Green Finance for Green Land: Coupling Economic and Ecological Systems Through Financial Innovation

Abstract

The coupled development of economic and ecological systems is crucial for achieving sustainable growth, with the financial system playing a pivotal adaptive role. Green financial innovation (GFI) is central to enhancing this adaptation. Urban land use eco-efficiency (ULUEE) serves as an effective measure of economic–ecological coupling. Using China’s Green Finance Reform and Innovation Pilot Zones (GFRPZs) as a quasi-natural experiment, this study assesses the impact of GFI on ULUEE, employing panel data from 283 prefecture-level cities (2013–2021). The results show that GFI significantly enhances ULUEE through technological spillovers, strengthened environmental regulation, industrial upgrading, and resource agglomeration. Heterogeneity analyses further reveal that GFI’s positive effects are more pronounced in economically developed regions, cities without legacy heavy-industry reliance, and those with deeper financial development. Additionally, GFI demonstrates cross-regional spillover effects, effectively interacting with other environmental policies. While GFI’s impact is more pronounced in economic growth, its ecological governance improvements are modest. This study provides critical insights for tailored green financial policies aimed at harmonizing economic and ecological objectives.

1. Introduction

Ecosystems are challenged by persistent market failures, technological bottlenecks, collective action dilemmas, and insufficient policy implementation [1,2]. According to the World Economic Forum, about 50% of global GDP directly depends on ecosystem services, but currently more than 60% of regional ecosystem resistance is declining. Economic–ecological system coupling and coordination (EESC) can enhance resource utilization efficiency, optimize ecosystem services, reduce ecological risks and economic losses, and promote green innovation and technological development [3,4]. To achieve the transition from “gray growth” to sustainable development, it is urgent to promote EESC. As the core hub for resource allocation, the financial system can effectively drive this transformation by channeling capital into low-carbon projects and using financial tools such as green bonds, carbon finance, and ESG investments to reduce transaction costs in ecological governance. Studying the role of the financial system in economic–ecological integration not only helps address the market-driven “tragedy of the commons” but also provides viable pathways to balance economic growth with ecological sustainability within global decarbonization targets.

The coupling and coordination of economic and ecological systems (EESC) urgently requires the innovation and expansion of adaptive financial instruments to address the dynamic complexities of ecosystems, bridge the financing gap for sustainable development, and create market incentives for green investments [5,6]. Traditional financial systems struggle to price ecological value and face significant structural challenges. Green finance innovation (GFI) can overcome these obstacles by diversifying and digitalizing financial instruments, effectively internalizing ecological costs into financial decision-making processes, and enhancing the financial system’s adaptability within the EESC framework [7,8,9]. This approach not only improves system adaptability but also reduces financing costs for green projects through clearer market signals, positioning GFI as a crucial tool for achieving sustainable development [10,11]. However, existing studies primarily focus on the impact of finance on individual systems, with less attention given to the distinctive role of financial innovation as a coordinating mechanism for economic–ecological integration.

As a shared carrier of ecological and economic systems, urban land use eco-efficiency (ULUEE) requires maximizing the economic output per unit area while simultaneously optimizing ecological value. It has thus become a significant indicator reflecting EESC [12,13]. The conflict between economic growth and ecosystem conservation has become increasingly evident, with land use facing negative ecological externalities such as soil degradation, biodiversity loss, and a reduced carbon sink capacity [14]. Approximately 25% of global land area has experienced moderate to severe degradation, exacerbated by the ineffective management of agricultural surface pollution due to regulatory challenges. Improving ULUEE signifies the coordinated optimization of economic output and ecological protection [15], serving as a key approach to addressing these issues [16]. Improving ULUEE has emerged as a vital investment target for green finance [17]. One of the core objectives of green finance is to drive the green transformation of the economy by allocating capital, with land, as a resource carrier, playing a crucial intermediary role [18,19]. Green financial tools can influence land management by incentivizing low-carbon initiatives and land use changes, ultimately contributing to ecological improvements [20,21]. Integrating land factors into green finance mechanisms is key to understanding how financial innovations impact ecological outcomes. As shown in Figure 1, there is a clear positive correlation between the share of green credit and the ULUEE in China, which necessitates further investigation to establish causal relationships. However, current research lacks an exploration of the causal relationship between GFI and ULUEE. Thus, there is a need to develop research frameworks based on the three-dimensional coupling of finance, land, and ecology. This study aims to analyze the policy implications of green financial innovation (GFI) within the framework of finance–land–ecology coupling.

Therefore, we conduct a quasi-natural experiment using data from 283 prefecture-level cities in China, spanning from 2013 to 2021, with ULUEE as the research subject, to thoroughly examine the specific impacts of GFI on ULUEE and its underlying mechanisms. The results indicate that GFI significantly enhances ULUEE, primarily by promoting technological advancement through incentives for green innovation, reinforcing environmental regulations associated with green finance, and facilitating the regional agglomeration of technology, financial services, and financial capital, thereby comprehensively driving industrial upgrading. Additionally, our analysis identifies key enabling pathways, such as innovations in green financial products and services, the development of green financial markets, and improvements in green financial policy frameworks, which collectively support the self-evolving path of green finance. Consequently, this paper recommends strengthening the national green finance system and promoting green financial innovation, thus providing directions for research on financial system adaptation to eco-economic coupling and coordination.

The contributions of this paper are mainly reflected in the following aspects:

First, this paper provides additional evidence of the financial system’s adaptability to EESC. We analyze the mechanism of GFI and, for the first time, incorporate the institutional innovation of green finance reform pilot zones into the analytical framework. By constructing a theoretical pathway of “policy incentives–technological innovation–industrial upgrading–environmental regulation–economic agglomeration”, we investigate the internal transmission mechanisms and reveal the multidimensional impact of innovative financial instruments on ULUEE. The research on the financial system primarily focuses on resource allocation efficiency, risk management, and financial stability [22,23,24], examining impacts such as investment efficiency and risk mitigation. Studies on financial instruments typically emphasize innovation mechanisms and pricing capabilities [25,26,27], with green finance research largely concentrated on the design of green financial instruments and policy effects [28]. On the one hand, existing studies focus on combining multiple instruments into a comprehensive green finance indicator to analyze coupling and coordinated development [29]. On the other hand, studies on green financial policies evaluates their effectiveness through policy implementation metrics [30]. Some scholars argue that green financial policies enhance green total factor productivity, promoting a sustainable development model [31]. Conversely, other studies suggest these policies may reduce financing availability for high-emission enterprises, crowding out their investment in green innovation, or highlight potential issues of formality without substance, negatively affecting policy implementation. Additionally, the literature on the financial system focuses primarily on enterprises or financial institutions, analyzing their impacts on economic or ecological systems, with insufficient attention given to aspects like urban land use. This paper examines the policy effects embodied in GFI concerning intensive land use, providing a more comprehensive and targeted analytical approach to studying green financial policies and green finance.

Second, this paper fills a gap in the literature on the mediating role of land elements in economic–ecological systems by analyzing the financial system’s role in resource allocation and revealing the dynamic mechanisms of ULUEE. ULUEE reflects the intensity of land use under sustainable conditions. Other studies examine ULUEE from both natural and human-oriented perspectives. From the natural perspective, research has focused on the spatio-temporal characteristics of land resources within specific regions [32,33], while the human dimension includes socio-economic conditions, industrial structure, and urbanization levels [34,35]. Other studies have examined the spatial and temporal evolution of land use [36], eco-efficiency across industries and materials [37,38], and the role of traditional financial development in ecological efficiency [39,40,41]. However, most of these studies overlook the financial system as a driver of effective resource allocation. Research on the mechanisms linking GFI and ULUEE remains scarce. This paper addresses this gap by examining how GFI influences ULUEE through financial resource allocation, thereby enriching the understanding of ULUEE and extending the theoretical framework of land elements as mediators in economic–ecological system coupling.

Third, this paper enriches understanding of economic, financial, and ecological system interactions by addressing gaps in their coupled development. It analyzes the impact of GFI on ULUEE, demonstrates GFI’s economic and ecological benefits, and reveals the financial system’s adaptability in promoting eco-economic coordination. Existing studies on system coordination mainly focus on evaluating coupling levels, constructing regional sustainable development paths, and developing ecological economic models [42,43,44,45], while those addressing a financial system’s role typically emphasize resource allocation efficiency, ecological value pricing, and risk management [46,47,48,49]. However, limited attention has been given to how innovation and diversification in green financial instruments can improve a financial system’s coordination capacity. Moreover, current research often isolates the financial system’s impact on either the ecological or economic system [50,51], overlooking their integrated and synergistic evolution. The mechanisms by which GFI influences ULUEE also remain underexplored. Although some studies have recognized the roles of tools [52,53], few have empirically examined the effects of policies implemented in Green Financial Innovation Pilot Zones. Furthermore, while land is widely acknowledged as a key factor in eco-economic coordination, limited research has examined this issue from the perspective of ULUEE [54]. This paper addresses this limitation by analyzing the impact of GFI—an indicator of financial system adaptability—on ULUEE within a finance–economy–ecology framework, providing empirical evidence for the integrated development of these systems.

