Spatiotemporal Coordination and Driving Mechanisms of Green Finance and Green Technology Innovation in China

Abstract

Promoting the synergistic development of green finance (GF) and green technology innovation (GTI) is crucial for achieving sustainable economic development. Based on the sample data of 30 provinces in China from 2010 to 2023, this study first investigates the theoretical mechanism of interactive coupling and then employs methods including Dagum Gini coefficient, spatial kernel density estimation, spatial correlation analysis, and a GTWR model to explore the spatiotemporal pattern, evolution trend, and driving factors of the coupling coordination between GF and GTI. The findings are as follows: (1) The coupling coordination degree (CCD) is about to transition from the moderate imbalance stage to the near imbalance stage, presenting a distinct spatial pattern of “higher levels and faster development in the east, and lower levels and slower development in the west”. (2) The Gini coefficient of the CCD shows an upward trend, with the degree of imbalance increasing year by year; the main sources of the overall differences follow this order: intra-regional disparity (G_w) > inter-regional disparity (G_b) > transvariation density (G_t). (3) The CCD between GF and GTI exhibits a positive spatial correlation, and the agglomeration degree is constantly increasing; the High-High Cluster areas are mainly concentrated in northern China. (4) Economic development level, financial development level, population scale, and urbanization level drive the coupling coordination between GF and GTI. This study provides new theoretical and empirical evidence for the complex coupling relationship and driving factors of GF and GTI and offers a key scientific basis for the Chinese government to formulate differentiated regional policies, thereby promoting the effective implementation of the green and low-carbon development strategy.

1. Introduction

With the growth of the global economy and the significant improvement in living standards, global energy and resource consumption has increased sharply. This has led to worsening environmental pollution, a growing scarcity of resource stocks, and enormous challenges for the ecological environment [1,2,3]. In 2022, China put forward its “dual carbon goals”. This demonstrates an unwavering commitment to promoting the strategy of ecological priority, less intensive resource use, and green and low-carbon development.

Developing green finance (GF) is a key path to establishing environmental protection mechanisms, improving environmental quality, controlling pollutant emissions, promoting green industries, and advancing the progress of production technologies [4]. As a core tool for guiding capital flow to green fields [5], GF can stimulate the application of green technologies [6], promote green innovation in enterprises [7], and drive the transformation and upgrading of industrial structures. Its impact in promoting green innovation among polluting enterprises is particularly notable [8].

Meanwhile, green technology innovation (GTI) also opens up new development space for green finance. GTI concentrates technology and capital on green industries such as pollution control, emission reduction, and new energy, which can improve production efficiency, reduce production costs, and mitigate environmental pollution. When the green economy develops to a certain stage, it will form green economic growth points, thereby leading to green dividends such as improved investment returns [8], an optimized financial environment, and an expanded investment market [9], highlighting the ecological value and economic benefits of GF.

Existing studies mainly analyze the impact from the perspectives of listed companies [9,10], heavy-polluting industrial enterprises [11], agriculture-related enterprises [12], and new-energy enterprises [13,14]. However, research on the reverse promotion of GF by GTI remains relatively scarce. Some scholars have begun to use the CCD and GTWR model to study the coupled and coordinated development of green finance with digital technology [15], green low-carbon transition [16,17], and sustainable economic development. Nevertheless, in-depth research on the environmental impact mechanism of the GF and GTI coupling coordination mechanism is still lacking, and empirical studies on its spatiotemporal characteristics, evolution path, and driving factors are even scarcer.

Based on this framework, this study focuses on the coupling coordination between green finance (GF) and green technology innovation (GTI) at the provincial level in China and seeks to answer the following research questions: (1) What are the overall level and spatiotemporal evolution characteristics of the coupling coordination between GF and GTI across Chinese provinces? (2) Are there significant regional disparities and spatial correlations in the GF–GTI coupling process? (3) What key factors drive the spatiotemporal heterogeneity of the coupling coordination between GF and GTI?

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature and analyzes the coupling coordination mechanism between green finance (GF) and green technology innovation (GTI). Section 3 outlines the research design, including the indicator system, data sources, and methodological framework. Section 4 presents and discusses the empirical results, focusing on the spatiotemporal evolution, regional disparities, spatial dependence, and driving factors of GF–GTI coupling coordination. Section 5 concludes the paper by summarizing the main findings and highlighting the theoretical contributions. Section 6 discusses the practical implications of the results for policy design and green development strategies. Finally, Section 7 acknowledges the limitations of the study and outlines directions for future research.

2. Literature Review and Mechanism Analysis of Coupling Coordination

2.1. Relevant Studies on the Relationship Between GF and GTI

2.1.1. Relevant Studies on GF and GTI

International research on the definition of GF began relatively early. White et al. [18] defined the structure of the financial system and argued that financial development should be integrated with environmental protection and promote the synergistic development of the economy and the environment. As GF has continued to develop, its concept has been continually refined. Linnenluecke et al. [19] argued that GF refers to innovation in financial instruments, systems, and models that serve environmental protection projects and energy conservation. At the G20 Hangzhou Summit in 2016, the G20 Comprehensive Report on Green Finance clarified the meaning of GF [20]. Later, with the release of the Guidelines for Establishing the Green Finance System, “green finance” gradually gained increasing attention, and China’s green finance entered a phase of rapid development.

GF can expand China’s green development space [21], optimize the economic structure without suppressing total output and total employment, and promote sustainable regional economic development [22]. Its role is more prominent for technology-intensive enterprises with high dependence on external financing [23].

Microscopically, GF can improve the production efficiency of green enterprises [24] and significantly promote their social responsibilities [25]. It is more conducive to stimulating the innovation motivation of green enterprises. Some scholars [26] found that green credit impacted the real operations of heavy-polluting enterprises through financing constraints, significantly curbing their pollution discharge behavior. However, these enterprises do not proactively transform and upgrade through technological innovation, and the Porter Hypothesis is not obvious. Lu et al. [27] found that the cost-following effect and credit-constraint effect caused by green credit policies have reduced the technological innovation of heavy-polluting enterprises, failing to generate the Porter effect. Using a mediation effect model, He et al. argued that the green credit level and R&D investment have a significant promoting effect on environmental protection enterprises [28].

As a type of technological innovation that integrates economic benefits and ecological benefits, GTI provides crucial technological support for green development. Existing studies mainly rely on the “Porter Theory” and have developed a systematic theoretical framework and empirical evidence focusing on aspects such as the driving factors, environmental benefits, empirical testing, and policy application of GTI. Scholars generally agree that GTI is a vital engine for green development—through GTI, outdated production capacity can be eliminated, production efficiency improved, and environmental pollution reduced [29]. Scholars have also examined issues related to financial support for the development of GTI [30], using game theory models to deduce that GF provides financing support for enterprises, thereby improving energy efficiency and curbing carbon dioxide emissions [31].

2.1.2. Studies on the Relationship Between GF and GTI

There are two main viewpoints in the study of the relationship between GF and GTI. First, scholars argue that GF significantly improves the level of green productivity [32] and exerts a relatively obvious positive impact on technological innovation [33,34,35]. Second, some hold the view that the impact on technological innovation shows a U-shaped trend [36]. Specifically, some studies agree that as the environmental regulation role of GF continues to strengthen, the crowding-out effect on enterprises’ innovation input gradually intensifies. However, after crossing the inflection point of the U-shaped curve, enterprises can obtain more financial support for their green environmental protection projects, which is then allocated to technological innovation. Additionally, another viewpoint suggests that the impact on green innovation has a double-threshold effect. This indicates that the driving effect weakens as the intensity of environmental regulation increases, implying that environmental regulation needs to be moderate [37].

