Does Green Finance Drive New Quality Productive Forces? Evidence from Chinese Listed Companies

Abstract

Productivity has long been the fundamental driver of human social progress and national prosperity. Against the backdrop of technological advancement and social development, New Quality Productive Forces (NQPFs) have emerged as a new form of productivity, serving as a key focus for corporate transformation and upgrading as well as sustainable national development. Based on the panel data of 28,107 listed companies in China from 2011 to 2022, this study employs a three-way fixed-effects model to investigate the impact of green finance (GF) on corporate NQPFs. The main findings are as follows: First, GF exhibits a significant positive correlation with the enhancement of corporate NQPFs. Second, financing constraints and corporate social responsibility strengthen the empowering effect of GF on corporate NQPFs, while environmental law enforcement weakens this effect, reflecting a “synergistic dilemma” between government intervention and market mechanisms in promoting corporate NQPFs. Third, the effect of GF on corporate NQPFs shows significant heterogeneity depending on environmental and social risks, the nature of property rights, public attention, and firm size. These findings provide important insights for optimizing green finance policies and enhancing corporate productivity.

1. Introduction

In the context of globalization, countries around the world are actively seeking new engines of economic growth and sustainable competitive advantages, making productivity a central topic in fields such as economics and management. From classical labor productivity theories to contemporary multi-factor productivity frameworks, academia has continuously deepened its understanding of the nature and drivers of productivity growth [1]. With the rapid advancement of technology and the profound restructuring of the global economic landscape, traditional productivity theories are confronting new challenges as well as opportunities for development. Against this backdrop, China introduced the concept of “New Quality Productive Forces” (NQPFs) in 2023, emphasizing innovation and technological progress as the core drivers for sustainable and green development [2]. This idea has garnered widespread international attention and offers a new paradigm for corporate sustainable transformation and environmental governance [3]. Research on NQPFs will aid in understanding China’s innovation-driven development and offer insights for other countries to boost productivity in a changing global economy.

Green finance (GF) serves as a critical financial instrument for supporting corporate technological innovation and environmental improvement, playing a key role in promoting sustainable development [4]. By directing capital flows toward green projects and low-carbon technologies, it provides essential funding for enterprises to adopt greener production practices. However, in a market economy, the external incentives offered by GF may misalign with the intrinsic developmental objectives of firms, giving rise to the risk of “greenwashing”—where companies superficially comply with GF requirements without making substantive investments in environmental technology upgrades or innovation [5]. Against this backdrop, whether GF can genuinely stimulate corporate technological innovation, thereby enhancing NQPFs and generating an innovation compensation effect analogous to the Porter Hypothesis—where environmental regulation simultaneously incentivizes innovation and improves competitiveness—remains a crucial yet underexplored issue. Although existing studies have preliminarily examined the impact of GF on total factor productivity (TFP) [6], such work has largely been confined to technical efficiency perspectives, leaving the multi-dimensional mechanisms and pathways through which GF influences NQPFs systematically unexamined.

The innovations and theoretical contributions of this study can be summarized in the following three aspects. First, unlike the conventional practice of simply equating NQPFs with TFP, we systematically elaborate on NQPFs from the perspective of structural transformation and construct a multi-dimensional indicator system to quantify it at the micro-level of enterprises, thereby expanding the theoretical purview of NQPFs from a macroeconomic concept into a measurable firm-level construct. Second, by incorporating GF and corporate NQPFs into a unified analytical framework, we not only identify the key factors affecting corporate NQPFs from a financial perspective but also extend the theoretical boundaries of GF. This approach theoretically elaborates GF as a market-based mechanism that influences corporate decisions and capabilities, thereby clarifying its pathways for driving qualitative economic growth and providing a more comprehensive revelation of the impact pathways and underlying mechanisms through which GF enhances enterprise productivity. Third, in contrast to the majority of existing studies that neglect the micro-level examination of the synergistic effects of government and market, we empirically examine how environmental enforcement moderates GF’s effectiveness. This analysis provides a theoretical nuance to the discussion of government–market synergies by revealing that the interaction between market incentives and command-and-control regulations can be complex and even counteractive. By revealing the three influencing mechanisms of financing constraints, environmental enforcement, and social responsibility, our study contributes to a more refined theoretical understanding of policy mix design for cultivating NQPFs.

The remainder of this paper is structured as follows: Section 2 and Section 3 present the literature review and theoretical framework. Section 4 introduces the empirical methodology, variable definitions, and data sources. Section 5 reports and discusses the empirical findings. Finally, Section 6 concludes with conclusions, policy implications, and directions for future research.

2. Literature Review

2.1. Research on NQPFs

Productive forces are a continuously evolving dynamic process that measures the degree of social and economic progress in each historical period. Marx and Engels posited that productive forces encompass three elements: laborers, means of labor, and objects of labor [7]. The creative capacity of individuals in social production is an important factor to consider in the transformation of productive forces [8]. Furthermore, as one of the constituents of productive forces, the level of development of the means of labor can bring qualitative changes to productive forces. Therefore, scientific and technological advancements are particularly important, as they can enhance labor efficiency and thereby foster the development of productive forces [9].

New productivity is not defined by the existence of a certain technology or a certain worker, productive forces of today have many new characteristics that far exceed those of the era of Marx [10]. In September 2023, the concept of NQPFs was first proposed in China, and since then, this concept has been repeatedly emphasized and deeply discussed in national conferences. Unlike quantitative changes, NQPFs is a qualitative change in the production system, which often changes the composition and structure of the economic system [11]. Therefore, compared with TFP, NQPFs not only symbolizes the improvement in production efficiency, but also reflects the structural transformation of the basic paradigm of productivity. It emphasizes that scientific and technological innovation is the core driver of the systematic and qualitative transition of the economic system in the factor structure, industrial structure, and growth momentum.

Compared with Marxist Productivity Theory, NQPFs focuses on the qualitative change in productivity under the conditions of contemporary technological revolution, especially the integration and innovation of digital, green, and advanced elements [12]. It is a new productivity paradigm with distinctive characteristics of the times and historical stages. In addition, compared with the traditional productivity development mode that heavily relies on resource input and consumption, NQPFs plays a leading role in technological innovation. Its basic connotation is the enhancement and optimal combination of workers, labor materials, and labor objects [13]. While TFP and Green TFP primarily serve as indicators of economic efficiency and sustainable development, NQPFs encompasses not only green and high-efficiency dimensions but, more critically, emphasizes structural changes in the underlying drivers of efficiency improvement. These include the reconfiguration of production functions brought about by disruptive technologies, such as artificial intelligence, new energy, and biotechnology [14]. With broader connotations, deeper implications, and greater systematic and leading significance, NQPFs starts with the structural transformation of productivity itself, providing a fundamental technological path and industrial foundation for achieving high-quality development.

In summary, NQPFs can be understood as an advanced productivity with high-tech, high-efficiency, and high-quality characteristics, which is driven by scientific and technological innovation and formed through technological revolutionary breakthroughs, innovative allocation of factors, and in-depth industrial transformation and upgrading. Importantly, the concept of innovation here not only refers to technological innovation but also contains the synergistic effect of management innovation and institutional innovation, which is also one of the most important differences between NQPFs and TFP. Therefore, some scholars have incorporated TFP into their indicator system as part of NQPFs [15]. In addition, from a practical perspective, NQPFs represents the structural transformation of the productivity paradigm. It not only focuses on efficiency improvement and green development, but also emphasizes the reconstruction of the production function through emerging technologies such as digital intelligence and green low-carbon technologies to promote the economic system to achieve dynamic change [16], efficiency change, and quality change, which is the internal basis and key support for leading high-quality development.

