Empirical Analysis of the Influence of Green Credit on the Industrial Structure: A Case Study of China

Abstract

In order to explore the influence of green credit on the optimization and rationalization of the industrial structure in China, based on the relevant data of the green credit balance, interest expenditure in six high-energy-consuming industries, and industrial structure in China from 2007–2019, the paper first measured the green credit index and the index of industrial structure optimization and rationalization by the methods of entropy weight and Theil index. Then, the coupling model was adopted to study the coupling degree and the coupling coordination degree between them, and the regression model was employed to further study the influence coefficient of green credit on the optimization and rationalization of industrial structure. Research showed that the degree of coupling between green credit and industrial structure rationalization presents three stages—extremely low coupling, low coupling, and moderate coupling—and the degree of coupling coordination presents two stages—extremely low coordination and low coordination. Similarly, the degree of coupling between them presents two stages—extremely low coupling and low coupling—and the degree of coupling coordination presents two stages—extremely low coordination and low coordination. Regression analysis showed that the influence coefficients of the green credit index on rationalization and optimization of industrial structure were 0.56 and 0.03, respectively, which supported the conclusion that the coupling degree between the former two is higher than that between the latter two on the one hand, and made it clear that green credit positively and effectively guides the rational allocation of resources and promotes secondary and tertiary industries on the other hand.

1. Introduction

Finance is the lever of economic development, and the rate of return has always been an important factor affecting the flow of funds. In recent years, as China’s economy has entered a new normal, environmental problems caused by high-energy-consuming and high-pollution industries have become increasingly prominent, and all sectors of society have reached a consensus on resource conservation and green development, Environmental benefits are increasingly being valued. Green development and protecting the natural environment have become an important indicator for considering the benefits of economic development. People have begun to pay attention to the social responsibility of financial institutions to promote industrial structure optimization and greening by guiding capital flow to modern green industry. Scholars at home and abroad have successively put forward the concepts of “environmental finance” and “green finance”. They believed that environmental finance refers to financial innovation activities carried out by the financial sector in order to meet the capital needs of the environmental protection industry [1,2]. It is a financing activity carried out by financial institutions to avoid risks caused by environmental problems and promote environmental protection [3]. Scholtens (2007) proposed that green finance is mainly through the internal optimization and improvement of financial products., guiding enterprises to reduce pollution and energy consumption through sustainable operation, so as to produce a situation that is conducive to the coordinated progress of economic development and environmental protection [4]. Farhad (2019) pointed out that green finance means that the financial industry uses financial leverage to select the environmental protection industry as the key support object and accordingly gives it preferential treatment in terms of capital acquisition, credit issuance, interest rates, and terms [5].

Green finance has different forms, such as green credit, green securities, green funds, and carbon emission trading rights. Comparatively speaking, green credit is more mature in China and constitutes the main part of green finance. Green credit refers to credit activities that take the environmental protection and social responsibility standards of enterprises as core indicators [4,6]. In 2007, the State Environmental Protection Administration, the People’s Bank of China, and the China Banking Regulatory Commission jointly put forward the credit policy “Suggestions for Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks” to curb the blind expansion of high-energy-consuming and high-pollution industries, which kicked off the prelude to green credit. In 2011, the People’s Bank of China issued “the Green Credit Guidelines”, giving the green loans of commercial banks regulations to follow. From the end of 2012 to the end of 2019, the balance of green credit increased from 0.33 trillion yuan to 10.22 trillion yuan, becoming the Chinese government’s main form of economic leverage in guiding industrial structure optimization and greening.

Due to the practices of green economy and sustainable development, green credit has become an important financial policy, so economic leverage can be used to influence the optimization of the industrial structure [7]. In the context of green development and sustainable development, financial institutions realize the function of controlling environmental pollution and optimizing and upgrading industrial structure by using financial tools such as green credit to guide funds flowing to the environmental protection industry [8,9]. Therefore, underdeveloped countries should guide the allocation and circulation of capital in various industries through the implementation of financial policies, improve the efficiency of resource allocation by increasing the investment in sunrise industries and reducing investment in declining industries, and promote the adjustment and upgrading of their industrial structures [10].

The key to China’s current industrial structure adjustment lies in guiding the green upgrading of traditional industries and the cultivation of new green and environmental protection industries through green credit. On the one hand, green credit supports the development of environmentally friendly industries. On the other hand, it imposes strict restrictions on loans to high-energy-consuming and high-pollution enterprises. This differentiated credit policy helps to accelerate the transformation and upgrading of industrial structures, promote the merging and reorganization of pollution-generating enterprises, and realize the dual optimization of industries and technology [11]. The implementation of green credit by commercial banks is conducive to the improvement of the core competitiveness of banks, enabling banks to obtain rich “green profits” and improve their ability to manage environmental risks [12]. Chami et al. believe that the promotion of green financial policies can not only improve the reputation of institutions and meet the needs of stakeholders, but also help enterprises to better control operational risks and make strategic decisions about development so as to truly achieve “profit while meaningful” [13].