2.2. Analysis of the Coupling Coordination Mechanism

These two systems are not mutually independent; instead, there is an interlocking relationship of mutual interaction between them. The synergistic development of GF and GTI is an indispensable path for sustainable economic development.

Fundamentally, GF provides necessary capital support for GTI to alleviate capital bottlenecks and insufficient financing [38]. Zhu et al. [39] confirmed that GF promotes technological innovation through R&D subsidies and environmental regulation. Zhao et al. [40] pointed out that green credit has a significant incentive effect on GTI and will drive the improvement in enterprises’ technological innovation level. Moreover, GF guides capital flow to environment-friendly enterprises through its “resource allocation” and “capital orientation” functions, accelerating the transformation and upgrading of green industrial structure [41]. GF can help enterprises reduce financing costs and expand financing channels, thereby reducing financing constraints [42], alleviating the capital pressure of green technology transfer, and promoting green technology innovation in enterprises [43]. Conversely, many scholars have also started discussing the implications of the lag in green finance. Hou et al. [44] pointed out that an insufficient supply of green capital and high financing costs for green finance are the core manifestations of green finance at present. Additionally, Pasquale et al. [45] argued that the insufficient coverage of green finance makes it challenging for small- and medium-sized enterprises to participate in it. It is a significant factor contributing to the slow development of green finance.

Conversely, the dividend feedback from GTI can boost the development of GF. GTI concentrates technology and capital on green industries (e.g., new energy, pollution control, and energy conservation). It can improve production efficiency, reduce production costs, and mitigate environmental pollution. When the green economy develops to a certain stage, it can replace the traditional economy, form green economic growth points, and highlight the ecological value and economic benefits through green dividends. In this process, the scale of financial institutions will further expand. However, the two systems of GF and GTI have long been in a running-in stage, and insufficient coordination between the systems restricts the improvement in their overall development level.

The aforementioned research findings indicate that there is an objective coupling effect between GF and GTI. At the enterprise level, GTI projects require capital injection to conduct R&D and marketization of achievements, generate investment returns, and feed back to green financial capital, thereby achieving a win-win outcome between enterprises and financial institutions. At the industrial level, the guidance of GF effectively directs capital to withdraw from the “high-energy consumption, high-emission, and overcapacity” industries and flow toward environment-friendly industries. This enhances the overall magnitude and degree of green innovation and ultimately forms green economic growth points through the industrial agglomeration effect [30].

2.3. Impact of Different Development Situations of GF and GTI on Coupling Coordination

When both GF and GTI are at a relatively high level, the two systems are in a state of high coupling. At this stage, after obtaining sufficient capital support through green finance, green enterprises concentrate resources on R&D and innovation of green technologies, convert these achievements into profits through marketization, and return the profits to financial institutions in the form of investment returns. Meanwhile, financial institutions gain the expected green investment returns, which increases their willingness to engage in green investment. This will further optimize the financial environment and expand the scale of the green finance market, ultimately forming an industrial agglomeration effect and a scale effect.

When either GF or GTI lags behind in development, the two systems are in a state of insufficient coupling or coupling at a low development level. If GF lags behind, its functions of “risk diversification” and “alleviating capital shortages” will be weakened, reducing its ability to provide capital support for GTI projects. To obtain green capital with low financing costs but limited overall scale, green enterprises will compete for market share in the field of GTI. This will lead to green products showing signs of homogenization and low-end orientation, thereby seriously hindering the sustainable development of GTI. Additionally, when GTI is in a backward state, the scale of GF will shrink due to the lack of suitable investment projects in the market. The lagging GTI conditions and generally low market innovation will lead to convergence in green product offerings. This will reduce the “green investment returns” of financial institutions, dampen the enthusiasm for green investment, and further lead to capital outflow, ultimately exacerbating the financing constraint problem of GTI.

2.4. Analysis of Driving Factors for the Coupling Coordination Between GF and GTI

To effectively promote in-depth integration and coordinated development, it is essential to thoroughly analyze the issue of the driving factors that have a significant impact on the CCD. This will enable the more targeted formulation of relevant policies and the optimization of resource allocation, thereby further improving the coupling level and facilitating the smooth achievement of sustainable development goals. Drawing on the existing literature [16,17], this study selects economic development level (ED), financial development (FD), population scale (PS), and urbanization level (UR) as the influencing factors to analyze the driving forces.

The economic development (ED) level serves as the core material foundation for the synergistic evolution of GF and GTI, and its driving role is reflected in the dual pathways of “supply guarantee + demand pull” [46]. GTI (e.g., low-carbon technology R&D and environmental protection equipment iteration) is characterized by high investment, long cycles, and high risks, requiring sustained financial support. Enterprises in economically developed provinces have stronger profitability and can independently bear a higher proportion of R&D costs; meanwhile, local governments possess more robust fiscal strength and can reduce the risks and costs of innovation entities through green subsidies and tax reductions, thereby providing a “financial buffer” for GTI. When the economy develops to a certain stage, society’s demand for “green development” will shift from “passive compliance” to “active pursuit,” forcing enterprises to accelerate GTI. Meanwhile, the expanded market demand for green financial products drives financial institutions to optimize their green service systems. Ultimately, this realizes a positive cycle of “green demand—technological innovation—financial support”.

The financial development (FD) level directly determines the “support efficiency” of GF for GTI and serves as a “bridge and link” for the coupling of GF and GTI. Provinces with a high financial development level have a larger-scale financial market and more varied entities, enabling them to provide a richer range of green financial instruments [47]. For instance, commercial banks can provide targeted support for enterprises’ innovation projects through green credit; security firms can assist in issuing green corporate bonds to raise R&D funds; and green insurance can hedge against the risks of innovation failure. These measures collectively address the issues of “difficulties in financing and high financing costs” for GTI. A well-developed financial market can reduce information asymmetry in green projects through information disclosure mechanisms, screen high-quality innovation projects via risk-pricing mechanisms, and guide capital to flow from high-pollution and low-efficiency sectors to the field of GTI. This directly promotes the in-depth coupling of GF and GTI.

GTI relies on high-quality labor. Provinces with a large population scale (PS) have more sufficient human capital reserves, which easily form a “talent pool” and provide a “human core” for GTI. In addition, a large population scale means a broader consumer market for green products and services, which will stimulate enterprises to increase investment in green technology R&D. Meanwhile, large-scale market demand can reduce the unit costs of green technologies and encourage financial institutions to increase the supply of GF. The “human capital dividends” and “market scale dividends” brought by population scale are important guarantees for the synergy between GF and GTI [48].

Urbanization level (UR) serves as the spatial agglomeration force for the coupling coordination. With the acceleration of urbanization, industries agglomerate at an accelerated pace, which can reduce the cost of technological cooperation among enterprises, promote the sharing and iteration of green technologies, and also enhance the availability of green financial services. The environmental pressure brought by urbanization will strengthen local governments’ demand for green governance and promote green technologies. Therefore, urbanization is the spatial support for the synergy between GF and GTI [49], and the improvement in its level can provide “physical space” and “policy impetus” for the coupling of GF and GTI.

Taking 30 provinces in China as the research object, this study constructs a spatial coupling framework for the dual system of GF and GTI by the CCD model and breaks through the traditional isolated perspective to reveal the system’s synergistic effects. Methodologically, this study innovatively integrates a suite of advanced techniques, including the entropy-weighted TOPSIS method, CCD model, kernel density estimation, Dagum Gini coefficient, GTWR model, and ArcGIS technology. Through this integration, a comprehensive methodological system is established. This system encompasses multiple analytical dimensions: objective indicator weighting, quantitative measurement of synergistic evolution, identification of spatial dependence, analysis of spatiotemporal heterogeneity, and research on driving factors. This system effectively identifies the spatial dependence and spatiotemporal heterogeneity of system coupling.