The structure of this paper is organized as follows. The second part constructs a theoretical framework for analyzing the impact of GFI on ULUEE. It draws upon classical economic and ecological theories, combined with practical cases from China’s Green Finance Reform and Innovation Pilot Zones (GFRPZs), to explore the transmission mechanisms of GFI. Based on this, relevant research hypotheses are proposed, focusing on both the theoretical foundation and the mechanism through which GFI influences ULUEE. The third part conducts an empirical investigation into the impact of GFI on ULUEE using a quasi-natural experiment design. It includes the selection of data and models, as well as variable definitions. The empirical analysis proceeds through descriptive statistics, benchmark regressions, robustness checks, mechanism analysis, and heterogeneity analysis. Further analysis explores the spillover effects of GFI, policy synergies, and a multi-dimensional efficiency decomposition based on the Slack-Based Measure (SBM) model. Based on the findings from the theoretical and empirical analyses, the fourth part summarizes the main conclusions of the study. It then offers policy recommendations aimed at promoting GFI, strengthening the coordinated development of financial, economic, and ecological systems through improved ULUEE. The conceptual framework of system coupling is illustrated in detail in Figure 2.

2. Research Hypotheses

2.1. GFI as a Driver of EESC Through the Enhancement of ULUEE

As resource constraints tighten globally, high-quality development has become a guiding principle for sustainable growth. Against this backdrop, ULUEE serves not only as a technical indicator for assessing land resource efficiency and environmental performance, but also as a critical link in achieving coordinated development between economic and ecological systems, based on the systems theory proposed [55].

From the perspective of the economic system, improving ULUEE optimizes factor allocation and increases the marginal productivity of land and other resources. Efficient land use minimizes waste and directs land, capital, and labor toward green, high-value-added industries, promoting a “green transformation” in the industrial structure. Guided by green finance, the traditional pattern of inefficient land use and extensive development has been corrected. Polluting and high-consumption industries are gradually replaced by clusters of green technology, energy-efficient, and environmentally friendly industries, as well as new energy sectors. This shift facilitates a transition toward a more sustainable and low-consumption resource allocation model, enhancing land output value and strengthening the economic system’s resilience under environmental and resource constraints.

From an ecological perspective, enhancing ULUEE directly alleviates ecological pressure and restores ecosystem functionality. Traditional development models have often sacrificed ecological integrity, resulting in habitat fragmentation, soil erosion, and reduced ecosystem stability. Efficient land use curbs urban sprawl and low-efficiency land development, helps prevent the loss of farmland, wetland encroachment, and forest fragmentation, and maintains ecosystem integrity. Moreover, optimizing land use structures improves the ecosystem’s ability to regulate external disturbances such as pollution, carbon emissions, and climate change, thereby enhancing both ecological capacity and environmental efficiency. For example, green technologies and financial incentives support land redevelopment, remediation, and reuse—reducing the environmental costs of new land development while strengthening ecological compensation on existing land. In urban areas, policies promoting compact, mixed-use land development have increased the ecological contribution per unit of land and fostered more symbiotic relationships between human activity and natural systems.

Finally, from a system coupling perspective, improving ULUEE facilitates a shift from passive conflict between the economic and ecological systems to active synergy. Under the guidance of GFI, land use efficiency improvements reduce the economic system’s overdependence on ecological resources while enabling the ecosystem to contribute to economic processes through “ecological productization”. A dynamic feedback loop is formed: green financial incentives drive higher land-based ecological efficiency, improving ecosystem health, which in turn enhances the economic and natural value of land resources, promoting further economic system optimization. This mutually reinforcing evolution creates a multi-objective, multi-dimensional, and multi-scalar framework for coupled system development.

Given the composite nature of ULUEE, it serves as a key proxy for analyzing the coupling between economic and ecological systems. GFI promotes this coupling by diversifying financial instruments to achieve optimal resource allocation. Therefore, investigating the mechanisms through which GFI influences ULUEE is central to this study. Under the framework of ecological modernization [56,57] and financial development theory [58,59], we explore how GFI enhances both the economic output and ecological performance dimensions of ULUEE, thereby facilitating economic–ecological coordination.

From the perspective of environmental land protection, GFI quantifies ecological benefits and uses market-based tools to create positive incentives and optimize land use patterns. On the one hand, it meets the financing needs of enterprises undergoing green transitions. On the other hand, it integrates ecological performance into financing conditions, linking environmental outcomes with financing costs. To access green finance, firms are incentivized to internalize environmental costs in land planning and decision-making, thereby improving land use efficiency and protecting ecosystems through technological upgrades and process innovations.

From the perspective of land’s economic and social value, GFI advances financial development and enhances ULUEE through several channels. First, GFI lowers financing costs for green projects by increasing the supply of green credit, alleviating financial constraints, and enabling greater investment in green research and development (R&D) and industrial upgrading. Second, it broadens access to capital by expanding the green financial toolkit, allowing for a more efficient economic output per unit of land without requiring major increases in land input. Third, product innovation enables precise financial support tailored to different land use scenarios. Targeted green finance addresses inefficiencies in traditional development models and supports performance improvements. Finally, service innovation and infrastructure upgrades—such as ecological data platforms integrating remote sensing, IoT sensors, and blockchain—improve the allocation of green finance and the pricing of land use externalities. These advances support ULUEE by enabling real-time monitoring, guiding resource allocation, and strengthening financial product design, issuance, and risk management. Differentiated pricing mechanisms also guide land users to adjust toward more sustainable and efficient practices, rewarding high-efficiency projects with preferential rates while penalizing ecologically harmful ones.

Therefore, GFI effectively incorporates ecological objectives into economic decision-making. It not only channels resources to support ecological land use but also strengthens the financial foundation for upgrading land productivity. In this way, GFI facilitates the synergistic coupling of economic and ecological systems, advancing a coordinated and mutually reinforcing development trajectory. We propose the following hypotheses:

H0: 

GFI does not enhance the adaptation tools of the financial system, and does not improve ULUEE.

H1: 

GFI enhances the adaptation tools of the financial system and improves ULUEE.

2.2. Mechanisms of GFI on ULUEE

2.2.1. Technology Facilitation

GFI can promote technological innovation by reducing the financing cost of the production sector as well as strengthening the external constraints, which in turn improves the ULUEE. Based on the theory of the innovation compensation effect proposed by Porter et al. [60], appropriate green environmental restrictions can enhance industrial competitiveness and promote technological progress, providing a theoretical basis for EESC. On the one hand, GFI alleviates the financial constraints on corporate technology R&D through market-oriented approaches, creating advantageous circumstances for technology development; on the other hand, GFI forms external pressure, creating a social atmosphere in which enterprises need to implement technological innovation. This technology-driven mechanism not only accelerates the penetration of technological elements in the production function, but also promotes the systematic enhancement of production efficiency using technology, prompts the transformation of land resources from extensive utilization to intensive and intelligent allocation, realizes the synergy between economic value and ecological value, and ultimately achieves the enhancement of ULUEE. Specifically, technological progress realizes the relative price reduction of technical factors, drives the systematic optimization of the production efficiency of the whole industry, promotes the transformation of land resources from extensive utilization to intensive configuration, and realizes the improvement of ULUEE. Technological progress changes the traditional pattern of production factor allocation, reconstructs the allocation and utilization intensity of land resources in the production process, and brings a more abundant supply of technical factors to enterprises and prompts their relative prices to go down. So enterprises will tend to increase the proportion of technical factor inputs to replace the rest of the relative prices of the same or higher factors of production in the decision-making of production factor combinations, forming the factor substitution effect. In addition, technological progress can also enhance the production efficiency of enterprises. Technological progress through industrial linkage and market competition gradually evolved into the systematic optimization of the production efficiency of the whole industry, altering the unit of land necessary to create a higher economic value while reducing the environmental load as much as possible, and ultimately enhancing the ULUEE.

Therefore, we propose the following hypotheses:

H2a: 

GFI promotes ULUEE through technology facilitation.

H2b: 

GFI does not significantly promote ULUEE through technology facilitation.

2.2.2. Environmental Regulation

Through environmental regulation, GFI pushes enterprises to improve land use, realizing the double enhancement of economic and ecological benefits, and promoting the ULUEE. Based on environmental regulation theory [61,62,63], the environmental regulations within pilot zones (GFRPZs) can be classified into four types: incentive-based regulation, direct regulation, public-participatory regulation, and voluntary environmental agreements. Incentive-based regulation has positive and negative incentives, in which positive incentives encourage financial institutions to develop green products through risk compensation and other measures, while negative incentives establish an environmental credit evaluation system and implement joint disciplinary measures for highly polluting enterprises; direct regulation directly restricts corporate behavior through administrative orders; public-participatory regulation forms flexible constraints with the help of information disclosure and public opinion supervision; voluntary environmental agreement facilitates non-mandatory environmental pacts through stakeholder negotiation. Based on regional resource endowment and development orientation, each pilot zone uses different types of environmental regulations to build a differentiated, multi-level policy system, which changes the behaviors of market stakeholders through external intervention to improve the efficiency of resource use. Direct regulation mainly consists of binding regulation, incentive-based regulation, public-participatory regulation, and voluntary environment agreements which have both binding effects and some incentives, but their cores goals are to achieve the policy objectives by way of constraints or incentives, and they have used the reversed transmission of the pressure for easing the monetary condition to realize the role of green financial environmental regulation to enhance the level of the intensive utilization of land through these paths.

Accordingly, we propose the following hypothesis:

H3a: 

GFI promotes ULUEE through environmental regulation.

H3b: 

GFI does not significantly promote ULUEE through environmental regulation.

