Spatiotemporal Evolution of Green Finance and High-Quality Economic Development: Evidence from China

Abstract

Utilizing panel data spanning from 2010 to 2021 across 30 Chinese provinces, this study examines the spatiotemporal dynamics of green finance and its correlation with high-quality economic development. Initially, the indicators for green finance and high-quality economic development were quantified by employing the entropy weight method. Secondly, we conducted a preliminary analysis of the spatiotemporal evolution patterns of green finance using the Mann-Kendall coefficient method and kernel density estimation, revealing an increasing trend in China’s green finance development level with regional disparities. Next, utilizing the Mann-Kendall coefficient method and spatial dynamic Markov model, we analyzed the spatiotemporal trends of green finance development and its coupling coordination with high-quality economic development across the 30 provinces in China. The research findings indicate a general upward trend in the degree of coordinated development between green finance and high-quality economic development from 2010 to 2021. Significant spatial differences in coupling coordination levels exist among different regions, with the highest level in the western regions, followed by the eastern regions, and the weakest in the central regions. This suggests an overall trend toward coordinated development between green finance and high-quality economic development in China, with green finance playing a significant role in promoting high-quality economic development. However, the growth rate of coupling coordination is relatively slow and exhibits regional heterogeneity. Lastly, drawing from these research findings, we put forward policy recommendations aimed at effectively advancing the development of green finance and high-quality economic growth in China.

1. Introduction

As a sustainable financial model, green finance has gradually become an important research field. The core idea is to guide financial resources to invest in environmental protection and low-carbon industries, thus promoting high-quality economic development. High-quality economic development is primarily manifested in five key dimensions: innovation, coordination, sustainability, openness, and inclusive growth. This concept represents a progressive shift in the new era, transforming economic growth from a focus on quantity to an emphasis on quality. High-quality economic development is an innovative concept in the new era, and it is the development and transformation of economic growth from quantity to quality [1]. The level of development is the value of the index corresponding to a specific time in the time series, which indicates the level reached by the objective phenomenon at different times. Since the implementation of reform and opening-up, China’s economy has developed rapidly. As an innovative mechanism in the financial sector designed to better support the transformation and growth of the green economy, green finance can achieve the Pareto optimal allocation of environmental protection funds, thereby facilitating the rapid development of China [2]. In 2016, seven ministries and commissions, including the People’s Bank of China, issued “Guiding Opinions on Building a Green Finance System” [3], which established the top-level framework system of green finance for the first time. The advancement of green finance can facilitate the shift from traditional economic models to new green growth paradigms while also supporting the development and technological innovation within emerging green energy science and technology sectors [4]. Green finance can increase the scale of green investment, increase the potential for green economic development, and drive green economic investment and financial development [5]. Green investment is helpful in promoting the rapid development of green industries, enhancing the driving force of green economic development, and developing the potential of green finance [6]. By measuring the supply of green industry and the development level of green finance among provinces, Li Xiaoxi found that green finance is a general trend of benign economic development in the long term [7], which contributes to the balance and growth of the green economy in different regions. With the continuous improvement in the green financial system, green finance has gradually become a significant way and strong support to propel China’s green and low-carbon development. China will continue to improve its green financial system to boost the green transformation of development mode. Green finance is the orientation of future financial institutions; China’s economic development has entered a new normal, and high-quality development is the only way that must be passed. Then, how to measure the spatial and temporal regional distribution of China’s green finance development, further analyze its causes and nature, and put forward targeted solutions has become a topic worthy of study. Financial development is key to the high-quality development of the modern market economy, and an imbalance in green financial development will not be conducive to the stable and high-quality development of the economy. In addition, in the development process of China’s financial industry, relevant research on green lags behind reality in a certain sense. By comparing the empirical results with the actual situation, our study can deduce whether the current development status of green finance meets the requirements of China’s economic development, determine the correlation mechanism between the two, identify the problems in a targeted manner, and improve the policy plan and system design for the problems. Therefore, it is very vital to measure the development level of green finance and study the coupling and coordinated spatiotemporal evolution of green finance development level and high-quality economic development to realize the transformation of green industry and promote high-quality economic development. Therefore, measuring the development of green finance and analyzing its temporal and spatial evolution and distribution dynamics hold significant theoretical and practical importance. This analysis is crucial for enhancing the green financial system, fostering economic upgrading and transformation, and enhancing the sustainable development of China’s economy.

Yang et al. employed data regression analysis and a two-step Generalized Method of Moments (GMM) to investigate the relationship between green finance, financial technology, and high-quality economic development [8]. Gao et al. examined the interplay between green finance, environmental pollution, and high-quality economic development using provincial panel data from China [9]. Wang et al. utilized the spatial Durbin model and the intermediary effect model to investigate the role of green finance and energy development in fostering high-quality economic growth [10]. Existing studies on the relationship between green finance and high-quality economic development have predominantly focused on analyzing their correlation [11,12], with relatively less attention given to the spatiotemporal evolution characteristics of green finance and high-quality economic development. Zhou et al. calculated the coupling coordination degree (CCD) and spatial association strength of green finance and high-quality economic development [13], but there is a lack of research on its spatiotemporal evolution laws. To promote the coordinated development of green finance and high-quality economic growth, it is essential to further analyze the evolutionary patterns of both green finance and high-quality economic development. In this paper, the entropy weight method is employed to measure the levels of green finance development and high-quality economic development, exploring the dynamic evolution patterns between the two. It identifies key factors influencing the coupling coordination of green finance and high-quality economic development and explores mechanisms for promoting their coordinated development, offering valuable insights for governmental departments and financial institutions. The specific research content is outlined as follows: firstly, the Mann-Kendall coefficient method and kernel density estimation are employed to analyze the dynamic evolution trends of green finance development levels. The results indicate an upward trend in China’s green finance development levels, with inter-provincial disparities observed. Over time, these disparities gradually diminish, especially after 2016, as provinces with lower levels of green finance development exhibit catch-up effects, leading to the narrowing of regional differences.

Subsequently, using a dynamic spatial Markov model, this study investigates the spatiotemporal evolution characteristics of coupling coordination between green finance and high-quality economic development. This study reveals an overall increasing trend in the degree of coordination between green finance and high-quality economic development in China from 2010 to 2021. Significant spatial disparities are observed in the degree of coupling coordination among the provinces, with the western regions showing the highest levels, followed by the eastern areas, and the central regions exhibiting the weakest coordination. This implies a trend toward coordinated development between green finance and high-quality economic development in China overall, with green finance playing a significant role in driving high-quality economic development. However, the growth rate of their coupling coordination is relatively slow, constrained by “path dependence”.

By comparing with the existing literature [9,13], this study contributes to two main aspects: (1) By delving into the spatiotemporal evolution characteristics of green finance and high-quality economic development, this study proposes targeted recommendations based on existing issues in their coordinated development in China. It enriches the theoretical framework of research on green finance and sustainable development and, to some extent, provides theoretical guidance for green finance policy formulation. (2) It unveils the coupling coordination characteristics of green finance and high-quality economic development in different periods and regions, offering practical guidance for promoting their coordinated development. Therefore, this study holds significant theoretical and practical value for advancing sustainable economic development and fostering a virtuous cycle between green finance and high-quality economic development.