2.5. Research Purpose and Analytic Framework

Based on the above literature review and mechanism analysis, this study aims to systematically investigate the coupling coordination between green finance (GF) and green technology innovation (GTI) at the provincial level in China from a spatiotemporal perspective. Existing studies have primarily examined the unilateral impact of green finance on technological innovation or environmental performance, often relying on linear econometric models that overlook the interactive, dynamic, and spatially heterogeneous nature of green development systems. As a result, the long-term coordination process and regional differentiation between GF and GTI remain insufficiently understood.

The necessity of this study arises from three aspects. First, GF and GTI constitute two interdependent subsystems within the broader green development framework, and their effectiveness depends not only on their individual development levels but also on their degree of coordination. Ignoring this systemic interaction may lead to biased assessments of green development performance. Second, China exhibits significant regional disparities in economic development, financial maturity, and innovation capacity, which may generate pronounced spatial heterogeneity in the GF–GTI coordination process. Third, under the “dual carbon” goals and the rapid expansion of green finance instruments, understanding how GF and GTI evolve jointly across space and time is crucial for formulating differentiated and targeted policy interventions.

Accordingly, this study seeks to (i) measure the coupling coordination degree (CCD) between GF and GTI across Chinese provinces, (ii) reveal its spatiotemporal evolution patterns and regional disparities, and (iii) identify the heterogeneous driving mechanisms underlying the coordination process. By doing so, this research provides a more comprehensive and policy-relevant understanding of green development dynamics in a large transitional economy.

This study does not formulate explicit, testable hypotheses regarding the relationship between green finance (GF) and green technology innovation (GTI). This methodological choice is closely aligned with the analytical objectives and the intrinsic characteristics of coupling coordination analysis. Unlike conventional econometric models that emphasize linear causality and parameter estimation, the coupling coordination degree (CCD) framework is designed to capture the systemic interaction, mutual feedback, and dynamic coordination between multiple subsystems. Therefore, following a growing body of literature on coupling coordination analysis in fields such as green development, innovation systems, and regional economics, this study adopts a mechanism-driven and exploratory analytical framework. Instead of testing narrowly defined hypotheses, the research focuses on measuring the degree of coordination between GF and GTI, examining its spatiotemporal evolution, identifying regional disparities, and revealing heterogeneous driving mechanisms [49,50,51]. This approach allows the empirical results to be guided by observed data patterns rather than constrained by restrictive theoretical assumptions.

3. Data Sources and Research Methods

3.1. Data Sources and Construction of Index System

3.1.1. Data Sources

In 2008, GF had already achieved initial development in China, and statistical data on GTI was also gradually improving. After the release of the Green Credit Guidelines in 2012, data on GF became increasingly comprehensive. Considering this situation, and combining the availability and continuity of data, this study takes 30 provinces (excluding Tibet, Hong Kong, Macau, and Taiwan) across China from 2010 to 2023 as the research sample.

The original sample data are sourced from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, statistical yearbooks and bulletins of various provinces, China Insurance Yearbook, official websites of local governments, and the National Bureau of Statistics website, among others. In addition, data related to the calculation of GTI, ED, FD, PS, and UR are derived from the CNRDS Database, China Environmental Statistical Yearbook, and Wind Database. Some missing data were supplemented using the linear interpolation method.

3.1.2. Construction of the Evaluation Index System

The research on green finance in Chinese academic circles has a relatively short history. Some scholars use single indicators to measure, such as green credit and green insurance [48,49]. As China’s green finance policies continue to evolve, GF has achieved considerable development in China. A wide variety of green financial instruments have emerged, and their scale has gradually expanded. Thus, the use of a single indicator can no longer meet the requirements for evaluating China’s green finance.

Considering the availability of data, the scientific nature of index selection, and based on the status of green finance in China, this study refers to the design of the green finance evaluation index system by Hua et al. [9,52] and constructs an evaluation system from four dimensions. These four dimensions include six secondary indicators, forming a relatively comprehensive and objective green finance evaluation system.

Green technology innovation mainly refers to the R&D and application of enterprises in the fields of environmental protection, low-carbon, new energy, and other related areas. Specifically, the number of green invention patents is an important indicator for measuring enterprises’ GTI and refers to granted invention patents with technical and innovative attributes in the green field. This indicator covers six major technical fields, namely, energy conservation and environmental protection, pollution prevention and control, environmental materials, ecological restoration, resource recycling, and clean energy. Compared with the indicator of patent applications, it more accurately reflects the market value of technological innovation and its emission reduction potential. Therefore, the number of green invention patents can more effectively capture enterprises’ R&D capabilities and application–transformation efficiency in GTI.

Referring to Wang et al. [15,53], the number of green patent grants per 10,000 persons in a given year can be used to represent GTI and indicate the situation of GTI in a region.

The selection of specific indicators is shown in

Table 1. Comprehensive evaluation index system of GF and GTI in China.

System	Primary Indicators	Secondary Indicators	+/−	Weight
Green Finance (GF)	Green Credit	proportion of interest in high-energy-consumption industries	−	0.084
	Green Investment	share of investment in environmental pollution control	+	0.252
		share of environmental protection expenditure	+	0.115
	Green Insurance	agricultural insurance density	+	0.430
		agricultural insurance loss ratio	+	0.102
	Carbon Finance	carbon emission intensity	−	0.017
Green Technology Innovation (GTI)		number of green patents granted per 10,000 persons	/	/

.

3.2. Research Methods

This study innovatively integrates a suite of advanced techniques, including the entropy-weighted TOPSIS method, CCD model, kernel density estimation, Dagum Gini coefficient, GTWR model, and ArcGIS technology. It encompasses multiple analytical dimensions: objective indicator weighting, quantitative measurement of synergistic evolution, identification of spatial dependence, analysis of spatiotemporal heterogeneity, and research on driving factors.

3.2.1. Entropy-Weighted TOPSIS Method

This study adopts the entropy-weighted TOPSIS method to evaluate the development level of green finance [15,41,54]. This method combines the advantages of the entropy-weighted method and TOPSIS method, enabling the measurement results to be more objective and reasonable. The specific calculation steps are as follows:

Step 1: Data Standardization. The min-max normalization method is used to normalize the green finance evaluation indicators, as shown in Equations (1) and (2).

$$Positive indicators: x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}} ( i = 1, 2, \dots , m; j = 1, 2, \dots , n)$$

(1)

$$Negative indicators: x_{ij}^{\prime }={\displaystyle \frac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}}} ( i = 1, 2, \dots , m; j = 1, 2, \dots , n)$$

(2)

Next, calculate the proportion of each indicator. Specifically, calculate the proportion p_ij of the j-th evaluation indicator $x_{ij}^{\prime }$ for the i-th evaluation object, as shown in Equation (3).

$$p_{ij}={\displaystyle \frac{{x\prime }_{ij}}{\sum _{i=1}^m{x\prime }_{ij}}}$$

(3)

Step 3: Calculate the entropy value of the j-th indicator, as shown in Equation (4).

$$e_j=-k\sum _{i=1}^m(p_{ij}\times \ln p_{ij})$$

(4)

The constant k is related to the number of samples m.

