Hypothesis 1 (H1). Increasing the proportion of green credit will increase the operating profits of commercial banks.

4. Do Green Credit Policies Boost Profits?

Ithe end of 2020, bank the last section, thecredit financial data for green industries and the “Two High and One Surplus” industries were compared, and the reasons for the differences between these two types of industries were analyzed to build a theoretical basis for howng still accounted for 61% of total social financing. Therefore, green credit policies affect the asset management of commercial banks. However, according to the literature, relevant empirical analyses have failed to find a relatively consistent answer to the question of whether increasingis the most important force affecting green finance in China. In fact, CIB Research estimates that green credit can have positive effects on commercial banks. This paper will continue to try to explain the differences in the conclusions from the literature through empirical analysis and will put forward inferences based on facts.

4.1. Description of the Data, Variables, and Models

Theaccounts for more than 90% of all green financing. (As of the first half of study period was from 2007 to 2020 and included 2016019, the first year of green finance in China; the global financial crisis in 2008; and the COVID-19 outbreak in 2019. Samplecredit balance of China’s 21 major banks included 43 types of large state-owned commercial banks, national joint-stock commercial banks, urban commercial banks, and rural commercial banks (including non-listed banks). During the sample period, the selected banks were very representative in terms of the proportion of deposit balance (69.2–80.5%) and loan balance (71.4–74.7%) for the whole industry. From the perspective ofwas CNY 10.6 trillion, and the balance of domestic green bonds was only CNY 708.78 billion. The bank mainly issued green financial bond funds to issue green credit data, as of 2016 . Excluding the green credit balance of the sample banks accounted for 77% ofinancial bonds, the balance of the whole industry (the CBRC has only releasednon-financial enterprise green credit statistics from 2013 to 2016). The relevant financial data,bonds was only CNY 293.96 billion, less than 3% of the green credit, and operational data from commercial banks were collected manually from their official websites and from their annual reports and social responsibility reports.

4.2. Different Models Have Different Results

Th scale. The balances of the other green financing scales were esmpirical analyses that have been carried out in the literature use the relative index—return on total assets (ROA) or return on equity (ROE)—as the explained variable. The value of the relative index is easily affected by the financing behavior of commercial banks. For example, capital expansion increases the denominator of the relative index. In order to further confirm that, this paper will compare the modeling resultaller. (Source: http://greenfinance.xinhua08.com/a/20191227/1905011.shtml (accessed on 16 March 2022))). On the other hand, since the beginning of the 21st century, China has faced the problem of environmental pollution during the process of the absolute index and the relative index, respectivelycountry’s development. The explained variable of the absolute index model is the natural logarithm of net profit, and the core explanatory variable is the natural logarithm of green credit. The relative index model corresponds to return on total assets (or return on equity) and the ratio of green credit corresponds to the total loan scale. The control variables include macro indicators, such as GDP, interest rates, and exchange rates, and industrial indicators, such as the average net interest margin, market concentration, asset size, age (from the beginning of its establishment), and income cost ratio (operating efficiency). The descriptive statistics of these variables are in Table 1. Using the F Test, it can be found that the F statistics in Table 2 (line “F-test”) significantly reject the null hypothesis that there is no fixed effect; so, the fixed-effect model is superior to the mixed-effect model. Using the Hausman test (“Hausmangovernment has always emphasized environmental protection and sustainable development and has frequently introduced policies for industrial structure adjustments, but these policies have always achieved poor results [4–7]. In essence, the massive development of high energy consumption and heavy-pollution industries has benefited from large inflows of credit funds (based on the bank loan data of the listed enterprises, Liu Xiliang and Wen Shuyang (p v2019) alue)”), it iso found that the fixed-effect models are mostly superior to the random-effect models. Therefore, the fixed-effect model of individual characteristics is established as follows:

The  is “six of the top ten industries with the amount of credit are highly polluting industries, and the avexplained variablerage emission of the relative index model, namely the ROA or ROE;  corresponds to the explained variable of the absolute index model, namely the natural logarithm of net profit.  and  are the natural logarithm of green credit and the proportop ten industries with the amount of loan is about twice that of other industries”) [8,9]. The unbalanced allocation of green credit in the total loan, respresources objectively. .  and  are random disturbance terms.  and  are individual fixed effects.

