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An evolutionary analysis of low-carbon strategies based on the government–enterprise game in the complex network context

业务 背景(考古学) 碳纤维 计算机科学 政府(语言学) 算法 语言学 生物 复合数 哲学 古生物学
作者
Bin Wu,Pengfei Liu,Xuefei Xu
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:141: 168-179 被引量:214
标识
DOI:10.1016/j.jclepro.2016.09.053
摘要

Low-carbon development patterns have gradually attracted more attention because of strong shocks to society and the economy from environmental problems. Developing a low-carbon economy is essential for every country to improve sustainable economic development, and doing so is at the forefront of low-carbon research. Policy and government intervention play a key role in the development of a low-carbon economy. Using game-based learning theory for reference, this paper builds an evolutionary model of low-carbon strategies based on the game between the government and enterprises in the context of a complex network. It then studies the effects of government incentives on enterprises regarding the diffusion of low-carbon policies and how enterprises compete and transform in the Newman-Watts small-world network. We introduce government policy encouragement as a factor in the decision-making process of companies' adoption of a low-carbon strategy, thus enriching the literature on the diffusion of low-carbon strategies. The model proposed in this paper can be used as a tool to evaluate the diffusion and application of low-carbon strategies among companies. The findings suggest that enterprises’ expectation of government incentives including subsidy and regulation determines whether low-carbon strategies can be diffused, and the diffusion speed. The more quick enterprises adjust their expectations in the government–enterprise game, the more enterprises will learn and follow to adopt effective low-carbon strategy. When enterprises attach great importance to the expected earnings from government incentives, the less effective low-carbon strategy adopted initially can be replaced by another more effective one.
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