Scholars have carried out abundant empirical studies on the impact of green credit on the optimization and upgrading of the industrial structure. By using a grey correlation model with the panel method on data from 31 provinces from 2004 to 2015 relating to the impact of green credit on the rate of industrial structure optimization, Xu (2018) concluded that China’s overall green credit upgrades to the industrial structure are remarkable, and the effects of the green credit on industrial structure optimization are different in the eastern, central, and western regions [14]. Qian et al. (2019) studied the influence of green credit on the optimization rate of the industrial structure based on interprovincial panel data from 2004 to 2017 and concluded that green credit promoted the optimization of the industrial structure to a certain extent, but the influence was limited [15]. Li et al. (2020) used the panel data from 2000–2016 in 31 provinces of China to study to the impact of green credit on the second and tertiary industries as a percentage of GDP. They found that green credit had a significant positive role in promoting overall industrial structure upgrades and that there were differences in the impact of green credit on the industrial structure between the eastern and the western regions [11]. In addition, various studies have focused on agricultural and industrial sectors for estimation of resources and institutional barriers in the land use. Elahi et al. (2019) evaluated the energy efficiency utilization by using an ANN neural network model and reached the conclusion that the current energy input is overused, and Peng et al. (2018) used the ecological footprint accounting model to study the regional ecological environment of Jiangsu Province and concluded that the land resources are currently being used irrationally and arable land resources are under great pressure [16,17,18,19,20,21].

Since green credit has only been developed for a little more than ten years in China, and the balance of green credit is relatively low, the empirical analysis of the influence of green credit on the industrial structure has not been fully developed yet. Therefore, the current study focuses on the mechanism and empirical analysis of the influence of the green credit index on the structure of the industries. This paper is structured as follows. In Section 2, the development process of green credit in China is presented; in Section 3, theoretical analysis and hypotheses of credit-guided industrial structure are presented; in Section 4, the coordination degree and influence coefficient between the green credit index and industrial structure are analyzed using a coupling model and regression model. This paper constructs a green credit index system, considering two aspects: the relative amount of green credit and the proportion of interest expenditure in six high-energy-consuming industries to measure the green credit index. Then, the coupling relationship between the green credit index and the rationalization and upgrading of the industrial structure is studied, respectively, and the influence of the green credit index on the industrial structure is further studied using the regression model. In Section 5, the research results are presented, and in Section 6, the conclusions and suggestions are provided.

2. Development of Green Credit

Since the “Suggestions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks” was issued, the People’s Bank of China and Shanghai, Jiangsu, Hebei, Zhejiang, Liaoning, Anhui, Fujian, Hubei, Guizhou, Shanxi, Gansu, and other provinces and cities have issued a number of green credit policies, laws, and regulations in order to control the poor environmental performance of enterprise credits, promote environmental protection, and reduce environmental risks. The judicial interpretations of the central laws and regulations include green credit guidelines, the evaluation of green credit implementation and evaluation indicators, and the approval of and statistics on the issuance of green credit financial bonds by the Industrial Bank and Shanghai Pudong Development Bank. Local laws and regulations focus on the green credit work guidance, monitoring, information sharing, and performance evaluation of financial institutions. China’s green credit has undergone a significant development, which can be divided into three implementation processes:

2.1. The Proposal Stage of Green Credit

In this stage, green credit was proposed, the concept of green credit was defined, and the implementation requirements were identified. In 2007, the state environmental protection administration, the China banking regulatory commission, and the People’s Bank of China jointly issued the first green credit policy, aiming to improve bank supervision, environmental protection, and credit risk guidance [22]. The banks must do their environmental due diligence and investigate credit management to ensure the industry policy complies with the environmental laws and regulations and relocate funds from pollution sources to environmental protection [9]. Because this measure was issued as an administrative guideline with relatively little authority, a lack of environmental information, imperfect supporting policies and laws, and inconsistent implementation standards across different industries, banks found it difficult to determine whether customers violated environmental regulations when implementing the policy [8]. To help the banks better manage the environmental and social risk management enterprise and project configuration, a “Green Credit Guide” was passed in 2012, which included a more complete definition of green credit, low carbon emissions, credit support for a circular economy, how to prevent the risks associated with environmental and social credit support, and how to improve the environmental and social benefits of credit support [23]. At the same time, the requirements of the China Banking Regulatory Commission (CBRC) regarding the support for green, circular, and low-carbon industries and the prevention and control of credit risks associated with high-energy-consuming and high-pollution industries were also considered within the scope of green credit.