2.2.3. Economic Agglomeration

Based on the theories of industrial agglomeration and financial agglomeration effects, GFI can enhance land use efficiency through agglomeration effects [64,65]. Under the framework of GFRPZs, GFI significantly enhances the efficiency of regional idle resource utilization, attracts the aggregation of technology, financial capital, and specialized financial services to the pilot zones (GFRPZs), gradually creates an economic agglomeration effect that is focused on green industry development, and achieves the spatial reconfiguration of resource allocation and an energy-level leap.

In terms of industrial geographic clustering, it is demonstrated that the policy guides industrial concentration through special financial support; in addition, the policy dividend strengthens the clustering advantage and forms industrial agglomeration. GFRPZs uses policy tools such as green bonds, park special-purpose bonds, and other policy tools to reduce the operating costs of enterprises, improve the infrastructure of the parks, form the location advantage, provide policy dividends, attract the industrial chain enterprises to concentrate, promote the overflow of knowledge and technology, and enhance the efficiency of land use. Compared with the green financial policy that plays an indirect leading role in industrial geographic clustering, financial factor concentration is a direct response to green financial policy and a more significant way for it to realize economic agglomeration. In terms of financial factor concentration, it is manifested in the formation and concentration of green financial capital pools, the accelerated expansion of specialized institutions, and the increased supply of products and services to form financial agglomeration. GFRPZs increase the number of franchised financial institutions, form an organizational system, and improve the supply of innovative financial products and services, thus reducing the demand for land unit output value, improving the density of factor allocation and scale effects, and promoting the transformation of land functions in the direction of high efficiency and benefits. Not only that, but the pilot zone governments also use financial subsidies and other policies to bear part of the external costs of green financial product and service innovation, realizing cost sharing and further enhancing the willingness to innovate green financial products and services. Therefore, green financial policies promote the concentration of elements such as green financial capital, financial institutions, and financial products and services, and ultimately realize financial agglomeration.

Industrial geographic aggregation as well as financial concentration constitute the economic agglomeration of GFRPZs, and ultimately realize ULUEE improvement through three paths. The first is to realize intensive land use. The concentration of similar industries or upstream and downstream industries under economic agglomeration brings about the sharing of regional infrastructure, reduces duplicated land construction, and easily triggers the spillover effect, where the knowledge, technology, innovation results and other elements of a single enterprise will spill over to other enterprises in the park. Such spillover effects will further attract the agglomeration of additional enterprises, making it easy to realize land intensification and further promote ULUEE. The second path is to enhance the carrying capacity of land. Economic agglomeration leads to the concentration of various factors to generally enhance the density of production factors, optimize the allocation of resources, and achieve the scale effect. This simultaneously increases the output per land unit and marginal returns while reducing land use costs, ultimately improving land productivity efficiency. Finally, the third path is land function optimization. Economic agglomeration brings about the concentration of high-end service industries, which promotes the development of urban land from having a single function to compound functions and the allocation of land in the direction of high efficiency reduces the land for low-end industries with low efficiency, such as eliminating the enterprises with serious pollution emissions and restricting the land for related industries, etc., so as to take into account the economic efficiency of the land and the ecological function, and to optimize the comprehensive use of the land.

Both economic agglomeration effects invariably reflect positive externalities, which not only reduces the explicit cost of land planning and use, but also realizes the value-added on the basis of the economic value of the land, and ultimately realizes ULUEE.

Accordingly, we propose the hypothesis:

H4a: 

GFI promotes ULUEE through economic agglomeration.

H4b: 

GFI does not significantly promote ULUEE through economic agglomeration.

2.2.4. Industrial Upgrading

GFI realizes industrial upgrading by releasing signals of green governance and financial innovation, creating momentum for green development enterprises and strengthening the environmental awareness of the whole society, etc., and the gradually upgraded and advanced industry helps land resource use to present a new balance between economic land use and environmental load, and promotes ULUEE.

From a signal transmission perspective, based on signaling theory [66], the GFI policy document conveys policy signals and provides differentiated green loan rates. GFI policy documents set up a catalog of encouragement as well as the development of a negative list, implement incentives for trustworthiness and punitive measures for breaches of trust, and strengthen accountability for non-compliance. The second item in that list is the stepped green financial product interest rate signal. Through the issuance of green loans with different interest rates for enterprises with different environmental credit ratings, green development signals are sent to enterprises with low green ratings, indirectly encouraging them to increase their proportions of green industries, and then realize the upgrading of the whole industry. From the perspective of the reputation incentive mechanism [67], green financial policy strengthens the reputation advantage of green enterprises, influences public awareness, and realizes the positive feedback mechanism. GFI policy guides the transformation of enterprises to form the upgrade path of “policy guidance—enterprise response”. Green financial policies encourage enterprises to consciously disclose environmental information, and these enterprises accumulate verifiable “reputational assets” through the regular release of ESG reports. The reputation advantage of green enterprises not only enhances their self-discipline, forms a supervision of themselves, and motivates them to continue to carry out green innovation, but also enables them to become industry benchmarks that generate positive feedback among their peers, and then this radiates and drives the whole industry to move towards greening and high-end advancement. In addition, green financial policies can influence public awareness. The official media announce the latest policy progress and results in a timely manner to deepen the public’s environmental awareness, so that the concept of the financial realization of green sustainable development is deeply rooted in people’s hearts. Under the dual role of the policy guidance and public awareness of social responsibility, the consumer market gradually shows a preference for environmentally friendly products and services, and this market demand orientation has forced manufacturing enterprises to gradually shift from the second and tertiary industries to modern service industries and high-tech industries, and has prompted service-oriented green enterprises to continue to carry out green innovation, and to promote the upgrading of industrial development.

Industrial upgrading has promoted the gradual transformation of the economy from the second and tertiary industry to intensive industries, such as modern service industries and high-tech industries, which has changed the extensive land use pattern in the past. The ecological transformation of industries has reduced industrial land pollution; the development and reuse of abandoned land, such as brownfield sites, has improved the compound utilization rate of land; and the deep excavation of three-dimensional space, such as green buildings and underground space, has improved the economic output per unit of land. Industrial upgrading brings about spatial reconfiguration, efficiency improvement and functional optimization, thus promoting the systematic improvement of ULUEE.

Building on this, we propose the following hypotheses:

H5a: 

GFI enhances ULUEE through industrial upgrading.

H5b: 

GFI does not significantly enhance ULUEE through industrial upgrading.

To facilitate the comprehension of the theoretical mechanism hypothesis in this study, we have constructed a logical diagram (Figure 3).

3. Research Design

3.1. Model Selection and Setting

3.1.1. Baseline Regression Model

This paper constructs the DID model. The policy of GFRPZs is an example of using an institutional design to improve the ecological environment, and this policy objective is in line with the theoretical analysis framework of ecological modernization [68], that is, environmental protection and economic development can go hand in hand, and there exists a positive interaction mechanism between the two. Economic growth can provide a material basis for environmental protection, and effective ecological and environmental governance can create the necessary conditions for economic growth, thus forming a virtuous cycle development path. The policy of GFRPZs inherently possesses environmental governance attributes, and its core lies in guiding the allocation of resources through financial means to improve the effect of environmental governance. GFRPZs have significantly increased the penetration of finance into ecological protection through “bottom-up” regional exploration, not only realizing financial growth and expansion, expanding financing channels for market players, revitalizing the stock of assets [69], and promoting GFI, but also providing new energy sources. It also provides powerful support for the innovative development of new energy and low-carbon technologies as well as the green transformation of industry and society.

Based on this, this paper conducts a city-level quasi-natural experiment by referring to the methodology of Wang [70], and constructs a DID baseline regression model according to whether a city is part of the GFRPZs. The model is specified as follows:

$${\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1·{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2\cdot {\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(1)

where ${\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ denotes the ULUEE of city $\mathrm{i}$ in year $\mathrm{t}$, ${\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ is a dummy variable for the policy of GFRPZs, set to 1 when city $\mathrm{i}$ serves as a GFRPZ in year $\mathrm{t}$, and 0 in the rest of the cases; ${\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ consists of the control variables; and ${\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$ is the random error. In order to alleviate the problem of omitted variable bias, this paper controls for the fixed effects of city and time to avoid their interference with the estimation results, with ${\mathsf{\mu }}_{\mathrm{i}}$ denoting the city fixed effects, and with ${\mathsf{\lambda }}_{\mathrm{t}}$ being the time fixed effects. ${\mathsf{\alpha }}_1$ is the coefficient of the core independent variable ${\mathrm{did}}_{\mathrm{i}\mathrm{t}}$.