Table 1. Desc promotes the continuous expansion of industriptive statistics.

Aes with shimple comparison of the empirical results (Table 2) shows that the conclusions drawn by the relative indicator model (Model 1–2) and the absolute indicator model (Model 7–9) are quite different. Under the relative indicator model, the increase igh rates of energy consumption and pollution and aggravates the environmental pollution and environmental costs incurred on the proportion of green credit inhibits the financial performance of commercial banks, while the conclusion drawn by the absolute indicator model is just the opposite. This contrast may be leath towards economic growth. Therefore, the environmental problems caused by the rapid expansion of the total assets (denominator) in the relative index. Before 2007, only 7 of the 43 banks in the sample had been listed. The vast majority of Chinese commercial banks were listed in succession during the samplindustrial structure [10,11] can be attributed to the “financial mismatch” status of credit resources.

Cofinancial institutions being required to regularly disclose financial information on their social and environmental responsibilities and on their support for energy conservation and emission reduction as well as to consideritang the interactive influence oftly refine their own asset scale (Model 3), the financial effect of the proportion of statistical standards for green credit is differentiated: banks with a lower asset scale than average (CNY 810.1 billion) are more likely to realize the positive effects of nformation. In terms of financial discounts for eligible green credit, green credit. On the one hand, under certain conditions, green credit policies are conducive to alleviating the decline in operating efficiency caused by the excessive capital expansion of Chinese co and green bonds should become eligible collateral for re-lending by commercial banks. On the other hand, it has been proven that sample selection plays an important role in the model results. Different studies in the literature have selected different sample individuals and sample periods, and the average asset size of those selected samples has also been different, causing different conclusions to be drawn.

Table 2. Regress Local governments are also rolling out policies to support green finance. For example, Zhejiang province took the lead by issuing the Guiding Opinions on Financial Suppon results for the different types of index models.

Robrt to Achieve Peak Carbon Neust t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

4.3. Cost Margin of Green Credit

According rality, esto the above analysis, the excessive capital expansion of Chinese commercial banks may lead to a decline in operating efficiency. Increasinglished a positive list of credit supports for green credit not only improves the operating profits, but also alleviates the problems caused by excessive capital expansion to a certain extent. In order to further verify and explore the relationship between green credit policies and the operation efficiency of commercial banks, this paper studies whether and how increasing green credit affects the operation of commercial banks by establishing elastic green credit models and input and output from the perspective of internal resource utilization efficiency:

, and low-carbon development, and set up “zero carbon” pilot units and low-carbon industrial parks. In addition, a series of measures have been introduced, such as setting up carbon accounts for key enterprises and public insthe input variable, represents the management costs; the number of employees and the number of business outlets are selected as the corresponding input variables, which are the explained variables taken from the natural logarithm.  is the logarithm of the amount of green credit,  represents a seritutions with carbon emissions and fostering regional environmental rights trading markets. Since 2016, Xiamen has offered different degrees of the control variables, and  is the random interference term. In order to compare the costs and benefits of green credit, the natural logarithm of the net profit is added tincentives, loan discounts, and risk-sharing compensation to the explained variable. In this paper, the individual fixed effect was used for regression analysis.

Tfinancial industry based on the results of Model 9–11(in Table 3) support the view tcale of green credit.

Table 3. Regressdevelopment policies will have a certain impact on the national economy, especially in some traditional industries with high levels of pollution and energy consumption. For developion of green credit on costs and profit.

