Can Green Credit Contribute to Sustainable Economic Growth? An Empirical Study from China

Abstract

Green development is an inevitable trend of sustainable development: how does it affect green economic growth as the main channel of green project financing and the core force of building a green financial system? The present article measures the relationship between green credit and sustainable economic growth using a benchmark regression analysis model and explores the main influencing factors and regional characteristics that affect the coupling development of green credit and sustainable economic growth by combining mechanism and heterogeneity tests. The results of the study show that: (i) Green credit has a significant positive contribution to sustainable economic growth. (ii) In terms of the transmission mechanism, industrial upgrading and environmental regulation have a significant impact on sustainable economic growth. (iii) In terms of heterogeneity, the effect of green credit on sustainable economic growth is the most pronounced in the west, followed by the central and eastern regions of China. The policy implications of this study are that green credit in China is an inevitable trend, and that a sound policy supporting the legal system and information communication mechanisms should be promulgated to ensure the effective allocation of resources, thereby promoting the coordinated, sustainable and stable development of environmental protection and the economy.

1. Introduction

The report of the 19th National Congress of the Communist Party of China clearly regards “pollution prevention and control” as one of the three most difficult challenges and points out that “the ecological environment must be treated as life”. The core of China’s future economic development is scientific development, and the top priority is to properly handle the relationship between resources and environment and sustainable economic growth, and to improve the quality of economic development. In 2002, because the administrative means could not effectively deal with environmental deterioration, global warming and other ecological problems caused by world economic development, the Equator Principles (Eps) and Green Credit (GC) emerged. In the process of modern economic development, credit funds are an important driving force, and credit supports and guides the development of the real economy.

In 2019, China’s total energy consumption, total coal consumption and total crude oil consumption increased by 3.3%, 1% and 6.8%, respectively, compared to 2018. Meanwhile, clean energy consumption accounted for only 23.4% of the total energy consumption, 53.4% of ambient air pollution exceeded the standard in 337 cities and the cumulative number of heavily polluted days increased by 88 days compared to 2018. In late 2015, China made a commitment to the world regarding the “total amount” and “intensity” of carbon emissions: in terms of “total amount”, total carbon emissions will peak by 2030 and in terms of “intensity”, i.e., carbon dioxide emissions per unit of GDP, they will be reduced by 60–65% by 2030 compared to 2005. The Fifth Plenary Session of the 19th Central Committee pointed out that “continuously improving the quality of the environment is an important task in building a beautiful China and a powerful means of promoting high-quality development”, and that the Plenary Session demanded that “by 2035, a green way of production and life will be widely formed”. Therefore, the GC model, which focuses on technologies to save resources, improve energy efficiency, prevent pollution and achieve sustainable development, has become an important driving force to support and guide the sustainable growth of the economy and achieve green transformation based on credit.

GC is known as sustainable finance or environmental finance, and according to Jose Salazar (1998), environmental finance is the combination of the financial sector and the environmental industry in order to protect the environment by means of finance [1]. According to domestic scholars Fang Hao and Ma Zhong (2010), the essence of environmental finance is an innovative financial model for the purpose of environmental protection [2]. Many scholars have argued that the implementation of green credit policies is beneficial to the development of the whole country [3,4,5,6]. Thus, promote the optimization and upgrading of green industries [7], the expansion of credit scale can achieve resource allocation [8,9,10,11,12,13] and reduce financing costs. This will add momentum to sustainable economic development [14]. This is consistent with Alan’s findings from the US Economic Census [15,16,17,18]. According to Schumpeter’s Theory of Economic Development, credit capital flows spontaneously into areas consistent with social development, promoting industrial upgrading, leading technological progress and driving economic development. Subsequently, other international scholars focused on the impact of environmental constraints on economic sustainability. There is currently a wide range of opinions on the economic effects and green impact mechanisms of GC. On the one hand, the relationship between GC and economic growth and industrial structure has been studied from a macro-level perspective [7,19,20,21,22,23,24] and the results suggest that credit policies are ineffective in the long run [25] or hinder macroeconomic development [26]. However, other scholars have confirmed the positive effects of GC on regional economies [27]. On the other hand, the impact of GC on enterprises and banks has been examined from a micro-enterprise perspective. Firstly, from the perspective of the impact on enterprises, some studies have found that GC has a positive effect on the technological progress of enterprises [28]. In particular, technological progress has a dampening effect on carbon emissions, with an inverted U-shaped relationship between economic growth and carbon emissions [16]. Chinese scholars have conducted further research on this basis and found that technological progress on carbon emissions [29,30] has a dampening effect that is influenced by industry and region [31]. In addition, the impact on medium and low carbon emissions is more significant than that of high carbon emission sectors [32,33,34]. However, an important aspect of GC policy is that it is a policy that can be used to reduce carbon emissions. An important orientation of green credit policy is to require the banking sector to reduce its credit exposure to the two high and one surplus sectors and to reduce energy consumption by controlling external financing, thereby achieving environmental benefits. Therefore, many scholars have tapped into this new research hotspot on the relationship between economic growth and energy consumption [35]. Some scholars have shown that there is no causal relationship between the two [36]. However, the mainstream view is positive, mainly on whether there is a one-way [37,38,39,40] or two-way causality [41,42], which is currently not uniform. Chinese scholars are more likely to explore the impact of GC on corporate investment and financing [19,20,21,22,23,24]. The policy has significantly increased the ease of access to finance for green-listed companies, but has not significantly reduced their financing costs (Niu, Haipeng et al., 2020) [43,44,45]. In particular, it exacerbates the financing penalties and investment disincentives for heavily polluting enterprises [46]. On the contrary, the policy on green innovation by micro enterprises has not significantly reduced the cost of financing (Niu et al., 2020). There is less literature on the green innovation behavior of micro enterprises, mostly from the perspective of environmental regulation and the impact of development zone policies on green innovation, but not from the perspective of financing constraints [47,48,49].

Secondly, in terms of impact on banks, GC is essentially a macro-regulatory approach to promote environmental protection through credit instruments and is the main option for banks and financial institutions to fulfil their environmental responsibilities [50]. It also helps to improve risk management capabilities [12,13,14]. Early explorations of the relationship between green finance and economic growth were largely qualitative in nature [51], but later case studies on sustainable finance emerged [52,53,54] and were reported in the literature. The impact of green finance on economic cycles and growth was explored through exogenously given green finance policies [55,56]. The literature on green finance has been largely qualitative in nature. Most of the research to date has been on the misallocation of financial resources [56], credit risk and policy effects [57,58].

