Exploring the Supporting Role of Finance in the Development of Clean Energy in China Based on the Panel Vector Autoregressive Model

Abstract

The efficient development and widespread utilization of clean energy hold global significance, particularly for developing countries like China, which has committed to carbon peak and neutrality targets. In this context, the financial sector plays a crucial role in supporting the renewable energy industry, ensuring a reliable energy supply for economic growth. To statistically assess the impact of financial characteristics—such as financial efficiency, financial size, and green finance—this paper employs a panel vector autoregressive (PVAR) model with province-level data from China spanning the period 1991 to 2018. The key findings demonstrate that (1) financial factors significantly contribute to the development of clean energy in China, and among these factors, financial scale has a greater impact than financial efficiency and green finance; (2) there are distinct regional variations in how financial development affects the clean energy sector, and the role of financial scale is particularly pronounced in the central and western regions of China while the impact of financial efficiency on the clean energy industry is not significant across all regions; and (3) other drivers—including industrial structure, financial expenditure, and technological advancements—also spur the growth of the clean energy industry. However, due to diminishing marginal effects, the forces driving its growth may gradually diminish. Therefore, the article proposes critical policy suggestions for promoting clean energy development in China. These policies should consider the regional context and address both financial and non-financial aspects. Understanding the interplay between finance, regional dynamics, and clean energy development is crucial for achieving sustainable and resilient energy systems in China.

1. Introduction

1.1. Research Background

Traditional fossil fuels present numerous challenges to the sustainability of ecosystems, economies, and societies worldwide. These challenges include resource depletion, substantial CO₂ emissions, and environmental pollution [1]. According to the United Nations reports, climate change has heightened the risk of poverty. Between 2010 and 2019, an average of approximately 23.1 million people faced displacement annually due to weather-related events while around 13 million lives were lost each year due to environmental factors. These interconnected issues create significant tensions across human, socioeconomic, climate, and environmental systems. In response to these challenges, global clean energy development has become paramount. Achieving carbon peaking and carbon neutrality targets is essential to combat current climate change and mitigate the environmental impact of fossil fuels. The International Energy Agency (IEA) estimates that clean energy initiatives will create more than 14 million job opportunities by 2030, with an estimated annual market value of approximately $65 billion, a positive contribution to addressing climate change. Finance can play a significant role in this transition by strategically allocating financial resources and promoting clean energy investments is widely acknowledged, as demonstrated during the Financial Day event at the 26th United Nations Climate Change Conference (COP26 Summit) held in the UK on 3 November 2021.

According to information released by the National Center for Climate Change Strategy and International Cooperation in China, the country has become the world’s largest emitter of greenhouse gases, surpassing the combined emissions of the United States, the European Union, and Japan. China now accounts for approximately a quarter of global emissions, making its impact on global climate change highly significant. Therefore, developing clean energy sources, including hydropower, nuclear power, wind power, and optoelectronics, is a priority for China. By gradually reducing dependence on traditional fossil fuels, this shift can address the quantitative constraints of fossil energy depletion and mitigate the externalities that cause climate warming and severe environmental pollution, providing a fundamental force for China’s high-quality growth.

At present, China’s clean energy consumption is on the rise. According to information from the National Energy Administration, in 2020, China’s primary clean energy sources included solar, hydropower, nuclear power, and wind power, which together accounted for 24.3% of total energy consumption. This exceeded the target of achieving approximately 15% non-fossil energy consumption by 2020. Furthermore, in 2021, China’s clean energy consumption increased to 25.5%, resulting in a reduction in coal consumption to 56%, a decrease of nearly 12.5 percentage points from 2012. Notably, new energy generation exceeded 1 trillion kilowatt-hours for the first time. China’s installed renewable energy capacity has exceeded 1.1 billion kilowatts, with hydropower, wind power, solar power, and biomass power generation capacities ranking first globally. Despite these achievements, there remains ample room for further development.

In the “Fourteenth Five-Year Plan,” released in June 2022, China has outlined a comprehensive renewable energy development strategy, emphasizing several key aspects, including total consumption, total power generation, total absorption, and non-electricity utilization. The primary goal of this plan is to achieve an annual renewable energy power generation of about 3.3 trillion kilowatt-hours by 2025. During this “14th Five-Year Plan” period, the targeted renewable energy generation is expected to surpass 50% of the country’s total electricity consumption. Specifically, wind and solar power generation are projected to double, reaching 3.3 trillion kilowatt-hours annually, marking a 50% increase compared to 2020.

In the pursuit of high-quality development, the transition from traditional energy to sustainable clean energy necessitates significant financial investment. Consequently, relevant state agencies have introduced a series of measures. For example, in 2021, the National Development and Reform Commission, in collaboration with five other divisions, jointly announced enhanced oversight and increased financial provision to promote the systematic development of wind power, photovoltaic power generation, and related industries. The announcement put forward several measures, including the issuance of clean energy subsidies and the promotion of preferential loans. This initiatives align with the principles of legalization and marketization, aiming to strengthen the financial industry’s backing for renewable energy development.

In recent times, China has introduced green bonds to strengthen finance support for clean energy development, with expectations for significant growth in 2021. On 18 March 2021, the China Development Bank issued its inaugural bond, specifically designed to link green finance with carbon neutrality. The issuance amounted to CNY 20 billion, making it China’s largest green bond issuance to date. By issuing these carbon-neutral financial bonds, financial institutions can better meet credit system’s demand for clean energy projects, unleashing the tremendous potential of the credit system to support green finance initiatives.

The research results of Olmos et al. [2], Polzin et al. [3], and Temmes et al. [4] all indicate that investing substantial financial resources into the development process will inevitably create robust conditions for large-scale clean energy projects, thereby driving the advancement of solar and wind energy. However, these efforts require further testing and comprehensive systematic analysis.

1.2. Research Significance

The rapid development of China’s financial policy system has created a growing demand for alignment between the clean energy industry and financial institutions, particularly in the context of low-carbon development initiatives. It is crucial to focus on advancing both finance and the clean energy sector, actively exploring their interaction to achieve mutual integration and collaborative development. Globally, energy plays a vital role in economic development, providing essential support for sustaining economies and forming a critical foundation for enhancing living standards. In contrast, the development of clean energy holds significant importance for a nation’s sustainable progress, with financial support playing a crucial role in constructing a robust clean energy infrastructure. Therefore, it is essential to analyze the financial mechanisms in China that influence the clean energy industry. Assessing the impact of financial development on clean energy advancement is equally critical. Based on this analysis, corresponding policy recommendations can be proposed to accelerate the role of financial development in better serving the clean energy sector, ultimately securing China’s sustainable energy future.

Based on the studies by Acheampong [5] and Charfeddine and Kahia [6], we apply the panel vector autoregressive (PVAR) model, which is a widely used tool in energy policy analysis, to investigate the role of financial factors in promoting clean energy development. The innovation can be formulated as follows.

Firstly, more focus should be drawn toward the financial sector within the context of clean energy development. While previous studies predominantly focused on financial scale and financial efficiency as representations of economic growth and their roles in promoting new energy, they often overlooked the crucial aspect of green finance. Alsedrah [7] empirically examined the impact of green finance, financial technology, and renewable energy on eight mineral-rich countries, exploring the moderating role of renewable energy in the relationship between financial technology, green finance, and reducing carbon dioxide emission. The findings were significant: increasing green finance practices, adopting financial technology, and leveraging renewable energy resources all contributed to a meaningful reduction in CO₂ emissions, providing valuable guidance for policymakers. Moreover, this research not only expands the understanding of the intricate interrelationships among these factors but also establishes a foundation for future research and informed policy-making. To comprehensively assess development levels, the study employs three key indicators: (a) financial scale, (b) financial efficiency, and (c) green finance. Furthermore, additional empirical analysis delves into the role of financial development in advancing the clean energy industry, thereby establishing a theoretical framework for further exploration in this vital area.

Secondly, regarding the impact of financial development on the clean energy industry, current studies tend to focus primarily on the national, provincial, or city level. But they often lack empirical research that specifically examines the influence of financial development, technology, fiscal expenditure, and other variables relating to energy distribution. As a result, comparing and analyzing regional differences becomes challenging. To address this gap, this paper takes a comparative approach, investigating the promotion of financial progression in China across various regions, all with a specific focus on the clean energy industry. By doing so, this research enriches the empirical landscape in this field and opens up more options for an in-depth study of how finance impacts clean energy development across diverse regions.

Thirdly, this paper dynamically examines the impact of various influencing factors, particularly finance, on clean energy production. The paper employs the framework of the panel vector autoregressive model, allowing the academic community to explore the dynamic characteristics and underlying laws governing the relationship between finance and clean energy production. Furthermore, this research also provides valuable guidance for the rational and effective allocation of financial resources within the clean energy production process.