2.2. The Audit and Evaluation Stage of Green Credit

In this stage, a green credit audit standard and green credit evaluation protocol were proposed [24]. In 2014, the China Banking Regulatory Commission (CBRC) launched the key audit standards for green credit in 2014, guiding banks to apply green credit guidelines and establish key performance indicators for green credit [12]. In 2014, the audit standard was applicable to all offices of the China Banking and Insurance Regulatory Commission (CBIRC), as well as policy banks, state-owned commercial banks, joint-stock commercial banks, and the Postal Savings Bank. All of these institutions are required to conduct annual self-audits and submit annual audit reports to CBIRC on their level of compliance with the standards. As departmental guidelines, the 2012 Green Credit Directive, like earlier sustainable finance policies, is relatively low impact, meaning that the standards are soft standards rather than explicit mandates. Conversely, the 2014 audit standards allow CBIRC to assess bank compliance with the guidelines and institutionalize the green credit assessments of banks [25].

2.3. The Market Incentive Stage of Green Credit

In this stage, we should further expand the development of green credit by proposing preferential interest rates and issue green bonds to expand the sources of green credit funds. In 2015, the People’s Bank of China and the United Nations’ Environmental Program Finance Initiative (UNEP–FI) cooperation formed an initial template of the green financial policy [24] and, in particular, put forward the green credit plan, which included government finance departments, policy banks, and commercial banks implementing the possibility of new incentives, such as preferential rates and eligibility requirements for green credit as well as issuing green bonds as an important source of enterprise green credit funds. In 2015, the People’s Bank of China and the National Development and Reform Commission introduced separate green bond issuance rules [26]. From 2015 to 2018, the Banking and Insurance Regulatory Commission (CBIRC) approved financial institutions, such as the Industrial Bank and Shanghai Pudong Development Bank, to issue a total of 280 billion yuan of green bonds.

It can be seen that, as the relevant policies were implemented, the financial system in China gradually expanded the green credit. It began with the demand for energy conservation and emissions reduction and expanded to include auditing and assessments and market-oriented incentive processes, such as green credit development, which will direct the development of China’s financial industry and have profound effects on the evolution of the industrial structure.

3. Theoretical Analysis and Research Hypothesis

The optimization and upgrading of the industrial structure and economic growth are two processes that follow each other closely. Kuznets (1971) defined industrial structure optimization as follows. With the development of the national economy, the proportion of the national income and labor in the primary industry in the whole national income and the whole labor force continues to decline, while the proportion of the national income and labor in the secondary industry first increases and then declines, and the proportion of the tertiary industry keeps rising. In related studies, the rationalization, upgrading, and efficiency of the industrial structure are also important dimensions for measuring the change in the industrial structure [27,28,29]. The rationalization of the industrial structure refers to the degree of coordination between industries and the degree of the effective utilization of resources, which is an important index for measuring the coordination degree of the input structure and output structure of factors. The optimization of the industrial structure refers to the evolution process of the industry from simple to complex and from low to high. The high efficiency of the industrial structure refers to the process in which the proportion of low-efficiency industries keeps decreasing and the proportion of high-efficiency industries keeps increasing, reflecting the structural dividend caused by the optimization of resource allocation. Generally, the proportion of the sum of the added value of the secondary and tertiary industries in GDP is adopted to measure the rationalization of the industrial structure, and the Theil index method, weighting method, and super-efficiency SBM model are used to measure the rationalization, optimization, and efficiency of the industrial structure [27].

In the context of the credit-oriented financial model in China, the adjustment of the industrial structure is mainly achieved through the credit market in the field of finance. On the one hand, banks have an important function. They guide the optimization of the industrial structure by adjusting the capital flow, quantities, and differential interest rates. Commercial banks distribute capital to different industries by issuing loans and guide capital to support the development of certain enterprises or industries, thus promoting the dynamic adjustment of the industrial structure. The usual path is manifested as a financial operation: —bank credit market—production loan—capital flow—distribution of financial elements C fund supply mechanism—industrial structure adjustment [30]. On the other hand, governments also play a very important role. By advocating the corporate social responsibility of financial institutions, they supervise the credit behavior of financial institutions, guide banks to provide green credit to industries with sustainable development, and guide the industrial structure toward advancement.