As for the regional development level of NQPFs, the existing research mostly establishes the evaluation index system based on the three factors of productivity [17], or constructs the evaluation framework based on the different performance of productivity advancement [15]. On this basis, the entropy weight method is widely used in quantitative evaluation [3]. As for the importance of NQPFs, Zhang et al. proposed that technological innovation is conducive to promoting the development of NQPFs, while discussing government attention and urban green total factor productivity [18], but they have not yet conducted empirical analysis on this assertion. Liu et al. further explored the positive impact of industrial collaborative agglomeration on the high-quality development of the manufacturing industry, using NQPFs as an intermediary variable [2]. In addition, some scholars have discussed the importance of cultivating NQPFs from the perspectives of high-quality development in carbon cutting, pollution reduction, and economic growth [19]. However, existing research has yet to develop a systematic understanding of the influencing factors and transmission pathways of corporate-level NQPFs, which provides significant theoretical space for this study to expand upon.

2.2. Research on Corporate NQPFs

Corresponding to the fundamental connotation of NQPFs, corporate NQPFs can be regarded as the combination and enhancement of productivity factors within enterprises. First, enterprise employees can enhance work efficiency through the accumulation of knowledge and skills, as well as improvements in workforce structure and management models [20]. Second, compared to the physical land, capital, and labor, data elements with virtuality and mobility have broken the constraints of spatial distance and have become one of the most important factors in the development and production of enterprises in the digital age [21]. Third, enterprises related to strategic emerging industries and future industries are gradually becoming more extensive objects of labor under the backdrop of economic globalization. In this process, enterprises can not only introduce high and new technologies (such as artificial intelligence, biotechnology, and low-carbon technology), but also reconstruct production functions, business processes, and value networks through technology integration and collaborative innovation, and deeply embed new elements such as data, technology, and green energy into the value creation process [22]. Compared with the traditional total factor productivity of enterprises, the new quality productivity of enterprises not only focuses on the improvement in the efficiency of resource allocation, but also emphasizes the fundamental transformation of the growth mode through the transformation of underlying power and system structure [23]. Therefore, any leap in productivity that can be achieved through the qualitative evolution of corporate production factors belongs to the NQPFs of enterprises, and any factor that can present high-tech, high-performance, and high-quality characteristics of corporate production factors and affects corporate productivity is an influencing factor of corporate NQPFs.

In terms of research on factors that affect corporate NQPFs, Song constructed an evaluation index system for corporate NQPFs, verifying the positive impact of ESG (environment, social responsibility, governance) development on corporate NQPFs [23]. However, the majority of scholars’ research is based on the main body of corporate TFP, and they have carried out research on influencing factors from aspects such as digital transformation [24], fiscal and tax policies [25], venture capital [26], green credit policies [27], and internet development [28]. Although both corporate TFP and corporate NQPFs play important roles in promoting enterprise development and enhancing competitiveness, they are not the same. Corporate TFP focuses more on production efficiency and optimization of resource utilization, while corporate NQPFs focuses more on promoting qualitative improvements in productivity through technological innovation and industrial transformation. Therefore, corporate NQPFs will become a key area of research in the future and is crucial for promoting the high-quality development of enterprises.

2.3. Research on the Impact of GF on Corporate NQPFs

Enterprises are vital micro-entities for technological innovation, resource allocation, and job creation [29]. Corporate NQPFs represents an advanced form of productivity aligned with the new development philosophy. Consequently, the corresponding financial structure and scale must also adapt to the development of corporate NQPFs. Many scholars have conducted explorations at the corporate level based on the implementation effects of GF policies, believing that GF policies play a guiding and regulatory role in corporate environmental behavior. They not only encourage local enterprises to reduce carbon emissions [30], but also effectively enhance the capacity utilization efficiency of enterprises and promote the green transformation of production methods [31]. However, some scholars have only focused on the impact of a single green financial instrument, such as green credit or green insurance, on corporate innovation and green development [32,33].

At present, the theoretical and empirical analysis of corporate NQPFs is still in the exploratory stage, especially the impact of GF on corporate NQPFs, which remains to be further examined. Scholars have often focused their research on corporate TFP or green TFP [34]. Moreover, this beneficial effect is more pronounced in areas with higher green output [35]. Scholars also found empirically that green credit can enhance the promotion of industrial green TFP as companies strengthen their R&D efforts [36]. It is evident that GF is an important driving force in promoting green technological innovation, improving corporate TFP, and facilitating corporate upgrades [37]. Therefore, the development of GF should be further encouraged and guided to support the enhancement of corporate production, operation, and technological innovation capabilities.

3. Theoretical Analysis and Research Hypothesis

3.1. The Effect of GF on Corporate NQPFs

The corporate NQPFs is not only reflected in the increased output of research and development innovation, especially green technology innovation, but also involves the new combination of production factors and the new driving force for development formed by enterprises in the process of scientific and technological research and development. Existing research has confirmed that environmental sustainability has a significant positive effect on enterprise productivity [38]. Concurrently, GF exerts a pronounced influence on alleviating environmental degradation and realizing sustainable development at the national level [39]. Although GF is essentially a financial service that supports projects with environmental benefits, its positive enabling effect permeates every link of the enterprise innovation chain [40]. From financial security to innovation incentives, from research and development breakthroughs to supervision and guidance, GF can comprehensively facilitate the development of corporate NQPFs.

Innovation and research and development activities often require substantial upfront investment and are fraught with high risks and uncertainties. GF provides significant financial support for enterprise innovation and research through diversified financing channels and preferential financing policies [27]. On the one hand, the establishment of green credit and the issuance of green bonds have provided effective ways for enterprises to raise large-scale funds [41]. Large environmental protection enterprises have attracted numerous institutional and individual investors who focus on green investment through the issuance of green bonds. The funds raised have powerfully propelled the research and development process of green technologies in enterprises [37]. On the other hand, GF not only provides financial support for enterprises but also, through a series of incentive mechanisms, encourages enterprises to more actively engage in the research and application of green technologies [42]. Meanwhile, green technologies and diversified financial instruments are also key factors in reducing investors’ green investments [43]. In addition, the risk compensation mechanism of GF can provide financial compensation to enterprises when their research projects fail, thereby reducing their losses [44]. This guiding mechanism encourages enterprises to focus their innovation resources on technology research and development that meets green standards. Based on the above theoretical analysis, this paper proposes the following hypothesis.

Hypothesis 1 (H1):

GF is positively associated with corporate NQPFs.

3.2. Mechanism Analysis of the Impact of GF on Corporate NQPFs

The cultivation and enhancement of corporate NQPFs depend not only on the integration and optimization of internal enterprise resources but are also influenced by the interactive relationships between enterprises and external stakeholders. This process involves complex social networks, within which each actor shapes the enterprise’s innovation capabilities and competitive advantages. Therefore, based on the actual situation of corporate development in China, this paper analyzes the impact mechanism of GF on corporate NQPFs from the perspectives of the enterprise’s interactions with financial institutions, governments, consumers, and investors (Figure 1).