The remainder of this paper is organized as follows. In Section 2, we discuss the relevant literature. The main research methodology and indicator construction used in this study is concluded in Section 3, and Section 4 presents the spatiotemporal evolution analysis of green finance development.

2. Literature Review

2.1. Green Finance

Green finance refers to the financial activities that align with the concept of sustainability and promote environmental initiatives. It can also be interchangeably referred to as “environmental finance”, “climate finance”, and “carbon finance” [14]. Existing literature generally measures the establishment of a comprehensive evaluation index system for the development level of green finance, consisting of five dimensions: green credit, green securities, green investment, green insurance, and carbon finance [14].

Based on the aforementioned measurement methods of green finance, scholars have investigated its role in various fields. Green finance has a positive impact on climate change and provides significant advantages for renewable energy [15]. Its development can sustainably promote carbon neutrality [16]. Governments can use green finance as a potential policy tool to encourage industrial enterprises to reduce pollution emissions [17]. Zhang et al. studied the restrictive effects of green credit policies on sulfur dioxide and wastewater emissions [18]. Shang et al. applied the ARDL (Autoregressive Distributed Lag) method and found that green bonds have a positive and significant long-term impact on the green efficiency of China’s tourism industry [19].

Additionally, a large number of scholars have studied green finance as an effective tool for promoting sustainable economic growth [20]. Research indicates that developing green finance can facilitate high-quality economic development with significant heterogeneity [21]. Han suggests that collective efforts from national and local governments, financial institutions, and enterprises are necessary to improve the green finance system and promote high-quality economic development in China [22].

2.2. High-Quality Economic Development

Yin et al. emphasized that the key to high-quality economic development lies in achieving effective qualitative improvement and reasonable quantitative growth, which is essential for China to attain sustainable development [23]. Zhang et al. summarized the connotation of HQED, believing it should be both green and sustainable while also meeting people’s needs [24]. Zhang et al. argued that high-quality economic development (HQED) represents the latest conceptual evolution from sustainable development to quality economic growth. They emphasize the negative consequences of focusing solely on economic growth rates and the need to balance economic growth objectives by prioritizing both quantity and quality [25].

In addition, the literature examines high-quality economic development from various perspectives. The assessment system for HQED lacks a unified measurement method, with different scholars employing varied approaches. Wang et al. established an evaluation index system for HQED, comprising three dimensions: capability, structure, and efficiency, and utilized the entropy method to obtain the weights of each indicator, thus deriving a comprehensive index for HQED [26]. Li et al. constructed a high-quality economic development index using the entropy method based on the five development concepts of innovation, coordination, greenness, openness, and sharing. Shan et al. established an evaluation index system for HQED from 4 dimensions: power conversion, structural upgrading, outcome sharing, and environmental protection, and studied the impact of renewable energy consumption and tourism development on HQED [27].

Overall, most literature concentrates on the conceptual definition and measurement of high-quality economic development (HQED), with insufficient exploration of its spatiotemporal evolution patterns. Green finance offers a financing pathway for green development and serves as an essential tool for achieving high-quality economic development. The advancement of regional economic development toward higher quality is influenced by inter-regional interactions, showcasing distinctive spatial dynamic characteristics that warrant the application of spatial econometrics methodologies [28].

3. Research Methodology and Indicator Construction

3.1. Research Methodology

3.1.1. Entropy Weight Model

Based on the concept of information entropy, the entropy value of an index indicates its dispersion degree. A smaller entropy value signifies a higher dispersion degree of the index and a greater impact on the overall evaluation. The steps for determining the indicator weights using the entropy weight method are as follows:

• Step 1. Normalization matrix:

$$p_{ij}=\frac{x_{ij}^{\prime }}{{\sum }_{i=1}^{n^{\prime }}{x}_{ij}},i=\mathrm{1,2},\dots ,m$$

(1)

• Step 2. The entropy value of the evaluation index is calculated to control the entropy value range to within (0,1). The formula for calculating the entropy value uses the natural logarithm and introduces a constant term. The formula is as follows:

$$e_j=\frac{1}{ln{n}^{\prime }}\sum _{i=1}^{n^{\prime }}{p}_{ij}ln{p}_{ij},i=\mathrm{1,2},\dots n$$

(2)

• Step 3. The calculation of weights of evaluation indicators is as follows:

$${\omega }_i=\frac{1-{\mathrm{e}}_{\mathrm{j}}}{{\sum }_{j=1}^m\left(1-{\mathrm{e}}_{\mathrm{j}}\right)}$$

(3)

In sum, the weight vector w of m evaluation indexes can be calculated as

$$W=({\omega }_1,{\omega }_2,\dots ,{\omega }_M),\sum _{j=1}^m{\omega }_j=1$$

(4)

3.1.2. Coupling Coordination Model

The coupling degree is primarily used to describe the interaction and mutual influence between two or more systems. In order to more accurately describe the interaction and coordination degree between the two systems of green finance and high-quality economic development, combined with relevant literature research, a coupling coordination degree model is constructed, and the specific model is established as follows:

$$C=\frac{2\times \sqrt{U_1+U_2}}{U_1+U_2}$$

(5)

C is the level of green financial development-high-quality economic development, is the comprehensive score of the green financial system, and is the comprehensive score of the high-quality economic development system.

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

(6)

$$T=a{U}_1+b{U}_2$$

(7)

D is the coupling coordination degree of the green financial development level and high-quality economic development and is the comprehensive score of this system. In Formula (7), and are constant coefficients. In research on coupling coordination degree, Wang et al. studied the misunderstanding and correction of the China coupling coordination degree model [29]. Sun et al. constructed a coupling coordination degree model to analyze the coordinated development among the consumption effect [30], investment effect, government purchase effect, and external demand effect, while Zhang et al. used the Beijing-Tianjin-Hebei urban agglomeration as an example to analyze the coupling coordination degree of a social-economic-resource-environment system in urban areas [31]. Taking the Taihu Lake Basin as an example, Xu et al. studied the coupling coordination degree between social and economic development and the water environment [32], and the coupling coordination degree models they built solved the studied problems well. Therefore, the coupling coordination degree between green financial development and high-quality economic development, as studied in this paper, can be categorized into ten levels based on the grading standards established by the aforementioned scholars, as shown in

Table 1. Classification of coupling coordination degrees.

D-Value Interval of Coupling Coordination Degree	Coordination Grade	Coupling Coordination Range	D-Value	Coordination Grade	Coupling Coordination Range
[0,0.1)	1	Extreme disorder	[0.5,0.6)	6	Reluctantly coordinate
[0.1,0.2)	2	Serious maladjustment	[0.6,0.7)	7	Primary coordination
[0.2,0.3)	3	Moderate disorder	[0.7,0.8)	8	Intermediate coordination
[0.3,0.4)	4	Mild disorder	[0.8,0.9)	9	Good coordination
[0.4,0.5)	5	On the verge of disorder	[0.9,1)	10	Quality coordination

Note. D-value is the D-value interval of the coupling coordination degree.

.