The capital flow is guided by the green credit policy in the following way. To support environmental protection and energy-saving projects or enterprises, the appropriate credit policy means, such as loan varieties, terms, interest rates, credit lines, and other means, are adopted. To punish high-polluting projects or enterprises that violate the relevant laws and regulations on environmental protection and energy conservation, credit penalty measures, such as credit deferment or even loan withdrawals, are adopted. Third, the lender uses credit means to urge the borrower to prevent environmental risks and fulfill the relevant social responsibilities so as to reduce credit risks. When the economy is faced with the development requirements of environmental sustainability, relying on the green credit of financial institutions to guide the adjustment of the industrial structure is an important way to achieve the upgrading of the industrial structure. Green credit mainly influences the optimization of China’s industrial structure and technological innovation of environmental protection enterprises through capital formation, capital orientation, information transmission, industrial integration, policy incentive, and risk allocation mechanisms [14,15,31]. Accordingly, Hypotheses 1 and 2 are proposed.

Hypothesis 1:

Green credit and industrial structure optimization are mutually coordinated.

Hypothesis 2:

Green credit can promote the rationalization and upgrading of the industrial structure.

4. Materials and Methods

4.1. Research Design

4.1.1. The Measure of the Green Credit Index

Referring to the green credit evaluation ideas proposed by Li and Xia (2014), the green credit index from 2007 to 2019 was calculated using the entropy method, with the proportion of green credit in the total RMB credit and the proportion of the interest expenditure of six high-energy-consuming industries in the total interest expenditure of the industrial sectors as evaluation indexes [32]. The entropy weight method is used to calculate the weight of indicators. The information entropy of each indicator is determined according to the information entropy formula, and the weight of each indicator is further determined:

$$W_i=\frac{1-E_i}{{\displaystyle \sum _{i=1}^m{E}_i}}$$

(1)

where $0\le W_i\le 1$ and ${\displaystyle \sum _{i=1}^m{W}_i=1}$, and the entropy weight of each index of green credit is obtained.

The weighted synthesis method was used to calculate the index values of green credit, and the proportion index of the green credit balance and the proportion index of the interest expenditure of six high-energy-consuming industries were calculated. After weighting, the green credit index was obtained. Let the green credit index of year $t$ be $G{C}_t$. Its expression is as follows:

$$G{C}_t={\displaystyle \sum _{i=1}^m{W}_i{p}_{ti}}$$

(2)

where $P_{ti}$ is the standardized value of the $i$ index in year $t$. The higher the $G{C}_t$ value, the higher the development level of green credit.

4.1.2. The Measure of Industrial Structure Rationalization

Industrial structure rationalization points to the aggregation quality between industries, reflecting the degrees of the coordination of industrial proportion and the effective allocation of resources. Referring to the treatment methods of Gan Chunhui (2011), Wu Chuanqing (2020), Shao Xuefeng (2021), etc. [27,28,33], the Theil index was introduced to measure industrial structure rationalization. The Thiel index considers the relative importance of industries and retains the theoretical basis and economic implications of the degree of structural deviation.

$$T{L}_t={\displaystyle \sum _{i=1}^n\left(\frac{Y_{it}}{Y_t}\right)\ln \left(\frac{Y_{it}/Y_t}{P_{it}/P_t}\right)}$$

(3)

where $T{L}_t$ represents the Theil index in year $t$, $Y_i$ represents the output value of industry $i$, $Y$ represents the gross regional product, $P_i$ represents the number of employees in industry $i$, and $P$ represents the total number of employees in the region, $i=1,2,3$.

When $T{L}_t$ is 0, the rationalization level of the industrial structure is the highest. The smaller the $T{L}_t$ measure value, the smaller the deviation degree between the industrial structure and equilibrium state, and the higher the rationalization level of the industrial structure.

4.1.3. The Measure of the Optimization of the Industrial Structure

The advanced change of the industrial structure is the non-agricultural change of the industrial structure. According to Petti-Clark’s law, the non-agricultural industrial structure is the rule of industrial evolution from the dominant output value of the primary industry to the dominant output value of the secondary and tertiary industries. In this paper, the proportion of the output value of the secondary and tertiary industries in the gross regional product is used to measure the advancement of the industrial structure [27,28,29,30].

$$isu{x}_t=\frac{Y_{t2}+Y_{t3}}{GD{P}_t}$$

(4)

where $isu{x}_t$ represents the measurement value of the advancement of the industrial structure in year $t$; $Y_{t3}$ and $T_{t2}$ represent the output value of the tertiary industry and the secondary industry in year $t$, respectively; and the larger the value of $isu{x}_t$, the more advanced the industrial structure.

4.1.4. Coupled Coordination Model of the Green Credit Index and Industrial Structure Rationalization and Upgrading

The term coupling derives from physics and refers to the dynamic development of two or more systems that depend on each other. The coupling coordination model can be used to measure the degree of interaction between two or more systems [34]. Shao X.F. (2021) studied the dynamic relationship between the development of regional green finance and the optimization of the industrial structure from 2010 to 2019 through the coupling coordination degree mode [33]. Liu (2020) discussed the coordination between the green finance and green economy in China and evaluated the relationship between the two from 2007 to 2016 [35]. In this study, the degrees of coupling and coupling coordination between the green credit index and the industrial structure were analyzed by referring to the above methods to verify Hypothesis 1.