3.2.1. Financing Constraint Channel

For a long period, the profit-seeking nature of financial institutions and the high risk of technological innovation have exacerbated the financing difficulties for energy-saving and environmental protection enterprises [45]. GF makes it easier for enterprises and projects that meet green standards to obtain financial support from financial institutions, greatly reducing the difficulty of financing and financial risks [46]. However, the impact of GF on the financing constraints of different enterprises is heterogeneous and is influenced by factors such as enterprise size and property rights [47]. On one hand, the type of environmental and social risks faced by an enterprise will directly affect the banking and financing constraints it faces. (The “Key Evaluation Indicators for the Implementation of Green Credit” classify the grades of enterprise environmental and social risks into three categories: Class A, Class B, and Class C. Class A enterprises are those whose construction, production, and business activities may significantly change the original environmental status and the adverse environmental and social consequences are not easy to eliminate. Class B enterprises are those whose construction, production, and business activities will have adverse environmental and social consequences but are relatively easy to mitigate through mitigation measures. Class C enterprises are those whose construction, production, and business activities will not have significant adverse environmental and social consequences.) On the other hand, enterprises with different ownership structures also show different tendencies in innovation activities when facing innovation pressures [48]. Generally, private and foreign-funded enterprises often cannot obtain a large market share in the heavy chemical industry due to capacity restrictions and national security reasons, while state-owned enterprises in China are often dominated by heavy industry, and their heavy asset model has intensified the pressure for carbon reduction and technological innovation, and credit incentives are not obvious for their innovative effects [49]. Non-state-owned enterprises often have better performance in promoting productivity improvement through R&D [50]. Therefore, the nature of property rights of the enterprise also affects its green credit restrictions, thereby affecting financing constraints. Based on this, this paper proposes the following hypothesis.

Hypothesis 2 (H2):

Alleviating financing constraints enhances the positive correlation between GF and corporate NQPFs.

3.2.2. Environmental Law Enforcement Channel

The government and the market often regulate the operation of the market economy through the “visible hand” and the “invisible hand” [51]. Despite both the government and the market sharing the common goal of promoting corporate green transformation and enhancing NQPFs, in certain circumstances, overly stringent environmental enforcement may to some extent be detrimental to enterprises’ cooperation and green technological innovation [52]. When the government intensifies its environmental law enforcement, enterprises that do not meet environmental standards or have potential environmental risks may face stricter regulation and penalties, which could lead to greater operational pressure, economic costs, and market risks in the short term [53]. In such cases, if the support from GF does not keep pace with the increased stringency of government environmental enforcement, enterprises facing stricter environmental regulations may feel financial and resource constraints, potentially allocating more funds to meet environmental regulatory demands and reducing the investment in technology research and development with green funds [54]. Therefore, the government and the market may fall into a “synergy dilemma”, whereby environmental policies and market instruments fail to form a concerted effort during implementation and may even suppress corporate innovation vitality. However, this does not imply that the government should weaken environmental enforcement to accommodate the needs of GF; instead, there should be enhanced cooperation and policy coordination with green financial institutions to maximize the role of GF in promoting corporate NQPFs. Accordingly, this paper proposes the following hypothesis.

Hypothesis 3 (H3):

Excessive environmental enforcement intensity may weaken the positive correlation between GF and corporate NQPFs.

3.2.3. Social Responsibility Channel

As public awareness of environmental protection increases, investors are paying more attention to the environmental performance of companies [55]. Corporate ESG principles are highly aligned with the goals of GF, both emphasizing the reduction in adverse environmental impacts while fostering economic development [56]. Specifically, corporate social responsibility promotes the allocation of resources for green technological innovation within enterprises and enhances the motivation mechanism for green development by encouraging innovation in green products, processes, and procedures, thereby having a positive impact on corporate performance [42]. Therefore, the enterprise’s social responsibility is the most important and direct mechanism affecting its production decisions and NQPFs. Additionally, ESG is also a key factor considered by GF in assessing corporate risk management [57]. By evaluating the ESG performance of companies, financial institutions can better identify and manage potential environmental risks, ensuring that funds are directed towards low-carbon, energy-saving, and emissions-reduction projects. This not only benefits companies in optimizing their financing structure but also helps to accelerate technological innovation and improves the efficiency of resource utilization, promoting sustainable development. Consequently, this paper proposes the following hypothesis.

Hypothesis 4 (H4):

Corporate ESG promotes the positive correlation between GF and corporate NQPFs.

4. Research Design

4.1. Model Specification

We constructed the following three-way fixed-effects model to test the impact of GF on corporate NQPFs:

$$NQPF{s}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\alpha }_2{X}_{it}+{\varphi }_{in}+{\delta }_t+{\lambda }_m+{\epsilon }_{it}$$

(1)

In the model, NQPFs_it represents the level of NQPFs of corporate i in year t, and GF_it represents the development level of GF in the city where firm i is located in year t. To control for other economic characteristics that affect corporate NQPFs, following the practices in the existing literature [4,40], we introduce a series of control variables X_it, including the proportion of independent directors (Ind), firm age (Age), cash ratio (Cash), equity concentration (Owner), debt-to-asset ratio (Debt), return on assets (Roas), the dual position of chairman and CEO (Dual), and audit opinion (Opin). These variables are selected based on their established relevance on corporate productivity and financial governance, as they capture key dimensions of corporate governance, financial structure, and operational performance that may confound the relationship between GF and corporate NQPFs. Furthermore, to obtain more robust regression results, we employ a three-dimensional fixed-effects regression method that controls for firm (${\varphi }_{in}$), year (${\delta }_t$), and city (${\lambda }_m$); this approach controls for time-invariant heterogeneity across sectors and regions, as well as common temporal shocks. ${\epsilon }_{it}$ is the random disturbance term; ${\alpha }_0$ is the constant term; and ${\alpha }_1$ is the parameter to be estimated, characterizing the impact of GF on corporate NQPFs. To account for the potential correlation of error terms within city–year clusters, all standard errors are clustered at the city–year level.

Meanwhile, we acknowledge potential endogeneity issues, such as reverse causality—where firms with higher corporate NQPFs may locate in regions with better GF development—and unobserved confounding factors. Although the three-way fixed-effects model alleviates some of these concerns, we further address them in subsequent analyses through instrumental variable (IV) estimation, double difference method, and other robustness tests.

To further analyze the mechanism by which GF affects corporate NQPFs, we add three moderating variables (SA, Punish, and ESG) to model (1) as interaction terms with the explanatory variable (GF),

$$NQPF{s}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\beta }_{1}S{A}_{it}+{\beta }_{2}G{F}_{it}\times S{A}_{it}+{\alpha }_2{X}_{it}+{\varphi }_{in}+{\delta }_t+{\lambda }_m+{\epsilon }_{it}$$

(2)

$$NQPF{s}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\beta }_{1}Punis{h}_{it}+{\beta }_{2}G{F}_{it}\times Punis{h}_{it}+{\alpha }_2{X}_{it}+{\varphi }_{in}+{\delta }_t+{\lambda }_m+{\epsilon }_{it}$$

(3)

$$NQPF{s}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\beta }_{1}ES{G}_{it}+{\beta }_{2}G{F}_{it}\times ES{G}_{it}+{\alpha }_2{X}_{it}+{\varphi }_{in}+{\delta }_t+{\lambda }_m+{\epsilon }_{it}$$

(4)

where SA represents financing constraints, Punish measures environmental penalties, and ESG is for corporate social responsibility. The significance and sign of the coefficients ${\beta }_2$ reflect the impact of the corresponding moderating effects.

