment, 7(1), 1–15. https://doi.org/10.1080/20430795.2016.1269190

30.

Cohen

W. M.

Levinthal

D. A.

(1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553

31.

Cremers

Pareek

(2016). Patient capital outperformance: The investment skill of high active share managers who trade infrequently. Journal of Financial Economics, 122(2), 288–306.

32.

Dafe

Upadhyaya

(2024). Varieties of capitalism and patient capital in developing and emerging economies: Evidence from Kenya. Socio-Economic Review, mwae074. https://doi.org/10.1093/ser/mwae074

33.

Dalton

D. R.

Daily

C. M.

Johnson

J. L.

Ellstrand

A. E.

(1999). Number of directors and financial performance: A meta-analysis. Academy of Management Journal, 42(6), 674–686. https://doi.org/10.5465/256988

34.

David

O’Brien

J. P.

Yoshikawa

(2008). The implications of debt heterogeneity for R&D investment and firm performance. The Academy of Management Journal, 51(1), 165–181.

35.

Dean

T. J.

McMullen

J. S.

(2007). Toward a theory of sustainable entrepreneurship: Reducing environmental degradation through entrepreneurial action. Journal of Business Venturing, 22(1), 50–76.

36.

Deeg

Hardie

Maxfield

(2016). What is patient capital, and where does it exist? Socio-Economic Review, 14(4), 615–625. https://doi.org/10.1093/ser/mww030

37.

Dyck

Lins

K. V.

Roth

Wagner

H. F.

(2019). Do institutional investors drive corporate social responsibility? International evidence. Journal of Financial Economics, 131(3), 693–714.

38.

Eccles

R. G.

Ioannou

Serafeim

(2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984

39.

Edmans

(2011). Does the stock market fully value intangibles? Employee satisfaction and equity prices. Journal of Financial Economics, 101(3), 621–640. https://doi.org/10.1016/j.jfineco.2011.03.021

40.

Fichtner

Heemskerk

E. M.

(2020). The New Permanent Universal Owners: Index funds, patient capital, and the distinction between feeble and forceful stewardship. Economy and Society, 49(4), 493–515. https://doi.org/10.1080/03085147.2020.1781417

41.

Flammer

(2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516. https://doi.org/10.1016/j.jfineco.2021.05.010

42.

Flammer

Bansal

(2017). Does a long-term orientation create value? Evidence from a regression discontinuity. Strategic Management Journal, 38(12), 1827–1847. https://doi.org/10.1002/smj.2629

43.

Zhao

(2025). Influence of green finance on new-quality productivity of enterprises: Evidence from Chinese A-share listed companies. International Review of Financial Analysis, 105, 104356. https://doi.org/10.1016/j.irfa.2025.104356

44.

Gao

Chu

Zheng

(2022). Environmental, social and governance performance: Can it be a stock price stabilizer? Journal of Cleaner Production, 379, 134705. https://doi.org/10.1016/j.jclepro.2022.134705

45.

Xiao

Dai

(2022). Does ESG performance promote high-quality development of enterprises in China? The mediating role of innovation input. Sustainability, 14(7), 3843. https://doi.org/10.3390/su14073843

46.

Global SWF. (2023). Assets under management (AUM) of the National Social Security Fund (NSSF) in China from 2014 to 2023 (in billion U.S. dollars) [Graph]. Statista. https://www.statista.com/statistics/1422344/aum-of-national-social-security-fund-china/

47.

Government of China. (2024, May 27). China’s Ministry of Ecology and Environment releases new policies on sustainable development. https://english.www.gov.cn/news/202405/28/content_WS6655152bc6d0868f4e8e78b8.html#:~:text=BEIJING,%20May%2027%20—%20China's%20Ministry%20of

48.

(2024). Empirical study on the impact of new-quality productivity on regional economic development in China. Journal of Economics and Public Finance, 10(4), p198. https://doi.org/10.22158/jepf.v10n4p198

49.

Guo

G. Y.

Hong

(2023). How does ESG information disclosure affect total factor productivity of enterprises? – Based on empirical evidence of A-share listed companies. Wuhan Finance, 7, 13–22.

50.

Han

Sheng

Xing

(2025). Study on the influence of new quality productivity on carbon emissions of the manufacturing industry. Scientific Reports, 15, 26180. https://doi.org/10.1038/s41598-025-10827-z

51.