As environmental challenges continue to escalate, the imperative for sustainable development becomes ever more urgent. This paper provides crucial policy recommendations for both the clean energy industry and regional sustainable development, unveiling potential solutions toward a greener future. By exploring into the synergies between the clean energy sector and finance, this research not only informs policy decisions but also shapes industry practices and technological advancements. It paves the way for a more resilient and sustainable future. Furthermore, this comprehensive work has the potential to significantly bridge the existing knowledge gap connecting the intricate relationship between financial factors and global clean energy development.

1.3. Literature Review

Upon collecting and organizing the relevant literature, it becomes evident that scholars both at home and abroad mainly investigate the potential of clean energy industry development from different perspectives. They analyze the impact of financial scale and financial efficiency on new energy using various methods and examine the influence of green finance on the growth of the clean energy industry. In light of this, this paper reviews the literature from the following three dimensions, and we aim to clarify the breakthroughs in this paper.

1.3.1. Studies on Financial Support for the Development of the Clean Energy Industry

Wiser and Pickle [8] conducted a study on the impact of renewable energy policy and energy financing costs, concluding that there is a certain correlation between the formulation of new energy policies and associated financing costs. Lipp [9] analyzed renewable energy power generation policies in Germany, Denmark, and the United Kingdom, emphasizing the crucial role of national financial support policies in fostering renewable energy development. Labatt and White [10] argued that carbon finance mechanisms could mitigate financial risks and significantly boost investment in the clean energy market. Joshi [11] explored the potential of asset securitization in clean energy development, envisioning a future securitization market for renewable energy assets. MacGillivray et al. [12] explored the financial aspects of the new energy industry, examining investment, financing, and cost considerations.

Focusing on OECD countries, Mamun et al. [13] investigated the intricate relationship between financial markets, innovations, and cleaner energy production and found that the financial markets, particularly equity and credit, promote biomass and non-biomass renewable energy production. Based on the findings of a time-series analysis of macro-level data, Ji and Zhang [14] concluded that financial development plays an important role in renewable energy development in China, contributing 42.42% to the variation in renewable energy growth. Taking a different perspective, Mu et al. [15] focused on Beijing as a research object and conducted an experiential analysis of the relationship between the consumption of clean energy, energy intensity, and financial credit and identified that financial credit plays a crucial role in promoting clean energy, with a contribution rate of up to one-third. Le et al. [16] applied a two-step system generalized method of moments (GMM) to analyze the relationship between financial sector development and renewable energy deployment. The findings established that while financial development significantly impacts renewable energy in high-income countries, its effect is statistically insignificant for low-and middle-income countries. Luo [17] focused on the role of financial support in clean-energy transformation from the perspectives of clean energy development and alternative market supply. Luo emphasized that the rapid and efficient development of the clean energy industry requires market mechanisms and the improvement of financial services. Constructing a series including a spatial autoregressive model, spatial error model, and spatial Durbin model, Yang and Wang [18] studied the spatial spillover effects of financial development on clean energy and concluded that local financial development stimulated clean energy in the studied region but impacted surrounding areas negatively. Xu et al. [19] conducted a comprehensive analysis using the component data method, cointegration analysis method, and scenario analysis method. They presented 27 scenarios for achieving carbon neutrality, with the initial step being to reach a carbon emission peak through adjustments in economic growth rates and energy consumption structures. The findings indicated that under low economic growth and low-carbon energy consumption scenarios, China’s carbon emissions will peak in 2026, ultimately achieving carbon neutrality by 2056. Khan et al. [20] empirically studied the short- and long-term effects of new technology, finance, and foreign direct investment (FDI) on renewable energy, non-renewable energy, and CO₂ emissions across sixty-nine countries included in the “Belt and Road Initiative (BRI)” between 2000 and 2014. They concluded that development in the financial sector is a significant positive determinant for the renewable energy sector. Additionally, the findings of the Granger non-causality test showed two-way causal links between renewable energy, innovative technology, finance, and FDI. Using data from 276 cities in China from 2011 to 2019, Chen [21] explored the impact of the digital economy using a double fixed-effects model and a spatial econometric model. Chen argued that the digital economy can drive the development of clean energy, with technological innovation and bank lending playing critical mediating roles in this process.

1.3.2. Research on the Impact of Financial Scale and Financial Efficiency on the Development of the Clean Energy Industry

While there have been few studies regarding the contributions of financial scale and financial efficiency in the energy industry, some literature has summarized the findings of finances related to the new energy industry. For example, Yin et al. [22], using data from 2000 to 2013, found a positive correlation between financial scale, financial efficiency, financial structure, and the development of a new energy industry. Both financial scale and financial efficiency were identified as Granger reasons for the development of the new energy industry. Taking monthly indicators from 2016 until 2019, Cao et al. [23] studied the relationship between the agglomeration degree of the new energy industry and financial support from four aspects: credit scale, security scale, financial efficiency, and financial structure. They considered that improving financial efficiency would promote the agglomeration degree of the new energy industry. Based on provincial panel data from 2008 to 2019, Wei and Yang [24] empirically investigated the financial size threshold effect on clean energy industry development and on labor force employment. The findings suggested that clean energy development significantly boosts the overall employment size when financial size serves as the threshold variable.

1.3.3. Studies on the Impact of Green Finance on the Development of Clean Energy Industry

Nie et al. [25] argued, by employing a microeconomic model and considering all relevant stakeholders, that government subsidies could increase the outputs and debt levels of renewable-energy-related firms. They also found that financial debts could stimulate a firm’s outputs but decrease the net profit per unit of debt due to the limited liability effects. Li et al. [26] analyzed the technological innovation of enterprises, banks, and government departments using green loans and concluded that green loans and financial subsidies could promote clean energy production. Peng and Liu [27] analyzed data from annual reports (2013 and 2014) of 58 listed clean energy-oriented companies in China and concluded that clean energy development was significantly encouraged by the formulation and implementation of appropriate GSA and GSB policies. Anton and Nucu [28] proposed that financial institutions should proactively introduce innovative green financial products to support the development of clean energy enterprises. They also emphasized that clean energy enterprises should strengthen their internal management practices and diversify their financing channels. Tian [29], greatly inspired by the progress of green finance in China, conducted a comparative analysis of the current situation and challenges related to financial support for clean energy development in the United States. He et al. [30] employed the Richardson model to assess the effectiveness of investments in renewable energy enterprises in China and argued that the issuance of bank loans negatively impacted green finance development and hindered improvements in investment efficiency within the renewable energy sector. As a solution, they recommended that financial institutions develop more innovative green financial products. Wei et al. [31] summarized that green finance policy played a positive role in promoting the development of new and renewable energy. Lei and Wang [32] argued that green finance supports clean energy and other industries through capital investment.

1.3.4. Summative Evaluation

The literature analysis herein indicates that the clean energy industry continues to capture people’s attention.

From the perspective of research methodology, case analysis and theoretical analysis have been the main focal points when assessing financial improvement in clean energy industry development. In contrast, quantitative analysis methods—such as econometric models are rarely employed to investigate the financial support for clean energy development. Although some scholars do use econometric models for empirical analysis, they typically yield coefficients that represent the impact of finance and other variables on the development of the clean energy industry. However, when a comprehensive analysis of variables is carried out, it may be challenging to fully understand and compare the various factors influencing clean energy development.

From the perspective of research data indicators, most experts and scholars have primarily focused on conducting empirical analysis and research regarding the financial impact of China’s clean energy industry at the national or provincial levels. However, there has been limited research on clean energy industry development in different regions of China, particularly investigations that establish correlations between finance and clean energy development. As a result, conducting a comprehensive and systematic regional analysis of the financial promotion of clean energy industry development remains challenging.

When considering the research object, most experts mainly utilize financial scale and financial efficiency as indicators to express the level of financial development and analyze the financial promotion of new energy. It is important to note that while clean energy is included in this context, it is not equivalent to new and renewable energy. Consequently, there is a need for more research on the impact of financial scale and financial efficiency specifically within the clean energy industry.

In terms of study conclusions, numerous researchers have demonstrated the contribution of the financial market to clean energy industry development. They have emphasized that finance (such as via mechanisms like green loans or financial subsidies) plays a significant role in advancing clean energy adoption. For instance, Xu et al. [33] conducted an analysis of financial investment’s impact on renewable energy development using a panel threshold model from an investment perspective. Based on the findings from these research works, scholars have put forward relevant financial policy recommendations to foster clean energy development.

1.4. Innovation Points

Compared with the above literature achievements, the article introduces innovative points that can be stated as follows:

(1)

In previous studies, most scholars mainly utilized financial scale or financial efficiency as indicators to express the degree of financial development when analyzing the financial promotion of new energy or exploring the relationship between green finance and clean energy. Therefore, this paper takes a more comprehensive approach by measuring financial development using three key indicators: financial scale, financial efficiency, and green finance, and through empirical analysis, it aims to uncover the specific role of financial development in promoting China’s clean energy industry.

