Spatial-Temporal Evolution and Driving Factors of the Synergistic Development of Green Finance and Low-Carbon Innovation

Abstract

Under the context of the “dual carbon” goals, the synergistic development of green finance and low-carbon innovation plays a significant role in driving the green transformation of the economy and high-quality development. This paper, based on provincial panel data from China from 1990 to 2022, employs the coupling coordination degree model, Dagum Gini coefficient, spatial autocorrelation model, and spatial Durbin model for empirical analysis. The research findings indicate the following: (1) the level of synergistic development of green finance and low-carbon innovation shows an upward trend, with the eastern region performing well, while the western region’s development remains weak, leaving much room for improvement; (2) the spatial differences in the synergistic development of green finance and low-carbon innovation are mainly due to inter-regional differences, followed by intra-regional differences, with the impact of super-variation density being relatively small; (3) regarding spatial correlation, there is a significant spatial autocorrelation in the synergistic development of green finance and low-carbon innovation, with the eastern region showing high-level clustering, while the western region exhibits low-level clustering; (4) the positive driving factors influencing the synergistic development of green finance and low-carbon innovation are ranked as follows: Government policy support > Human capital > Economic development, with industrial structure having a significant negative impact. Based on these conclusions, recommendations are made to strengthen differentiated policy support mechanisms, build cross-regional innovation collaboration networks, systematically promote the green transformation of industrial structures, expand markets, and strengthen regional cooperation.

1. Introduction

Achieving the “dual carbon” goals is not only a major strategic initiative for China’s high-quality economic development but also aligns with the global trend of sustainable development and climate governance. During the “13th Five-Year Plan” period, China emphasized advancing energy conservation, emission reduction, and low-carbon innovation in key sectors and regions, controlling energy intensity and total carbon emissions, and vigorously developing a low-carbon economy. It further proposed to “improve fiscal, financial, investment, and pricing policies and standards that support green development” (Fanbin K et al., 2024) [1]. Notably, China’s exploration is not an isolated case but rather reflects common challenges faced globally in pursuing sustainable development. The European Union, relying on the Sustainable Finance Action Plan and the green bond market, has developed a relatively mature path integrating policy instruments with market mechanisms (Merler, 2025) [2]. The United States, by contrast, has promoted clean energy investment and carbon emission regulation, leveraging market-based mechanisms to drive low-carbon technology R&D and industrialization. Emerging economies such as Brazil and India place greater emphasis on policy-oriented financial tools, including green credit and green funds, to address their dependence on fossil energy structures and environmental governance pressures. However, in recent years, due to path dependence and self-locking effects, both developed provinces in China and EU member states with strong low-carbon innovation capacity tend to preserve their innovation advantages internally, resulting in pronounced spatial disparities in low-carbon innovation (Evro et al., 2024) [3]. Similarly, in emerging economies such as Brazil and India, green finance continues to face limitations in financing capacity and low efficiency in supporting technological innovation (Novák et al., 2025) [4]. To address these challenges, it is imperative to strengthen the supply capacity of green finance and enhance the efficiency of low-carbon innovation transformation. On the one hand, green finance should optimize resource allocation, reduce financing costs, and diversify investment risks to channel capital into the R&D and diffusion of low-carbon technologies, thereby accelerating breakthroughs in renewable energy, energy conservation, and cleaner production, and providing sustained financial supply and market incentives for low-carbon innovation. On the other hand, low-carbon innovation should focus on accelerating technological R&D and commercialization, enhancing both the economic and environmental benefits of green products, thereby increasing the return certainty and risk controllability of green finance investments (Feng et al., 2023; Qamruzzaman and Karim, 2024) [5,6]. The synergy between the two offers a systemic solution for achieving high-quality development under the “dual carbon” goals and advancing global climate governance. Therefore, examining the spatiotemporal evolution and driving factors of the coupling coordination between green finance and low-carbon innovation is not only of practical significance for China’s green and low-carbon economic transition but also provides important references for the international community in exploring sustainable development pathways and addressing climate change challenges.

Research related to the theme of this study has mainly focused on the following aspects:

(1)

The Connotation and Measurement of Green Finance: Höhne et al. (2012) [7] defined green finance as financial resources directed toward green development projects, environmental products, and policies that encourage sustainable economic growth. Berrou et al. (2019) [8] emphasized that green finance encompasses financial stocks and flows aimed at achieving environmentally related sustainable development goals. Lindenberg (2014) [9] argued that green finance is often used interchangeably with green investment, referring to the allocation of capital into green development sectors to improve the environment, address climate change, and enhance resource efficiency. Regarding the measurement of green finance, existing studies primarily evaluate its performance from single dimensions, such as green credit, green bonds, green investment, green funds, or green equity. These indicators have been widely employed to assess the impact of green finance on corporate green production efficiency, renewable energy innovation, ecological improvement, and sustainable development [10,11,12,13,14]. With the continuous refinement of China’s green finance policy framework, some scholars have attempted to construct multi-dimensional evaluation systems. For instance, Zhou et al. (2022) [15] assessed the level of green finance development comprehensively from the perspectives of green credit, green investment, and green insurance. D’Orazio and Thole (2022) [16] proposed a “Climate-related Financial Policy Index” to compare cross-country differences in green finance policy performance. Similarly, Bhatnagar (2024) [17] designed a transnational Green Finance and Investment Index to evaluate the overall development of green finance across 15 countries.

(2)

Measurement and Evaluation of Low-Carbon Innovation: From the input perspective, Furman et al. (2002) [18] and Riddel and Schwer (2003) [19] argued that regional innovation represents a comprehensive capability determined by the potential to produce related or innovative products, and it is typically measured through research and development (R&D) investment. Cantone et al. (2023) [20] developed a negative binomial regression model based on carbon pricing to evaluate the efficiency of low-carbon innovation, suggesting that higher carbon prices provide effective incentives for low-carbon innovation. From the output perspective, the performance of low-carbon innovation reflects its direct outcomes. Li et al. (2021) [21] assessed low-carbon technological innovation efficiency using firm performance as the ultimate output indicator. Ma (2024) [22] employed carbon emissions as a measure of low-carbon innovation output, finding that in firms with low ownership concentration or operating in high-tech industries, high carbon emissions intensify emission-reduction pressures, thereby encouraging increased R&D investment to foster low-carbon innovation. Bonnet (2020) [23], on the other hand, evaluated low-carbon innovation capacity using patent data and further incorporated the application of low-carbon energy technologies to assess innovation efficiency. Johnstone et al. (2010) [24] analyzed OECD patent data to investigate international differences in renewable energy technology innovation.

(3)

The Impact of Green Finance on Low-Carbon Innovation: From the perspective of green credit, Chen et al. (2022) [25] found that green credit policies exert a more significant positive effect on low-carbon technological innovation among state-owned enterprises and ESG-certified firms. Specifically, such policies promote corporate low-carbon innovation by increasing R&D investment and improving managerial efficiency; Similarly, Aizawa and Yang (2010) [26] highlighted that green credit policies can improve capital allocation efficiency and risk assessment mechanisms, thereby guiding capital flows into low-carbon technological innovation in emerging markets. In contrast, Pang et al. (2022) [27] argued that green credit may have a short-term inhibitory effect on low-carbon innovation. From the perspective of green bonds, Li et al. (2025) [28] reported that green bond policies significantly increase the number of corporate green patent applications, particularly green invention patents, and have a more pronounced effect on stimulating green technological innovation in highly polluting industries. However, Flammer et al. (2021) [29] noted that the relationship between green bonds and low-carbon innovation is not entirely positive. In practice, some issuers exhibit forms of environmental misrepresentation, including overstating environmental commitments, selectively disclosing sustainability goals, or making misleading claims about the environmental benefits of projects. Such practices, aimed at securing easier access to green financing channels and related policy support, constitute instances of greenwashing in green finance. Likewise, Larcker and Watts (2020) [30] revealed evidence from U.S. firms indicating a disconnect between environmental disclosures in the green bond market and firms’ actual emission-reduction behaviors. From the perspective of green insurance, Hu et al. (2023) [31] demonstrated a positive correlation between green insurance and low-carbon innovation. Green insurance significantly increases the number of green patent applications, enabling insured enterprises to access more resources, bear greater risks, and consequently enhance their willingness to engage in low-carbon innovation.

In summary, extensive studies have examined the impact of green finance on low-carbon innovation and generally suggest a positive relationship between the two. However, several issues remain to be addressed: (1) At the theoretical level, there is a lack of systematic analysis of the “synergistic development” and coupling mechanisms between green finance and low-carbon innovation. Existing studies primarily emphasize the linear narrative of “green finance promoting low-carbon innovation,” which fails to account for the substantial heterogeneity and stage-specific outcomes observed across different regions and development phases. (2) Empirical research on the dynamic characteristics and spatial correlation of the synergistic development level of green finance and low-carbon innovation remains insufficient. Existing studies primarily focus on macro-level theoretical discussions or case studies of single regions, lacking systematic quantitative investigations into the interaction patterns between green finance resource allocation and low-carbon innovation activities across different regions. Specifically, there is a lack of sufficient empirical evidence on key issues such as the spatial heterogeneity of the synergistic development level, spatial spillover effects, and evolutionary paths, making it difficult to reveal the complex spatial mechanisms of their interaction at the regional scale. (3) There is significant room for improvement in the analysis of the driving factors and mechanisms behind the synergistic development of green finance and low-carbon innovation. Current research has identified the core driving factors in a fragmented manner, mostly focusing on a single dimension, and has yet to establish a comprehensive analytical framework that encompasses multiple dimensions.