3.1.3. Markov Chain

As research progresses, the Markov chain model is increasingly being applied to studies of spatiotemporal evolution. Hu S. et al. innovatively introduced a non-static spatial Markov chain model, building on the static Markov chain model, to study the spatiotemporal patterns and influencing factors of urban tourism development in China [32]. Liu et al. utilized a spatial Markov chain to investigate the impact of the regional environment on soil erosion in the Xiangxi River Basin [33]. Wang et al. explored the dynamic transition characteristics, spatial spillover effects, and future development trends of the urban Human Development Index (HDI) using both Markov chain and spatial Markov chain models [34]. Liao et al. explored the spatiotemporal evolution characteristics and influencing mechanisms of China’s urban tourism green innovation efficiency (TGIE) from 2000 to 2020 using the SBM-Undesired model, kernel density estimation, and spatial Markov chain [35]. Furthermore, Alyousifi et al. explored the potential influence of spatial dependence on the distribution of air pollution using a spatial Markov chain (SMC) model [36]. Drawing on the research methods of the aforementioned scholars, this paper employs the Markov chain model to describe the spatiotemporal evolution characteristics of green finance and high-quality economic development in China.

The Markov chain model is primarily used to discretize the continuous attribute values of geographical phenomena across different periods. Data are typically divided into k categories based on data grading. By taking a measurement of the probability distribution and changes in each category, the evolution and development process of green finance and high-quality economic development can be approximated as a Markov process. A certain type of distribution at time $t$ is represented by a state probability vector $E_t=[E_{1,t},E_{2,t},\cdots ,E_{k,t}]$ of $1\times k$, and the state transition process of the whole thing can be represented by a Markov probability transition matrix with the probability value of $k\times k$ as $M_{ij}$. $M_{ij}$ represents the probability that the spatial unit of type $i$ at time $t$ will be transformed into type $j$ at time $t+1$, and the formula is:

$$M_{ij}=\frac{n_{ij}}{n_i}$$

(8)

where: $n_{ij}$ represents the sum of the number of space units of type $j$ at the moment when type $i$ changes to $t+1$; $n_i$ represents the sum of the number of all type $i$ spatial units during the study period. The spatial and temporal evolution of the coupling coordination degree between green finance and high-quality economic development is not a random distribution. Its regional correlation and dependence within the geographical space are significant and cannot be overlooked. The static spatial Markov chain combines the concept of “spatial lag” to make up for the traditional Markov’s neglect of spatial interaction. The spatial Markov chain introduces a spatial weight matrix to calculate the weighted average attribute value (spatial lag) of neighboring regions so as to investigate the neighboring region state of spatial units and construct a Markov matrix under different spatial lag conditions. The spatial Markov chain transition probability moment matrix can be decomposed into $k\times k$ conditional transition matrices and $m_{ij}(k)$ represents the spatial transition probability value, which is transformed into $j$ type at $t+1$ under the condition of the spatial lag type k of the spatial unit at $t$. The spatial lag type of a spatial unit is determined by its spatial lag value, which is the spatial weighted average of the neighborhood attribute values of the spatial unit. The formula is:

$$Lag=Y_i{W}_{ij}$$

(9)

where: $Y_i$ is the attribute value of the spatial unit; $W_{ij}$ is the element in row $i$ and column $j$ of the spatial weight matrix $W$, that is, the relationship matrix between spatial units and neighboring areas.

3.1.4. Mann-Kendall

Mann-Kendall test method is suitable for data analysis with non-normal distribution, incompleteness or a few abnormal values, which are often encountered in time series analysis. Therefore, this study employs the Kendall test method to examine the development level of green finance in mainland China, excluding Hong Kong, Macao, and Taiwan. The principle of the Kendall test is to compare the data on the development level of green finance for each year. If the later value (in time) is higher than the previous value, it will be marked with “+”; otherwise, it will be marked with “-”. If the number of plus signs is more than minus signs, there may be an upward trend; similarly, if there are more minus signs than plus signs, there may be a downward trend. If there is no upward or downward trend in the data of the green financial development level, the number of positive and negative signs should account for 50%, respectively.

For the sequence X_t = (x_1, x_2, x_3, x_4…, x_n), first determine the size relationship between x_i and x_j in all dual values (x_i, x_j, j > i) (set to τ). The statistics of the trend test are as follows.

$$U_{MK}=\frac{\tau }{{[Var(\tau )]}^{1/2}}$$

(10)

$$\tau =\sum _{t=1}^{n-1}\sum _{j=i+1}^{n}sgn\left(x_j-x_i\right);sgn(\theta )=\left\{\begin{array}{c}1if\theta >0\\ 0if\theta =0\\ 1if\theta <0\end{array}\right.$$

(11)

$$Var=\frac{n\left(n-1\right)\left(2n+5\right)-{\sum }_{i=1}^{n-1}{t}_{i}i(i-1)(2i+5)}{18}$$

(12)

When $n>10$, the Mann-Kendall test statistic UMK converges to the standard normal distribution. The null hypothesis assumes that the sequence exhibits no trend. Using a two-sided trend test at a significance level α, the critical value Uα/2 is obtained from the standard normal distribution table. If ${|U}_{MK}|<U_{\alpha /2}$, the null hypothesis is accepted, indicating a non-significant trend. Conversely, if ${|U}_{MK}|>U_{\alpha /2}$, the null hypothesis is rejected, suggesting a significant trend.

3.2. Indicator Construction

3.2.1. Data Source and Preprocessing

This paper selects provincial-level green finance and high-quality economic development indicators for 30 provinces in China from 2010 to 2020, excluding Hong Kong, Macao, and Taiwan. Relevant data on green finance are primarily sourced from the Wind database, China Industrial Statistics Yearbook, China Energy Statistics Yearbook, China Economic Census Yearbook, China Forestry and Grassland Yearbook, China Water Conservancy Investment Yearbook, China Insurance Statistics Yearbook, and China Environmental Statistics Yearbook. The selection of high-quality economic development data comes from Mark Data Network. The descriptive statistics of the variables are shown in

Table 2. Descriptive statistics of variables.

Variable Name	Sample Size	Average Value	Standard Deviation	Minimum Value	Maximum Value
Total Loans of Listed Environmental Companies in Provinces	360	7,132,839,124.8134	14,970,039,045.7010	1,029,148.1714	106,201,520,423.8300
Total Loans of Listed Companies	360	9,879,550,748,193.8600	3,912,153,623,064.2400	3,664,801,873,101.2200	15,752,890,686,336.6000
Interest Expenditure of Highly Polluting Industries (billion)	360	186.4285	120.7301	8.3300	616.8400
Total Interest Expenditure of Industrial Enterprises above Designated Size	360	382.6589	302.9966	17.3000	1482.7100
Total Market Value of A Shares (billion)	360	17,075.3297	32,357.6108	338.0909	199,775.7700
Total Output Value of Environmental Protection Enterprises (billion)	360	925.0729	1486.5926	3.0000	10,749.2600
Total Market Value of High Energy Consumption Industries (yuan)	360	212,280,002,189.3830	302,810,636,758.3880	2,203,488,738.0000	2,432,215,696,795.1000
Total Market Value of High Energy Consumption Industries (billion)	360	2122.8000	3028.1064	22.0349	24,322.1570
Total Investment in Environmental Pollution Control (billion)	360	257.0568	197.4296	5.3237	1416.2000
GDP (billion)	360	25,665.8567	21,319.0860	1350.4300	124,369.6716
Agricultural Insurance Income (million)	360	1499.1824	1413.9448	9.2000	7869.5700
Agricultural Output Value (billion)	360	1899.6050	1336.3367	92.0725	6564.8311
Agricultural Insurance Expenditure Compensation (million)	360	1020.7336	1099.9707	12.3700	6842.0000
Local Fiscal Energy Conservation and Environmental Protection Expenditure (billion)	360	145.6566	98.1552	14.8874	747.4388
Local Fiscal General Budget Expenditure (billion)	360	5006.5602	2948.5499	557.5285	18,247.0084
Carbon Dioxide Emissions (million tons)	360	377.6300	307.6709	30.7936	1874.6100

.

