The dynamic coupling and spatio-temporal differentiation of green finance and industrial green transformation: Evidence from China regions

Abstract

Promoting the development of green finance and industrial green transformation is of great significance for achieving high-quality economic development in China's regions. A deep exploration of the dynamic coupling relationships and interaction mechanisms between green finance development and industrial green transformation has important theoretical value and practical implications. Based on relevant data from 2014 to 2019 for 30 provincial regions in China, this paper selects the Eastern, Central, Western, and Northeastern regions as the subjects of study. It constructs a comprehensive evaluation index system for the levels of green finance development and industrial green transformation. Since sorting out the interactive coupling theoretical mechanisms between the two, the paper employs a coupling coordination model to explore the coupling and coordinating relationships between green finance and industrial green transformation. Furthermore, using the Theil index, Moran's index, and Markov chain algorithms, the paper conducts a comparative analysis of the spatiotemporal differences and patterns in coupling coordination degrees between green finance and industrial green transformation in the four major regions, and identifies their causes. The results show that: overall, there is regional heterogeneity between green finance and industrial green transformation, and the mean coupling coordination degree is east, west, central and northeast in order from high to low. From the perspective of dynamic distribution, the coupling coordination of the four regions is moving to a high level, and it is difficult to achieve leapfrog development. As far as the sources of differences are concerned, intra-regional differences are the main cause of the differences in the coupling and coordinated development of the four regions, but the contribution rate shows a downward trend, and the gap between the four regions is gradually narrowing. To further reduce the coupling and coordination differences between green finance and industrial green transformation and development in the four regions, the region should strengthen mutual penetration and mutual radiation, increase the innovation of green financial products, improve the efficiency of green finance allocation, and provide an important reference for the realization of high-quality development of China's industrial green transformation.

1. Introduction

In recent years, China's economy has witnessed continuous growth and advancement. However, this progress has also brought to the forefront the issues of resource depletion, environmental degradation, and excessive production capacity. The conventional path of extensive development has encountered obstacles, necessitating the pursuit of green development as a crucial objective for achieving high-quality and sustainable economic growth in the new era. As the driving force behind economic development, the industrial sector plays a pivotal role in facilitating the transition towards a greener and low-carbon economy. The symbiotic relationship between green finance and industrial green transformation is becoming increasingly profound, as the development of green finance, centered around energy conservation and emission reduction, aligns with the goals of industrial green transformation. Finance and industry are the blood of economic development and a community of shared interests. Making good use of the transformation and upgrading of the financial innovation service industry is an important measure for healthy economic development. Green finance takes green capital as the carrier to invest in social and economic construction, promote the development of green technology and green industry, and realize the green transformation of economic structure. With the continuous advancement of ecological civilization construction, how to promote the high-quality coordinated development of green finance and industrial green transformation is an important direction in the process of transforming economic development mode and optimizing economic structure in the future. As a developing country with the world's second largest economy, China is paying more attention to green development in its current economic transformation. At the same time, the coupling coordination degree and dynamic distribution of green finance and industrial green transformation in different regions are studied, which is of great significance to help China form a long-term and effective coordination mechanism between green finance and industrial green transformation and improve the collaborative efficiency of green finance development and industrial green transformation. It also provides a reference for other developing countries to achieve sustainable economic development.

Based on the green development strategy, our study establishes an index system to assess the level of development in green finance and green industrial transformation. Utilizing a coupled coordination model, the study empirically examines the coupling coordination degree, spatial distribution, and temporal dynamics of green finance and green industrial transformation across 30 provinces, cities, and regions in China from 2014 to 2016. Based on these findings, the paper proposes a dynamic coupling mechanism and a pathway to enhance the development of green finance and facilitate industrial green transformation in China. These strategies are aligned with the objectives of the green low-carbon transformation strategy and serve as a foundation for the successful implementation of the 14th five-year plan (2021–2025), facilitating the early realization of high-quality green low-carbon transformation in China.

By establishing a comprehensive evaluation index system to reflect the development of green finance and industrial green transformation, we make up for the shortcomings of the existing research indexes which are relatively one-sided; based on the background of green development strategy, we take green finance as the entry point to explore the dynamic coupling mechanism of green finance and industrial green transformation, providing a new vision for the research of green finance and industrial green transformation development; meanwhile, based on empirical analysis, we examine the coupling coordination degree and spatial and temporal differentiation of green finance and industrial green transformation development, revealing the mutual relationship between green finance and industrial green transformation development. The study on the dynamic coupling mechanism and guiding strategy of green finance and industrial green transformation development can provide a new vision for the research on green finance and industrial green transformation development; meanwhile, based on the empirical analysis, the coupling coordination degree and spatial and temporal differentiation of green finance and industrial green transformation development are examined, and the mutual relationship between green finance and industrial green transformation development is revealed. The research on the dynamic coupling mechanism and guidance strategy of green finance and industrial green transformation development can provide new ideas for the formation of long-term effective coordination mechanisms of green finance-industrial green transformation development in China and improve the synergistic efficiency of green finance and industrial green transformation development.

The second part of this study briefly reviews the relevant literature on the performance evaluation and interrelationship of green finance and industrial green transformation and establishes a coupling theoretical model of green finance and industrial green transformation development, which lays a theoretical foundation for further research on the relationship between the two. The third part explains the setting of the research model, the selection of indicators, the source of data and the analysis method. The fourth part uses entropy weight method, coupling coordination model, Moran index, Markov chain and Theil index to study the dynamic coupling and spatiotemporal differentiation between them. The last part is the research conclusions and suggestions, which further explains the empirical results and puts forward the corresponding countermeasures and suggestions. The specific research flow chart is shown in Fig. 1.

Fig. 1.

Research flow chart.

2. Literature review and theoretical analysis

2.1. Literature review

2.1.1. Green finance performance evaluation

Under the guidance of the green development concept, there is a growing scholarly interest in the evaluation of green finance performance. Concerning the influence of green finance on the industrial green transformation and development, three key aspects are typically considered. That is, the emission reduction effect of green finance, the impact of green finance on technological innovation and the role of green finance on the upgrading of industrial structure.

In promoting carbon emission reduction, the study shows that based on the existing transformation financial products such as sustainable development-linked bonds and special refinancing loans for clean and efficient utilization of coal, innovative transformation funds, transformation credits, and other financial products [1]. The development of green credit helps energy conservation and emission reduction, and the amount of carbon emission reduction is positively related to the average growth rate of green credit, the larger the scale of green credit and the faster the growth rate, the more energy conservation and emission reduction [2]. Its mechanism of action is to promote industrial decarbonization by increasing the cost of debt financing for high-polluting industries and directing more capital to green industries[3,4]. In addition to directly intervening in the flow of funds to achieve carbon emission reduction, Liu Chuanzhe and Ren Yi [5] found that green credit also promotes the decarbonization of energy consumption structure through the expansion effect and technology effect to achieve carbon emission reduction [6]. proposed that green finance is the core of developing a green economy in the new era and that green financial development is conducive to improving resource utilization efficiency and reducing carbon emissions. Green finance is a bridge between the development of financial innovation and resource and environmental protection, which can maintain the balance between ecological environment and economic development, and effectively guide enterprises in the direction of energy saving and emission reduction[7,8].

For the innovation of firms, some studies have agreed that the issuance of green credit has played a positive role. A key mechanism is to ease the financial pressure by providing credit funds, forcing heavily polluting enterprises to reform and innovate, and thus upgrading the industrial structure. Financial development can contribute to economic growth in two ways: by increasing the accumulation of capital and by promoting technological innovation[[9], [10]]. Kumari [11] identify SMEs as the dominant market force in the green economy transition process [12]. suggested that industrial firms should actively invest in research and development, cultivate green technology innovation, establish green production bases, and promote the establishment of a green project database suitable for regional green projects [13]. found that green credit supports R&D and innovation activities of green firms, which has a more obvious impact on eastern China and private green enterprises [14]. believe that green finance promotes green economic growth by promoting technological progress and is an inevitable choice to promote green economic transformation. Green investment is also essential for sustainable development[15]. [16] considers green investment as a kind of “socially responsible investment”, which is a way for investment companies to make investments in “environmentally friendly”, “ethical”, “green”, “socially responsible” or “sustainable” criteria, “green”, “socially responsible” or “sustainable” standards [17]. Moreover, green finance can be combined with industrial policies to guide funds to invest in green technology innovation, energy conservation and environmental protection and other fields related to green industry, to promote green technology innovation.