Robug countriest, t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

So fsuch industries ar, we have proved Hypothesis 1; that is, green credit policies improve the operating profits of commercial banks. In athe pillar of their national economy in the early stages of development. Addition, it was determined that compared to general loans,ally, the role of green credit also significantly increases the cost input of commercial banks. In order to through financial leverage has further analyze the mechanism iincreased the influencing green credit policies and to combine with the conclusions that have already been obtained, this paper puts forward Hypothesis 2:

Hypothesis 2 (H2). Increasing the proportion of green credit will improve the operating efficiency of commercial banks by improving the utilization rate and allocation efficiency of internal resources.

5. The Operation Efficiency of Commercial Banks

Accore of this impact. On the other hand, the global economy is under downward pressure dingue to the conclusions of Section 4, excessive capital expansion speed may lead to a decrease in the internal resource allocation efficiency of Chinese commercial banks and improving the green credit ratio not only pushes up the business costs of banks, it also createsimpact of COVID-19, while it is also being buffeted by various extreme events that have several times more profit. These findings provide market evidence for green credit policies improving the operational efficiency of commercial banks, but these findings only support this inference from the results, and there is a lack of systematic and dynamic discussion on the principles and processes. Is the rate at which the assets of commercial banks are expanding really reasonable? Is there an optimal asset size or allocation structure for internal resources? Additionally, can increasing green credit have an impact oely affected financial stability. In this context, should we strengthen our confidence and further promote economic transformation with green credit? Are green credit policy incentives compatible in the resource allocation efficiency of commercial banks? Thmarket economy? Before answers to ting these questions are the key to , we must further exploring how green credit policies affect the oclarify the market operational efficiency of commercial banks mechanism of green credit. This requires us to analyze the relationship betweenpaper studies the market logic of green credit policies and the operational efficiency of from the perspective of Chinese commercial banks, not only from the perspective of the operating results, but also from the perspective of the operating processes. In view of this, this paper will take the input–output efficiency as and tries to determine the long-term market effect of green credit from the starting point to explore the impact of changes in the proportion of of whether green credit on business efficiency and will carry out research from ais in line with the interests of commercial banks.

Here,-called green credit input–output efficienolicy refers to the concept of technical efficiency in economics; that is, under certain technical conditions, when it is impossible to increase any output without reducing other output, or it is impossible to reduce any input without increasing other input, then the input–output is technically efficient [53]. Therefore, technical efficiency refers to thea series of policies that allow commercial banks, policy banks, and other financial institutions to offer preferential interest rates for loans to enterprises or institutions that are engaged in ecological protection and the development and utilization level of the existing technology. Common influencing factors include the quality of the workersof new sources of energy and the management level. Farrell [54] useat develop and production frontier analysis to measure technical efficiency for the first time, which was widely recognized by the theoretical circle of researchers and became the basis of the efficiency measurements. The so-called production frontier refers to the maximum output set corresponding to various proportional inputs at a certain technical level. The core oe anti-pollution facilities; meanwhile, the policy also includes a high, punishment interest rate that is implemented to limit loans for the investment and working capital of new projects carried out by polluting means of production, frontier analysis, is the effective frontier, that is, analysis in and enterprises. The purpose is to guide the form of the production function. In terms of methods, it includes parametric analysis with a specific function form and a non-parametric method without a specific function form. The former is represented by stochastic frontier analysis (SFA) and parametric metafrontier analysis. The latter is represented by data envelope analysis (DEA).

