Green Finance Reform: How to Drive a Leap in the Quality of Green Innovation in Enterprises?

Abstract

Improving green innovation quality is a critical component for speeding green transformation and generating high-quality growth. This study examines the link between the pilot zone for green finance reform and innovations (PZGFRI) policy and the quality of green innovation in Chinese A-share listed firms from 2010 to 2020. This study demonstrates that the PZGFRI may greatly enhance the quality of enterprises’ green innovation. Additionally, by promoting environmental investment and reducing financial barriers, we use the mediating effect model to confirm that the PZGFRI improves the enterprises’ quality of green innovation. Meanwhile, the heterogeneity analysis demonstrates that the PZGFRI is more successful in raising the green innovation quality in state-owned, large-sized, and heavily polluting businesses. Our study’s findings offer a strong theoretical basis for improving the PZGFRI and encouraging businesses to undergo high-quality transformation.

1. Introduction

The advancement of green finance is a crucial element in fostering high-quality growth. However, the large amount of capital investment in the green economy transformation has imposed a huge burden on the national treasury. To solve this problem, China has conducted a series of explorations of green financial systems [1]. Green finance aims to facilitate the flow of social capital into green industries to overcome the challenge of securing capital at the early stages of green industries. However, an inevitable consequence of building the financial system is regional differences in economic development, thereby reducing the efficiency and effectiveness of policy implementation. Therefore, in 2017, the Chinese government created a pilot zone for green financial reform and innovation (PZGFRI), aiming to develop green finance policies in a more targeted manner to promote the economy’s green transformation [2].

The economic background of the PZGFRI is underpinned by the intensification of global environmental risks and the “dual carbon” goals. The demand for high-quality economic development and green transformation is urgent. Meanwhile, the green finance market is booming, and financial institutions need to transform urgently. Against this backdrop, the pilot zone policy emerged. It aims to promote green development through financial innovation and facilitate economic transformation and upgrading [3]. Meanwhile, enterprises, functioning as microeconomic agents, exert a pivotal influence on the green transition of the macroeconomy through their capabilities in green innovation [4,5,6]. As participants in the green development of the economy, enterprises also directly influence green finance [7]. However, few studies have analyzed whether the implementation of the PZGFRI has promoted enterprises’ green innovation quality. At the early stage of the establishment of the PZGFRI, five main tasks were set. First, assisting financial institutions in establishing green financial divisions or sub-branches. Second, promoting the advancement of green credit. Third, investigating the establishment of a market for the trade in environmental rights and interests, such as emission allowances. Fourth, prioritizing the establishment of governmental service channels for green companies and projects. Fifth, implementing a mechanism for the protection of financial risks associated with green initiatives. These five main tasks can bring certain policy preferences to enterprises. In particular, such policies might facilitate financing for enterprises to develop green technologies, further enhancing technology and development over the long run. In addition, the PZGFRI, taking into account the current economic development status and resource endowment advantages of the regions, has facilitated the flow and allocation of green finance resources through the innovation of green finance policy tools, thereby enhancing the quality of green innovation. This novel form of financial supply can provide direct resource support for the green transformation. For example, green credit can expand the funding acquisition channels for enterprises and diversify their financing pathways for green transformation, thus improving the quality of enterprise green innovation.

Although the PZGFRI has not been extensively discussed in academia, there have been many empirical findings relating to the influence of green financial systems on green development. In general, scholars are positive about green finance’s effect on enterprises’ green transformation. They claim that policies like green bonds and funds can substantially enhance the proliferation of green innovations [8,9]. However, the discussion of these policies does not evaluate the PZGFRI and the effect of the green financial system on firms’ green innovation quality (GTQ). In the context of carbon neutrality, transforming green innovation from improving quantity to increasing quality is necessary for enterprises to achieve sustainable development and enhance their competitiveness [10]. Exploring how to improve quality and increase efficiency in green technology innovation can provide enterprises with a clearer strategic positioning and provide the government with a direction for policy support to realize the economy’s green transformation. Hence, discussing the impact of the PZGFRI on enterprise GTQ can not only enhance theoretical research but also contribute to further promoting a greener financial system and sustainable economic transformation.

However, regarding the measurement of GTQ, there is no comprehensive research and unified standard in the academic community [11,12]. Due to data availability, some of the literature constructs patent quality based on indicators such as R&D expenditures, patent applicant information, weighted number of patents, and the number of patent citations [13]. Nevertheless, most of these patent quality indicators are plagued by problems related to measurement accuracy and data availability. Therefore, based on the knowledge width method, this paper matches and compares the IPC classification numbers in the enterprise patent documents of China with the international green patent list to construct green technology innovation quality indicators. Additionally, to explore the relationship between the PZGFRI and firms’ GTQ, this paper utilizes the Difference-in-Difference (DID) model to elucidate the mechanism through which the PZGFRI exerts its influence on GTQ. We also examine two potential pathways: enhancing investment in environmental protection and alleviating corporate financing constraints.

This study contributes to the present literature in the three respects. Firstly, this paper explores the economic effects of PZGFRI policies on micro enterprises within the theoretical framework of Hartwick’s Law, supplementing the literature on firms’ GTQ. Specifically, taking the idea revealed by Hartwick’s law, a complete analytical framework for the impact mechanism of green finance is constructed from the dual perspectives of “cost optimization–investment incentives”. On the one hand, policies force enterprises to optimize resource allocation and improve the efficiency of green innovation by increasing pollution costs; on the other hand, policies guide enterprises to reinvest their excess returns in high-quality green technologies through long-term financial incentives [2]. This framework overcomes the fragmented defect of “only focusing on a single cost effect or a single incentive effect” in the existing literature, and achieves a systematic characterization of the action path of green finance.

Secondly, this paper systematically constructs a theoretical derivation model for the impact of green financial policies on the quality of green innovation. Based on Hartwick’s law and integrating local static equilibrium analysis with Shepard’s lemma, this paper constructs a theoretical framework that characterizes how green financial policies affect the quality of green innovation, and systematically explains the internal mechanism by which policy incentives enhance the quality of green innovation in enterprises. This analysis aids in elucidating the actual economic conditions for enterprises’ green development transformation, addressing the deficiency of the existing literature lacking quantitative theoretical derivations [4,8].

Thirdly, this paper draws on the construction principle of the Herfindahl–Hirschman Index and proposes a method for measuring the quality of enterprise green innovation based on IPC. It also empirically analyzes the impact of PZGFRI on the GTQ of enterprises and its mechanism. The existing literature predominantly relies on metrics such as the volume of patent applications [14] or the number of patent citations [15] to evaluate green innovation quality. However, these methods may encounter strategic citation issues, such as over-citation or under-citation, which can obscure innovative knowledge and lead to the loss of critical information. This paper adopts the knowledge breadth method, tailored to the Chinese research context and data availability, to assess green innovation quality comprehensively.

The rest of this paper is arranged as follows: Section 2 systematically analyzes the relevant research. Section 3 explicates the theoretical mechanism and research hypotheses in detail based on Hartwick’s Law. Section 4 primarily elucidates the model, variable definitions, and sources employed. Section 5 interprets the empirical findings. Section 6 presents further analysis. Section 7 encapsulates the conclusion.

2. Literature Review

There are three closely related themes in the literature, namely the policy effects of the green financial system on micro enterprises, the measurement of and influencing factors in the quality of enterprises’ green innovation, and the research on the relationship between green finance and enterprises’ green innovation. The first category in the literature focuses on the policy effects of the green financial system on microenterprises. The PZGFRI denotes the institutional framework that facilitates the economic transition toward sustainability using financial instruments and associated regulations such as green credit, green bonds, a green stock index, and related products, such as a green development fund, green insurance, and carbon finance [16,17,18]. Comprehensive academic research exists regarding the impact of this system on firms, primarily emphasizing the influence of green finance policies on production processes, business decision-making, and optimal resource allocation within organizations [19,20]. For example, Gilbert and Zhou [21] conclude that green financial products such as green funds and green insurance are crucial media to motivate private capital to enter the energy conservation and environmental protection field. He et al. [22] suggest that green credits may encourage enterprises to invest in renewable energy, contributing to the green economy. Flammer [23] further reports that investors are more concerned about the disclosure of green information by corporations and the risks associated with investing in environmentally sensitive companies.