Regarding the upgrading of industrial structure, the influence of green credit is twofold. Firstly, through effective information transfer, the continuous cycle of positive feedback, and the utilization of information catalytic, fund formation, and fund orientation mechanisms[18]. Finance plays a key role in the reallocation of resources and economic development[19]. [20] suggest that green finance is a way to reduce pollution and energy consumption through internal optimization and improvement of financial products and to guide companies to operate sustainably to promote the harmonious development of the economy and environmental protection. Tan et al. [21] suggest that green finance can effectively link the environment and finance, thus promoting economic development and protecting the environment [22]. found that green funds, green bonds, etc. Are important components of the innovation of green financial instruments to promote economic development by green financial instruments. Green credit guides social funds towards green projects, green enterprises, and green industries, thereby promoting industrial structure upgrading[23]. Suggest that green credit policies significantly reduce the debt financing capacity of heavily polluting entities. Furthermore, green credit not only has a substantial positive impact on overall industrial structure upgrading but also exerts a stronger promoting effect on the secondary industry compared to its inhibiting effect on the tertiary industry [5]. Additionally, Li, Penglin. et al. [24] highlights that green finance provides the necessary environmental conditions for technological research, development, and innovation in green industries. By fostering technological innovation, green finance helps optimize and upgrade industries, enabling the attainment of green development objectives.

2.1.2. Industrial green transformation development evaluation

Research on the development of industrial green transformation primarily focuses on three key areas. The first is the evaluation study of the efficiency of industrial green development, the second is the study of the influencing factors of industrial green development, and the third is the study of the impact of green finance on industrial green transformation.

In the evaluation research of industrial green development efficiency, some scholars used the DEA method to evaluate industrial green development efficiency, and [25] measured the industrial green efficiency of Qinghai, Henan, and Fujian using the super-efficient DEA analysis method [26]. used the SBM-GML index model to measure the industrial green development efficiency of the Yangtze River Economic Belt from 2011 to 2015 with a steady growth trend [27]. used the Super-SBM model with the Malmquist index to analyze the industrial green development efficiency of 11 provinces and cities in the Yangtze River Economic Belt during 2007–2016 [28]. constructed an industrial green development index system based on the connotation of green development with the coordination of resources, environment, and economy, and calculated the weights using a combination of AHP and an improved entropy weighting method with the introduction of time variables.

Research on the influencing factors of industrial green development, some scholars believe that the slow progress of green technology is an important factor limiting the improvement of industrial green development level, and the increase of investment in scientific and technological innovation and environmental protection can effectively improve the level of industrial green development [[29], [30], [31]]. There is a degree of the spatial spillover effect of digital finance on industrial green development, and it has a positive promotion effect on industrial green development in the region and neighboring regions[32]. It is necessary to continuously promote environmental supply-side reform, actively build a green industrial system, strengthen the institutional mechanism guaranteeing green development, promote the economic development mode from factor- and investment-driven to innovation-driven and build an all-round innovation system including management innovation, technological innovation, and institutional innovation[33].

The research on the impact of green finance on industrial green transformation argues that industrial green transformation is the process of industrial production to achieve sustainable development through technological research and development with less environmental consumption[34]. The research and development of new technologies require huge financial support, and green finance can precisely provide an important financial guarantee for the transformation of enterprises. Green finance supports green projects with credit preferences and low-interest rates by regulating credit supply (e.g., credit approval, loan restrictions, and risk management) and restricts funds for high pollution, high emission, and high consumption enterprises that do not undergo green transformation[35]. Academic research on green finance and industrial green transformation has focused on efficiency [36], financial support [37], and innovation promotion [38], and concluded that green finance can provide financial support for industrial enterprises, which promotes technological innovation and efficiency of industrial enterprises, which in turn promotes The development of green transformation of industrial enterprises are promoted [3,[39], [40]]. Further, due to the improvement of green finance, relevant regulators also require enterprises to disclose their environmental responsibility [41], which in turn increases the market's attention to green industries and helps attract high-tech personnel to invest in green innovation and R&D industries, providing human resources for industrial green transformation [24,42]).

2.1.3. Interrelationship between green finance and industrial green transformation

In the field of research on the interrelationship between green finance and industrial green transformation, the existing research mainly focuses on the impact of green finance on industrial green transformation, mainly including two major aspects of research on the mechanism of action and impact mechanism; while there are few research results on the impact of industrial green transformation on green finance and a small number of studies have been conducted to deal with theoretical research results and lack of empirical research results.

Regarding the mechanism [43], highlights that green finance plays a crucial role in optimizing capital allocation and facilitating green and sustainable socioeconomic development [44]. argues that, in terms of industrial green total factor productivity, China has shown a positive upward trend. The study suggests that technological progress plays a more significant role in driving total factor productivity than technical efficiency, indicating the importance of innovation and advancements in green technologies for industrial green transformation. In the study on the impact of green financial policies, Su.. and Lian [45] discovered that these policies have a restraining effect on the investment and financing behavior of heavily polluting enterprises. Through the financing penalty effect, green financial policies discourage investments that are detrimental to the environment and encourage enterprises to pursue greener practices. This, in turn, promotes the green transformation of enterprises by incentivizing them to adopt more sustainable approaches.

In the mechanism of mutual influence between green finance and industrial green transformation, Tan and Shu [39] argued that the financing business provided by traditional financial institutions will deviate from the optimal state to affect the transformation and upgrading of industry, and the innovation of traditional financial products needs to be accelerated to promote the development of green finance. When [46] conducted an empirical study by constructing PVAR, they found that there is a mutual promotion effect of green finance development, local industry greening, and economic growth, economic growth is the basis for the healthy and stable long and stable development of local green finance and green industry, and green finance and green industry influence each other, develop synergistically, and have a significant promotion effect on local economic growth.

The existing literature has provided a solid foundation for studying the development of green finance and industrial green transformation. However, there are still some limitations that need to be addressed. Firstly, the selection of indicators in many studies is narrow and lacks integration. Most studies tend to focus on measuring the level of green finance development or industrial green transformation individually, without fully considering the key factors that encompass both aspects. Secondly, there is a prevalence of parallel studies rather than cross-sectional studies. Existing research often focuses on studying green finance development and industrial green transformation separately, with limited exploration of the one-way impact of green finance on industrial green transformation. Consequently, there is a lack of cross-sectional studies and empirical analyses that examine the mutual influence, paths, and effects between the two phenomena. Thirdly, there is a scarcity of studies that specifically explore the coupling relationship between green finance and industrial green transformation in their research content. The existing literature predominantly consists of qualitative analyses at the theoretical level, and there is a notable absence of empirical studies that examine the dynamic coupling and spatio-temporal divergence between these two aspects.

Our study will build a comprehensive evaluation index system of green finance and industrial green transformation development based on the entropy weight method in the context of a green development strategy based on existing relevant studies. Data from 30 provinces and cities in China from 2014 to 2019 are selected to empirically study the dynamic coupling relationship between green finance development and industrial green transformation. The dynamic coupling mechanism and spatiotemporal divergence analysis of green finance and industrial green transformation are examined. An attempt is made to provide empirical evidence for analyzing the realization paths of green finance development and industrial green transformation.

2.2. Green finance and industrial green transformation development coupling theory

The green transformation of industry is developed through green finance, and green financial innovation relies on the leadership of the development concept of industrial green transformation to maintain a positive interaction with social demand. Both originate from social demand, and they are both development-centered and development-oriented, and achieve synergy in the process of interaction, and their coupling and coordination relationship is shown in Fig. 2.

