To get rid of the development dilemma of green credit, we constructed a stochastic evolutionary game model of local government, commercial banks, and loan enterprises. We gave sufficient conditions for the stability of strategy based on the stability discriminant theorem of
Green credit means that financial institutions provide loan support and preferential treatment to the enterprises, which satisfy the national industrial policy and environmental protection standards, and restrict or refuse to lend to other enterprises with heavy pollution and high energy consumption. As an effective way to develop green finance, green credit plays an important role in expanding green enterprises and promoting the transformation and upgrading of the economic structure. In order to pursue green development, since 2007, the People’s Bank of China (PBOC) and China Banking Regulatory Commission (CBRC) have begun to promote the establishment of a green credit standard system framework and successively issued a number of green credit-related systems. The major systems include the Green Credit Guidelines and Guiding Opinions on Building a Green Financial System. In May 2020, in Premier Keqiang Li’s government work report, he stressed out that it is necessary to innovate financial instruments such as green credit, expand applicable business scenarios, increase the proportion of green projects, and further promote ecological environmental governance.
To promote the construction of ecological civilization and realize the sustainable development of the economy, China has attached great importance to the development of green credit and encouraged more social capital to flow into the green industry [
The green credit market possesses obvious characteristics of the game. Under the framework of game theory, many scholars have conducted research on the interests relationship among green credit participants. Xue et al. [
In the earlier period, some scholars have captured the existence of high uncertainty in the objective world and considered it in the study of game theory. Foster et al. [
In this context, we construct a three-party stochastic evolutionary game model of local governments, commercial banks, and loan enterprises. The contribution of this study is reflected in the following two aspects: first, the three-party stochastic evolutionary game model is applied to the behavior research of green credit transaction subjects, and the sufficient conditions for the stability of subject strategy are given under uncertain environment; second, the impact of key parameters on the convergence rate and the changing degree of players’ strategy is comparatively analyzed by numerical simulation, and the effectiveness of reward and punishment mechanism is further discussed.
The structure of this paper is as follows: in Section
The concept of “evolutionary game” was first proposed by Smith [
We assume that, in the implementation of green credit, there are three main players—local government (including regulatory agency), commercial banks, and loan enterprises. Based on evolutionary game theory, the following hypotheses are made for the three players:
The strategy that can be taken by the local government (including regulatory agency) is “nonsupport” or “support,” and the probability of action selection of it is
The strategy that can be taken by the commercial banks is “nonperformance” or “performance,” and the probability of action selection of it is
The action that can be taken by the loan enterprises is “nongreen production” or “green production,” and the probability of action selection of it is
Based on the above analysis and parameter setting, we can get the specific payoff matrix of local government, bank, and loan enterprise, as shown in Table
Payoff matrix of local government, bank, and loan enterprise.
Strategic combination | Local government | Bank | Loan enterprise |
---|---|---|---|
(Nonsupport, nonperformance, nongreen production) | |||
(Nonsupport, nonperformance, green production) | |||
(Nonsupport, performance, nongreen production) | |||
(Nonsupport, performance, green production) | |||
(Support, nonperformance, nongreen production) | |||
(Support, nonperformance, green production) | |||
(Support, performance, nongreen production) | |||
(Support, performance, green production) |
Assuming that the expected income of the “nonsupport” or “support” strategies adopted by the local government is
According to equations (
Assuming that the expected income of the “nonperformance” or “performance” strategies adopted by the bank is
The replication dynamic equation of the bank can be further expressed as follows:
Similarly, assuming that the expected income of the “nongreen production” or “green production” strategies adopted by the loan enterprise is
The replication dynamic equation of the loan enterprise can be further obtained as follows:
Since
During the implementation of green credit, the game among the government, bank, and loan enterprise faces great uncertainty. On the one hand, in addition to the production and operation status of enterprises, the emotional changes and risk preferences of each group will directly affect the strategies of the players. On the other hand, external factors such as changes in the credit system and macroeconomic policies will also interfere with the behavior of players. Therefore, the original evolutionary game cannot truly reflect the strategic adjustment process of each participant in green credit. In order to overcome this flaw, it is necessary to add random factors to the evolutionary game model. In this paper, Gaussian white noise is introduced into the replication dynamic equations:
In this equation,
Assuming that the initial moment is
A stochastic differential equation is given as
Let If there is a positive constant If there is a positive constant
For equations (
According to
When the above three conditions (
Since equations (
In this equation,
In the above expansion,
This paper adopts the Milstein numerical method [
Therefore, the Milstein method can be used to numerically solve the SDEs (
According to equations (
In this section, we use
Keeping the value of other parameters unchanged, the influence of the change of tax reduction ratio
The impact of tax reduction ratio
The impact of tax reduction ratio
From the perspective of the convergence rate of the strategy, the impact of increasing the tax reduction ratio
Next, we consider the changing degree of the strategy. The worst case of local government’s strategy selection in the evolution process is that when
Keeping the value of other parameters unchanged, let the degree of financial subsidies
The impact of the degree of financial subsidies
The impact of the degree of financial subsidies
Considering the convergence rate of the strategy, it can be seen that when
Then, we analyze the influence of
Similarly, keeping the value of other parameters unchanged, let the degree of punishment
The impact of the degree of punishment
The impact of the degree of punishment
The impact of the degree of punishment
Considering the convergence rate of the strategy, we can observe that when
Next, we focus on the impact of
Based on the simulation results, we comparatively analyze the impact of incentive and penalty parameters on the convergence rate and changing degree of subject strategy, as shown in Tables
Impact of incentive and penalty parameters on the convergence rate of subject strategy.