Environmental regulation has been adopted in various regions of the world to address the challenges of environmental pollution and sustainable economic growth by implementing green credit policies in terms of corporate social responsibility, risk management and credit environment [59,60,61,62,63,64]. Of course, the development of GC in China has not been smooth due to the lack of entrepreneurship or local government regulation, insufficient implementation of GC and inadequate supporting systems [65,66]. Although there is a growing body of research on green finance, none of the above studies have verified the impact of GC policies on sustainable economic growth and have dug deeper into the economic effects created by the influencing factors. In view of the serious resource and environmental problems currently faced by China’s economic development and the importance of credit funding for enterprise development, the purpose of this study is to explore whether GC can help to promote sustainable economic growth and achieve the goal of improving a high-quality economy. Taking developing countries such as China as an example, we conduct in-depth research into the economic effects of industrial upgrading and environmental regulation on GC supporting sustainable economic growth. Then, we implement suitable credit policies for different regional economic levels. The empirical analysis section tests the mechanism from the perspectives of industrial advancement and environmental regulation to study their impacts. As for the specific impact path, please refer to Section 3 of this paper (Theory Model). Considering the different levels of economic development and finance in different regions, China is divided into eastern, central, and western regions in the heterogeneity test, and according to the overall level of financial development, it is divided into [0, 0.1), [0.1, 0.2), [0.2, 0.3) and [0.3, 1). In order to avoid the endogeneity problem of explanatory variables, the generalized estimation of moments (GMM) method is used for the analysis. At the same time, in order to ensure the reliability of the research conclusions and the comparability of the research samples, a robustness test is carried out. This research is mainly divided into seven sections: Introduction, Methods, Current Status, Theory Model, Empirical Test Results, Discussion, Conclusions, and Recommendations. The main research contributions are highlighted in Section 5, Section 6 and Section 7 (See Appendix A, Figure A1: A flowchart of the analytical process).

2. Methods

In this paper, we constructed a benchmark regression analysis model to explore whether GC can support sustainable economic growth. The model depicts the correlation between GC variables (e.g., industrial sophistication, environmental regulation and urbanization rate) and the development of GC in China. Under the benchmark regression analysis model, there are mechanism, heterogeneity, endogeneity and smoothness tests. Our main contributions in this paper are to investigate whether GC can support sustainable economic growth using data from 30 Chinese provinces and cities as examples; to discuss specifically the mechanistic effects of industrial sophistication and environmental regulation on sustainable economic development through the four tests mentioned above; and to explore the differences, influencing factors and regional characteristics of the effects of GC under different economic conditions and financial levels. In this paper, we differ from the existing literature in that, in order to examine a range of effects of green credit on sustainable economic growth, we divide the tests into regional and financial levels from two main perspectives, namely industrial sophistication and environmental mechanisms, in order to reduce error lengthening time. In the mechanism test section, the two perspectives of industrial sophistication and environmental regulation are examined separately. Previous scholars [14] also mentioned that GC has a more significant impact through industrial upgrading, especially in secondary and tertiary industries, driving regional economic growth. At the same time, GC mainly provides innovative and environmentally friendly enterprises with access to finance [67]. Given the theory of environmentally constrained economic growth, environmental regulations can raise the production costs of industries and hinder economic growth. Conversely, previous scholars have mentioned that there is a significant positive correlation between sustainable economic growth and environmental improvements [15].

3. Current Status of GC and Sustainable Economic Growth in China

Since the reform and opening-up, China’s economy has developed rapidly. Despite the epidemic in recent years, China’s GDP reached USD 18.9 trillion in 2021. Although China’s economy has entered a new normal, along with the continuous development of economic construction, there are also certain problems, such as environmental pollution, resource consumption and overcapacity. In 2021, the scale of China’s sanitation market was about USD 37.233 billion, and the high cost of environmental governance has sounded the alarm for sustainable economic development in the future. With the deployment of the “14th Five-Year Plan”, GC has become an important part of the development of green finance. Its main role is to promote the healthy development of the economy in the environment of resource conservation, efficient utilization, energy conservation and emission reduction, and to realize the coordinated development between the ecological environment and green sharing.

According to Figure 1, the development level of green finance in China has gradually improved, and the scale of GC has gradually expanded. In June 2020, the balance of domestic and foreign currency green loans of major financial institutions in China reached USD 1.71 trillion. According to the data from the Shanghai Clearing House, the green bond financing instruments in 2019 were USD 10.120 billion. According to the practice of scholar Su Dongwei [4], the GC index was selected to reflect the development status of GC in various provinces and cities in China. At present, the GC index levels of Beijing, Guangdong and Shanghai are relatively high, occupying the top three in the country, reaching 0.839, 0.421 and 0.403, respectively; Tianjin, Jiangsu, Zhejiang and Shandong all exceed the national credit index average by 0.2389, while the GC index level of the remaining 23 provinces and cities was basically between 0.1 and 0.2. Comparatively speaking, the GC index levels in the eastern and southeastern coastal areas are relatively high, while those in the central and western regions are generally lower, which is mainly related to the local economic level. As the coastal areas in the east and southeast belong to developed economy parts of China, their financial development is far more saturated and has a better foundation for promoting GC. On the contrary, the economy of the central and western regions is relatively backward, especially the backward regions, and remote regions that are excluded from traditional finance. Their financial demand and efficiency are not satisfactory, and their financial development is relatively lagging behind. As a result, the effect of GC policy is not enough in breadth and depth, which needs further guidance and improvement.