Based on the above gaps, this study employs provincial panel data from China covering the period 1990–2022 to investigate the interactive relationship between green finance and low-carbon innovation. The marginal contributions of this paper are as follows: (1) It proposes and systematizes a conceptual framework for the synergistic development of green finance and low-carbon innovation. Unlike previous studies that primarily relied on single-dimensional measurements, this study, grounded in sustainable development theory, integrates financial resource allocation with technological innovation dynamics into a unified analytical logic. By applying the coupling coordination degree model, it measures the level of synergistic development, thereby offering a new operational tool for uncovering the interaction mechanisms between the two systems and extending the theoretical boundaries of synergy research. (2) It deepens the theoretical understanding of regional disparities and spatial effects. Employing the Dagum Gini coefficient and spatial autocorrelation models, the study verifies the spatial heterogeneity and spillover effects of synergy, reveals the spatial dependence of inter-regional collaboration, and provides a new theoretical lens for examining cross-regional green transformation. (3) It further identifies the key driving factors of synergistic development by applying the spatial Durbin model, thereby providing robust empirical evidence to support the advancement of higher levels of coordination between green finance and low-carbon innovation.

In summary, this paper is structured into five sections: The first section is the introduction, which introduces the research background, defines the research questions, and outlines the contributions. The second section is mechanism of action, which focusing on the “synergistic development of green finance and low-carbon innovation” and the “driving mechanisms underlying their synergy.” The third section describes the research design, including the selection of indicators, methodological approaches, and data sources. The fourth section is the empirical analysis, which reports results from the perspectives of temporal characteristics, spatial disparities, spatial correlations, and driving factor analysis. The fifth section is the Discussion, which discusses the findings, offering policy recommendations derived from them, along with the study’s limitations and directions for future research.

2. Mechanism of Action

2.1. The Mechanism of the Synergistic Development of Green Finance and Low-Carbon Innovation

The synergy between green finance and low-carbon innovation refers to the close coordination between the green allocation of financial resources and the iterative advancement of low-carbon technologies within the broader framework of China’s “dual carbon” strategy and the Sustainable Development Goals. This coordination unfolds across multiple dimensions—including financial flows, information flows, institutional regulations, and technological pathways—forming a systemic mechanism that integrates economic growth, social equity, and ecological protection. In contrast to prior studies that examined either the expansion of green finance or the efficiency of low-carbon innovation in isolation, this perspective underscores the dual driving roles of capital and technology throughout their entire life cycles. Specifically, green finance channels capital into low-carbon sectors through resource allocation and risk diversification, while low-carbon innovation enhances resource efficiency and environmental capacity through continuous technological iteration and diffusion. Accordingly, the synergistic development of green finance and low-carbon innovation is grounded in the “triple bottom line” logic of sustainable development. It not only responds to the practical demands of green transformation and high-quality growth but also carries universal relevance and institutional value for global climate governance and international cooperation on low-carbon transitions. Within this conceptual framework, the interactive mechanisms can be analyzed from a bidirectional perspective.

The impact of green finance on low-carbon innovation can be explained through the following three mechanisms. First, green financial instruments—such as green credit, green bonds, and green funds—reduce financing costs and alleviate credit constraints, thereby providing enterprises with the necessary financial support for low-carbon technological research and development. This, in turn, facilitates breakthroughs and applications in energy-saving and emission-reduction technologies (Zhu et al., 2023) [32]. Second, risk management tools such as green insurance help to mitigate the technological, market, and policy risks that low-carbon innovation projects face during the stages of R&D, pilot testing, and implementation. By improving the risk-bearing capacity of both firms and investors, green insurance enhances the likelihood of project success (Hu et al., 2023) [31]. Third, green finance promotes the establishment of green classification standards, green certification systems, and ESG evaluation frameworks, which provide recognition and credit endorsements for low-carbon innovation outcomes. These mechanisms enhance the credibility and acceptance of low-carbon innovations in both capital markets and among consumers, thereby improving financing accessibility and further strengthening the capacity of the green financial system to support low-carbon technologies (Zheng et al., 2025; Shao & Huang, 2023; Zhang et al., 2021) [33,34,35].

Low-carbon innovation also exerts a reverse effect on the development of green finance, and its mechanisms can be summarized in the following four aspects: First, with the support of green finance, low-carbon innovation accelerates the large-scale application and industrial deployment of renewable energy, clean energy, and energy-saving and environmental protection technologies. This enhances the investment returns and risk controllability of green projects, thereby optimizing the allocation structure and efficiency of green financial resources (Lu et al., 2022; Wen et al., 2021) [36,37]. Second, low-carbon innovation generates a significant demand for green technology R&D, cleaner production, and green infrastructure projects. This creates new investment opportunities that encourage banks, funds, and other financial institutions to design and introduce corresponding green credit, green bond, and equity investment products, thereby expanding the supply of green finance (Yang, 2025) [38]. Third, by improving energy efficiency, reducing pollutant emissions, and extending equipment lifecycles, low-carbon innovation mitigates both environmental and operational risks at the firm level. This helps reduce loan default rates and investment risks, thereby enhancing the willingness of financial institutions to participate in green finance activities (Safiullah et al., 2024; Meles et al., 2023) [39,40]. Fourth, the diversified characteristics of low-carbon innovation projects—such as distributed energy systems, carbon capture, utilization and storage (CCUS), and new material applications—provide financial institutions with new asset types and financing models. This diversity drives innovation in green financial instruments, including structured financing, green insurance, and carbon financial derivatives.

In summary, the overall mechanism of the synergistic development of green finance and low-carbon innovation is illustrated in Figure 1.

2.2. The Driving Mechanisms of the Synergistic Development of Green Finance and Low-Carbon Innovation

The synergistic development of green finance and low-carbon innovation is not the direct outcome of a single factor but rather the result of a multidimensional system shaped by the combined influences of economic development, human capital, industrial structure, and institutional design. Through interactive feedback, these dimensions form complex pathways that affect the stability, adaptability, and diffusion of the synergistic process. The main mechanisms are as follows:

Economic Development provides both resource support and demand-driven momentum. In regions with higher levels of economic growth, financial resources are more abundant and firms face lower financing constraints, thereby facilitating the market-oriented adoption of green financial instruments. At the same time, growing consumer preferences for green products and low-carbon lifestyles further stimulate the diffusion of green projects and low-carbon technologies (De Haas and Popov, 2023) [41]. Thus, economic development, through capital accumulation and market expansion, enhances the practical application of low-carbon innovation and strengthens the stability of synergy between the two systems.

Human Capital acts as the knowledge base and adaptive capacity that invigorates the internal vitality of the synergy mechanism. Its operation depends on multiple specialized skills, including green project evaluation, environmental risk assessment, low-carbon technology appraisal, and pricing modeling. Such cross-disciplinary expertise is essential for sustaining synergy. Particularly in regions with higher educational attainment, stronger policy adaptability and institutional learning capacity facilitate the diffusion, replication, and iteration of the synergy mechanism(Song et al., 2023; Kogan et al., 2017; Nesta et al., 2014) [42,43,44].

Industrial Structure determines the transmission channels and conversion efficiency of the synergy mechanism. In regions dominated by traditional energy-intensive industries, the mechanism faces multiple barriers, including the limited availability of green projects, difficulties in risk identification, and inadequate policy instruments. These constraints often give rise to mismatches, such as “finance with willingness but innovation without recipients” (Zhu et al., 2019) [45]. By contrast, regions characterized by high-tech manufacturing and modern services generally have well-developed green application scenarios and institutional infrastructure, which enable more efficient pathways for synergy and establish robust feedback loops (Costantini and Mazzanti, 2012) [46].

Government Policy Support provides the institutional foundation that sustains the operation of the synergy mechanism. Policy instruments such as green finance classification standards, project databases, fiscal subsidies, tax incentives, and carbon trading markets not only strengthen institutional stability and shape behavioral expectations but also demonstrate integrative capacity (Zhang et al., 2024) [47]. These instruments help bridge the “information silos” between finance and technology by establishing unified systems for evaluation, financing, and regulation. In doing so, they mitigate systemic frictions and reduce the risks of fragmentation within the synergy mechanism (Aglietta and Espagne, 2016; Campiglio, 2016) [48,49].

In summary, the driving mechanisms of the synergistic development of green finance and low-carbon innovation are illustrated in Figure 2.

3. Research Design

This study employs provincial panel data from China for the period 1990–2022 as the research sample and conducts the following empirical analyses. First, the coupling coordination degree model is applied to measure the level of synergistic development between green finance and low-carbon innovation, thereby assessing their degree of coordination. Second, the Dagum Gini coefficient decomposition and spatial autocorrelation analyses are used to investigate the spatiotemporal evolution and dynamic trends of this synergy. Finally, a spatial Durbin model is constructed to provide an in-depth examination of the driving factors and mechanisms influencing their synergistic interaction.

3.1. Variable

In terms of measuring the development level of green finance, in August 2016, the People’s Bank of China, together with the Ministry of Finance and five other ministries, jointly issued the Guidelines for Establishing a Green Financial System, which clearly outlined the development direction of green finance. These guidelines emphasized actively promoting green credit, green investment, green funds, and green insurance, as well as improving equity trading mechanisms. In line with the relevant studies of Zhou et al. (2022) [50] and Lv et al. (2021) [51], this paper constructs a provincial-level evaluation index system for green finance development in China from seven dimensions: green credit, green investment, green insurance, green bonds, government policy support, green funds, and green equity (see

Table 1. Evaluation Index System for Provincial-Level Green Finance Development in China.