Harrison

R. T.

Botelho

Mason

C. M.

(2016). Patient capital in entrepreneurial finance: A reassessment of the role of business angel investors. Socio-Economic Review, 14(4), 669–689. https://doi.org/10.1093/ser/mww024

52.

Tian

(2013). The dark side of analyst coverage: The case of innovation. Journal of Financial Economics, 109(3), 856–878. https://doi.org/10.1016/j.jfineco.2013.04.001

53.

Hebb

(2008). No small change: Pension funds and corporate engagement. Cornell University Press.

54.

Heinkel

Kraus

Zechner

(2001). Investor activism and financial capital allocation. Journal of Financial Economics, 60(1), 107–141. https://doi.org/10.1016/S0304-405X(01)00039-0

55.

Hofstede

(1991). Cultures and organizations: Software of the mind. McGraw-Hill.

56.

Hofstede

G. J.

Minkov

(2010). Cultures and organizations: Software of the mind (3rd, Revised and Expanded ed.). McGraw-Hill.

57.

Hsiao

(2014). Analysis of panel data. Cambridge University Press. https://doi.org/10.1017/CBO9781139839327

58.

Investment Company Institute. (2024). 2024 Investment company fact book. Investment Company Institute. https://www.ici.org/fact-book

59.

Jang

Kang

Lee

J.-Y.

Bae

K.-H.

(2020). ESG scores and the credit market. Sustainability, 12(8), 3456. https://doi.org/10.3390/su12083456

60.

Jensen

M. C.

Meckling

W. H.

(1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X

61.

Jia

Zhang

(2024). A study on the impact of ESG development on new productive forces of enterprises——Empirical evidence from A-share listed companies in China. Contemporary Economic Management, 46(6), 1–11.

62.

Jinping

(2024, May 11). Title of the webpage. Qiushi, China Daily. https://subsites.chinadaily.com.cn/Qiushi/2024-05/11/c_985265.htm

63.

Kahane

(2020). The new economy: A financial climate for climate finance. DOAJ (Directory of Open Access Journals). https://doaj.org/article/0a6514b55f4147a58a9f744a87b17574

64.

Kaplan

S. B.

(2021). Globalizing patient capital: The political economy of Chinese finance in the Americas. Cambridge University Press.

65.

Kaplan

S. N.

Schoar

(2005). Private equity performance: Returns, persistence, and capital flows. Journal of Finance, 60(4), 1791–1823.

66.

Kim

E. H.

Lyon

T. P.

(2015). Greenwash vs. Brownwash: Exaggeration and undue modesty in corporate sustainability disclosure. Organization Science, 26(3), 705–723. https://doi.org/10.1287/orsc.2014.0950

67.

Kim

Park

(2018). Do long-term institutional investors promote corporate social responsibility activities? Journal of Banking & Finance, 101, 256. https://doi.org/10.1016/j.jbankfin.2018.11.015

68.

Kim

Cho

Okafor

C. E.

Choi

(2022). Does environmental, social, and governance drive the sustainability of multinational corporation’s subsidiaries? Evidence from Korea. Frontiers in Psychology, 13, 899936. https://doi.org/10.3389/fpsyg.2022.899936

69.

Klingler-Vidra

(2016). When venture capital is patient capital: Seed funding as a source of patient capital for high-growth companies. Socio-Economic Review, 14(4), 691–708. https://doi.org/10.1093/ser/mww022

70.

Krueger

(2015). Corporate goodness and shareholder wealth. Journal of Financial Economics, 115(2), 304–329. https://doi.org/10.1016/j.jfineco.2014.09.008

71.

Krüger

(2015). Corporate goodness and shareholder wealth. Journal of Financial Economics, 115(2), 304–329. https://doi.org/10.1016/j.jfineco.2014.09.008

72.

Kumar

Ting

I. W. K.

Sujit

K. S.

Sumod

S. D.

(2020). Environmental, social and governance initiatives and wealth creation for firms: An empirical examination. Managerial and Decision Economics, 41(5), 710. https://doi.org/10.1002/mde.3131

73.