Green GDP refers to the total accounting index to measure the real national wealth newly created by countries after deducting the loss of natural assets. To put it simply, it is the real total national wealth obtained by deducting the cost of economic losses caused by certain factors, such as environmental pollution, natural resource degradation, low education, uncontrolled population and poor management from the current GDP statistics. In order to strengthen the government’s guidance, the support for green industry, to vigorously expand the investment and financing channels of green industry is an important direction of developing green economy. In 2017, Guangdong Province took the lead in promulgating the “Implementation Rules for the Construction of a Green Financial Reform and Innovation Pilot Zone in Guangzhou, Guangdong Province”, actively building a mechanism for green financial services and leading the upgrading and development of the industrial structure. By September 2019, the balance of green loans of regional banking institutions in Guangzhou had exceeded USD 46.503 billion. It can be observed from Figure 2 that, in 2020, the per capita green GDP of Shanghai, Beijing and Tianjin reached USD 22,482, USD 21,999 and USD 19,887, respectively. Among them, eight provinces and cities, including Jiangsu, Fujian, Guangdong, Zhejiang, Chongqing, Shandong, Guangxi and Hubei, also exceeded the national average per capita green GDP by USD 102.04 million. The per capita GDP of the remaining 19 provinces and cities was between USD 4300 and 1024. Areas with a high level of economic development generally have greater advantages in environmental regulation, science and technology, and ecological economy and gather more green industries to promote green economic development. Sustainable economic growth is the key to achieving “lucid waters and lush mountains”, and the development of a green economy also requires the support of green industries. GC allocates resources rationally through certain mechanisms, such as capital formation, capital orientation and information transmission. By controlling the scale and direction of credit supply, funds will be transferred to green industries, such as new energy, energy conservation and environmental protection, and enterprises and projects with high energy consumption and high pollution will be eliminated. The project will ultimately realize the transformation of the industrial structure to be green and high-end, and promote the development of green industries and regional economic growth. In turn, the development of green industries also has a positive effect on regional economic growth.

4. Theory Model

(i)

Baseline regression analysis model

This paper selected the panel data of 30 provinces and cities in China from 2001 to 2020 to verify the impact of GC on sustainable economic growth. To this end, this paper constructed a benchmark regression model as follows [68]:

$$LGD{P}_{it}={\alpha }_0+{\alpha }_{1}GC{R}_{it}+{\alpha }_{2}G{F}_{it}+{\delta }_i+{\gamma }_t+{\epsilon }_{it}$$

(1)

Equation (1) represents the green GDP of each province and city i in year t. This paper selected Green GDP (GDP–depletion value of natural resources—environmental pollution loss value + environmental improvement benefit) to represent sustainable economic growth. This paper selected the green credit index to represent GC. Referring to the People’s Bank of China’s green credit evaluation method, we selected five indicators: the proportion of green loan balance, the proportion of green loan increment, the year-on-year growth rate of green loan balance, and the green loan non-performing ratio. The weight of each indicator is 20% for measurement calculation. $GC{R}_{it}$ is the GC index of each province and city i in year t, and its coefficient ${\alpha }_1$ measures the impact of GC on sustainable economic growth, which is the core parameter of this paper. If ${\alpha }_1$ remains significantly positive after controlling for a range of provincial and municipal characteristics $GC{R}_{it}$, GC will positively contribute to sustainable economic growth, and vice versa. α₀ is the intercept of the equation and ${\alpha }_2$ measures the impact of provincial and municipal characteristics on sustainable economic growth. In addition, this paper also controlled for provincial and municipal fixed effects ${\delta }_i$ and time fixed effects ${\gamma }_{t }$, thus further reducing the problem of omitted variable bias. ${\epsilon }_{it}$ denotes the error term. All regressions were estimated in Stata15.1. *, ** and *** indicate the statistical significance at the 10%, 5% and 1% levels.

(ii)

Indicator system

This study was constructed based on a system of key indicators from a benchmark regression analysis model, taking into account the fact that the impact of green credit on sustainable economic growth evolves with changes in industrial sophistication and environmental regulation, as well as presenting different regional characteristics. Due to the poor availability and completeness of bank data on GC, and the different statistical calibers of published data, different statistical standards were used. Therefore, the GC Index was chosen as the core explanatory variable, and the larger the index, the higher the level of GC. According to Section 3 (the current situation), the current water average of the green credit index of 30 Chinese provinces and cities is at 0.2389.

Referring to the research of Li Jianglong et al. (2018) [69], factors affecting sustainable economic development were selected as the control variables. The main control variables were: (1) Urbanization rate: A higher level of urbanization can effectively improve the development level of China’s green economy; (2) The number of students in schools: In terms of human capital formation indicators, the study takes into account the education input factor. Based on the number of students in schools, it reflects the education level of the school-age groups in various provinces and cities, and provides sufficient labor production factors for the sustainable development of the economy; (3) Financial budget: The cost of sustainable and healthy economic development is reflected in the per capita financial expenditure, especially energy conservation and environmental protection financial expenditure, and financial security can promote industrial development in an orderly manner; (4) Foreign direct investment: The amount of foreign investment reflects the situation of import and export trade contracts, and has a positive role in promoting the economic development of the region. Some scholars have also confirmed that external financing also stimulates economic growth [44,46]. The data descriptions of the explained variables, core explanatory variables and a series of control variables are shown in

Table 1. Data description.

Variable Category	Variable Name	Metric or Description	Unit
Explained variable	Green GDP	GDP–natural resource depletion value–environmental pollution loss value + environmental improvement benefit	CNY ten thousand
Core explanatory variables	GC Index	Weighted average of GC observations	/
Control variable	Urbanization rate	Urban population, total population	%
	Number of students in schools	Number of students in colleges and universities	People
	Public budget	Budgetary expenditure	CNY hundred million
	Foreign investment	Foreign direct investment	USD ten thousand

.

(iii)

Data sources and processing

The enquiry into the calculation of the green GDP was obtained from the China Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook and China Price Statistical Yearbook. The prices involved in the accounting are mainly from the China Environmental Economic Accounting Guide. The rest of the data were obtained from the China City Statistical Yearbook, WIND database and CSMAR database. See Appendix B for data sources and download dates. In order to maintain the stability of the data and prevent certain problems, such as heteroskedasticity from affecting the regression results, the natural logarithms of the green GDP and fiscal budget were processed in this paper. Missing values and outliers were excluded for individual years, and the descriptive statistics of the variables are shown in

Table 2. Descriptive statistics of each variable.

VarName	Obs	Mean	SD	Min	Median	Max
Green GDP	570	6.7377	1.9374	0.5653	6.8606	12.4637
GC Index	570	0.1406	0.0930	0.0418	0.1151	0.7930
Urbanization rate	570	0.5172	0.1485	0.2280	0.5025	0.8960
Number of students in schools	560	39.5933	26.2221	0.2889	35.8960	116.0830
ln public budget	570	7.5014	1.1673	4.3626	7.6189	17.5048
Use of foreign capital	570	57.2708	70.6226	0.0000	27.2985	357.9091

.

In order to effectively measure the closeness between the variable factors, a bivariate correlation test was selected for this study. The purpose was to initially explore the relationship between the explanatory and explained variables and secondly to test the correlation between the explanatory variables. In the correlation matrix in

Table 3. Correlation test.