• (1)Green finance, as a professional financial system arrangement that considers environmental factors, plays an important role in the process of industrial green transformation and upgrading. It mainly promotes the development of industrial green transformation by improving resource allocation, promoting technology upgrading and optimizing industrial structure. First, through the implementation of differentiated credit policies, green finance restricts the investment and financing activities of sunset industries with high pollution and low added value, directs capital to green industries with low carbon, environmental protection, and high capital efficiency [47], and promotes the transfer of capital from declining industries with excess capacity to emerging industries with advanced technology, thus improving the efficiency of resource allocation. We will optimize both industry and technology and accelerate industrial transformation and upgrading. Second, from the internal perspective of enterprises, because green technology has the characteristics of “reverse market logic”, firms lack incentives for green innovation[48]. Green credit and other environmental protection industrial policies can effectively reduce the financing cost of green innovation projects, improve the cost-benefit ratio of green innovation, and provide internal impetus for firms to innovate and transform. From the external perspective of firms, the development of green finance will convey information to the whole society about the policy arrangements for the development of green economy and green transformation and attract the public's attention to green products[49]. This will encourage firms to develop new green products, transform existing production lines, achieve intensive and efficient production, and improve the market competitiveness of products. Third, under the goal of “carbon neutrality”, the business model with traditional energy as the core will inevitably gradually change to the business model with clean energy as the core. The traditional “High-pollution, High-energy consumption of resources and Overcapacity” industries have fewer channels to obtain funds under the green finance policy, and the approval process of funds is longer than that of green industries, which increases the cost of holding funds. For traditional industries to obtain financial support, they must continue to upgrade their technology from the entire industrial chain, from the extensive production and operation mode based on labor and resources to the intensive production and operation mode based on technology, data, and other factors.
• (2)Industrial green transformation provides a good material basis and market demand for the development of green finance. Mainly through the implementation of the construction of the green industrial system, green technology innovation, energy saving, and emission reduction to improve the green financial system. First, green laws and regulations and the improvement of the green financial market. With the continuous innovation of green financial products and the improvement of the green financial market, firms are in the process of realizing green transformation and need targeted laws and regulations to guarantee the operation of the whole green financial system[21]. In the improvement of the green financial market, enterprises face investment and financing problems in the process of green transformation. This has prompted financial institutions to participate actively and steadily in the construction of the carbon financial market[50]. Second, the innovation of green financial products and services. With the continuous innovation of industrial green technology, more enterprises are driven to imitate and invest in the innovation of green technology. For firms of different development scales and natures, diversified green financial products and services need to be launched to meet the demand[51].

Fig. 2.

The coupling relationship between Green Finance and Industrial Green Transformation. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

3. Data and research design

3.1. Index system and data source

3.1.1. Index system

• (1)Construction of green finance index system

There have been studies on constructing a green financial development indicator system, mostly measured by a single indicator. It mainly includes two cases of measuring according to green credit indicators[4,14,52] or green investment indicators [53]; Mao, Yanjun. and Xu Wencheng [54]. In addition, according to the connotation of the development of green finance, the evaluation index system of the development level of green finance is constructed, which mainly includes green credit, green investment, carbon finance.1 [55,56]. Considering that the development level of green finance is influenced by green credit, green investment, and carbon finance, the government's policy support for green finance is conducive to the expansion of green finance demand, thus effectively promoting the development level of green finance [57], therefore, based on the study of Su, Hongting and Zhang, Wenxiu [56], the evaluation index system of green finance is enriched and improved by adding government support indicators.

Our study constructs a green finance development index system from four dimensions: green credit, green investment, carbon finance, and government support (Table 1), with four secondary sub-indicators. Among them, the percentage of interest expenditure in high energy-consuming industries positively affects green credit; the percentage of completed investment in industrial pollution control positively affects green investment; the intensity of carbon emission negatively affects the level of carbon finance development; and the support of government departments for green finance development positively affects the level of government support for green finance. The above index system is integrated, and the entropy weight method2 is used to comprehensively evaluate the development level of green finance in each province of China[58] [[,59].

• (2)Construction of industrial green transformation index system

Table 1.

Green finance and Industrial Green Transformation index system.

Category	Indicators	Second-level indicators	Definition	Target direction
Green Finance Development (GF)	Green Credit	The proportion of interest expenditure of energy-intensive industries (×1)	Interest expenses of six energy-intensive industries/Total industrial interest expenditure	+
	Green Investment	Industrial pollution control completed investment proportion (×2)	Investment in industrial pollution control will be completed/GDP	+
	Carbon Finance	Carbon intensity (×3)	Carbon dioxide emissions/GDP	–
	Government Support	The proportion of expenditure on energy conservation and environmental protection (×4)	Local fiscal expenditure on environmental protection/Fiscal expenditure	+
Green industrial transformation (GD)	Intensive use of resources	Water consumption per unit of industrial-added value (×5)	Industrial water consumption/Industrial added value	–
		Electricity consumption per unit of industrial-added value (×6)	Consumption of electric power/Industrial added vale	–
	Industrial production emission reduction	Sulfur dioxide emissions per unit of industrial-added value (×7)	Industrial sulfur dioxide emissions/Industrial added vale	–
		Wastewater discharge per unit of industrial-added value (×8)	Discharge amount of wastewater/Industrial added value	–
		Wastewater discharge per unit of industrial-added value (×9)	Carbon dioxide emissions/Industrial added value	–
	Sustainable Development	The comprehensive utilization rate of industrial solid waste (×10）	The comprehensive utilization of solid waste/Output	+

For the construction of industrial green transformation indicators, studies have used data envelopment analysis (DEA) to measure the degree of industrial green total factors to measure the degree of industrial transformation. However, the connotation of industrial green transformation includes not only the improvement of production efficiency but also the process of its growth mode from sloppy to intensive and pollution control from high carbon pollution to green emission reduction. Therefore, it is too single to measure the degree of industrial green transformation from the perspective of input efficiency. Therefore, this paper draws on the construction method of Mao, Y. J., and Xu, W. C. (2021) and selects three dimensions of resource-intensive utilization, industrial production emission reduction, and sustainable development, under which six secondary sub-indicators are set. Among them, water consumption per unit of industrial value-added and electricity consumption per unit of industrial value-added inversely affect resource-intensive utilization; sulfur dioxide emission per unit of industrial value-added, wastewater emission per unit of industrial value-added and carbon dioxide emission per unit of industrial value-added inversely affect industrial production emission reduction; comprehensive utilization rate of industrial solid waste inversely affects sustainable development level. The above index system is integrated, and the entropy method is used to synthesize the industrial green transformation. The specific meaning is shown in Table 1.

3.1.2. Data source

We consider the availability of relevant data and the promulgation of the latest environmental protection law in 2014, and the significant improvements in China's ecological environmental quality in 2019, this paper conducts empirical research using data from 2014 to 2019 for 30 provincial and municipal regions in China (excluding Tibet, Taiwan, Hong Kong, and Macau due to data availability). The data are obtained from the <China Statistical Yearbook> and the <China Environmental Statistics Yearbook>.

3.2. Spatio-temporal dynamic evolution model of coupling coordination degree

Coupling degree measures the level of mutual application and interdependence between economic systems, reflecting the extent of interaction between them. However, it has certain limitations as it cannot reflect the hierarchical level and coordinated development status between economic systems. That is, even with the same degree of coupling, there may be a difference between low-level and high-level coupling among different economic systems. On the other hand, the coordination model can not only reflect whether the systems have a good level but also show the interaction relationships between systems. It can better reflect the synergistic effects and balanced state between the two. Only when economic systems achieve benign coordinated development on the premise of sufficient matching coupling can true integrated development be achieved. Therefore, this paper further introduces a coupling coordination model based on the calculation of the coupling degree between green finance and industrial green transformation, to analyze the overall efficacy and synergistic effects of green finance and industrial green transformation.

Using the coupling coordination model can only provide a preliminary analysis of the coupling and coordination strengths and weaknesses in the development of green finance and industrial green transformation across the four major regions. For a more in-depth analysis of the dynamic evolution of their coupling and coordination, it is necessary to employ tools such as the Moran's index, Markov chain algorithms, and the Theil index. The Moran's index is an important indicator reflecting the global spatial correlation of related variables. The global Moran's index takes the whole as the entry point and cannot reveal the spatial correlation features within each region of the whole, thus necessitating the introduction of local Moran's indices for analyzing the spatial correlation features of coupling coordination in each province (region).

Spatial Markov chains introduce the concept of spatial lag because of traditional Markov chains, making up for the traditional Markov chain's neglect of the spatial correlation of economic phenomena. In this paper, spatial Markov chains are introduced to explore the state transition characteristics of individual coupling coordination under spatial interactive effects. This section further discusses the inter-regional differences in the development level of green finance and industrial green transformation. Existing research mainly uses Gini coefficients, Theil indices, and polarization indices to estimate regional differences. However, the Theil index can decompose regional differences into interval differences and intra-regional differences, making it more conducive to identifying the internal structure of inter-regional gaps and their underlying reasons. Therefore, this paper adopts the Theil index to measure the regional differences in the integration levels of green finance and industrial green transformation.

Overall, we use the coupling coordination degree model, Moran's index, Markov chain algorithm, and Thiel index. We compare and analyze the spatio-temporal differences in the coupling coordination degree between green finance and industrial green transformation development in four major regions of China and point out their causes.

