Has Green Credit Improved Ecosystem Governance Performance? A Study Based on Panel Data from 31 Provinces in China
1
Business School, Soochow University, Suzhou 215021, China
2
School of Business, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11008; https://doi.org/10.3390/su151411008
Submission received: 6 June 2023
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Revised: 8 July 2023
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Accepted: 12 July 2023
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Published: 13 July 2023
(This article belongs to the Special Issue What Influences Environmental Behavior?)
Abstract
:Pollution prevention enhancement and ecological civilization construction serve as the keys to optimizing economic structure and promoting green sustainable development in China. Employing the balanced panel data of 31 provinces from 2011 to 2020, this study empirically examines the impacts of green credit on ecosystem governance performance. The results demonstrate that green credit can significantly contribute to the improvement of ecosystem governance performance in each province. Additionally, regional heterogeneity in the impacts of green credit on the performance of ecological environmental governance, which passed the significance test in economically developed and underdeveloped regions, eastern and non-eastern regions and resource-based and non-resource-based regions, respectively, was further confirmed. Hence, we suggest further improving the green credit policy system, continuously stimulating green financial innovation, releasing the right signals for green development and boosting the balanced development of green credit in all regions.
1. Introduction
Green credit occupies a pivotal position in China’s green financial system and is an important source of funds for the green and low-carbon development of the real economy. As the definition of green credit varies in China, this study defines it as the loan support and preferential interest rate provided by commercial banks and other financial institutions for projects, enterprises and organizations related to environmental protection, such as pollution control facility invention, ecological protection and construction, green manufacturing, ecological agriculture, new energy exploitation, etc., based on national environmental economic and industrial policy. It also refers to the financial means of imposing credit limits and punitive high interest rates on projects that involve polluting production and operation.
Currently, there are three main international green lending implementation standards, namely, the Equator Principles, the Green Lending Principles and the Linked Sustainability Lending Principles. The Equator Principles, introduced in 2003, are known for their comprehensiveness and have been adopted by most commercial banks around the world. The Green Lending Principles, launched in 2018, further restrict the use of loan funds compared to the Equator Principles and effectively direct the flow of funds to green industries. Under these Principles, financial institutions are required to first accurately classify financing projects to ensure that they can deliver environmentally sustainable benefits. Financing projects include, but are not limited to, nine broad categories: renewable energy; energy efficiency improvements; pollution prevention; the environmentally sustainable management of biological and land resources; terrestrial and aquatic biodiversity conservation; clean transport; sustainable water and wastewater management; climate change adaptation; and eco-efficient and circular economy products, production technologies and processes. The Principles for Sustainable Development-Linked Loans, launched in 2019 by the Loan Market Association and the Asia Pacific Loan Market Association in association with the Loan Syndication Association, further standardize the implementation criteria for sustainable development-linked loans and encourage borrowers to achieve sustainable benefits on an ongoing basis [1]. In the current international context, green credit also suffers from the following disadvantages: low levels of financing, poor green project selection and management, risk–return trade-offs and a lack of analytical tools and expertise to identify and assess green project risks, so green financing gaps are often observed [2].
Green credit falls within the scope of the green financial system. The earliest research on how green finance acts on the ecosystem can be traced back to the hypothesis of the environmental Kuznets curve, which, although it analyzes the relationship between economic growth and the ecological environment, still provides references for exploring the relationship between finance, especially green finance, and the ecological environment. For example, some scholars qualitatively analyzed the relationship between the two from different perspectives, and argued that the externalities in the field of environmental protection lead to low motivation for social capital intervention. Therefore, green finance can leverage more social capital to invest in green industries through policy incentives. Likewise, green credit, as an important part of the green financial system, can contribute to more investment in green industries [3,4,5]. Notably, green credit requires commercial banks to meet environmental testing standards and undertake pollution control and ecological protection as important prerequisites for credit approval in their credit activities. Through differentiated audit requirements, enterprises are guided to voluntarily improve their production behaviors and standards and invest their funds in projects and technologies that are conducive to environmental protection [6]. Pan [7] found that green credit instruments significantly curbed the pollution emissions of highly polluting enterprises. There are also large quantities of studies analyzing the relationship between green credit and carbon emission reduction. You [8] claimed that green credit is the optimal way to achieve carbon emission reduction by looking into green credit, green industry investment, and green bonds. And Mao et al. [9] stated that green credit can not only reduce local CO2 emissions, but also make neighboring areas’ CO2 emissions decline through a spatial spillover effect (i.e., a “local-neighborhood” effect).