DEA ilow of funds and loans into the enterprises and institutions that promote the national environmental protection cause and to appropriately withdraw from the enterprises and non-parametric analysis method that was first proposed by A. Charnes, W.W. Cooper, and E. Rhodes [55] in 1978 and was developed on the basis of evaluating relative efficiency. As a non-parametric analysis, DEA can calculate the efficiency value without setting the specific fprojects that destroy and pollute the environment to realize the “green allocation” of funds [12]. The capital allocation function form of production, which has a wide range of applications. In the process of analyzing the input–output efficiency of commercial banks, it is difficult to find an effective functional form between input (such as staff and outlets) and output (such as net profit). Therefore, this paper adopts the DEA method to analyze the operating efficiency of commercial banks (if it is a functional form, then net profit is the explained variable, and the number of employees and outlets is the explanatory variable. It should be considered whether there is an optimal proportion between employees and outlets, which often varies greatly for different types of commercial banks in different regions, and this optimof green credit means that the role of environmental governance can be carried out from the initial stages of the production process and can run through the whole production stage; that is, the incentive and restraint effect of green credit has the characteristics of initial governance and whole-cycle governance [13]. The earliest official proportion often varies over time. Therefore, insufficient degrees of freedom should be considered during parameter estimation).

Ssals for green credit date back to therman and Gold [56] were the first to apply DEA to banking efficiency, and later scholars followearly 1990s, when the United Nations Environmental Program Finance Initiative (UNEPFI) issued their methods [57–59]. A key to measuring the operational efficiency of commercial banks with DEA is to identify the inpu “Statement by Financial Institutions on the Environment and output. There are three main classification methods: production methods, asset methods, and intermediary methods. Production methods view banks as general productive enterprises. The input production Sustainable Development” [14], which stressed the need to incorporate environmental factors mainly include employees, machinery and equipment, capital, etc., and the output factors include deposits and loans. According to the intermediary method,into standard risk-assessment processes. Its main goal was to require the bank is the medium of borrowing and lending; that is, it produces the absorbed deposits as loans, and other inputs also include human resources, financial resources, material resources, and other elements. Foring industry to consider environmental factors in its management activities and to encourage the property method, the index classification is closely related to the balance sheet. The input index is the liability item, and the output is the asset item. The amount of a loan is often taken as the most important output indicator, while deposits can be classified differently. Other indicators, such as manpower, equipment assets, and capital input, generally appear in the form of input factors.

The DEA model iivate sector to invest in environmentally beneficial technologies and services. The Equator Principles, advocated for by the World Bank Group, provide a framework for environmental and social-risk ass essentially a target optimization problem under a given constraint. As a modern enterprise, the ultimate goal is undoubtedly to pursue profit maximization rather than to maximize the loan amount. In fact, commercial banks expand the size of depositsment in project financing, including the risk classification of different types of projects, and loans to maximize their total profit by increasing the scale. The classification methods for deposits are the inputist topics related to environmental assessment processes, monitoring, and loans are the output, showing how commercial banks continue to consider debt management similarly to how it was considered in the 1960s and 1970s. This paper does not take loans as output and instead replaces them with net profit. Considering the importance of the asset security of commercial banks to the ffollow-up guidance. At present, 56 financial institutions around the world, including Citigroup, Standard Chartered, and HSBC, have become Equator Principle Financial system and even to the national economy, we added the non-performing loan rate as an undesirable output to the optimization target. In fact, trade-offs between safety and profitability often occur; so, coInstitutions (“EPFIs”), accounting for more than 90% of the global project financing market. By the end of 2021, nine Chinese mainland commercial bank managers and regulators need to make two-goal trade-offs. In s had incorporated the Equator Principles, which are also the practice, commercial banks have different priorities for different goals in different periods. For example, in a period when the non-performing loan ratio is lower than 1%, it is more important to improve the net profit level than to further reduce the non-performing loan ratio, while the opposite is true when the non-performing loan ratio is higher than 5%. In view of this, we considered a mal blueprint for green credit in China’s banking industry (From the Preamble of the Equator Principles, www.equator-principles.com (accessed on 16 March 2022)).