Step 4: Calculate the coefficient of variation d_j and weight w_j of the j-th indicator, as shown in Equation (5).

$$w_j={\displaystyle \frac{d_j}{\sum _{j=1}^n{d}_j}}$$

(5)

Step 5: Calculate the weighted matrix C_ij.

$$C_{ij}=\{p_{ij}\times w_j{\}}_{m\times n}$$

(6)

Step 6: Determine the positive ideal solution $C_j^{+}$ and negative ideal solution $C_j^{-}$.

The calculation equations are as follows:

$$C_j^{+}=(\max c_{i1},\max c_{i2},\dots ,\max c_{in})$$

(7)

$$C_j^{-}=\left(\min c_{i1},\min c_{i2},\dots ,\min c_{i\eta }\right)$$

(8)

Step 7: Calculate the Euclidean distances $d_i^{+}$ and $d_i^{-}$.

$$d_i^{+}=\sqrt{\sum _{j=1}^n(c_{ij}-C_j^{+}{)}^2}$$

(9)

$$d_i=\sqrt{\sum _{j=1}^n(c_{ij}-C_j{)}^2}$$

(10)

Step 8: Calculate the relative closeness degree f_i (comprehensive score level) for each evaluation object.

$$f_i={\displaystyle \frac{d_i^{-}}{d_i^{+}+d_i^{-}}},f_i\in \left[\mathrm{0,1}\right]$$

(11)

3.2.2. Coupling Coordination Degree Model

This study establishes a CCD model to measure the coupling coordination of GF and GTI. The equations, referenced from Li et al. [55], are shown in Equations (12)–(14):

$$C={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(12)

$$T=\alpha U_1+\beta U_2$$

(13)

$$D=\sqrt{C\times T}$$

(14)

In the equations, U₁ represents green finance, U₂ represents green technology innovation, C denotes the coupling degree of the two systems, and T is the comprehensive development index. Based on the view of Shao et al. [15,56], this study maintains that GF and GTI are equally important, so the weights are set as α = β = 0.5; D stands for the CCD. Drawing on existing research findings, the levels of CCD are classified [57], as shown in

Table 2. Classification criteria for CCD.

CCD	Classification Criteria
0 ≤ C < 0.2	Serious Imbalance
0.2 ≤ C < 0.4	Moderate Imbalance
0.4 ≤ C < 0.5	Near Imbalance
0.5 ≤ C < 0.6	Barely Coordination
0.6 ≤ C < 0.8	Intermediate Coordination
0.8 ≤ C ≤ 1	Excellent Coordination

.

3.2.3. Dagum Gini Coefficient

The Gini coefficient is a key method for measuring disparities in economic development. Drawing on existing studies, this study adopts the Gini coefficient proposed by Dagum and its decomposition method to measure the spatial disparities of the CCD as well as their sources, as shown in Equations (15) and (16).

$$G={\displaystyle \frac{1}{2n^2\mu }}\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}|g{f}_{ji}-g{f}_{hr}|$$

(15)

G = G_w + G_b + G_t

(16)

In Equation (15), k denotes the number of regions. gf_ji and gf_hr represent the CCD of any province in the j-th (h-th) region; n stands for the number of provinces; and μ is the average CCD of the 30 provinces across the three major regions.

In Equation (16), G_w is the contribution of intra-regional disparity, which describes the source of intra-regional disparity; G_b is the contribution of inter-regional disparity, representing the source of inter-regional disparity; and G_t is the contribution of transvariation density, which measures the cross-overlapping effect of samples among regions. These three components together constitute the Gini coefficient G. The Dagum Gini coefficient further decomposes the overall disparity G into intra-regional disparity (G_w), inter-regional disparity (G_b), and transvariation density (G_t). This approach not only enables the examination of the impact of different subgroups on the overall disparity but also effectively overcomes the problem of cross-overlapping among the measured samples.

3.2.4. Kernel Density Estimation

This method intuitively reflects the overall spatial agglomeration effect by measuring the density of spatial elements within their surrounding areas. Assume that n spatial samples are independent and identically distributed samples drawn from a population with probability density function f [58]. The calculation equation is shown in Equation (17).

$$f_n(x)={\displaystyle \frac{1}{nh}}\sum _{i=1}^{n}k\left({\displaystyle \frac{x-x_i}{h}}\right)$$

(17)

In the equation, f_n(x) denotes the kernel density value at point x; k() represents the kernel function; h stands for the bandwidth; and x − x_i is the distance from the estimated point x to the sample point x_i.

3.2.5. Spatial Autocorrelation Model

Drawing on Du et al. [59], in statistics, spatial autocorrelation is usually measured using the Global Moran’s I index. Given a set of elements and sample data, the Global Moran’s I index describes the average degree of correlation between all spatial units and their surrounding areas in the entire region, which is used to determine whether the samples follow a clustered, dispersed, or random pattern. Its calculation equation is as follows.

$$I={\displaystyle \frac{\sum _{i=1}^n\sum _{j=1}^n{\omega }_{ij}(x_i-\overline{x})(x_j-\overline{x})}{S^2\sum _{i=1}^n\sum _{j=1}^n{\omega }_{ij}}}$$

(18)

$$S^2={\displaystyle \frac{1}{n}}\sum _{j=1}^n{\omega }_{ij}{\left(x_i-\overline{x}\right)}^2$$

(19)

where ω_ij is the spatial weight between element i and element j. If the sample points are spatially adjacent, then ω_ij = 1; otherwise, ω_ij = 0. S² represents the sample variance.

3.2.6. Spatiotemporal Geographically Weighted Regression (GTWR)

Considering the spatial and temporal non-stationarity, this study adopts the GTWR model [60] to more effectively reveal the influencing factors of the CCD between GF and GTI. The mathematical expression is shown in Equation (20).

$$y_i={\beta }_0(u_i,v_i,t_i)+\sum _j{\beta }_j(u_i,v_i,t_i)x_{ij}+{\epsilon }_i$$

(20)

In the equation, y_i and x_i represent the observed values of the dependent variable y and independent variable x at the point (u_i, v_i, t_i), where (u_i, v_i, t_i) denotes the three-dimensional coordinates of the i-th sample point. The regression coefficient β_j(u_i, v_i, t_i) is a function of the geographic coordinates (u, v, t) (with i = 1, 2, …, n), and ε_i is the random error of the i-th sample point, which follows a normal distribution with a mean of 0 and a variance of σ².

For the spatiotemporal geographically weighted model, the weighted least squares estimation is still adopted as its estimation method, and this is shown in Equation (21).

$$f({\widehat{\beta }}_{i0},{\widehat{\beta }}_{i1},\dots ,{\widehat{\beta }}_{im})=\underset{{\beta }_0,{\beta }_1,\dots ,{\beta }_n}{\min }\sum _k{w}_{ik}{\left(y_k-{\beta }_{i0}-\sum _j{\beta }_{ij}{x}_{ij}\right)}^2$$

(21)

In the equation, w_ik denotes the spatial weight function matrix based on the geographical distance between the i-th regression point and the k-th sample point.

Based on the above research, this study constructs a spatiotemporal geographically weighted regression model for the CCD between GF and GTI, as shown in Equation (22).

$$\begin{array}{ll}{y}_i& ={\beta }_0(u_i,v_i,t_i)+{\displaystyle \sum _{j=1,k}}{\beta }_1(u_i,v_i,t_i)x_i(ED)+{\displaystyle \sum _{j=1,k}}{\beta }_2(u_i,v_i,t_i)x_i(FD)+\\ & {\displaystyle \sum _{j=1,k}}{\beta }_3(u_i,v_i,t_i)x_i(PS)+{\displaystyle \sum _{j=1,k}}{\beta }_4(u_i,v_i,t_i)x_i(UR)+{\epsilon }_i\end{array}$$

(22)

The explanation of indicators and symbols are shown in

Table 3. Explanation and symbol of variables of driving factors.