4.2. Description of Variables

4.2.1. Explained Variable

Corporate NQPFs essentially reflects the comprehensive ability of enterprises to realize factor reconstruction, efficiency improvement, and structure optimization under the development mode dominated by scientific and technological innovation. Based on the theoretical framework of the three elements of productive forces in Marxist political economy, and incorporating the contemporary connotations of NQPFs under modern techno-economic conditions [23], this paper constructs an indicator system from three dimensions: new quality labor force, new quality labor object, and new quality labor tools. Subsequently, we employed the entropy weight method (Appendix A) to assign weights to the relevant indicators.

Table 1. Corporate NQPFs indicator system.

Indicator	Primary Dimension	Secondary Indicator	Description
Corporate NQPFs	New quality labor force	Proportion of R&D staff salary	(Salary in R&D expenses)/operating income
		Proportion of R&D personnel	Number of R&D personnel/total number of employees
		The proportion of highly educated personnel	Number of employees with master’s and doctoral degrees/total number of employees
	New quality labor object	Proportion of fixed assets	Net fixed assets/total assets
		Percentage of manufacturing expenses	(Subtotal of cash outflows from operating activities + depreciation of fixed assets + amortization of intangible assets + provision for asset impairment–cash paid for goods and services purchased–cash paid to and for employees)/(Subtotal of cash outflows from operating activities + depreciation of fixed assets + amortization of intangible assets + provision for asset impairment)
	New quality labor tools	Proportion of R&D depreciation and amortization	(Depreciation and amortization in R&D expenses)/operating income
		Proportion of R&D leasing expenses	(Leasing expenses in R&D expenses) /operating income
		Proportion of direct R&D investment	(Direct investment in R&D expenses) /operating income
		Proportion of intangible assets	Intangible assets/total assets
		Total asset turnover	Operating revenue/average total assets
		Reciprocal of equity multiplier	Owner’s equity/total assets

presents the details.

Regarding the new quality labor force, this dimension measures the upgrading and knowledge intensity of human resources within enterprises, emphasizing the role of innovative talent in value creation. It is proxied by the ratio of R&D personnel salaries (reflecting investment intensity), the proportion of R&D personnel (indicating talent density), and the share of highly educated employees (representing the reserve of high-skilled talent).

As for new quality labor object, their upgrading embodies the intelligent and green transformation of production materials. This dimension focuses on the asset structure closely related to innovation activities in resource allocation. In addition to the proportion of fixed assets, this paper also considers that enterprises embodying NQPFs are predominantly in high-precision technology sectors such as advanced equipment manufacturing. These enterprises largely rely on high-end machinery and instruments for production, and thus their manufacturing cost ratio tends to be higher than that of other firms. Accordingly, the manufacturing cost ratio is also incorporated into the indicator selection.

The dimension of new quality labor tools, reflecting how enterprises deploy technology in production, emphasizes both R&D investment intensity and asset utilization efficiency. To measure R&D investment, this study uses the ratio of direct R&D input, depreciation and amortization, and leasing expenses to operating income, along with the proportion of intangible assets. Asset utilization efficiency is captured by total asset turnover and the inverse of the equity multiplier—the latter serving as an inverse proxy for financial risk, where a higher value indicates lower risk and thus higher productivity.

4.2.2. Explanatory Variable

This paper takes into account the consideration of green credit, green investment, green insurance, green bonds, green support, green funds, and green equity, using the entropy weight method (Appendix A) to measure the GF development index of cities [58].

Table 2. GF indicator system.

Indicator	Primary Dimension	Secondary Indicator	Description
GF	Green credit	Proportion of environmental project credit	Total credit amount for environmental protection projects in the city/total credit amount in the province
	Green investment	Investment intensity in environmental pollution control	Investment in environmental pollution control/GDP
	Green insurance	Promotion level of environmental pollution liability insurance	Environmental pollution liability insurance income/total premium income
	Green bond	Development level of green bonds	Total issuance of green bonds/total issuance of all bonds
	Green support	Intensity of fiscal environmental protection expenditure	Financial environmental protection expenditure/general budget expenditure
	Green fund	Development of green funds	Total market value of green funds/total market value of all funds
	Green equity	Development of green rights and interests	Total amount of carbon trading, energy use rights trading, emissions trading/equity market trading

shows the specific indicators and calculation methods.

4.2.3. Moderating Variables

This paper introduces three moderating variables to examine the contextual mechanisms through which GF influences corporate NQPFs. Moderating variables help clarify under what conditions or for which groups the effect of GF is strengthened or weakened, offering deeper insight into the boundary conditions of GF’s role.

The first moderating variable is financing constraints. Financing constraints reflect the difficulties firms face in accessing external capital, which may inhibit their ability to invest in innovation and green transition. Scholars often employ single indicators or comprehensive indices to gauge the extent of these constraints [59]. In this paper, we adopt the SA index, which offers advantages in mitigating endogeneity concerns, as it relies exclusively on time-invariant and externally determined firm characteristics [60]. SA index is a linear combination of size, the square of size, and corporate age. The index takes negative values; thus, a larger absolute value indicates more severe financing constraints. The calculation formula is as follows [61]:

$$SA=-0.737\times Size+0.043\times Siz{e}^2-0.04\times Age$$

(5)

The second moderating variable is Punish, which measures environmental penalties. This paper utilizes the municipal-level environmental protection penalty data from the Peking University Legal Database (the Peking University Legal Database, also known as “PKULaw”, is a comprehensive online platform providing access to Chinese legal resources, including laws, regulations, judicial interpretations, and case laws) spanning 2011 to 2022 to measure the intensity of local governments’ punishment for environmental violations based on the environmental penalty cases in various regions. Concurrently, acknowledging that the punitive strength of local governments for environmental offenses is subject to the influence of governmental attention to environmental protection, this study adopts the median value of the proportion of 15 environmental protection keywords in the work reports of prefecture-level city governments as the basis for group division (the 15 environmental protection keywords include environmental protection, eco-friendly, pollution, energy consumption, emissions reduction, pollution discharge, ecology, green, low-carbon, air, chemical oxygen demand, carbon dioxide, sulfur dioxide, PM10, and PM2.5). A group with a keyword frequency ratio greater than the median is considered to have a higher frequency of environmental protection keywords, indicating a higher level of government attention to environmental protection or a more stringent degree of environmental regulation, and vice versa. This variable helps to test whether stronger regulatory pressure amplifies GF’s impact on corporate NQPFs.

The third moderator is corporate social responsibility, proxied by ESG performance. ESG offers a multi-dimensional assessment of a firm’s environmental, social, and governance conduct. Although ESG rating systems emerged later in China than in Western markets, the Huazheng ESG rating is among the most comprehensive domestic frameworks. It classifies firms into nine tiers (C to AAA), which are assigned numerical scores from one to nine, with higher values indicating better ESG performance. This paper uses the annual average of these ratings to measure corporate social responsibility.

Additionally, recognizing that public scrutiny and firm size may influence corporate social responsibility, the study incorporates the Baidu Search Index (reflecting public attention to a firm’s environmental conduct) and operating income (as a proxy for firm size). Both are split into high/low groups based on median values, enabling group comparisons. This facilitates a more nuanced analysis of how external monitoring and firm resources condition the role of ESG.