Coupling Degree Measurement Model

The coupling degree model is used to describe the degree of the coupling relationship between two systems, which can be measured by the following formula:

$$C=\sqrt{\frac{H_1{H}_2}{{(H_1+H_2)}^2}}$$

(5)

where $C$ represents the coupling degree between green credit and the industrial structure, $H_1$ represents the green credit index, and $H_2$ represents the rationalization and enhancement index of the industrial structure. According to the level of coupling degree, the coupling status of green credit and industrial structure is divided into six stages, as shown in

Table 1. Type division of the coupling degree between green credit and the industrial structure.

Coupling Degree	Coupling Type	Coupling Degree	Coupling Type
$C\in \left[0,0.3\right)$	Extremely low coupling stage	$C\in \left[0.5,0.6\right)$	Intermediate high coupling stage
$C\in \left[0.3,0.4\right)$	Low coupling stage	$C\in \left[0.6,0.7\right)$	High coupling stage
$C\in \left[0.4,0.5\right)$	Moderate coupling stage	$C\in \left[0.7,1\right)$	Extremely high coupling stage

.

Coupling Coordination Degree Measurement Model

While the coupling degree can reflect the degree of interaction between the green credit index and the rationalization and advancement of the industrial structure, it cannot represent whether each system promotes another at a high level or affects another at a low level. Therefore, the coupling coordination model is introduced in this paper, and the specific calculation formula is as follows:

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

(6)

$$H=\alpha H_1+\beta H_2$$

(7)

where $\alpha =\beta =0.5$.

Similarly, according to the level of coupling coordination degree, the coupling coordination between green credit and industrial structure is also divided into six types, as shown in

Table 2. Type division of the coupling coordination degree between green credit and the industrial structure.

Coupling Coordination Degree	Coupling Coordination Level	Coupling Coordination Degree	Coupling Coordination Level
$D\in \left[0,0.3\right)$	Extremely low coordination	$D\in \left[0.5,0.6\right)$	Intermediate high coordination
$D\in \left[0.3,0.4\right)$	Low coordination	$D\in \left[0.6,0.7\right)$	High coordination
$D\in \left[0.4,0.5\right)$	Moderate coordination	$D\in \left[0.7,1\right)$	Extremely high coordination

.

4.1.5. Regression Model of the Green Credit Index and the Rationalization and Upgrading of the Industrial Structure

On the premise of a clear coupling and coordination between green credit implementation and the industrial structure, a regression model of the green credit index and rationalization and upgrading of the industrial structure is built to verify Hypothesis 2. The regression model is as follows:

$$isr{x}_t=c+{\alpha }_0\cdot \ln gc{x}_t+{\alpha }_1\cdot \ln fg{r}_t+{\alpha }_2\cdot \ln fi{r}_t+{\alpha }_3\cdot \ln go{v}_t+{\epsilon }_t$$

(8)

$$isu{x}_t=e+{\beta }_0\cdot \ln gc{x}_t+{\beta }_1^{}\cdot \ln fg{r}_t+{\beta }_2^{}\cdot \ln fi{r}_t+{\beta }_3^{}\cdot \ln go{v}_t+{\delta }_t$$

(9)

where the explained variable $isr{x}_t$ is the rationalization level of the industrial structure, considering the reciprocal of the Theil index, and $isu{x}_t$ is the advanced level of the industrial structure.

The explanatory variable is the green credit index ($gc{x}_t$), which adopts the ratio of the green credit balance to the total credit of financial institutions.

The control variables are $fg{r}_t$, $fi{r}_t$, and $go{v}_t$. Among them, the variable $fg{r}_t$ is the ratio of the added value of the national financial industry to GDP, representing the status and development level of the financial industry; the variable $fi{r}_t$ is the ratio between the loan balance of financial institutions nationwide and the deposit balance of financial institutions nationwide, representing the current development situation of the financial industry; and the variable $go{v}_t$ is the ratio of government expenditure to GDP, which is used to indicate the guidance of government policies on green credit.

4.2. Related Data

This paper uses the industrial structure data for three industries (including the output value structure and the labor force structure of the three industries), the green credit balance of the top 21 major banks, the added value of the national financial industry, the loan and deposit balance of financial institutions nationwide, and the ratio of government expenditure to GDP, etc., from 2012 to 2019. The data are mainly from the China Statistical Yearbook, the China Banking Social Responsibility Report, and the Wind website (see

Table 3. Description and analysis of the variables.