Variable Name	Variable Measurement	Unit	Direction	Weight
Green credit	Share of total credit allocated to green and environmental protection projects in the province	%	+	0.141
Green investment	Ratio of investment in environmental pollution control to GDP	%	+	0.141
Green insurance	Share of environmental liability insurance premium income in total premium income	%	+	0.141
Green bonds	Share of green bond issuance in total bond issuance	%	+	0.140
Government policy support	Share of fiscal expenditure on environmental protection in total general budget expenditure	%	+	0.144
Green funds	Share of green fund market value in total fund market value	%	+	0.142
Green equity	Market capitalization share of environmental protection enterprises in the A-share market	%	+	0.151

).

In terms of measuring the level of low-carbon innovation, this study follows the research approaches of Ma et al. (2021) [52] and Block et al. (2025) [53]. By compiling provincial-level low-carbon patent data in China from 1990 to 2022, we employ disparity indicators to quantitatively measure low-carbon innovation from the perspectives of mean value, average annual growth rate, and ranking, and further provide a detailed descriptive analysis.

3.2. Modeling and Research Methods

3.2.1. Entropy Weight Method

The entropy weight method is an objective weighting approach that assigns values to indicators based on the magnitude of their information entropy (Wang et al., 2021) [54]. In this study, the entropy weight method is employed to calculate the index of each evaluation indicator. To eliminate dimensional differences among the data, the threshold method is first applied for data standardization:

For positive indicators:

$$Y_{ij}={\displaystyle \frac{X_{ij}-Min(X_j)}{Max(X_j)-Min(X_j)}},i=1,2,3\cdots \cdots ,n;j=1,2,3\cdots \cdots ,m$$

(1)

For negative indicators:

$$Y_{ij}={\displaystyle \frac{Max(X_j)-X_{ij}}{Max(X_j)-Min(X_j)}},i=1,2,3\cdots \cdots ,n;j=1,2,3\cdots \cdots ,m$$

(2)

where $X_{ij}$ represents the observed value, $Y_{ij}$ is the standardized value, $Max(X_{ij})$ denotes the maximum value of indicator $i$; $Min(X_{ij})$ denotes the minimum value of indicator $i$.

Second, the standardized data are normalized to obtain the proportional value of each province in each indicator.

$$p_{ij}={\displaystyle \frac{Y_{ij}}{{\displaystyle {\sum }_{i=1}^m{Y}_{ij}}}},i=1,2,3\cdots \cdots ,n;j=1,2,3\cdots \cdots ,m$$

(3)

where $p_{ij}$ denotes the normalized proportion.

Third, the information entropy ($e_j$) and the indicator weights ($w_j$) are calculated based on the normalized proportions:

$$e_j=-k{\displaystyle \sum _{i=1}^n{p}_{ij}}\ln (p_{ij}),i=1,2,3\cdots \cdots ,n;j=1,2,3\cdots \cdots ,m$$

(4)

where $k$ is a constant term, $k={\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\ln (n)$}\right.}$, and $e_j\in [0,1]$.

$$w_j={\displaystyle \frac{1-e_j}{{\displaystyle \sum _{j=1}^m(1-e_j)}}}$$

(5)

Finally, the comprehensive score is computed using the weighted sum of all indicators $S_{ij}$.

$$S_{ij}={\displaystyle \sum _{j=1}^m{w}_j}{X}_{ij}$$

(6)

3.2.2. Coupling Coordination Model

Drawing on research approaches and modeling methods from physics, this study analyzes the dynamic changes and evolutionary process of the coupled development between green finance and low-carbon innovation. Given that both processes are inherently nonlinear, a nonlinear model is constructed as follows:

$$f(Gf)={\displaystyle {\sum }_{j=1}^n{a}_j{x}_j},j=1,2,\cdots ,n$$

$$f(Lc-inno)={\displaystyle {\sum }_{i=1}^n{b}_j{y}_j},i=1,2,\cdots ,n$$

where $f(Gf)$ denotes the function of green finance development, $f(Lc-inno)$ represents the function of low-carbon innovation development, $x$, $y$ are influencing factors within the two systems (both being functions of time), and $a$ bare the weights of the respective influencing factors. To further investigate the degree of coupling between green finance and low-carbon innovation, this study introduces the coupling coordination degree model following previous studies:

$$C=2{\displaystyle \frac{\sqrt{f(Gf)\cdot f(Lc-inno)}}{f(Gf)+f(Lc-inno)}}$$

(7)

$$T=gf(Gf)+hf(Lc-inno)$$

(8)

$$D[f(Gf),f(Lc-inno)]=\sqrt{CT}$$

(9)

where represents the comprehensive development index of green finance and low-carbon innovation, reflecting the overall level of their integrated development; $g$, hare undetermined reference coefficients; $C$ denotes the coupling degree, and $D[f(Gf),f(Lc-inno)]$ represents the coupling coordination degree, which reflects the synergistic development level between green finance and low-carbon innovation.

Following Li et al. (2022) [55], Dou et al. (2025) [56], the coupling degree $C$ is divided into four categories, and the coupling coordination degree $D$ is classified into ten levels, as shown in

Table 2. Classification system and criteria for coupling degree.

Coupling Degree	Coupling Level
[0,0.3)	The coupling degree remains at a relatively low level, with limited mutual influence between the two systems.
[0.3,0.5)	The coupling degree is in an antagonistic state, where the two systems find it difficult to fully absorb and integrate each other’s effects.
[0.5,0.8)	The coupling degree gradually evolves toward a benign coupling, with the two systems progressively converging toward a common trend and stable state.
[0.8,1.0]	The coupling degree reaches a high level of coordinated development, with the two systems mutually integrated and inclusive.

and

Table 3. Classification system and criteria for coupling coordination degree.

Stage	Coupling Coordination Degree	Coordination Level
Coordination Level	[0,0.1)	Extreme disorder and decline
	[0.1,0.2)	Severe disorder and decline
Low Coordination Stage	[0.2,0.3)	Moderate disorder and decline
	[0.3,0.4)	Mild disorder and decline
Moderate Coordination Stage	[0.4,0.5)	On the verge of disorder and decline
	[0.5,0.6)	Barely coordinated development
High Coordination Stage	[0.6,0.7)	Primary coordinated development
	[0.7,0.8)	Intermediate coordinated development
Extreme Coordination Stage	[0.8,0.9)	Good coordinated development
	[0.9,1.0]	Excellent coordinated development

.

3.2.3. Dagum Gini Coefficient and Decomposition

The Dagum Gini coefficient decomposition method is an effective approach to measuring regional spatial disparities, as it can adequately address the issue of cross-overlapping among sub-samples. This study applies the method to investigate both the regional spatial differences in the synergistic development level of green finance and low-carbon innovation and the underlying sources of such disparities.

$$G={\displaystyle \frac{{\displaystyle {\sum }_{j=1}^n{\displaystyle {\sum }_{l=1}^n{\displaystyle {\sum }_{i=1}^{k_j}{\displaystyle {\sum }_{r=1}^{k_l}\left|s_{ij}-s_{lr}\right|}}}}}{2k^2\overline{S}}}$$

(10)

where $G$ represents the overall Gini coefficient; k denotes the number of provinces; n is the number of divided regions; $\overline{S}$ is the mean coupling coordination degree of the 30 provinces under study.

The Gini coefficient can be decomposed into three components: the contribution of intra-regional differences ($G_w$), the contribution of inter-regional differences ($G_{nb}$), and the contribution of transvariation density ($G_t$). Specifically, $G_w$ measures the distributional differences in the coupling coordination degree within a given region. $G_{nb}$ captures the distributional differences in the coupling coordination degree between two regions. $G_t$ reflects the impact of cross-overlapping items in the coupling coordination degree between regions on the overall disparity when sub-groups are defined. The decomposition can be expressed as follows:

$$G_w={\displaystyle \sum _{j=1}^n{G}_{jj}{Y}_j{S}_j}$$

(11)

$$G_{nb}={\displaystyle \sum _{j=2}^n{\displaystyle \sum _{l=1}^{j-1}{G}_{jl}(Y_j{S}_l+Y_l{S}_j)D_{jl}}}$$

(12)

$$G_t={\displaystyle \sum _{j=2}^n{\displaystyle \sum _{l=1}^{j-1}{G}_{jl}(Y_j{S}_l+Y_l{S}_j)(1-D_{jl})}}$$

(13)

where $Y_j={\displaystyle \frac{k_j}{k}}$, $S_j={\displaystyle \frac{k_j\overline{s_j}}{k_{\overline{s}}}}$, $j=1,2,3\cdots \cdots ,n$.

3.2.4. Spatial Autocorrelation Model

The spatial autocorrelation model is applied to examine the spatial patterns, associations, and dynamic transitions of the spatiotemporal coupling, thereby effectively reflecting the evolutionary process of geographic spatial distribution. The global spatial autocorrelation is measured using the global Moran’s I index, which evaluates whether the coupling coordination degree exhibits spatial dependence.