5.1. Theory and Methods

Ih of the research on green this papecr, the directional distance function (DDF) was used to measure the input–output efficiency. xedit is based on a corporate perspective. represents tChe inputs, and the outputs are divided into desirable outputinese enterprises y and non-desirable outputs b. The prode usuction possibiallity set is:

and ty he corresponding non-radial DDF is

,) is the ghly direction of improvement, and  is the adjustment amount for the input–output that is not limited by a fixed proportion. Based on the studies related to SBM-DDF [60–62], we also dynamically set the weights w of the inputs and outputs in the objective function.It was transformed into a linear programming problem using panel data with an individual number J and time dimension Γ (assuming the same scale remuneration production mode, variable scale remuneration only needs to add convexity conditions):

In thpendent on external financing, and credit resources are the “blood” of enterprise development. There is a long-term “financial mismatch” in China, where financial resources are concentrated in state-owned ente above exrpression, the letter with lower indices is a scalar valueises, large enterprises, and the letter without lower indices is a vector. In accordance with the above, x, y, ahot industries [15,16], and the end b terepresent input, desirable output, and undesirable output, respectively. ,) and ,) are the adjustment directions and adjustment quantities of these three variables, respectively; ,  and  are the relaxation variables representing the three variables, respectively;  is theises with high levels of pollution and energy consumption mentioned above are mainly subject to government intervention [17,18] and credit rationing [19,20]. The green credit λ vapolue (intensity variable) of the Jth DMU at time τ.

The setticy tries to change of the efficient reference frame, namely the effective frontier, is crucial. The effective frontier is usually dividedthis “financial mismatch” phenomenon by introducing high-pollution investment into the biennial, sequential, and global frontiers in the scope of time. The main difference lies in the period range of data used to determine the effective frontier. It corresponds to the data from the previous two years and to the sequential and sample data for the whole period. In order to facilitate the efficiency comparison in different periods, this paper adopts the global SBM-DDF.

5.2. Variable Selection and Data Description

Ihigher financing constraints and by internalizing the negative externality of corporate pollution emissions, thus having a profound impact on China’s industrial pattern this pape.

Table 4. Dcredit-approval features of Chinese commercial banks that emphascriptive statistics for operational efficiency analysis.

5.3. Analysis of Business Efficiency

Theize mortgages [24] cause green credit npon-radial distance was obtained. The smaller the value of the distance, the closer it is to the corresponding effective point and the higher the efficiency value. The non-radial distance between the points on the efficiency boundary was 0. For better understanding, this distance was further normalized by means of min-max normalization, resulting in an efficiency score of 0–100, with higher scores resulting inlicies to have less of an impact on highly polluting enterprises with high fixed assets [25]. Sun et al. [26] found that green credit policies can motivate Chinese enterprises to focus on front-end prevention and control rather than on terminal emission reduction.

Over the sociall, as we can see in Figure 4, the operating efficiency of Chinese image of commercial banks has declined since they reached their peak in 2011.This is consistent with the previous discussion on how “excessive capital expansion leads to a decline in operating efficiency”. In 2008–2011, the efficiency scores rose rapidly as each slack variable declined (in order to increase the comparability between different slack variables, this paper represents the change rates in the slack variables. The larger the change rate, the lower the factor efficiency corresponding to the slack variable). It can be seen that the most important force leadfrom the perspectives of media attention and recognition from customers, employees, and partners. For example, more social reports on green credit [27] and the disclosure of public announcements [28] can enable commercial banks to gain more public trust by improving this wave of operational efficiency improvements is the sharp increase in net profit, which is closely related to rapid economic recovery in the post-financial crisis, and a good economy promotes the improvement of the utilization efficiency of various input factors. During the period of 2011–2017, the overall operating efficiency gradually decreased. In terms of slack variables, the efficiency of all of the inputs and outputs shows varying degrees of decline during this periodeir reputation. In contrast, there is evidence that commercial banks regard some major polluting enterprises as important loan-provision objects that will affect their green reputation, lead to the loss of high-quality customer resources, and the most obvious feature is the decline in the control efficiency of the non-performing loan ratio. All of the indicators showed a brief small rally in 2017–2019. Affected by the COVID-19 epidemic, the operating efficiency in 2020 showed a sharp decline. As seen from the figure, the most important reason for this comes from the rapid decline in net profitsresult in reductions and fluctuations in the financial performance level [29]. Second, the implantation of a green policy can reduce the environmental risks of the loan customer.