The second type of research mainly explores the influencing factors in green innovation quality and their measurement methods. Firstly, the existing literature focuses on examining the influencing factors in enterprises’ green innovation quality from the perspective of environmental regulatory policies. The research finds that both environmental policies and environmental supervision are key influencing factors in enterprises’ green innovation [24]. Li et al. [24] find through their study that when the government implements environmental regulatory policies, this affects banks’ lending preferences and can promote banks’ lending on green projects, enhancing the amount of green innovation. In the study by Wang et al. [25], it is proposed that environmental policies can encourage enterprises to diversify their types of investments, including increasing investments in technology and finance, and thus to have abundant funds for green technology innovation. Shen et al. [26] argue that environmental policies mainly restrict high-energy-consuming and high-emission enterprises from facing stricter access standards in their projects. Enterprises have to improve their competitiveness through technological directional changes, thus promoting green innovations.

Most existing studies tend to equate enterprise green innovation solely with “quantitative expansion,” thereby overlooking the critical role that innovation quality plays in determining corporate market value [27]. Although some of the existing literature has attempted to evaluate the quality of enterprise green innovation through indicators such as the number of invention patent applications [14] and the number of patent citations [15], these metrics face certain limitations in accurately capturing the technical complexity of patents. First, highly cited patents may generate a “herd effect” due to their “recognized label,” which may encourage strategic citation behavior by firms during the application process, leading to the simultaneous occurrence of both “over-citation” and “under-citation.” Second, the number of citations alone does not necessarily reflect the depth of breakthroughs in core technologies. In contrast, the breadth of patent knowledge offers a more accurate representation of green technology quality (GTQ) by capturing the complexity and diversity of the knowledge embedded within patent texts [28]. Although a few studies have evaluated the GTQ based on the knowledge breadth method, most focus on overall green innovation activities in China rather than providing analysis at the micro-enterprise level.

The third category of research examines the impact of enterprises’ green innovation from the perspective of green finance. Existing research has found that, compared with other environmental regulations, green finance policies, which use capital allocation as a policy tool, can guide financing entities to achieve green transformation and represent market-oriented environmental regulation. However, regarding the impact of green finance on enterprises’ green innovation, at present, no consensus has been reached in the research. Regarding the incentive effect of green finance policies on enterprises’ green innovation, the research mainly focuses on two viewpoints: the effective incentive theory and the ineffective incentive theory. Some scholars believe that green finance policies have increased credit constraints on highly polluting enterprises, thereby suppressing their research and development innovation [29]. Another group of scholars has found that green finance can have a significant incentive effect on enterprises’ green innovation [10]. Therefore, the relationship between green finance and enterprises’ green innovation needs to be further clarified.

To sum up, the extant literature largely agrees that environmental policies have a significant positive effect on green innovation. However, whether the quantity of green innovation can effectively drive enterprises’ green transformation remains a matter of debate. While technological innovation is advancing rapidly, enterprises must prioritize the quality of innovation. Few studies have determined the role of the PZGFRI in green technology quality. Nevertheless, as a crucial part of the green finance process, the study of green finance innovation pilot zones, is of vital importance for both supplementing existing research and informing policymaking. Finally, the existing research on GTQ has certain limitations, and most of the relevant studies examine overall green innovation activities, lacking micro-enterprise-level data.

3. Theoretical Analysis and Research Hypothesis

China has been investigating the green credit situation of various banks and promulgating policies related to green finance since 2013. In September 2015, the State Council launched the General Program for the Reform of the Ecological Civilization System, putting forward the strategy of a green financial system for the first time. However, in the process of green finance development, there are still problems such as uneven regional development and uneven distribution of resources [30]. Hence, the government established the PZGFRI in 2017. The innovative pilot zones contain five provinces and eight districts, aiming to develop targeted green financial policies through regional exploration.

Encouraging the participation of various kinds of capital in green investment, such as microfinance and insurance, may more strongly promote the development of green finance. At the same time, this innovation pilot zone covers provinces with different economic characteristics, not only provinces such as Zhejiang and Guangdong, where investors are favorable, but also provinces such as Xinjiang and Guizhou, where financing is relatively difficult. Investigating the heterogeneous policy effects among provinces with different geographical locations and economies is crucial for future policymakers to formulate the policy in a more feasible and targeted manner. Concurrently, the development of relatively uniform and clear green finance standards can help green finance policies in different areas be better implemented.

3.1. Theoretical Analysis

Based on the Hartwick law proposed in 1977 and grounded in the research paradigm established by Hartwick [31,32], this paper systematically investigates the influence mechanism of green finance on GTQ. Originally introduced in “Intergenerational Equity and Rent for Depletable Resources,” the Hartwick rule was formulated to elucidate how intergenerational equity can be maintained in the context of non-renewable resource utilization. With the growing awareness of environmental sustainability and the increasing demand for a green economy, the Hartwick rule has increasingly been recognized as a pivotal theoretical foundation for green finance practices [33,34]. Nevertheless, most existing studies have primarily focused on macroeconomic perspectives and have seldom explored the interaction mechanism between the Hartwick rule and green finance from the standpoint of micro-level enterprises.

As environmental taxation operates through the economic mechanism of taxation, green finance autonomously balances the supply of and demand for environmental goods by assigning scarcity rents to natural resources and internalizing external environmental costs into market accounting [35]. As illustrated in Figure 1a, the level of environmental governance can be interpreted as the supply of environmental goods. Scarcity rents serve as compensation for corporate investments, leading to a rightward shift in the supply curve. Consequently, the equilibrium point moves from Q to Q’, thereby increasing the availability of environmental goods. Regarding environmental demand, under conditions of constant consumption levels, this approach reduces total pollution and shifts the overall environmental demand curve to the left without diminishing the value of environmental capital stock. As shown in Figure 1b, when all scarcity rents are allocated to environmental investment, the pollution level decreases from Q to Q’.

Specifically, an effective green financial market system should achieve cost optimization of the accumulation mechanism and maximize the benefits of the investment mechanism. This study takes Hartwick’s law as the core and proposes two operational principles of green finance: The first is to ensure that the stock of environmental resources does not decrease through an accumulation mechanism [3,8]. The second is to achieve effective replacement of environmental investment and environmental resources through investment mechanisms [16,17]. Based on this, we have constructed the framework of the green finance system in Figure 2, providing a theoretical basis for how green finance can achieve both its stability and environmental sustainability simultaneously.

From a more essential perspective, Hartwick’s law not only emphasizes the importance of the sources and allocation of funds, but also endows micro-dynamics for the quality leap of green technologies in enterprises [36]. Specifically, the accumulation mechanism provides a stable source of funds by ensuring that the scarce rent generated by environmental assets is used for environmental investment. The stability of this kind of fund provides solid financial support for enterprises to effectively carry out high-quality green innovation, thereby significantly improving the quality of green technologies [25,37].

However, most of the existing literature regards environmental policies as exogenous shocks and discusses how they can expand the total number of green patents, but neglects the quality of green innovation in enterprises. For instance, Li et al. [24] found that command-based environmental regulations can stimulate an increase in the number of green patent applications. Meanwhile, the investment mechanism, through the incentive effect of the market, directs funds toward technologies and projects with the advantage of the marginal benefits of capital. This market-oriented system is conducive to the optimal allocation of resources and encourages enterprises to continuously improve their technology and technological quality through dynamic competition. In the green financial system, maximizing the efficiency of capital allocation means that resources will flow to innovative technology projects with the highest environmental and economic benefits, thereby promoting the green technological progress of the entire industry. Therefore, this paper is dedicated to exploring whether and under what conditions green finance can significantly enhance the quality of green innovation in enterprises [10,38].