The precondition for the sustainable development of the Chinese nation lies in the ecological environment, which is also an important foundation for economic structure optimization and green sustainable development. In recent years, the task of pollution prevention and control in China has been successfully completed. However, the structural, root and trend pressures of ecological environmental protection have not yet been fundamentally alleviated, with prominent pollution problems occurring in key areas and industries, as well as the arduous tasks of achieving carbon peak and carbon neutrality still remaining. As a key link in promoting green and low-carbon economic and social development and achieving high-quality advances, green credit is calling for more relevant studies to investigate whether it can improve ecosystem governance performance.
In a bid to explore whether green credit can improve ecosystem governance performance, this study takes 31 provinces in China from 2011 to 2020 as samples, and the procedure comprises three steps. First of all, it theoretically examines the relationships between green credit and ecosystem governance performance. Secondly, the ecosystem governance performance index is constructed to test the impacts of green credit on ecosystem governance performance from a holistic perspective. Lastly, it studies whether there exists heterogeneity in the efficiency improvement of ecosystem governance performance brought about by green credit, with the model tested for robustness.
The marginal contribution of this paper is that: (1) The existing research scarcely combines environmental pollutant emissions and carbon emissions, which are two critical aspects of ecological civilization construction and ecological environment protection. Additionally, it’s of great significance to comprehensively and systematically explore the impacts of green credit on ecosystem governance performance. This paper integrates general industrial solid waste production, sulfur dioxide emissions, carbon dioxide emissions and chemical oxygen demand into the framework of ecosystem governance performance, and analyzes the impacts of green credit on ecosystem governance performance. (2) Secondly, the model is tested for heterogeneity in terms of economic development level, geographic location and resource abundance, and specific recommendations are proposed for the development of green credit in regions with different characteristics.
2. Theoretical Analysis and Research Hypothesis
- (1)
- Green credit and environmental pollution control
Studies have shown that green credit can enable the achievement of ecological and environmental governance by reducing pollution emissions. The impacts of green credit on environmental governance have been explored in a vast body of studies. Grossman and Kuerger [10] and Antweiler et al. [11] both proposed three effects of environmental pollution, that is, green credit can improve the governance of the ecological environment through enterprise technology innovation, industrial structure adjustment and social and economic growth. Zhang [12] pointed out that the continued development of green credit has a significant negative correlation with the emission of “three waste” pollutants, thus indicating that the development of green credit can inhibit environmental pollution. Using the green credit policy issued in 2007 as an exogenous event, Cai and Wang et al. [13] found that its implementation significantly reduced the emissions of air and water pollutants in cities, and effectively improved environmental quality by employing the difference-in-difference model. It has been further proven that green credit policy can reduce carbon emissions in direct and indirect ways, and the level of technological progress, industrial structure upgradation, foreign direct investment and environmental regulation play a partly mediating role in the process of green credit inhibiting carbon emissions [14]. Additionally, according to Kim et al. [15], green credit projects serve as an effective measure to promote green retrofitting to reduce energy consumption in old buildings, and political factors such as loan support and carbon taxes are found to have influenced green credit projects in Korea. With a maximum support rate of 4.45%, greenhouse gas (GHG) emission reductions can be increased approximately three to three times from current levels. Dursun-deNeef et al. [16] examined the development of the environmental, social and governance (ESG) performance of enterprises after granting “green loans” dedicated to green projects versus “sustainable loans” benchmarked against ESG criteria, and concluded that enterprises with green loans could effectively reduce their environmental emissions. Based on the above findings, the following hypothesis is proposed:
Hypothesis 1.
An increase in the level of green credit can significantly improve ecosystem governance performance.
- (2)
- Green credit and differences in governance structure
Based on provincial data from 2010 to 2019, Lv et al. [17] studied the regional disparities, spatial patterns and trend evolution of green finance development in China, and further proposed that the development index of green finance in China is on the rise, and that the development of green finance demonstrates a trend of polarization, with obvious disparities among regions. The pilot policies of the green Finance Reform and Innovation Pilot Zone have heterogeneous effects on the ecological development of industrial structure in cities with different levels of financial development, urban administrative hierarchies, urban geographical locations and urban industrial characteristics [18]. Hu et al. [19] conducted an empirical study using a fixed-effects model based on sample data in eastern, central and western part of China from 2006 to 2016, and found that green credit makes a difference to industrial structure mainly through the capital and capital channels of enterprises. Moreover, green credit in China exerts a significant impact on industrial structure transformation, which differs to a great extent across regions. And green credit can reduce environmental pollution through three mechanisms, that is, improving enterprise performance, stimulating enterprise innovation as well as upgrading industrial structure. Notably, the inhibitory impacts of green credit policies on carbon emissions are regionally heterogeneous. To put this another way, green credit is more conducive to improving environmental quality in resource-based regions, and the emission reduction effect is more significant in regions with developed financial markets [20,21]. In summary, there are clear spatial patterns and trends evolving in the development of green finance and the development of green credit, and there is a positive impact of green credit on environmental governance in China. Based on the above findings, the following hypothesis is proposed:
Hypothesis 2.