Value of parameters | The moment of the proportion of noncooperative strategies first reaching zero | Change in convergence rate |
---|---|---|
Impact of incentive and penalty parameters on the changing degree of subject strategy.
Value of parameters | Maximum proportion of noncooperative strategies | Change in strategy proportion |
---|---|---|
As shown in Table
As can be seen from Table
In this paper, we constructed a stochastic evolutionary game model of local government, commercial bank, and loan enterprise. The sufficient conditions for the stability of the subject strategy are given, and the influence of incentive and penalty parameters on the convergence rate and changing degree of players’ strategy is analyzed by simulation. Through the analysis of the model results, we got some important conclusions and management implications as follows.
First, the strengthening of reward and punishment by local governments can help banks implement green credit policy and enterprises choose green production mode, but increasing incentives is not conducive to the governments’ own performance of regulatory duties. When the local government increases tax reduction ratio to the green enterprises and increases financial subsidies to the commercial banks that carry out green credit business, although it can effectively increase the enthusiasm of enterprises to adopt green production and commercial banks to implement green credit policies, the local government may inevitably produce some deviated behaviors such as inadequate supervision and intervention due to financial pressure and other self-interest factors. Accordingly, local government should consolidate regulatory responsibilities and establish relevant evaluation and accountability mechanisms to promote the implementation of green credit. At the same time, the government’s support for green credit should be kept within a certain range. In the context of limited financial resources, the superior authorities need to fully conduct investigations and studies, set up scientific and reasonable incentive policies, and make a balance between the subjects to be regulated.
Second, when the superior department is committed to improving the convergence rate of subject strategy, the punishment mechanism can exert a better regulatory effect. Although incentives such as tax cuts and financial subsidies can effectively shorten the time when loan enterprises and banks reach the state of full cooperation, they all come at the cost of a slight delay in the time when the government reaches full support, and the effect of regulation is not significant. However, when superior authorities take punitive measures to increase the risk cost of noncooperation among the three parties, the time when local government, commercial banks, and loan enterprises reach the fully cooperative state is significantly earlier, and local government is most sensitive to risk costs. Therefore, according to the priority of variable adjustment, the superior authorities should first increase the punishment on local government and increase the risk cost of dereliction of duty such as inadequate intervention and insufficient support. And then, it is necessary to increase the penalties for commercial banks with illegal lending and enterprises with nongreen production, strengthen the responsibility assessment on banks’ green credit business, timely disclose enterprises’ environmental information, and urge them to assume social responsibilities.
Lastly, when the superior department hopes to improve the changing degree of subject strategy, both reward mechanism and punishment mechanism can play an obvious regulatory role. The financial subsidy incentives for commercial banks, as well as the punishment on unqualified governments and nongreen production enterprises all show good results in increasing the proportion of green credit participants. So, when building a long-term mechanism for green credit, the relevant departments should combine reward and punishment, give priority to punishment, and supplement with rewards. It is vitally important to impose severe accountability and punishment for illegal lending by banks and illegal production by polluting enterprises. At the same time, under the premise of sustainability, the government should establish a reasonable financial subsidies system and actively lead the flow of social funds to the enterprises with green production and the banks with green credit.
In addition, there are still some limitations in this study: (1) in reality, the implementation process of green credit is subject to numerous and complex random disturbance, but the explanation and analysis of random factors in this article are not comprehensive enough, which can be the direction of future research; (2) in the simulation stage, it is difficult to obtain actual case data, so the research results can only reflect the general situation of the players’ strategy evolution; and (3) the variable setting in this article is based on common scenarios. To be closer to reality, perhaps we can try to refine variables and consider more complex green credit scenarios.
The data used to support the findings of this study are included within the article.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by National Natural Science Foundation of China (Grants nos. 11671229 and 11871309); National Key R&D Program of China (Grant no. 2018YFA0703900); and Natural Science Foundation of Shandong Province of China (Grants nos. ZR2020MA032 and ZR2019MA013).