We take Hartwick’s law as the logical starting point, combine local static equilibrium analysis and the Shephard lemma, and construct a mathematical description of how green financial policies enhance the GTQ, enriching the relevant literature on green finance and green innovation [4,8]. From the perspective of institutional theory, enterprises must make adaptive responses to external institutions to obtain legitimacy and resource endowments [39]. Against the backdrop of the implementation of green financial policies, enterprises could adapt to government requirements to obtain green financial resources, making the production of green innovative products the production goal. According to the technical and sustainable characteristics of green innovation, the realization of the production goal requires the input of green production factors that meet the requirements of low-carbon transformation and technological research and development. The cost of green production factors can be regarded as the cost of environmental R&D investment. Based on the principle of minimizing production costs, enterprises only need to spend such costs when conducting green innovation, and the input amount is constrained by the accumulation of green financial funds and the efficiency of market resource allocation. Therefore, green factors can be regarded as quasi-fixed production factors, while capital, labor, and technological level are considered variable production factors. Now, taking the minimization of production costs as the decision-making criterion for the input of production factors, with a total of X variable factors and Y quasi-fixed factors, the variable cost function of the enterprise’s green innovation can be expressed as

$$\begin{array}{c}C=\left(GTQ,X_1,X_2\dots X_X,M_1,M_2\dots M_Y\right)\end{array}$$

(1)

where C represents the variable cost of green innovation, GTQ represents the enterprise’s green innovation quality, $X_x$ (x = 1, 2, …X) is the price of the Xth variable element, and $M_y$ (y = 1, 2, …Y) is the input quantity of the Yth type of “quasi-fixed” factor. According to Shepard’s lemma, the demand function of variable factor capital E can be represented by the quality of green innovation GTQ, the price of the variable factor $X_x$, and the price of the quasi-fixed factor $M_y$. This paper refers to the practice of Berman and Bui [40] and selects the linear function E to represent it as follows:

$$E\left(GTQ,X_1,X_2\dots X_X,M_1,M_2\dots M_Y\right)=q+\phi GTQ+\sum _{x=1}^X{r}_x{X}_x+\sum _{x=1}^Y{\gamma }_y{M}_y$$

(2)

where q represents the marginal impact of an enterprise’s enhancement of green innovation technology levels on the size of variable capital requirements, reflecting the variable factor demands brought about by an enterprise’s current promotion of green innovation. This paper assumes that γ > 0, that is, the relationship between the input of green production factors caused by green financial policies and variable factor capital is determined by substitution. We assume that G represents the green finance policy, and θ represents the marginal impact of the green finance policy on the changes in variable factor capital. If μ represents the influence effect of factors other than green finance policies, then the relationship between variable factor capital and policies can be expressed as

$$E\left(GTQ,X_1,X_2\dots X_X,M_1,M_2\dots M_Y\right)=\theta G+\mu$$

(3)

By differentiating Equation (3), the formula for the impact of green finance policy G on variable factor capital can be obtained:

$$\theta ={\displaystyle \frac{dE}{dG}}=\phi {\displaystyle \frac{dGTQ}{dG}}+\sum _{x=1}^X{r}_x{\displaystyle \frac{d{X}_x}{dG}}+\sum _{y=1}^Y{\gamma }_y{\displaystyle \frac{d{M}_y}{dG}}$$

(4)

Under the theoretical framework of Hartwick’s law, the fund accumulation mechanism provides a stable source of funds by ensuring that the scarce rent generated by environmental assets is used for environmental investment. Meanwhile, the investment mechanism, through the incentive effect of the market, directs funds toward technologies and projects with the advantage of the marginal benefits of capital, ${\displaystyle \frac{dE}{dG}}>0$. Therefore, assuming that the variable factor market is perfectly competitive, the prices of various variable production factors in the market will not change due to a certain producer or consumer, and green financial policies will not have an impact on the prices of variable factors, that is,

$$\sum _{x=1}^X{r}_x{\displaystyle \frac{d{X}_x}{dG}}=0$$

(5)

Then the formula for the quality impact of green innovation can be obtained as

$${\displaystyle \frac{dGTQ}{dG}}={\displaystyle \frac{1}{\phi }}\left(\theta -\sum _{y=1}^Y{\gamma }_y{\displaystyle \frac{d{M}_y}{dG}}\right)$$

(6)

where green finance policies use capital allocation as a policy tool, which can guide financing entities to achieve green transformation. Enterprises will respond to external systems to obtain legitimacy or the resources needed by the organization and carry out green transformation, that is, ${\displaystyle \frac{d{M}_y}{dG}}>0$. ${\gamma }_y$ reflects whether green activities and capital are complementary or substitutive. Generally speaking, the increase in green production factors (such as clean energy and environmental protection technologies) will directly lead to the reduction in traditional variable factor capital (such as highly polluting equipment and inefficient labor), that is, ${\gamma }_y$ < 0. Therefore, ${\displaystyle \frac{dGTQ}{dG}}>0$, which indicates that green financial policies can promote the improvement of the quality of green innovation in enterprises.

3.2. Research Hypothesis

Existing research has broadly established a consensus regarding the “financing relief” function of green finance: its primary role is to drive the green transformation of the economy, achieve a dual rebalancing of economic growth and environmental sustainability, and alleviate the long-term funding gap associated with environmental investments [3,4,7]. Financial instruments are instrumental in addressing the challenges related to long-term and stable capital inflows. The green financial system has demonstrated effectiveness in resolving capital shortages commonly encountered during the transition to a green economy [33,38]. Empirical findings from Rao et al. [9] indicate that green bonds significantly reduce corporate financing constraints, thereby promoting an increase in green innovations. However, theoretical perspectives remain divided on the impact of green finance on the quality of corporate green innovation, with two dominant views: the “effective incentive theory” and the “ineffective incentive theory.” Some scholars argue that green finance policies have imposed stricter credit constraints on highly polluting enterprises, consequently suppressing their research and development activities [29].

From the perspective of principal-agent theory, this paper argues that GF can provide positive incentives for corporate green innovation quality. First, discrepancies in utility functions between financial institutions and enterprises may give rise to managerial moral hazard, thereby exacerbating principal-agent problems. Owing to its distinctive environmental regulatory attributes, green finance can effectively mitigate such agency conflicts, thus playing a constructive role in promoting corporate green innovation [41]. This paper develops the conceptual framework illustrated in Figure 3. Second, as the green financial system continues to evolve, GF has emerged as a critical incentive mechanism for advancing the green economy [20,42]. In its initial stages, the green financial system was predominantly established under government leadership as a core element of global sustainable development strategies [19]. However, due to the inherent presence of regional heterogeneity, green finance encounters practical challenges such as implementation difficulties, insufficient credibility, and inadequate regional policy support during its dissemination [43]. The PZGFRI seeks to address these challenges by granting enhanced policy autonomy to pilot regions, enabling green finance policies to align precisely with local economic features, and customizing financial support programs for enterprises. This approach effectively facilitates corporate green transformation and enhances GTQ. Under this policy framework, each pilot zone has been able to construct a regionally tailored green financial system and actively develop green financial services [7,19]. Given that the business orientation of local enterprises often closely corresponds with regional economic characteristics, these pilot policies can directly address the key constraints enterprises face during the green innovation process, thereby significantly improving firms’ GTQ. Therefore, we propose the following hypothesis:

Hypothesis 1. 

The PZGFRI can improve enterprises’ green innovation quality.