O In the olither hand, compared to other inputs, the inefficient use of fixed assets and business outlets is more prominent and has existed for a long time. Among them, the number of business outlets before 2009 was the minimum efficiency investment, which is consistent with the conclusion of the research performed by Wang Bing and Zhu Ning [63]. The rate at which personnel are utilized and the management expenses gradually converge, a phenomenon that can be explained by saying that the efficiency of management expenses is gradually determined by how efficiently employees are used. The biggest factor causing the fluctuations in the overall operating efficiency ierature, environmental risk generally includes climate risk and environmental-regulation risk. China’s current credit resources flow into industries with high energy consumption, high pollution, and excess capacity (hereinafter referred to as the “Two High and One Surplus”) [8,9]; this process makes the substantial adjustments in net profit, followed by the non-performing loan ratio.

From the pervalue of the credit asspective of different typets of commercial banks(in Figure 5), the operating efficiency of large state-owned banks has risen to the highest level in the industry through a series of measures such as capital injection, bad debt stripping, shareholding reform, among other more sensitive to environmental regulations, energy price fluctuations, and market demand changes. These measures have resulted in strong national joint-stock commercial banks. In contrast, the city commercial banks cannot compete with the national banks due to the restrictions of their operating areas, and they have to compete with rural commercial banks for tight regional resources, resulting in the operating efficiency declining. Rural commercial banks have a low level of operating efficiency. The efficiency stratification of different types of banks is basically consistent with the study of Li Lifang et al. [64re is evidence that green credit can effectively reduce the probability of loan losses caused by environmental regulations [30,31]. Climate risk affects the ability for loans to be repaid by changing the business cash flow of borrowers [32]. From t

Figure 4. Average operlars with the opposite point of view believe that green credit increases business costs (Ma and Liu, 2013; Hu and Zhang, 2016) and squeezes out the “Two High and One Surplus” loating efficiency and slack variabless (Chen and Lu, 2011).

Figure 5. Operating Evefficiencies of the different types of n the decision by commercial banks.

Figure 6. O to adopt therating efficiencies of commercial banks in different regions.

6. Green Credit and Bank Operating Efficiency

Wh Equator Principles will bring a doublen comparing the trends in the blow to operating efficiency of commercial banks and the proportion of green credit, the average value of green credit is easily affected by extreme values due to the limitations in the amount of information that is publicly disclosed, thus affecting the accuracy costs and profits (Scholtens and Dam, 2007). However, Maya et al. (Finger and Gavious et al., 2018) showed that most of the conclusions. As shown in the left figure below, if the proportion of green credit is simply averaged, then there is a certain negative correlation with the operating efficiency; that is, the higher the proportion of green credit is, the lower the operating efficiency of the banks is as well. In cases where the sample size is small, this simple average method is easilyse results occurred in developing countries, while the situation was reversed in developed countries. The study of Cao Junxin and Yao Bin (Cao and Yao, 2014) found that carbon emission reduction measures would reduce industry profits and affected by large values, thus resulting in wrong judgments. For example, the green the quality of bank credit ratio in industrial banks has been 3–7 times assets.

Figure 7. Gree [33]. However, the research results of Zhang Lin and Lian Yonghui [34] are in contrast to this. Tong Menghua et al. [35] believe that commercial banks will lose customers due to “one-size-fits-all” carbon credit ratio and operating efficiency (trend comparison under different averaging methods).