Heterogeneity exists in the impacts of green credit on the performance of ecosystem governance.
3. Variable Selection and Study Design
3.1. Definition of Variables
3.1.1. Core Explanatory Variable: Green Credit
Referring to the measurement method for green credit by Zhou et al. [22], this study adopts the interest expenditure ratio of green industry as an indicator of green credit in each province, calculated as 1—the interest expenditure of highly energy-consuming industrial industries/the interest expenditure of industrial industries, with data from Chinese Industrial Statistics Yearbook from 2009 to 2021, in which relevant data from 2017 are supplemented via interpolation.
3.1.2. Explained Variable: Ecosystem Governance Performance Index
Ecological conservation is an activity that protects natural resources, involving individuals, organizations and governments [23], and emphasizes addressing issues arising from the interactions between ecosystems and humans, air pollution, water pollution, the overuse of energy and the loss of biodiversity [24]. With regard to the impacts of local fiscal capacity on environmental pollution prevention and control, Zhou [25] selected industrial wastewater discharge, urban industrial sulfur dioxide emissions and urban industrial dust emissions from cities at prefecture level and above as indicators to measure the level of local environmental pollution and control. Drawing on the measurement indicators selected in the existing literature, this study chooses general industrial solid waste generation, sulfur dioxide emissions from exhaust gases, carbon dioxide emissions and chemical oxygen demand in China’s provinces as indicators to measure the level of local ecosystem governance performance, and utilizes the technique for order preference by similarity to ideal solution (TOPSIS) to synthesize the ecosystem governance performance index (Table 1). The data were obtained from the CSMAR database and the China Statistical Yearbook.
3.1.3. Control Variables
The local ecosystem is also affected by other socio-economic factors. The control variables selected in this study involve the degree of opening up (i.e., the ratio of total import and export of trades goods to GDP according to the location of the business unit), regional urbanization rate (i.e., the proportion of the urban population to the total population in each region), energy consumption per ten thousand yuan of GDP (i.e., the ratio of total energy consumption to GDP in each province), technological innovation (i.e., the ratio of technology market turnover to GDP), green finance (i.e., the share of fiscal expenditure on environmental protection of the total fiscal expenditure in each province). The data weregathered from the National Bureau of Statistics, the Chinese Statistical Yearbook and the Provincial Statistical Yearbook. The definitions of the main variables are presented below (see Table 2).
3.2. Model Construction
A panel data model is adopted as the data selected for this study comprise both time series data and cross-sectional data. Additionally, the panel data model takes into account both individual differences and time dynamics, which can contribute to improving the validity of the econometric model estimation and provide a more accurate account of the research subjects’ performance. A static panel model refers to one in which the explanatory variables do not contain lagged terms of the explained variables, and meanwhile, random effects, fixed effects, or mixed effects may appear. This study aims to determine which model to choose using the F-statistic and Hausman tests.
Model (1) is constructed in this study to analyze the effects of green credit on ecosystem governance performance.
Yit = β0 + β1 × GreenCredit + β × Xit + γi + εit
Note: t denotes the year; i represents the province; Yit stands for the index of ecosystem governance performance in each province; Xit refers to the control variables, including the degree of opening up, regional urbanization rate, energy consumption per ten thousand yuan of GDP, technological innovation, and green finance; γit represents the individual fixed effects; εit denotes the random error term. Notably, the variables are logarithmically processed so as to solve the problems of heteroscedasticity and collinearity.