Existing research suggests that the scale of environmental protection investment reflects the strategic priorities of enterprises in resource allocation [44]. According to externality theory, environmental protection investments exhibit significant positive externalities, resulting in market-determined investment levels that are typically lower than the socially optimal level under spontaneous equilibrium. Under the guidance of government policies, social capital is more likely to support corporate environmental protection initiatives, thereby facilitating easier access to financing. Higher levels of environmental investment can stimulate corporate green innovation and consequently enhance GTQ [29,45]. For example, Cheng and Zhu [46] found that increased environmental protection investment effectively encourages enterprises to engage in green technological innovation, thus improving the quality of such innovation. However, corporate environmental investment decisions hinge on a cost–benefit trade-off, aiming to minimize the total long-term costs associated with environmental investment and environmental tax payments. Unlike traditional long-term investments, environmental protection investments are characterized by externalities and high risk, and therefore require stable and sufficient external financial support. In the absence of accessible external financing, profit-maximizing enterprises may reduce their environmental investment. The establishment of a green financial system has effectively eased corporate capital constraints, created new sources of economic growth, and facilitated green transformation [47,48]. Therefore, the PZGFRI can provide financial support for corporate environmental investments, thereby promoting GTQ. Based on the above analysis, we propose the following hypothesis:

Hypothesis 2. 

The PZGFRI improves enterprises’ green innovation quality by incentivizing firms to increase investment in environmental protection.

According to the financing constraint theory, due to the high-investment nature of green innovation itself, enterprises often encounter certain financing constraints when conducting green innovation [6,7,49,50]. As green technological innovation belongs to capital-intensive R&D activities, financing constraints have become one of the key factors restricting the improvement of enterprises’ innovation capabilities [51,52]. Existing research has found that green finance can significantly alleviate the financing constraints of enterprises and provide financial support for enterprise innovation [20,41]. For instance, Chen et al. [41] found that green finance can change the financing costs of enterprises and thereby promote the simultaneous increase in the quantity and quality of the green innovation of enterprises. This study holds that the PZGFRI can effectively alleviate the financing constraints of enterprises and thereby promote their GTQ. On the one hand, the PZGFRI can effectively promote the innovation of green financial products within the jurisdiction. Under the impetus of this policy framework, various innovative green financial tools have been constantly emerging, injecting new vitality into the green financial system. This new type of financial supply can provide direct resource support for the green transformation. For instance, green credit can increase the channels for enterprises to obtain funds, broaden the financing approaches for their green transformation, and thereby alleviate financing constraints. In addition, green financial tools such as green funds and green insurance can guide private capital into the field of clean production, thereby stimulating green innovation. On the other hand, under policy guidance, local governments can more effectively introduce a series of green preferential policies that are beneficial to local enterprises [53]. This can not only provide targeted fiscal and tax incentives to solve problems such as difficult financing for enterprises, long payback periods for technological innovation, and high repayment pressure, but also help enterprises increase their GTQ. Therefore, we propose the following hypothesis:

Hypothesis 3. 

The PZGFRI improves enterprises’ green innovation quality by reducing corporate financing constraints.

Existing studies have consistently pointed out that the incentive effects of green finance policies have significant heterogeneity [54,55,56]. For instance, Fu and Zhao [55] confirmed that state-owned enterprises benefited more prominently, while Gao et al. [56] found that large-scale enterprises also enjoyed policy dividends. Under the framework of the PZGFRI, this “scale-ownership” disparity has been further magnified by the targeted optimization of financial resource allocation. Specifically, based on the hypotheses of “ownership premium” and “scale signal”, the implicit government guarantee attached to the state-owned background and the reliable mortgage assets of large enterprises jointly lower the information asymmetry and default risk premium of banks, enabling the two types of entities to obtain green credit at lower interest rates, for longer periods, and with more simplified guarantee procedures. This effectively offsets the high adjustment costs and long payback period required for green technological innovation [55,57]. Secondly, following the theory of resource allocation, state-owned and large enterprises, with their abundant human capital, technological reserves, and vertical integration capabilities, can rapidly internalize external green financing into a “research and development–production–market” collaborative chain, significantly enhancing the marginal output per unit of green investment [54]. Secondly, large enterprises are subject to multiple reputation constraints from the media, the public, and regulators. Green transformation can not only reduce potential compliance and litigation costs, but also raise market valuations through “green reputation capital”, forming a positive feedback loop. In conclusion, PZGFRI, by allocating financial resources, has magnified the inherent endowment advantages of state-owned and large enterprises, resulting in a significantly higher improvement in the quality of their green technological innovation compared to non-state-owned and small enterprises. This has verified the “scale-ownership” heterogeneity effect of green financial policies. Therefore, we propose the following hypothesis:

Hypothesis 4. 

The PZGFRI can promote the enterprises’ green innovation quality in state-owned and large enterprises.

4. Models and Data

4.1. Models

4.1.1. Baseline Regression Model

This paper treats the green financial reform and innovation pilot zone as a quasi-natural experiment and uses a DID model, which can effectively control the time trend and individual characteristics by comparing the differences in changes between the treatment and the control group before and after policy implementation, enhancing the accuracy and credibility of causal inference, as follows:

$${GTQ}_{it}={\beta }_0+{\beta }_1{PZGFRI}_{it}+{\beta }_2{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(7)

In Equation (7), the subscripts $i$ and $t$ correspond to Chinese A-share listed firms and years, respectively. The ${GTQ}_{it}$ denotes that the firm $i$ in $t$ year corresponds to the quality fraction of the invention patent. ${PZGFRI}_{it}$ is the focusing term, representing the product of the policy dummy and the time dummy variable. The policy dummy variable is taken as 1 if the listed company is headquartered in a green finance pilot city, and 0 otherwise; the time dummy variable is taken as 1 if the year is after the enactment of the green finance pilot policy, and 0 otherwise. In the underlying regression, we focus on the coefficient ${\beta }_1$, which reflects the effect of establishing a green financial reform and innovation pilot zone on firms’ GTQ.

4.1.2. Mediating Effects Model

Referring to the test method for mediating effect proposed by Gao et al. [58], this paper establishes the specific model of mediating effect, which is excellent for showing how the independent variable affects the dependent variable through the mediating variable. It also helps to explain the model better, as well as to develop theories and use them in practice, and makes the research more scientific:

$$M_{it}={\alpha }_0+{\alpha }_1{PZGFRI}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(8)

$${QGTI}_{it}={\rho }_0+{\rho }_1{PZGFRI}_{it}+{\rho }_2{M}_{it}+{\rho }_3{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(9)

In this paper, $M_{it}$ represents two mediating variables, which are the amount of corporate environmental investment (Einv) and corporate financing constraints (Sa). As shown in the above models, Equations (8) and (9) test the direct effect of the mediating variables on the explanatory variables, respectively, when the estimated coefficients of ${\alpha }_1$ and ${\rho }_2$ both are statistically significant, and the mediating effect is significant.

4.2. Variables

4.2.1. Dependent Variable

Referring to Aghion et al. [28], we use the knowledge width method for calculation, which has been widely applied by Yin et al. [59] and Xi et al. et al. [60] to measure the quality of innovation. In contrast, the breadth of patent knowledge can more accurately capture the quality of green innovation because it more reasonably reflects the depth and breadth of innovation by analyzing the diversity of the technical fields covered by patents, avoiding the deviations of traditional methods. Generally, the more IPC classification number categories a patent involves, the stronger its cross-application in different technical fields. This also means that the patent’s review cycle is longer, its application scope is broader, and its technical complexity is higher. Subsequently, it is more likely to be cited by patents in multiple fields, that is, the quality level embedded in the patent is often higher. Design patents require a shorter application period and lower innovation standards than the other two patent categories, and such patents may not measure the GTQ accurately. Therefore, we only calculate the knowledge width of invention and utility model patents.