3.2.1. Coupling coordination degree measure model

After the entropy weight method is used to calculate the development level of green finance and green industrial transformation, the interaction degree of these two systems can be calculated, namely the coupling coordination degree. We set up the coupling coordination model as follows:

• (1)Calculate the degree of coupling.

$$C=2\times {\left[\frac{GF\times GD}{{\left(GF+GD\right)}^2}\right]}^{\frac{1}{2}}$$ (1)

As shown in formula (1), C is the degree of coupling between 0 and 1. The closer the index is to 1, the better the coupling state between the two major systems.

• (2)Calculate the coupling coordination degree.

$$\{\begin{array}{c}T=\alpha \times GF+\beta \times GD\\ D=\sqrt{CT}\end{array}$$ (2)

D is the coupling coordination degree, which ranges from 0 to 1. The higher the coupling coordination degree index, the higher the coupling coordination degree of the two systems. As shown in formula (2), $\alpha$ and β are undetermined coefficients, indicating the importance of green finance and green industrial transformation. This paper considers green finance and green industrial transformation equally important, i.e., both α and β are 0.5. In addition, referring to the classification of coupling coordination degree by Ref. [60]; we divide the coupling coordination degree of the two systems into ten levels. The specific classification and corresponding coupling coordination degree are shown in the following table:

3.2.2. Dynamic distribution of coupling coordination degree based on Moran index

The calculation of the global Moran's index is as shown in formula (3). In it, x_i(x_j)represents the variable value for the i(j) province (region, city), which in this context refers to the coupling coordination degree; $\bar{x}$ is the mean value of the variable, i.e., the mean value of the coupling coordination degree; w_ij is the spatial weight matrix. In this paper, a binary adjacency matrix is chosen; if provinces (regions) are adjacent, the weight value is set to 1, otherwise, it is 0. The range of the global Moran's index is [−1,1]. A positive or negative value indicates that the coupling coordination degree of the object of study is positively or negatively correlated. The closer the value is to −1, the stronger the spatial negative correlation; the closer it is to 1, the more obvious the spatial positive correlation. A global Moran's index value of 0 indicates a random spatial distribution, meaning there is no spatial correlation.

$$Mora{n}^{\prime }I=\frac{1}{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}}•\frac{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\left(x_i-\bar{x}\right)\left(x_j-\bar{x}\right)}{\frac{1}{n}\sum _{i=1}^n{\left(x_i-\bar{x}\right)}^2}$$ (3)

The significance test formula of spatial autocorrelation is as follows:

$$Z\left(I\right)=\frac{I-E\left(I\right)}{\sqrt{VAR\left(I\right)}}$$ (4)

where, formula (4) Z(I) is the significance level of spatial autocorrelation, E(I) is the expectation of the Moran's index and VAR(I) is the variance.

The calculation of the Local Moran's index is shown in formula (5). In it, LMI stands for the local Moran's index, and the meanings of x_i, x_j, and w_ij are the same as mentioned earlier. LMI>0 indicates that the spatial units are clustered as high-high or low-low; LMI<0 indicates that the spatial units are clustered as low-high or high-low.

$$LMI=\frac{\left(x_i-\bar{x}\right)}{\frac{1}{n}\sum _{i=1}^n{\left(x_i-\bar{x}\right)}^2}•\sum _{j=1}^n{w}_{ij}\left(x_i-\bar{x}\right)$$ (5)

3.2.3. Dynamic prediction of coupling coordination degree based on Markov chain

Markov chain is a method to study the law of state change of stochastic processes. At each probability shift the distribution change will only be related to the previous distribution state and will not be affected by the earlier data. Let (Wn,n ≥ 0) have n states of the stochastic process, and the n states at moment t are set as G1, G2, …, Gn. The states located at moment t are only related to the states Gi and G_j and not to n. The matrix P= (P_ij) is said to be the transfer matrix. Each element of the matrix needs to satisfy: P_ij≥0, and the elements of each row of the matrix sum to 1.

$$\mathrm{P}={\mathrm{P}}_{\text{ij}}=\begin{array}{c}{\mathrm{P}}_{11}{\mathrm{P}}_{12}\dots {\mathrm{P}}_{1\mathrm{j}}\\ {\mathrm{P}}_{21}{\mathrm{P}}_{22}\dots {\mathrm{P}}_{2\mathrm{j}}\\ \begin{array}{c}\dots \dots \dots \dots \\ {\mathrm{P}}_{\mathrm{i}1}{\mathrm{P}}_{\mathrm{i}2}\dots {\mathrm{P}}_{\text{ij}}\end{array}\end{array}$$

Let the conditional probability be the k-step transfer probability of the Markov chain, the probability of transferring from state Gi to state Gj after k steps. If the probability of transferring the system from state Gi to state Gj is represented by the transfer matrix Pij then the recurrence according to the K–C equation yields: G(n) = G(n-1) × P = G(0) × ${\mathrm{P}}^{\mathrm{n}}$.Where the main diagonal element is the probability of retaining the original state and the non-diagonal row element is the probability of transitioning from a development state to another state. where G(n) is the state vector of the predictor variable at time n and P is the one-step transfer probability matrix. Let D(n) be the coupling coordination state vector at moment n. The weight of the state vector is {De(n), Dd(n), Dc(n), Db(n), Da(n)}, and the one-step transfer probability matrix of the coupling coordination development state from moment n to moment n+1 is P(n). Where the main diagonal element is the probability of retaining the original state and the non-diagonal row element is the probability of transitioning from a development state to another state.

$$\mathrm{D}\left(\mathrm{n}\right)=\left\{\text{De}\left(\mathrm{n}\right),\text{Dd}\left(\mathrm{n}\right),\text{Dc}\left(\mathrm{n}\right),\text{Db}\left(\mathrm{n}\right),\text{Da}\left(\mathrm{n}\right)\right\}$$

$$\mathrm{P}\left(\mathrm{n}\right)=\left(\begin{array}{ccccc}{\mathrm{p}}_{\mathrm{e}\to \mathrm{e}}\left(n\right)& {\mathrm{p}}_{\mathrm{e}\to \mathrm{d}}\left(n\right)& {\mathrm{p}}_{\mathrm{e}\to \mathrm{c}}\left(n\right)& {\mathrm{p}}_{\mathrm{e}\to \mathrm{b}}\left(n\right)& {\mathrm{p}}_{\mathrm{e}\to \mathrm{a}}\left(n\right)\\ {\mathrm{p}}_{\mathrm{d}\to \mathrm{e}}\left(n\right)& {\mathrm{p}}_{\mathrm{d}\to \mathrm{d}}\left(n\right)& {\mathrm{p}}_{\mathrm{d}\to \mathrm{c}}\left(n\right)& {\mathrm{p}}_{\mathrm{d}\to \mathrm{b}}\left(n\right)& {\mathrm{p}}_{\mathrm{d}\to \mathrm{a}}\left(n\right)\\ {\mathrm{p}}_{\mathrm{c}\to \mathrm{e}}\left(n\right)& {\mathrm{p}}_{\mathrm{c}\to \mathrm{d}}\left(n\right)& {\mathrm{p}}_{\mathrm{c}\to \mathrm{c}}\left(n\right)& {\mathrm{p}}_{\mathrm{c}\to \mathrm{b}}\left(n\right)& {\mathrm{p}}_{\mathrm{c}\to \mathrm{a}}\left(n\right)\\ {\mathrm{p}}_{\mathrm{b}\to \mathrm{e}}\left(n\right)& {\mathrm{p}}_{\mathrm{b}\to \mathrm{d}}\left(n\right)& {\mathrm{p}}_{\mathrm{b}\to \mathrm{c}}\left(n\right)& {\mathrm{p}}_{\mathrm{b}\to \mathrm{b}}\left(n\right)& {\mathrm{p}}_{\mathrm{b}\to \mathrm{a}}\left(n\right)\\ {\mathrm{p}}_{\mathrm{a}\to \mathrm{e}}\left(n\right)& {\mathrm{p}}_{\mathrm{a}\to \mathrm{d}}\left(n\right)& {\mathrm{p}}_{\mathrm{a}\to \mathrm{c}}\left(n\right)& {\mathrm{p}}_{\mathrm{a}\to \mathrm{b}}\left(n\right)& {\mathrm{p}}_{\mathrm{a}\to \mathrm{a}}\left(n\right)\end{array}\right)$$

