期刊
JOURNAL OF CLEANER PRODUCTION
卷 141, 期 -, 页码 168-179出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.09.053
关键词
Complex network context; Government-enterprise game; Low-carbon; Evolutionary
资金
- National Natural Science Foundation of China [71390333]
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. (C) 2016 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据