Specifically, this paper matches and compares the IPC classification numbers in the corporate patent documents of the State Intellectual Property Office of China with the IPC classification numbers on the international green patent list. The IPC classification numbers in the international green patent list are green patents (in patents for inventions and utility models, the format of the IPC patent number is “Division–Major Class–Minor Class–Major Group–Group”, e.g., C10L 5/00. Specifically, the first letter indicates the Division. There are eight major categories in the Ministry, which are represented by the letters A–H. The second and third digits indicate the major group, the fourth letter indicates the minor group, the digit following the minor group indicates the major group, the digit indicating the major group may have one or two digits, the digit following the major group indicates the group, and the major group and the group will be separated by a “/”. For example, C10L 5/00 means Part C, Class 10, Subclass C, Group 5, Panel 00, which corresponds to solid fuels in the Patent List IPC classification). Subsequently, we apply the Herfindahl–Hirschman index at the large group scale for weighing. After measuring the proportion of each enterprise’s patent classification number in each large group classification, we further generate the enterprise’s green patent quality score based on the following principle: the greater the difference between the patent classification numbers at the large group level, the greater the breadth of knowledge required by the enterprise for patent innovation. The calculation method is as follows:

$${GTQ}_{i,t}=1-\sum {\alpha }^2$$

(10)

where $\alpha$ is the ratio of primary group classifications in the patent classification number. As shown in Equation (10), the greater the difference between the patent classification numbers at the major group level, the greater the breadth of knowledge used by the enterprise’s green patent, and the higher the quality of its patent. After completing the knowledge width information indicators at the level of green patents, the knowledge width information at the level of applied and granted patents is summed up at the enterprise level according to the “enterprise–year–patent” dimensions. Moreover, because of the uneven distribution of the data, we employ the median method to summarize the knowledge width information of green patents at the enterprise level.

4.2.2. Independent Variable

${PZGFRI}_{it}$ is the difference-in-difference term, representing the interaction of two dummy variables (${treat}_i$ and ${time}_t$). The ${treat}_i$ denotes 1 if the headquarters of the listed company sits in a pilot zone, and 0 otherwise. The ${time}_t$ represents the implementation of the policy in year t. Although Gansu province established an innovation pilot zone in 2020, the data used in this article are up to 2020. Due to the time-limited restrictions on the data, it is challenging to conduct a thorough and detailed analysis of its actual operation and development situation after its establishment. Furthermore, the Gansu Pilot Zone was established relatively recently, and the system may still be in the early stages, with various systems, policies, and innovative practices being gradually improved and advanced. Therefore, to ensure the scientificity and accuracy of the analysis, referring to Gao et al. [19], this paper temporarily excludes the sample of Gansu province from the scope of this analysis. Figure 4 presents the distribution map of the pilots in the green finance reform and innovation pilot zones.

4.2.3. Mediating Variables

Environmental Investment (Einv). Referring to Yin et al. [61], through a detailed analysis of the construction in progress sections in the annual reports of listed companies, we focus on investment expenditures directly related to environmental protection, pollution control, energy conservation, and emission reduction. These investment expenditures cover multiple aspects ranging from the construction and upgrading of pollution control facilities to the application of energy conservation and emission reduction technologies, reflecting the proactive actions and financial input of enterprises in environmental protection. To ensure the scientific nature and comparability of the research results, we standardized the Einv. Specifically, we divide the amount of environmental protection investment by the total assets of the enterprise to obtain a standardized proportion of environmental protection investment. This proportion can effectively eliminate the influence of enterprise scale differences on investment data, making the environmental protection investment levels of enterprises of different scales comparable. Furthermore, to make the results more intuitive and easier to understand, we multiply the normalized environmental protection investment ratio by 100 and convert it into a percentage.

Financing Constraint (Sa). According to Zhang et al. [62], we use the financing constraint coefficient Sa to represent the financing constraint problem faced by the firm. The construction formula of the entity is Sa = −0.737 × Enterprise scale + 0.043 × Enterprise scale 2 − 0.04 × Enterprise age. In this formula, “scale” is expressed as the natural logarithm of the total assets of an enterprise and is measured in millions. “age” is the term used to describe the operating years of an enterprise. The Sa index is usually negative. It is clear that the larger the absolute value, the higher the degree of financing constraints on the enterprise. This index’s key advantage is its independence from corporate financial indicators, making it a robust and reliable metric [63]. Enterprises often need to rely on a large amount of external financing to enhance their performance levels. The financing constraint coefficient must be positive for PZGFRI enterprises to find it difficult to obtain support from relevant policy preferences to alleviate financing constraints. This will prevent them from effectively enhancing the quality of green innovation and may even have negative impacts. The financing constraint coefficient could be negative for PZGFRI to help enterprises reduce external financing costs. This will enable them to invest more cash flow in non-productive business activities such as green innovation.

4.2.4. Control Variables

To obtain objective estimates of the policy effects, according to Bu et al. [64], we also add as the following variables: (1) Firm size (Size). Large-scale enterprises generally have stronger human resources and capital to promote green technology innovation than small-scale enterprises. (2) Enterprise age (Age). The longer the establishment has run, the more likely it is to have the operational experience and human capital required for R&D and a higher level of innovation. (3) Profitability of assets (ROA). Since the time between investment in green technology innovation and profitability is generally long, enterprises with higher operational capacity as well as profitability are more likely to have the ability to develop green technology. (4) Corporate liabilities (Lev). Corporate indebtedness significantly influences capital expenditure and allocation within the firm. (5) First largest shareholder shareholding ratio (Hol). The Hol can better reflect the governance structure of the enterprise. The specific variable descriptions are shown in

Table 1. Variable definitions.

	Variable Name	Measurement	Data Source
Explained variable	GTQ	See Section 4.2.1 for details	CSMAR
Explanatory variable	PZGFRI	See Section 4.2.2 for details	The National Development and Reform Commission and the Ministry of Finance
Control variable	Size	Ln (the total assets of an enterprise)	Wind
	Age	Ln (Observation year—year of the listed)	CSMAR
	ROA	The net profit of the firm at the end of the year is divided by the total assets.	Wind
	Lev	The total liabilities of the firm at the end of the year are divided by the total assets.	CSMAR
	Hol	The shareholding of the largest shareholder is divided by the total share capital	CSMAR

.

4.3. Data Sources

We employed IPC data from 2010 to 2020, which include information on 2679 Chinese A-share listed companies, amounting to a total of 15,054 data entries. The IPC data to measure the quality of green innovation in this paper were obtained from the State Patent Office, and the IPC classification of the international green patent list was downloaded through the official website of WIPO. The CSMAR database provides enterprise financial data. In this paper, the obtained data are merged and matched, and the samples are screened according to the following principles: (i) exclude the financial sector samples; (ii) exclude the ST, PT, and insolvent samples; (iii) exclude the samples with missing relevant variables. To avoid outlier interference, we also apply a bilateral 1% level tail reduction to all variables and adjust firm-level clustering for standard errors in the regression analysis.

Table 2. Descriptive statistics.

Variable	N	Mean	S.D	Min	Max
GTQ	15,054	0.232	0.272	0.000	0.945
$PZGFRI$	15,054	0.122	0.147	0.000	1.000
Size	15,054	12.801	1.169	8.358	19.195
Age	15,054	2.822	0.326	1.386	3.988
Hol	15,054	3.459	0.441	1.162	4.511
ROA	15,054	0.033	0.221	−0.125	0.275
Lev	15,054	0.438	0.195	0.080	0.899

reports the results of descriptive statistics.

According to the data in Table 2, the minimum value of GTQ is 0 and the maximum value is 0.945, signifying substantial disparities in green innovation quality among firms. Some enterprises may invest significant resources and technological power in green innovation, while others may invest little or nothing. In addition, the data from the PZGFRI show that about 12.2% of the samples belong to the pilot zone. In addition, among the control variables selected in this paper, the maximum value of Hol is 4.511, and the minimum value is 1.162. The maximum value of Age is 3.988, and the minimum value is 1.386, which indicates a disparity in the relevant indicators.