Driving Factors	Symbol	Definition of Indicators
Economic Development	ED	ln GDP/ln Resident Population (10,000 persons)
Financial Development	FD	ln (Total Deposits and Loans)/GDP
Population Scale	PS	ln (Year-End Resident Population)
Urbanization	UR	Urban Population/Resident Population

.

Figure 1 illustrates the overall research framework and outlines the main methodological stages of the study, including coupling mechanism analysis, model construction, and empirical analysis.

4. Results and Discussions

4.1. The Coupling Coordination Degree Between GF and GTI

As shown in

Table 4. The coupling coordination degree between GF and GTI across provinces and years.

Province	2010	2012	2014	2016	2018	2020	2022	2023
Beijing	0.461	0.563	0.687	0.771	0.877	0.928	0.990	0.912
Tianjin	0.288	0.331	0.399	0.477	0.461	0.477	0.445	0.415
Hebei	0.220	0.234	0.253	0.288	0.326	0.350	0.346	0.334
Shanxi	0.263	0.252	0.266	0.317	0.323	0.355	0.343	0.321
Inner Mongolia	0.251	0.261	0.285	0.291	0.303	0.366	0.331	0.351
Liaoning	0.273	0.318	0.307	0.292	0.295	0.312	0.326	0.320
Jilin	0.259	0.265	0.273	0.295	0.318	0.367	0.396	0.388
Heilongjiang	0.258	0.272	0.299	0.311	0.312	0.359	0.325	0.303
Shanghai	0.341	0.429	0.476	0.531	0.606	0.679	0.737	0.704
Jiangsu	0.299	0.352	0.396	0.433	0.465	0.470	0.466	0.423
Zhejiang	0.281	0.289	0.327	0.400	0.440	0.467	0.448	0.398
Anhui	0.239	0.277	0.321	0.434	0.461	0.433	0.430	0.419
Fujian	0.224	0.224	0.233	0.340	0.349	0.356	0.322	0.301
Jiangxi	0.207	0.222	0.215	0.265	0.297	0.331	0.354	0.362
Shandong	0.251	0.296	0.292	0.330	0.364	0.343	0.344	0.341
Henan	0.240	0.180	0.213	0.262	0.324	0.290	0.267	0.239
Hubei	0.228	0.231	0.257	0.328	0.360	0.359	0.365	0.357
Hunan	0.223	0.236	0.268	0.307	0.330	0.362	0.336	0.305
Guangdong	0.328	0.278	0.291	0.331	0.438	0.434	0.466	0.454
Guangxi	0.217	0.226	0.280	0.296	0.243	0.267	0.271	0.249
Hainan	0.205	0.221	0.272	0.255	0.287	0.320	0.322	0.301
Chongqing	0.265	0.281	0.290	0.315	0.341	0.370	0.386	0.350
Sichuan	0.210	0.235	0.257	0.301	0.335	0.316	0.322	0.303
Guizhou	0.211	0.111	0.203	0.213	0.246	0.247	0.261	0.254
Yunnan	0.210	0.197	0.210	0.231	0.255	0.245	0.267	0.290
Shaanxi	0.311	0.277	0.300	0.304	0.333	0.366	0.370	0.339
Gansu	0.217	0.227	0.240	0.255	0.367	0.284	0.321	0.302
Qinghai	0.212	0.222	0.232	0.317	0.321	0.368	0.403	0.384
Ningxia	0.237	0.259	0.295	0.320	0.373	0.371	0.377	0.361
Xinjiang	0.217	0.251	0.281	0.281	0.262	0.288	0.280	0.271

, the CCD between GF and GTI across provinces in China presents an overall trend of “phased growth followed by a slight decline in the final period”. From 2010 to 2022, the coupling coordination degree of the vast majority of provinces showed a steady upward trend, reflecting the gradual improvement of China’s green finance system, the continuous enhancement of green technology innovation capabilities, and the constant optimization of the synergistic development mechanism between the two over the last decade and more.

Beijing has consistently maintained the highest coupling coordination degree nationwide, significantly outperforming other provinces. This is attributed to its multiple advantages as a national financial management center, a pioneer pilot zone for green policies, and an agglomeration of high-tech industries, boasting the optimal allocation efficiency of green financial resources and technological innovation factors. Relying on its status as an international financial center and the construction of a science and technology innovation center, Shanghai has long remained in second place.

For central provinces (Hubei, Hunan, Anhui, and Jiangxi), their coupling coordination degree generally ranges from 0.300 to 0.420, indicating that driven by the “dual carbon” goals, the supporting role of green finance for technological innovation in the central region has gradually come to the fore. Western provinces (Guizhou, Guangxi, and Xinjiang) have long had a coupling coordination degree below 0.300, falling into the category of regions with a low level of synergy. This reflects the constraints of scarce financial resources and a weak foundation for technological innovation in underdeveloped western areas.

Henan, however, exhibits special fluctuating characteristics, which may be related to the short-term impact of regional industrial structure transformation (such as the adjustment in the proportion of high-energy-consuming industries) regarding green synergy.

Figure 2 shows that the CCD between GF and GTI ranges from 0.25 to 0.39 and is generally in the stage of moderate imbalance. China’s current level of CCD between GF and GTI is not high [15,30]. Inter-regional differences are also relatively obvious; some regions are relatively backward in development level and have failed to achieve coordinated development.

From a temporal perspective, the CCD between GF and GTI was in a stage of fluctuating changes from 2010 to 2012. This is because national policies on green finance had not yet been introduced, the green finance development system was incomplete, and at the same time, enterprises had a low awareness of GTI. All of these factors became the main bottleneck restricting the improvement in the coordination degree [14].

From 2013 to 2020, it entered a stage of rapid growth. The reason lies in the issuance of the Green Credit Guidelines in 2012 and the gradual improvement of green finance data. As the national green development strategy advanced in depth and the green finance policy was established, it entered a channel of rapid improvement [41]. The two systems began to show positive interaction; however, in some regions, the development speed of GF lagged behind relative to the practice of GTI, which still restricted synergistic improvement.

After 2020, the growth rate of the CCD slowed down and even declined slightly. Affected by the COVID-19 pandemic, some green projects were hindered, the rhythm of green finance capital investment was disrupted, and green technology innovation activities were also suppressed by factors such as restrictions on personnel mobility and interruptions in the supply chain of key technology products. These factors, in turn, hindered the coordinated development process to a certain extent.

From a spatial perspective, the CCD between GF and GTI presents a distinct spatial distribution pattern. It has higher levels and faster development in the east, and lower levels and slower development in the west. Specifically, the CCD in eastern regions is significantly higher than the other two regions and is entering the stage of basic coordination. Eastern regions boast a more developed economy and obvious advantages in resource endowments such as geographical location, capital, and technology. The east’s GTI score is higher than the other two regions. In turn, GF holds advantages in industries and capital, forming a sound interactive cycle with GTI—thus resulting in a relatively high degree, which has increased from 0.29 to 0.47.

In recent years, the central region has achieved steady economic growth, with various economic indicators gradually improving. However, in the key field of GTI, there remains a notable gap between the central region and eastern China. Such gaps are reflected not only in the quantity and quality of innovative achievements and the R&D investment in green technologies but also in the promotion, application, and industrialization process of green technologies. Meanwhile, due to the lag in GTI, the central region also faces corresponding constraints in the development of GF: the scale and maturity of its green finance market are both inferior to those of eastern China. This prevents the CCD between GTI and GF in the central region from reaching the desired high level; instead, it remains at a medium level. Therefore, the central region is in urgent need of further resource input and policy support to narrow the gap with the eastern region and elevate the regional green economy’s overall development level [30,47].