	Green GDP	GC Index	Urbanization Rate	Number of Students in School	Public Budget	Use Foreign Capital
Green GDP	1
GC Index	0.7003 ***	1
Urbanization rate	0.6523 ***	0.7270 ***	1
Number of students in schools	0.7046 ***	0.4667 ***	0.3276 ***	1
Public budget	0.2961 ***	0.2522 ***	0.1450 **	0.1673 **	1
Use of foreign capital	0.6572 ***	0.5511 ***	0.4605 ***	0.7630 ***	0.1260 **	1

** and *** indicate the statistical significance at the 5% and 1% levels.

below, the correlation coefficients between the variables were below 0.8, indicating that there was no co-linearity between the variables.

5. Empirical Test Results

(i)

Research hypothesis

According to Equation (1), its coefficient measures the impact of GC on sustainable economic growth and is the core parameter of this paper. In the mechanism test, the impact of industrial upgrading and environmental regulation on sustainable economic growth is measured from two perspectives: factor input and environmental constraints [30], and innovative technological innovation [35]. On the one hand, the factor input perspective considers the allocation of resources, innovative technological innovations and high-tech talents to promote the optimization and upgrading of industrial structure and sustainable economic development. On the other hand, the negative impact of increased costs for entrepreneurs and inadequate enforcement of government regulations is explored through the lens of environmental constraints. Combining the need for mechanism testing and heterogeneity testing in Section 2 (Methods), this study proposes the hypothesis:

Hypothesis 1 (H1).

Green credit is positively associated with sustainable economic growth.

Hypothesis 2 (H2).

Industrial upgrading has a positive effect on green credit to support sustainable economic growth.

Hypothesis 3 (H3).

Environmental regulation has a negative effect on green credit to support sustainable economic growth upfront.

Hypothesis 4 (H4).

Green credit supports sustainable economic growth at different regional levels.

Based on the above hypotheses, the impact of green credit on sustainable economic growth was subjected to benchmark regression analysis and mechanism, heterogeneity, endogeneity and robustness tests in turn. The specific findings of the study are presented below.

(ii)

Baseline regression analysis

The regression results of the benchmark model of Equation (1) in Section 3 are shown in

Table 4. GC and sustainable economic growth: regression results of the benchmark model.

	(1)	(2)	(3)	(4)	(5)
	Green GDP	Green GDP	Green GDP	Green GDP	Green GDP
GC	15.2456 ***	5.0014 ***	5.6210 ***	5.6302 ***	6.3193 ***
Index	(0.6650)	(0.6446)	(0.7290)	(0.7290)	(0.7704)
Urbanization		12.8822 ***	14.8643 ***	14.8710 ***	14.5156 ***
rate		(0.5545)	(0.8325)	(0.8325)	(0.8386)
Number of students			0.0119 ***	0.0119 ***	0.0 1 68 ***
in schools			(0.0037)	(0.0037)	(0.0042)
Public budget				0.0002	0.0002
				(0.0002)	(0.0002)
Use of foreign capital					0.0029 ***
					(0.0011)
_cons	4.5946 ***	−0.6284 ***	−1.2485 ***	−1.2532 ***	−1.2038 ***
	(0.1014)	(0.2360)	(0.3056)	(0.3056)	(0.3045)
N	570	570	560	560	560
r2_a	0.7661	0.8831	0.8832	0.8832	0.8845

Standard errors in parentheses: *** p < 0.01.

. From the results in column (1), the GC index is significantly positively correlated with green GDP [3,4,5,6]. Hypothesis 1 is verified in this case, which is also consistent with the results of numerous scholarly studies with an impact coefficient of 15.2456. The improvement of the GC index improves the financing environment; the expansion of the credit scale realizes the allocation [8,9,10,11,12,13] of resources, the scale effect is generated, credit cost is reduced and the level of green finance is accelerated [27]. It can be observed that the improvement of the GC index of each city reduces the financing cost and promotes the development of the green economy. Based on the results in column (2), after adding the control variable urbanization rate [7,70], the GC index, urbanization rate and green GDP are all significantly positive. Places with a high level of urbanization have a relatively high financial level, which is more conducive to solving the implementation and investment of GC and promoting the development of the green GDP. After adding certain elements, such as human capital, public budget and foreign capital into columns (3)–(5), the results of human capital, foreign investment and green GDP are still significant [7,70], with the influence coefficients reaching 0.0168 and 0.0029, respectively, and the influence coefficients are consistent with the actual meaning. The results of fiscal budget and green GDP are not significant, and the impact coefficient is relatively small.

(iii)

Testing the mechanism

A number of scholars have studied the impact mechanism of GC on sustainable economic development by promoting technological progress [28]. Considering that GC has many factors affecting economic sustainability, scientific research expenditure provides financial support for the optimization and upgrading of industrial structure, and environmental regulation mainly refers to the ratio of investment in pollution control to industrial added value, which can effectively reflect local efforts to achieve sustainable development. According to the previous theoretical analysis, the article choses to verify the two transmission mechanisms of industrial advancement and environmental regulation. Referring to the practice of the scholar Gan Chunhui [71], the ratio of the tertiary industry to the secondary industry was used to measure industrial upgrading [16]. Column (1) of the mechanism test results in

Table 5. Mechanism inspection: industrial advancement.

	(1)	(2)	(3)
	Green GDP	Industrial Sophistication	Green GDP
GC Index	6.3193 ***	0.6687 ***	5.4933 ***
	(0.7704)	(0.0984)	(0.7943)
Urbanization rate	14.5156 ***	1.5948 ***	12.5458 ***
	(0.8386)	(0.1071)	(0.9885)
Number of students	0.0068	0.0002	0.0070 *
in schools	(0.0042)	(0.0005)	(0.0041)
Public budget	0.0002	0.0001	0.0002
	(0.0002)	(0.0000)	(0.0002)
Use of foreign capital	0.0029 ***	0.0001	0.0028 ***
	(0.0011)	(0.0001)	(0.0011)
Industrial			1.2351 ***
sophistication			(0.3377)
_cons	−1.2038 ***	5.6437 ***	−8.1745 ***
	(0.3045)	(0.0389)	(1.9295)
N	560	560	560
r2_a	0.8845	0.9296	0.8872

Standard errors in parentheses: * p < 0.10, *** p < 0.01.

