Three-Party Stochastic Evolutionary Game Analysis of Reward and Punishment Mechanism for Green Credit
Author(s) -
Qingfeng Zhu,
Kaimin Zheng,
Yilin Wei
Publication year - 2021
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2021/5596015
Subject(s) - incentive , punishment (psychology) , mechanism (biology) , evolutionarily stable strategy , loan , stability (learning theory) , government (linguistics) , economics , dilemma , microeconomics , computer science , game theory , mathematics , finance , machine learning , psychology , social psychology , philosophy , linguistics , epistemology , geometry
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 It o ^ ' s stochastic differential equation (SDE). Then, we discussed the impacts of incentive and penalty parameters on green credit. Through the above analysis, we got the following conclusions: (1) rewards and punishments always benefit green production and green credit, but increasing incentives is not conducive to the governments’ performance of regulatory duties; (2) punishments can better improve the convergence rate of players’ strategy than rewards; and (3) both rewards and punishments can exert an obvious effect in improving the changing degree of players’ strategy. Finally, we put forward some suggestions to optimize the green credit mechanism.
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