(2)

The existing literature has primarily focused on assessing the impact of financial development on the clean energy industry at the national level or within particular provinces or cities. However, there has been limited empirical study regarding the impact of variables such as financial development, technological progress, and financial expenditure on the development of the clean energy industry across different regions based on energy distribution. As a result, regional differences remain less apparent. This paper aims to enrich our understanding by examining the impact of financial development on the clean energy industry at the national level and in the eastern, central, and western regions.

(3)

Upon reviewing the existing literature, it becomes evident that scholars may not have thoroughly tested the specific influence mechanisms while studying the relationship between financial development and clean energy. Consequently, this paper analyzes the mechanism of the influence of financial development on the clean energy industry. The analysis reveals that financial development directly and indirectly influences the development of the clean energy industry through the mediating variable of clean energy investment, with a pronounced mediating effect.

2. Model, Variables, and Data

2.1. Model

2.1.1. PVAR

The general expression of the PVAR model is as follows:

$$Y_{i,t}={\Gamma }_0+{\displaystyle {\sum }_{j=1}^n{\Gamma }_j}{Y}_{i,t-j}+{\beta }_j+d_t+{\epsilon }_{i,t}$$

(1)

Here, $Y_{i,t}$ represents variable vectors in the 8 × 1 dimension of panel data; $i$ represents provinces, cities, and autonomous regions; $t$ represents the year; ${\Gamma }_j$ represents the lag effect matrix of the variable to be evaluated; fixed effect ${\beta }_j$ represents the heterogeneity of individuals; time dummy variable $d_t$ represents the differences in different periods; and ${\epsilon }_{i,t}$ represents the random disturbance period.

2.1.2. PVAR Model Identification

Determining the lag order is an essential part of the panel VAR model. The model typically relies on real data, and its economic and financial data generally do not provide a clear indication of the most appropriate lag order of the PVAR model and the timing when the variables affect the system. Therefore, researchers frequently employ the information criterion method to determine the optimal lag order. In this paper, the Akaike information criterion (AIC), Schwartz information criterion (BIC), and Hannan Quinn information criterion (HQ) are employed to determine the lag order. By estimating different lag term values, we obtain corresponding AIC, BIC, and HQ values. The final optimal lag order must be the one lag value that minimizes AIC, BIC, or HQ values. Typically, all three criteria yield the same optimal lag order value, but if they happen to diverge, one can be chosen flexibly according to specific situations (although differences are generally minor).

2.1.3. PVAR Model Parameter Estimation

When using PVAR model to estimate coefficients, deviation can occur due to model’s fixed effect and time effect. Therefore, it is necessary to remove the fixed effect from individual samples. But a correlation may exist between the fixed effect and the regression quantity due to the influence of the lag value of the dependent variable. In order to resolve this, the mean differencing method, namely the Helmert process [34], is employed to exclude individual fixed effects. This involves subtracting the average of all individuals and the average of future observations within each cycle. By doing so, the lagged variable and the transformed variable become orthogonal and independent of the error term. Furthermore, the lag variable is regarded as an instrumental variable in the model, and the generalized method of moments (GMM) is applied to estimate the parameters of the model system. On the other hand, time dummy variable is introduced into the model to represent differences across each period. The time effect is then eliminated by applying the mean of differencing method to the cross-section.

2.1.4. Panel Granger Causality Test

The Granger causality test analyzes whether lagged values of one variable significantly impact other variables, thereby determining the temporal causal relationship between variables. This method is standard in economic research for assessing interactions between variables. When compared to traditional Granger time series analysis, the use of panel data introduces two dimensions: time and section (cross-sectional data). This expansion of the sample size improves the test freedom and reduces the multicollinearity between explanatory variables. Therefore, the panel Granger causality test offers higher accuracy and stability. In our paper, we mainly employ the panel Granger causality test to examine the causality between financial development and the clean energy industry.

2.1.5. Impulse Response and Variance Decomposition

The explanatory variables of the traditional VAR model include the lag values of both dependent and independent variables. For that reason, the model’s t-test cannot directly indicate whether the alteration in a variable have either a positive or negative effect on other variables within the model. Additionally, it does not reveal how long such changes will persist or the specific impact they will have over time. In order to address these limitations inherent to the VAR model, researchers often turn to two key techniques after parameter estimation: impulse response analysis and variance decomposition.

The impulse response function is mainly applied to analyze the impact of the combined disturbance term and the standard deviation of model variables on both existing and forthcoming value of each variable within the model. By observing the dynamic response of each variable, we can assess the degree and specific direction of its impact. One advantage of using this method is that each model contains the same lag structure without too many exogenous and endogenous variables.

Given that the impulse response alone cannot directly compare the strength of the response, variance decomposition becomes a valuable tool. By analyzing the changes in internal variables resulting from each structural impact, we can better assess and evaluate the degrees of various structural impacts.

2.2. Variables, Data

When considering data indicators for clean energy industry development, Xu and Chen [35] utilized power production from renewable energy sources (excluding hydropower) and total power production as indicators to estimate the development of the new energy industry. Their study focused on understanding the financial support role of new energy development. In our case, we have chosen a readily available comprehensive index: the ratio of clean energy production to total energy production. Clean energy encompasses mainly primary power sources such as wind power, hydropower, nuclear power, solar energy, and other renewable energies.

The financial correlation ratio (FIR) represents the ratio of total deposits and loans from financial institutions to GDP, denoted as a measurement index of the financial scale. Macro-financial efficiency (FE) encompasses three aspects: savings mobilization ability, savings–investment transformation efficiency, and investment orientation efficiency. The financial efficiency evaluation index system is constructed using the entropy method. The ratio of interest expenditures of non-six high-energy-consuming industries to total interest expenditure of industrial industries in each province is used as the reverse index to represent the measurement index of green finance, expressed in GCL, and the calculation formula is as follows:

$$GCL=1-{\displaystyle \frac{{\displaystyle {\sum }_{i=1}^6{I}_i}}{I}}$$

(2)

Here, $I_i$ represents the interest expense of type-$i$ high-energy-consumption industry and $I$ represents the total interest expense of industrial enterprises above the designated size.

In addition, this paper introduces control variables such as industrial structure, technological progress, fiscal expenditure, and relative energy price to further analyze whether influencing factors other than financial factors will affect the development of the clean energy industry. Among these, energy price is expressed by the purchase price of raw materials, fuels, and electricity in different regions.

Table 1. Variable meaning and calculation method.

Variable Category	Variable	Variable Code	Calculation Method and Source	Unit
Dependent variable	Development of clean energy industry	CE	Clean energy production/total energy production *100%	%
Independent variable	Financial scale	FIR	(0.5*(deposits and loans at the end of a year + deposits and loans at the end of last year))/GDP*100%	%
	Financial efficiency	FE	Calculated by constructing the index system	%
	Green finance	GCL	One-interest expense of six high-energy-consuming industries/interest expense of industrial enterprises above designated size	%
Control variable	Industrial structure	IS	Added value of secondary industry/GDP*100%	%
	Technical progress	TI	Technology market turnover/GDP*100%	%
	Fiscal expenditure	PFE	National fiscal expenditure/GDP*100%	%
	Relative price of energy	PCE	Purchase prices of raw materials, fuels and electricity in different regions	%

Note: all the above data indicators have been converted from nominal values to actual values.

shows the meaning and calculation method for specific variables.

This paper mainly utilizes the panel data of 30 states in China, except for Tibet, between 1991 and 2018 for empirical analysis. All statistical indicators have been obtained from the China Statistical Yearbook, China Energy Statistical Yearbook, National Data Network, and Statistical Yearbooks of Provinces and Cities. The data on clean energy production of individual provinces from 1991 to 1993 are calculated by the average five-year growth rate.

Additionally, if the mode experiences severe multicollinearity problem, it can lead to increased variance in the regression coefficient, making the research results unstable or difficult to interpret. Therefore, the variance expansion factor (VIF) is used in this paper to assess the severity of multicollinearity. The calculated VIF value is 1.66, far less than 10, showing no severe multicollinearity in the model.

3. Results

3.1. Stationary Test

To prevent the pseudo-regression phenomenon, testing the stationarity of each data variable is essential. In order to ensure the robustness and comprehensiveness of the empirical analysis results, this paper uses the LLC, IPS, Fisher ADF, and Fisher PP methods in EViews 9.0 to identify their stationarity.

Table 2. Unit root test results of data variables.