4. Empirical Results and Analysis
4.1. Descriptive Statistics
As shown in the descriptive statistics of the variables in Table 3, (1) the standard deviation of each variable is small, indicating small dispersion of each variable in this study; (2) the mean value of green credit is 0.472, the median value is 0.478, the number of samples above the mean is 162, the number below is 148, the maximum value is 0.972 in the Tibet Autonomous Region in 2011, the minimum value is 0.094 in Qinghai Province in 2017 and the difference between the maximum and minimum values is 0.878; (3) the mean value of the ecosystem governance performance index is 0.743, the median value is 0.274, the number of samples above the mean is 138, the number below is 172, the maximum value is 0.727 in Hebei Province in 2011 and the minimum value is 0.012 in Hainan Province in 2016, achieving the best ecosystem governance performance among all the samples.
4.2. Correlation Analysis
Spearman’s rank correlation coefficient is utilized in this study to examine the correlations among ecosystem governance performance index, green credit, degree of opening up, regional urbanization rate, energy consumption per ten thousand yuan of GDP, technological innovation and green finance. The results are displayed in Table 4, verifying the correlations among all the variables. The correlation coefficients between green credit and the ecosystem governance performance index are strongly negative, which is consistent with the status quo.
4.3. Regression Analysis
STATA16 was used to conduct regression tests on the selected panel data, and Hausman’s test was first adopted to determine the most suitable model for this study (i.e., a fixed-effects model or a random-effects model). The traditional Hausman test assumes that the random-effects model is the most efficient when H0 holds, implying that the perturbation terms must be homoskedastic; however, it cannot be applied in this study due to the heteroskedasticity-robust standard errors utilized in the subsequent regressions. Therefore, the robust Hausman test is employed, and the regression underlying the test is performed using cluster-robust standard errors. The results indicate that the original hypothesis of the random-effects model should be rejected at a significance level of 99%. In other words, the individual fixed effects are correlated with the explanatory variables, and thus, the fixed-effects model ought to be utilized. In the subsequent empirical process, the fixed-effects model (FE) and heteroskedasticity-robust standard errors were adopted.
Table 5 delineates the regression results of the fixed-effects model. It is noteworthy that better ecosystem governance performance results in a lower index score as it is a negative indicator. Therefore, the positive impacts of green credit on ecosystem governance performance should be reflected by a reduction in its index scores. As the regression coefficient of green credit on ecosystem governance performance is −0.334, significant at the 10% level, it is demonstrated that the reduced emissions of the four pollutants are accompanied by an increased proportion of green credit in each province, contributing to better ecosystem governance performance, which is in line with the economic significance test.
The regression coefficient of green finance in ecosystem governance performance for each province is −0.096, though it is not significant. The regression coefficient of urbanization rate on ecosystem governance performance is −1.393, significant at the 1% level, indicating that an increase in urbanization rate can improve the ecosystem governance performance of each province. Furthermore, the regression coefficient of technological innovation in ecosystem governance performance is −0.060, which is not significant, either. The regression coefficients of the degree of opening up and energy consumption on ecosystem governance performance are −0.261 and 0.015, respectively, showing the negative impacts of these two factors on ecosystem governance performance.
4.4. Robustness Tests
4.4.1. Reducing Control Variables One by One
Table 6 below illustrates the coefficients of the effects of the core explanatory variables on ecosystem governance performance after reducing the control variables one by one. With no control variables involved, the regression coefficient of the core explanatory variable GreenCredit is significantly negative at the 1% level, indicating the significant impact of green credit on ecosystem governance performance. Overall, the regression coefficient values of the core explanatory variable GreenCredit differ greatly, all passing the significance test.
4.4.2. Lagging Core Explanatory Variables
Drawing on Li and Shi [26] ’s practice, this study lagged green credit by one period, and then, carried out the regression analysis in a gesture to address the endogenous problem among control variables. The results display that the impacts of lagged green credit on ecosystem governance performance are significantly negative at the level of 1%, verifying the inhibitory effect of green credit on environmental pollution.
Compared with the regression results in Table 5, green credit that lagged for one period has a greater and more significant impact on the improvement of ecosystem governance performance than the current one, and its regression coefficient is significant at the 1% level, while the regression coefficient of the current one is merely significant at the 10% level.
4.4.3. Tail Reduction
The two-sided 1% tail reduction in all the variables involved shows that the influence coefficient of green credit on ecosystem governance performance is significantly negative at the 5% level, confirming the robustness of the estimated results (Table 7).
4.5. Heterogeneity Analysis
Green credit can greatly reduce urban pollutant emissions and carbon emissions, thus effectively improving urban ecosystem governance performance and promoting the transformation and upgradation of industries towards a green and sustainable direction. However, due to the differences in economic development, resource endowment and pollution control basis, the impacts of green credit in different provinces vary to some degree. A further question is put forward: are there any differences in the impacts of green credit on ecosystem governance performance in different provinces?
