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环境规制中地方政府与中央政府的演化博弈分析 被引量:32

Evolutionary Game Analysis of Local Government and Central Government in Environmental Regulation
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摘要 针对环境规制中地方政府与中央政府之间的行为互动,从演化博弈论的研究视角探讨了地方政府与中央政府的决策演化过程。通过建立地方政府与中央政府的非对称演化博弈模型,考察了环境规制中参与者的行为特征。根据复制动态方程得到了参与者的行为演化规律、分析了参与者的演化稳定策略及其影响因素。研究表明,环境规制系统的初始状态、地方政府的环境规制成本和环境规制收益、中央政府的监查成本以及中央政府对地方政府的处罚额都会影响地方政府与中央政府的演化稳定策略。降低中央政府的监查成本、加强中央政府对地方政府的监查力度和违规处罚力度,降低环境规制成本、提高环境规制收益,将有利于促使地方政府执行环境规制,从而达到改善环境质量的目的。 For the interaction between local government and central government in environmental regulation, the evolutionary process of decision is discussed based on the evolutionary game theory. In this paper, an asymmetric evolutionary game model between local government and central government is established to study the behavioral characteristics of participants in environmental regulation. Behavioral evolutionary law of participants is found out according to the replicator dynamics equation, evolutionarily stable strategy is analyzed, and the related influencing factors are also considered. The results show that the evolutionarily stable strategy of local government and central government is affected by the initial states of environmental regulation system, the cost of environmental regulation of local government, the income of environmental regulation of local government, the cost of supervision of central government, the punishment of central government to local government. The implementation of environmental regulation of local government will be promoted by reducing the cost of supervision, strengthening supervision and punishment to local government, reducing the cost of environmental regulation, increasing the income of environmental regulation, which contributes to the improvement of environmental quality.
出处 《运筹与管理》 CSSCI CSCD 北大核心 2015年第3期88-93,204,共7页 Operations Research and Management Science
基金 国家社会科学基金重点项目(12AGL010) 国家自然科学基金资助项目(61074133)
关键词 环境规制 演化稳定策略 演化博弈论 地方政府 中央政府 environmental regulation evolutionarily stable strategy evolutionary game theory local government central government
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