Methods	Variable
	CE	FIR	FE	GCL	IS	TI	PFE	PCE
LLC	11.558	3.463	−7.796 ***	−3.978 ***	−4.039 ***	−20.448 ***	3.598	−9.723 ***
IPS	12.940	8.177	−5.931 ***	−1.331 *	1.228	−9.848 ***	8.131	−7.217 ***
Fisher ADF	20.579	12.796	155.319 ***	62.535	46.615	270.448 ***	6.907	154.944 ***
Fisher PP	20.133	4.576	78.883 *	68.612	81.745 **	22.547 ***	4.475	192.653 ***

Note: ***, **, and * indicate significance at the significance levels of 1%, 5%, and 10%, respectively.

and

Table 3. Test results of first-order difference unit root of each data variable.

Methods	Variable
	dCE	dFIR	dFE	dGCL	dIS	dTI	dPFE	dPCE
LLC	10.362 ***	−8.479 ***	11.5293 ***	10.2820 ***	−6.126 ***	1.789	−7.465 ***	−26.061 ***
IPS	−11.118 ***	−8.235 ***	12.5945 ***	12.2214 ***	−6.591 ***	−5.673 ***	10.233 ***	−26.341 ***
Fisher ADF	322.225 ***	187.612 ***	276.410 ***	269.556 ***	150.492 ***	143.667 ***	220.216 ***	593.915 ***
Fisher PP	376.837 ***	159.462 ***	344.306 ***	450.742 ***	164.123 ***	143.667 ***	440.017 ***	809.693 ***

Note: *** indicate significance at the significance levels of 1%.

below show that the level values of financial efficiency, technological progress, and relative energy price have passed the assessment of the four test methods. But the IPS and ADF test results for the level value of industrial structure are unstable, and the ADF and PP test results for green finance are unstable. Additionally, the four test results for the development of the clean energy industry, financial scale, and financial expenditure level exhibit instability. Furthermore, the stationarity of the first-order difference of each variable significantly rejects the original hypothesis, indicating that each variable follows a first-order single-integer sequence. Consequently, this paper establishes a PVAR model based on the first-order difference of all variables. Moreover, the unit root test results for each respective variable by region are listed in

Table 4. Unit root test outcomes of data variables in regions.

Variable	Eastern Region		Central Region		Western Region
	LLC	IPS	LLC	IPS	LLC	IPS
CE	4.434	6.008	7.316	7.373	2.956	4.248
FIR	0.605	4.265	1.923	4.773	−0.230	3.498
FE	−6.885 ***	−6.808 ***	−3.300 ***	−1.239	−3.952 ***	−1.943 **
GCL	−2.274 **	−1.386 *	−1.469 *	−0.665	−3.174 ***	−0.197
IS	−2.699 ***	−0.094	−2.154 **	0.852	−2.125 **	1.384
TI	−1.729 **	2.290	−1.187	2.351	−1.050	1.878
PFE	2.840	5.732	2.649	5.162	0.555	3.066
PCE	−8.477 ***	−5.757 ***	−4.111 ***	−3.518 ***	−3.614 ***	−3.103 ***
dCE	−0.918	−3.917 ***	−2.787 ***	−3.736 ***	−5.552 **	−6.063 ***
dFIR	−10.247 ***	−9.148 ***	−8.484 ***	−6.564 ***	−5.963 ***	−6.411 ***
dFE	−8.060 ***	−8.987 ***	−5.258 ***	−6.101 ***	−6.543 ***	−6.622 ***
dGCL	−7.656 ***	−8.430 ***	−3.778 ***	−5.093 ***	−6.712 ***	−7.625 ***
dIS	−5.321 ***	−5.064 ***	−1.513 *	−2.277 **	−1.376 *	−2.233 **
dTI	−4.771 ***	−8.142 ***	−6.064 ***	−7.198 ***	−3.758 ***	−5.226 ***
dPFE	−4.161 ***	−7.030 ***	−4.325 ***	−5.446 ***	−4.484 ***	−5.169 ***
dPCE	−12.491 ***	−14.451 ***	−16.897 ***	−15.907 ***	−16.073 ***	−15.348 ***

Note: ***, **, and * indicate significance at the significance levels of 1%, 5%, and 10%, respectively.

. Due to space constraints, only the results of LLC and IPS test methods are listed, and the first-order difference of all variables in each region rejects the original hypothesis.

Since several variables used in this paper are first-order single-integration variables, the cointegration test method is used to analyze long-term and stable cointegration relationships between variables. This paper also applies the Johansen cointegration test to observe the cointegration relationships among eight variables. The results are shown in

Table 5. Johansen cointegration test results.

Original Hypothesis	Whole Country		Eastern Region		Central Region		Western Region
	Fisher Joint Trace Statistics	Fisher Joint Eigenvalue Statistics	Fisher Joint Trace Statistics	Fisher Joint Eigenvalue Statistics	Fisher Joint Trace Statistics	Fisher Joint Eigenvalue Statistics	Fisher Joint Trace Statistics	Fisher Joint Eigenvalue Statistics
None	3461 ***	994.3 ***	1333 ***	363.1 ***	1114 ***	312.6 ***	1013 ***	318.5 ***
At most 1	1337 ***	974.9 ***	517.0 ***	363.9 ***	432.1 ***	342.8 ***	387.7 ***	268.2 ***
At most 2	744.4 ***	421.2 ***	302.6 ***	181.0 ***	225.7 ***	123.5 ***	216.1 ***	116.6 ***
At most 3	391.2 ***	235.5 ***	153.9 ***	86.14 ***	120.1 ***	60.66 ***	117.2 ***	88.73 ***
At most 4	203.8 ***	132.7 ***	82.10 ***	51.85 ***	72.67 ***	41.17 ***	49.00 ***	39.72 ***
At most 5	110.9 ***	81.65 **	44.51 ***	29.71	42.66 ***	35.62 **	23.70	16.32
At most 6	72.02	52.62	31.24 *	25.19	22.80	15.56	17.98	11.86
At most 7	97.48 ***	97.48 ***	35.04 **	35.04 **	33.01 **	33.01 **	29.43 **	29.43 **

Note: ***, **, and * indicate significance at the significance levels of 1%, 5%, and 10%, respectively.

.

We can observe that there are at least four cointegration vectors in the entire nation and in the eastern, central, and western regions. This suggests the possibility of establishing a long-term and stable cointegration relationship between the variables. Therefore, the PVAR model can be established, enabling subsequent analysis.

3.2. Estimation Results of PVAR Model

The optional lag order of the model needs to be determined before estimating the parameters of the PVAR model. This decision is mainly based on information criteria, which help find the optimal lag order for the variable data. Considering the values of the AIC, BIC, and HQIC, this paper finally determines the best lag order of each model specifically, order 4 and establishes the PVAR (4) model.

Table 6 shows the national and regional GMM estimation results of the panel VAR model. It is found that the GMM results of variable dCE are not entirely significant, but some conclusions can be drawn as follows:

(1)

The relationship between financial scale and clean energy industry development

In the development equation of the clean energy industry, the coefficient estimates in the national model show that the lag of financial scale in the third period has an unfavorable impact on the clean energy industry (the coefficient is −2.2503), significant at the 10% level. In contrast, during the second and fourth periods, we observe positive coefficients: 2.8019 and 2.8017, respectively. These are significant at the levels of 5% and 1%. This indicates a positive relationship between China’s clean energy industry development and the lag of the financial development scale in phases II and IV at the present stage. In the early stages, the expansion of the financial scale contributes positively to the growth of the clean energy industry. In the eastern region, the dynamic response of clean energy to the lag in financial scale during the second and third periods is weak while the response to the lag of the financial scale in the first and fourth periods is significantly positive at the levels of 10% and 5% (coefficients are 2.2143 and 2.1810). In the central region, the response of clean energy industry development to financial scale is negative in the first and third periods and positive in the second period, and the impact effect is significantly positive at the 5% level in the fourth period (coefficient is 3.8453). In the western region, the correlation between clean energy and financial scale is positive in the lag phases I and IV (the coefficients are 4.9715 and 4.1834) and is significant at the levels of 5% and 10%, respectively. In the third phase, the correlation becomes significantly negative at the level of 10%. Therefore, the eastern, central, and western regions show a more balanced impact compared to the national level. Notably, the development of the clean energy industry is positively affected by the lag of the financial scale in the fourth phase. This indicates that an early focus on financial scale can stimulate the development of this industry.

Regarding the influence of financial efficiency on clean energy industry development in the eastern and central regions, the role of financial efficiency in the lag phases I to IV is not significant. This indicating that early financial efficiency has little influence on clean energy industry development in these regions. In the western region, however, the lag of financial efficiency in the second stage is significant at the level of 5% (the coefficient is −144.1532). Therefore, it appears that early financial efficiency plays a limited impact in shaping the clean energy industry. This observation could be attributed to the priority placed on addressing capital scarcity rather than focusing primarily on fund utilization efficiency.