The exploration of this question is conducive to gaining a more profound perception of the impacts of green credit on ecosystem governance performance. Therefore, this study investigates the heterogeneous impacts from three aspects, namely, economic development level, geographic location and resource abundance.
4.5.1. Economic Development Level
The classification of the 31 provinces into economically developed and underdeveloped regions was based on whether the per capita GDP of each province exceeded 65,000 yuan in 2021. Thirteen provinces were accordingly classified into economically developed regions, including Guangdong, Zhejiang, Jiangsu, Shanghai, Beijing, Shandong, Hubei, Hunan, Fujian, Chongqing, Tianjin, Liaoning and Shaanxi, while the other provinces were considered economically underdeveloped ones.
Based on the empirical results of this study, the regression coefficient of green credit on ecosystem governance performance in economically developed areas is −0.845, significant at the level of 1%. In an economic sense, this means that a 1% increase in green credit in economically developed regions is able to reduce the Environmental Pollution Management Performance Index by 0.845%. Meanwhile, that in economically underdeveloped areas is −0.106, failing to pass the significance test. This shows that green credit can give full play to the role of improving ecosystem governance performance in economically developed areas and that the positive impacts of green credit on ecosystem governance performance in economically underdeveloped areas are weaker than those in economically developed areas.
4.5.2. Geographic Location
The differences in geographic location have sparked an imbalance in regional economic development to a certain extent, which is directly tied to regional ecosystem governance performance. Due to the existence of heterogeneous factors such as the type of regional ecological environment, environmental regulations, financial development and macroeconomic policies, the inhibitory effect of green credit in different regions varies. Thus, this study classifies its research subjects into two categories, eastern regions and non-eastern regions, and explores the impacts of green credit on ecosystem governance performance in different regions through the proposed model (1).
In accordance with the results of the regression analysis, the positive impacts of green credit on ecosystem governance performance in eastern regions are significant at the 1% level, while those in non-eastern regions are significant at the 5% level. In economic terms, this means that in the east, a 1% increase in green credit can reduce the Environmental Pollution Management Performance Index by 0.494%. In the midwest, a 1% increase in green credit was able to reduce the Environmental Pollution Control Performance Index by 0.264%. Since the financial system in eastern regions is generally well developed and enterprises have access to a greater variety of funding channels, the incentive and restriction impacts of green credit may be more apparent.
4.5.3. Resource Abundance
Resource-based cities traditionally refer to those whose pillar industries involve the development and processing of natural resources such as minerals, energy, and forests, serving as the backbone for ensuring the supply of national energy resources and promoting industrialization. However, dominated by industries with high energy consumption, high pollution and high emission, most cities are faced with severe ecological pollution and resource sustainability issues. Considering that the role of green credit in ecosystem governance performance may be affected by the differences between resource-based cities and non-resource-based cities, this study further categorizes the samples into resource-based and non-resource-based regions to examine its heterogeneous impacts.
The State Council defined China’s 262 resource-based cities for the first time in the document National Plan for the Sustainable Development of Resource-based Cities (2013–2020). Based on the list of resource-based cities nationwide disclosed in the circular, we used the number of resource-based cities each province contained in the list as a criterion for classifying whether it was a resource-based province. Based on the number of resource-based cities included in the document, we ranked the 31 provinces from highest to lowest and selected 13 cities—Shanxi, Guizhou, Anhui, Henan, Shandong, Heilongjiang, Yunnan, Liaoning, Hebei, Jilin, Jiangxi, Hunan and Sichuan—as resource-based provinces.
Based on the regression results, green credit in resource-based provinces has not displayed a positive impact on ecosystem governance performance, while the regression coefficient of green credit in non-resource-based regions is −0.432, significant at the level of 5%. In economic terms, this means that a 1% increase in green credit in a non-resource-based region can reduce the Environmental Pollution Management Performance Index by 0.432%. To sum up, compared with resource-based regions, enhancing green credit to strengthen local ecosystem governance performance is much more effective for non-resource-based regions. As traditional industrial development centers in China, problems in environmental protection and industrial transformation in resource-based provinces are still prominent, and the active role of green credit has not been fully exploited (Table 8).