Kweh

Q. L.

Alrazi

Chan

Y. C.

Abdullah

W. M. T. W.

(2017). The effects of environmental, social and governance on the corporate performance of Malaysian government-linked companies. SHS Web of Conferences, 36, 22. https://doi.org/10.1051/shsconf/20173600022

74.

Lerner

(2002). When bureaucrats meet entrepreneurs: The design of effective public venture capital programmes. Economic Journal, 112, 73–84. https://doi.org/10.1111/1468-0297.00004

75.

Ibrahim

(2024). Executive green perception and green innovation improve new quality productivity in Chinese listed firms. International Journal of Financial Studies, 12(4), 102. https://doi.org/10.3390/ijfs12040102

76.

Noorliza

Zhang

(2024). Enhancing environmental, social, and governance performance through new quality productivity and green innovation. Sustainability, 16(11), 4843. https://doi.org/10.3390/su16114843

77.

(2018). China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0.” Technological Forecasting and Social Change, 135, 66–74. https://doi.org/10.1016/j.techfore.2017.05.028

78.

Yin

Liu

(2022). Evaluation of ESG ratings for Chinese listed companies from the perspective of stock price crash risk. Frontiers in Environmental Science, 10, 933639. https://doi.org/10.3389/fenvs.2022.933639

79.

(2021). Government regulation, ESG disclosure and corporate green transformation in China. Journal of Cleaner Production, 278, 123634.

80.

Xia

(2020). State ownership and corporate responses to policy signals: Evidence from China. Journal of Comparative Economics, 48(4), 885–903.

81.

(2024). ESG performance and innovation quality. International Review of Economics and Finance, 92, 1361–1373. https://doi.org/10.1016/j.iref.2024.02.063

82.

Lin

(2017). Does institutional ownership influence firm performance? Evidence from China. International Review of Economics & Finance, 49, 17. https://doi.org/10.1016/j.iref.2017.01.021

83.

Lins

K. V.

Servaes

Tamayo

(2017). Social capital, trust, and firm performance: The value of corporate social responsibility during the financial crisis. The Journal of Finance, 72(4), 1785–1824. https://doi.org/10.1111/jofi.12505

84.

Liu

Yang

Zheng

Lin

(2024). Research on the impact exerted by green innovation corporations’ internationalization depth on innovation performance and sustainability—An empirical analysis based on data from china’s specialized, fine, special, and innovative enterprises. Sustainability, 16(21), 9457. https://doi.org/10.3390/su16219457

85.

Liu

(2024). Scientific understanding and practical development of new quality productive forces. Economic Research Journal, 59, 4–11.

86.

Liu

(2023). Substantial innovation or strategic innovation: The influence of ESG performance on corporate innovation strategy. SHS Web of Conferences, 169, 1063. https://doi.org/10.1051/shsconf/202316901063

87.

Liu

(2023). Brand premium and carbon information disclosure strategy: Evidence from china listed companies. Sustainability, 15(6), 5240. https://doi.org/10.3390/su15065240

88.

Gao

Sun

(2022). Does ESG performance promote total factor productivity? Evidence from China. Frontiers in Ecology and Evolution, 10, 1063736. https://doi.org/10.3389/fevo.2022.1063736

89.

Liu

Chen

(2024). Corporate ESG performance, green innovation, and green new quality productivity: Evidence from China. Sustainability, 16(22), 9804. https://doi.org/10.3390/su16229804

90.

Marquis

Qian

(2014). Corporate social responsibility reporting in China: Symbol or substance? Organization Science, 25(1), 127–148.

91.

McCahery

J. A.

Pudschedl

P. C.

Steindl

(2022). Institutional investors, alternative asset managers, and ESG preferences. European Business Organization Law Review, 23(4), 821. https://doi.org/10.1007/s40804-022-00264-0

92.

McCarthy

M. A.

Sorsa

V.-P.

Van Der Zwan

(2016). Investment preferences and patient capital: Financing, governance, and regulation in pension fund capitalism. Socio-Economic Review, 14(4), 751–769. https://doi.org/10.1093/ser/mww020

93.

McConnell

J. J.

Servaes

(1990). Additional evidence on equity ownership and corporate value. Journal of Financial Economics, 27(2), 595–612. https://doi.org/10.1016/0304-405X(90)90069-C

94.