Examining the impact of green finance on the development of the clean energy industry at the national level, green finance shows significant positive coefficients at the levels of 10%, 10%, and 1% in the first, second, and third lag periods, respectively (coefficients are 6.6051, 6.3226, and 12.5604). This indicates that green finance plays a crucial role in promoting the development of the clean energy industry, with its effect gradually increasing over time. It signifies that financial institutions, such as banks, investing funds into the clean energy industry through loans play an essential role in promoting clean energy development. The three-phase lag coefficient of green finance in the eastern region is 9.6776, which is significant at the level of 5%. Green finance in the central region indicates a similar financial efficiency and has no remarkable impact on clean energy. In the western region, the lag of green finance in the first, second, and third phases is significant at the levels of 1%, 10%, and 10%, respectively (the coefficients are 19.7367, 17.0915, and 18.0902). This suggests that early green finance in the western region also promotes the development of clean energy, with the most significant impact being observed during the first phase.

(2)

The correlation between industrial structure and clean energy industry development

The regression coefficients for the first and third stages of industrial structure lag are highly negative at the national level. In contrast, the regression coefficient for the fourth stage lag is significantly positive at the level of 5% (the coefficient is 0.2951). In the eastern region, the direction of impact of the first three lag periods remains negative while the effect of the fourth lag period is positive but not significant. In the central region, the lag-phase-I coefficient is negative and significant at the level of 10% whereas the lag-phase-IV coefficient is 0.5368, significantly positive at the 5% level. In the western region, lag phase I exhibits significance at the 10% level (coefficient is −1.0060), and lag phase II is also significant at the 10% level (coefficient is 1.1438). These findings indicate that the development of clean energy in different regions is affected by the lag values of the industrial structures, with varying directions and magnitudes of impact mainly due to the distinct industrial distribution across these regions.

(3)

The relationship between technological progress and clean energy industry development

The impact of technological improvement on clean energy industry development is multifaceted. At the national level, the lag of technological progress in phases I and IV is significantly positive at the level of 10% (coefficients are 10.5479 and 6.8792). This indicates that the improvement of early technological progress contribute to the development of the clean energy industry, likely by promoting research and development (R&D) in clean energy technologies. In the eastern region, the change direction varies, it is negative, positive, positive, and negative, with the lag in the second period being significant at the level of 10% (the coefficient is 9.9946). In the central region, the lag in technological progress in the fourth period is significantly positive at the level of 10% (the coefficient is 56.8265), indicating that early technological progress in the central region evidently plays a significant role in promoting clean energy industry development. In the western region, although the change directions of technological progress are positive, negative, positive, and positive, none of these are significant. This regional heterogeneity highlights that the impact of technological improvement on clean energy development varies across different parts of the country. In the central region, the impact of early technological progress on the clean energy industry appears more pronounced.

(4)

The relationship between fiscal expenditure and clean energy industry development

The lag coefficient of national fiscal expenditure in the second phase is 0.2364, which is significant at the level of 5%. The lag coefficient of the fourth period in the eastern region is 0.5875, which is also significant at the level of 5%. But the impact of fiscal expenditure in the central region is weak. In the western region, the fiscal expenditure lag in the first period is significant at the level of 1% (coefficient is 0.3098). This indicates that the increase in early fiscal expenditure at the national level (whole country) and in the eastern and western regions can promote the level of improvement in the clean energy industry. With the increased fiscal expenditure, some funds will flow to the clean energy industry, providing policy and financial guarantees for industrial growth and promoting clean energy development.

(5)

The relationship between the relative price of energy and clean energy industry development

In the development equation of the clean energy industry at the national level, the lag coefficient for relative energy price in phases I and II is positive. The lag coefficient in phase II is significant at the level of 5% (the coefficient is 0.0562). The lag coefficient in phases III and IV is negative and not significant, indicating that early-stage energy prices have a certain role in promoting clean energy industry development. When energy prices increase in the previous stage, the clean energy industry tends to increase R&D and energy utilization to promote further development. For the eastern region, the coefficients for the first four periods are positive, with the coefficient of the second period being 0.1121, and significant at the level of 10%. In the central region, the impact of energy prices on clean energy industry development is not significant. In contrast, the western region shows that the lag of energy prices in the third and fourth periods is significant at the level of 10%, with coefficients of −0.0668 and −0.0688, respectively. This regional variation highlights that the influence of energy prices on clean energy development varies across regions, with a more pronounced impact observed in the eastern region.

Table 6. GMM estimation results.

Explanatory Variable	Whole Country	Eastern Region	Central Region	Western Region
L.h_dCE	−0.2156 **	−0.1088	−0.4082 **	−0.1144
L.h_dFIR	−1.0407	2.2143 *	−2.1605	4.9715 **
L.h_dFE	−2.4855	7.1959	−33.7673	32.4142
L.h_dGCL	6.6051 *	4.0802	−1.3670	19.7367 **
L.h_dIS	−0.7542 ***	−0.3497	−0.7792 *	−1.0060 *
L.h_dTI	10.5479 *	−3.3286	57.9755	83.9756
L.h_dPFE	−0.1222	0.2295	−0.0449	0.3098 ***
L.h_dPCE	0.0045	0.0541	−0.0284	−0.0048
L2.h_dCE	−0.1194	−0.0928	−0.0422	−0.1652 *
L2.h_dFIR	2.8019 **	1.1112	3.3673	3.3943
L2.h_dFE	−36.3244	−23.1487	62.0592	−144.1532 **
L2.h_dGCL	6.3226 *	4.1794	−0.9055	17.0915 *
L2.h_dIS	0.1068	−0.0127	−0.1121	1.1438 *
L2.h_dTI	0.4980	9.9946 *	−43.5916	−98.9376
L2.h_dPFE	0.2364 **	−0.0887	−0.2090	−0.0453
L2.h_dPCE	0.0562 *	0.1121 **	0.0330	0.0300
L3.h_dCE	−0.0455	0.1216	0.0975	−0.2675 ***
L3.h_dFIR	−2.2503 *	−0.0300	−0.7967	−5.2357 *
L3.h_dFE	−41.0763	−53.3339	−41.4995	−46.1671
L3.h_dGCL	12.5604 ***	9.6776 **	0.1399	18.0902 *
L3.h_dIS	−0.3267 *	−0.0415	−0.4600	−0.7337
L3.h_dTI	1.8403	1.6199	−26.3017	66.6797
L3.h_dPFE	−0.1277	−0.3871	−0.2141	−0.1747
L3.h_dPCE	−0.0079	0.0271	−0.0024	−0.0668 *
L4.h_dCE	−0.0174	0.1847 **	−0.0240	−0.0806
L4.h_dFIR	2.8017 ***	2.1810 **	3.8453 **	4.1834 *
L4.h_dFE	18.0852	28.4388	32.2997	−32.0841
L4.h_dGCL	−2.7015	−4.2336	2.5200	10.9307
L4.h_dIS	0.2951 **	0.3193	0.5368 **	0.5820
L4.h_dTI	6.8792 *	−2.3467	56.8265 *	5.2269
L4.h_dPFE	0.1037	0.5875 **	0.2692	0.1085
L4.h_dPCE	−0.0153	0.0128	0.0202	−0.0688 *

Note: these data have been obtained by stata16.0 operation. ***, **, and * indicate significance at the levels of 1%, 5%, and 10%, respectively.

Table 6. GMM estimation results.

Explanatory Variable	Whole Country	Eastern Region	Central Region	Western Region
L.h_dCE	−0.2156 **	−0.1088	−0.4082 **	−0.1144
L.h_dFIR	−1.0407	2.2143 *	−2.1605	4.9715 **
L.h_dFE	−2.4855	7.1959	−33.7673	32.4142
L.h_dGCL	6.6051 *	4.0802	−1.3670	19.7367 **
L.h_dIS	−0.7542 ***	−0.3497	−0.7792 *	−1.0060 *
L.h_dTI	10.5479 *	−3.3286	57.9755	83.9756
L.h_dPFE	−0.1222	0.2295	−0.0449	0.3098 ***
L.h_dPCE	0.0045	0.0541	−0.0284	−0.0048
L2.h_dCE	−0.1194	−0.0928	−0.0422	−0.1652 *
L2.h_dFIR	2.8019 **	1.1112	3.3673	3.3943
L2.h_dFE	−36.3244	−23.1487	62.0592	−144.1532 **
L2.h_dGCL	6.3226 *	4.1794	−0.9055	17.0915 *
L2.h_dIS	0.1068	−0.0127	−0.1121	1.1438 *
L2.h_dTI	0.4980	9.9946 *	−43.5916	−98.9376
L2.h_dPFE	0.2364 **	−0.0887	−0.2090	−0.0453
L2.h_dPCE	0.0562 *	0.1121 **	0.0330	0.0300
L3.h_dCE	−0.0455	0.1216	0.0975	−0.2675 ***
L3.h_dFIR	−2.2503 *	−0.0300	−0.7967	−5.2357 *
L3.h_dFE	−41.0763	−53.3339	−41.4995	−46.1671
L3.h_dGCL	12.5604 ***	9.6776 **	0.1399	18.0902 *
L3.h_dIS	−0.3267 *	−0.0415	−0.4600	−0.7337
L3.h_dTI	1.8403	1.6199	−26.3017	66.6797
L3.h_dPFE	−0.1277	−0.3871	−0.2141	−0.1747
L3.h_dPCE	−0.0079	0.0271	−0.0024	−0.0668 *
L4.h_dCE	−0.0174	0.1847 **	−0.0240	−0.0806
L4.h_dFIR	2.8017 ***	2.1810 **	3.8453 **	4.1834 *
L4.h_dFE	18.0852	28.4388	32.2997	−32.0841
L4.h_dGCL	−2.7015	−4.2336	2.5200	10.9307
L4.h_dIS	0.2951 **	0.3193	0.5368 **	0.5820
L4.h_dTI	6.8792 *	−2.3467	56.8265 *	5.2269
L4.h_dPFE	0.1037	0.5875 **	0.2692	0.1085
L4.h_dPCE	−0.0153	0.0128	0.0202	−0.0688 *

Note: these data have been obtained by stata16.0 operation. ***, **, and * indicate significance at the levels of 1%, 5%, and 10%, respectively.