Due to geographical constraints and economic limitations, the western region faces numerous challenges in the R&D, promotion, and application of green technologies, making it difficult to form a robust technological innovation system. Financial institutions have a low willingness to invest in green projects. Moreover, green financial products and services are relatively scarce. This creates obstacles to effectively meeting GTI’s capital needs. This leads to poor interaction between GF and GTI and a failure to form a sound synergistic effect between the two. Consequently, among the three regions, the western region has the lowest CCD between GF and GTI.

As shown in Figure 3, the CCD of most provinces in China fell into the category of serious imbalance in 2010, while only a few provinces were at the levels of moderate imbalance and near imbalance. This is because the green finance system was still underdeveloped at that time, with limited financial support for GTI, and many innovation projects struggled to advance due to insufficient funds. Meanwhile, the market demand for GTI had not been fully stimulated, and enterprises lacked enthusiasm for conducting green technology R&D, resulting in a low output of GTI achievements. Additionally, policy guidance and incentive measures were insufficient, failing to effectively promote the in-depth integration and coordinated development between GF and GTI.

By 2016, the overall CCD had improved: most provinces had moved up to the moderate imbalance level, Jiangsu Province and Anhui Province reached the near imbalance level, and the Shanghai Municipality and Beijing Municipality took the lead in entering the excellent coordination level. This indicates that their GF systems and GTI systems had formed an efficient synergy mechanism. The reason lies in the gradual improvement of the green finance system during this period: the government increased support for green finance and introduced a series of incentive policies, and financial institutions also responded actively by providing more financial support for GTI projects, effectively promoting in-depth integration and coordinated development. Guided by the normative framework of green finance policies, financial institutions in the Shanghai Municipality, Beijing Municipality, Jiangsu Province, and Anhui Province continued to innovate green financial products and services. Simultaneously, these regions also established a sound green technology evaluation system, providing a basis for financial institutions to accurately identify and assess GTI projects. This further reduced the investment risks of financial institutions and enhanced their enthusiasm for participating in GTI.

In 2023, the coupling coordination level improved further, with the number of provinces at the serious imbalance level reducing to five, and Guangdong Province also advancing to the near imbalance level. This is attributed to the more mature development of green finance during this period: society’s awareness of and demand for green development increased significantly, and the market demand for green technology products became increasingly strong. This greatly stimulated enterprises’ enthusiasm for conducting green technology R&D and innovation, leading to a continuous emergence of GTI achievements, which further promoted the positive interaction and coordinated progress between GF and GTI. During this period, with strong support from local green finance policies, numerous enterprises in Guangdong Province had sufficient capital to increase investment in the green technology field and carry out a series of forward-looking and innovative R&D projects. Meanwhile, Guangdong’s sound industrial supporting system and abundant talent resources also created highly favorable conditions for GTI, thereby achieving rapid momentum in the coordinated development of the two systems.

4.2. Regional Differences in the Degree of Coupling Coordination

4.2.1. Analysis of Regional Differences

As shown in

Table 5. Regional differences in the CCD between GF and GTI.

Year	Dagum Gini Coefficient				Average Dagum Gini Coefficient (G) by Region
	G	Gw	Gb	Gt	Eastern	Central	Western
2010	0.102	0.030	0.051	0.021	0.125	0.043	0.063
2011	0.132	0.039	0.071	0.022	0.146	0.062	0.100
2012	0.140	0.041	0.078	0.021	0.155	0.068	0.098
2013	0.139	0.042	0.071	0.027	0.165	0.082	0.084
2014	0.138	0.041	0.075	0.022	0.175	0.073	0.070
2015	0.147	0.044	0.077	0.026	0.181	0.082	0.080
2016	0.142	0.042	0.083	0.018	0.179	0.079	0.065
2017	0.147	0.043	0.086	0.018	0.185	0.062	0.077
2018	0.151	0.044	0.088	0.019	0.182	0.065	0.085
2019	0.150	0.043	0.095	0.012	0.182	0.053	0.081
2020	0.151	0.044	0.091	0.016	0.187	0.053	0.087
2021	0.158	0.047	0.090	0.021	0.200	0.046	0.098
2022	0.157	0.047	0.088	0.021	0.201	0.071	0.086
2023	0.153	0.047	0.083	0.023	0.197	0.088	0.079
Mean	0.143	0.042	0.081	0.021	0.176	0.066	0.082
Max	0.158	0.047	0.095	0.027	0.201	0.088	0.1
Min	0.102	0.03	0.051	0.012	0.125	0.043	0.063

Note: G denotes the overall Dagum Gini coefficient of the coupling coordination degree (CCD) between green finance and green technology innovation; Gw denotes intra-regional disparity; Gb denotes inter-regional disparity; and Gt denotes transvariation density. Columns 6–8 report the average Dagum Gini coefficient of the CCD for the eastern, central, and western regions, respectively.

, the Gini coefficient of the CCD shows a slow upward trend, indicating that the degree of imbalance in the CCD has increased yearly. In terms of values, the Gini coefficients of China’s three major economic zones show an obvious characteristic of “eastern region > western region > central region”. In terms of change trends, the change trends of the Gini coefficients of the coupling degree in China’s three major economic zones vary from each other: the eastern region shows a steady upward trend yearly, while the central and western regions show a fluctuating upward trend.

Figure 4 shows the changes in the contribution rates of Gw, Gb, and Gt to the total Gini coefficient G. Since 2010, the inter-regional gap in the CCD between GF and GTI has been in a dominant position, with its contribution rate to the total gap ranging from 50% to 60%. It is followed by the intra-regional gap, whose contribution rate is around 30%. The smallest contributor is transvariation density (Gt), accounting for approximately 10∼20% of the total gap; this indicates that the cross-overlapping effect of the CCD has a relatively small impact on the overall spatial disparity. The three major contributing factors show a relatively stable trend, which suggests that in the coming period, the overall disparity will still be dominated by inter-regional gaps [21]. Therefore, investigating how to narrow the inter-regional development imbalance is an important direction for the spatial governance of the coordinated development of the coupling between GF and GTI.

4.2.2. Analysis of Inter-Regional Differences

As shown in

Table 6. Inter-regional differences among the three groups.

Year	Central and Western	Eastern and Central	Eastern and Western
2010	0.063	0.118	0.133
2011	0.087	0.149	0.169
2012	0.085	0.163	0.182
2013	0.085	0.167	0.174
2014	0.075	0.173	0.175
2015	0.086	0.179	0.184
2016	0.081	0.166	0.189
2017	0.082	0.169	0.199
2018	0.088	0.170	0.203
2019	0.082	0.167	0.209
2020	0.086	0.164	0.208
2021	0.086	0.175	0.213
2022	0.087	0.180	0.206
2023	0.090	0.178	0.194
Mean	0.083	0.166	0.188
Max	0.09	0.18	0.213
Min	0.063	0.118	0.133

, the inter-regional differences among the three groups (Eastern and Central, Eastern and Western, and Central and Western) all show an increasing trend, which has driven the overall increase in regional imbalance in the coordinated development of the coupling.

Among these groups, the inter-regional disparity between the Eastern and Western regions exhibits the largest variation, increasing from 0.133 to 0.213 during the study period, which corresponds to a relative expansion of 60.15%. The rapid widening of this gap has severely undermined the equilibrium of national coordinated development. It also reflects substantial and growing disparities among different regions in terms of policy implementation, market conditions, and resource allocation.