5. Research Findings and Policy Recommendations
Based on the panel data of 31 provinces in China from 2011 to 2020, the interest expenditure ratio of green industry is selected as an indicator of green credit in each province, and is measured to analyze its impacts on ecosystem governance performance in each province. The results are elucidated as follows: Firstly, green credit exerts a positive lagging impact on ecosystem governance performance in each province. Secondly, the impact of green credit on ecosystem governance performance is heterogeneous. Specifically, the positive impact of green credit on ecosystem governance performance in economically underdeveloped areas is weaker than that in economically developed areas, and the inhibitory effect of green credit on environmental pollution in eastern regions is more significant compared with that in non-eastern regions.
This paper selects four representative environmental pollutants, incorporates them into an integrated framework, constructs an eco-environmental governance performance index, empirically tests the role of green credit on eco-environmental governance and deepens the understanding of the relationship between green credit and eco-environmental governance. This paper extends the existing literature on the heterogeneity of the impact of green credit by demonstrating that there are significant regional differences and there is structural heterogeneity in the positive impact of green credit on environmental governance in China. The limitations of this paper are that more indicators are not included in the construction of the environmental pollution governance performance index due to the unavailability and incompleteness of the data, and that a more in-depth analysis of the institutional pathways through which green credit affects ecological governance is not undertaken.
In the future, we can continue our research in the following two directions: First, we can continue to collect important data on environmental pollution emissions and include them in the construction of the index system, so as to minimize the bias caused by omitted variables. Secondly, we should continue to explore the impact mechanisms and pathways of green credit on ecological and environmental governance, and analyze whether there are differences in the mediating or moderating effects across regions, in order to make better recommendations in line with specific realities.
Based on the above findings, the following policies are recommended:
Further improve the green credit policy system. The positive impact of green credit on ecosystem governance performance has been confirmed in this study, but there are still some basic tasks in the field of green finance in China that require improvement. For instance, a gap between the development of green credit standards, high-quality data and information disclosure and current demand restricts the development of green credit and is not conducive to exerting the carbon emission reduction effect of green credit. Therefore, the government ought to further improve the green credit policy system, formulate disclosure standards, and strengthen the mutual communication between the environmental department and the financial department to urge more funds to flow to the green environmental protection industry, so as to provide technical support and create a better market environment for the effective implementation of green credit.
Continuously expand the scale of green credit. Since the release of the green credit policy, investment has increased year by year, but there is still much room for improvement. The government can take more effective measures to expand the scale of green credit. To be specific, subsidies should be provided for banks to reduce the costs associated with the implementation of green credit. On the other hand, the government ought to strengthen the supervision of environmental pollution, reduce the loans of high-pollution enterprises, offer relevant subsidies to environmentally friendly enterprises, and arouse the enthusiasm of banks and enterprises to expand the scale of green credit. For example, by focusing on major strategic deployments such as carbon peaking and carbon neutrality targets, we can gradually improve the dual incentive and constraint mechanisms, and actively guide financial institutions to increase green investments and enhance the ability of green credit business to support the development of green and low-carbon industries through policies such as performance evaluation and interest discount incentives.
Promote the balanced development of green credit in different regions. Referring to the heterogeneity analysis of this study, it can be found that the scale and development of green credit differ greatly in different regions, resulting in differences in ecosystem governance performance. For instance, not only should non-eastern regions actively develop their economies and formulate perfect financial supervision policies, but more preferential measures should be taken to offer banks subsidies, increase the efficiency of dealing with environmental pollution and realize the balanced development of green credit in each region as soon as possible. Resource-based provinces should also enhance their green financial systems, stimulate green financial innovation, and improve their levels of green credit. There are many old industrial base cities in resource-based provinces, which are characterized by a low level of industrial structure, a relatively sloppy approach to development, a high intensity of exhaust emissions, serious environmental pollution and many “three high” enterprises. Therefore, green credit funds can focus on scientific and technological innovation industries, green and sustainable industries, etc., so as to adjust the industrial structure and promote the improvement of enterprise performance in environmental protection. Additionally, under the guidance of the carbon peak and carbon neutrality goals, governments at all levels and financial regulators can broaden the sources of green credit funds by setting up green funds, issuing green special bonds for electricity and using policy and developmental financial instruments to supplement the funding gap to improve ecosystem governance performance.