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

(14)

where $i\ne j$, $n$ denotes the number of provinces, $x_i$ represents the coupling coordination degree of province $i$, $S^2$ is variance, ${\omega }_{ij}$ is the spatial weight matrix. The Moran’s I ranges from [−1,1], A larger absolute value indicates a stronger spatial autocorrelation of the coupling coordination degree. However, the global Moran’s I index alone cannot reveal the detailed spatial clustering characteristics. Therefore, the local spatial autocorrelation model is further employed to measure the correlation between each province and its neighboring regions in terms of the coupling coordination degree of green finance and low-carbon innovation, thereby illustrating the spatial clustering or dispersal patterns.

$$I={\displaystyle \frac{x_i-\overline{x}}{S^2}}{\displaystyle \sum \left[{\omega }_{ij}(x_i-\overline{x})\right]}$$

(15)

When the local Moran’s I index exhibits significant spatial correlation, Moran’s I scatter plots and LISA cluster maps are used to classify spatial relationships into four categories (Jiang et al., 2025) [57], thereby describing the spatial association between the focal province and its neighboring regions, as shown in

Table 4. Classification of spatial relationships between the study area and neighboring provinces.

Quadrant	Type	Relationship
First Quadrant	H-H	Both the study area and its neighboring provinces exhibit a high coupling coordination degree.
Second Quadrant	L-H	The study area shows a low coupling coordination degree, while neighboring provinces show a high level, reflecting spatial heterogeneity.
Third Quadrant	L-L	Both the study area and its neighboring provinces exhibit a low coupling coordination degree.
Fourth Quadrant	H-L	The study area shows a high coupling coordination degree, while neighboring provinces show a low level, reflecting spatial heterogeneity.

.

3.2.5. Spatial Durbin Model

The Spatial Durbin Model (SDM) captures the spatial linkage processes among regional variables and can reveal the interactions and spillover mechanisms between dependent and independent variables within a spatial structure. In this study, we construct an SDM to examine how spatial proximity and regional characteristics affect the coupling coordination degree of green finance and low-carbon innovation, thereby further testing the driving factors of their synergistic development. The specific model is as follows:

$$Y_{it}=\rho W{Y}_{it}+\beta x_{it}+\lambda W{x}_{it}+{\epsilon }_{it}$$

(16)

where $Y_{it}$ represents the synergistic development level of green finance and low-carbon innovation in the province $i$ during year $t$, $X_{it}$ is the set of influencing factors, $\rho$ and $\lambda$ are the parameter vectors of the explanatory variables and their spatially lagged terms, respectively, $W$ represents the spatial economic weight matrix constructed using inverse squared distance, and ${\epsilon }_{it}$ is the random error term. Following Zhou et al. (2022) [50], Yang et al. (2020) [58], and considering the driving factors of the synergistic development of green finance and low-carbon innovation, this study introduces four explanatory variables—economic development, human capital, industrial structure, and government policy support—to control for other factors affecting the empirical results. The detailed definitions and measurement of these variables are presented in

Table 5. List of variable definitions.

Variable Name	Variable Code	Variable Measurement
Economic development	Eco	GDP per capita
Human capital	Hum	The sum of students enrolled in regular higher education institutions per 10,000 people
Industrial structure	IS	By the natural logarithm of the interest expenditure of energy-intensive industries
Government policy support	Gov	Fiscal general budget expenditure divided by gross domestic product

.

3.3. Data Source and Processing

This study employs provincial panel data from China for the period 1990–2022. Data on green finance were obtained from authoritative statistical yearbooks, while data on low-carbon innovation were constructed using the number of green patent grants extracted from the China National Intellectual Property Administration (CNIPA) patent disclosure database, aggregated by province and year. To ensure consistency with existing research, patents classified under IPC codes beginning with Y02 (including Y02B, Y02C, Y02E, Y02P, Y02T, and Y02W) are selected as proxies for low-carbon patents. These categories encompass typical fields of low-carbon innovation, such as building energy efficiency, transportation emission reduction, industrial process optimization, renewable energy, and waste management. Patent data are assigned to provinces according to the registered address of applicants, with Hong Kong, Macao, Taiwan, and Tibet excluded. For a small number of missing observations, interpolation methods were applied to complete the dataset.

4. Empirical Analysis

4.1. Evaluation of Green Finance and Low-Carbon Innovation Development Levels

4.1.1. Evaluation of Green Finance Development Levels

Based on the entropy weight method, this study obtained the evolutionary trend of China’s green finance development level from 1990 to 2022 (see Figure 3). The results show that the green finance development index exhibits a clear upward trajectory during the sample period, indicating that China’s green finance development level has been steadily improving. Furthermore, the provinces can be divided into four tiers. The first tier, including Guangdong, Beijing, Zhejiang, and Shanghai, records green finance development indices significantly higher than the national average. The second tier, consisting of Jiangsu, Heilongjiang, Hubei, Hunan, Fujian, Guangxi, Hainan, Shandong, and Chongqing, also maintains indices notably above the national average. The third tier, covering Hebei, Liaoning, Jiangxi, Tianjin, Anhui, Shanxi, Henan, and Sichuan, falls below the national average. Finally, the fourth tier, comprising Jilin, Shaanxi, Gansu, Xinjiang, Guizhou, Inner Mongolia, Yunnan, Qinghai and Ningxia, demonstrates relatively low levels of green finance development, suggesting considerable potential for further improvement.

At the regional level, based on the general standards of the National Bureau of Statistics of China and the geographical division of China, the 30 provinces under study are classified into four major regions: the eastern region (Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan), the central region (Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan), the western region (Inner Mongolia, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang), and the northeastern region (Liaoning, Jilin, Heilongjiang). In 1990, the green finance development index of the eastern region was 0.112, ranking first, while the northeastern region recorded the lowest index at 0.028. By 2022, the eastern region’s index had risen to 0.493, still ranking first, with an increase of 340.18% compared to 1990; meanwhile, the northeastern region reached 0.121, showing significant growth but remaining at the lowest level (see

Table 6. Measurement results of green finance development levels across four regions in China, 1990–2022, based on the entropy weight method.

Region	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000
East	0.112	0.122	0.143	0.146	0.155	0.164	0.186	0.194	0.214	0.219	0.223
Central	0.050	0.053	0.060	0.062	0.064	0.071	0.076	0.083	0.090	0.084	0.096
West	0.045	0.049	0.052	0.055	0.058	0.069	0.071	0.081	0.079	0.078	0.093
Northeast	0.028	0.032	0.034	0.033	0.039	0.042	0.045	0.047	0.052	0.053	0.051
Region	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011
East	0.235	0.249	0.259	0.268	0.286	0.298	0.298	0.310	0.336	0.343	0.353
Central	0.097	0.100	0.107	0.122	0.121	0.130	0.123	0.135	0.140	0.131	0.135
West	0.094	0.097	0.104	0.107	0.107	0.117	0.114	0.120	0.117	0.133	0.123
Northeast	0.062	0.056	0.059	0.067	0.066	0.073	0.081	0.078	0.080	0.084	0.082
Region	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
East	0.363	0.374	0.387	0.403	0.418	0.407	0.450	0.436	0.477	0.471	0.493
Central	0.153	0.160	0.168	0.173	0.160	0.172	0.183	0.190	0.203	0.198	0.210
West	0.134	0.131	0.148	0.161	0.144	0.165	0.168	0.171	0.172	0.177	0.189
Northeast	0.086	0.087	0.100	0.087	0.095	0.103	0.099	0.107	0.114	0.109	0.121

).

At the same time, as shown in Figure 4, during the sample period, the eastern region of China exhibited a relatively high level of green finance development with a faster growth rate; the western and central regions developed at a comparatively slower pace with only minor differences between them, whereas the northeastern region lagged significantly behind.

4.1.2. Evaluation of Low-Carbon Innovation Development Levels

The measurement results of China’s low-carbon innovation level based on disparity indicators are presented in

Table 7. China’s low-carbon innovation level (1990–2022).

Region	Average (Number of Patents)	Average Ranking	Average Annual Growth Rate (%)	Growth Rate Ranking
Beijing	1655	4	60.87	11
Tianjin	442	12	58.15	12
Hebei	417	13	73.42	9
Shanxi	173	19	31.06	17
Inner Mongolia	109	21	25.85	18
Liaoning	376	14	19.90	21
Jilin	81	26	18.61	23
Heilongjiang	94	25	16.18	24
Shanghai	893	6	153.03	2
Jiangsu	2915	2	68.34	10
Zhejiang	1725	3	94.41	6
Anhui	732	7	137.33	3
Fujian	653	8	118.88	5
Jiangxi	310	17	47.03	13
Shandong	955	5	93.94	7
Henan	534	9	44.55	14
Hubei	506	11	119.58	4
Hunan	347	15	16.16	25
Guangdong	3868	1	336.80	1
Guangxi	109	21	9.02	29
Hainan	43	29	42.70	15
Chongqing	227	18	19.21	22
Sichuan	511	10	81.09	8
Guizhou	109	21	9.02	29
Yunnan	153	20	15.23	27
Shaanxi	336	16	36.80	16
Gansu	73	27	13.33	28
Qinghai	40	30	7.58	30
Ningxia	72	28	15.67	26
Xinjiang	97	24	20.24	20

. The findings show that from 1990 to 2022, China’s overall low-carbon innovation exhibited a continuous upward trend. In terms of average annual growth, Guangdong Province stood out most significantly, with an increase of 336.80 times, followed by Shanghai, Anhui, and Hubei. The number of low-carbon patent grants in Guangdong rose sharply from just 2 in 1990 to 22,231 in 2022, with the most rapid growth occurring during the “13th Five-Year Plan” period, which was closely linked to policies such as “ecological civilization construction” and “energy conservation and emission reduction.” By contrast, regions such as Qinghai, Guizhou and Gansu demonstrated relatively limited growth; nevertheless, they also achieved multiple-fold increases. This indicates that in recent years, China’s low-carbon innovation level has improved significantly, with the economic development model gradually shifting from an extensive pattern characterized by high input, high consumption, and high emissions to an intensive, sustainable model emphasizing technological progress, green transition, and efficient resource utilization.