4.3. Sample Selection and Data Sources

This study examines A-share listed companies from 2011 to 2022. Sample selection followed these steps: (1) removal of financially distressed firms (ST, *ST, PT), (2) exclusion of observations with missing key variables, (3) elimination of companies with extreme financial conditions, such as negative or exceeding-1 asset–liability ratios, (4) exclusion of financial and insurance firms, and (5) winsorization of continuous variables at the 1% level. The final sample consists of 28,107 firm–year observations.

The data used in this study at the corporate level are all derived from the Wind database, which is a comprehensive financial data platform that provides detailed information across various financial sectors. City-level data were obtained from the National Bureau of Statistics, the Ministry of Science and Technology, the People’s Bank of China, and various authoritative statistical yearbooks, including national and provincial statistical yearbooks, environmental condition bulletins, and some specialized statistical yearbooks, such as China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, China Financial Yearbook, China Agricultural Statistical Yearbook, China Industrial Statistical Yearbook, China Tertiary Industry Statistical Yearbook. Environmental penalty data were obtained from the Peking University Legal Database.

Table 3. Descriptive statistics.

Variable	Observations	Mean	Std. Dev.	Min	Max
NQPFs	28,107	5.005	2.529	0.031	32.467
GF	28,107	0.406	0.123	0.086	0.651
SA	28,107	−3.832	0.273	−5.358	−1.805
ESG	28,107	4.086	1.066	1	8
Punish	28,107	0.94	1.584	0	12.737
Ind	28,107	0.379	0.062	0.167	1
Age	28,107	2.344	0.7	0.693	3.497
Cash	28,107	0.008	0.022	0	1.675
Owner	28,107	0.565	0.153	0.013	1.012
Debt	28,107	0.437	0.207	0.008	0.998
Roas	28,107	0.033	0.131	−2.834	10.401
Dual	28,107	0.258	0.437	0	1
Opin	28,107	0.969	0.172	0	1

presents the descriptive statistics of the main variables. For detailed definitions and sources of all variables, see Appendix B.

5. Empirical Results

5.1. Benchmark Regression

Table 4. Benchmark regression results.

	NQPFs
	(1)	(2)	(3)	(4)
GF	1.0046 ***	9.5826 ***	0.9267 ***	1.3655 ***
	(2.8080)	(18.3652)	(2.6819)	(2.9836)
Ind			1.2817 ***	−0.0050
			(5.2282)	(−0.0212)
Age			0.1929 ***	1.1800 ***
			(4.0233)	(20.0047)
Cash			−7.7146 ***	−8.9396 ***
			(−4.3805)	(−3.8756)
Owner			−0.3740 ***	−0.0193
			(−2.9659)	(−0.1248)
Debt			−1.1672 ***	−0.4354 ***
			(−9.7143)	(−3.1791)
Roas			−0.4747 ***	−0.4397 ***
			(−3.0714)	(−3.8441)
Dual			−0.0997 ***	0.0141
			(−2.7508)	(0.4877)
Opin			0.0535	−0.1619 **
			(0.5707)	(−2.2773)
Constant	4.5977 ***	1.1183 ***	4.4671 ***	2.1318 ***
	(39.6679)	(4.9893)	(21.4387)	(8.7173)
Firm FE	No	Yes	No	Yes
Year FE	No	Yes	No	Yes
City FE	No	Yes	No	Yes
Observations	28,107	28,104	28,107	28,104
R-squared	0.0024	0.7286	0.0149	0.7505

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. Significance levels are indicated by **, and ***, corresponding to 5%, and 1%, respectively. FE denotes fixed effects, the same applies hereafter.

presents the baseline regression results examining the relationship between GF and corporate NQPFs. Whether control variables or fixed effects are included, the estimated coefficient of GF is significantly positive at the 1% level, supporting Hypothesis H1 that GF is positively correlated with corporate NQPFs. In the specification that includes industry, year, and city fixed effects, all control variables, and cluster standard errors at the city–year level (Column 4), the coefficient for GF is 1.3655. This indicates that a one-unit increase in the city-level GF index is associated with an average increase of approximately 1.37 units in corporate NQPFs. This finding suggests that GF provides robust financial support for corporate green innovation and quality–efficiency enhancement, incentivizing firms to increase R&D investment, adopt more energy-efficient production technologies, attract and cultivate high-quality talent, and promote the development of strategic emerging industries and future-oriented sectors [62], thereby injecting strong momentum into the cultivation and development of corporate NQPFs.

The regression results for the control variables offer additional insights into the organizational and governance factors influencing corporate NQPFs. Specifically, a higher proportion of independent directors (Ind) is positively associated with NQPFs, suggesting that board independence enhances oversight and strategic guidance, thereby facilitating resource allocation toward innovation and productivity-enhancing activities. Similarly, longer firm age (Age) correlates positively with NQPFs, likely reflecting accumulated experience, established routines, and greater access to resources that support sustained productivity growth. In contrast, a higher cash ratio (Cash) exhibits a negative relationship with NQPFs, which may indicate risk-averse financial management that prioritizes liquidity over strategic investments in green technologies or structural upgrades. Furthermore, the negative association between return on assets (Roas) and NQPFs implies that firms with strong short-term profitability may lack the impetus to pursue disruptive innovation and structural transformation, potentially due to satisfaction with existing business models or path dependence, thereby hindering the cultivation of long-term-oriented new quality productivity.

5.2. Robustness Analysis

Firstly, this study conducts a robustness test by using the alternative explained and explanatory variable. Because both corporate NQPFs and TFP are important indicators for measuring production efficiency, this paper uses TFP as the alternative explained variable. In terms of measurement method, the OP method overcomes simultaneity bias in production function estimation by using firm investment as a proxy for unobserved productivity shocks, while the LP method instead uses intermediate inputs as a proxy, which applies more broadly to firms with zero investment periods. At the same time, the paper considers that finance can extend its service terminals through information networks and has the characteristics of cross-regional services. This paper uses provincial green credit (measured by the proportion of interest expenses in the six major high-energy-consuming industries, with data sourced from the China Industrial Statistical Yearbook) as the explanatory variable to measure the level of regional GF development [63]. In addition, this paper also uses principal component analysis to re-measure GF.

Table 5. Robustness test of replaced explained and explanatory variables.

	(1) TFP_OP	(2) TFP_LP	(3) NQPFs	(3) NQPFs
GF	1.5179 ***	1.5144 ***
	(11.6548)	(13.0556)
Gcred			−1.9857 ***
			(−5.4871)
GF2				0.0045 *
				(1.9127)
Constant	5.1250 ***	5.6559 ***	3.3304 ***	2.2962 ***
	(74.3095)	(84.6539)	(14.5714)	(9.9876)
Control variables	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Observations	28,104	28,104	28,104	28,104
R-squared	0.8570	0.8660	0.7520	0.7504

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. significance levels are indicated by * and *** corresponding to the 10%, and 1%, respectively.

presents the regression results for corporate TFP using the OP and LP methods in columns (1) and (2), respectively. Columns (3) and (4) report the regression results using provincial green credit and an alternative measure of GF, respectively. It is observed that the estimated coefficient of GF exerts a statistically significant positive effect on TFP at the 1% level. Moreover, the coefficient of GF measured by the negative indicator (Gcred) is significantly negative at the 1% level, while that of the alternative GF measure (GF2) is significantly positive; both indicate a positive relationship between GF and corporate NQPFs. These results robustly confirm the findings of our baseline regression.