Author Contributions
Methodology, Y.Z.; Formal analysis, Y.Z.; Writing—original draft, Y.Z.; Writing—review & editing, Y.Z.; Supervision, R.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Ecosystem governance performance index.
| Level 1 Indicators | Level 2 Indicators | Indicator Symbols | Definition of Indicators | Indicator Attributes |
|---|---|---|---|---|
| Ecosystem governance performance index (EGPI) | General industrial solid waste generation | SolidWaste | General industrial solid waste generation in each province (million tons) | Reverse indicator |
| Sulfur dioxide emissions from exhaust gases | SO2 | Sulfur dioxide emissions from exhaust gases in each province (million tons) | Reverse indicator | |
| Carbon dioxide emissions | CO2 | Carbon dioxide emissions in each province (million tons) | Reverse indicator | |
| Chemical oxygen demand | COD | Amount of oxygen required to oxidize one liter of organisms in wastewater in each province (mg/L) | Reverse indicator |
Table 2.
Definitions of main variables.
| Variable Name | Variable Symbols | Variable Definition | |
|---|---|---|---|
| Explained variables | Ecosystem governance performance index | EGPI | (See Table 1) |
| Explanatory variable | Green credit | GreenCredit | 1—interest expenditure of highly energy-consuming industrial industries/interest expenditure of industrial industries |
| Control variables | Degree of opening up | OPE | Ratio of total import and export of traded goods to GDP by location of business unit |
| Regional urbanization rate | URB | Share of urban population in total population in each region | |
| Energy consumption per ten thousand yuan of GDP | ENER | Ratio of total energy consumption to GDP in each province | |
| Technological innovation | TECH | Ratio of technology market turnover to GDP | |
| Green finance | GRE | Share of fiscal expenditure on environmental protection of total fiscal expenditure in each province |
Table 3.
Descriptive statistics.
| Variables | N | Mean | p50 | sd | Min | Max |
|---|---|---|---|---|---|---|
| EGPI | 310 | 0.257 | 0.264 | 0.161 | 0.012 | 0.727 |
| Greencredit | 310 | 0.472 | 0.478 | 0.154 | 0.094 | 0.972 |
| GRE | 310 | 0.030 | 0.028 | 0.010 | 0.012 | 0.068 |
| URB | 310 | 0.581 | 0.570 | 0.131 | 0.227 | 0.896 |
| TECH | 304 | 0.015 | 0.006 | 0.028 | 0 | 0.175 |
| OPE | 310 | 0.261 | 0.141 | 0.293 | 0.008 | 1.548 |
| ENER | 300 | 0.732 | 0.584 | 0.438 | 0.187 | 2.446 |
Table 4.
Correlation analysis.
| EGPI | GreenCredit | GRE | URB | TECH | OPE | ENER | |
|---|---|---|---|---|---|---|---|
| EGPI | 1 | ||||||
| GreenCredit | −0.116 *** | 1 | |||||
| GRE | 0.0110 | −0.135 ** | 1 | ||||
| URB | −0.115 ** | 0.412 *** | 0.0720 | 1 | |||
| TECH | −0.295 *** | 0.137 ** | 0.154 *** | 0.552 *** | 1 | ||
| OPE | −0.074 | 0.600 *** | −0.249 *** | 0.597 *** | 0.305 *** | 1 | |
| ENER | 0.225 *** | −0.634 *** | 0.230 *** | −0.437 *** | −0.337 *** | −0.647 *** | 1 |
Note: ** p < 0.5, *** p < 0.01.
Table 5.
Fixed effects regression results.
| EGPI | |
|---|---|
| GreenCredit | −0.334 * |
| (0.188) | |
| GRE | −0.096 |
| (0.117) | |
| URB | −1.393 *** |
| (0.465) | |
| TECH | −0.060 |
| (0.061) | |
| OPE | 0.261 *** |
| (0.091) | |
| ENER | 0.015 |
| (0.136) | |
| _cons | −2.781 *** |
| (0.485) | |
| N | 300 |
| R2 | 0.366 |
Note: Standard errors in parentheses. * p < 0.1, *** p < 0.01.
Table 6.
Robustness tests (1).
