考虑羊群效应的低碳创新网络搭便车行为演化——基于Moran过程的随机博弈分析
Evolution of Free Riding Behavior in Low-carbon Innovation Network Considering Herd Effect:Stochastic Game Analysis Based on Moran Process
汪熠杰 1吕宛青 2张一3
作者信息
- 1. 云南财经大学工商管理博士后流动站 云南 昆明 650500
- 2. 云南大学工商管理与旅游管理学院
- 3. 四川大学商学院 四川 成都 610064
- 折叠
摘要
低碳创新是实现"碳达峰、碳中和"目标的关键动力与路径,由众多创新主体组成的低碳创新网络则是我国低碳创新的主要力量.然而,合作网络中的搭便车行为,却严重制约了我国低碳创新网络的稳定发展与深度创新.为此,在相关研究基础上考虑羊群效应理论,并引入Moran过程构建随机博弈模型,对低碳创新网络中搭便车行为的形成演化进行分析.结果表明:首先,当不考虑羊群效应或羊群效应较低时,低碳创新网络仅存在唯一稳定状态.而当羊群效应作用较强时,创新网络中将存在多重稳定状态;其次,低碳创新网络中搭便车行为羊群效应大小受合作创新成本与收益、制度惩罚实施力度与概率、双重外部性、专利政策及创新成功率等因素影响;最后,根据博弈结果和数值模拟,对我国低碳创新网络中搭便车行为的防治提供政策建议与理论支持.
Abstract
Low-carbon innovation is the key driving force and path to achieve the goal of"carbon peak and carbon neutrali-ty",and the low carbon innovation network composed of numerous innovation entities is the main force of low-carbon inno-vation in China.However,the freeriding behavior seriously restricts the stable development of low-carbon innovation net-work.Therefore,based on the theory of herd effect and Moran process,a stochastic game model is constructed to analyze the formation and evolution of free riding behavior in low-carbon innovation networks.The results show that,firstly,when the herd effect is not considered or is low,there is only one stable state in the low-carbon innovation network;when the herd effect is strong,there is multiple stable states in the innovation network;secondly,the degree of herd effect of free-riding behavior in low-carbon innovation networks is influenced by factors such as the cost and benefit of cooperative inno-vation,the implementation strength and probability of institutional penalties,double externalities,patent policies,and in-novation success rates,etc.;finally,based on the game results and numerical simulations,we provide policy suggestions and theoretical support for the prevention of free-riding behavior in low-carbon innovation network.
关键词
低碳创新/碳中和/羊群效应/稳定性/随机博弈Key words
low-carbon innovation/carbon neutrality/herd effect/stability/stochastic game引用本文复制引用
基金项目
国家自然科学基金(71663061)
云南省教育厅科学研究基金(2022Y091)
西藏教育厅科学研究基金(xzjykt521001)
云南财经大学科学研究基金(80059900421)
出版年
2024
国家科技期刊平台
NSTL
万方数据