Secondly, this study conducts three robustness tests. The first test considers the severe impact of the COVID-19 pandemic by deleting samples from the 2020–2022 period. The second test excludes the five provinces and eight areas (the specific areas of the first batch of green finance reform and innovation pilot zones are Huzhou City and Quzhou City in Zhejiang Province, Guian New District in Guizhou Province, Hami City, Changji Prefecture, and Karamay City in Xinjiang Uygur Autonomous Region, Guangzhou City in Guangdong Province, and Ganjiang New District in Jiangxi Province) designated as the first batch of GF reform and innovation pilot zones in 2017. The third test excludes financial center cities: specifically, based on the authoritative China Financial Center Index (CFCI), we identified and removed all 31 cities classified as national or regional financial centers (including those in the northeast, northern coastal, eastern coastal, southern coastal, central, and western regions). These robustness tests aim to mitigate potential inaccuracies caused by abnormal years (2020–2022), sample enterprises in policy pilot zones, and the unique characteristics of major financial hubs.

Table 6. Robustness test of deleting abnormal years and cities.

	NQPFs
	(1) Exclude Abnormal Years	(2) Exclude Pilot Zone	(3) Exclude Financial Center Cities
GF	1.7641 ***	1.3720 ***	1.3178 ***
	(2.6195)	(2.9191)	(2.5804)
Constant	1.2361 ***	1.9790 ***	2.0891 ***
	(4.3258)	(7.7889)	(8.0072)
Control variables	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
City FE	Yes	Yes	Yes
Observations	20,274	26,820	11,470
R-squared	0.7573	0.7524	0.7062

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. *** denotes significance at the 1% level.

presents the results. We observed that the GF coefficients remain positive and statistically significant across all specifications. This confirms that the positive relationship between GF and corporate NQPFs is robust and not driven by these special samples, thereby strengthening the generalizability of our findings.

Thirdly, to mitigate possible endogeneity issues arising from omitted variable bias and sample selection, this study employs the lagged one-period values of the explanatory variables as instrumental variables for the endogeneity test [64]. The results of the two-stage least squares (2SLS) regression are presented in

Table 7. Robustness test using instrumental variable.

	GF	NQPFs	GF	NQPFs
	(1) First Stage	(2) Second Stage	(3) First Stage	(4) Second Stage
GF		0.7114 ***		0.4454 ***
		(4.9145)		(2.8905)
L1.GF	0.2892 ***
	(7.6793)
L2.GF			0.2566 ***
			(7.3640)
Constant	0.1142 ***	4.9032 ***	0.1066 ***	5.4047 ***
	(9.6012)	(27.7416)	(9.0797)	(29.2790)
Control variables	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Kleibergen–Paap rk LM	7800.432 ***		6935.694 ***
Kleibergen–Paap rk Wald F	300,000		230,000
	{16.38}		{16.38}
Observations	25,344	25,348	22,701	22,704
R-squared	0.9577	0.0109	0.9612	0.0110

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. *** denotes significance at the 1% level. The { } value is the critical value of the Stock–Yogo weak identification test at the 10% level.

. Columns (1) and (2) report the first-stage and second-stage results using the one-period lagged value of GF (L1.GF) as the instrument, while Columns (3) and (4) present the results using its two-period lagged value (L2.GF).

In the first stage, the coefficients on the instrumental variables are positive and statistically significant at the 1% level, indicating a strong positive correlation between the instrument and the endogenous variable GF. The Kleibergen–Paap rk LM statistics (7800.432 and 6935.694) are significant at the 1% level, rejecting the null hypothesis of under-identification and confirming the relevance of the instruments. Furthermore, the exceptionally high Kleibergen–Paap rk Wald F statistics (300,000 and 230,000) far exceed the critical value of 16.38, providing robust evidence against the weak instrument problem.

In the second stage, the estimated coefficients of GF remain positive and statistically significant at the 1% level, indicating that GF has a significant promoting effect on corporate NQPFs after controlling for endogeneity. The consistency of these results with our baseline findings reinforces the conclusion that the positive impact of GF on corporate NQPFs is causal and not driven by endogenous selection.

Lastly, this study uses the double difference method to further investigate the causal effect of the establishment of green financial reform and innovation pilot zone (2017) on corporate NQPFs. As shown in

Table 8. Difference-in-differences estimates of the green finance pilot policy.

	NQPFs
	(1)	(2)	(3)	(4)
DID	0.4342 ***	0.7820 ***	0.4030 ***	0.6199 ***
	(5.7747)	(11.2656)	(5.3644)	(9.0026)
Constant	4.9891 ***	4.9763 ***	5.3337 ***	6.5118 ***
	(323.9786)	(545.2074)	(37.2550)	(41.6953)
Firm FE	No	Yes	No	Yes
Year FE	No	Yes	No	Yes
City FE	No	Yes	No	Yes
Observations	28,107	28,104	28,107	28,104
R-squared	0.0010	0.7054	0.0114	0.7239

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors (firm–year level). *** denotes significance at the 1% level.

, the coefficient of the core explanatory variable DID (treat × post) is significantly positive at the 1% level, whether the control variables are included or not, indicating that the establishment of the pilot area has significantly promoted the corporate NQPFs, and hypothesis 1 has been verified.

In order to test the parallel trend hypothesis, this paper further uses the event study method to verify it. The year before the implementation of the policy is taken as the benchmark period. The results of parallel trend test are shown in Figure 2. There is no significant difference between the estimated coefficients of dummy variables in each period before the implementation of the policy and 0, indicating that the experimental group and the control group met the conditions of parallel trend before the policy impact.

Further analysis of the dynamic effect of the policy found that the impact of the establishment of the GF pilot zone on corporate NQPFs was mainly short-term. After the implementation of the policy, it increased slightly and was statistically significantly different from 0, but after the fourth year, the effect gradually weakened and returned to the non-significant level. This dynamic change may be due to the strong signal release and demonstration effect at the initial stage of the policy, which quickly stimulated the innovation willingness of enterprises. However, over time, the marginal decline in the efficiency of policy implementation, the failure of supporting measures to follow up in time, the “green floating” behavior caused by some enterprises’ strategic response, and the dilution effect of market competition on innovation funds may have jointly weakened the continuous incentive force of the policy.

5.3. Mechanism Analysis

5.3.1. Financing Constraint

Based on the theoretical analysis, it is evident that corporate financing constraints are influenced by the degree of green credit restrictions and the nature of property rights. We match the industry classification of listed companies with the environmental and social risk types (A, B, C) outlined in the “Key Evaluation Indicators for the Implementation of Green Credit” (industries included in Class A companies encompass nuclear power generation, hydropower generation, water conservancy and river port engineering construction, coal mining and washing, oil and natural gas extraction, ferrous metal ore mining and dressing, non-ferrous metal ore mining and dressing, non-metallic mineral mining and dressing, and other mining industries, which totals up to nine sectors; Class B companies are involved in industries such as oil processing, pharmaceutical manufacturing, railway transportation, rubber and plastic products, and pipeline transportation, comprising 25 industries; companies not classified as A or B are identified as Class C companies), where Class A and B companies are considered the green credit high-restricted group, and Class C is considered as the general-restricted group. Furthermore, the sample is also divided into state-owned enterprises and non-state-owned enterprises based on the nature of the property rights.