5. Empirical Results

5.1. Benchmark Regression

Table 3. Benchmark regression.

	GTQ	GTQ	GTQ	GTQ
	(1)	(2)	(3)	(4)
$PZGFRI$	0.761 ***	0.485 ***	0.291 ***	0.379 ***
	(0.153)	(0.052)	(0.025)	(0.049)
Size		0.213 **	0.179 ***	0.260 ***
		(0.098)	(0.002)	(0.007)
Hol		−0.019 *	−0.009 *	−0.051 ***
		(0.011)	(0.005)	(0.017)
Age		0.044 *	0.0375 ***	0.113 ***
		(0.025)	(0.008)	(0.026)
ROA		0.089	0.108	0.072 **
		(0.071)	(0.099)	(0.032)
Lev		0.347 ***	0.581 ***	0.193 ***
		(0.093)	(0.012)	(0.041)
Constant	9.421 ***	5.244 ***	4.843 **	4.254 ***
	(2.412)	(1.425)	(2.362)	(1.001)
Firm FE	√	×	√	√
Year FE	√	√	×	√
City FE	√	√	√	√
Observation	15,054	15,054	15,054	15,054
Adj-R2	0.210	0.276	0.306	0.439

Note: Parentheses surround the firm-level cluster standard errors. *: significance at the 10% level, **: significance at the 5% level, and ***: significance at the 1% level.

presents the results of the baseline regression. In particular, column (1) includes only control fixed effects, while columns (2)–(4) include control variables and the fixed effects of company, city, and year, in that order. Overall, the coefficients of the PZGFRI in all columns are significantly positive. As shown in Table 3, although significantly positive, the estimated coefficient in column (2) is 0.485 when including all control variables, which is slightly smaller than the 0.761 in column (1) without including control variables. The results in columns (3) and (4) remain significant after the year- and city-fixed-effects control. These results tentatively verify hypothesis 1, that the PZGFRI is conducive to the enterprises’ GTQ. This outcome may stem from the observation that, first, the PZGFRI guides financial resources to precisely flow toward high-quality green innovation projects, screens out innovation directions with greater potential and benefits, avoids resource waste on the inefficient expansion of non-valuable innovations, and thereby enhances the quality of innovation [41]. Second, the incentive mechanism has been refined. Through differentiated incentives, such as providing higher subsidies or tax preferences for high-quality green innovation achievements, enterprises can be guided to focus on improving the quality of innovation rather than pursuing quantitative growth. Thirdly, externalities are internalized. The policy transforms the environmental and social benefits of green innovation into quantifiable economic gains for enterprises, encouraging them to invest more resources to enhance the quality of innovation and thereby obtain greater internalization benefits from externalities.

For the control variables, under the assumption that other conditions remain, the age of an enterprise positively correlates with the quality of its green innovation. Conversely, a higher shareholding ratio for the largest shareholder is associated with lower green innovation quality. This may stem from the fact that major shareholders with large shareholdings tend to prioritize short-term interests. Given that green innovation typically requires substantial upfront investment and exhibits a relatively long payback period, major shareholders might reduce their commitment to such investments in pursuit of short-term profit maximization, thereby leading to diminished GTQ. The coefficients of these variables are largely consistent with Nepal et al. [47].

5.2. Parallel Trend Test

Compliance with common trends is crucial for causal inference using the DID method [65]. Therefore, the parallel trend assumption, as delineated in Equation (11), indicates that the temporal progression of green innovation quality across firms in both pilot and non-pilot cities is the same before the policy’s implementation. After the implementation of the policy, the parallel trends of the experimental and the control groups could be significantly different. This paper sets up a test model as follows:

$${GTQ}_{it}={\beta }_0+{\sum }_{-4\le j\le 3,\ne 0}{\beta }_j{PZGFRI}_{it}+\lambda X_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(11)

where ${\beta }_j$ is the estimated coefficient concerned. Figure 5 illustrates the parallel trend results. Since the PZGFRI started in 2017, we selected the year 2017 as the base period and removed it from the figure. It can be seen from Figure 5 that during the four years before the implementation of the PZGFRI, the estimated policy coefficient crossed the zero horizontal line. This indicates that before the policy is introduced, there is no significant difference between the enterprises under the jurisdiction of the experimental zone and those outside the experimental zone. After policy intervention, the coefficients of the interaction term are significantly positive at the 5% confidence level. According to the graphical trend, the diversity between the groups progressively escalates over the years. Based on the above analysis, the parallel trend hypothesis is satisfied.

5.3. Robustness Tests

5.3.1. PSM-DID

Divergences in the industrial structure and capital size of the firms covered in the treatment and control groups may have led to biased estimation. We selected control group samples using propensity score matching (PSM). Specifically, the sample enterprises were scored using logit and conduct 1:1 nearest neighbor matching. In this case, there is no substantial disparity between the two groups before the pilot zone policy shock. Hence, the endogeneity problems related to self-selection bias generated from establishing green finance pilot zones are alleviated. The PSM-DID results are reported in

Table 4. PSM-DID.

	(1)	(2)	(3)	(4)
$PZGFRI$	0.591 ***	0.411 **	0.426 **	0.412 **
	(0.188)	(0.179)	(0.191)	(0.196)
Constant	11.874 ***	9.214 ***	7.148 ***	7.828 ***
	(3.081)	(2.722)	(1.258)	(2.021)
Controls	×	√	√	√
Firm FE	√	×	√	√
Year FE	√	√	×	√
City FE	√	√	√	√
Observation	13,125	13,125	13,125	13,125
Adj-R2	0.314	0.417	0.325	0.558

Note: The initial column offers findings without control factors, whereas the following columns exhibit results that incorporate control variables and fixed effects, respectively. The rest is the same as in Table 3.

. In the first column, the coefficient of the $PZGFRI$ term is 0.391 and is significantly positive at the 5% level. This result is still stable after adding the control variables and fixed effects of enterprises, years, and cities in columns (2)–(4), demonstrating that the PZGFRI exerts a substantial influence on GTQ. The results generally imply the robustness of the baseline regressions.

5.3.2. Placebo Test

Figure 6 depicts the outcomes of the placebo test. This paper conducts a placebo test through randomization to eliminate the influence of other invisible elements, including economic, environmental, and political factors, on firms in the test area from the pure policy effect. Specifically, we employ 500 samples among listed companies in all 31 provinces, and each sample randomly selects listed companies in five provinces as the treatment group. We further estimate Equation (1) based on the newly selected sample. As can be seen from the results in Figure 6, the distribution of randomly assigned estimates for the placebo test is concentrated around 0, and the estimated coefficients are all much smaller than the estimated value of 0.379 in the base regression model (1), implying the robustness of previous findings.

5.3.3. Exclusion of Contemporaneous Policy

During the sample period, China implemented several important policies closely related to enterprise green innovation, especially the carbon emission trading (CET) policy, which might interact with the PZGFRI. The confusion effect caused by these concurrent policies could restrict the validity of the core results. Therefore, this paper further controlled the dummy variable of the CET and re-conducted the regression. The results are shown in columns (1)–(2) of

Table 5. Exclusion of contemporaneous policy.

	(1)	(2)	(3)	(4)
	DID		PSM-DID
$PZGFRI$	0.412 ***	0.366 **	0.326 ***	0.394 **
	(0.107)	(0.179)	(0.111)	(0.169)
CET	0.357 *	0.279 *	0.398 *	0.301 *
	(0.208)	(0.157)	(0.226)	(0.172)
Constant	3.782 ***	4.176 ***	2.499 ***	5.201 ***
	(0.846)	(1.233)	(0.561)	(1.792)
Controls	×	√	×	√
Firm FE	√	√	√	√
Year FE	√	√	√	√
City FE	√	√	√	√
Observation	15,054	15,054	12,624	12,624
Adj-R2	0.302	0.453	0.377	0.598

Note: All the variables and symbols are the same as in Table 3.