Mou

(2024). The impact of digital finance on technological innovation across enterprise life cycles in China. Heliyon, 10(14), e33965. https://doi.org/10.1016/j.heliyon.2024.e33965

95.

Munari

Toschi

(2015). Assessing the impact of public venture capital programmes in the United Kingdom: Do regional characteristics matter? Journal of Business Venturing, 30(2), 205–226. https://doi.org/10.1016/j.jbusvent.2014.07.009

96.

Musacchio

Lazzarini

S. G.

(2014). Reinventing state capitalism: Leviathan in business, Brazil and beyond. Harvard University Press.

97.

Petersen

M. A.

(2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22(1), 435–480.

98.

Pfeffer

Salancik

G. R.

(1978). The external control of organizations: A resource dependence perspective. Harper & Row.

99.

Porter

M. E.

van der Linde

(1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9(4), 97–118. https://doi.org/10.1257/jep.9.4.97

100.

Ren

(2024). The logic of forming new quality productive forces through the transformation of modernization of productive forces. Economic Research, 59(3), 12–19.

101.

Reuters. (2025, January 22). China unveils plan to encourage insurance funds into stock markets. https://www.reuters.com/markets/asia/china-unveils-plan-encourage-insurance-funds-into-stock-markets-2025-01-22/

102.

Ruan

Liu

(2021). Environmental, social, governance activities and firm performance: Evidence from China. Sustainability, 13(2), 767. https://doi.org/10.3390/su13020767

103.

Ruanzhou

Guo

(2025). New quality productivity and intellectual property protection. Finance Research Letters, 83, 107650. https://doi.org/10.1016/j.frl.2025.107650

104.

Schoenmaker

Schramade

(2019). Principles of sustainable finance. Oxford University Press.

105.

Shao

Dong

Gao

(2024). New quality productivity and industrial structure in china: The moderating effect of environmental regulation. Sustainability, 16(16), 6796. https://doi.org/10.3390/su16166796

106.

Simerly

R. L.

(2000). Environmental dynamism, capital structure and performance: A theoretical integration and an empirical test. Strategic Management Journal, 21(1), 31–49. https://doi.org/10.1002/(SICI)1097-0266(200001)21:1<31::AID-SMJ76>3.0.CO;2-T

107.

State Council of the People’s Republic of China. (2015). Made in China 2025. http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm

108.

State Council of the People’s Republic of China. (2025). State council releases policy document on promoting economic development. https://english.www.gov.cn/policies/latestreleases/202501/08/content_WS677db5ddc6d0868f4e8ee961.html

109.

Sun

Rao

Yao

Feng

(2025). Driving enterprise new quality productivity: The role of big data tax collection. International Review of Financial Analysis, 103, 104184. https://doi.org/10.1016/j.irfa.2025.104184

110.

Tang

Chen

(2022). Does the social security fund help non-financial enterprises to transform from the virtual to the real? China Journal of Accounting Research, 15(2), 100238. https://doi.org/10.1016/j.cjar.2022.100238

111.

Teece

D. J.

Pisano

Shuen

(1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

112.

Thatcher

Vlandas

(2016). Overseas state outsiders as new sources of patient capital: Government policies to welcome Sovereign Wealth Fund investment in France and Germany. Socio-Economic Review, 14(4), 647–668. https://doi.org/10.1093/ser/mww028

113.

Wang

Mei

Yang

Peng

(2023). ESG performance and corporate resilience: An empirical analysis based on the capital allocation efficiency perspective. Sustainability, 15(23), 16145. https://doi.org/10.3390/su152316145

114.

Wang

Zhou

Dong

(2025). Can new quality productivity drive the low-carbon transformation of carbon-intensive industries? Macro and micro evidence from China. Energies, 18(13), 3278. https://doi.org/10.3390/en18133278

115.

Wang

(2022). A study on the relationship between ESG information disclosure and corporate value from the cost-benefit perspective. Journal of Shanghai University of Foreign Trade, 29(04), 74–86.

116.

Wang

(2024). Intelligent manufacturing as a new quality productive force to support the construction of a modern industrial system. Contemporary Economic Research, 2, 105–115.

117.

Wooldridge

J. M.

(2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press.

118.

Sercu

Yao

(2002). Reexamining the relation between debt mix and growth in Japan. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.296719

119.