From the perspective of average values, Guangdong Province recorded the highest number of low-carbon patent grants, averaging 3868, followed by Jiangsu, Zhejiang, and Beijing. Conversely, Qinghai, Hainan and Ningxia ranked among the lowest. These results clearly reveal that developed regions in China are far ahead of less developed regions in terms of low-carbon innovation, primarily because developed regions benefit from higher levels of capital accumulation, more mature financial systems, and the concentration of high-level research institutions and innovative enterprises. This combination provides stable and diversified funding sources for low-carbon innovation activities, thereby facilitating the continuous iteration and rapid application of low-carbon technologies.

4.2. Temporal Characteristics of the Synergistic Development of Green Finance and Low-Carbon Innovation

4.2.1. National Temporal Characteristics of the Synergistic Development of Green Finance and Low-Carbon Innovation

Using the coupling coordination model, this study measures the overall level of synergistic development between green finance and low-carbon innovation in China, along with the degree of coupling coordination. The results are presented in the figure below.

As shown in Figure 5:

(1)

Overall, the synergistic development level of green finance and low-carbon innovation in China demonstrates a steady upward trajectory, with the coupling coordination degree increasing rapidly and consistently.

(2)

From the perspective of the coupling degree, its values during the sample period fall within the range of [0,0.3), indicating that the coupling between green finance and low-carbon innovation remains relatively weak. This suggests that their interactions are not yet significant and that the two systems have not fully achieved synchronized or synergistic development.

(3)

In terms of coupling coordination degree, during 1990–2010 it remained within the interval of [0,0.2), reflecting that green finance and low-carbon innovation were in a prolonged state of severe imbalance. From 2011 to 2016, the degree rose to the range of [0.2,0.4), entering a stage of low coordination where interactive effects began to emerge. By 2017–2022, the degree had improved to the interval of [0.4,0.6), indicating that the two systems had stepped into a stage of moderate coordination, with their coupling relationship becoming more balanced.

4.2.2. Temporal Characteristics of the Synergistic Development of Green Finance and Low-Carbon Innovation Across Chinese Provinces

From Figure 6, several patterns emerge:

(1)

1990–2016: All provinces remained in the stage of severe imbalance, with little improvement in their coupling coordination degree.

(2)

2017–2019: Although most provinces did not experience fundamental changes in their coordination stages, the overall coordination degree improved moderately, and several provinces achieved upward shifts. Specifically, in 2017, Jiangsu and Guangdong were the first to enter the low coordination stage, with Guangdong further advancing to the moderate coordination stage in 2018. In the same year, Beijing and Shandong also progressed to the low coordination stage, followed by Zhejiang and Henan in 2019. Provinces that took the lead in upgrading their coordination levels were predominantly concentrated in eastern China.

(3)

2019–2022: Guangdong took the lead by reaching a coupling coordination degree of 0.610 in 2019, becoming the first province to enter the high coordination stage. Jiangsu followed in 2021 with a score of 0.701. Both provinces continued to rise rapidly, reaching 0.921 and 0.853, respectively, in 2022, thereby entering the stage of extreme coordination and demonstrating a clear leading effect.

(4)

Regional disparities by 2022: Despite these advances, the majority of provinces still remained in the stage of severe imbalance, particularly in central, northeastern, and western China. Within these regions, Anhui in central China (0.173), Liaoning in the northeast (0.065), and Sichuan in the west (0.101) performed relatively better than their regional counterparts but still remained at low overall levels.

In summary, the coupling coordination degree of green finance and low-carbon innovation across Chinese provinces has followed a gradual upward trajectory. However, pronounced inter-provincial disparities persist, suggesting that the synergistic development of the two systems remains in a dynamic evolutionary stage with substantial room for improvement. From a regional perspective, the eastern region demonstrates the highest level of coupling coordination, highlighting its first-mover advantages in both green finance support and low-carbon innovation capacity. The central and northeastern regions occupy a moderate level, while the western region remains relatively low, indicating that its development trajectory still requires optimization and retains significant potential for improvement.

4.3. Spatial Differences in the Synergistic Development Level of Green Finance and Low-Carbon Innovation

4.3.1. Overall Spatial Differentiation Analysis

According to

Table 8. Decomposition of spatial differences in the synergistic development level of green finance and low-carbon innovation and contribution rates, 1990–2022.

Year	Overall Disparity	Gw	Contribution Rate	Gnb	Contribution Rate	Gt	Contribution Rate
1990	0.940	0.247	26.291%	0.4778	50.817%	0.215	22.891%
1991	0.942	0.250	26.570%	0.468	49.650%	0.224	23.780%
1992	0.942	0.252	26.677%	0.472	50.116%	0.219	23.207%
1993	0.940	0.247	26.278%	0.478	50.876%	0.215	22.846%
1994	0.941	0.249	26.473%	0.470	49.919%	0.222	23.609%
1995	0.942	0.250	26.566%	0.467	49.644%	0.224	23.791%
1996	0.945	0.256	27.111%	0.491	52.005%	0.197	20.885%
1997	0.940	0.246	26.196%	0.482	51.271%	0.212	22.532%
1998	0.944	0.256	27.093%	0.490	51.929%	0.198	20.977%
1999	0.946	0.258	27.318%	0.500	52.905%	0.187	19.777%
2000	0.940	0.247	26.268%	0.478	50.910%	0.215	22.827%
2001	0.944	0.256	27.069%	0.490	51.855%	0.199	21.076%
2002	0.943	0.253	26.835%	0.479	50.861%	0.210	22.303%
2003	0.945	0.258	27.319%	0.501	52.986%	0.186	19.695%
2004	0.937	0.242	25.853%	0.495	52.853%	0.200	21.294%
2005	0.944	0.256	27.160%	0.495	52.409%	0.193	20.431%
2006	0.939	0.248	26.414%	0.470	50.008%	0.221	23.578%
2007	0.938	0.247	26.301%	0.473	50.439%	0.218	23.261%
2008	0.936	0.245	26.140%	0.478	51.070%	0.213	22.790%
2009	0.935	0.247	26.387%	0.467	49.928%	0.221	23.685%
2010	0.928	0.242	26.070%	0.467	50.360%	0.219	23.570%
2011	0.925	0.248	26.844%	0.499	53.964%	0.178	19.192%
2012	0.910	0.241	26.515%	0.501	55.068%	0.168	18.417%
2013	0.893	0.229	25.639%	0.477	53.380%	0.187	20.982%
2014	0.869	0.218	25.110%	0.474	54.553%	0.177	20.338%
2015	0.844	0.210	24.920%	0.493	58.379%	0.141	16.702%
2016	0.807	0.197	24.369%	0.507	62.761%	0.104	12.870%
2017	0.788	0.185	23.441%	0.492	62.444%	0.111	14.115%
2018	0.760	0.175	23.005%	0.504	66.350%	0.081	10.645%
2019	0.747	0.168	22.540%	0.511	68.390%	0.068	9.070%
2020	0.734	0.163	22.198%	0.514	70.032%	0.057	7.770%
2021	0.716	0.156	21.756%	0.521	72.829%	0.039	5.146%
2022	0.688	0.145	21.043%	0.499	72.599%	0.044	6.357%

, (1) in terms of overall disparity, China’s overall Gini coefficient during 1990–2022 fluctuated within the range of 0.668–0.946, showing an evolutionary pattern of “initial fluctuating increase—subsequent fluctuating decline—eventual linear decrease.” The Gini coefficient was 0.940 in 1990, rose to its peak of 0.946 in 1999, and then entered a fluctuating downward phase. Since 2006, it has exhibited a continuous and significant linear decline. This evolutionary process reflects the substantial initial regional differences in the development of green finance and low-carbon innovation in China, but with the gradual improvement of the national “dual carbon” strategy and green finance institutions, these disparities have been progressively reduced, moving toward overall coordinated development. (2) Regarding the sources of disparity, inter-regional differences have consistently been the dominant factor driving the imbalance in the synergistic development of green finance and low-carbon innovation, with an average contribution rate of 55.259%. In contrast, intra-regional differences ranked second, with an average contribution rate of 25.629%, while the impact of hypervariable density was the smallest, averaging only 19.103%.

4.3.2. Intra- and Inter-Regional Disparity Analysis

According to

Table 9. Intra-regional disparities in the synergistic development level of green finance and low-carbon innovation.