The proportion of new bank loans for listed environmental protection companies in the A-share market is sourced from the CSMAR database. For missing values, we addressed them by excluding severely missing data and consulting the companies’ balance sheets for additional information. The interest expenditure ratio of high-energy-consuming industries is obtained from the “China Industrial Statistics Yearbook”. Data related to green securities and green investments are sourced from the Wind database and the EPS database. The general investment in environmental pollution control has been interpolated to extend the data up to 2021. The green insurance data, including agricultural insurance payouts, agricultural insurance premium income, and property insurance premium income, are derived from the “China Insurance Yearbook”. These comprehensive data provide a solid basis for revealing the spatial evolution characteristics of green finance and high-quality economic development in China.

3.2.2. Measurement of Green Finance Development

On the Measurement of the Development Level of Green Finance, after consulting a substantial amount of relevant literature, it was found that Chen et al. measured the development level of green finance in China from five aspects: green credit, green securities, green investment, green insurance, and green bonds [37]. Similarly, Wang et al. assessed the development level of green finance by considering dimensions such as green credit, green securities, green investment, green insurance, and carbon finance to ensure the validity of the calculation results as much as possible [38]. Referring to the index construction of green financial development level by Chen, Wang, Gao, and Zhou et al. [37,38,39,40], this paper constructs the measurement standard of green financial development from five sides: green credit, green securities, green investment, and green insurance. Additionally, it compares metrics such as the interest expense ratio of high-energy-consuming industries, the borrowing scale of listed environmental companies, the market value of environmental protection enterprises, the market value of high-energy-consuming industries, the depth of agricultural insurance, and the payout ratio of agricultural insurance.

Table 3. Establishment of a Green Finance Index System.

	Secondary Index	Three-Level Index	Calculation Method	Indicator Attribute
DGF	Green credit	Proportion of interest expenditure in high energy-consuming industries	Interest Expenditure of Six High Energy Consumption Industries/Industrial Interest Expenditure	−
		Borrowing scale of environmental protection listed companies	A-share environmental protection listed company loans/A-share listed company loans	+
	Green bond	Market value ratio of environmental protection enterprises	Total output value of environmental protection enterprises/total market value of A shares	+
		Market value ratio of energy-intensive industries	Total market value of six high energy-consuming industries/total market value of A shares	−
	Green insurance	Agricultural insurance depth	Agricultural insurance income/total agricultural output value	+
		Agricultural insurance payout ratio	Agricultural insurance expenditure/agricultural insurance income	+
	Green investment	Proportion of investment in environmental pollution control	Environmental pollution control investment/total investment	+
		Local financial and environmental protection	Local fiscal and environmental expenditure/GDP	+
	Carbon finance	Financial carbon intensity	Carbon emissions/GDP	−

Note: “+” and “−” represent the positive and negative indicators, respectively. See Section 3.2.1: Data Sources for an explanation of the orientation of the selected variables.

presents the establishment of a Green Finance Index System.

3.2.3. Measurement of High-Quality Economic Development

Currently, academic standards for measuring high-quality economic development have not yet reached a unified consensus. When considering describing the concept of high-quality economic development as much as possible, we should refer to the research of Wei and Li, Zeng et al., Zhao et al., and establish five indicators to measure the level of high-quality economic development (

Table 4. Establishment of an evaluation system for high-quality economic development.

Primary Index	Secondary Index	Three-Level Index	Calculation Method	Indicator Attribute
HQED	Innovative development	GDP growth rate	Regional GDP growth rate	+
		R&D investment intensity	R&D expenditure/GDP of industrial enterprises above the designated size	+
		Efficiency of investment	Investment rate/regional GDP growth rate	−
		Technology trading activity	Technology transaction turnover/GDP	+
	Coordinated development	Demand structure	Total retail sales of social consumer goods/GDP	+
		Urban-rural structure	Urbanization rate	+
		Government debt burden	Government debt balance/GDP	−
		Industrial structure	The increase in the ratio of tertiary industry output value to regional GDP	+
	Green development	Elastic coefficient of energy consumption	Energy consumption growth rate/GDP growth rate	−
		Wastewater per unit output	Sulfur dioxide emissions/GDP	−
		Exhaust gas produced by unit	Sulfur dioxide emissions/GDP	−
	Open development	Degree of dependence on foreign trade	Total import and export/GDP	+
		Proportion of foreign investment	Total foreign investment/GDP	+
		Degree of marketization	Regional marketization index	+
	Shared development	Resilience of residents’ income growth	Labor compensation/regional GDP	+
		Resilience of residents’ income growth	Labor remuneration/regional GDP	+
		Urban-rural consumption gap	Per capita consumption expenditure of urban residents/rural residents	−
		Proportion of people’s livelihood financial expenditure	The proportion of housing security expenditure, medical and health expenditure, local financial education expenditure, social security and employment expenditure in local financial budget expenditure	+

Note: “+” and “−” represent positive and negative indicators, respectively. See Section 3.2.1 Data Sources for an explanation of the orientation of the selected variables.

) [41,42,43].

4. Results and Discussion

4.1. Green Finance Development Trend Analysis in China

4.1.1. Calculation of Green Finance Development Level

In order to enhance the comparability across various indicators, the data within this investigation underwent standardization, and the allocation of weights for each indicator was determined utilizing the entropy weight method. This method of entropy weighting constitutes an impartial technique for assigning weights involving the calculation of information entropy for each individual indicator. Smaller entropy values indicate greater variability of the indicator and, correspondingly, higher weights. These final weights are utilized to assess the overall development levels across various regions, as detailed in

Table 5. Weights of entropy method for each index.

Name of Index	Weight
Loan scale of environmental protection listed companies	0.128832189
High energy consumption industry interest ratio	0.01859477
Proportion of market value of environmental protection enterprises	0.045771758
Proportion of market value of high energy-consuming listed companies	0.042207575
Agricultural insurance payout ratio	0.019301144
Agricultural insurance depth	0.072396371
Proportion of investment in environmental pollution control	0.030980883
Proportion of public expenditure on energy conservation and environmental protection	0.020736253
carbon finance	0.059909386
increasing rate of GDP	0.002390718
R&d investment intensity	0.022433119
efficiency of investment	0.001554517
Technology trading activity	0.127977376
pattern of demand	0.006146925
urban and rural structure	0.019717388
Government debt burden	0.047359268
industrial structure	0.023338433
Energy consumption elasticity coefficient	0.007435737
Wastewater per unit of output	0.009189148
Unit of exhaust gas produced	0.092084944
ratio of dependence on foreign trade	0.068515851
Proportion of foreign investment	0.067901841
marketization degree	0.017682412
Proportion of workers’ compensation	0.01356555
Resident income elasticity	0.00688337
Urban-rural consumption gap	0.017956841
The proportion of public fiscal expenditure	0.009136231

, for specific weight allocations.