shows that the GC index has a significant positive impact on the green GDP, with an impact coefficient of 6.3193, indicating that GC can effectively promote sustainable [3,4,5,6] economic development. In column (2), the influence coefficient of the GC index on industrial upgrading is 0.6687, and it is significant at the 1% level. The advanced industrial structure reflects the characteristics of being high-end, high-value-added, knowledge-intensive, etc., which is more conducive to optimizing the allocation of resources, guiding the flow of funds from low-end industries to new energy, new technologies and new industries, and realizing the goal of optimizing the industrial structure. Column (3) shows that the coefficient of influence of the GC index on the green GDP is 5.5933, whose coefficient of influence of industrial advancement on the green GDP is 1.2351, and industrial advancement played a positive intermediary effect in GC supporting sustainable economic development [7]. In this case, hypothesis 2 is verified. The introduction of GC can effectively promote the sustainable development of the economy by optimizing the industrial structure to achieve advanced level. (4) Considering the other influencing factors of economic development, after adding the control variables, it was found that the urbanization rate, human capital and foreign investment have a significant impact on the green GDP, with the impact coefficients reaching 12.5458, 0.007 and 0.0028, respectively. The improvement of the level, the optimization of human capital and the increase in foreign investment can effectively promote the sustainable growth of the economy [7,70].

In the theory of environmentally constrained economic growth, the endogenous growth model given by Bovenberg and Smulders (1995) is an important reference point in this field [55,72]. In addition, Nordhaus (1994) and Nordhaus and Yang (1996) developed an endogenous growth model and explore the implications of sustainable growth by developing a model of economic growth that takes into account greenhouse gas emissions and recycling (DICE-RICE model [52,53]). The sustainable growth model examines the relationship between financial resource allocation and environmental pollution and economic development [56]. The environmental regulation was selected to test the mechanism of green credit and sustainable economic growth.

Environmental regulation was measured by selecting the ratio of the completed investment in pollution control projects in each province to the industrial added value. Column (1) of the test results in

Table 6. Mechanism check: environmental regulation.

	(1)	(2)	(3)
	Green GDP	Environmental Regulation	Green GDP
GC Index	6.3193 ***	−6.2618 ***	6.1626 ***
	(0.7704)	(0.7619)	(0.7600)
Urbanization rate	14.5156 ***	13.1404 ***	12.3890 ***
	(0.8386)	(0.8566)	(0.9750)
Number of students	0.0068	43.5617 ***	0.0098 **
in schools	(0.0042)	(10.9066)	(0.0042)
Public budget	0.0002	0.0681	0.0002
	(0.0002)	(0.4371)	(0.0002)
Use of foreign capital	0.0029 ***	7.9286 ***	0.0023 **
	(0.0011)	(2.8223)	(0.0011)
Environment			0.0001 ***
			(0.0000)
_cons	−1.2038 ***	−1.4504 ***	−0.2234
	(0.3045)	(0.3519)	(0.3833)
N	560	560	560
r2_a	0.8845	0.7664	0.8879

Standard errors in parentheses: ** p < 0.05 and *** p < 0.01.

shows that the impact of the GC index on scientific research investment is significantly positive, with an impact coefficient of 6.3193. The coefficient of influence of the GC index on environmental regulation in column (2) is −6.2618, mainly because the allocation of GC resources can promote the development of green industries, testing hypothesis 3, but the high cost of environmental pollution control affects the amount of GC negative impact [59,60,61,62,63,64]. However, some scholars have also found that economic sustainability has a negative impact on the amount of GC. Some scholars have found a positive correlation between sustainable economic growth and environmental improvement, with an inverted ‘U’ shape between environmental quality and income levels [16]. This study did not specifically compare environmental factors, which is a shortcoming. After the third (column) control variable is added, the impact of the GC index on the green GDP reaches 6.1626, and the impact mechanism of environmental regulation on the green GDP is significant, with an impact coefficient of 0.0001. A study in 2022 found that the intensity of environmental regulation in each province has a significant “inverted U-shaped” relationship [73] with the level of local green and low-carbon technologies. Environmental pollution control affects the amount of green investment at the early stage. With the development of the green industry and the innovation of low-carbon technology, this situation further promotes the sustainable development of the economy. This also shows that there is a dynamic and benign interaction between the investment of GC and environmental regulation and the growth of the regional green economy, showing a similar U-shaped characteristic of first depressing and then rising.

(iv)

Heterogeneity analysis

As can be observed from

Table 7. Heterogeneity analysis by region.

	(1)	(2)	(3)
	Green GDP	Green GDP	Green GDP
	East	Central	West
GC Index	6.5016 ***	8.4860 **	22.1882 ***
	(0.9339)	(3.6968)	(5.3577)
Urbanization rate	4.1472 *	9.9755 ***	9.6879 ***
	(2.2503)	(1.6912)	(2.1765)
Number of students	0.0170 **	0.0048	0.0308 **
in schools	(0.0077)	(0.0071)	(0.0120)
Public budget	0.0001	3.2120 ***	1.0261
	(0.0002)	(0.9434)	(0.8650)
Use of foreign capital	0.0025 *	0.0165 ***	0.0023
	(0.0014)	(0.0021)	(0.0036)
_cons	3.6177 ***	0.3571	−0.2589
	(1.0984)	(0.5430)	(0.5404)
N	184	205	171
r2_a	0.7790	0.8837	0.8796

Standard errors in parentheses: * p < 0.10, ** p < 0.05 and *** p < 0.01.

, the results of heterogeneity analysis based on regional differences show that, in the eastern, central and western regions, the GC index has a significant positive impact on the green GDP and, in contrast, the impact on the eastern region is relatively small, followed by the central and the west, which is relatively large. Hypothesis 4 is tested in this analysis, especially as it is also subject to industry and regional influences [32,33,34]. Due to the differences in economic level development, energy factors and ecological environment in the eastern, central and western regions [37,38,39,40,41,42], there are obvious regional differences in the impact of GC investment on sustainable economic growth. From the results in column (1) of Table 7, it can be observed that the impact coefficient of the GC index on the green GDP of the eastern region reaches 6.5016. After adding the control variables, the urbanization rate, the number of students in schools and foreign investment have a significant impact on green GDP, with impact coefficients of 4.1472, 0.0170 and 0.0025, respectively. The results in column (2) of Table 7 show that the influence coefficient of the GC index on the green GDP of the central region reaches 8.4860. After adding the control variables, the urbanization rate, public budget and foreign investment have a significant impact on green GDP, and the impact coefficients are 9.9755, 3.2120 and 0.0165, respectively. In the results in column (3) of Table 6, we can observe that the impact coefficient of the GC index on the green GDP of the western region is as high as 22.1882, which is higher than that of the eastern and central regions. After adding the control variables, the urbanization rate and the number of students in school have a significant impact on green GDP, with impact coefficients of 9.6879 and 0.0308, respectively. It can be observed that the impact of the GC index on green GDP is gradually increasing from east to west. The specific heterogeneity analysis results are as follows:

Based on

Table 8. Heterogeneity analysis by green finance development level.