	Mean	Median	Max	Min	Std. Dev.	Observations
Proportion of output value of primary industry	0.0872	0.0894	0.1025	0.0704	0.0106	13
Proportion of output value of primary industry	0.4339	0.4418	0.4697	0.3859	0.0324	13
Proportion of output value of primary industry	0.4789	0.4688	0.5427	0.4286	0.0426	13
Proportion of labor force in primary industry	0.3221	0.3140	0.4080	0.2510	0.0541	13
Proportion of labor force in second industry	0.2858	0.2870	0.3030	0.2680	0.0117	13
Proportion of labor force in third industry	0.3921	0.3850	0.4740	0.3240	0.0527	13
GE1	4.3545	4.6916	6.4939	1.2484	1.7755	13
GE2	7.7750	7.5738	8.6488	6.8873	0.6198	13
GE3	4.7632	4.7674	5.1729	4.4039	0.2163	13
GE4	8.5391	8.9111	10.0740	6.4135	1.2590	13
GE5	4.7910	4.9102	5.2951	3.8844	0.4891	13
GE6	3.0411	3.0098	3.4566	2.8001	0.2009	13
GE7	19.7853	19.0417	24.0095	16.5275	2.5039	13
Government intervention	0.2293	0.2359	0.2553	0.1843	0.0208
Financial development	3.0370	2.9729	3.6164	2.4105	0.3879
Proportion of financial output to GDP	0.0696	0.06964	0.0817	0.0563	0.0089

).

5. Analysis of Empirical Results

5.1. Green Credit Index

The Green Credit Index consists of two types of evaluation indicators. One is the proportion of green credit, and the other is the proportion of interest expenditure of high-energy-consuming industries. The proportion of green credit, namely, the proportion of green credit balance in the balance of national RMB loans, reflects the positive strength of commercial banks in supporting environmental optimization and is a positive indicator. The proportion of the interest expenditure of high-energy-consuming industries is the ratio of the interest expenditure of six high-energy-consuming industries to the total interest expenditure of the industrial industries, which reflects the strength of commercial banks in controlling the development of high-consumption and high-pollution industries and curbing the deterioration of resources and the environment. This is a reverse indicator [32].

The research shows that, in the evaluation index for green credit, the weight of the green credit balance is 0.75, and the total weight of the interest expenditure of six high-energy-consuming industries is 0.25 (see

Table 4. Evaluation indicators and weights of green credit.

Green Credit Evaluation Index	Weight
Ratio of green credit to RMB loans in China (GE1)	0.75
Interest expenditure ratio of chemical raw materials and chemical products manufacturing (GE2)	0.02
Interest expenditure ratio of the non-metallic mineral products industry (GE3)	0.01
Interest expenditure of ferrous metal smelting and rolling processing industry (GE4)	0.10
Interest expenditure ratio of non-ferrous metal smelting and rolling processing industry (GE5)	0.04
Interest expenditure ratio of petroleum processing and coking industry (GE6)	0.02
Interest expenditure of electricity and heat production (GE7)	0.07

). Since the implementation of green finance in China, the green credit index has continued to rise, rising from 0.0370 in 2007 to 0.1038 in 2019 (see

Table 5. Green credit index.

	Indicator	GE1	GE2	GE3	GE4	GE5	GE6	GE7	GCI
Year
2007		0.0164	0.0015	0.0007	0.0088	0.0024	0.0012	0.0061	0.0370
2008		0.0259	0.0015	0.0006	0.0093	0.0024	0.0014	0.0060	0.0471
2009		0.0317	0.0016	0.0007	0.0084	0.0025	0.0012	0.0065	0.0525
2010		0.0391	0.0015	0.0007	0.0087	0.0028	0.0012	0.0057	0.0597
2011		0.0475	0.0015	0.0007	0.0092	0.0030	0.0012	0.0051	0.0683
2012		0.0549	0.0017	0.0007	0.0092	0.0031	0.0012	0.0050	0.0759
2013		0.0618	0.0017	0.0007	0.0085	0.0030	0.0013	0.0050	0.0820
2014		0.0679	0.0018	0.0007	0.0079	0.0030	0.0013	0.0046	0.0872
2015		0.0731	0.0018	0.0007	0.0071	0.0031	0.0012	0.0044	0.0915
2016		0.0774	0.0018	0.0007	0.0068	0.0031	0.0011	0.0044	0.0953
2017		0.0808	0.0018	0.0007	0.0064	0.0031	0.0012	0.0046	0.0985
2018		0.0834	0.0017	0.0007	0.0061	0.0031	0.0012	0.0050	0.1012
2019		0.0855	0.0016	0.0006	0.0059	0.0031	0.0014	0.0058	0.1038

).