Year	Northeast	East	Central	West
1990	0.667	0.900	0.833	0.646
1991	0.527	0.900	0.833	0.661
1992	0.531	0.900	0.833	0.583
1993	0.531	0.900	0.833	0.615
1994	0.537	0.900	0.833	0.566
1995	0.507	0.900	0.833	0.558
1996	0.504	0.900	0.833	0.535
1997	0.480	0.900	0.833	0.491
1998	0.498	0.900	0.833	0.518
1999	0.510	0.900	0.833	0.530
2000	0.521	0.899	0.833	0.557
2001	0.506	0.899	0.833	0.569
2002	0.517	0.899	0.833	0.573
2003	0.555	0.899	0.833	0.542
2004	0.547	0.898	0.833	0.559
2005	0.568	0.897	0.833	0.611
2006	0.556	0.896	0.833	0.638
2007	0.547	0.894	0.833	0.642
2008	0.543	0.890	0.833	0.637
2009	0.551	0.884	0.832	0.661
2010	0.536	0.869	0.830	0.668
2011	0.539	0.857	0.825	0.667
2012	0.548	0.824	0.817	0.665
2013	0.540	0.791	0.812	0.657
2014	0.545	0.744	0.799	0.638
2015	0.549	0.700	0.778	0.612
2016	0.536	0.639	0.737	0.611
2017	0.529	0.596	0.734	0.606
2018	0.520	0.556	0.686	0.600
2019	0.510	0.532	0.659	0.580
2020	0.507	0.516	0.613	0.560
2021	0.490	0.494	0.508	0.551
2022	0.477	0.458	0.496	0.541

Note: In this table, the eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; the western region includes Inner Mongolia, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang; and the northeastern region includes Liaoning, Jilin, and Heilongjiang.

, the intra-regional Gini coefficients follow the pattern: East > Central > Northeast > West.

(1)

Eastern region. As the hub of China’s most developed capital markets and green finance infrastructure—including the Shanghai and Shenzhen stock exchanges and the green finance reform and innovation pilot zones—the eastern region offers a significantly higher supply of green finance and greater financial instrument diversity than other regions. However, resource distribution follows a “core–periphery” pattern, with Beijing, Shanghai, Shenzhen, Guangzhou, Jiangsu, and Zhejiang forming highly concentrated financial and innovation “cores.” The coexistence of digital services, advanced manufacturing, and traditional processing industries produces a highly differentiated industrial structure, further amplifying disparities in project reserves, technology conversion efficiency, and financial accessibility across provinces.

(2)

Central region. Dominated by manufacturing industries, with a relatively high share of energy-intensive sectors such as energy and transportation equipment, the central region exhibits a relatively homogeneous industrial structure. Green finance here is primarily policy- and bank-driven, while equity and bond markets remain underdeveloped. Nonetheless, Hubei, benefiting from its carbon market pilot and strong educational resources, has enabled the Wuhan metropolitan area to take the lead in green credit and innovation transformation, generating moderate divergence. Consequently, intra-regional disparity in the central region ranks second only to that of the east.

(3)

Northeast region. Long constrained by a heavy reliance on state-owned enterprises and heavy industries, coupled with population decline and shrinking demand, the northeast mainly relies on the green transformation of existing credit stock. Market-based capital and innovation platforms are scarce, and project pipelines and technology applications are generally weak and homogeneous. As a result, intra-regional disparities are relatively limited, yielding lower Gini coefficients compared to the east and central regions.

(4)

Western region. Most provinces face common bottlenecks, including shallow financial depth, the absence of robust project identification and certification systems, and limited capacity to attract talent and research resources. Green finance and low-carbon innovation generally remain at the stage of “low-level homogeneous initiation.” Although Sichuan and Chongqing benefit from hydropower and equipment manufacturing advantages, these strengths are offset by common constraints, leading to the lowest intra-regional Gini coefficients.

Across the four regions, Gini coefficients have generally declined over time, reflecting a convergence of internal disparities. This trend has been facilitated by the unification of national green classification standards, the launch of the national carbon market, convergence in green credit and bond guidelines, fiscal–financial coordination, and the diffusion of green technologies. On the one hand, institutional unification has enhanced the consistency of green asset recognition and information disclosure, thereby reducing “institutional disparities.” On the other hand, the dissemination of green technologies and project evaluation methods has improved the financing capacity of projects in the central, western, and northeastern regions, driving intra-regional convergence in the synergistic development of green finance and low-carbon innovation.

From Figure 7, the differences and evolutionary trends in the synergistic development of green finance and low-carbon innovation between 1990 and 2022 can be summarized as follows:

(1)

From 1990 to 2010, Regional disparities fluctuated slightly within the range of 0.06–0.10, reflecting low-level volatility and moderate variation. This suggests that China was still in the institutional groundwork stage of green finance and low-carbon innovation, with policies and market-based instruments not yet generating significant regional differentiation. Between 2011 and 2016, as green credit, green bonds, carbon trading pilots, and disclosure rules were gradually introduced, the eastern region was the first to benefit. Consequently, the gaps between “East–Northeast,” “East–West,” and “East–Central” widened, while other regional pairs experienced relatively mild changes. From 2016 to 2022, a divergence pattern emerged with the east as the “core pole.” Disparities in “East–Northeast,” “East–West,” and “East–Central” expanded rapidly, reaching nearly 0.375 for the first two and approximately 0.253 for the latter by 2022. In contrast, the Gini coefficients for “Central–West” and “Central–Northeast” increased only slightly, while the “West–Northeast” gap remained minimal and persistently close to zero.

(2)

East–West. Institutional construction and the completeness of the green industrial chain are the decisive factors. The east established comprehensive standards and disclosure systems earlier, enabling rapid and effective policy implementation. The west, by contrast, exhibited delays in both institutional execution and market cultivation, resulting in weak policy transmission. Moreover, while the east has developed a complete low-carbon industry chain encompassing R&D, production, and application—facilitating deep integration of green finance and technological innovation—the west, despite abundant energy resources, lacks adequate supporting industries and innovation chains, leading to a mismatch between green finance support and low-carbon technology adoption.

(3)

East–Northeast. Disparities here stem primarily from industrial path dependence and differences in the pace of economic transition. The east has steadily advanced institutional reforms and low-carbon industry cultivation, creating interactive advantages between finance and innovation. In contrast, the northeast has long relied on heavy industries and energy-intensive sectors, facing structural obstacles to green transformation. Even under green finance policies, its green patent output and low-carbon technology iteration remain limited. Capital outflows and population decline further erode its financial vitality, exacerbating the gap.

(4)

Central–West. Differences are mainly linked to industrial upgrading and resource allocation efficiency. The central region has fostered clusters of green manufacturing and clean energy industries by absorbing industrial transfers from the east, supported by green credit and government initiatives. The west, though rich in renewable resources, often confines its green finance and innovation activities to primary resource utilization, with insufficient industrial chain extension. A weaker financial system and limited availability of green financial products further constrain synergistic interaction.

(5)

Central–Northeast. Divergence reflects differences in policy implementation capacity. The central region has demonstrated strong execution in green finance pilots and fiscal guidance, effectively translating policy resources into support for low-carbon innovation. By contrast, the northeast, despite adopting supportive measures, suffers from limited fiscal capacity and weak execution, which dilute policy effectiveness and widen disparities.

(6)

West–Northeast. Both regions demonstrate a “dual disadvantage.” The west suffers from inadequate financial depth and limited market openness, while the northeast remains constrained by rigid industrial structures and a weak foundation for green innovation. Neither region has formed a robust ecosystem integrating green finance with technological innovation, resulting in persistently low interaction levels and disparities that remain close to zero, reflecting a state of low-level convergence.

4.4. Spatial Correlation Analysis

4.4.1. Global Spatial Autocorrelation

Table 10. The synergistic development level of green finance and low-carbon innovation in China, 1990–2022.

Year	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000
Moran’s I	0.144	0.138	0.135	0.145	0.140	0.138	0.121	0.146	0.122	0.114	0.145
P	0.020	0.020	0.020	0.020	0.020	0.020	0.019	0.020	0.019	0.019	0.020
Year	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011
Moran’s I	0.122	0.130	0.114	0.152	0.119	0.141	0.143	0.146	0.139	0.144	0.119
P	0.019	0.020	0.019	0.021	0.019	0.020	0.020	0.021	0.020	0.020	0.018
Year	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Moran’s I	0.124	0.147	0.158	0.159	0.148	0.156	0.138	0.095	0.120	0.094	0.147
P	0.016	0.016	0.014	0.011	0.017	0.033	0.057	0.129	0.089	0.131	0.062

reports the results of synergistic development between green finance and low-carbon innovation across 30 provinces. During 1991–2022, the global Moran’s I values remained positive. Except for 2008 and 2021, the p-values in all other years passed the significance test, indicating that the synergistic development of green finance and low-carbon innovation in China exhibited significant positive spatial correlation in most years.

4.4.2. Local Spatial Autocorrelation

To gain deeper insights into the spatial clustering of provincial synergistic development between green finance and low-carbon innovation in China, this study plots the local Moran scatterplots for 1990 and 2022 (see Figure 8). In the scatter plots, the first quadrant (H–H) indicates clusters of regions with high coupling coordination, while the third quadrant (L–L) indicates clusters of regions with low coupling coordination. The second (L–H) and fourth (H–L) quadrants represent clusters where regions with high and low coupling coordination coexist. As shown in Figure 8, H–H clusters are mainly concentrated in the eastern region, such as Shandong and Jiangsu. L–L clusters are largely distributed in western provinces, including Inner Mongolia, Ningxia, Gansu, Qinghai, and Xinjiang, as well as in northeastern provinces such as Heilongjiang, Jilin, and Liaoning. This indicates that these areas and their neighboring regions generally exhibit low coordination levels and weak regional linkages. L–H clusters appear in some central provinces, such as Jiangxi and Anhui, while H–L clusters are primarily located in certain eastern provinces, including Guangdong and Beijing, where local coordination levels are relatively high but the supporting capacity of neighboring regions is comparatively weaker.