4.3. The Evolutionary Trends of Coupling Coordination

Figure 5 illustrates the dynamic distribution evolution of the CCD between GF and GTI. From the peak of the distribution curve, the following can be seen: the main peak of the CCD distribution curve narrows in width and increases in height, which indicates that the spatial disparity in the CCD shows an increasing trend. Additionally, the distance between the two peaks gradually widens; this is because the gap gradually expands. The side peak of the CCD distribution curve shifts in position to the left with little change in height. This suggests that in the process of improving the CCD, the CCD of a small number of provinces has not improved, thus widening the gap with the average level.

4.4. Spatial Correlation Analysis

According to the First Law of Geography, the CCD may exhibit spatial correlation among adjacent regions. This study employs the spatial autocorrelation model and the high/low clustering model, taking the CCD as the measurement indicator, and uses ArcGIS 10.8 software to conduct a spatial correlation analysis on it.

Table 7. Global Moran’s I index of the CCD from 2010 to 2023.

Year	2010	2011	2012	2013	2014	2015	2016
Moran’s I	0.108	0.260	0.244	0.258	0.265	0.269	0.326
p-value	0.081	0.006	0.009	0.005	0.002	0.003	0.001
Year	2017	2018	2019	2020	2021	2022	2023
Moran’s I	0.302	0.255	0.319	0.254	0.186	0.160	0.127
p-value	0.001	0.004	0.001	0.004	0.023	0.044	0.100

presents the Moran’s I index and its corresponding p-value from 2010 to 2023. In most years (2011–2022), the Moran’s I index is significantly positive at the 5% significance level under the spatial weight matrix. This result indicates there is significant positive spatial correlation in the CCD. The spatial correlation reached its peak in 2016 (I = 0.3256) and showed a fluctuating downward trend thereafter. A possible reason for this is the intensive introduction of policies related to GF and GTI in China. In particular, in 2016, China issued the Guidelines for Establishing the Green Finance System, which clearly defined the definition and classification of green finance and proposed several specific measures to drive the growth of green finance [11].

Figure 6 illustrates the high/low clustering distribution of the CCD between GF and GTI. The High-Low Outlier indicates that the attribute value of the research variable is relatively high, but the region is surrounded by areas with lower attribute values, showing a negative spatial correlation with the surrounding areas; the meanings of other clustering results are similar.

Through comparison, it can be found that the High-High Cluster is mainly concentrated in northern China. Adjacent provinces exhibit “neighboring imitation” in policy interpretation and the promotion of pilot experience. For example, the Beijing–Tianjin–Hebei region jointly established a green technology trading market [27]. These practices led to the simultaneous improvement of the CCD of adjacent provinces in these regions, forming a High-High Cluster, which ultimately manifests as a significant positive spatial correlation.

In addition, the spatial agglomeration characteristics of various provinces underwent significant changes in 2016. This may be attributed to the fact that the Chinese government promulgated the Guidelines for Establishing the Green Finance System, while provinces differed in the pace and intensity of policy implementation.

In general, the impact of each driving factor is not static but exhibits significant spatiotemporal heterogeneity. Policy formulation should abandon the “one-size-fits-all” paradigm and implement regionally differentiated, targeted strategies to promote coordinated regional development.

4.5. Analysis of Driving Factors

4.5.1. Model Selection and Validation

To verify the rationality of the model, the data on the CCD between GF and GTI were initially standardized. Subsequently, a multicollinearity test was conducted on each explanatory variable. The results show that the tolerance of each variable is greater than 0.1, and the VIF value is less than 1.680, indicating no multicollinearity. Thus, the next step of model construction can be carried out.

Following the diagnostic tests, Ordinary Least Squares (OLS) regression was performed on the driving factors. The results show that the OLS regression coefficients of all driving factors are significant at the 5% significance level, which indicates that economic development level, financial development level, population scale, and urbanization all have a significant positive impact on the CCD. Therefore, the selection of these four factors is meaningful.

Regarding the final model selection, the GTWR plugin in ArcGIS 10.8 software was used to conduct OLS, GWR, and GTWR analyses. As shown in

Table 8. Comparison of related parameters of OLS, GWR, and GTWR.

Parameters	OLS	GWR	GTWR
R2	0.690	0.883	0.911
R2 Adjusted	–	0.882	0.910
AICc	−1067.049	−1405.481	−1487.161
RSS	1.893	0.714	0.543

Note: ‘–’ indicates that the value is not reported.

, GTWR results have a larger R², smaller Residual Sum of Squares (RSS), and smaller corrected AICc. Given that it exhibits the highest goodness of fit and the most obvious spatial heterogeneity, outperforming the OLS model and GWR model, this study selected the GTWR model to analyze the coupling development between GF and GTI in China’s provincial-level regions and its influencing factors.

4.5.2. Spatiotemporal Effects of Driving Factors

The economic development level (ED) is positively correlated with the CCD, as shown in Figure 7, but it exhibits complex non-linearity and spatiotemporal heterogeneity. Economically developed eastern provinces have provided market demand and financial support for GTI through industrial structure upgrading and high-tech industry agglomeration. In contrast, economically underdeveloped western provinces are constrained by the low level of green finance development and the small scale of the financial market, resulting in a weak driving effect on GTI and even a negative regulatory effect in some local areas.

The financial development level (FD) has a significant positive impact on the CCD, with its regression coefficient being significant in most provinces and years, showing an obvious pattern of “higher levels and faster development in the east, and lower levels and slower development in the west”. This fully indicates that in eastern coastal areas, the abundance and allocation efficiency of financial resources directly determine the capital support for GTI R&D and transformation [27], effectively promoting the R&D and industrialization of green technologies. However, in some central and western regions, the marginal contribution of financial development level is relatively low, which may be related to the uneven distribution of financial resources and insufficient coverage of green financial products.

The impact of population scale (PS) on the CCD presents a complex and changeable spatial pattern. Regions such as Heilongjiang, Jilin, and Liaoning, which initially had high impact coefficients, have gradually evolved to a low level. This may be due to the economic recession, population outflow, or aging in northeast China in recent years. The population scale has a significant positive effect on the CCD in central China, with the highest effect in provinces such as Sichuan, Hubei, Hunan, and Jiangxi. This may be because China is promoting the transfer of coastal industries, and the resource potential and labor cost advantages of central China have begun to emerge. Therefore, when promoting green transformation, population factors must be incorporated into comprehensive considerations, and the potential pressure brought by population scale should be offset by improving resource utilization efficiency and advocating green lifestyles.

The impact of urbanization level (UR) on the CCD shows an obvious pattern of “higher in the east and lower in the west”. Highly urbanized regions (e.g., Beijing, Tianjin, and Shanghai) have promoted the in-depth integration of green technologies and urban construction through improved infrastructure, agglomeration of innovative resources, and policy pilot advantages, but their promoting effect tends to stabilize. In contrast, the urbanization process in western regions is still dominated by scale expansion, with insufficient capacity for green technology integration and application, leading to a weak driving effect. The Xinjiang Uygur Autonomous Region shows strong particularities, which may be due to its large geographical area and single economic and industrial structure, but this does not affect the analysis results.

Figure 8 shows the average GTWR coefficients of each driving factor. The impact of economic development level and urbanization on the CCD between GF and GTI has been on an upward trend in each year. This indicates that by expanding the market scale and promoting population agglomeration, the diffusion of technology and industrial synergy have been accelerated, which provides stable financial support and demand-driven impetus for GTI, thus continuously strengthening the coupling coordination effect.