3.3. Panel Granger Causality Test

Before conducting the Granger causality test, the impulse response and variance decomposition of panel data are also essential to assess the stability of the model. Specifically, the stability of the panel vector autoregressive (PVAR) model hinges on whether all characteristic equation roots lie within the unit circle. In Figure 1, we observe a distributed scatter diagram of each feature root placed on the complex plane. It is also visible that both the data feature roots for the whole country and those for all regions are placed within the unit circle. This observation confirms that the PVAR model established in this paper is highly stable.

The cointegration test above shows a long-term equilibrium correlation between clean energy industry development, financial progression, and other variables. But due to an unclear causal direction, it becomes imperative to conduct a panel Granger causality test on the data to help determine the direction of causality. According to the optimal lag order, the development of the clean energy industry, financial scale, financial efficiency, green finance, and other control variables have lagged in four periods. The panel Granger causality test is carried out using Eviews 9.0 software to assess the direction of causality between each variable and clean energy industry development. The results are given in

Table 7. Granger causality test results.

Causal Relationship	Whole Country	Eastern Region	Central Region	Western Region
DFIR does not Granger-Cause DCE	2.40511 **	0.29353	2.11952 *	2.76571 **
DCE does not Granger-Cause DFIR	0.57985	0.16651	0.26806	0.88117
DFE does not Granger-Cause DCE	0.45630	0.23063	0.57874	2.35747 **
DCE does not Granger-Cause DFE	2.85197 **	0.57625	2.37085 *	3.49746 ***
DGCL does not Granger-Cause DCE	1.99875 *	2.29428 *	0.58916	1.06009
DCE does not Granger-Cause DGCL	0.37138	0.68928	1.43874	0.71572
DIS does not Granger-Cause DCE	4.11083 ***	3.31518 **	2.44465 **	2.67825 **
DCE does not Granger-Cause DIS	0.20734	0.72962	0.20180	0.28657
DTI does not Granger-Cause DCE	2.10157 *	2.14703 *	1.71213 *	2.71378 *
DCE does not Granger-Cause DTI	0.92303	3.08413 **	2.22979 **	3.13659
DPFE does not Granger-Cause DCE	2.21411 *	2.68133 *	0.43817	3.21205 ***
DCE does not Granger-Cause DPFE	1.71857	0.60056	0.80529	1.02880
DPCE does not Granger-Cause DCE	2.02123 *	0.79355	2.36527 *	1.96295 *
DCE does not Granger-Cause DPCE	1.63217	1.09926	0.55624	1.70772

Note: ***, **, and * indicate significance at the significance levels of 1%, 5%, and 10%, respectively.

.

(1)

At the national level, the financial scale is a Granger cause of clean energy development at a significance level of 5%, and green finance is also a Granger cause of clean energy industry development at a significance level of 10%. Therefore, financial scale and green finance have a considerable impact on the clean energy industry. However, the test results indicate that the clean energy industry development is not a Granger cause of financial scale and green finance, and it is accepted that financial efficiency is not the Granger cause of clean energy industry development. Furthermore, at a significant level of 5%, the clean energy industry development is a Granger cause of financial efficiency. Therefore, there is only a one-way causal relationship between three financial variables and clean energy industry development. In addition to this, green finance in the eastern region is the Granger reason for the clean energy industry development there, financial scale in the central region plays a pivotal role as the Granger cause for clean energy industry development, and both financial scale and financial efficiency in the western region act as the Granger causes for clean energy industry development.

(2)

Among the control variables, we observe Granger effects for clean energy industry development including the industrial structure across the entire nation as well as in the eastern, central, and western regions. Fiscal expenditure impacts clean energy development in the entire country, as well as in the eastern and western regions. The relative price of energy influences clean energy development in the whole country, particularly in the central and western regions. Lastly, technological progress plays a significant role in clean energy development across the entire nation and in the eastern, central, and western regions. Furthermore, clean energy industry development in the eastern and central regions is also a Granger cause for technological progress. Therefore, we observe a two-way causal correlation between technological advancement and clean energy industry development in the eastern and central regions.

3.4. Impulse Response and Variance Decomposition

3.4.1. Impulse Response

While the GMM estimation method for the panel VAR model allows us to obtain estimation coefficients between variables, it is important to recognize that the PVAR model is dynamic. Consequently, GMM estimation may not fully capture the intricate relationships between these variables. To address this limitation, we turn to panel impulse response analysis, a dynamic tool that stimulates the standard impact of one variable on another without affecting other variables. Through 500 Monte Carlo simulations, this paper obtains an impulse response diagram for clean energy industry development, financial scale, financial efficiency, green finance, industrial structure, technological progress, financial expenditure, and relative energy prices.

Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7 show the impulse response function diagrams simulated through Monte Carlo analysis, assessing the impact of various variables on clean energy industry development.

(1)

The impact of the clean energy industry on itself

It is noticeable from Figure 2 that the impact of the national and regional clean energy industry on itself shows a positive response in phase 0, reaching its maximum. This indicates that China’s clean energy industry development is highly self-reliant during this initial phase. However, this dependence gradually diminishes over time. In phase I, it shows a negative effect, which subsequently evolves. Ultimately, the overall value stabilizes near zero in the long run.

Figure 2. Response of dCE to its own impact in the entire country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis represents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 2. Response of dCE to its own impact in the entire country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis represents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

(2)

The impact of financial scale on the clean energy industry

As shown in Figure 3, the influence of financial scale on clean energy development exhibits a lag effect at the national level. After a unit impact on financial scale, the response of the clean energy industry in the current period is 0, and there is a negative response in phase I, which then gradually rises, reaching a positive maximum in phase IV. Subsequently, the effect decreases gradually while remaining above zero. Initially, financial scale tends to restrain the clean energy industry, but it does not persistently inhibit its growth. In the long run, however, financial scale contributes positively to promoting clean energy industry development. Expanding financial scale aligns with the needs of the clean energy industry as an emerging sector requiring initial growth funding. Notably, in the central and western regions, after a financial impact, the clean energy industry shows a response pattern similar to that of the whole country in the first two periods. In the central region, the development of the clean energy industry increases in the first period, reaching a positive maximum in the third period, and then decreases while still maintaining an overall positive impact. Similarly, in the western region, there is a positive maximum in the fifth period, followed by a slight decrease. However, the overall effect remains positive, indicating that financial scale in these regions positively influences clean energy industry development and fosters sectoral growth. The eastern region exhibits slight differences from the other regions. As shown in Figure 3b, financial scale consistently has a positive impact on the clean energy industry. Although the reaction degree varies slightly in each period, the impact remains relatively stable, demonstrating that financial scale plays a consistently significant role in promoting the clean energy industry in the eastern region.

Figure 3. Response of dFIR to dCE impact in the whole country and eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 3. Response of dFIR to dCE impact in the whole country and eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

(3)

The impact of financial efficiency on the clean energy industry

In Figure 4, at the national level, after exerting a positive control on financial efficiency, the response of the clean energy in the current period is 0, reaching a positive maximum in phase IV and then slightly decreasing while remaining above zero. In the eastern region, when impacted by financial efficiency, it shows a positive effect in phase I, indicating that financial efficiency can promote clean energy in the short term. However, over the long run, the impact of financial efficiency on clean energy industry development exhibits a negative trend. Interestingly, the central region differs from the other regions: apart from a short-lived negative effect in the third period, it consistently maintains an overall positive promoting effect. Meanwhile, the western region shows a pattern similar to that of the whole country, displaying a positive effect in phase I and negative effects in phases II–IV. Overall, the general trend of regional financial efficiency’s impact on clean energy industry development varies across regions.