3.2.4. Regional spatial difference analysis model based on Theil index

We adopts Thiel Index to measure the regional difference in the integration degree of green finance and industrial green transformation. The formula for calculating Theil index is as follows:

$${\mathrm{T}}_{\mathrm{b}}=\sum _{\mathrm{j}=1}\left(\frac{{\mathrm{D}}_{\mathrm{j}}}{\mathrm{D}}\right)\ln \left(\frac{{\mathrm{D}}_{\mathrm{j}}/\mathrm{D}}{{\mathrm{N}}_{\mathrm{J}}/\mathrm{N}}\right)$$

$${\mathrm{T}}_{\text{wj}}=\sum _{\mathrm{i}}\left(\frac{{\mathrm{D}}_{\text{ij}}}{\mathrm{D}}\right)\ln \left(\frac{{\mathrm{D}}_{\text{ij}}/\mathrm{D}}{1/{\mathrm{N}}_{\mathrm{j}}}\right)$$

$${\mathrm{T}}_{\mathrm{w}}=\sum _{\mathrm{j}}\left(\frac{{\mathrm{D}}_{\mathrm{j}}}{\mathrm{D}}\right){\mathrm{T}}_{\text{wj}}=\sum _{\mathrm{j}}\sum _{\mathrm{i}}\left(\frac{{\mathrm{D}}_{\mathrm{j}}}{\mathrm{D}}\right)\left(\frac{{\mathrm{D}}_{\text{ij}}}{\mathrm{D}}\right)\ln \left(\frac{{\mathrm{D}}_{\text{ij}}/\mathrm{D}}{1/{\mathrm{N}}_{\mathrm{j}}}\right)$$

$$\text{Theil}={\mathrm{T}}_{\mathrm{b}}{+\mathrm{T}}_{\mathrm{w}}$$

where, i = 1, 2, …, 9, 10, 11, 30, denote 30 provinces respectively; j = 1, 2, 3, 4, denote the east, central, west and northeast regions respectively; D is the sum of the coupling coordination degree of each province; D_j is the sum of the coupling coordination degree of each region; D_ij is the coupling coordination degree of each province; N is the sum of the number of provinces; N_j is the number of provinces in the j region. T_b is the Thayer index of inter-regional coupling coordination; T_wj is the Thayer index of coupling coordination of each basic unit within the region, T_w1 for the east, T_w2 for the center, T_w3 for the west, and T_w4 for the northeast; T_w is the Thayer index of intra-regional coupling coordination; Theil is the overall Thayer index, and the overall difference is equal to the inter-regional difference plus the intra-regional difference.

4. Empirical research and analysis

According to the index system, entropy weight method, and coupling coordination model we constructed, the development level of green finance and industrial green transformation and the coupling coordination degree of both 30 provinces and cities in China were measured using the data of each province and city from 2014 to 2019. The coupling coordination degree model can only provide a preliminary analysis of the good and weak coupling coordination of the development level of green finance and industrial green transformation in the four major regions. For an in-depth analysis of the dynamic evolution of the coupling and coordination between green finance and industrial green transformation, the results of the transfer probability matrix derived from the Thiel index, Moran index, and Markov chain stochastic process are needed. Therefore, we conducted further analyzed the spatial-temporal differentiation and patterns of the coupling coordination based on the Moran's index, Markov chain, and Thiel index.

4.1. Evaluation and analysis of green finance development and industrial green transformation development

4.1.1. Development level of green finance

Fig. 3 compares the mean value of green finance in China and its eastern, northeastern, central, and western regions. The development of green finance in China is on the rise. After the rapid growth in 2015, the relative decline in 2016 started to pick up gradually in 2017. The main reason for this change is that the Chinese government has promulgated several policies on green finance, investment, and industries since 2017. It provides a stable development environment and a reliable policy basis for the overall development of green finance in China.

Fig. 3.

Green finance overall and four regional development. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

From a regional perspective, the western region leads in the development level of green finance and has shown rapid growth within the research period. The central region displays a steadily rising trend, while the eastern region falls below the national average. The development in the Northeast fluctuates the most, highlighting the regional imbalance in China's green financial development. The reasons for this include the western region effectively utilizing national rural revitalization strategies and western development strategies, actively promoting the development of new energy and green finance, and gradually improving its overall image of an underdeveloped financial system. The central region started its economic development later but has grown rapidly. Its green financial efficiency shows a relatively stable trend with lower extensionality and polarization. In the eastern region, green financial development and industrial green transformation mutually promote each other, but there is a time lag between their interactions. Since the revitalization strategy was implemented in the Northeast in 2003, national development policies, support funds, and key industrial projects have accelerated the overall development speed. However, there are still challenges in structural transformation and sustainable development in manufacturing, which limit the development of green finance in the region.

Additionally, based on the green finance development scores of various provinces, the Beijing-Tianjin-Hebei region performs well in the east, while Shanghai and Guangdong lag behind. In the central region, the development levels of the provinces are close, but Hubei exhibits noticeable lag. In the western region, due to national policy support for green credit in Xinjiang, its green financial development takes the lead, while Ningxia and Inner Mongolia perform poorly, and the rest of the provinces have similar development levels.

4.1.2. Development level of industrial green transformation

As shown in Fig. 4, there is a positive trend in industrial green transformation across various regions in China within the research period. In 2014, China's Ministry of Industry and Information Technology proposed the “Green Development Special Initiative,” outlining specific implementation plans for industrial green transformation. As a result, the national average was slightly affected in the following year. Starting from 2016, China overall showed a steadily rising trend in industrial green transformation, and the state of green industrial transformation is good, although significant regional disparities remain. The eastern region's industrial green transformation is generally higher than the national average, further confirming that since 2011, the eastern region has actively promoted industrial transfer, capitalizing on the geographical resource advantages of the central and western regions, thereby systematically transferring the achievements of industrial transformation from the eastern region. Both the western and central regions show a fluctuating upward trend. The Northeast has the lowest national average in industrial green transformation. It has significant coal, iron, and petroleum resources and holds an important position in China. In 2018, China's Ministry of Natural Resources issued green mining construction standards for nine major industries including chemicals, coal, iron, and mining. The policy had some impact on the Northeast's overall average the following year, but due to sufficient corresponding policies and support, it saw a significant improvement in 2019, showing a good development trend.

Fig. 4.

Green industrial transformation as a whole and four regional developments. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Simultaneously, the development level of China's industrial green transformation shows a significant regional imbalance, represented as Eastern > Central-Western > Northeastern, further reflecting the regional imbalance in China's industrial green transformation. Looking at each province, in the eastern region, the overall development level is high, with Tianjin, Beijing, and Guangdong performing well, while Shanghai, Jiangsu, and Hainan lag. In the central region, Shanxi and Henan perform well, while Jiangxi lags. In the western region, Shaanxi leads significantly in industrial green transformation, while the other provinces are similar and lower in level. In the Northeast, Liaoning performs better in industrial green transformation, while the other two provinces are similar and lower in level.

4.2. Green finance and industrial green transformation coupling coordination degree

We have calculated the development level of green finance and the index of green industrial transformation. Next, the coupling coordination degree of green finance and green industrial transformation in China's provinces and cities from 2014 to 2019 is analyzed. The results are shown in Table 3 and Fig. 5.

Table 3.

Green finance and Green industrial transformation coupling coordination degree.