However, the impact of financial development level and population scale on the CCD between GF and GTI has shown a downward trend in each year. A possible reason lies in the dual effects of financial market saturation and policy adjustments. As the financial market gradually matures, the marginal contribution of green financial instruments decreases; in addition, tighter regulation in some regions has restrained innovation vitality. The negative impact of population scale, on the other hand, may be related to uneven resource allocation during the urbanization process, which weakens the scale effect. Concurrently, the environmental pressure caused by rapid urbanization has offset the positive contribution of the demographic dividend.

In general, the impact of each driving factor on the CCD between GF and GTI is not static but exhibits significant spatiotemporal heterogeneity. Policy formulation should implement differentiated and targeted regional coordination strategies.

4.6. Discussions

The empirical findings of this study are broadly consistent with, yet meaningfully extend, the existing literature on green finance and green technology innovation. Specifically, regarding the positive impact of green finance, Wang et al. [7] demonstrated that green credit policies effectively reduce the R&D risks for enterprises, thereby stimulating patent output. Complementing this view, Liu et al. [15] highlighted that diverse green financial instruments drive environmental performance by optimizing resource allocation in high-polluting sectors. Our results align with these observations, confirming that green finance acts as a vital catalyst for technological upgrading.

In terms of systemic interaction, recent research employing coupling coordination models has also documented similar trends. For instance, Chen et al. [17] and Sun et al. [30] observed moderate coordination levels and distinct regional disparities between green finance and digital technology. Consistent with these prior studies, our analysis reveals that while GF–GTI coupling has improved, it remains constrained by regional imbalances and structural bottlenecks.

However, this study advances the literature in several important respects. Most notably, compared with Zhao et al. [41] and Chen et al. [17], who mainly focus on static coupling levels or short time spans, this research provides a long-term (2010–2023) spatiotemporal analysis that reveals a three-stage evolution path and a post-2020 slowdown in coordination dynamics. This finding highlights the sensitivity of GF–GTI coordination to external shocks such as the COVID-19 pandemic and global economic uncertainty.

Regarding regional imbalances, while prior studies often rely on overall inequality measures, this study employs the Dagum Gini coefficient decomposition to explicitly identify inter-regional disparity as the dominant source of imbalance. This decomposition-based evidence deepens the understanding of why national-level improvements may coexist with widening regional gaps.

Furthermore, by integrating spatial autocorrelation analysis and the GTWR model, this study uncovers significant spatial dependence and spatiotemporally heterogeneous driving mechanisms. Unlike conventional regression approaches that estimate average effects, the GTWR results demonstrate that the impacts of economic development, financial development, population scale, and urbanization vary substantially across regions and over time. These findings complement and refine the conclusions of firm-level studies such as Song et al. [11], which do not explicitly account for spatial heterogeneity.

5. Conclusions

This study investigated the coupling coordination between green finance (GF) and green technology innovation (GTI) across Chinese provinces from a spatiotemporal perspective. By integrating the coupling coordination degree (CCD) model with spatial and spatiotemporal analytical techniques, several robust conclusions can be drawn.

The results indicate that the overall level of coupling coordination between GF and GTI remains relatively low, even though it exhibits a clear upward trend over the study period. Specifically, the coordination state has gradually evolved from moderate imbalance toward near imbalance, indicating incremental improvement but insufficient systemic integration. From a regional perspective, pronounced regional heterogeneity persists. The eastern region consistently outperforms the central and western regions and has begun to enter a stage of weak or initial coordination. In particular, economically advanced provinces such as Beijing and Shanghai have achieved a transition from moderate imbalance to high or excellent coordination. In contrast, the majority of provinces remain trapped in a state of moderate imbalance, reflecting persistent institutional, financial, and technological constraints in the nationwide green transformation process.

Furthermore, spatial correlation analysis reveals a significant and increasingly strong positive spatial dependence in GF–GTI coupling coordination. High-High Cluster areas are mainly concentrated in the Beijing–Tianjin–Hebei (BTH) region and surrounding northern provinces, indicating the presence of spatial spillover effects and regional synergy. Conversely, High-Low outliers are predominantly located in central China, suggesting that some provinces with relatively high coordination levels are surrounded by regions lagging in green finance–innovation integration. This spatial pattern highlights the importance of inter-regional linkages and coordinated policy design.

Regarding the underlying driving mechanisms, the spatiotemporally heterogeneous driving mechanism analysis demonstrates that economic development (ED), financial development (FD), population scale (PS), and urbanization level (UR) exert differentiated impacts across regions. ED, FD, and UR play a stronger positive role in the eastern region, where financial markets and innovation systems are relatively mature. Meanwhile, population scale exerts a more pronounced influence in the central region, reflecting the role of labor supply and market size in supporting green innovation. However, the western region shows weaker and, in some cases, negative responses to these driving factors, indicating structural bottlenecks and limited absorptive capacity.

Beyond these empirical findings, this study contributes to the theoretical literature by conceptualizing GF and GTI as two interdependent and dynamically interacting subsystems within a unified green development framework. This coordination-oriented perspective complements existing causality-based approaches by emphasizing long-term alignment, feedback mechanisms, and systemic compatibility. Moreover, the application of a hypothesis-free coupling coordination framework demonstrates the value of mechanism-driven analysis in contexts characterized by structural complexity and spatial heterogeneity. By allowing coordination patterns and dominant driving forces to emerge endogenously from the data, this study provides a transferable analytical framework for future research on integrated finance–innovation systems and other dimensions of sustainable development.

6. Practical Implications

The findings of this study offer several important practical implications for policymakers seeking to advance coordinated green development under China’s dual carbon goals.

Paramount among these is the need to strengthen the synergistic interaction between green finance and green technology innovation. As financing constraints remain a key bottleneck for GTI, the government should enhance targeted financial support mechanisms, such as dynamically updated green technology catalogs, national-level green innovation funds, and diversified green financial products (e.g., green transformation loans and green patent-backed financing). Establishing linkage platforms between GF and GTI can improve capital allocation efficiency and accelerate the commercialization of green technologies.

Simultaneously, promoting cross-regional coordination is crucial to narrowing regional disparities in green transformation. Given the persistent imbalance across regions, inter-regional green development coordination mechanisms should be strengthened. The eastern region can leverage its technological and financial advantages to facilitate technology diffusion and industrial upgrading in the central and western regions. In parallel, increased policy support and infrastructure investment are necessary to enhance the absorptive capacity and green development potential of less-developed regions.

Finally, in light of the heterogeneous driving mechanisms identified, regionally differentiated policy instruments should be adopted to account for heterogeneous driving mechanisms. In developed regions, policy efforts should focus on improving the efficiency and quality of green financial resource allocation. In developing regions, coordinated measures are needed to balance economic expansion with ecological protection, including fiscal incentives for green innovation and urbanization strategies that integrate green infrastructure and sustainable construction standards.

7. Limitations of the Study and Future Research

This study still has several limitations that warrant further in-depth exploration. The analysis is currently conducted at the provincial level, which may obscure intra-provincial heterogeneity. Future research could employ prefecture-level or urban agglomeration data to provide more granular insights and reduce potential aggregation bias.

Furthermore, although the indicator system for GF and GTI is relatively comprehensive, the definitions of both systems can be further enriched. Future studies may expand the indicator set by incorporating additional dimensions such as green equity financing or distinguishing GTI inputs and outputs more explicitly.

From a methodological perspective, while the coupling coordination framework effectively captures systemic interaction and spatiotemporal heterogeneity, it does not explicitly identify causal mechanisms. Future research could integrate quasi-experimental or structural modeling approaches to further investigate the causal pathways linking green finance and green technology innovation.