5.2. The Coupling and Coordination Evolution of the Green Credit Index and Industrial Structure Rationalization and Upgrading

From 2007 to 2019, the industrial structure rationalization index decreased from 0.2407 to 0.1144, and the industrial structure upgrading index increased from 0.8975 to 0.9286, indicating a significant trend toward industrial structure rationalization and upgrading (see

Table 6. Industrial structure rationalization index and industrial structure upgrading index.

Year	Industrial Structure Rationalization Index	Industrial Structure Upgrading Index
2007	0.2407	0.8975
2008	0.2278	0.8983
2009	0.2158	0.9036
2010	0.2045	0.9067
2011	0.1852	0.9082
2012	0.1698	0.9089
2013	0.1495	0.9106
2014	0.1348	0.9136
2015	0.1251	0.9161
2016	0.1234	0.9194
2017	0.1273	0.9254
2018	0.1268	0.9296
2019	0.1144	0.9286

).

The coupling degree of the green credit index and industrial structure rationalization index shows that from 2007 to 2009, it was in the low coupling stage; from 2010 to 2015, it was in the moderate coupling stage; and from 2016 to 2019, it entered the high coupling stage. The coupling coordination degree of the two indicates that from 2007 to 2008, they had a low coordination; and from 2009 to 2019, they had a moderate coordination. Both the coupling degree and coupling coordination degree showed an upward trend (see

Table 7. Coupling degree and coupling coordination degree of the green credit index (GCI) and industrial structure rationalization.

Year	GCI and Industrial Structure Rationalization		GCI and Industrial Structure Upgrading
	Coupling Degree	Coupling Coordination Degree	Coupling Degree	Coupling Coordination Degree
2007	0.34	0.37	0.20	0.31
2008	0.38	0.39	0.22	0.33
2009	0.40	0.41	0.23	0.34
2010	0.42	0.42	0.24	0.35
2011	0.44	0.44	0.26	0.36
2012	0.46	0.46	0.27	0.36
2013	0.48	0.47	0.28	0.37
2014	0.49	0.48	0.28	0.38
2015	0.49	0.49	0.29	0.38
2016	0.50	0.49	0.29	0.38
2017	0.50	0.49	0.29	0.39
2018	0.50	0.49	0.30	0.39
2019	0.50	0.49	0.30	0.40

).

The coupling degree of the green credit index and industrial structure advancement index shows that from 2007 to 2017, it was in the very low coupling stage; and from 2018 to 2019, it entered the low coupling stage. The coupling coordination degree of the two indicates that from 2007 to 2018, they had a very low coordination; and in 2019, they had a low coordination. Both the coupling degree and coupling coordination degree showed an upward trend (see Table 7).

5.3. Further Study on the Influence of the Green Credit Index on the Industrial Structure

The coupling coordination study shows that the green credit index has a certain coordination with the rationalization of the industrial structure and the high polarization of the industrial structure, respectively, which indicates that the green credit index and the evolution of the industrial structure are interdependent. On this basis, the rationalization and upgrading of the industrial structure are analyzed by regression. The explained variables are the rationalization and upgrading of the industrial structure, and the core explanatory variable is the green credit index. The rationalization level of the industrial structure is the reciprocal of Theil index: the greater the value, the more reasonable the industrial structure. The advancement level of the industrial structure is the ratio of the sum of the output value of the secondary and tertiary industries to GDP.

Regression analysis shows that the main influencing factors of industrial structure rationalization are the ratio of the financial industry output value to GDP, government intervention, and the green credit index, and the influence coefficients are 1.40, −1.10, and 0.56, respectively, as showed in

Table 8. Regression coefficient of the green credit index on the rationalization of the industrial structure.

	Explained Variable	$\mathit{i}\mathit{s}\mathit{r}{\mathit{x}}_{\mathit{t}}$
Explanatory Variables		(1)	(2)	(3)	(4)
$gc{x}_t$		0.7632 ***	0.3565 **	0.3919 **	0.5546 ***
$fg{r}_t$			1.1293 ***	1.3282 **	1.3964 ***
$fi{r}_t$				−0.2969
$go{v}_t$					−1.1004 **
$R^2$		0.94	0.97	0.97	0.99

Note that $\alpha =0.10$, ** and *** were significant at the levels of 5% and 1%, respectively.

. The main influencing factors for the advancement of the industrial structure are government intervention, the financial development index, and the green credit index, and the influence coefficients are −0.11, 0.07, and 0.03, respectively, as showed in

Table 9. Regression coefficient of the green credit index on the optimization of the industrial structure.