3.1.2. Mediating Effects Model

In order to explore whether GFI enhances ULUEE through enhancing the level of green technological innovation, environmental regulation, promoting economic agglomeration and industrial upgrading in several dimensions, taking into account the endogeneity problem that may be caused by the traditional “three-step method” in the testing process, this paper therefore considers the endogeneity problems that may be caused by the traditional “three-step method” in the testing process, and this paper uses the “two-step method” of the mediation effect test, combining it with the aforementioned benchmark regression model (1), and constructs the mediation effect model in the second step as follows:

$${\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{y}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(2)

$${\mathrm{a}\mathrm{u}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}_{\mathrm{i}\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(3)

$${\mathrm{e}\mathrm{n}\mathrm{v}\mathrm{i}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}\mathrm{t}}={\mathsf{\delta }}_0+{\mathsf{\delta }}_1{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_2{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(4)

$${\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{r}\mathrm{y}}_{\mathrm{i}\mathrm{t}}={\mathsf{\theta }}_0+{\mathsf{\theta }}_1{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\theta }}_2{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(5)

$${\mathrm{a}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}={\mathsf{\rho }}_0+{\mathsf{\rho }}_1{\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\rho }}_2{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(6)

where the explanatory variables in Equations (2) and (3) are proxies under the green technology innovation effect; ${\mathrm{p}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{r}\mathrm{y}}_{\mathrm{i}\mathrm{t}}$, and ${\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{r}\mathrm{y}}_{\mathrm{i}\mathrm{t}}$ are proxies for environmental regulation, industrial structure upgrading, and economic agglomeration, respectively; ${\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ is a control variable for city $\mathrm{i}$ in year $\mathrm{t}$; ${\mathsf{\mu }}_{\mathrm{i}}$, ${\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$, and ${\mathsf{\lambda }}_{\mathrm{t}}$ have the same meanings as previously stated.

3.2. Variable Definitions

3.2.1. Dependent Variable

The explanatory variable is ULUEE ($\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}$). As an important link between the environment and the economy, this is an indicator that can well measure the development of the coupling of an economic system and the ecosystem. This indicator is measured using the SBM-Undesirable model, which has its roots in the theoretical framework of Data Envelopment Analysis (DEA) [71], and is an important part of DEA, belonging to the non-radial extension model of its methodology, which is used in this paper for the measurement of ULUEE. This paper uses this method to measure ULUEE, which not only inherits the advantage of the DEA method that can deal with the comprehensive evaluation system of multiple indicators, but also breaks through the limitations of its radial optimization that ignores slack variables and cannot deal with non-desired outputs directly [72], and captures the trade-off relationship between the economic system and the environmental system in land use more accurately. Meanwhile, in order to better measure the reality of complex systems such as urban development and environmental governance, the SBM-Undesirable model based on variable returns to scale is finally chosen to measure the ULUEE. The model formula is as follows:

$${\mathsf{\eta }}^{\ast }=\mathrm{m}\mathrm{i}\mathrm{n}{\displaystyle \frac{1-\frac{1}{\mathrm{m}}{\sum }_{\mathrm{i}=1}^{\mathrm{m}}\frac{{\mathsf{\alpha }}_{\mathrm{i}\mathrm{o}}^{-}}{{\mathrm{x}}_{\mathrm{i}0}}}{1+\frac{1}{{\mathsf{\alpha }}_1+{\mathsf{\alpha }}_2}({\sum }_{\mathrm{j}=1}^{{\mathsf{\alpha }}_1}\frac{{\mathsf{\alpha }}_{\mathrm{j}}^{\mathrm{e}}}{{\mathrm{y}}_{\mathrm{j}0}^{\mathrm{e}}}+{\sum }_{\mathrm{k}=1}^{{\mathsf{\alpha }}_2}\frac{{\mathsf{\alpha }}_{\mathrm{k}}^{\mathrm{b}}}{{\mathrm{y}}_{\mathrm{k}0}^{\mathrm{b}}})}}$$

The formula needs to be satisfied:

$${\mathrm{x}}_0=\mathrm{X}\mathsf{\lambda }+{\mathsf{\alpha }}_0^{-}$$

$${\mathrm{y}}_0^{\mathrm{e}}={\mathrm{Y}}^{\mathrm{e}}\mathsf{\lambda }-{\mathsf{\alpha }}^{\mathrm{e}}$$

$${\mathrm{y}}_0^{\mathrm{b}}={\mathrm{Y}}^{\mathrm{b}}\mathsf{\lambda }+{\mathsf{\alpha }}^{\mathrm{b}}$$

$${\mathsf{\alpha }}_0^{-}\ge 0;{\mathsf{\alpha }}^{\mathrm{e}}\ge 0;{\mathsf{\alpha }}^{\mathrm{b}}\ge 0;\mathsf{\lambda }\ge 0$$

Among these variables, ${\mathsf{\eta }}^{\ast }$ represents the ULUEE of prefecture-level cities, and its value ranges from 0 to 1. If ${\mathsf{\eta }}^{\ast }$ is 1, it means that the decision-making unit is in the state of an optimal combination of “inputs—desired outputs and non-desired outputs”, which is effective; if ${\mathsf{\eta }}^{\ast }$ is less than 1, it means that the decision-making unit is in the state of Pareto Improvement, and there is a loss in efficiency. From the input side, $\mathrm{m}$, ${\mathrm{x}}_0$, ${\mathsf{\alpha }}_0^{-}$, and $\mathrm{X}$ represent the number of input factors, input quantity, input slack variables, and the input matrix; from the desired output side, ${\mathsf{\alpha }}_1$, ${\mathrm{y}}_0^{\mathrm{e}}$, ${\mathsf{\alpha }}^{\mathrm{e}}$, and ${\mathrm{Y}}^{\mathrm{e}}$ represent the number of desired outputs, desired output quantity, desired output slack variables, and the desired output matrix; from the non-desired output side, ${\mathsf{\alpha }}_2$, ${\mathrm{y}}_0^{\mathrm{b}}$, ${\mathsf{\alpha }}^{\mathrm{b}}$, and ${\mathrm{Y}}^{\mathrm{b}}$ represent the number of non-desired outputs, non-desired output quantity, non-desired output slack variables, and the non-desired output matrix. And $\mathsf{\lambda }$ consists of the weights.

For the selection of inputs, desired outputs, and non-desired outputs, referring to the existing [73,74] practice, the input indicators are divided into land inputs, capital inputs, and labor inputs, which are measured by the urban construction area (square kilometers), the total investment in social fixed assets (RMB 10,000), and the number of employees in the industry and service industry (RMB 10,000), respectively. Desired output indicators reflect economic and social development, and are measured by the value added by industry and service industry (RMB 10,000) and the total urban population (RMB 10,000); non-desired outputs show environmental benefits. PM2.5 and carbon emissions are high-frequency monitoring indicators for environmental performance assessment, and both have a direct correlation with land use; they can comprehensively characterize the environmental externalities of land use. Therefore, PM2.5 and carbon emissions were chosen to measure the undesired outputs.

3.2.2. Core Independent Variable

The core independent variable constructed in this paper is ${\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ of the DID model. The ${\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ is obtained from the product of $\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}$ and $\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}$, i.e., ${\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}=\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\times \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}$. When ${\mathrm{d}\mathrm{i}\mathrm{d}}_{\mathrm{i}\mathrm{t}}=\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\times \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}=1$, it indicates that the prefecture-level city is impacted by the policy of GFRPZs.

3.2.3. Control Variables

The selection of control variables follows this principle, covering economic structure, policy capacity and environmental pollution, which conforms to the analytical framework of the “economic–ecological” coupled system. So this paper refers to the past studies [75,76], and chooses to control the following variables to study the impact of the implementation of the policies of GFRPZs on the ULUEE. The primary industry is an important sector in land utilization, and its proportion in GDP directly reflects the degree of a region’s reliance on agricultural land. The proportion of urban construction land reflects the degree of urbanization encroachment on natural land and directly affects the integrity of the ecosystem. Therefore, referring to the research of Chen et al., the level of the primary industry and urban land use were selected as control variables [77]. The level of primary industry ($\mathrm{a}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}$) is measured by the proportion of the gross domestic product of primary industry to the GDP of prefecture-level cities in the same year, and the proportion of agriculture can directly reflect the use of land, especially agricultural land, and its relationship with ULUEE is relatively close. Urban land use ($\mathrm{s}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{r}\mathrm{e}$) is measured by the proportion of land area used for construction and development to the total urban area. Industrial sulfur dioxide emissions are typical indicators of environmental pollution, not only reflecting the industrialization status of cities but also directly relating to the health of the ecosystem. The technological innovation capacity is the key driving force for green transformation, influencing the research and development of clean technologies and the efficiency of resource utilization. These are related to our core explanatory variable. So, referring to the research of Wang et al. [78], industrial sulfur dioxide emissions ($\mathrm{l}\mathrm{n}\mathrm{s}\mathrm{o}2$) are measured by the logarithmic value of the annual industrial SO₂ emissions of prefecture-level cities. The level of scientific and technological innovation ($\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}$) is represented by the ratio of science and technology expenditure to public fiscal expenditure. The level of financial development determines the efficiency of capital allocation and affects the availability of financing for green projects. The local fiscal capacity determines the intensity of environmental protection investment and infrastructure construction. Therefore, referring to the research of Ge et al. and Yang [79,80], financial development ($\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}$) is measured by the ratio of the year-end RMB loan balance of financial institutions in prefecture-level cities to the regional GDP. The fiscal level ($\mathrm{f}\mathrm{i}\mathrm{s}\mathrm{c}$) is defined as the ratio of annual public revenue to fiscal expenditure in prefecture-level cities. The level of informatization reflects the degree of digitalization in a region and influences the capacity for environmental monitoring and intelligent management. Referring to the research of Zhang et al. [81], the informatization level ($\mathrm{t}\mathrm{e}\mathrm{l}\mathrm{e}$) is calculated as the ratio of the number of cell phone subscribers to the resident population. The specific definitions are provided in

Table 1. Variable definition.