	2014	2015	2016	2017	2018	2019	Mean
Beijing	0.88	0.81	0.80	0.70	0.63	0.66	0.75
Tianjin	0.59	0.72	0.60	0.64	0.74	0.88	0.70
Hebei	0.82	0.86	0.72	0.73	0.87	0.84	0.81
Shanghai	0.36	0.39	0.46	0.64	0.58	0.62	0.51
Jiangsu	0.54	0.51	0.52	0.55	0.65	0.64	0.57
Zhejiang	0.51	0.70	0.54	0.67	0.67	0.69	0.63
Fujian	0.42	0.48	0.50	0.52	0.67	0.74	0.56
Shandong	0.58	0.73	0.69	0.78	0.76	0.79	0.72
Guangzhou	0.70	0.66	0.49	0.63	0.66	0.72	0.64
Hainan	0.36	0.40	0.45	0.47	0.64	0.68	0.50
Eastern mean	0.58	0.63	0.58	0.63	0.69	0.73	0.64
Liaoning	0.55	0.70	0.56	0.62	0.63	0.67	0.62
Jilin	0.63	0.53	0.47	0.48	0.51	0.73	0.56
Heilongjiang	0.51	0.54	0.46	0.47	0.51	0.47	0.49
Northeast mean	0.56	0.59	0.50	0.53	0.55	0.63	0.56
Shanxi	0.69	0.72	0.64	0.79	0.76	0.74	0.72
Anhui	0.36	0.39	0.44	0.59	0.61	0.70	0.52
Jiangxi	0.39	0.44	0.45	0.48	0.56	0.64	0.49
Henan	0.55	0.71	0.69	0.73	0.74	0.74	0.69
Hubei	0.40	0.42	0.43	0.50	0.56	0.65	0.49
Hunan	0.51	0.50	0.50	0.53	0.57	0.61	0.54
Median mean	0.48	0.53	0.53	0.60	0.63	0.68	0.58
Inner Mongolia	0.84	0.85	0.76	0.80	0.76	0.66	0.78
Guangxi	0.39	0.47	0.43	0.50	0.53	0.58	0.48
Chongqing	0.53	0.54	0.50	0.64	0.67	0.72	0.60
Sichuan	0.51	0.40	0.38	0.60	0.65	0.69	0.54
Guizhou	0.39	0.43	0.47	0.48	0.54	0.53	0.47
Yunnan	0.41	0.52	0.49	0.47	0.70	0.76	0.56
Shaanxi	0.71	0.74	0.67	0.66	0.68	0.79	0.71
Gansu	0.51	0.46	0.50	0.52	0.73	0.66	0.56
Qinghai	0.82	0.54	0.83	0.71	0.75	0.74	0.73
Ningxia	0.54	0.51	0.80	0.77	0.76	0.70	0.68
Xinjiang	0.60	0.67	0.60	0.68	0.72	0.55	0.64
Western mean	0.57	0.56	0.58	0.62	0.68	0.67	0.61
National mean	0.55	0.58	0.56	0.61	0.66	0.69	0.61

Fig. 5.

The overall development of the coupling coordination degree of the two systems and the four regions.

According to the hierarchical division of coupling coordination degree set above, China's average coupling level was slightly disordered from 2014 to 2019. The coupling degree between the eastern and western regions presents a primary coordination state, while the central and northeast regions need to be more coordinated. The eastern region is superior to the western and central regions, and the northeast region has the lowest significant imbalance. At the same time, the coordination of green finance development and green industrial transformation in various regions of our country is rising within the study range. From the perspective of provinces in the eastern region, Hebei has achieved a reasonable degree of coordination. However, Guangxi, Guizhou, Hubei, Jiangxi, and Heilongjiang are on the verge of dissonance and need to promote their coordinated development further. In the central region, the coordination degree of Jiangxi and Hubei could be better, and the coordination gap with other provinces is large. The western provinces generally show a state of reluctancy coordination, among which Guangxi and Guizhou are on the verge of disharmony. This means that a suitable coupling mechanism between green finance development and green industrial transformation has been initially formed in various regions of China. However, the degree of coordination is generally low. To a certain extent, there is still a large room for improvement in the collaborative efficiency of green finance development and industrial green transformation system.

4.3. Further analysis

4.3.1. Dynamic evolution analysis of coupling coordination degree based on Moran's index

We further examine whether spatial clustering characteristics have statistical significance using both Global Moran's index and Local Moran's index. The result of the Global Moran's index calculation is shown in Table 4. The Moran indexes in the study period all passed the significance test, indicating that the coupling coordination degree has a significant spatial correlation. The Moran indexes are all positive, indicating that the specific performance is a spatially positive correlation characteristic. That is provinces (regions and cities) with high coupling coordination degree cluster with each other, and provinces (regions and cities) with low coupling coordination degree cluster with each other. From the changing trend, the Global Moran's index shows a fluctuating upward trend in general. 2014–2015 shows a small increase; 2015–2017 is in a low oscillation, and the Global Moran's index is 0.252 in 2017, reaching the lowest point; 2017–2018 shows an obvious upward trend and a significant increase in the rise, and the Global Moran's index reaches the maximum value of 0.338 in 2018. It indicates that during this period The spatial clustering trend of the coupling and coordination degree of green finance and industrial green transformation is further enhanced.

Table 4.

Global Moran's index of coupling coordination degree of green finance-industrial green transformation.

Variables	I	E(I)	sd(I)	z	p-value*
y_2014	0.256	−0.034	0.123	2.327	0.010
y_2015	0.315	−0.034	0.125	2.798	0.003
y_2016	0.300	−0.034	0.124	2.700	0.003
y_2017	0.252	−0.034	0.125	2.321	0.010
y_2018	0.338	−0.034	0.123	3.022	0.001
y_2019	0.273	−0.034	0.121	0.888	0.020

To further explore the spatial correlation characteristics of the coupling and coordination degree among various provinces (regions), we selected data from the years 2014, 2016, 2018, and 2019. The results are compiled into Table 5 based on the Local Moran's Index scatter plots.

Table 5.

Local Moran's index of coupling coordination degree of green finance-industrial green transformation.

Year	High-High clustering	Low-High clustering	Low-Low clustering	High-Low clustering
2014	Beijing-Tianjin-Hebei, Shanxi, Inner Mongolia, Jilin, etc	Liaoning, Heilongjiang	Shanghai, Jiangsu, Zhejiang, Anhui, etc	Shandong, Guangdong, Gansu, Tibet
2016	Beijing-Tianjin-Hebei, Shanxi, Inner Mongolia, Liaoning	Jilin, Heilongjiang	Shanghai, Jiangsu, Zhejiang, Anhui, etc	Gansu
2018	Beijing-Tianjin-Hebei, Shanxi, Inner Mongolia, Shandong	Liaoning, Sichuan	Shanghai, Jiangsu, Jilin, Heilongjiang, etc	Zhejiang, Fujian, Guangdong, Chongqing, etc
2019	Beijing-Tianjin-Hebei, Shanxi, Anhui, Shandong, etc	Inner Mongolia, Jiangsu	Shanghai, Jiangxi, Hunan, Guangxi, etc	Jilin, Zhejiang, Fujian, Guangdong, etc

As Table 5 shows, except for 2019, the provinces (regions) in the high-high clustering zone include Beijing-Tianjin-Hebei, Shanxi, and Inner Mongolia. This confirms the closely linked integration of Shanxi and Inner Mongolia into the Beijing-Tianjin-Hebei region through efficient financial services. These provinces actively use financial services to promote industrial layout restructuring, structural transformation, and factor and market integration. This facilitates orderly capital flow, promotes the free flow and optimal allocation of financial resources within the region, and has a significant positive impact on regional economic development. It also easily influences neighboring provinces (regions) through demonstration effects. The low-low clustering areas in 2014 and 2016 include Shanghai, Jiangsu, Zhejiang, and Anhui, all located in the Yangtze River Delta. This is related to the development phase and industrial structure of the region at the time. One of the significant shortcomings facing the high-quality development of the Yangtze River Delta is green development. This is due to both spatial and resource endowments, as well as issues in the ecological governance system, economic structural development efficiency, and the business environment. With the government's deepening implementation of green development measures and the acceleration of the Yangtze River Delta's ecological and green integration, industrial economics may be impacted in the short term, hindering the improvement of the coupling and coordination level. Due to differences in industrial resource endowments, financial supply, or technological applications, its positive spatial spillover effect is also relatively low. In 2018, Jilin and Heilongjiang moved from the low-high clustering area to the low-low clustering area, possibly due to a sudden drop in their industrial water resource efficiency. In 2019, there were significant changes in the provinces (regions) in the low-low clustering area; Jiangsu moved to the low-high clustering area, while Jilin and Heilongjiang moved to the high-low clustering area. The low-high and high-low clustering areas include fewer provinces (regions) and are more volatile. The low-high clustering area has two provinces (regions) every year, and only Gansu has always been in the high-low clustering area. The coordination development level of Jiangsu's surrounding areas (Shanghai, Zhejiang, Anhui) has increased rapidly, leading it to move from the low-low clustering area to the low-high clustering area. In contrast to Shanghai, which has always been in the low-low clustering area, Jiangsu's entry into the low-high clustering area indicates that it is positively influenced by the surrounding provinces (regions). Gansu has a high level of coordinated development, but due to its similarities in industrial structure and endowment characteristics with neighboring low-coordination provinces (regions), there is some homogenized competition. Therefore, it should strengthen interactions and contacts with neighboring provinces (regions) for staggered development, enhance the positive driving effect, and promote overall regional coordinated development. In summary, the number of provinces (regions) in the low-low clustering area is slightly higher than that in the high-high clustering area during the research period. However, the scope of the low-low clustering area is constantly changing, while the high-high clustering area always includes Beijing-Tianjin-Hebei and Shanxi. This reveals that the level of coupling and coordinated development of green finance and industrial green transformation presents a “the strong get stronger” spatial clustering feature.