Figure 4. Response of dFE to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 4. Response of dFE to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

(4)

The impact of green finance on clean energy industry

In Figure 5, we observe that both at the national level and in the eastern region, a positive impact of green finance initially results in a response of clean energy of 0 and then gradually increases, showing a positive maximum in phase III. A notable divergence occurs in phase IV: while the national response decreases to 0, the eastern region shows a negative effect before gradually rebounding, ultimately showing a positive promoting effect. This indicates that green finance, both nationally and in the eastern region, contributes to long-term clean energy development. In the central region, green finance initially shows a negative impact during the first two periods, but then the response of clean energy turns positive. This indicates that green finance in the central region initially hinders clean energy development in the short term, but it does not persistently impede progress. Over the long run, green finance still promotes clean energy development, particularly during the initial phase. Similarly, in the western region, clean energy initially responds to green finance positively, reaching a positive maximum. Although clean energy subsequently experiences a decrease, green finance continues to play a positive role in fostering clean energy industry development. Despite these short-term fluctuations, green finance consistently encourages clean energy development across regions.

Figure 5. Response of dGCL to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 5. Response of dGCL to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

(5)

The impact of industrial structure on clean energy industry

As shown in Figure 6, at the national level, we observe that the industrial structure positively impacts clean energy development. There is no response in the initial period; it then begins to decline, reaching its lowest value in phase II. Then, it begins to show an upward trend, indicating that the industrial structure initially inhibits the development of clean energy due to its weak sustainability level. But, over the long run, improving the industrial structure becomes a driver for promoting clean energy development. In the eastern region, the development of clean energy consistently maintains a positive trend. This signifies that the improvement of industrial structure in the eastern region plays a crucial role in promoting clean energy development. The central region shows a nuanced pattern: although the effect of industrial structure on clean energy initially shows a negative effect during earlier periods, an analysis of impulse response results over 12 periods (not listed in this paper) indicates that industrial structure ultimately promotes clean energy development in the long term. Thus, the impact of industrial structure on clean energy development in the central region is sustained. In contrast, the western region initially experiences no response to changes in industrial structure. Subsequently, it starts showing a negative trend. Although there is a brief positive effect in the second period, the long-term trend remains negative. This suggests that the industrial structure in the western region inhibits the development of clean energy.

Figure 6. Response of dIS to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 6. Response of dIS to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

(6)

The impact of technological progress on clean energy industry

In Figure 7, we identified that after technological progress, clean energy development in China initially exhibits no immediate response and then gradually began to produce a positive trend. Although there are slight fluctuations in the mid-term, it maintains an upward trend in the long run, underscoring the expanding impact of technological progress on clean energy industry development. This elevation, however, will varies slightly over time. The underlying reason is the dramatic influence of technology during the early improvement and utilization of clean energy. As time passes, the degree of clean energy technology innovation and development gradually slows down, leading to fluctuations in clean energy development. In the eastern region, following a unit impact of technological progress, clean energy development rises rapidly in phases I and II. Similar to the entire country, despite slight fluctuations in the middle, it consistently shows a positive impact. This suggests that technological improvements in the eastern region continuously uphold clean energy industry development. In the western and central regions, the patterns resemble those of the overall country. But there is a brief negative impact in the third period. Notably, due to the abundance of energy resources in the western region, the reaction degree shows that clean energy development is significantly impacted by technological progress.

Figure 7. Response of dTI to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

Figure 7. Response of dTI to dCE impact in the whole country and the eastern, central, and western regions. Note: (a–d) refer to the overall country and the eastern, central, and western regions, respectively, the same as below. The horizontal axis represents the lag period of influence, the vertical axis rep-resents the degree of influence, the solid line represents the impulse response function curve, while the upper and lower dashed lines represent the boundaries of the 95% confidence interval.

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Impact of fiscal expenditure on clean energy industry

As shown in Figure 8, at the national level, the clean energy industry initially does not respond immediately after a positive impact from the national fiscal expenditure. However, it then shows a positive response in phase I, mainly because the clean energy industry relies on government financial subsidies during its early stages of survival and development. This highlights the importance of government funding alongside market-oriented finance and crucial assistance from relevant government departments. After phase I, the response begins to decline, forming a negative trend in phase II. It subsequently starts to expand gradually. In the long run, the overall impact remains positive, indicating that the expansion of national fiscal expenditure is conducive to improving the developmental level of the clean energy industry. In the eastern region, clean energy remains stable in the current period but then gradually rises. In general, it plays a positive role in promoting clean energy development. From the perspective of the reaction degree, fiscal expenditure in the eastern region significantly impacts clean energy development compared to the other regions. In the central region, fiscal expenditure has an effect in terms of response degree, but it shows a positive promotion effect as a whole. Meanwhile, in the western region, fiscal expenditure has a constructive impact on clean energy development. The response degree in the western region aligns closely with that observed in the entire country.

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The impact of relative energy prices on the clean energy industry

From Figure 9, we can see that the impact of energy prices on the development of the clean energy industry in China does not respond initially, but it shows a negative response in phase I and a positive maximum in phase II. Subsequently, in phase II, there is a positive maximum, followed by a gradual convergence toward zero over time after phase V. In the eastern region, clean energy remains stable in the current period after the impact of energy prices. It then shows a positive maximum in phase II. The subsequent response gradually decreases to a negative value, experiences an increase again after phase III, and finally settles near zero after reaching a positive value in phase V. This pattern shows that energy prices play a promoting role in clean energy industry development, even with some short-term fluctuations. Although there is a temporary reduction phase, a slightly positive long-term trend emerges for clean energy. The central region’s response is comparable to that of the entire country. In the initial stage, the relative price of energy impacts clean energy industry development, but this effect is not consistently sustained. Instead, it alternates between inhibiting and promoting clean energy industry development, showing substantial overall volatility. Similarly, in the western region, the clean energy response follows a parallel to that observed in the eastern region. Although there are fluctuations in the long-term response process, it becomes evident that the promotional effect of energy prices on clean energy increases over time.

3.4.2. Variance Decomposition

To further investigate the influence degree of each variable, variance decomposition can be applied to analyze the variance contribution rates of the impulse responses from different equations to the fluctuation of individual variables. The results are presented in

Table 8. Variance decomposition of the whole country and the eastern, central, and western regions.

Variables	Periods	Whole Country	Eastern Region	Central Region	Western Region
dCE	5	97.13255	94.79161	95.90262	90.84486
dFIR	5	0.605383	0.134431	1.640744	1.3632
dFE	5	0.397961	2.071669	0.258463	0.835489
dGCL	5	0.427737	0.835189	0.35289	1.13151
dIS	5	0.538387	0.158457	1.044773	3.298526
dTI	5	0.169874	0.373197	0.457487	0.439469
dPFE	5	0.438242	0.903897	0.169858	1.872908
dPCE	5	0.289863	0.731551	0.173165	0.214037
dCE	10	97.03813	94.57334	95.39566	90.35776
dFIR	10	0.615191	0.237785	1.674518	1.357125
dFE	10	0.42061	2.0708	0.319465	0.85255
dGCL	10	0.434198	0.852151	0.42015	1.154205
dIS	10	0.546291	0.159246	1.040869	3.597552
dTI	10	0.203845	0.460871	0.64892	0.482699
dPFE	10	0.448374	0.902825	0.295523	1.972252
dPCE	10	0.293359	0.74298	0.204895	0.225853
dCE	15	97.03627	94.56798	95.37184	90.30537
dFIR	15	0.616138	0.238919	1.675175	1.359388
dFE	15	0.420779	2.070752	0.320049	0.870366
dGCL	15	0.434194	0.852362	0.42031	1.157017
dIS	15	0.546321	0.160618	1.041093	3.601576
dTI	15	0.204445	0.463577	0.651093	0.508061
dPFE	15	0.448484	0.902799	0.314931	1.972359
dPCE	15	0.293365	0.742998	0.205508	0.225866
dCE	20	97.03625	94.56798	95.37117	90.30308
dFIR	20	0.616141	0.238919	1.675236	1.359586
dFE	20	0.42078	2.070752	0.320084	0.870862
dGCL	20	0.434194	0.852362	0.420321	1.157385
dIS	20	0.546333	0.160618	1.041111	3.602583
dTI	20	0.204449	0.463577	0.651243	0.508295
dPFE	20	0.448484	0.902799	0.315291	1.972333
dPCE	20	0.293365	0.742998	0.205541	0.22588

, considering a total of 20 periods. This paper only focuses on the variance decomposition results related to the impact of variables within partial periods on dCE to save the length of the article.

The variance decomposition results between phase XV and phase XX show no significant difference, indicating that the interpretation degree of each variable on the error term of the dependent variable remains stable in the long term.