Xianju

Kai

Hui

Chunhui

(2024). The growth of SRDI enterprises: A review and prospects. Foreign Economics & Management, 46(1), 62–76. https://doi.org/10.16538/j.cnki.fem.20230912.101

120.

Xie

Zhang

(2021). Government subsidies and firm technological innovation performance: An empirical study based on panel data of high-tech firms. E3S Web of Conferences, 275, 3070. https://doi.org/10.1051/e3sconf/202127503070

121.

Xue

Chen

(2025). ESG performance and stability of new quality productivity forces: From perspective of China’s modernization construction. International Review of Economics & Finance, 98, 103911. https://doi.org/10.1016/j.iref.2025.103911

122.

Yao

(2019). Do innovation subsidies policy improve the innovation performance of enterprises? Open Journal of Social Sciences, 7(2), 1. https://doi.org/10.4236/jss.2019.72001

123.

Fang

(2025). The impact of new-quality productivity on environmental pollution: Empirical evidence from China. Sustainability, 17(7), 3230. https://doi.org/10.3390/su17073230

124.

Zeng

Xie

(2023). Local sourcing embeddedness, manufacturing relocation, and firm attitudes toward the US-China trade war: A survey analysis of China-based MNC subsidiaries. Business and Politics, 25(2), 91. https://doi.org/10.1017/bap.2022.27

125.

Zhan

(2023). ESG and corporate performance: A review. SHS Web of Conferences, 169, 1064. https://doi.org/10.1051/shsconf/202316901064

126.

Zhang

Liu

(2022). Does ESG performance enhance financial flexibility? Evidence from China. Sustainability, 14(18), 11324. https://doi.org/10.3390/su141811324

127.

Zhang

H. L.

Zhang

J. Q.

(2024). Exploration of the internal logic and philosophical foundation of the sinicization of Marxism. Journal of Harbin Municipal Party School, 6, 19–26.

128.

Zhang

Pan

Janardhanan

M. N.

Patel

(2022). Relationship analysis between greenwashing and environmental performance. Environment Development and Sustainability, 25(8), 7927. https://doi.org/10.1007/s10668-022-02381-9

129.

Zhang

Chang

(2024). ESG Studies the impact on enterprise investment and financing decisions. Journal of Economics, Finance and Accounting Studies, 6, 121–131.

130.

Zhang

Chen

Wang

(2023). Policy pressure, ESG adoption and corporate innovation: Evidence from Chinese listed firms. Sustainability, 15(4), 2156.

131.

Zhang

Wang

(2023). Can SMEs benefit equally from supportive policies in China? PLoS ONE, 18(3), e0280253. https://doi.org/10.1371/journal.pone.0280253

132.

Zhang

Shen

(2025). The impact of new quality productivity on carbon emission intensity: Evidence from China. Frontiers in Earth Science, 13. https://doi.org/10.3389/feart.2025.1546703

133.

Zhang

(2021). Innovation input on enterprise value: Based on the moderating effect of ownership structure. Emerging Markets Finance and Trade, 58(4), 1078–1091. https://doi.org/10.1080/1540496x.2021.1928492

134.

Zhao

Yang

Zhang

Jin

Guo

(2025). How can new quality productivity decrease carbon emission intensity? Evidence from 30 provincial regions in China. Energy, 327, 136487. https://doi.org/10.1016/j.energy.2025.136487

135.

Zhao-zi

(2020). Productivity, public capital, and socialism with Chinese characteristics – A critique of the doctrine of incompatibility between capital and public ownership. China Political Economy, 3(1), 121. https://doi.org/10.1108/cpe-05-2020-0011

136.

Zhou

Caldecott

Harnett

Schumacher

(2020). The effect of firm-level ESG practices on macroeconomic performance. SSRN Electronic Journal. Advance online publication. https://doi.org/10.2139/ssrn.3618748

137.

Zhou

Lazonick

(2021). Industrial policy, intellectual property rights, and China’s economic transformation. Industrial and Corporate Change, 30(1), 1–27. https://doi.org/10.1093/icc/dtaa059

138.

Zhu

H. L.

Yang

K. Z.

(2024). The spillover effect of ESG performance on green innovation—Evidence from listed companies in China A-shares. Sustainability, 16(8), 3238. https://doi.org/10.3390/su16083238