4.3.2. Analysis of state transition characteristics of coupling coordination degree based on Markov chain

The type of classification criterion shown previously (Table 2) divides the coupling coordination into four adjacent but non-overlapping completion intervals, namely, mild dissonance (0–0.3), low-level coordination (0.3–0.45), intermediate-level coordination (0.45–0.6), and high-level coordination (0.6–0.1). These four state types are represented by k = 1,2,3,4, respectively, and a larger k represents a higher level of coupling coordination. The resulting spatial Markov state transfer probability matrix is calculated as shown in Table 6.

Table 2.

Green finance-industrial green transformation coupling coordination level.

Coupling coordination degree	level	Coupling coordination degree	level
0.0–0.1	Extreme disorder	0.5–0.6	Forced coordination
0.1–0.2	Serious imbalance	0.6–0.7	Primary coordination
0.2–0.3	Moderate disorder	0.7–0.8	Intermediate coordination
0.3–0.4	Mild disorder	0.8–0.9	Good coordination
0.4–0.5	Near disorder	0.9–1.0	Quality coordination

Table 6.

Coupling coordination state transition probability matrix of green finance-industrial green transition.

Tradition	Spatial lag	Local status	frequency	Ⅰ	Ⅱ	Ⅲ	Ⅳ
	150	<25 %	25 %–50 %	50 %–75 %	>75 %
	No-lag	Ⅰ	44	0.614	0.273	0.114	0.000
		Ⅱ	41	0.195	0.293	0.366	0.146
Ⅲ		32	0.031	0.156	0.438	0.375
Ⅳ		33	0.000	0.091	0.242	0.667
Spatial	Ⅰ	Ⅰ	16	0.688	0.188	0.125	0.000
		Ⅱ	6	0.500	0.167	0.333	0.000
		Ⅲ	5	0.200	0.400	0.400	0.000
		Ⅳ	1	0.000	0.000	1.000	0.000
	Ⅱ	Ⅰ	22	0.636	0.273	0.091	0.000
		Ⅱ	13	0.077	0.538	0.231	0.154
		Ⅲ	12	0.000	0.083	0.333	0.583
		Ⅳ	6	0.000	0.167	0.333	0.500
	Ⅲ	Ⅰ	6	0.333	0.500	0.167	0.000
		Ⅱ	18	0.222	0.167	0.444	0.167
		Ⅲ	11	0.000	0.091	0.636	0.273
		Ⅳ	13	0.000	0.000	0.154	0.846
	Ⅳ	Ⅰ	0	0.000	0.000	0.000	0.000
		Ⅱ	4	0.000	0.250	0.500	0.250
		Ⅲ	4	0.000	0.250	0.250	0.500
		Ⅳ	13	0.000	0.154	0.231	0.615

When spatial factors are not considered, the dynamic evolution of the coupled green finance-industrial green transformation coordination has the following characteristics: (1) the overall leap of coupling coordination to a high level. Provinces (regions and cities) with mild dissonance have 0.273 probability of leaping to primary coordination; provinces (regions and cities) with primary coordination have 0.366 probability of leaping to intermediate coordination, while the probability of leaping downward is 0.195, which is significantly smaller than the probability of leaping upward; provinces (regions and cities) with intermediate coordination have 0.375 probability of leaping to high-level coordination, while the probability of leaping downward is almost zero. It reflects that the dynamic evolution process of green finance-industrial green transformation coupling coordination shows an overall trend toward better; (2) state leap only occurs in adjacent coupling coordination types, and it is difficult to achieve leapfrogging. The probability of leapfrogging from the mildly dysfunctional state across primary coordination to the intermediate coordination state is generally very low. Similarly, the probability of leapfrogging from primary coordination to advanced coordination in primary coordination provinces (regions and cities) is also very low. This indicates that the coordinated development of green finance and industrial green transformation is a gradual process; (3) there is a very high “state locking” effect in the evolution of coupled coordination. The diagonal element is larger than the non-diagonal element, and the minimum value of the diagonal element is 0.293, which indicates that the probability that the coupling and coordination between green finance and industrial green transformation in a province (region or city) will remain in the original state in the next year is at least 29.3 %. Breaking the low coordination track is an important way to improve the overall green finance and industrial green transformation coupling coordination development level.

The Markov chain with the concept of spatial lag is introduced to see the club convergence phenomenon of the coupling coordination degree of green finance and industrial green transformation: (1) the diagonal elements in the spatial Markov state transfer probability matrix are larger than the non-diagonal elements and the coupling coordination state has a higher probability of “locking” at the primary and advanced coordination levels, indicating that the dynamic evolution 4 convergence clubs exist in the process, and the coupled coordination level is more likely to converge to the primary and advanced coordination types; (2) spatial factors can have an impact on the state jump. $P_{21|1}=0.500>P_{21}=0.195$, indicates that the probability of a province (region or city) in primary coordination leaping downward is significantly higher when it is adjacent to a province (region or city) that is mildly dysregulated; $P_{23|3}=0.444>P_{23}=0.366$, indicates the increased probability of a primary coordinated province (district or city) leaping upward when adjacent to a secondary coordinated province (district or city); $P_{34|4}=0.500>P_{34}=0.375$, indicates that the probability of a province (region or city) with intermediate coordination leaping to an advanced coordination state increases when it is adjacent to a province (region or city) with advanced coordination. There is a spatial spillover effect in the process of coupled coordination state transfer, and provinces (regions and cities) with high coordination will drive the level of neighboring provinces (regions and cities) to improve so that the probability of their coupled coordination state jumping upward increases, which has a positive spillover effect; provinces (regions and cities) with low coordination will drag neighboring provinces (regions and cities) so that the probability of their state jumping downward increases, which has a negative spillover effect. Finally, the “club convergence” phenomenon is that high and low coordination provinces (regions and cities) are clustered, and the coupling coordination degree tends to be different in general but converges locally.

4.3.3. Regional spatial difference analysis based on Theil index

We measure the overall differences in coupling coordination between green finance and industrial green transformation in each province, the inter-group differences in coupling coordination among the four regions, and the intra-group differences in coupling coordination among the provinces within the regions by calculating the Theil index for the whole country and the four regions. It also screened whether the overall differences in coupling coordination between green finance and industrial green transformation originated from intra-regional differences or inter-regional differences.

As shown in Table 7 and Fig. 6, the Thiel index ranges between 0.27 and 0.40 for each province in the country, revealing slight yet widening regional disparities. A comparison of between-group and within-group differences indicates that the within-group differences are consistently larger than the between-group differences. This suggests that the variations within regions are more significant compared to the disparities among the four major regions. The provinces within the region exhibit substantial disparities in economic and social development, as well as green technology investment. The presence of the “Matthew effect” cannot be discounted, resulting in a scenario where the stronger provinces become even stronger, while the weaker provinces continue to lag. Consequently, future efforts should prioritize the coordination of intra-regional differences.

Table 7.

Theil index results of coupling coordination between green finance and industrial green transformation.

Year	Theil	Inter-regional differences		Intraregional differences
		Tb	Contribution rate	Tw	Contribution rate
2014	0.396316	0.091335	23.05 %	0.304981	76.95 %
2015	0.404104	0.108749	26.91 %	0.295356	73.09 %
2016	0.337829	0.078246	23.16 %	0.259582	76.84 %
2017	0.315038	0.082932	26.32 %	0.232106	73.68 %
2018	0.277036	0.078183	28.22 %	0.198853	71.78 %
2019	0.289987	0.095598	32.97 %	0.194390	67.03 %

Fig. 6.

Theil index results of coupling coordination between green finance and industrial green transformation. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Analyzing the contribution rates reveals that the contribution rate of intra-regional differences decreases from 76.95 % to 67.03 %, while the contribution rate of inter-regional differences increases from 23.05 % to 32.97 %. The quantitative disparity and opposite trend between the two indicate a growing impact of inter-regional differences on overall disparities.

As shown in Table 8 and Fig. 7, comparing the intra-group Theil index, it can be seen that T_w1 is significantly higher in the eastern region than in the northeast, central and western regions, indicating that the internal differences in the eastern region are significant, and there is a small landing but an expanding trend during the period; while the internal differences in the northeast, central and western regions are relatively small and decreasing, and the mean values of the coupling coordination Theil index in the western, central and northeastern regions during 2014–2019 are 0.1317, 0.1753, and 0.0258.

Table 8.

Theil index variation trend of coupling coordination in each region.