4.5. Analysis of the Driving Factors of the Synergistic Development Level of Green Finance and Low-Carbon Innovation

4.5.1. Model Selection

To ensure the robustness of model specification, we first examined the effect structure of the panel data. The Breusch–Pagan LM test indicated significant individual effects, thereby rejecting the applicability of pooled OLS and suggesting the use of panel models that account for unobserved heterogeneity. Given that provinces exhibit spatial dependence and spillover effects in factor mobility, industrial division, and policy coordination, spatial effects were incorporated into the econometric framework, and alternative spatial panel models (SAR, SEM, and SDM) were compared. The maximum likelihood estimation results of the SDM show that the p-values of the Wald_SAR and Wald_SEM restriction tests both approached zero, strongly rejecting the null hypothesis that “SDM can be simplified to SAR or SEM.” This demonstrates that the spatial lag terms of the explanatory variables cannot be ignored, and relying on weaker models (SAR/SEM) would yield biased coefficients and distorted interpretations. Accordingly, this study adopts a Spatial Durbin Model with fixed effects to mitigate omitted variable bias and enhance causal inference.

4.5.2. Spatial Weight Matrix Selection and Robustness Tests

To test the robustness of the model results, alternative spatial weight matrices were employed, and regression estimation was conducted based on Equation (16) using panel data constructed from principal component scores. Three types of spatial weight matrices were considered: the spatial distance matrix (inverse squared), the nested spatial–geographic weight matrix, and the spatial economic weight matrix (inverse squared). As shown in

Table 11. Stationarity tests of the panel data.

Variable	Spatial Distance Matrix (Inverse Squared)	Nested Spatial–Geographic Weight Matrix	Spatial Economic Weight Matrix (Inverse Squared)
$\mathsf{\rho }$	−0.006 (0.054)	−0.159 (0.083)	−0.008 (0.033)
Eco	4.10 × 10−6 *** (2.00 × 10−7)	4.40 × 10−6 *** (3.00 × 10−7)	3.50 × 10−6 *** (5.00 × 10−7)
Hum	0.001 *** (9.05 × 10−5)	0.001 *** (9.12 × 10−5)	7.71 × 10−4 *** (9.21 × 10−5)
IS	−0.028 *** (0.007)	−0.032 *** (0.007)	−0.027 *** (0.008)
Gov	0.323 *** (0.063)	0.341 *** (0.059)	0.391 *** (0.058)
W × Eco	−5.00 × 10−7 (5.00 × 10−7)	5.00 × 10−7 (1.30 × 10−6)	1.70 × 10−6 (7.00 × 10−7)
W × Hum	−0.001 *** (3.12 × 10−4)	−0.002 *** (5.52 × 10−4)	−5.19 × 10−4 (1.64 × 10−4)
W × IS	−0.019 (0.015)	−0.064 * (0.036)	−0.070 (0.010)
W × Gov	0.035 (0.150)	0.888 ** (0.363)	0.225 (0.126)
R2	0.474	0.484	0.491
${\sigma }^2$	0.003	0.003	0.003
N	990	990	990

Note: *, ** and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. Standard errors are reported in parentheses.

, the regression results remain robust across these specifications: the coefficients of the core explanatory variables are consistently significant and directionally stable, thereby confirming the reliability of the model results.

At the same time, to avoid the issue of ‘spurious regression,’ we further adopt ADF-type methods (Dickey and Fuller, 1979) [59] (including the Kao test and the Pedroni test) to conduct cointegration and stationarity analysis of the model residual series (see

Table 12. Residual cointegration and stationarity tests.

Method	Statistic	p Value	Conclusion
Kao test	−2.528	0.005	Stationary
Pedroni test	−4.202	0.000	Stationary

). All tests significantly reject the null hypothesis, indicating that the residuals are stationary, which supports the existence of a long-term stable relationship among the variables. This, in turn, validates the appropriateness of the model specification and the reliability of the regression results.

Additionally, to further validate the effectiveness of using the number of green patents as a measure of low-carbon innovation, this study adopts scientific research and the number of green technology-related workers per ten thousand people as alternative indicators, and conducts a coupling analysis with the green finance index. Given the potential for skewed distribution and heteroscedasticity in the data, we performed logarithmic transformation on all the data. This transformation aims to narrow the data range, reduce the impact of extreme values on the regression results, and linearize the relationships between variables, thus facilitating a more accurate interpretation of the economic relationships between variables and providing more reasonable statistical inferences. Based on this approach, we conducted regression analysis using the spatial economic weight matrix (inverse square) and the Spatial Durbin Model (SDM). The results indicate that the coupling analysis between the alternative indicators and the green finance index remains significant, further confirming the stability and reliability of using the number of green patents as a proxy for low-carbon innovation. The detailed results are presented in

Table 13. Robustness Test.

Variable	Coef.	Std. Err.	Z Value	p Value
Eco	1.00 × 10−6 ***	0.000	8.428	0.000
Hum	5.32 × 10−4 ***	2.80 × 10−5	18.781	0.000
IS	−0.006 ***	0.002	−2.617	0.009
Gov	0.056 ***	0.018	3.176	0.001
W × Eco	0.000 **	0.000	2.013	0.044
W × Hum	−1.61 × 10−4 ***	5.30 × 10−5	−3.054	0.002
W × IS	−0.024 ***	0.003	−7.990	0.000
W × Gov	0.084 **	0.039	2.178	0.029
R2	0.813

Note: ** and *** represent statistical significance at the 5%, and 1% levels.

.

4.5.3. Analysis of the Driving Factors

To more clearly reveal the mechanisms through which various factors influence the synergistic development of green finance and low-carbon innovation across different dimensions, the spatial effects of the estimated coefficients were further decomposed into direct and indirect effects, based on the spatial economic weight matrix (inverse squared) model presented in Table 11. The detailed results are reported in

Table 14. Driving factors of the synergistic development of green finance and low-carbon innovation.

Variable	Direct Effect	Indirect Effect	Total Effect
Economic development	4.00 × 10−6	1.00 × 10−6	5.00 × 10−6
Human capital	7.73 × 10−4	−5.23 × 10−4	2.50 × 10−4
Industrial structure	−0.0269	−0.0695	−0.0964
Government policy support	0.391	0.221	0.612

.

From the effect decomposition, the results show that government policy support exerts the strongest impact, with a direct effect of 0.3906, an indirect effect of 0.221, and a total effect of 0.612, making it the primary positive driver of synergistic development. Human capital demonstrates a total effect of 2.50 × 10⁻⁴, but with a negative spillover effect on neighboring regions (indirect effect –5.23 × 10⁻⁴), reflecting a “siphoning competition” of talent and innovation resources. Economic development has a total effect of 5.00 × 10⁻⁶, indicating a slow yet steady positive contribution. By contrast, industrial structure exhibits a total effect of –0.0964, acting as the main negative driver, suggesting that the predominance of heavy industries and structural rigidities hinder the improvement of synergistic development. According to path dependence theory, some regions have formed a “lock-in” phenomenon due to their historical over-reliance on traditional industries, which has led to significant difficulties in their transformation process, thereby affecting the synergy between green finance and low-carbon innovation. At the same time, industry upgrading theory also suggests that low-carbon technological innovation is often closely related to the transformation and upgrading of high-tech industries. If a region’s industrial structure has long been dependent on traditional energy and high-carbon-emitting industries, it is difficult to promote the synergistic development of low-carbon technological innovation and green finance. Taking the northeast region of China as an example, despite the gradual introduction of green finance policies, the region’s long-standing reliance on heavy industries and energy-intensive sectors has caused excessive rigidity in its industrial structure, leading to slow industrial transformation and technological innovation. As a result, green finance resources are difficult to effectively channel into low-carbon sectors, impacting the actual outcomes of low-carbon innovation.

In summary, the strength of the driving factors influencing the synergistic development level of green finance and low-carbon innovation follows the order: government policy support > human capital > economic development > industrial structure.

5. Discussion

Drawing on provincial panel data from China spanning 1990–2022, this study develops a new conceptual framework to measure the synergistic development of green finance and low-carbon innovation using the coupling coordination degree model. On this basis, it further examines their interactive relationship, identifies temporal characteristics, explores spatial disparities and spatial correlations, and investigates the driving factors and underlying mechanisms. The main findings are as follows:

(1)

From 1990 to 2022, the synergistic development level of green finance and low-carbon innovation followed a clear upward trajectory, progressing from severe imbalance to moderate coordination. Regionally, the ranking of coordination levels from high to low is as follows: eastern, central, northeast, and western provinces.

(2)

During 1990–2022, the overall Gini coefficient fluctuated between 0.688 and 0.946, showing an evolutionary pattern of “initial fluctuating increase—subsequent fluctuating decline—final linear decrease.” Inter-regional differences consistently dominated spatial disparities, contributing an average of 55.259%, followed by intra-regional differences (25.629%), while hypervariable density had the smallest effect (19.103%).