Variable Type	Variable Name	Variable Symbol	Variable Definition
dependent variable	Urban land use eco-efficiency	land	calculated using the SBM-Undesirable model
independent variable	green finance reform and innovation pilot zone	did	the research object belongs to GFRPZs, and the observation time is set to 1 after the implementation time, otherwise it is 0.
control variables	level of primary sector	agriculture	product of primary industry (CNY 100 million) divided by gross product (CNY 100 million)
	urban land use	square	urban land use area (sq·km) divided by total urban area (sq·km)
	industrial sulfur dioxide emissions	lnso2	industrial sulfur dioxide emissions (ton) in logarithms
	level of science, technology and innovation	tech	science and technology expenditure (CNY 10,000) divided by public finance expenditure (CNY 10,000)
	financial development	fina	RMB loan balance of financial institutions (CNY 10,000) divided by GDP (CNY 10,000)
	public economy level	fisc	public revenues (CNY 10,000) divided by public expenditures (CNY 10,000)
	informatization level	tele	number of cell phone subscribers (CNY 10,000) divided by area resident population (CNY 10,000)
mechanism variables	green patent applications	applygreen	green patent applications (piece) divided by resident population (100 persons)
	green patent grants	authorizegreen	green patents granted (piece) divided by resident population (100 persons)
	environment regulation	environment	total regional energy consumption (104 tce) divided by GDP (CNY 100 million)
	economic agglomeration	agglomeration	Herfindahl–Hirschman index for prefecture-level cities’
	industrial upgrading	tertiary	value added by tertiary industry (CNY 10,000) divided by resident population (10,000 persons)

.

3.2.4. Mechanism Variables

The GFI promotes ULUEE through four paths, realizing the systematic coupling development of economy–ecosystem coupling in multiple dimensions. Therefore, this paper selects four mechanism variables for research and analysis. The green innovation level of the city is measured by the number of green patent applications ($\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{y}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$) and the number of green patent authorizations ($\mathrm{a}\mathrm{u}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$) per 100 people in each prefecture-level city [82]. The environmental regulation ($\mathrm{e}\mathrm{n}\mathrm{v}\mathrm{i}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}$) is measured by the ratio of the total annual energy consumption of prefecture-level cities to the gross regional product [83], and the GFRPZs can empower the adjustment of the city’s energy structure, which influences the ULUEE in terms of realizing environmental norms and constraints. The economic agglomeration ($\mathrm{a}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$) is measured by the Herfindahl–Hirschman index of prefecture-level cities [84], and green financial policies can bring economic agglomeration to the city where the pilot zone is located, and utilize the positive externality generated by agglomeration to promote the ULUEE. The industrial structure upgrading ($\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{r}\mathrm{y}$) is measured by the per capita value added by tertiary industry in terms of CNY ten thousand per year in prefecture-level cities [85], and most of the financial support from green finance will flow to the tertiary industry that is less destructive to the environment to promote its transformation and upgrading, and to enhance the ULUEE by way of industrial development.

3.3. Sample Selection and Data Sources

The GFRIZ policy is essentially a “coupled financial innovation”, whose design logic goal is not simply to stimulate a green economy or ecological restoration, but rather to force or incentivize a synergistic coupling of the economic system and the ecosystem through financial instruments. At the same time, as a system innovation, it also catalyzes instrument innovation and market innovation, and is a collection of green financial innovations. Therefore, it is an ideal proxy variable. The GFRPZs have been implemented since 2017, with a total of three batches of pilot zones approved to date. The first batch, established in 2017, included eight regions across five provinces/autonomous regions. The second batch was launched in 2019 in Lanzhou New District, Gansu Province. The third batch, proposed for Chongqing Municipality in 2022, is not included in the experimental group due to the limited time since its implementation and the lack of observable policy effects. Additionally, Hami City and Changji Hui Autonomous Prefecture in Xinjiang are excluded from the study due to severe data limitations. The selection of pilot zones focuses on the diversity of economic–ecological contradiction types to verify the applicability of green finance in different scenarios. As a result, the final experimental group consists of the following cities: Huzhou, Quzhou, Guangzhou, Nanchang, Jiujiang, Guiyang, Anshun, Lanzhou, and Karamay. This paper ultimately selects panel data from 283 prefecture-level cities covering the period from 2013 to 2021 as the research sample. The spatial distribution of the GFRPZs is shown in Figure 4.

The data are sourced from provincial statistical yearbooks and statistical bulletins, the China Research Data Service Platform (CNRDS), DMSP nighttime light data, commercial bank license information released by the China Banking and Insurance Regulatory Commission (CBIRC), the China Urban Statistical Yearbook, the China Urban Construction Statistical Yearbook, the China Energy Statistical Yearbook, and the Center for Social and Economic Data and Applications at Columbia University.

4. Empirical Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	N	Mean	SD	Min	Max
land	2547	0.590	0.173	0.186	1
did	2547	0.0169	0.129	0	1
agriculture	2547	0.114	0.0751	0.00350	0.379
square	2547	0.0695	0.0826	0.00102	0.447
lnso2	2547	9.489	1.248	6.194	12.03
tech	2547	0.00293	0.00253	0.000311	0.0145
fina	2547	1.094	0.585	0.377	3.402
fisc	2547	0.440	0.215	0.0935	0.998
tele	2547	1.047	0.308	0.121	4.632
applygreen	2547	0.0111	0.0167	0	0.146
authorizegreen	2547	0.00681	0.0101	3.74 × 10−5	0.0971
environment	2547	0.7022	0.3534	0.0695	3.836
tertiary	2547	2.596	1.801	0.3543	15.02
agglomeration	2547	0.173	0.0672	0.0677	0.333

below presents the results of the descriptive statistics for all kinds of variables, respectively. The dependent variable ULUEE is proportional data. At the same time, ULUEE shows a relatively obvious polarization phenomenon, and the ULUEE between regions is not balanced. Similarly, the data of the seven control variables also reflect the obvious heterogeneity among different prefecture-level cities, but they are within a reasonable range.

4.2. Baseline Regression

The baseline regression results are listed in

Table 3. Baseline regression results.

	(1)	(2)
Variables	Land	Land
did	0.0370 **	0.0475 ***
	(0.0184)	(0.0184)
tech		−2.145
		(1.420)
fina		−0.0407 ***
		(0.00835)
agriculture		−0.196 ***
		(0.0722)
square		0.0166
		(0.0392)
tele		−0.0256 **
		(0.0117)
lnso2		0.00910 ***
		(0.00343)
fisc		−0.00150
		(0.0317)
Constant	0.620 ***	0.614 ***
	(0.00479)	(0.0435)
City FE	YES	YES
Year FE	YES	YES
Observations	2547	2547
R-squared	0.038	0.058

Note: Robust t-statistics are in parentheses. Statistical significances at the 1% and 5% levels are denoted by *** and **, respectively, the same as below.

. Column (1) shows the results of the regression that controls only for city fixed effects as well as time fixed effects. Column (2) considers all control variables. Regardless of whether control variables are included, GFI enhances ULUEE by 4.75% and promotes EESC. Therefore, Hypothesis H1 is validated, demonstrating that GFI significantly enhances ULUEE.

4.3. Parallel Trends Test

Before the policy implementation in the GFRPZs, the changes between the experimental group of prefecture-level cities and the control group of prefecture-level cities should keep roughly the same trend. So we refer to existing studies [86,87] to conduct the parallel trends test by using the event study method and setting the base period as one year before the implementation of GFRPZs. The specific model used is as follows:

$$\begin{array}{c}{\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}={\mathsf{\phi }}_0+{\displaystyle \sum _{\mathrm{k}=2}^6}{\mathsf{\omega }}_{-\mathrm{k}}\cdot {\mathrm{p}\mathrm{re}}_{\mathrm{k}\mathrm{t}}+{\mathsf{\phi }}_1·{\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}\mathrm{t}}+{\displaystyle \sum _{\mathrm{k}=1}^4}{\mathsf{\omega }}_{\mathrm{k}}{·\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{k}\mathrm{t}}+{\mathsf{\phi }}_2\cdot {\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}\hfill \end{array}$$

(7)

Within the model, ${\mathrm{p}\mathrm{r}\mathrm{e}}_{\mathrm{k}\mathrm{t}}$, ${\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{k}\mathrm{t}}$ and ${\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$ denote the dummy variables before, after the implementation, and during the period of policy implementation in the pilot zones, respectively, and the rest of the variables are the same as the baseline regression settings. As shown in Figure 5, the regression coefficients are not significant before the policy is implemented and there is no significant pre-treatment trend. The estimated coefficients are significantly positive one year after being subjected to the policy implementation. This indicates that before the policy was implemented, there was no significant difference in the level of ULUEE between the experimental and control prefecture-level cities, and the reason why the policy effect was not significant in the first period after the policy was implemented may be that the policy has a time-lag in transmission. But all in all, the use of DID models in this paper for the evaluation of the policy meets the prerequisites.

4.4. Robustness Tests

4.4.1. Time Placebo Test

We refer to the existing practice [88] and use a counterfactual framework to advance the implementation of the GFRPZs by three years, so as to set a new $\mathrm{d}\mathrm{i}\mathrm{d}-3$ variable for the regression, and the regression results are shown in

Table 4. Robustness test regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Land	Land	Land	Land	Land	Land
did			0.0473 **	0.0504 ***	0.0322 *
			(0.0185)	(0.0194)	(0.0180)
did-3	0.0354
	(0.0270)
did1		0.0357 *
		(0.0186)
didxcovid19						0.118 *
						(0.0622)
Constant	0.612 ***	0.612 ***	0.609 ***	0.619 ***	0.628 ***	0.608 ***
	(0.0435)	(0.0435)	(0.0440)	(0.0436)	(0.0418)	(0.0436)
Observations	2547	2547	2511	2538	1976	2547
R-squared	0.056	0.057	0.057	0.059	0.061	0.057
Controls FE	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significances at the 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively, the same as below.