	2014	2015	2016	2017	2018	2019	Mean
Northeast region	0.014647	0.029279	0.021771	0.028229	0.023073	0.037735	0.025789
East region	0.585605	0.532825	0.510639	0.416639	0.379699	0.383435	0.468140
Central region	0.211028	0.204828	0.162634	0.192302	0.154951	0.126324	0.175344
West region	0.168779	0.176102	0.137090	0.128811	0.093940	0.085652	0.131729

Fig. 7.

Theil index change trend chart of coupling coordination in each region from 2014 to 2019.

Based on the Theil index scores for the regions, the Beijing-Tianjin-Hebei region leads in the eastern area's first echelon, due to its clear focus on collaborative green financial development. This collaboration aims to assist in synchronized carbon reduction, pollution control, green expansion, and growth to achieve high-quality development. Zhejiang, Guangdong, and Shandong form the second echelon in the eastern area as they are national-level green financial reform and innovation pilot zones. These provinces are actively promoting industrial transformation and upgrades to meet green development needs. The remaining provinces have also started to place high importance on regional green and low-carbon transformation and high-quality development, making up the third echelon in the eastern region. This shows that there is a considerable difference in the level of coordinated development between green finance and industrial transformation among the provinces in the eastern region. In contrast, the differences in system coordination for environmental protection, ecological restoration, and green development among provinces in the Northeast, Central, and Western regions are relatively small. There is some degree of clustering effect and developmental consistency within these regions.

5. Conclusions and recommendations

5.1. Conclusions

Based on the relevant data from 30 provincial-level regions in China for the years 2014–2019, we employ comprehensive evaluation models and coupling-coordination models to calculate the development index and coupling-coordination degree of green finance and industrial green transformation. On this basis, we introduce the Theil Index, Moran's Index, and Markov Chain algorithms to accurately grasp the coordinated development situation of green finance and industrial green transformation from a spatiotemporal perspective. This is aimed at providing theoretical support and policy recommendations for the synergistic and sustainable development of green finance and industrial green transformation in China. The research results show that: China's green finance and industrial green transformation have strong interaction and a high degree of matching. However, there is still a large space for deep integration, and there are obvious regional differences in the level of integration. Intra-regional differences are the main source of the overall imbalance. Meanwhile, the coupling coordination degree of the four major regions in China has significant spatial correlation and shows positive spatial correlation characteristics.

First, development level aspects. China is currently developing fast in green finance, but there are still problems such as unbalanced regional development. Specifically, the development of green finance is better in the western region and more volatile in the central region. The overall development in the eastern region is on an upward trend, slightly lower than the national average, and the northeastern region is the most backward in green finance development. Meanwhile, in terms of industrial green transformation, the development of China's regions is generally slow, showing a clear characteristic of high in the east and low in the west. Specifically, the eastern region has the best development of industrial green transformation, which is much higher than the national average. The development of the central and western regions shows a trend of steady improvement. The Northeast region has the lowest industrial green transformation score. However, the Northeast region has a better development prospect as the industrial green transformation score has increased significantly since 2018.

Second, in terms of coupling and coordination, spatially, there exists heterogeneity in the level of coordination between green finance and industrial green transformation across various regions in China. Regionally, it manifests as Eastern > Western > Central > Northeastern, and a preliminary effective coupling and coordination mechanism for green finance and industrial green transformation has been formed. Temporally, over the course of six years, China's overall green finance and industrial green transformation have transitioned from a barely coordinated state to a preliminary coordinated state, with both exhibiting a relatively low level of coupling and coordination. Specifically, the eastern and western regions have relatively good scores in terms of coupling and coordination and are in a state of preliminary coordination. Conversely, the central and northeastern regions lag the most in coordination and are in a barely coordinated state.

Fourth, from the analysis of the dynamic evolution of the Moran index, the coupling coordination degree of the four major regions in China has significant spatial correlation and shows positive spatial correlation characteristics; from the changing trend, the Moran index shows an overall fluctuating upward trend, indicating that the spatial clustering of the coupling coordination degree of green finance and industrial green transformation is further enhanced during this period.

Third, in terms of spatial differences. From the dynamic evolution analysis of the Moran's Index, the coupling coordination degrees of China's four major regions have significant spatial correlation and exhibit spatially positive correlation characteristics. In terms of the changing trend, the Moran's Index generally shows a fluctuating upward trend, indicating that the spatial clustering tendency of the coupling and coordination degrees between green finance and industrial green transformation has further strengthened during this period, showing a “the strong get stronger” spatial clustering feature.

As far as the Markov Chain stochastic process is concerned, under the current levels of green finance and economic structure, the overall coupling and coordination are leaping towards higher levels. State transitions only occur between adjacent types of coupling and coordination, making leapfrog development difficult to achieve. Additionally, there is an extremely high “state-locking” effect in the evolutionary process of coupling and coordination. The coupling and coordination degrees of the four major regions display a phenomenon of “club convergence,” characterized by overall divergence and local convergence.

From the perspective of the spatial differences in the Theil Index, the Theil Index of various provinces nationwide fluctuates between 0.27 and 0.40, indicating relatively small but expanding regional spatial differences. The inter-group differences are always smaller than the intra-group differences, and there are significant disparities among the provinces within each region in terms of socio-economic development, green technology investment, etc. Although different regions each have their own development disparities, it is the intra-regional differences that primarily contribute to the overall coordination development disparities among the four major regions.

5.2. Recommendations

Based on the development levels of green finance and industrial green transformation, as well as the spatiotemporal evolution patterns and current spatial differences of their coupling and coordinated development, the following policy recommendations are proposed.

First, pay attention to the regional imbalance in the development of green finance and promote the further development of green finance in the central and western regions. At present, the development of green finance in China shows obvious regional differences. As the central and western regions of China are the key regions for the national green development strategy and the realization of energy saving and emission reduction and green innovation, the country should further improve the green financial development system. Meanwhile, relying on green innovation to further enhance the level of green financial development in central and western China and narrow the regional differences. The government should improve the laws, regulations, and supervision policies related to green finance to provide a good external environment for the development of green finance.

Second, actively construct an evaluation system for green finance and industrial green transformation and establish a docking platform for green finance and industrial transformation. Given the current situation in China, where a comprehensive coordination mechanism for green finance and industrial green transformation has not yet been formed, efforts should be increased to strengthen financial support for energy conservation and environmental protection. By leveraging internet platforms, an innovative and effective evaluation system should be constructed to enhance the coupling and coordination between the two.

Third, regions should give full play to their regional advantages and establish differentiated development strategies. The eastern region has a better overall green finance and industrial green conversion, strengthening high-quality technological innovation to increase the speed of industrial transformation further effectively. Human resources are the core driving force of development and transformation. While promoting industrial green transformation and financial development, the central and western regions should also increase investment in local education and cultivate high-quality talents. The northeast region is China's old industrial base, gathering many heavy industries and facing numerous difficulties in the process of transformation. Building a new energy industry zone and forming an integrated development model of industry, academia, and research will enhance the transformation efficiency to the maximum.

Fourthly, the green financial dividend should realize mutual penetration and radiation within the region. For example, the three northeastern provinces can make full use of their location advantages and fully draw on the resources and technologies of developed provinces to accelerate the quality of industrial green transformation. As well as enhance the level of their economic quality development to form intra- and inter-regional synergistic development. At the same time, all places should also develop more local characteristics of green pillar industries to create high-quality development space. Continuously narrow the gap of green transformation level within the region, break the regional and inter-regional boundaries, and promote the coordinated development level of green finance and industrial green transformation with high-quality development.

5.3. Limitations and future prospects

The limitations of this paper are as follows.

First, the paper constructs an evaluation indicator system for green finance and industrial green transformation. Due to some dimensions of green financial tools and green development data not being updated, this paper has selected some academically recognized indicators to estimate the level of green financial development. Therefore, there are issues related to the acquisition of research indicators and the timing of data selection. We plan to further update the data and improve the evaluation indicator system in subsequent research.

Second, the development indicators for green finance are jointly constituted by three major subsystems: green credit, green investment, and carbon finance, along with government support. We were unable to further disaggregate the analysis regarding government support. For example, exploring in greater depth the impact and operating mechanisms of government support on the level of green financial development across various dimensions such as human resources and capital will become a major direction for future research.

Data availability statement

The data are available from the corresponding author on reasonable request.

CRediT authorship contribution statement

Yuanping Zhao: Writing – original draft, Software, Methodology. Na Zhao: Writing – review & editing, Data curation, Conceptualization. Rui Lyu: Visualization, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.