(3)

Global Moran’s I values were positive and statistically significant in most years, confirming the existence of significant spatial correlation. At the local clustering level, H–H clusters were primarily concentrated in the eastern region (e.g., Shandong, Jiangsu), whereas L–L clusters were concentrated in the western region (e.g., Inner Mongolia, Ningxia, Gansu, Qinghai, Xinjiang) and the northeast (e.g., Heilongjiang, Jilin, Liaoning). L–H clusters appeared in some central provinces such as Jiangxi and Anhui, while H–L clusters were observed in several eastern provinces including Guangdong and Beijing.

(4)

Economic development, human capital, industrial structure, and government policy support exert heterogeneous effects on the synergistic development and spatial dynamics of green finance and low-carbon innovation. Among the positive drivers, the relative strength is as follows: government policy support > human capital > economic development. Industrial structure serves as the main negative driver, suggesting that the predominance of heavy industries and structural rigidity significantly constrain synergy improvement.

Based on the above findings, this paper proposes the following policy recommendations to promote the synergistic development of green finance and low-carbon innovation:

Firstly, strengthen the differentiated policy support mechanism to enhance the precision and efficiency of policy interventions. Given that government policy support has the strongest positive effect on collaborative development and significant spillover effects, the reform should be guided by an integrated approach of “projects—funding—data—standards,” consolidating pilot practices into a normalized mechanism. The experience of California’s Clean Energy Fund, which provides financial support for the commercialization of low-carbon technologies through tax incentives and subsidy programs, has attracted a significant amount of private capital into the clean energy sector and promoted low-carbon technology innovation. This approach can serve as a reference for China to achieve the collaborative development of green finance and low-carbon innovation. China should establish a unified green project database and due diligence templates, coordinate fiscal interest subsidies and green re-lending, and encourage multiple banks to engage in “joint assessment, joint credit, and joint risk management” to improve the financing accessibility and implementation rate of projects. Additionally, a “projects—funding—data” collaborative platform should be established, incorporating risk-sharing clauses and third-party verification for mutual recognition, amplifying the positive spillover effects from the eastern regions to surrounding areas. Furthermore, carbon allowances or voluntary emission reductions should be integrated into the enhancement and repayment structure of green/transformation bonds, forming a “carbon price—financing—technology” linkage that reduces financing costs and uncertainty. Many provinces in China have already developed replicable and verifiable policy combinations and operational paradigms, as detailed in the

Table 15. Local case comparison table.

Policy Recommendation	Local Practices (Examples)	Operational Highlights
Provincial Green Project Database and Joint Credit Granting.	In the Guangzhou Green Finance Reform and Innovation Pilot Zone, the Administrative Measures for the Green Enterprise and Project Database were implemented to specify entry standards and promote multi-institutional coordination. As a result, the balance of green credit more than doubled within five years.	Unified project entry and due diligence templates → fiscal subsidies + relending (aligned with PBoC tools) → multiple banks engaging in “joint evaluation, joint credit granting, and joint risk management”
Cross-Provincial Collaborative Platform.	In the Yangtze River Delta Integration Demonstration Zone, a unified project management platform and parallel approval mechanism were established. The Development and Construction Management Authority was granted provincial-level project management authority, enabling the unified acceptance and circulation of cross-regional projects.	Joint construction of a “projects–finance–data–standards” platform → establishment of dedicated task forces and risk-sharing clauses → mutual recognition of third-party verifications
Approval and Certification Mutual Recognition.	The Guangzhou Project Database Measures were aligned with the national Green Bond and Green Credit Guidelines, the Catalogue of Green Bond-Supported Projects, and relevant exchange regulations. This alignment opened multi-tiered market channels from the municipal project database, facilitating consistency in project standards and enhancing the circulation of green financial products across different markets.	Unified ESG/carbon certification standards → routine spot checks and information disclosure → establishment of interprovincial mutual recognition lists
Carbon Price–Financing Linkage.	The Shanghai Environment and Energy Exchange introduced pioneering carbon finance products, including carbon quota pledge loan guarantee insurance and grassland carbon sink index insurance. These instruments enhanced credit support and risk mitigation mechanisms, thereby strengthening the linkage between carbon pricing, financing, and technological innovation.	Matching cash flows with emission reduction pathways → using quota/CCER revenues for credit enhancement or repayment support → integration with exchange and registry systems

.

Secondly, human capital and innovation platform construction should be the core focus to alleviate regional disparities and promote knowledge spillover, thereby building a cross-regional innovation and collaboration network. First, the successful experiences of BRICS countries (Brazil, Russia, India, China, South Africa) in talent exchange and technology cooperation can be used as a reference. These countries have successfully facilitated the exchange of green finance and low-carbon technology expertise. China can establish dual-mentor training and practical bases for “green finance × low-carbon technology”, providing standardized training on project evaluation, carbon certification, and ESG disclosures. Second, it is crucial to promote inter-provincial mutual recognition of ESG, carbon management, and green assessment qualifications, while also issuing training vouchers and job-sharing plans to facilitate the two-way flow of talent, projects, and funds within metropolitan areas and economic zones. Third, relying on cross-regional platforms, joint projects and joint laboratories should be established, closing the loop of research, demonstration, and investment financing to dissolve information barriers.

Thirdly, systematically promote the green transformation of industrial structures by implementing an integrated approach of “technology diagnostics—EPC contracting—transformational finance—insurance/guarantees—performance-based payment” to retrofit high-energy-consumption assets. On the one hand, drawing on Germany’s successful experience with the implementation of green industrial funds to support the transformation and upgrading of high-energy industries, third-party energy audits can be organized in traditional industrial bases and key industrial parks. Suitable retrofitting points should be selected, with EPC contracts for bundled implementation. This should be combined with transformation loans, green bonds, and guarantee insurance or policy-based guarantees to effectively disperse early-stage technology and market risks. On the other hand, it is essential to encourage lead enterprises in supply chain management to promote green supply chain financial tools, integrating low-carbon performance into supplier evaluation systems and linking it with core transaction conditions such as procurement orders and settlement cycles, thus accelerating the pace of low-carbon technology innovation among small and medium-sized enterprises.

Fourthly, expand the market and strengthen regional collaboration, advancing tiered and categorized governance. For low-coordination regions, China can draw on the successful experience of the Indian government in using sovereign green bonds to effectively promote the green finance market. The priority should be to address the issue of “financing capability” by starting with guarantees and demonstration projects. This can be achieved by establishing a green project database and standardized due diligence templates. An integrated approach of “EPC + transformation loans/green bonds + guarantee insurance/guarantees” should be adopted, initially focusing on wind-solar-storage integration and technological improvements in park-wide projects to generate visible cash flow. For transition and upgrading regions, the focus should be on joint credit and green/transformation bonds, bridging the “pilot-to-commercialization” gap. Through a “project whitelist + provincial interest subsidies,” the financing capability and conversion efficiency of pilot results can be improved. For regions with medium or higher coordination, emphasis should be placed on “spillover output + anti-greenwashing audits.” An ESG/carbon disclosure sampling and negative list system should be established, and for “greenwashing” projects, penetrating audits and accountability measures should be implemented.

Unlike existing studies, this paper takes the synergistic development of green finance and low-carbon innovation as its theoretical foundation and systematically depicts their spatiotemporal characteristics. This approach moves beyond prior research that primarily examined either the level of green finance development or the efficiency of low-carbon innovation in isolation, often lacking a holistic analysis of their interrelationship. Building on this, the study establishes a comprehensive analytical framework capable of dynamically revealing the dimensions of “evolution–disparity–mechanism.” It provides an in-depth assessment of the spatiotemporal evolution, spatial disparities, spatial correlations, and driving factors underlying the synergy between green finance and low-carbon innovation. In doing so, it not only fills methodological gaps but also contributes a replicable paradigm for the study of synergistic development. More importantly, the findings offer practical guidance for achieving China’s “dual carbon” targets and serve as a valuable reference for promoting the joint advancement of green finance and low-carbon innovation globally, thereby supporting sustainable development and international climate governance.

Nevertheless, this study has several limitations. First, constrained by data availability and completeness, the analysis primarily relies on provincial-level statistics from official government sources. While these data capture overall trends, they do not fully reveal firm-level nuances in the “quality” of green finance or the “effectiveness” of low-carbon innovation, thereby limiting measurement precision and raising issues of heterogeneity in statistical calibers. Future research could integrate multi-source information, such as field surveys in green finance pilot zones, institutional index reports, and firm-level microdata, to improve accuracy and timeliness. Second, the study employs the number of authorized low-carbon patents as a proxy for low-carbon innovation. Although this proxy captures technological innovation to some extent, it inadequately reflects non-patented forms of innovation, such as organizational improvements, process optimization, and efficiency gains. Future research should incorporate additional indicators—such as corporate green performance metrics, energy efficiency indices, or green technology certifications—to provide a more comprehensive measure of low-carbon innovation. Third, although this paper applies the coupling coordination degree model, the Dagum Gini coefficient, spatial autocorrelation, and the spatial Durbin model, it does not perform robustness checks against alternative methods (e.g., DEA models, non-spatial panel regressions). This leaves room for improvements in explanatory power and robustness. Future studies could enhance methodological rigor through cross-validation and comparative analyses. Finally, within the framework of the Sustainable Development Goals (SDGs), it is essential to further investigate the green upgrading of industrial structures, the low-carbon transformation of energy systems, and ecological protection. Future research should adopt a multi-level perspective—micro (firms), meso (industrial chains/parks), and macro (regions, nations, or international cooperation)—to uncover the driving mechanisms, optimization pathways, and regional coordination strategies underlying the synergy between green finance and low-carbon innovation.