	(1)	(2)	(3)	(4)
	Green GDP	Green GDP	Green GDP	Green GDP
	[0, 0.1)	[0.1, 0.2)	[0.2, 0.3)	[0.3, 1)
Green finance	7.3078 ***	6.8296 **	6.4513 **	2.2126 *
level	(1.3377)	(2.9519)	(2.0220)	(1.2345)
Urbanization	7.3579 ***	24.4079 ***	18.1074 *	16.4162
rate	(1.6813)	(1.9339)	(10.2867)	(11.5706)
Number of	0.0142 *	0.0122	0.1354 ***	0.2979 ***
students in schools	(0.0076)	(0.0079)	(0.0498)	(0.0545)
Public budget	3.3095 ***	0.0001	4.8405 *	1.8901
	(0.7978)	(0.0001)	(2.6218)	(1.2887)
Use of foreign capital	0.0012	0.0031 ***	0.0086 *	0.0010
	(0.0048)	(0.0011)	(0.0047)	(0.0016)
_cons	1.5976 ***	−4.3045 ***	−0.1193	5.5892
	(0.4644)	(0.6534)	(5.9062)	(7.9646)
N	219	254	59	twenty two
r2_a	0.9055	0.8355	0.6901	0.9780

Standard errors in parentheses: * p < 0.10, ** p < 0.05 and *** p < 0.01.

, a heterogeneity analysis was carried out based on the development level of green finance in each province. Different provinces and cities have different levels of financial development. GC is an important part of the green financial system. Many scholars use the proportion of GC balances in total loans to measure the development level of green finance. In order to more effectively reflect the degree of transfer of social funds by financial institutions, we referred to Chai Jingxia (2018) to measure the proportion of GC balance and overall deposits [74]. Because the overall level of green finance is relatively low, it was divided into four categories according to the sample frequency range: [0, 0.1), [0.1, 0.2), [0.2, 0.3) and [0.3, 1). Specifically, (1) under the development level of green finance in different provinces and cities, the GC index has a significant positive impact on green GDP. Areas with a high (low) level of economic development are adjacent, and areas with a high (low) level of green finance development are also adjacent. (2) In areas with higher levels of green finance, on the contrary, the influence of GC index on green input GDP decreased, which were 7.3078, 6.8296, 6.4513 and 2.2126, respectively. Areas with a low economic level have high financial demands, and GC investment can directly support local green enterprises to promote local economic development [8,9,10,11,12,13].

From the results in column (1) of Table 8, the green finance level in [0, 0.1) has a significant positive impact on green GDP, with an impact coefficient of 7.3078. After adding the control variables, the urbanization rate, the number of students in schools and the public budget have a significant positive impact on green GDP, with coefficients of 7.3579, 0.0142 and 3.3095, respectively. The results in column (2) of Table 8 show that the level of green finance in the [0.1, 0.2) category has a positive effect on green GDP, with an influence coefficient of 6.8296. After adding the control variables, the impact of urbanization rate and foreign investment on green GDP is significantly positive, and the impact coefficients are 24.4079 and 0.0031, respectively. From the results in column 8 (3), it can be observed that the green finance level in [0.2, 0.3) has a significant positive impact on green GDP, with an impact coefficient of 6.4513. After adding the control variables, the urbanization rate, the number of students in schools, the public budget and foreign investment have a positive effect on green GDP, and the influence coefficients are 18.1074, 0.1354, 4.8405 and 0.0086, respectively. The results in column (4) of Table 8 show that the level of green finance in [0.3, 1) has a positive impact on the green GDP, with an impact coefficient of 2.2126. After adding the control variables, the impact of the number of students on the green GDP is significantly positive. In short, with the improvement of the level of green finance, the driving effect of the level of finance on the green GDP gradually decreases. This also shows that it is necessary to optimize financial resources and maximize investment in areas with financial needs, especially in areas with low financial levels and unsatisfactory credit scale, which can give more play to their advantages and promote sustainable economic growth.

(v)

Endogeneity analysis

There may be endogeneity issues with the explanatory variables. First, the GC system accompanies the development of green finance and may have a positive causal relationship with the green economy; second, the measurement error of variables and the omission of important variables will cause endogenous bias in the estimation results. In order to ensure the robustness of the results, this paper continued to use the Generalized Moments Estimation (GMM) method for the endogeneity analysis. The estimation results in column (1) of

Table 9. Endogenous test.

	(1)	(2)
	Green GDP	Green GDP
Green GDP	0.9335 ***	0.8978 ***
	(0.0246)	(0.0293)
GC Index	1. 0510 *	1.080 5 *
	(0.6650)	(0.6459)
Urbanization rate		0.1680
		(0.1703)
Number of students		0.0026 **
in schools		(0.0013)
Public budget		0.00007 *
		(0.00004)
Use of foreign capital		0.00007
		(0.0005)
Constant term	0.5036 ***	0.5342 ***
	(0.1058)	(0.1220)
N	540	540
AR(2)	0.164	0.188

Standard errors in parentheses: * p < 0.10, ** p < 0.05 and *** p < 0.01.

show that the GC index has a significant positive impact on green GDP, and the impact coefficient is 1.0510. The instrumental variables and endogenous variables satisfy the correlation, and the exogeneity test shows that the instrumental variables are exogenous. Therefore, the selection of instrumental variables is reasonable. According to the results in column (2), after considering the endogeneity problem, the GC index still has a positive relationship with the green GDP, and the influence coefficient is 1.0805. After adding the control variables, the number of students in school and the public budget has a positive effect on the green GDP, and the influence coefficients are 0.0026 and 0.00007.

(vi)

Robustness tests

Table 5, Table 6, Table 7, Table 8 and Table 9 focus on examining the impact transmission mechanism and heterogeneity and endogeneity tests of GC and green economy. In order to further ensure the reliability of the research conclusions, this paper conducted a series of robustness tests. Considering that the research samples are more comparable,

Table 10. Robustness test.