6.1. Model and Variables

Comparedmission reduction policies and advocate for the establishment to the panel data from the mixed-effect model, the individual fixed-effect model eliminates the influence of individual characteristics that do not change the average effect in the form of difference over time and can eliminate the influence of extreme individuals without reducing the sample size. Therefore, this paper takes the f a “carbon emission reduction credit risk warning system for industrial enterprises” to ensure that commercial banks can maximize their interests when implementing green credit policies.

 and  aexplained variables, many scholars take return on total assets as the explained variable [34,36]. Some scholares the non-radial distance and green credit ratio, respectively. representhave also used self-built comprehensive indexes as the slackexplained variables of the inputs and outputs, and  and  are, such as the bank competitiveness index [37] or the control variables for two modelsmprehensive evaluation index [38,39].

Table 5. Des in a cripontive statistics (regression of green credit ratio and operating efficiency).

6.2. Empirical Results

Frolled variable linearom the regression results, it can be seen that increasing the proportion of green credit in commercial banks will improve all aspects of their operating efficiency. All else being equal, each percentage point increase inseries. These rich research methods and empirical conclusions allow us not only to further explore the green credit brings commercial banks closer to the effective boundary by 0.07 units on average (Table 6). This proves that green credit policies can effectively improve the allocation efficiency of the internal resourcfinancial market mechanism and to lay a solid foundation, but also to leave space for further research, with potential avenues of commercial banks. Through the regression of theresearch including updating the data, as most of China’s green credit ratio to the slack variable (Table 7), it can be seen that during the operation of esearch data are from 2017 and are related to commercial banks, increasing the proportion of . The existing green credit can significantly improve the efficiency of all of the inputs and outputs. Among them, improvdata imperfectly disclose regulations, meaning the efficiency has the biggest effect on the net profits. All other things being equal, an average percentage point in at free disclosure exists in terms of disclosing the green credit ratio of one can promote the net profit index towards its corresponding effective standard by 3.31. Improving the efficiency of the non-performing loan ratio is also obvious, a finding that is supported by the market data of commercial banks. Thus, due to the lack-of-interruption phenomenon, it is difficult to update the data. For example, according to data from the CBRC in June 2016, the non-performing green credit ratio of 21 major banks was only 0.41%, 1.35% lower than that of other loans during the same period (http://www.cbirc.gov.cn/cn/view/pages/ItemDetail_gdsj.html?docId=22943&docType=0, accessed on 8 April 2022).

TAdditionally, 2016 represents the first year of green finance in China, and the excessive speed of capital expansion leads to Chinese commercial banks having excessive internal resources, resulting in the gradual decline of the overall operating efficiency after 2011. In essence, green credit policies increase the green credit ratio; on the one hand, they make more effective use of the excess internal resources, and on the other hand, they improve the allocation efficiency of internal resources, alleviating the problem of low operating efficiency. This proves Hypothesis 2.

Table 6. The ata from subsequent years are more meaningful. The sample size needs to be expanded. Additionally, due to limited relevant information regarding disclosure regressulation results for the green credit ratio on non-radial distances.

Sdefects, the vast majority of the literatandaurd errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 7. Regresse is limion results of the green credit ratio on the slack variables.

Sed to listandared errors in parentheses. ***companies p < 0.01, ** p < 0.05, * p < 0.1.

7. Discussion of Endogeneity and the Robustness Test

7.1. Discussion of Endogeneity

As there is policy space for thet disclosure of green credit data,e their data to commercial banks can choose the green credit data that they wish to publish to a certain extent. Therefore, the samples that can be collected may be from commercial banks with a high proportion of green credit, and most banks and some influential city businesses come to the market late. This potential problem leads to the possibility of endogeneity caused by a non-random sample (self-selection bias); that is,not only causes under-represented defects, but also partly explains the use of the sample selection is endogenous. Oe methods in the other hand, some important variables related to the proportion of green credit may be omitted from the model. Heckman’s two-stage methodliterature; however, different results have been achieved, and the instrumental variablethree research method are used to discuss the above two types of endogeneity problems.