Ultimately, the aggregate sum of indicators was calculated utilizing the established weights to ascertain the comprehensive developmental status of each geographic area. The formula employed for computing the comprehensive developmental level is expressed as follows:

$$S_i=\sum _{j=1}^m{w}_j\ast X_{ij}^{、}$$

(13)

$S_i$ represents the comprehensive development level of region $i$; $X_{ij}^{、}$ denotes the standardized value of indicator $j$ for region $i$;$w_j$ signifies the weight assigned to indicator $j$. The resultant development levels are presented in

Table 6. Comprehensive development levels of all regions in China.

Region	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Shanghai	0.257	0.266	0.26	0.258	0.265	0.278	0.268	0.273	0.283	0.289	0.299	0.319
Yunnan	0.187	0.18	0.176	0.169	0.163	0.161	0.161	0.289	0.273	0.247	0.233	0.23
Inner Mongolia	0.264	0.263	0.261	0.241	0.243	0.238	0.212	0.214	0.192	0.184	0.182	0.194
Beijing	0.318	0.323	0.349	0.358	0.364	0.367	0.386	0.406	0.417	0.44	0.455	0.48
Jilin	0.158	0.151	0.145	0.143	0.148	0.147	0.147	0.162	0.172	0.185	0.181	0.183
Sichuan	0.148	0.137	0.139	0.141	0.148	0.146	0.141	0.143	0.15	0.161	0.164	0.175
Tianjin	0.229	0.219	0.22	0.221	0.234	0.233	0.226	0.236	0.253	0.281	0.272	0.278
Ningxia	0.284	0.295	0.278	0.264	0.275	0.285	0.244	0.273	0.247	0.253	0.261	0.275
Anhui	0.154	0.149	0.152	0.155	0.151	0.157	0.158	0.161	0.158	0.167	0.173	0.179
Shandong	0.168	0.164	0.173	0.178	0.168	0.163	0.161	0.179	0.185	0.197	0.208	0.219
Shanxi	0.212	0.19	0.194	0.237	0.235	0.253	0.253	0.221	0.214	0.225	0.209	0.186
Guangdong	0.231	0.201	0.203	0.209	0.21	0.202	0.193	0.231	0.243	0.26	0.256	0.264
Guangxi	0.189	0.152	0.153	0.138	0.144	0.155	0.141	0.143	0.141	0.149	0.176	0.208
Xinjiang	0.195	0.2	0.217	0.209	0.212	0.203	0.191	0.186	0.167	0.182	0.18	0.172
Jiangsu	0.19	0.189	0.191	0.194	0.185	0.181	0.175	0.175	0.177	0.18	0.189	0.199
Jiangxi	0.167	0.166	0.171	0.158	0.155	0.149	0.157	0.162	0.157	0.158	0.173	0.185
Hebei	0.166	0.158	0.153	0.151	0.151	0.159	0.143	0.151	0.148	0.155	0.163	0.164
Henan	0.151	0.135	0.133	0.133	0.136	0.143	0.136	0.138	0.137	0.135	0.14	0.154
Zhejiang	0.157	0.163	0.162	0.167	0.168	0.168	0.167	0.17	0.176	0.184	0.196	0.218
Hainan	0.16	0.159	0.158	0.144	0.148	0.146	0.175	0.168	0.179	0.186	0.176	0.179
Hubei	0.17	0.163	0.16	0.158	0.158	0.158	0.166	0.165	0.169	0.198	0.199	0.206
Hunan	0.178	0.158	0.164	0.151	0.143	0.148	0.137	0.148	0.148	0.152	0.155	0.161
Gansu	0.219	0.205	0.231	0.229	0.224	0.229	0.208	0.211	0.208	0.204	0.202	0.205
Fujian	0.153	0.146	0.144	0.144	0.139	0.146	0.154	0.145	0.145	0.159	0.151	0.154
Guizhou	0.291	0.239	0.215	0.201	0.195	0.183	0.157	0.157	0.149	0.147	0.141	0.144
Liaoning	0.219	0.206	0.218	0.2	0.213	0.207	0.198	0.217	0.215	0.218	0.218	0.221
Chongqing	0.218	0.218	0.204	0.193	0.191	0.181	0.164	0.163	0.165	0.159	0.164	0.204
Shaanxi	0.192	0.17	0.167	0.174	0.173	0.183	0.174	0.175	0.177	0.194	0.201	0.201
Qinghai	0.269	0.265	0.254	0.256	0.242	0.261	0.245	0.236	0.228	0.218	0.215	0.218
Heilongjiang	0.175	0.167	0.173	0.181	0.17	0.17	0.17	0.178	0.172	0.187	0.183	0.181

as calculated.

4.1.2. Analysis of the Changing Trend of Green Finance Development Level

Monitoring and analyzing the annual development of green finance across Chinese provinces and cities from 2010 to 2021 was conducted using the Mann-Kendall test method to assess the significance of trends in green finance development levels across regions. Detailed steps are as follows: Firstly, calculate the $\mathrm{S}$ statistic by comparing each pair of data points in the time series, computing the difference signs between these data points, and summing these signs to derive the $\mathrm{S}$ statistic. Next, compute the variance of the $\mathrm{S}$ statistic, $\mathrm{V}\mathrm{a}\mathrm{r}(\mathrm{S})$, while accounting for duplicate values in the data. Then, calculate the $\mathrm{Z}$ statistic, which is the standardized (S) statistic used to evaluate the significance of trends. Based on the $\mathrm{Z}$ statistic, compute the p-value to determine the significance of the results. Finally, assess the significance of trends based on the p-value, typically choosing a significance level of 0.05; trends are considered significant if the p-value is less than 0.05 (

Table 7. Trend analysis results of green finance development levels.