In the decomposition of the error term for clean energy industry development across the entire nation and in the eastern, central, and western regions, the explanation degree of its fluctuations changes from 100% in phase I to 90.30% in phase XX. This finding demonstrates the strong inertia inherent in clean energy industry development and shows that that its self-explanatory capacity gradually diminishes over the long term.

At the same time, we observe from Table 8 that eventual changes in clean energy development across various regions are significantly influenced by their own dynamics. The explanation degrees of the contribution of financial scale are 0.616% in the 20th period at the national level and 0.239%, 1.675%, and 1.36% in the eastern, central, and western regions, respectively. The contribution degrees of financial efficiency in the overall country and in the eastern, central and the western regions are 0.421%, 2.07%, 0.32%, and 0.871%, respectively. The contributions of green finance at the whole-country level and in the eastern, central, and western regions are 0.434%, 0.852%, 0.42%, and 1.157%, respectively. Additionally, the effect of financial efficiency in the eastern region is greater than that in the central and western regions. The impact of financial scale in the central region is greater than that in the eastern and western regions. Green finance in the western region plays a greater role than that in the eastern and central regions.

Lastly, when considering the impact of other factors, it becomes evident that national fiscal expenditure plays the most significant role in driving clean energy development in the eastern region. Following closely are the relative price of energy and technological progress. This indicates that fiscal expenditure significantly contributes to clean energy development in the eastern region. In the central region, it is the industrial structure that exerts the greatest impact, closely followed by technological progress. These findings underscore the pivotal roles played by both fiscal policies and structural factors in promoting clean energy adoption. In the western region, the industrial structure emerges as the primary driver of clean energy development, closely followed by financial expenditure and technological progress. This finding underscores the pivotal role played by the existing industrial framework in promoting clean energy adoption within the western context. Moreover, while the impact of various variables on the development of clean energy industry has undergone certain changes, it remains evident that these factors collectively possess relatively weak explanatory power compared to the actual growth of the clean energy sector.

4. Conclusions, Policy Recommendations and Future Research

4.1. Conclusions

Based on China’s provincial panel data from 1991 to 2018, this paper has employed a panel VAR model to investigate the impact of finance and other variables on the clean energy industry development in China. The key conclusions are as follows:

(1)

Financial development has significantly promoted the development level of the clean energy industry in China. There are substantial positive correlations between clean energy industry development and both financial scale and green finance. These factors contribute significantly to improving the overall development level of the clean energy industry. This finding aligns with prior research by Fan and Liu [36] and Zhang et al. [37], who also observed a positive impact of green finance on the clean energy industry. However, it is essential to note that their studies were limited to specific economic regions whereas this, our analysis, has explored these effects at a national level. Conversely, the effect of financial efficiency on the clean energy industry is insufficient. From the perspective of China’s broader economic context, financial efficiency does not emerge as a leading factor in the development of the clean energy industry. The variance decomposition results reinforce this perspective: the expansion of financial scale contributes most significantly to supporting clean energy industry growth, followed by green finance. In contrast, the impact of financial efficiency remains relatively weak.

(2)

In contrast to previous studies, this paper has investigated the impact of finance on the clean energy industry at a regional level. From this regional perspective, both financial scale and green finance at the national level serve as Granger causes for clean energy industry development. This means that financial institutions have effectively implemented relevant national policies, providing crucial financial support for the development of clean energy initiatives and meeting the imperative of sustainable energy development. In addition, it is important to recognize that the influence of financial development on the clean energy industry exhibits significant regional disparities. The energy storage capacities in the western and central regions surpass that of the eastern region. Despite increased government support in recent years, certain necessary resources are utilized due to geographical constraints, environment factors, and other natural conditions.

(3)

Regarding the control variables, industrial structure, technological progress, and fiscal expenditure have played positive roles in clean energy industry development. However, it is essential to recognize that the impact of technological progress has waned over time due to the increasing complexity of clean energy research and development (R&D). As for the relative price variable related to energy, it falls short in accurately representing the true cost of clean energy, so its effect on clean energy remains somewhat unstable.

4.2. Policy Recommendations

The development of clean energy, energy transformation, reducing fossil energy consumption, and building robust clean energy systems represent essential steps in reducing carbon dioxide emissions and ultimately achieving global carbon neutrality. Based on the conclusion of the empirical analysis, this paper puts forth several measures to foster the long-term development of the clean energy industry and enhance the effectiveness of financial products in supporting this critical sector.

4.2.1. Expand Financial Scale and Increase Bank Support for the Clean Energy Industry

The initial stages of establishing and growing a clean energy industry involve substantial investment, often with extended return periods. Consequently, operational risks for enterprises in this sector are notably high. To navigate this complexity successfully, robust support from credit funds provided by banks and other financial institutions becomes essential. Considering regional particularities and specific conditions, particularly in the central and western regions, responsible authorities should strategically leverage existing energy advantages. Adequate financial resources must be allocated to fully realize the role of financial scale in promoting clean energy industries. At the national level, the government should continue to transform credit management systems within state-owned commercial banks. Establishing a professional credit decision-making framework, along with dedicated personnel overseeing credit approvals, will broaden the scope of credit support for clean energy initiatives, ensuring long-term benefits. This approach should not prioritize short-term gains; it must also consider broader social values. Furthermore, enhancing the quality of credit services and expanding credit lines within banking institutions specifically in favor of clean energy enterprises is critical. Finally, streamlining the loan approval process will ensure efficient financial support for the clean energy industry, fostering competitiveness and driving innovation.

4.2.2. Improve Financial Efficiency and Rationally Allocate Financial Resources

To foster a standardized allocation of financial resources, financial institutions must actively contribute to promoting and supporting the clean energy industry. Once the initial promotional impact stabilizes, it becomes crucial to focus on improving financial efficiency to maintain a robust and sustainable clean energy sector.

Step 1 is to enforce measures that restrict banks and other financial institutions from granting loans to environmentally polluting or resource-intensive energy enterprises. They must increase credit support for projects aligned with environmentally friendly practices and resource conservation. This strategic shift would encourage energy enterprises to transition toward greener development.

Step 2 is to accelerate the construction of a robust clean energy service system. This involves empowering the financial market to efficiently allocate social capital and financial resources. Specifically, one must increase support for high-tech clean energy industrialization projects and foster innovation in clean energy technologies. The ultimate goal is to drive the transformation of the entire energy industry toward a high-energy paradigm.

Step 3 must harness financial leverage effectively and allocate resources judiciously. The goal is to transform the linear development pattern of the energy industry—focused primarily on resource consumption—into a diversified circular model dominated by renewable energy resources. This strategic shift not only promotes sustainability but also lays the groundwork for a circular, resilient development paradigm. This approach aligns with Luo’s [17] perspective. In his assessment of the financial support pathways for clean energy development, Luo emphasizes the importance of enhancing financial innovation and service efficiency to foster the growth of the clean energy industry.

4.2.3. Improve the Preferential Policies for Green Finance and Accelerate Innovation of Green Credit Finance

China should improve its preferential policies for green finance to accelerate and innovate financial services related to environmental protection. The role of green finance in promoting the clean energy industry shows noticeable regional disparities, with the western region playing a pivotal role. To foster green finance in the eastern and central regions, local economic incentive policies can be introduced to reduce enterprise financing costs. Banks and other financial institutions can grant green credit to enterprises. Additionally, enterprises can raise funds by issuing green bonds dedicated to constructing and operating of clean energy projects. Implementing preferential policies—such as loan interest discounts, fee subsidies, and green guarantees—will incentivize both enterprises and financial institutions to actively participate in sustainable clean energy initiatives. Moreover, China should consider establishing a green development fund or environmental protection fund to drive innovation and the development of green finance products. Exploring non-credit financing elements—such as financial leasing, non-financial enterprise bond financing instruments, and green credit asset securitization—will further enrich the landscape of green financial products, ultimately bolstering the clean energy industry.

4.3. Future Research

This article has employed the panel vector autoregression (PVAR) model to investigate both short- and long-term impacts as well as the magnitudes of various factors, financial scale, financial efficiency, green finance, and other variables, on clean energy development. The paper has taken a comprehensive approach, examining national and regional perspectives, including the eastern, central, and western regions. Undoubtedly, this research holds significant value for shaping the future of clean energy. However, it is essential to acknowledge certain limitations. The chosen comprehensive index for measuring clean energy industry development level (CE) is accessible but relatively less comprehensive. To enhance the model’s alignment with real-world dynamics, future research could incorporate additional control variables. Factors such as national environmental and energy policies, population levels, and overall development status significantly influence the clean energy industry. Moreover, the division of 30 provinces into broad eastern, central, and western regions lacks specificity. A more nuanced approach in future studies—categorizing and comparing provinces based on their unique development levels—could provide insights into balanced development strategies, especially in regions with varying economic contexts.