. After excluding the interference of concurrent policies, the green finance policy still maintained a good incentive effect on the enterprise’s green innovation quality. In addition, columns (3)–(4) of Table 5 present the PSM-DID regression results after controlling for the dummy variable of the CET and PZGFRI policies. These results show that the green finance policy still proves the robustness of baseline results.

5.3.4. The Omitted Variables Test

Referring to the practice of Altonji et al. [66], by controlling the set of finite observable variables, the difference ratio of the coefficients of explanatory variables is calculated, and thereby the possibility of the effect of bias in the omitted variables on the baseline results is evaluated. According to the results in

Table 6. The omitted variables test.

Finite Sets Control Variables	Finite Set Coefficients	Universal Set Coefficients	Difference Ratio
No control variables and no fixed effects	0.761	0.379	0.657
No control variables but fixed effects of enterprise and city years	0.528	0.379	2.256
Only control variables and enterprise fixed effects	0.443	0.379	4.689

, when no control variables or fixed effects are included, the difference ratio is 0.657, which is much lower than that in other cases. At this time, the degree of bias caused by the omission of variables is relatively high. When only the multiple fixed effects are included, the difference ratio is 2.256. The difference ratio when both control variables and enterprise fixed effects are included is 4.689. It should be indicated that the self-selection based on unobservable variables must be at least 2.256 times larger than that based on observable factors in order to cause the benchmark results to be biased. Therefore, the possibility of estimation result bias caused by omitting unobservable variables is relatively small.

5.3.5. Replacing the Metric of the Explained Variable

To further test the robustness of this paper, we draw on Li and Yang [15] and use the citation count of green patents to measure the quality of green innovation. Specifically, the company’s green innovation quality is measured by taking the logarithm of the number of citations in the two years following the application plus one. The regression results are shown in

Table 7. Replacing the metric of the explained variable.

	(1)	(2)	(3)	(4)
$PZGFRI$	1.285 ***	1.109 ***	0.986 **	0.829 **
	(0.348)	(0.311)	(0.391)	(0.388)
Constant	5.411 ***	4.450 ***	3.866 ***	2.573 ***
	(1.724)	(1.289)	(1.084)	(0.870)
Controls	×	√	√	√
Firm FE	×	×	√	√
Year FE	×	√	×	√
City FE	×	√	√	√
Observation	15,054	15,054	15,054	15,054
Adj-R2	0.297	0.380	0.450	0.483

Note: All the variables and symbols are the same as in Table 3.

. The results indicate that even if the metric of the explained variable is replaced, green financial policies still have a significant promoting effect on the enterprises’ GTQ.

6. Further Analysis

6.1. Mechanism Analysis

Columns (1) and (3) in

Table 8. Mediating effects model.

	(1)	(2)	(3)	(4)
	Einv	GTQ	Sa	GTQ
$PZGFRI$	0.531 **	0.112 *	−0.046 ***	0.012 **
	(0.269)	(0.066)	(0.0142)	(0.006)
Einv		0.023 ***
		(0.005)
Sa				−0.002 **
				(0.001)
Cons	2.111 ***	3.244 ***	1.473 ***	3.016 ***
	(0.735)	(1.184)	(0.459)	(1.010)
Controls	√	√	√	√
Firm FE	√	√	√	√
Year FE	√	√	√	√
City FE	√	√	√	√
Observation	15,054	15,054	15,054	15,054
Adj-R2	0.414	0.514	0.325	0.418
p-value of Sobel test	0.009		0.023

Note: Einv denotes environmental protection investment, Sa denotes financing constraint. The rest is the same as in in Table 3.

show the regression results of the effects of the $PZGFRI$ on the mediating variables, while columns (2) and (4) show the regression results of the effects of the $PZGFRI$ and mediating variables on the GTQ. The coefficient of PZGFRI in column (1) of Table 8 is 0.531, indicating that this policy has had a direct effect on enterprises’ environmental protection investment; that is, PZGFRI has significantly promoted the environmental protection investment of local enterprises. The coefficient of $PZGFRI$ in column (2) is 0.112 and is significantly positive at the 10% confidence level, and the p-value of the Sobel test is 0.009, which indicates that the PZGFPRI serves as a partial mediator in the process of enhancing the enterprises’ green innovation quality. This means that the PZGFRI has the effect of improving the enterprises’ green performance by increasing those firms’ environmental investment. These findings indicate that after introducing the PZGFRI, the government encourages them to invest in the clean program, and this subsidy promotes the enterprises’ GTQ. Therefore, hypothesis 2 is verified.

Column (3) of Table 8 explores the extent of regional firm financing constraints under the PZGFR. We find that the estimated coefficient of the PZGFRI is −0.046 and is significant at the 1% confidence level. This indicates that this policy has a direct effect on the financing constraints of enterprises, that is, the PZGFRI can help enterprises reduce the cost of external financing. Column (4) jointly explores the impact of firm financing constraints and the PZGFRI on enterprises’ GTQ. We find that the coefficient of $PZGFRI$ is still significantly positive, and the coefficient of financing constraints is significant. Furthermore, the p-value of the Sobel test was 0.023, which verified the effectiveness of this intermediary channel. Therefore, hypothesis 3 is verified. Through the introduction of green preferential policies, local authorities can enhance the GTQ by alleviating enterprises’ financial constraints.

6.2. Heterogeneity Analysis

The previous findings suggest that the PZGFRI significantly enhances firms’ GTQ. However, innovation pilot zone policies may have distinct impacts on different enterprises due to their unique attributes. To better explore the micro effects of the PZGFRI on green innovation under different firm attributes, we conducted a heterogeneity analysis on three aspects: the nature of enterprise property rights, enterprise scale, and enterprise pollution level. The heterogeneity analysis results are in

Table 9. Heterogeneity analysis.

	(1)	(2)	(3)
	Firm Property Right	Firm Scale	Firm Pollution Degree
$PZGFRI$ * Group1	0.437 ***
	(0.125)
$PZGFRI$ * Group2		0.337 *
		(0.189)
$PZGFRI$ * Group3			0.265 **
			(0.133)
Constant	2.572 ***	3.048 ***	2.929 ***
	(0.853)	(0.974)	(0.795)
Controls	√	√	√
Firm FE	√	√	√
Year FE	√	√	√
City FE	√	√	√
Observation	15,054	15,054	15,054
Adj-R2	0.665	0.407	0.562

Note: The first column is grouped by the nature of ownership, the second column is grouped by the scale of the enterprise, and the third column is grouped by the firm’s pollution degree. The rest is the same as in Table 3.

.

The intersection term of the property nature variable (Group1) and the PZGFRI is taken as a new explanatory variable to re-analyze the entire sample. Specifically, if the enterprise is a state-owned enterprise, Group 1 is set to 1. The result is shown in column (1). The results show that the coefficient of PZGFRI*Group1 is 0.437 and is significant at the 1% level, indicating that the promoting effect of green finance on the GTQ of state-owned enterprises is more obvious. This aligns with the conclusions of Yu et al. [67]. This may be due to the following three reasons: first, state-owned enterprises (SOEs) have complete internal governance structures and resource allocation capabilities. This enables them to respond better to the policies of the green finance reform pilot zones. SOEs must focus their resources on high-quality green innovation projects. They should avoid wasting resources on the inefficient expansion of the number of innovations. SOEs can focus more on improving quality in green innovation thanks to the advantage of internal governance. Second, SOEs are usually highly consistent with the national policy orientation according to property rights theory. The policies of the PZGFRI provide a clear direction for green innovation for state-owned enterprises, which must integrate green innovation with national strategies and promote industrial upgrading and sustainable development through high-quality green innovation projects. This strategic alignment enhances the quality of green innovation and strengthens the long-term competitiveness of SOEs.