Table 9. Mechanism test on financing constraints.

	NQPFs
	(1) High-Restricted Group	(2) General-Restricted Group	(3) State-Owned Enterprise	(4) Non-State-Owned Enterprises	(5) Full Sample
GF	0.2731	2.1623 ***	1.0432 *	0.8941	1.5368 ***
	(0.5986)	(3.8798)	(1.9009)	(1.3654)	(2.9369)
SA					−0.7937 ***
					(−5.5590)
GF × SA					4.6357 ***
					(5.1312)
Constant	2.1298 ***	2.1192 ***	1.2393 ***	2.9023 ***	−0.2680
	(7.1385)	(6.9160)	(3.3547)	(9.2179)	(−0.5589)
Control variables	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes
Observations	9560	18,457	11,090	16,819	28,104
R-squared	0.7075	0.7791	0.7885	0.7757	0.7520
Chow Test	9.8 ***		209.20 ***

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. Significance levels are indicated by * and ***, corresponding to the 10% and 1%, respectively.

presents the results. The coefficients in Columns (2) and (3) are significantly positive at the 1% and 10% levels, respectively, while the coefficients in Columns (1) and (4) are not statistically significant. These findings indicate that the positive impact of GF on corporate NQPFs is more pronounced for firms with lower financing constraints and for state-owned enterprises. Column (5) reports the regression results examining the moderating effect of the SA index, which measures the level of financing constraints based on the full sample of firms using Model (7). We observe that the estimated coefficient of SA and the interaction term between GF and SA are significantly positive at the 1% levels, respectively. Given that SA is a negative indicator (where larger, less negative values indicate milder constraints), the significantly positive coefficient of the interaction term suggests that financing constraints positively moderate the relationship between GF and corporate NQPFs. Specifically, for enterprises facing less severe financing constraints (i.e., those with larger SA values), GF exhibits a stronger promoting effect on corporate NQPFs.

These results demonstrate that alleviating financing constraints can enhance the positive relationship between GF and corporate NQPFs, thereby confirming Hypothesis H2. Furthermore, the significance of the Chow test (in the context of panel regression analysis, the Chow test is employed to examine whether there are significant differences in model parameters across different groups) indicates that higher environmental and social risks faced by firms are associated with stronger financing constraints, which impedes the cultivation of corporate NQPFs. Concurrently, the statistically insignificant coefficient of GF in Column (4) reveals the challenges faced by non-state-owned enterprises during the green transition. Influenced by their industrial attributes, non-state-owned enterprises are often smaller in scale. Due to a comparative lack of stable collateral, established credit histories, and implicit government guarantees, many non-state-owned enterprises are unable to translate policy incentives into innovation momentum as effectively or efficiently as their state-owned counterparts. Therefore, achieving the green transformation of non-state-owned enterprises requires more tailored supportive policies rather than relying solely on conventional credit constraints or incentives.

5.3.2. Environmental Law Enforcement

The intensity of government environmental enforcement is primarily influenced by the focus of the local government on environmental protection. Therefore, we have statistically analyzed the proportion of the frequency of words related to environmental protection in local government work reports, using it as an indicator to measure government attention to the environment. The sample companies are divided into two groups, high government attention and low government attention, based on the median, and the base model is estimated separately for each group.

Table 10. Mechanism test on environmental law enforcement.

	NQPFs
	(1) High-Government-Attention Group	(2) Low-Government-Attention Group	(3) Full Sample
GF	2.4597 ***	0.6263	0.8287 *
	(3.5294)	(0.9707)	(1.7638)
Punish			0.1225 ***
			(7.4935)
GF × Punish			−0.4294 ***
			(−4.1185)
Constant	1.4612 ***	2.7611 ***	2.5078 ***
	(3.9054)	(8.7210)	(10.9166)
Control variables	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
City FE	Yes	Yes	Yes
Observations	14,575	13,162	28,104
R-squared	0.7859	0.7906	0.7520
Chow Test	2.12

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. Significance levels are indicated by * and ***, corresponding to the 10% and 1%, respectively.

shows that the regression coefficients for GF are significantly positive in Column (1). It shows that the government’s environmental attention will promote the positive relationship between GF and corporate NQPFs. Furthermore, after introducing the interaction term of Punish and GF, it can be seen that the coefficient of the interaction term is −0.4294, and it has passed the significance test at the 1% level, indicating that there is a non-synergistic relationship between GF and Punish in influencing corporate NQPFs. Thus, Hypothesis H3 is supported.

Specifically, the government and the market often regulate the operation of the market economy through the “visible hand” and the “invisible hand”. Although Punish and GF have positive impacts on corporate NQPFs at different stages or aspects, their impact on corporate NQPFs is not a simple addition; instead, there is a certain degree of offset or weakening. As a deterrent governance tool, environmental administrative punishment may urge enterprises’ compliance when acting alone, but its joint implementation with GF shows a non-synergistic effect, which means that the compliance cost and operating pressure brought by punishment may crowd out the resources and energy of enterprises for long-term innovation. Enterprises are more inclined to adopt evasive strategies to avoid risks, rather than use funds for technological innovation and efficiency improvement, thus weakening the incentive effect of GF on corporate NQPFs.

5.3.3. Social Responsibility

We conduct a mechanism test on two major factors that affect corporate social responsibility: the public’s environmental concern and the scale of the enterprise. Following the literature [65], we use the Baidu Index of residents’ searches for the keywords “environmental pollution” and “smog” to reflect the public’s environmental concern indicator for listed companies. Additionally, we use the enterprise’s operating income as a measure of the scale of the enterprise, dividing companies into two groups based on the median.

Table 11. Mechanism test on social responsibility.

	NQPFs
	(1) High-Concern Group	(2) Low- Concern Group	(3) Large Enterprises	(4) Small- and Medium-Sized Enterprise	(5) Full Sample	(6) Full Sample
GF	1.2803 *	1.4420 ***	1.5537 ***	0.7883	1.3667 ***	1.3297 **
	(1.8583)	(3.0088)	(2.7935)	(1.3848)	(2.9882)	(2.5283)
ESG					−0.0169
					(−1.2017)
GF × ESG					0.2442 **
					(2.0417)
EID						−0.0022
						(−0.4064)
GF × EID						−0.1366 ***
						(−3.0296)
Constant	2.3324 ***	2.0214 ***	2.2443 ***	2.6607 ***	2.1985 ***	1.9358 ***
	(6.1478)	(8.2055)	(7.2901)	(8.1033)	(8.6005)	(7.2546)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	14,660	13,378	13,957	13,945	28,104	25,529
R-squared	0.7777	0.7146	0.8118	0.7531	0.7505	0.7541
Chow Test	124.61 ***		27.02 ***

Notes: The values in ( ) represent t-values adjusted using clustered robust standard errors. Significance levels are indicated by *, **, and ***, corresponding to the 10%, 5%, and 1%, respectively.

presents the findings. Comparing Columns (1) to (4), we concluded that the positive relationship between GF and corporate NQPFs is stronger in regions with lower public environmental concern and within larger enterprises. Moreover, both the estimated coefficients of ESG and the interaction term between GF and ESG are positive and statistically significant at the 1% level, confirming the significant role of corporate social responsibility in promoting the impact of GF on NQPFs, thereby supporting Hypothesis H4.