	(1)	(2)	(3)	(4)	(5)
	Green GDP	Green GDP	Green GDP	Green GDP	Green GDP
Green finance	8.7264 ***	4.9257 ***	2.9459 ***	2.9512 ***	2.8676 ***
	(0.6223)	(0.7192)	(0.5833)	(0.5839)	(0.5829)
Urbanization		4.4554 ***	4.3256 ***	4.3214 ***	4.0586 ***
rate		(0.4960)	(0.3974)	(0.3979)	(0.4130)
Number of students			0.0354 ***	0.0354***	0.0309 ***
in schools			(0.0019)	(0.0019)	(0.0028)
Public budget				0.0001	0.0001
				(0.0003)	(0.0003)
Use of foreign capital					0.0023 **
					(0.0010)
_cons	5.5110 ***	3.7408 ***	2.6583 ***	2.6589 ***	2.8527 ***
	(0.1046)	(0.2200)	(0.1842)	(0.1844)	(0.2027)
N	570	570	560	560	560
r2_a	0.5004	0.5636	0.7218	0.7214	0.7235

Standard errors in parentheses: ** p < 0.05 and *** p < 0.01.

shows the level of green finance needed to replace the GC index for testing. From the results in column (1) of Table 9, the impact of green finance on the green GDP is significantly positive, and the impact coefficient reaches 8.7264. The significance results of the robustness test are basically consistent with the benchmark regression results. After adding the control variable urbanization rate in column (2) of Table 10, the impact of green finance on the green GDP is still significant, and the impact coefficient of urbanization rate on the green GDP is 4.4554. After adding the control variables of the urbanization rate and number of students in column (3) of Table 9, the influence coefficients of green finance, urbanization rate and number of students on the green GDP are 2.9459, 4.3256 and 0.0354, respectively. After columns (4) and (5) are added to the public budget and the use of foreign capital, the impact of green finance on the green GDP is still positive and significant, with an impact coefficient of 2.876, and the regression results remain basically unchanged.

6. Discussion

The objective of this study was to explore whether GC can support sustainable economic growth using data from 30 Chinese provinces and cities (2001–2020). These factors included core explanatory variables (GC index) and control variables (urbanization rate, school attendance, public budget and foreign investment input), which we validated based on a benchmark regression analysis model. Meanwhile, in the mechanism test section, we selected two perspectives to study, industrial sophistication and environmental regulation, and examined the differentiation of the impact of GC on sustainable economic growth by region and financial level. In contrast to the existing literature, we focused more on the mechanism test and the structure of the heterogeneity analysis, the mechanism impact arising from industrial sophistication and environmental regulation, the disaggregated differential impact and the regional characteristics. In this section, we discuss the main findings and research results.

First, the results of the benchmark regression analysis show that GC is positively associated with economic sustainability. The results are consistent with the findings of many domestic and international scholars on the beneficial effects of GC policies, mainly in the areas of financing model innovation, scale effect, green industries, technological progress and cost reduction [1,2,8,9,10,11,12,13,14,15,27]. When the control variables are added, the urbanization rate, human capital and foreign investment are each significantly positively associated with economic sustainability growth [7,43,44,70]. Previously, international scholars have focused on the impact of environmental constraints on green credit to support sustainable economic development, for example, Grossman’s study on the North American Free Trade Area (NAFTA) shows that environmental quality tends to fall and then rise with per capita income in an inverted ‘U’ shape [71]. The OECD confirms the existence of an environmental Kuznets curve [73], which shows a significant positive correlation between sustainable economic growth and environmental improvement [18]. Nowadays, the concept of green consumption is gaining popularity and green industries have become the key to support sustainable economic growth. Chinese scholars have combined green credit, green industries and economic growth [14]. Green industries create a lot of employment opportunities [16]. In this context, the role of GC in quality economic development was examined.

On the one hand, according to Table 5, the impact coefficient of GC on economic sustainability growth is 5.5933, with the impact coefficient of industrial upgrading on economic sustainability growth reaching 1.2351. The mechanism of the impact of GC on economic sustainability through the promotion of technological progress was previously obtained by many scholars [28]. The optimization and upgrading of industrial structures promote the optimal allocation of resources and play a positive mediating effect [7], so we were able to refine the specific extent of the impact of industrial advancement on GC’s support of economic sustainability. On the other hand, the relationship between environmental constraints and economic growth has attracted considerable attention [53,54,55,56]. In Table 6, the coefficient of the GC index on environmental regulation is −6.2618, but the coefficient of the impact of environmental rules on sustainable economic growth is 0.0001 [16]. The high cost of combating environmental pollution has a negative impact on the amount of GC invested [59,60,61,62,63,64]. This is consistent with the positive correlation between sustainable economic growth and environmental improvement, with an inverted U-shape between environmental quality and income levels [16]. Industrial upgrading also accelerates innovations in carbon emission technologies, and there is a causal relationship between energy consumption, through carbon emissions and R&D stocks, and economic growth in both the long and short terms [35,36,37,38,39,40,41,42]. This is the case in China. China is also currently working on GC as a financing channel for innovative and environmentally friendly companies [67]. However, other scholars have found that the policy has significantly increased the ease of access to finance for green-listed companies, without significantly reducing their financing costs [45]. In particular, it has made it more difficult for heavily polluting firms to obtain loans from banks [45]. There is a paucity of the literature on the green innovation behavior of micro enterprises in China, with both the pilot emissions trading policy and the new environmental protection law examining the impact on the innovation behavior of micro-enterprises from the perspective of environmental regulation or development zone policy [47,48,49]. The impact on the innovation behavior of micro-enterprises has not been examined from the perspective of financing constraints.

Finally, in terms of heterogeneity results: We divided China’s 30 provinces and cities into east, central and west according to administrative divisions, due to the differences in economic development, energy factors and ecological environment among the eastern, central and western regions [37,38,39,40,41,42]. As a result, the impact of GC investment on the sustainable economic growth in each region is characterized by an increasing trend from east to west. In addition to sectoral differences, there are also regional differences in the impact of GC on sustainable economic growth [32,33,34]. At the same time, according to the results of the green finance level test, the higher the level of green finance, the lower the impact of GC on sustainable economic growth. Green credit can contribute to economic growth by providing a capital element through differentiated pricing [31]. A more detailed comparison of the evolution of regional and temporal differences has not yet been possible.