, and it was found that the coefficient of $\mathrm{d}\mathrm{i}\mathrm{d}-3$ does not reach the significance, thus proving that the view that the policy can significantly improve the ULUEE is relatively robust.

4.4.2. Lagged Independent Variables

This paper regresses the policy variables with one lag, the independent variables are set to $\mathrm{d}\mathrm{i}\mathrm{d}1$, and the results are in column (2) of Table 4. After lagging one period, the size and sign of the coefficients are relatively stable compared with the coefficients of the baseline regression, which indicates that the impact of the policy variable on the dependent variables in the test area is not generated by chance fluctuations.

4.4.3. Impact of Removing Municipalities Directly Under the Central Government

Municipalities directly under the central government have greater comparative advantages than other prefectural-level cities, mainly in terms of more concentrated policy support and state financial input; larger economic volume and more diversified industrial structures; and an administrative status that is equal to provinces and autonomous regions, which is superior to other prefecture-level cities. In summary, these cities are more likely to have tilted in favor of capital, policy, technology and other resources to support the improvement of ULUEE. Therefore, after excluding the four municipalities of Beijing, Shanghai, Tianjin, and Chongqing from the regression, the results are shown in column (3) of Table 4, indicating that the regression is significant and the coefficients are basically unchanged compared with the benchmark regression, so the empirical results are robust.

4.4.4. Excluding Lanzhou City from the Pilot Zone Test

Lanzhou city was included in the scope of the policy in 2019, but its implementation of the policy in the selected study area was late, so in order to better assess the long-term implementation effect of the policy, the second batch of pilot cities, i.e., the sample of Lanzhou city, is excluded, and the multi-period DID model is turned into an ordinary DID model, and then regression is carried out, and the regression results are as follows in column (4) of Table 4. The results show that the regression results are more significant after excluding Lanzhou city and the coefficients are raised, which not only indicates the robustness of the empirical evidence, but also shows more clearly that the policy has a stronger effect on the first batch of pilot cities, which may be due to the following reasons: firstly, there is a time-lag in the implementation of policies, and the effects of the policy are gradually revealed as the time goes by; secondly, the first batch of pilot cities were generally more mature in their policy implementation than the second batch, which was reflected in more obvious policy effects.

4.4.5. Removing the Impact of the COVID-19 Pandemic

The COVID-19 pandemic has had a significant negative impact on the economic and social situation in our country and in the world as a whole. The outbreak slowed down economic activity in China, halted production, triggered supply chain disruptions, and weakened demand on the consumption and investment sides, which leads to a corresponding shortfall in the supply of capital for land use. In order to avoid the interference of the COVID-19 pandemic shock on the policy effect itself, the data of 2020–2021 is excluded and then regressed. Column (5) of Table 4 shows that the COVID-19 pandemic did not affect the direction of the policy effect, which indicates the robustness of the model’s conclusions and the effectiveness of GFI. At the same time, we also set up an interaction term between GFI and the severity of the epidemic to be tested together, with the severity of the epidemic measured by the number of confirmed cases per 10,000 people in prefecture-level cities. As shown in column (6) of Table 4, the results indicate that the epidemic does not inhibit the effect of green financial innovation, but rather enhances it, reflecting the policy resilience, and its effect does not depend on external shocks. Instead, the epidemic will highlight the systemic risk of a highly polluting economic structure, forcing all parties to seek more resilient green alternatives, and indirectly promoting EESC.

4.4.6. Excluding the Effects of Other Policies

During the period of 2013–2021, the government of China also implemented a series of policy measures to promote the country’s green economy and financial development. Therefore, in order to avoid such policies interfering with the policy effects studied in this paper, this paper refers to the studies of Liu et al. [89,90], respectively, and examines whether the policy of the Ecological Civilization Advance Demonstration Zone (ECADZ) as well as the Environmental Protection Tax Law (EPTL) would have any research impact. As shown in

Table 5. Review results excluding other policy effects.

	(1)	(2)	(3)
Variables	Land	Land	Land
did	0.0426 **	0.0423 **	0.0381 **
	(0.0184)	(0.0184)	(0.0184)
ecology	0.0585 **		0.0545 ***
	(0.0151)		(0.0151)
tax		−0.0272 ***	−0.0256 ***
		(0.00653)	(0.00653)
Constant	0.612 ***	0.624 ***	0.621 ***
	(0.0434)	(0.0434)	(0.0433)
Observations	2547	2547	2547
R-squared	0.064	0.065	0.070
Controls FE	YES	YES	YES
City FE	YES	YES	YES
Year FE	YES	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significances at the 1% and 5% levels are denoted by *** and **, respectively, the same as below.

below, column (1) shows the results of including the ECADZ policy in the regression, and it can be seen that although the proxy variable for the ECADZ policy is significantly positive, the $\mathrm{d}\mathrm{i}\mathrm{d}$ coefficient is guaranteed to be significantly positive, indicating that the conclusions are not affected by the ECADZ. Column (2) shows the regression results considering both the GFRPZs and the EPTL. The coefficient of the proxy variable of the EPTL is significantly negative, and the coefficient of $\mathrm{d}\mathrm{i}\mathrm{d}$ is significantly positive, with little change, which indicates that the EPTL has no impact on the conclusions of this paper. Column (3) includes all the above policies in the regression for testing and analysis, where the coefficient of $\mathrm{d}\mathrm{i}\mathrm{d}$ is still significantly positive, and the coefficients of the remaining policy proxies have the same significance and signs as those previously statí.

5. Mechanism Analysis

According to the test and analysis above, GFI can enhance ULUEE and promote the coupling development of the economic system and ecosystem. Next, in order to study the ways through which GFI enhances ULUEE and promotes the EESC, this paper will verify the mechanism of GFI that enhances ULUEE from four perspectives: technology promotion, environmental regulation, economic agglomeration, and industrial upgrading.

5.1. GFI Promotes ULUEE Through Technology Facilitation

GFI occurs mainly through the pilot area policy to provide financial instruments and policy incentives, and it can reduce the financing constraints of green technology R&D and the application and formation of a pressure environment in the whole society in order to promote the green innovation of new technologies as well as the upgrading of the application of the original green technology. Green technology innovation can reduce the amount of pollutant emissions caused by immature industrial technologies in the process of land use and improve the land ecological environment; it can also restore the productivity and ecological functions of degraded land. In short, GFI reduces resource consumption and environmental damage in the process of land use in all aspects by promoting green technological innovation, forming a positive path from policy implementation to technological promotion, and ultimately enhancing the ULUEE and realizing the coupled development of ecological and economic systems.

The number of green patents in a city often reflects its green technology level, so it is of practical significance to use green patents as a proxy variable for technology promotion. In order to better verify the results, this paper also chooses $\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{y}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ and $\mathrm{a}\mathrm{u}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ for regression, which can undertake mutual corroboration. The regression results are shown in

Table 6. Regression results of mechanism analysis.

	(1)	(2)	(3)	(4)	(5)
Variables	Applygreen	Authorizegreen	Environment	Agglomeration	Tertiary
did	0.00675 ***	0.00409 ***	−0.0845 ***	0.00341 **	0.213 **
	(0.00138)	(0.000973)	(0.0319)	(0.00170)	(0.0990)
Constant	0.0169 ***	0.0154 ***	1.181 ***	0.188 ***	2.620 ***
	(0.00327)	(0.00230)	(0.0754)	(0.00401)	(0.234)
Observations	2547	2547	2547	2547	2547
R-squared	0.401	0.456	0.281	0.612	0.671
Controls FE	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significances at the 1% and 5% levels are denoted by *** and **, respectively, the same as below.

below. Column (1) and column (2) are the regression results of $\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{y}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ and $\mathrm{a}\mathrm{u}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$, respectively. The results show that green financial innovations can increase the number of green patent applications and grants per 100 persons by about 0.675% and 0.409%, respectively. GFI can improve the ULUEE by promoting the green technological innovation of the city, and Hypothesis H2a is established.

5.2. GFI Promotes ULUEE Through Environmental Regulation

Energy consumption per unit of GDP is a measure of the efficiency of energy use, but it can also be used to reflect the intensity of environmental regulation [91] because a decrease in this indicator usually reflects the adoption of more efficient energy use technologies or more environmentally friendly production methods by urban sectors, which is usually the result of environmental regulation.

Under the policy framework of the GFI, government departments or financial institutions can either directly constrain the unhealthy use of land resources by enterprises and social sectors through the introduction of environmental protection regulations and other coercive means, or through the introduction of incentives to encourage the optimization of land use in various sectors, in order to promote the efficient, intensive, and ecological use of land resources, the ecological utilization of land resources, and to ultimately achieve an improvement in the ULUEE, reflecting the EESC.

Table 6 shows the regression result and column (3) shows the regression of the mechanism proxy variable energy intensity per unit of GDP. The coefficients of the explanatory variables in column (3) are negative, indicating that GFI reduces urban energy consumption by 8.45% and that environmental regulations are effective. Also, it verifies that the hypothesis is valid in that GFI reduces energy consumption in all sectors of the society through effective environmental regulation, thus enhancing the ULUEE. Therefore, H3a is validated, demonstrating that GFI promotes ULUEE through environmental regulation.