Region	S Statistic	Var(S)	Z Statistic	p-Value	Sen’s Slope	Significance Level	MK Test Result	Evaluation Conclusion
Beijing	66	212.67	4.46	0.00	0.01	100.00%	4.46	increase significantly
Tianjin	48	212.67	3.22	0.00	0.01	99.87%	3.22	increase significantly
Hebei	−4	212.67	−0.21	0.84	0.00	16.30%	−0.21	There was no significant upward or downward trend
Shanxi	−4	212.67	−0.21	0.84	0.00	16.30%	−0.21	There was no significant upward or downward trend
Inner Mongolia	−56	212.67	−3.77	0.00	−0.01	99.98%	−3.77	significant reduction
Liaoning	16	212.67	1.03	0.30	0.00	69.63%	1.03	There was no significant upward or downward trend
Jilin	34	212.67	2.26	0.02	0.00	97.64%	2.26	increase significantly
Heilongjiang	26	212.67	1.71	0.09	0.00	91.35%	1.71	There was no significant upward or downward trend
Shanghai	54	212.67	3.63	0.00	0.00	99.97%	3.63	increase significantly
Jiangsu	−8	212.67	−0.48	0.63	0.00	36.88%	−0.48	There was no significant upward or downward trend
Zhejiang	58	212.67	3.91	0.00	0.00	99.99%	3.91	increase significantly
Anhui	54	212.67	3.63	0.00	0.00	99.97%	3.63	increase significantly
Fujian	20	212.67	1.30	0.19	0.00	80.74%	1.30	There was no significant upward or downward trend
Jiangxi	6	212.67	0.34	0.73	0.00	26.83%	0.34	There was no significant upward or downward trend
Shandong	36	212.67	2.40	0.02	0.00	98.36%	2.40	increase significantly
Henan	18	212.67	1.17	0.24	0.00	75.63%	1.17	There was no significant upward or downward trend
Hubei	34	212.67	2.26	0.02	0.00	97.64%	2.26	increase significantly
Hunan	−8	212.67	−0.48	0.63	0.00	36.88%	−0.48	There was no significant upward or downward trend
Guangdong	34	212.67	2.26	0.02	0.01	97.64%	2.26	increase significantly
Guangxi	6	212.67	0.34	0.73	0.00	26.83%	0.34	There was no significant upward or downward trend
Hainan	30	212.67	1.99	0.05	0.00	95.33%	1.99	increase significantly
Chongqing	−40	212.67	−2.67	0.01	−0.01	99.25%	−2.67	significant reduction
Sichuan	42	212.67	2.81	0.00	0.00	99.51%	2.81	increase significantly
Guizhou	−62	212.67	−4.18	0.00	−0.01	100.00%	−4.18	significant reduction
Yunnan	4	212.67	0.21	0.84	0.00	16.30%	0.21	There was no significant upward or downward trend
Shaanxi	36	212.67	2.40	0.02	0.00	98.36%	2.40	increase significantly
Gansu	−32	212.67	−2.13	0.03	0.00	96.65%	−2.13	significant reduction
Qinghai	−54	212.67	−3.63	0.00	0.00	99.97%	−3.63	significant reduction
Ningxia	−24	212.67	−1.58	0.11	0.00	88.52%	−1.58	There was no significant upward or downward trend
Xinjiang	−40	212.67	−2.67	0.01	0.00	99.25%	−2.67	significant reduction

).

Based on the application of the Mann-Kendall (MK) trend analysis method to assess the progression of green finance across various regions in China spanning the years 2010 to 2021, the findings yielded the following conclusions:

• Regions with Significant Increasing Trends: Beijing, Tianjin, Shanghai, Zhejiang, Anhui, Jilin, Shandong, Hubei, Guangdong, Sichuan, and Shaanxi, among others, showed positive and significant MK test results, indicating a significant upward trend in green finance development during the study period. These regions likely benefited from strong policy support, financial product innovation, and increased investments in green initiatives, facilitating the growth of green finance.
• Regions with Significant Decreasing Trends: Inner Mongolia, Chongqing, Guizhou, Gansu, Qinghai, Ningxia, Xinjiang, and others exhibited negative and significant MK test results, suggesting a notable decline in green finance development over the study period. This trend could be attributed to insufficient investment in green finance, inadequate policy implementation, and weaker environmental protection awareness in these areas.
• Regions with No Significant Trends: Regions such as Hebei, Shanxi, Jiangsu, Hunan, Jiangxi, Guangxi, Yunnan showed no discernible upward or downward trends in green finance development based on the MK test results. This may indicate a stable development in green finance within these regions or insufficient influence from policies and market dynamics.
• Localized Variations: In certain areas like Fujian and Hainan, although overall trends were not significant, brief periods of both upward and downward trends were observed in specific years. These fluctuations could be linked to local policies, economic conditions, and environmental events, necessitating a further detailed analysis.

Overall, the majority of regions in China demonstrated an increasing trend in green finance development, indicating some success in national and local government efforts to promote green finance. However, regions with decreasing trends or no significant changes highlight the need for enhanced efforts in green finance policies, market mechanisms, and public awareness to achieve a balanced nationwide development. Considering diverse factors, such as economic scale, policy environment, and market demand, is crucial for comprehensive evaluation. Additionally, advancing green finance depends not only on financial institutions and market dynamics but also on robust governmental support and collective societal efforts.

4.2. Spatial Evolution Analysis of Green Finance Development in China

Additional investigation unveils the spatial evolution traits pertaining to the advancement of green finance, exemplified through the kernel density distribution of provincial-level development across China from 2010 to 2020, as depicted in Figure 1.

The primary peak of green finance development in China demonstrates a leftward shift characterized by a reduction in peak height and an increase in peak value. Post-2015, there is an upward trend in peak values accompanied by a narrowing bandwidth. Overall, China’s green finance development displays a consistent upward trajectory despite notable inter-provincial variations. However, since 2016, there has been a declining trend, particularly evident across provinces. Regions with higher levels of green finance experience widened internal disparities, whereas those with lower levels exhibit a catch-up effect, contributing to convergence among regions.

In the eastern area, the kernel density curve of green finance development exhibits a multipeak phenomenon between 2010 and 2015, indicating a certain gradient effect in its development. In some years, a polarization phenomenon occurs, with an overall trend of bandwidth expansion from 2015 to 2021, showing a right-tail phenomenon and decreasing extensibility. This suggests a growing disparity between provinces with high and low levels of green finance development in the eastern region.

The central region shows a slight but rapid upward trend in the main peak of green finance development, with significant fluctuations in peak height, particularly a sharp increase in 2016, sustained bandwidth shrinkage, right-tail trend, and decreasing extensibility. Additionally, green finance development in the central region shows an overall upward trend after 2015, with a gradual narrowing gap between provinces with high and low levels of green finance development and relatively stable provincial-level green finance development within the region.

In the western region, the main peak of green finance development exhibits a slight fluctuating downward trend from 2010 to 2021, mainly decreasing slightly from 2010 to 2014 and showing a fluctuating trend from 2014 to 2020. Consequently, green finance development in the western region shows an overall stable trend, with the kernel density curve showing relatively gentle fluctuations in peak height but a large overall bandwidth width, indicating significant differences in green finance development among provinces in the region and demonstrating a multi-polarization phenomenon. Regions characterized by elevated levels of green finance development continue to experience robust growth, in contrast to the slower growth rates observed in regions with lower levels of development in this sector.

Further analysis elucidates the spatial evolution characteristics of green finance development across China’s provinces from 2010 to 2020, as depicted by the kernel density distribution in Figure 1. This analysis provides a nuanced understanding of the shifting dynamics of green finance across different regions over a specified period.

The kernel density estimation highlights a leftward shift in the primary peak of overall green finance development in China, suggesting an overall rise in the occurrence of lower levels of development in this domain. Simultaneously, the peak becomes shorter, and its value increases, signifying a concentration of provinces with higher development levels. After 2015, the peak value continues to rise, while the bandwidth narrows. This pattern suggests an overall upward trend in green finance development, albeit with substantial inter-provincial disparities that have diminished slightly over time, particularly post-2016. Provinces with higher levels of green finance development have exhibited relatively expanded internal differences, indicating greater variability within these regions. Conversely, provinces with lower levels of green finance development show a catch-up effect, leading to a narrowing gap between the different regions.