Fos need to be enriched. The return non-random sample problems, this paper refers to Heckman’s [66] two-stage method and introduces the non-randomness of the sample into the original regression model in the form of a on assets or return on equity is taken as the explained variable called the inverse Mills ratio (IMR) to solve the self-selection bias problem , and its fluctuation is easily affected by changes in the regression model.

 iasset s a dtructummy variable. If a bank publishes green credit data in that year, then the corresponding value is 1; otherwise, it is 0.  is a potential factor that affects whether banks publish green credit data, including the ages of the banks, whether they are listed, and the competition statusre (for example, the return on assets decreases due to capital and stock increases). However, most of the industry. In this paper, we established a logistic model to analyze the relationship between  and  through the intermediatestudies in the literature do not control this variable .  and  are the probability density function values and the cumulative distribution function values corresponding to the  fitting value, respectively, and the two are divided to calculate the inverse Mills ratio .  represents the green credit ratio, and  is the corresponding control variable (the same as above).  and  are the residual terms for the two models, respectively.  and  are individual fixed effects.

In, resulting in differences in the research conclusions. A single income index fails to fully describe the operation of commercial banks, while the comprehensive indicator method fails thiso paper, the instrumental variable method is used to analyze possible missing variable problems. In order to meet the relevance and externality principle of instrumental variable selection, we took the local implementation intensity of environmental protection as the direction of selection and constructed “the operating cost of grasp the phased coordination under multiple objectives (for example, when the non-performing loan ratio is low, the pursuit of the maximum return rate is a more important goal).

As seen from the regressionmpirical results (Table 8), the inverse Mills ratio (IMR) is significant, indicating the existence of non-random sample problems. After modification, the positive effect of green credit of the model. In contrast, we have carried out a full research methodology on the operating efficiency of banks is improved (the coefficient increases from 0.0663 to 0.0692). From the perspective of the instrumental variable method (Model 4–5), environmental tolerance (OEIWGpSo2) has a certain correlation with the proportionimpact mechanism: on the one hand, it reveals that the characteristics of green credit, and endogeneity problems may underestimate the promotion effect of green credit on improving the operational efficiency of banks.

Table 8. The regress“low risk and high income”, are important mechanion results of the endogenous tests.

Standams ford errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

7.2. Test of Robustness

Whether meaimproving the business suring the operational efficiency tuation of commercial banks can effectively reflect objective facts is the key to determining whether. On the other hand, combined with the conclusions of this paper are reliable. During the DEA analysisin the literature and of the efficiency, the weight change rule of the objective function is set in this paper, which may afinancial analyses, different index types affect the reliability of the conclusions. Therefore, the rule will be reset to test the robustness of the modeling results, with the modeling results. Specifically, the weight of net profit is still set as 1, and the tolerance of non-performing loans is reduced: when the non-performing loan ratio is lower than the industry average, the weight is set as 1; whe pointing out that capital expansion is too fast and that the decline in the non-performing loan ratio is higher than the average but lower than 5%, the weight is set as 1.2; if it is above 5%, the weight is 1.5. The other methods remain unchanged.

Byefficiency of internal resources is the main reason cfomparing the influences of different weight setting rules on the results (Tables 7 and 9), we can see that the significance of the regression coefficient remains unchanged after the rules that have been set for the dynamic weight have changed considerably. Although the size of the coefficient is reduced to a certain extent, such changes cannot overturn the original conclusions. That is, it still proves that green credit can significantly improver the decline in asset yields and that green credit policies can help improve the “overcapacity” in commercial banks. (2) In the empirical analysis, this paper establishes a data envelope analysis model with undesirable output and dynamically analyzes the operational efficiency of commercial banks using the allocation efficiency of input and output.

Table 9. Regin China, innovatively proving the argument thression results for the robustness test (objective function reconstruction).