Column (2) reports the regression results based on the differences in the size of enterprises. Companies of varying sizes exhibit substantial differences in operational efficiency and financial capacity. In this paper, the intersection term of the two variables, the enterprise size variable (Group2) and the PZGFRI, is taken as a new explanatory variable to re-analyze the entire sample. Specifically, if the enterprise is a large-scale enterprise, Group 2 is set to 1. Otherwise, it is 0. In this paper, the sizes of all enterprises are averaged. Those above the average are large-scale enterprises. Those below the average are small-scale firms. The results show that the coefficient of PZGFRI*Group2 is 0.337 and is significant at the 10% level. This shows that the PZGFRI plays a more significant role in promoting the GTQ in large enterprises. These economic explanations are as follows: First, large enterprises have ample financial, technological, and human resources. The PZGFRI guides these enterprises to focus their resources on high-quality green innovation projects by providing special green credit and other financial tools according to resource allocation theory. Large enterprises can use economies of scale more efficiently, achieving greater depth and breadth of green innovation and enhancing the quality of innovation. Second, the PZGFRI has clearly promoted the exchange in and cooperation with green technologies. As industry leaders, large enterprises can more easily obtain advanced green technologies and innovative concepts. This technological spillover effect enhances the green innovation efficiency of large enterprises and improves their innovation quality. Large enterprises are accelerating the transformation and application of green technologies by cooperating with universities and research institutions, further consolidating their leading position in the field of green innovation.

Column (3) reports the results based on the differences in enterprise pollution levels. The intersection term of the enterprise pollution (Group 3) and the PZGFRI is taken as a new explanatory variable to re-analyze the entire sample. Specifically, if the enterprise is a heavily polluting enterprise, Group 3 is set to 1. The results show that the coefficient of PZGFRI*Group3 is 0.265 and is significant at the 5% level, indicating that the promoting effect of PZGFRI is more obvious. This might be because, first, heavily polluting enterprises usually face stricter environmental supervision, and green financial policies further increase the cost of their environmental violations. To avoid financing restrictions or penalties, heavily polluting enterprises are more motivated to reduce pollution emissions and meet policy requirements through high-quality green innovation. Second, high-quality green innovation can enhance a company’s reputation and competitiveness in the market, attracting more investors and consumers according to signal transmission theory. Under the PZGFRI, heavily polluting enterprises are more inclined to demonstrate their commitment to environmental protection and sustainable development capabilities through high-quality green innovations. This market signal and reputation mechanism further drives heavily polluting enterprises to focus on the quality of green innovation.

7. Conclusions

7.1. Theoretical Implications

The theoretical contribution of this paper lies in the following: Taking Hartwick’s law’s dual mechanism of accumulation and investment as the starting point, this paper takes the lead in extending it to the context of green finance and systematically clarifies the internal logic of how green finance enhances the quality of green innovation in enterprises. Based on this, this paper, by drawing on the core conclusions of Hartwick’s law and integrating local static equilibrium analysis with Shephard’s lemma, constructs a theoretical model of how green finance policies affect GTQ, further explaining the theoretical mechanism. Most of the existing studies focus on the paradigm opposition between “effective incentives” and “ineffective incentives”, and no consistent conclusion has been reached yet. The relationship between green finance and enterprises’ green innovation needs to be further clarified. The research conclusion of this paper provides a new theoretical reference for whether and how green finance can substantially drive enterprises’ green innovation. In addition, this paper draws on the construction logic of the Herfindahl–Hirschman index, adopts the GTQ measurement index centered on the concentration of the IPC green patent portfolio, and empirically examines the impact and mechanism of action of PZGFRI. The traditional indicators commonly adopted, such as the number of patent applications or the number of patent citations, are prone to being troubled by “strategic citations”. Based on the availability of data in China and the research context, this paper replaces the single quantity or citation dimension with “the breadth of knowledge of green patents” and more stably characterizes the impact of green finance on the quality of enterprises’ green innovation.

7.2. Conclusions

Based on the data of China’s A-share listed companies from 2010 to 2020, this paper empirically examines the impact of the PZGFRI on these enterprises’ green innovation quality. This study shows that the PZGFRI has significantly boosted the GTQ of enterprises. Furthermore, through the analysis of the mediating effect model, the internal mechanism by which the PZGFRI affects the enterprises’ GTQ is through two paths: promoting enterprises’ environmental investment and alleviating financing constraints. In addition, the results of the heterogeneity analysis show that in state-owned, heavily polluting, and large enterprises, the effect of the PZGFRI is more significant, which indicates that the implementation effect of the policy varies among different enterprise types. The research conclusion not only enriches the theoretical research on the relationship between green finance and enterprise innovation, but also provides practical guidance for policy optimization, enterprises enhancing their green competitiveness, and promoting the coordinated development of the regional green economy.

Even though this study investigates the impact of green finance on the GTQ of enterprises based on Hartwick’s law and further examines the underlying mechanisms, it is subject to three main limitations. Firstly, the sample period covers the years from 2010 to 2020. It should be noted that policy effects generally require a longer time horizon to manifest fully. Future research can employ extended time frames in order to yield more comprehensive insights into the relationship between green finance and GTQ. Secondly, while this paper identifies certain mediating pathways between environmental investment and financing constraints, there may be additional influential channels that should be explored further. Further research could explore other potential transmission mechanisms. Thirdly, the empirical analysis focuses on listed Chinese enterprises, which somewhat restricts the generalizability of the findings. Further research is needed to examine whether the conclusions hold for unlisted firms and to extend the analytical framework beyond the Chinese context through cross-country comparative studies. In light of these findings, it is possible to draw several policy implications.

First, gradually promote and implement the PZGFRI on a larger scale, and vigorously improve the green finance system. Among these cities, there are not only coastal areas such as Zhejiang and Guangdong, but also central and western regions such as Jiangxi, Guizhou, and Xinjiang, covering relatively typical economic geographical areas in China and having a certain representativeness. Moreover, after a period of exploration, the experiences of the PZGFRI have been increasingly refined. Promoting these experiences across the country can facilitate the development of the green finance system in various provinces, and at the same time, better contribute to the transformation and development of the national green economy. Therefore, it is necessary to consider the leading advantages of the pilot zones, summarize and promote the beneficial experiences, bring green finance reform and innovation measures to more regions, and accelerate enterprises’ green transformation.

Second, the government should focus on encouraging enterprises to alleviate their financing difficulties. The PZGFRI mainly promotes the improvement of enterprises’ GTQ by encouraging them to make environmental protection investments and alleviating the financing constraint pressure they face when undergoing green transformation. Therefore, green financial policies should continue to play a guiding and supporting role in the green and low-carbon development of local economies and societies, promote enterprises’ environmental investment, improve the efficiency of enterprises’ environmental investment, and drive enterprises’ green transformation. Green finance reform and innovation pilot zones should, through multiple measures such as optimizing green credit policies, promoting the development of the green bond market, establishing green industry funds, improving green finance incentive mechanisms, and strengthening the construction of green finance infrastructure, give full play to the guiding and supporting role of green finance in the green and low-carbon development of local economies and societies.

Third, the government should provide differentiated incentives and assistance based on the heterogeneous characteristics of enterprises. It is known that non-state-owned and small-scale enterprises, due to their attributes, may face problems such as financing constraints when conducting green technological innovation compared to state-owned enterprises and large-scale enterprises. To this end, the government can introduce a series of targeted policies, such as encouraging financial institutions to provide microcredit specifically to meet the capital demands of small and medium-sized enterprises for green technological innovation. These policies could help small and medium-sized enterprises obtain sufficient financial support and a reasonable time cycle, thus enabling them to carry out green technological innovation more smoothly. Under the guarantee of the principle of enterprise profit, as the overall economy advances toward green transformation, these enterprises could have more confidence and strength to invest resources in improving the quality of green technological innovation, thereby promoting the green and sustainable development of the entire society.