Specifically, the significant positive correlation in low-attention regions may stem from the fact that in areas with weaker external supervision, green finance serves as a more pivotal market-based incentive, effectively compensating for the lack of informal regulation and guiding corporate green transformation. In contrast, in high-attention regions, although the coefficient is positive, its weaker significance (10% level) suggests that stringent public scrutiny may have already compelled firms to undertake environmental initiatives, thereby diminishing the marginal effect of GF. The heterogeneity in firm size indicates that, compared to small- and medium-sized enterprises, large enterprises demonstrate a more significant response to GF policies, owing to their more robust governance structures, stronger financing capacity, and systematic advantage in integrating green financial resources into long-term strategies. Most notably, a significant positive synergy exists between ESG performance and corporate response to GF. Firms with higher ESG standards generally possess more robust internal governance structures and more transparent environmental information disclosure, enabling them to absorb and allocate green financial resources more effectively, thereby facilitating technological innovation and enhancing production efficiency.

To further validate our argument, we introduced the quality of corporate environmental information disclosure (EID) as an alternative moderator. As shown in Column (6), the coefficient for the interaction term between GF and EID (GF × EID) is −0.1366, which is significant at the 1% level. While high-quality EID is intended to reduce information asymmetry and guide green finance to green enterprises, in practice, it may lead to unexpected distortions. It can become a heavy compliance burden for firms, diverting resources from green innovation. Some firms may also engage in “greenwashing”, meeting policy requirements with superficial reports rather than substantive actions. This misleads the allocation of green funds. Therefore, this result highlights the complex role of transparency mechanisms in the GF-NQPFs relationship, indicating that under certain institutional conditions, improved disclosure may initially constrain rather than facilitate the productivity benefits of GF.

6. Conclusions and Implications

6.1. Conclusions

This study employs a three-dimensional fixed-effects panel model to examine the impact of GF on corporate NQPFs using a sample of 28,107 Chinese listed companies from 2011 to 2022. Four main findings are observed. First, a significant positive correlation between GF and the cultivation of corporate NQPFs is confirmed, which aligns with conclusions from several macro-level studies on the contribution of green finance to economic green transformation and productivity growth [6]. However, by validating this relationship at the micro-enterprise level, this study moves beyond aggregate analysis to uncover firm-specific drivers.

Second, the positive impact of GF on corporate NQPFs varies across firm types. Specifically, non-state-owned enterprises, small- and medium-sized enterprises, and firms that attract substantial public attention while maintaining low environmental and social risks tend to derive more substantial benefits from GF initiatives. This finding is consistent with existing research on the differential responsiveness of firms to GF based on ownership and size [46].

Third, financing constraints and corporate social responsibility positively moderate the GF-corporate NQPFs relationship, whereas environmental enforcement exerts a negative moderating effect. This suggests that GF acts as a critical enabler—particularly for firms facing lighter financing constraints and lower environmental risks—by providing essential financial support for innovation and sustainable practices. This supports related research, emphasizing that enterprises should focus on green development to secure green funding [66]. Similarly, a strong commitment to social responsibility appears to create a favorable environment for GF to exert positive effects on corporate NQPFs, which may be related to the notion that strong ESG performance enhances the effectiveness of sustainable investments [67]. In contrast, although environmental enforcement is crucial for ensuring compliance and protecting the environment, it may also impose additional costs or constraints on firms, thereby attenuating the positive influence of GF on corporate NQPFs.

Fourth, a non-synergistic relationship is identified between GF and environmental penalties, highlighting the importance of policy coordination between governmental and GF institutions. While some studies have analyzed the complementary effects between market-based incentives and command-and-control regulations [51], our results highlight potential policy incongruity. This suggests that, under certain conditions, stringent enforcement may generate compliance costs that outweigh the benefits of financial incentives. This nuanced finding partially contrasts with studies advocating solely for regulatory stringency [52], and underscores the complexity of policy interactions in the Chinese context. These conclusions provide micro-level evidence of the significant relationship between the GF market and the cultivation and development of corporate NQPFs.

6.2. Policy Implications

Based on our empirical findings, several key recommendations emerge that collectively aim to enhance the role of GF in promoting corporate NQPFs. First, given the significant positive relationship between GF and corporate NQPFs, policymakers and financial institutions should strengthen support for green technology innovation, especially for state-owned and large enterprises, which demonstrate higher responsiveness to GF. Differentiated financial products—such as green credit lines with preferential terms and venture capital geared toward green startups—should be developed to better serve heterogeneous firms. Second, considering the negative moderating effect of environmental enforcement and its non-synergistic interaction with GF, greater policy coordination is essential. Rather than relying solely on compliance-driven penalties, environmental regulations should be aligned with GF mechanisms. For instance, integrating environmental performance into GF evaluation criteria and offering compliance incentives—such as lower financing costs for firms that exceed emissions standards—can harmonize regulatory pressures with financial incentives. Third, the government should play a pivotal role in establishing clear, standardized ESG disclosure frameworks and providing guidance for firms to improve ESG practices. Our results confirm that higher ESG performance enhances the positive impact of GF on NQPFs. Therefore, enhancing corporate sustainability governance and mandating transparent environmental information disclosure will not only strengthen market credibility but also help to direct GF resources toward firms with strong sustainability commitments. Lastly, given the moderating role of public environmental attention, efforts should be made to increase public participation and supervision. Platforms that enhance corporate–environmental information transparency can leverage societal oversight to reinforce GF’s effectiveness and encourage firms to adopt meaningful green innovations.

6.3. Directions for Future Research

The leap in productivity is a crucial force for human society to achieve leapfrog development and progress, and enterprises are important micro-subjects in driving this force. Existing studies mainly assess the production efficiency of enterprises through TFP [68], or further measure the environmental performance of enterprises with green TFP [20]. However, compared to the purely technical TFP, NQPFs has a broader and higher-dimensional significance. We focus on the empowering effect and mechanism of GF on the NQPFs of enterprises. The conclusions confirm that GF has a significant positive correlation between GF and enterprise NQPFs, and from the perspective of subject theory, the channels of the empowering effect of GF have been verified. This provides a new perspective for understanding how financial activities affect the production efficiency of enterprises, and also provides empirical support for the policy formulation and practical application of GF. Compared with existing research, this paper not only enriches the theoretical connotation of enterprise productivity and expands the research field of GF, but also closely links GF with the cultivation and development of corporate NQPFs. It deepens the understanding of the interaction between enterprises and relevant departments in cultivating and developing corporate NQPFs, providing theoretical support and practical guidance for GF to support corporate NQPFs.

This study acknowledges several limitations that highlight directions for future research. First, while we focus on direct effects and internal mechanisms, our empirical framework lacks full consideration of spatial dependence and cross-regional spillovers, which are confirmed in green technological innovation studies [69]. Future research should explore these spatial dynamics and their implications for corporate distribution and regional innovation. Second, we explicitly acknowledge that potential measurement error in the NQPFs index—as with any composite indicator—could lead to attenuation bias (a tendency for coefficient estimates to be biased toward zero). This may result in an underestimation of the true strength of the relationship between GF and NQPFs. Third, our measure of environmental enforcement, based on penalty counts, may reflect reporting intensity rather than actual enforcement severity. Future studies could seek alternative data sources or methods to better capture this dimension. Finally, future research should delve into the transmission pathways and heterogeneous effects of GF policies, considering factors like firm size, ownership, and industry characteristics.