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| EGPI | EGPI | EGPI | EGPI | EGPI | EGPI | |
| GreenCredit | −0.334 * | −0.337 * | −0.355 * | −0.417 *** | −0.477 ** | −0.529 *** |
| (0.188) | (0.188) | (0.223) | (0.151) | (0.216) | (0.222) | |
| GRE | −0.096 | −0.098 | −0.100 | −0.120 | −0.207 | |
| (0.117) | (0.110) | (0.118) | (0.111) | (0.148) | ||
| URB | −1.393 *** | −1.413 *** | −1.722 *** | −1.602 *** | ||
| (0.465) | (0.473) | (0.457) | (0.371) | |||
| TECH | −0.060 | −0.061 | −0.048 | |||
| (0.061) | (0.060) | (0.059) | ||||
| OPE | 0.261 *** | 0.263 *** | ||||
| (0.091) | (0.092) | |||||
| ENER | 0.015 | |||||
| (0.136) | ||||||
| _cons | −2.781 *** | −2.814 *** | −3.409 *** | −3.284 *** | −2.729 *** | −2.035 *** |
| (0.485) | (0.347) | (0.353) | (0.395) | (0.487) | (0.182) | |
| N | 300 | 301 | 301 | 310 | 310 | 310 |
| R2 | 0.366 | 0.366 | 0.334 | 0.267 | 0.079 | 0.064 |
Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Table 7.
Robustness tests (2).
| Lagging Core Explanatory Variables | Tail Reduction | |
|---|---|---|
| EGPI | EGPI | |
| GreenCredit | −0.672 *** | −0.408 ** |
| (0.231) | (0.164) | |
| GRE | −0.105 | −0.048 |
| (0.108) | (0.093) | |
| URB | −1.310 *** | −1.291 *** |
| (0.452) | (0.446) | |
| TECH | −0.056 | −0.069 |
| (0.062) | (0.059) | |
| OPE | 0.259 *** | 0.234 *** |
| (0.085) | (0.084) | |
| ENER | 0.067 | 0.021 |
| (0.123) | (0.139) | |
| _cons | −2.666 *** | −2.697 *** |
| (0.479) | (0.471) | |
| N | 300 | 300 |
| R2 | 0.362 | 0.389 |
Note: Standard errors in parentheses. ** p < 0.05, *** p < 0.01.
Table 8.
Heterogeneity analysis.
| EGPI | EGPI | EGPI | ||||
|---|---|---|---|---|---|---|
| Economically Developed Regions | Economically Underdeveloped Regions | Eastern Regions | Non-Eastern Region | Resource- Based Areas | Non-Resource- Based Areas | |
| GreenCredit | −0.845 *** | −0.106 | −0.494 ** | −0.264 * | −0.194 | −0.432 ** |
| (0.196) | (0.124) | (0.169) | (0.142) | (0.143) | (0.300) | |
| GRE | −0.006 | −0.023 | −0.144 | 0.144 | 0.104 | −0.292 |
| (0.078) | (0.145) | (0.105) | (0.133) | (0.138) | (0.171) | |
| URB | −3.554 *** | −1.033 ** | −0.942* | −1.328 *** | −1.220 ** | −2.162 * |
| (0.785) | (0.439) | (0.487) | (0.390) | (0.476) | (1.068) | |
| TECH | 0.042 | −0.089 | −0.085 | −0.051 | −0.079 | −0.045 |
| (0.075) | (0.060) | (0.061) | (0.074) | (0.058) | (0.101) | |
| OPE | 0.413 *** | 0.198* | 0.589 *** | 0.118 | 0.141 | 0.260 * |
| (0.116) | (0.109) | (0.108) | (0.091) | (0.116) | (0.138) | |
| ENER | −0.246 ** | −0.026 | −0.141* | 0.104 | 0.061 | −0.103 |
| (0.108) | (0.189) | (0.075) | (0.186) | (0.182) | (0.107) | |
| _cons | −3.151 *** | −2.420 *** | −2.995 *** | −1.992 *** | −1.883 *** | −4.324 *** |
| (0.300) | (0.593) | (0.312) | (0.665) | (0.576) | (0.614) | |
| N | 130 | 170 | 100 | 200 | 170 | 130 |
| R2 | 0.573 | 0.359 | 0.614 | 0.344 | 0.377 | 0.405 |
Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
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Zhou, Y.; Long, R. Has Green Credit Improved Ecosystem Governance Performance? A Study Based on Panel Data from 31 Provinces in China. Sustainability 2023, 15, 11008. https://doi.org/10.3390/su151411008
AMA Style
Zhou Y, Long R. Has Green Credit Improved Ecosystem Governance Performance? A Study Based on Panel Data from 31 Provinces in China. Sustainability. 2023; 15(14):11008. https://doi.org/10.3390/su151411008
Chicago/Turabian StyleZhou, Yiting, and Ruyin Long. 2023. "Has Green Credit Improved Ecosystem Governance Performance? A Study Based on Panel Data from 31 Provinces in China" Sustainability 15, no. 14: 11008. https://doi.org/10.3390/su151411008
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