5.3. GFI Promotes ULUEE Through Economic Agglomeration

Economic agglomeration has positive externalities. GFI realizes the economic agglomeration of pilot cities through the aggregation of green financial products, services, and other industrial resources, and the externality generated by this economic agglomeration spills over to ecological protection, which ensures the efficiency of the economic system and effectively promotes the reduction of ecosystem pollution prevention and control, and enhances the role of ULUEE.

Table 6 shows the results obtained by regressing the proxy variables of economic agglomeration, as shown in column (4). The results show that GFI increases urban economic agglomeration by 0.341%, and indicates that GFI can effectively enhance the level of economic agglomeration, and the positive externality brought by economic agglomeration can enhance the ULUEE. Hypothesis H4a is validated.

5.4. GFI Enhances ULUEE Through Industrial Upgrading

GFI has accelerated the upgrading of the industrial structure towards becoming greener and more high-end and has accelerated intensification by guiding funds from high-carbon to low-carbon industries through the financial system, promoting the green transformation of traditional industries and the cultivation of new green industries. This kind of industrial structure upgrading with finance as a link not only compresses the living space of high-pollution and high land-consuming industries, but also builds up a virtuous cycle of “industrial quality enhancement—land efficiency enhancement—ecological value-added” through technological empowerment and systematic innovation, ultimately realizing a win-win situation for the output of the economic system and the ecosystem’s carrying capacity. The core support of green finance depends on the substantial environmental protection contribution of the project. As most of the fields of the tertiary industry are characterized by light assets, low pollution, and high value-added characteristics, they have a high degree of synergy with the objectives of GFI, and are more likely to obtain green finance support.

Table 6 shows the test results of the mechanism, and column (5) shows the regression results of industrial upgrading. The column (5) $\mathrm{d}\mathrm{i}\mathrm{d}$ coefficient is 0.213, indicating that GFI is able to increase the per capita tertiary value added by about 21.3%., and Hypothesis H5a is validated.

6. Heterogeneity Analysis

The above empirical analysis shows that GFI can play a positive role in promoting ULUEE, and technological progress, environmental regulation, industrial upgrading, and economic agglomeration are four important mechanisms through which GFI can improve ULUEE, thus promoting EESC. In order to further examine the heterogeneity of GFI for the EESC, this paper analyzes the differences in the three dimensions of geographic location, industrial resource endowment, and the degree of financial marketization.

6.1. Geographic Location Heterogeneity

Based on geographic and economic disparities, China is roughly divided into three geographic zones: eastern, central, and western. The regression results for different geographic regions are shown in

Table 7. Geographic location heterogeneity regression results.

	(1)	(2)	(3)
Variables	Land	Land	Land
did	0.0907 ***	−0.0172	0.0225
	(0.0322)	(0.0402)	(0.0251)
Constant	0.815 ***	0.574 ***	0.550 ***
	(0.0827)	(0.0747)	(0.0748)
Observations	882	891	774
R-squared	0.101	0.127	0.045
Controls FE	YES	YES	YES
City FE	YES	YES	YES
Year FE	YES	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significance at the 1% level is denoted by ***, the same as below.

below. The regression results in Table 7 reveal significant regional disparities in the impact of GFI on ULUEE. Columns (1) to (3) represent the results of the eastern, central, and western regressions, respectively. In the eastern region, the policy exhibits a strongly positive effect, likely due to its well-developed financial markets, robust institutional capacity, and greater policy implementation efficiency. By contrast, the central region shows a marginally negative coefficient, possibly because its heavy reliance on secondary industries and ongoing structural transition temporarily offset policy benefits. Meanwhile, the western region reflects insignificant results, which may stem from weaker economic foundations and less-effective policy enforcement. These findings highlight how regional economic structures shape the effectiveness of GFI.

6.2. Heterogeneity of Industrial Resource Endowments

This section examines how the GFI affects ULUEE in old industrial base cities versus non-old industrial base cities, based on China’s National Old Industrial Base Adjustment and Rehabilitation Plan (2013–2022). Regression results in

Table 8. Regression results for heterogeneity of industrial characteristics.

	(1)	(2)
Variables	Land	Land
did	0.0601 ***	−0.00879
	(0.0208)	(0.0384)
Constant	0.650 ***	0.540 ***
	(0.0530)	(0.0777)
Observations	1710	837
R-squared	0.043	0.158
Controls FE	YES	YES
City FE	YES	YES
Year FE	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significance at the 1% level is denoted by ***, the same as below.

indicate that GFI significantly enhances ULUEE in non-old industrial base cities, which is shown in column (1). However, its impact is limited in old industrial base cities, which is shown in column (2), where heavy industries like machinery and chemicals dominate. These cities face greater ecological restoration pressures and struggle to transition toward service-oriented and high-tech industries, making it difficult for GFI to improve land use efficiency or reconcile economic and ecological systems.

6.3. Heterogeneity in the Level of Financial Marketization

This paper measures financial marketization using the factor market development sub-index from the Fan Gang Index, dividing regions into high and low marketization groups based on the median value. Regression results in

Table 9. Financial market heterogeneity regression results.

	(1)	(2)
Variables	Land	Land
did	0.0891 ***	0.0257
	(0.0308)	(0.0230)
Constant	0.797 ***	0.367 ***
	(0.0559)	(0.0717)
Observations	1377	1170
R-squared	0.078	0.090
Controls FE	YES	YES
City FE	YES	YES
Year FE	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significance at the 1% level is denoted by ***, the same as below.

show that GFI significantly improves ULUEE in regions with higher financial marketization, which is shown in column (1), while the effect is insignificant in less-marketized regions, which is shown in column (2). This suggests that financial market maturity enhances the effectiveness of GFI. Regions with higher financial marketization demonstrate three key advantages in GFI. First, their well-established information disclosure and risk assessment systems significantly improve the allocation efficiency of green funds. Second, the highly competitive financial environment compels institutions to continuously innovate green financial products and services. Third, such competition further drives down financing costs for green projects, creating a sustainable mechanism that effectively supports energy conservation and emission reduction initiatives. Ultimately, ULUEE is improved even more dramatically.

7. Further Analysis

In order to reveal the spatial boundaries, synergies, and operational mechanisms of policies, this paper will further analyze the spillover effects, policy synergies, and multidimensional efficiency deconstruction.

7.1. Spillover Effects

GFI will not only have an impact on the pilot city, but may also radiate to the neighboring areas through spillover effects. In order to study the spillover effect of GFI, this paper sets a dummy variable, $\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}$, and sets $\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}=1$ for non-pilot cities within pilot provinces, otherwise it is 0.The model is constructed as follows:

$${\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}={\mathsf{\phi }}_0+{\mathsf{\phi }}_1\cdot {\mathrm{d}\mathrm{i}\mathrm{d}2}_{\mathrm{i}\mathrm{t}}+{\mathsf{\phi }}_2\cdot {\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(8)

Among these variables, ${\mathrm{d}\mathrm{i}\mathrm{d}2}_{\mathrm{i}\mathrm{t}}=\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}\times \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}$, the experimental group is the remaining prefecture-level cities in the same province where the GFRPZ is implemented, and the control group includes the other prefecture-level cities outside the province where the pilot zone is located. If ${\mathsf{\phi }}_1$ is positive and significant, it indicates that there is a positive spillover effect of GFI on the neighboring areas of the same province where the pilot site is located.

The results are shown in

Table 10. Regression results for further analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Land	Land	Land	Land	Expect	Unexpect
did2	0.0193 **
	(0.00932)
did		0.0475 ***		0.0178	0.0717 ***	−0.00535
		(0.0184)		(0.0193)	(0.0211)	(0.00749)
didcabon			0.0479 ***	0.0461 ***
			(0.0117)	(0.0116)
didfix				0.242 ***
				(0.0568)
Constant	0.625 ***	0.614 ***	0.595 ***	0.584 ***	0.222 ***	0.0685 ***
	(0.0442)	(0.0435)	(0.0436)	(0.0435)	(0.0499)	(0.0177)
Observations	2466	2547	2547	2547	2547	2547
R-squared	0.064	0.058	0.062	0.072	0.058	0.034
Controls FE	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES

Note: Robust t-statistics are in parentheses. Statistical significances at the 1% and 5% levels are denoted by *** and **, respectively, the same as below.

, column (1). The results reveal a statistically significant but economically modest positive spillover effect. This attenuated spillover aligns with the typical “center-periphery” diffusion pattern, where policy effects of GFI weaken as they radiate outward. Three factors contribute to this phenomenon: first, the inherent geographical decay of policy influence, second, the limited policy learning capacity in peripheral areas, and third, the absence of direct financial support mechanisms that are available to pilot zones. While the results confirm that the policy benefits of GFI do extend to neighboring regions, the effect intensity diminishes substantially beyond pilot boundaries.