In the eastern region, the kernel density curve for green finance development displays a multipeak phenomenon between 2010 and 2015. This indicates the presence of several development clusters and suggests a gradient effect in the region’s green finance evolution. During some years within this period, a polarization phenomenon is evident, characterized by the presence of distinct high and low development peaks. From 2015 to 2021, the overall trend shows an expansion in bandwidth accompanied by a right-tail phenomenon and decreasing extensibility. This indicates an increasing disparity between provinces with high and low levels of green finance development, highlighting growing inequality in the eastern region’s green finance landscape.

Within the central region, the kernel density curve demonstrates a subtle yet noticeable upward trend in the primary peak of green finance development. There are significant fluctuations in the peak height, with a particularly sharp increase observed in 2016. This region also shows sustained bandwidth shrinkage, a right-tail trend, and decreasing extensibility. This implies that while there is overall growth in green finance development, the differences between provinces with high and low levels are becoming less pronounced, leading to a more homogenous regional development profile. After 2015, the central region demonstrates a consistent upward trend in green finance development, with a gradual narrowing gap between provinces at different development levels, indicating relatively stable provincial-level development within the region.

In contrast, the western region’s kernel density curve for green finance development shows a slight fluctuating downward trend from 2010 to 2021. Initially, from 2010 to 2014, there is a mild decline, followed by a fluctuating trend from 2014 to 2020. Consequently, green finance development in the western region displays an overall stable trend, with the kernel density curve exhibiting relatively gentle fluctuations in the peak height. However, the overall bandwidth remains large, indicating significant disparities in green finance development among provinces within the region. This demonstrates a multi-polarization phenomenon, where provinces with higher levels of green finance development continue to grow while those with lower levels experience slower growth.

To summarize, the spatial dynamics of green finance development in China reveal discernible regional patterns and evolving trends. The eastern region, despite showing overall growth, faces increasing internal disparities. The central region, on the other hand, shows signs of convergence in development levels, suggesting a more balanced regional growth. The western region remains stable but polarized, with significant differences between high and low-development provinces. These findings underscore the importance of tailored regional policies to address unique challenges and opportunities in green finance development across different parts of China.

4.3. Analysis of the Spatiotemporal Evolution Patterns of the Coupling Coordination between Green Finance and High-Quality Economic Development

4.3.1. Temporal Evolution Analysis of Coupling Coordination between Green Finance and High-Quality Economic Development

Using Supermap (iDesktop 11i) drawing software, an analysis was conducted of the temporal evolution characteristics of the coupling coordination between green finance and high-quality economic development. The coupling coordination of 30 provinces in China (excluding Hong Kong, Macau, and Tibet) was classified into 3–4 categories from low to high, with darker shades of orange indicating higher coupling coordination in the respective year. The trends in coupling coordination from 2010 to 2020 are depicted in Figure 2a–f.

From a national perspective, the coupling coordination between green finance and high-quality economic development has shown a significant increase from 0.65078 in 2010 to 0.71796 in 2020, indicating a general upward trend over the decade. This trend suggests a nationwide tendency toward the coordinated development of green finance and high-quality economic growth, with green finance playing a crucial role in facilitating high-quality economic advancements. However, it is important to note that the growth rate of this coupling coordination is relatively slow, highlighting the need for more targeted policies and initiatives to accelerate this process.

In examining regional heterogeneity, the eastern region, when compared to the western region, exhibits a lower level of coupling coordination. Nevertheless, it generally maintains higher coordination levels than the central region. From 2016 to 2020, there is a noticeable upward trend in the eastern region’s coupling coordination, signifying a simultaneous focus on both high-quality economic development and the enhancement of green finance initiatives. This dual emphasis indicates the region’s progressive stance toward integrating environmental considerations with economic policies.

In the central region, the coupling coordination consistently remained below the national average from 2012 to 2018. The advanced economic development in the eastern region facilitates easier implementation of green finance policies by the government, with more substantial financial and resource allocation. Conversely, the western region, potentially due to its abundant natural resources, places greater emphasis on the development of traditional industries. This focus results in comparatively less policy support for green finance. The central region appears to be situated between these two extremes, lacking the robust support observed in the eastern region and the resource advantages found in the western region.

Figure 2 illustrates that coupling coordination in the western region has generally remained at a high level, reflecting a state of coordinated development between green finance and high-quality economic growth in the western provinces from 2010 to 2021. However, it is worth noting that there was a retrogressive trend in coupling coordination from 2012 to 2018. Although there were some improvements observed in 2020, it is imperative to thoroughly analyze and integrate the factors that constrain the development of coupling coordination in this region. Such an analysis is essential for formulating effective development policies that will promote the coordinated advancement of green finance and high-quality economic growth locally.

In summary, although there are encouraging trends in the coupling and coordination between green finance and high-quality economic development, regional disparities underscore the importance of tailored strategies. These strategies must be region-specific, addressing distinct challenges and leveraging unique strengths to enhance the overall efficacy of green finance policies and their alignment with economic development goals.

4.3.2. Spatial and Temporal Evolution Characteristics Analysis Based on Static (Spatial) Markov Model

Considering an approximately equal distribution of regions across each category, this study uses the average coupling coordination (D values) of provinces in China (excluding Tibet, Hong Kong, Macau, and Taiwan) from 2011 to 2021 as a reference point. Our study covers 30 provinces and municipalities, which are categorized into four types (k = 4): Type I represents relatively low coordination (D values below 86% of the national average), Type II represents low coordination (D values between 86% and 99% of the national average), Type III represents relatively high coordination (D values between 99% and 113% of the national average), and Type IV represents high coordination (D values above 113% of the national average). These categories are denoted as k = 1, 2, 3, and 4, respectively, with higher k values indicating stronger dependencies. Based on this classification, a first-order Markov transition matrix can be derived, as shown in

Table 8. Static Markov Transition Probability.

t/(t + 1)	Type I	Type II	Type III	Type IV	Frequency
Type I	0.6000	0.2889	0.0778	0.0333	90
Type II	0.2500	0.4091	0.2273	0.1136	88
Type III	0.0589	0.2118	0.4353	0.2941	85
Type IV	0.0299	0.0746	0.2388	0.6567	67

.

The elements along the diagonal of the table reflect the probability of the coupling coordination type remaining unchanged to a certain extent, whereas the off-diagonal elements indicate the probability of transition between different types of coupling coordination. From the table, it is evident that:

(1)

All diagonal elements have values greater than those off the diagonal, with the maximum being 0.656 and the minimum being 0.409. This suggests that the probability of coupling coordination remaining unchanged is at least 40%, which is greater than the probability of a type transition. This indicates a constrained growth of coupling coordination between green finance and high-quality economic development, exhibiting “path dependence”.

(2)

Off-diagonal elements are situated on either side of the diagonal, indicating the potential for coupling coordination levels to transition to higher levels over consecutive years. However, such transitions are challenging, as most cities shift up or down by only one level. This is primarily due to the long-term and sustained nature of green finance and high-quality economic development. The advancement of green finance requires substantial time investment, coupled with factors such as geographical location and economic foundation, making significant type transitions unlikely over short periods.