7. Conclusions, Implications and Further Research

(i)

Conclusions

Green growth is the future direction of global economic growth, and GC, as a major component of green finance, plays an important role in promoting the upgrading of China’s industrial structure and green economic growth. We should vigorously develop GC, continue to improve GC policies and, through the differential treatment of GC, guide the flow of capital into energy-saving and environmentally friendly green industries, and restrict the development of the two high and one surplus industries, thereby promoting the transformation of industrial structures. This should also encourage the development of green innovation projects, reduce the risk of green innovation through green finance, promote green technological progress and also contribute to green economic growth. GC should also be invested in the clean energy sector to optimize the energy consumption structure and achieve green development [35,36,37,38,39,40,41,42]. The practical significance of this study is its ability to analyze the impact of green credit on sustainable economic growth based on Chinese data and to explore two major impact mechanisms—industrial sophistication and environmental regulation—which have guiding implications for green credit to serve sustainable economic development. It also examined the differences by region and level of development, and the need to tailor green credit policies to local conditions in their implementation.

Based on the panel data of 30 provinces and cities in China from 2001 to 2020, the different role paths of GC in promoting sustainable economic development were tested and analyzed. The main conclusions are as follows: (1) GC has a significant positive effect on sustainable economic development. The improvement of urbanization level and human capital can accelerate the development of green industries and promote sustainable economic growth. China should make greater efforts to implement green credit policies, further improve the level of green credit and establish a sound green financial system. (2) GC has a positive role in promoting sustainable economic development through industrial upgrading. This also shows that the expansion and effective allocation of GC can further optimize the industrial structure, adjust the proportion of polluting energy and develop green and low-carbon technologies to develop a green economy. In particular, green-oriented industries that focus more on carbon emission technologies and new energy sources are now the main development path. (3) The impact of GC on green GDP through environmental regulation presents a U-shaped characteristic, that is, first decreases and then increases [16]. At an early stage, it is necessary to invest capital costs to control environmental pollution, which will naturally limit the scale of credit and the investment in research and development, thus having a negative impact. Following the reform of low-carbon technology, green cost is reduced in the later period, and the impact of environmental regulation on green GDP turns from negative to positive. Credit funds can support and guide the real economy. Green credit not only contributes to the development of strategic emerging industries, but also helps to improve the cleanliness of industries with high pollution and high energy consumption. (4) In the heterogeneity test, the influence coefficients of GC on sustainable economic growth in the eastern, central and western regions were 6.50, 8.49 and 22.19, respectively. Under different levels of financial development, the impact of GC on sustainable economic growth also has the characteristic of gradually increasing from east to west. The eastern region has a higher level of economic development and a relatively adequate allocation of financial resources, but due to the existence of a large amount of environmental pollution in the early development, mostly concentrated in the two high and one surplus industries, the environmental management costs are relatively high, thus the effect of promoting sustainable economic growth based on green credit is not obvious. Compared to the relatively less economically developed central and western regions, where financial resources are in great demand, especially as the west is vigorously developing environmentally friendly industries, green credit is essentially environmental financing, which opens up a green channel for sustainable economic growth in the central and western regions [1,67]. See Appendix C.

With the background of China’s economic structural transformation and supply side structural reform, promoting the reform of the financial system represented by green credit is the key to sustainable economic growth. From the perspective of sustainable economic strategy, it would reduce green costs and focus on the development of green industries. Combined with the above research results, the following suggestions are put forward:

(1) To accelerate the transformation of entities and establish an effective information communication mechanism. The current lack of environmental information in banks should build a mechanism for cooperation between government, banks and enterprises to share credit data platforms. The green credit business carried out by banks needs to provide joint environmental protection as part of the environmental impact approval results, pollution and investigation, and punishment of disciplinary enterprises list, strictly establishing a restraint mechanism for inhibiting the credit of “two high-energy and one surplus” industries and a GC incentive mechanism to support the development of green industries, optimizing GC channels and increasing GC. Drawing on international experience, we will open up channels for the disclosure of environmental information on enterprises, while banks and financial institutions will actively accept window guidance to promote the standardized operation of green credit.

(2) To accelerate technological innovation and professional talent development. The results of the transmission mechanism show that industrial upgrading and environmental regulation are both important transmission paths for GC to promote sustainable economic growth. To accelerate the transformation of business concepts, increase awareness of environmental protection and avoidance of environmental risks and gradually promote green credit business. To establish a dedicated department responsible for the operation, management and marketing of GC business. Financial institutions and the environmental protection sector should accelerate the training of comprehensive talent. To increase research expenditure and encourage technological innovation. In addition, strengthening green credit risk management will ensure the sustainable operation of green credit by banks. To correctly handle the relationship between economic benefits and environmental, ecological and social benefits. Promote the quota restraint role of the carbon emission trading mechanism and promote the orderly and healthy development of green industries in combination with environmental policy effects, thereby promoting economic sustainability and leading to sustained growth.

(3) To accelerate the construction of a legal system to support GC. In response to local protectionism, to implement an accountability system for environmental damage and impose severe penalties on enterprises and relevant departments that endanger the environment; to implement a market access system based on the “two high” category of enterprises and make it more difficult for the “two high” category of enterprises to gain market access. To regulate the investment and financing system of private capital and improve the legal construction of the financial market. Different preferential measures should be applied to different regions according to the actual situation, in accordance with the scale of credit investment at the financial level, through tax exemptions or policy subsidies.

(ii)

Limitations

The limitations of this study are related to both the data and the analysis. On the one hand, the data on indicators to measure green credit and sustainable economic growth are relatively from a macro-level perspective and mainly at the provincial level. There are significant differences in policy effects and levels of economic development between provinces and municipalities, which cannot be measured specifically for comparison. On the other hand, in conducting the heterogeneity analysis, this paper divided China into eastern, central and western regions according to administrative regions, and the specific study of regional differences in the impact of green credit on sustainable economic growth is still relatively unclear, as the policy impact of green credit can be further refined into regional in-depth studies in the future.

(iii)

Future direction of study

In view of the existing research results, future research can improve and extend the following two aspects. On the one hand, a more detailed analysis should be conducted. The administrative regions of China can be further subdivided (e.g., Northeast, Southwest and North China) into smaller units so as to study the regional differences in depth. On the other hand, microdata can be added to further research. The level of green credit from the perspective of the banking sector and enterprises can be measured, the evaluation index system can be improved and the heterogeneity and impact factors of green credit on regional economic growth can be explored. In the mechanism analysis, the green effect and influence mechanism of green credit policies were studied based on the green patent data of the listed companies [9].