7.2. Policy Synergies

Synergy is the effect of multiple policies working together to produce a whole that is greater than the sum of its parts. This study investigates the synergistic effects between GFRPZs and carbon emission trading policies, with the latter initiated in 2011 through the National Development and Reform Commission’s pilot program. And this paper sets up the following model:

$${\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}={\mathsf{\eta }}_0+{\mathsf{\eta }}_1\cdot {\mathrm{d}\mathrm{i}\mathrm{d}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\eta }}_2\cdot {\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(9)

$${\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}={\mathsf{\omega }}_0+{\mathsf{\omega }}_1\cdot {\mathrm{d}\mathrm{i}\mathrm{d}\mathrm{f}\mathrm{i}\mathrm{x}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\omega }}_2\cdot {\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$$

(10)

The variables ${\mathrm{d}\mathrm{i}\mathrm{d}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}=\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}\times \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}$ identify carbon trading pilot cities during post-implementation periods, where $\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}$ indicates the pilot status and $\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}$ marks the policy implementation timeline. If the city is both a pilot city and experiences policy shock, record ${\mathrm{d}\mathrm{i}\mathrm{d}\mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$ as 1, otherwise it is 0. For policies initiated in the second half-year, implementation is recorded as occurring in the following year to account for time-lag effects. Similarly, ${\mathrm{d}\mathrm{i}\mathrm{d}\mathrm{f}\mathrm{i}\mathrm{x}}_{\mathrm{i}\mathrm{t}}=1$ when a city implements both policies simultaneously and others are 0.

The model regression results are shown in Table 10. Column (2) shows the regression results of GFRPZ policy, column (3) shows the regression results of the carbon emission trading right pilot policy, and column (4) shows the regression results of the dual pilot policy. The results demonstrate that while both policies independently contribute to improved ULUEE, their combined implementation produces a synergistic effect that significantly surpasses the sum of individual impacts. This synergy emerges from the complementary nature of the two policy instruments: green finance mechanisms provide essential capital support for low-carbon transition, while carbon trading systems effectively internalize environmental costs through market-based pricing mechanisms. Together, these policies create an integrated “capital + market” governance framework that drives more comprehensive and sustainable urban land use transformations than could be achieved by either policy alone.

7.3. SBM Multi-Dimensional Efficiency Deconstruction

The multidimensional efficiency deconstruction reveals asymmetric effects of GFI on ULUEE components. Regression results in the fifth and sixth columns of Table 10 show that GFI significantly enhances the desired output efficiency, which is the economic growth dimension, with positive coefficients, while its impact on reducing undesired outputs, which are pollution emissions, shows negative but statistically insignificant coefficients. This asymmetry suggests that economic benefits respond more rapidly to policy interventions, likely due to China’s developmental stage during GFI implementation where land use readily translated into economic gains. The limited environmental improvement may stem from two factors. On the one hand, there is an inherent time lag in pollution control, and a comprehensive industrial chain transformation may not be carried out until after the research period. On the other hand, there is potential “greenwashing” behavior, that is, some enterprises obtain financing without a substantial emission reduction.

8. Discussions

This study demonstrates that green financial innovation (GFI) enhances urban land use eco-efficiency (ULUEE) through technological innovation, environmental regulation, economic agglomeration, and industrial upgrading. This finding enriches and complements existing research. We not only echo the positive economic effects of green finance highlighted in studies such as those of Li et al.(2025) [92], Liu et al. (2023) [93], and Han et al. (2024) [94], but also add to the research on the positive environmental effects of green finance, as seen in the studies of He et al. (2023) [95] and Zhang et al. (2020) [96]. Furthermore, our research connects economic and environmental systems through land use, broadening the scope of the study.

In addition, we demonstrate that GFI performs better in areas with favorable locational economic conditions, more developed financial markets, and industries that are less reliant on pollution-heavy sectors. This conclusion is consistent with Xu et al. (2025)’s findings on the spatiotemporal differences in green finance [97]. Our research further enriches the evidence and clarifies the heterogeneous effects of GFI across different regions. Additionally, we demonstrate that GFI has environmental spillover effects, improving the ecological environment in surrounding areas. This finding aligns with studies by Wang et al. (2021) [98] and Huang et al. (2022) [99] regarding the positive spatial spillover effects of green finance on neighboring regions.

Lastly, our results show that GFI does not significantly reduce the undesired output, which aligns with the intuitive judgment made by Hu et al. (2025) [100] about the presence of short-term behavior and greenwashing in the demand for green finance. Additionally, our study confirms that green finance innovation policies have a time lag, complementing the perspective of Wang et al. (2020) [101] regarding policy misalignment over the policy duration in green finance reform.

9. Conclusions and Recommendations

9.1. Conclusions

This study explores the critical challenge of reconciling economic expansion with ecological protection, which is exacerbated by the depletion of natural capital through industrialization and systemic barriers such as market failures, technological constraints, and fragmented policies [102,103,104]. The core dilemma lies in the difficulty in achieving coordinated development between the economy and ecosystems. This research emphasizes the role of green finance innovation (GFI) as a mechanism for bridging this gap, with the financial system serving as a central conduit for facilitating this transformation. Urban land use eco-efficiency (ULUEE), as an indicator of the synergy between economic output and ecological protection, reveals the effectiveness of economic–ecological system coupling and coordination (EESC). By investigating China’s Green Finance Reform and Innovation Pilot Zones (GFRPZs) as the quasi-natural experiment, the findings indicate that GFI significantly improves ULUEE. GFI achieves this through four key pathways: technological upgrading, environmental regulation, economic agglomeration, and industrial restructuring.

However, while GFI shows potential in improving ULUEE, the study also acknowledges several important limitations. First, the study predominantly focuses on areas in China, which may not fully reflect the dynamics in countries with different levels of green finance development. Future research should explore the global applicability of these findings, particularly in regions with different levels of green finance development. Second, although the study highlights the positive effects of GFI on ULUEE, it does not fully address the potential endogeneity of the variables or the complex interactions between financial systems, ecological policies, and industrial development. This remains an area for further exploration using more detailed longitudinal data. Finally, the study addresses the direct effects of green finance on land use, but the broader implications for social and economic development remain unexplored. While GFI shows promise in enhancing land use efficiency and ecological governance, the environmental effects are relatively modest, particularly in regions with weaker regulatory frameworks. The potential for greenwashing and the short-term nature of some benefits are important factors that have not been sufficiently addressed in this study. Future research could expand the scope to consider how GFI impacts social outcomes within the context of sustainable urban development.

In terms of theoretical implications, this study contributes to the literature by advancing the understanding of how GFI enhances economic–ecological system coupling, particularly through the lens of land use efficiency. It fills a gap in the literature by examining the dynamic mechanisms through which financial systems influence ecological outcomes. Additionally, this research introduces a novel framework that integrates financial innovation, environmental regulation, and industrial restructuring as key drivers of sustainable economic development.

The findings highlight the importance of aligning green finance with technological advancement, industrial upgrading, and efficient resource allocation. Policymakers and financial institutions can draw actionable insights from this study to better integrate ecological sustainability into economic growth. Future research should extend this work by investigating the multi-dimensional impacts of GFI on various regions and industries and refining the theoretical models that link financial systems with eco-economic coupling.

9.2. Recommendations

Therefore, we propose the following policy implications. To enhance green finance’s role in sustainable development, policymakers should focus on building a comprehensive green finance system that integrates national standards with regional coordination. Financial institutions should receive targeted incentives to boost participation and innovation. These steps will foster the evolution of a multi-level green finance system, improving its ability to facilitate the coupling of economic and ecological systems.

From the perspective of financial system development, it is essential to accelerate the systematic construction of green finance to enhance the system’s capacity to support ecological-economic transformation. Future financial reform should adopt a systems-thinking approach, shifting from a focus on scale expansion to simultaneous breadth and depth in financial services. A comprehensive green finance system should be built by integrating national-level standardization with regionally coordinated implementation. Specifically, a “national standard + regional coordination” model should be established, accompanied by industry-specific green finance white papers to guide differentiated practice. Financial institutions that are actively engaged in green finance should receive targeted policy incentives to strengthen their participation and innovation capacity. This systemic construction will foster a multi-level, multi-agent, and multi-path evolution of the GFI support system, ultimately improving its ability to adapt to and facilitate economic–ecological coupling.

From the perspective of innovation-driven development, technological upgrading and application are key to supporting the coupling of economic and ecological systems and achieving a win–win outcome between growth and sustainability. Technological progress serves as a critical bridge that links productivity improvements with ecological protection—not only in land use, but across all areas of ecological resource utilization. Financial resources should be mobilized to support high-level R&D, the improvement of outdated technologies, and the integration of innovations across sectors. A composite innovation system—combining R&D investment, technological upgrading, and talent development—can reduce pollutant emissions, enhance the functional properties of natural resources, and increase productivity while minimizing environmental damage. Government support through policy and financial guarantees is also essential, as it provides the most direct leverage for strengthening the long-term sustainability of both economic and ecological systems.

From the perspective of coupled system development, it is crucial to promote green finance, enhance the innovation capacity of the financial system, and strengthen its adaptive and bridging function in economic–ecological coupling. Innovation is the key driver of financial system evolution. Strengthening GFI involves encouraging the development of diverse green financial products and services that align with ecological objectives and meet the growing needs of market participants. At the same time, integrating technologies such as big data and AI can enhance the intelligence and efficiency of green financial operations, and drive product innovation. Interdisciplinary collaboration is also necessary, as green finance inherently involves complex interactions between ecological and economic domains. Breaking disciplinary silos—by linking finance with fields such as ecology, meteorology, and environmental science—can foster new solution pathways beyond the scope of traditional finance. These measures collectively promote the integrated development of economic growth, financial resilience, and ecological sustainability, and strengthen the coupling across systems.