(3)

The current stage of development is a transitional period between the level of green finance development and high-quality economic development. Regions are inclined to maintain their own type probabilities. In regions with high coupling coordination, there is a 24% probability of transitioning to a lower type, while regions with lower coupling coordination have around a 29% probability of transitioning to a higher type. This observation indicates a propensity for regression in regions exhibiting higher levels of coordination, underscoring the necessity of these regions to devise tailored development policies that address constraints on coordination development. It is crucial to promote comprehensive coordination between green finance and high-quality economic initiatives in local contexts.

Table 9. Markov transition probability in static space.

Type	t/(t + 1)	Type I	Type II	Type III	Type IV	Frequency
Type I	Type I	0.6897	0.2069	0.1034	0	29
	Type II	0.2105	0.5263	0.1579	0.1053	19
	Type III	0	0.3125	0.4375	0.2500	16
	Type IV	0	0	0	1	4
Type II	Type I	0.6222	0.3111	0.0444	0.0222	45
	Type II	0.2000	0.5111	0.2000	0.0889	45
	Type III	0.1333	0.1333	0.5000	0.2333	30
	Type IV	0.0690	0.0690	0.2414	0.6207	29
Type III	Type I	0.5000	0.3333	0.0833	0.0833	12
	Type II	0.3750	0.1875	0.2500	0.1875	16
	Type III	0.0333	0.3000	0.3333	0.3333	30
	Type IV	0	0.0690	0.3103	0.6207	29
Type IV	Type I	0	0.5000	0.2500	0.2500	4
	Type II	0.3750	0	0.5000	0.1250	8
	Type III	0	0	0.5556	0.4444	9
	Type IV	0	0.2000	0	0.8000	5

further reports estimates of the transition matrix based on the static spatial Markov chain model. In all the sample cities, the probability of the first type city remaining in the first type in the next phase is 0.6897, the probability of transferring to the second type is 0.2069, and the probability of transferring to the third type is 0.1034, while no city is directly transferred to the fourth type, and the sample size is 29. The probability of the second type city remaining in the existing type is 0.5263, and the probability of transferring to the first, third and fourth types is 0.2105, 0.1579 and 0.1053, respectively, with a sample size of 19. The probability of the third type city remaining in the existing type is 0.4375, and the probability of moving to the second and fourth types is 0.3125 and 0.25, respectively, with a sample size of 16. The probability of the fourth type city maintaining the existing type is 1, and the sample size is 4, indicating its extremely high stability. These data reveal the dynamic transfer characteristics of different types of cities in different states, reflecting the stability and transfer trend between different types of cities.

Given the intricacies involved in the spatiotemporal evolution of coupling coordination between green finance and high-quality economic development, it is essential to verify the applicability of the Markov model to this dynamic process. Following the approach of Ray et al. [44]., this study constructs chi-square statistics for the transition probabilities between the different states of the spatial Markov model. Subsequently, a Pearson chi-square test is performed, with the null hypothesis showing the static nature of the Markov transition matrix.

Table 10. Model Static Testing.

Model Category	Chi-Square Statistic	p-Value
Static Markov Models	2.2235	0.9874
Type I—Spatial Markov Model	4.3754	0.9803
Type II—Spatial Markov Model	4.3754	0.9804
Type III—Spatial Markov Model	1.7006	0.9954
Type IV—Spatial Markov Model	3.3219	0.9502

reports the chi-square values and the right-tail probabilities under the assumption of the null hypothesis. The findings presented in Table 10 affirm the validity of the spatial Markov model, substantiating its suitability for the analysis at hand.

5. Conclusions and Prospect

The data employed in our research span from 2010 to 2021, and we evaluated the development of green finance and high-quality economic indicators across 30 regions in China (excluding Hong Kong, Macau, and Tibet) using the entropy weight method (Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G). We analyze the spatiotemporal evolution of green finance development utilizing the Kendall coefficient method and kernel density estimation. Furthermore, we investigate the interaction and coordination between green finance and high-quality economic development using a spatial dynamic Markov model.

Firstly, the results of MK test results indicate significant regional disparities in the development of green finance in China. Regions such as Beijing, Tianjin, and Shanghai exhibit a pronounced upward trend, likely due to policy support and financial innovation. In contrast, Inner Mongolia and Chongqing show a noticeable downward trend, possibly due to insufficient green finance investment and inadequate policy implementation. Provinces like Hebei and Shanxi display stable green finance development with no significant trends, while provinces such as Fujian and Hainan exhibit localized fluctuations without an overall discernible trend.

Secondly, the significant spatial differences in the maturity of green finance across China have impacted its balanced development.

Thirdly, between 2010 and 2021, the overall coordination between green finance and high-quality economic development has improved. However, this coordination varies significantly across regions, with the highest integration in the western regions, followed by the eastern regions, and the lowest in the midland.

Fourthly, the growth of coordination between green finance and economic development is constrained by “path dependence”, indicating that advancing green finance requires substantial time and cost investments. Rapid transitions are challenging due to geographical and economic conditions.

The inter-provincial financial disparity largely stems from the economic disparity between provinces, which is an objective reality. However, this study proposes several strategies and policy recommendations to mitigate these disparities. According to our study results, the suggestions are as follows:

First, enhancing inter-provincial cooperation to leverage regional advantages is crucial. Identifying and utilizing the comparative advantages of green finance development in each province is necessary. Although the development gap between the eastern, central, and western regions has narrowed, the western regions remain at the forefront of green finance and economic development. Therefore, the central and western regions should support the eastern regions to promote high-quality economic development nationwide. When planning environmentally green demonstration projects, regional connectivity should be considered to effectively coordinate provincial participation. Promoting regional coordinated development is key, and differentiated green finance policies should be formulated based on the development characteristics and resource advantages of different regions. The eastern regions should further enhance the implementation of green finance policies to boost financial innovation; the central regions require more policy support and resource input to address their shortcomings in green finance development; and the western regions should leverage their resource advantages to continue promoting green finance, forming a virtuous cycle.

Second, to facilitate the coordinated development of green finance and high-quality economic growth, targeted government subsidies and tax incentives should be provided for green projects, especially in underdeveloped areas. Establishing green funds can attract green capital and enhance the development of green finance in the eastern, central, and western regions of China. Strengthening policy guidance and support is also necessary. The government should increase policy support for green finance, particularly in the central and western areas, through fiscal subsidies and tax incentives to encourage more enterprises and financial institutions to participate in green finance. Additionally, relevant laws and regulations should be improved to provide institutional guarantees.

Third, promoting innovation in green financial products is an essential aspect of green finance development. Financial institutions should be encouraged to develop more market-oriented green financial products, such as green bonds and green funds, to attract more social capital to green industries. Additionally, a robust green finance risk assessment and management system should be established to enhance the transparency and credibility of green finance.

In summary, by implementing these targeted policy recommendations, we can alleviate inter-provincial economic and financial disparities to some extent and promote coordinated development across regions so as to provide strong support for achieving China’s goals of green and high-quality economic development.