	Explained Variable	$\mathit{i}\mathit{s}\mathit{u}{\mathit{x}}_{\mathit{t}}$
Explanatory Variables		(1)	(2)	(3)	(4)
$gc{x}_t$		0.0317 ***	0.0235 *	0.0109	0.0336 ***
$fg{r}_t$			0.0227
$fi{r}_t$				0.0578 **	0.0737 ***
$go{v}_t$					−0.1066 ***
$R^2$		0.85	0.86	0.91	0.98

Note that $\alpha =0.10$, and *, **, and *** were significant at the levels of 10%, 5%, and 1%, respectively.

. On the one hand, the influence coefficient of the green credit index on the rationalization and upgrading of the industrial structure is low, which supports the conclusion that the coupling and coordination degree between the green credit index and the rationalization and upgrading of the industrial structure is low. On the other hand, the influence of the green credit index on the rationalization of the industrial structure is more significant than that of the green credit index on the upgrading of the industrial structure, which supports the conclusion that the coupling degree between the former two is higher than that between the latter two.

6. Conclusions and Suggestions

The results reported in this study show that, as an important part of green finance, green credit can influence the optimization and adjustment of the industrial structure by guiding the capital flow of financial institutions.

First, from 2007 to 2019, the level of green credit in China continued to rise, which is inseparable from the rapid growth of the balance and proportion of green credit in China and the steady decline of the proportion of interest expenditure of high-energy-consuming industries. The rationalization level of the industrial structure continues to rise, indicating that the rationalization level of resource allocation and the degree of coordination between industries are rising day by day. The industrial structure is becoming increasingly advanced, reflecting the continuous decline of the proportion of the primary industry in China and the significant trend of the non-agricultural industrial structure.

Secondly, the relationship between the green credit index and the rationalization of the industrial structure is mainly in the low coupling stage, with a low coupling coordination between them. The relationship between the green credit index and the optimization of the industrial structure is mainly in the extremely low coupling stage, and it is mainly coordinated with a low coupling between them. This shows that China’s green credit is still in the primary stage, and there is still a large space for the degree of coupling and coordination between green credit and the industrial structure.

Thirdly, the coupling degree and coupling coordination degree of green credit and the rationalization level as well as optimization level of industrial structure show an upward trend. This shows that the influence of green credit on the rationalization and upgrading of the industrial structure is gradually improving, and it is necessary to continuously increase the balance of green credit and reduce the interest expenditure of high-energy-consuming industries to further support the adjustment and optimization of the industrial structure.

Fourthly, further regression analysis shows that the green credit index in China has a positive impact on the rationalization and upgrading of the industrial structure in the present stage, but the influence coefficient is not high, which is consistent with the conclusion that the two are in the low coupling stage, and the coupling coordination degree is not high.

Based on our analysis, the environmental information disclosure policy and related industry laws and regulations must be improved. Through the implementation of this policy, sufficient environmental performance information about enterprises must be obtained, the reasonable assessment and supervision of the environmental protection degree of enterprises must be strengthened by the banking industry, and the governance of enterprises must be enhanced by the government, so as to improve enterprise pollution reduction and emissions reduction and thus realize ecological environmental protection and economic development.

Second, green credit incentive policies must be promoted to stimulate banks’ ability to effectively manage environmental risks. The government can use market incentive policies, such as an environmental protection tax and subsidies, to improve banks’ attention to environmental risk contracts and properly subsidize banks’ “payment costs”, so that banks can provide a relatively favorable financing for projects with better environmental benefits.

Third, the employment of professional and technical personnel for the management of green credit must be accelerated, and the openness and innovation of green credit products must be improved. By recruiting professional and technical personnel, banks can effectively promote the development of and employment in green finance.

Fourth, policy guidance must be given a role in relation to green credit, the investment of green credit funds in the three industries must be increased, especially in the secondary industry, and the demand of market subjects for green credit must be expanded. National and local governments should play a more supportive and guiding role, and fiscal and tax subsidies and other measures should be used to encourage enterprises to invest in green credit in order to realize green development.

7. Results Discussion

It has been nearly 20 years since China implemented the green finance policy. Scholars have made rich achievements in the research on the effectiveness of green finance implementation, such as sustainable growth, industrial structure optimization, energy efficiency promotion, and environmental protection, etc. The existing studies have concluded that green finance played a positive role in the above aspects by using the panel model, grey correlation, data envelopment and differential difference methods, etc., respectively [36,37,38,39]. Comparatively speaking, this paper first constructed the green credit index through the entropy weight method and measured the index of the rationalization of industrial structure by Theil index, and then the methods of coupling analysis and regression analysis were adopted, respectively, to explore the synergies and regression coefficient between the green credit and the industrial structure. The results showed that there is coupling and synergy between the green credit and industrial structure, and the green credit has a positive effect on the industrial structure. The results also showed that the conclusions of the two methods could confirm each other. At last, the further research on green credit will focus on the analysis of the economic and social effects of the implementation of green credit by commercial banks.