An Evolutionary Game Study of Consumers' Low-Carbon Travel Behavior Under Carbon-Inclusive Policy

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Expert Systems Pub Date : 2024-11-29 DOI:10.1111/exsy.13804
Yaqin Liu, Xi Chen, Mengya Zhang, Ke Li, Daniel S. da Silva, Victor Hugo C. de Albuquerque
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引用次数: 0

Abstract

Carbon-inclusive policy is regarded as an incentive measure to personal low-carbon actions. However, its impacts are various for different parties under the government-led (including government and consumer) mode and the enterprise-led (including government, consumer and the enterprise) mode, while few studies reveal their difference and give reasonable implications. To fill these gaps, taking consumer's low-carbon travel as an example, this study develops two evolutionary game models—a two-party model (based on government-led adoption) and a tripartite model (based on enterprise-led adoption)—to investigate the effects of carbon-inclusive policy. The findings show that (1) the policy benefits all parties in both models, but the participation of the enterprise enhances the effectiveness of the policy; (2) the enterprise-led mode, that is, the operation of the carbon-inclusive platform by the enterprise is preferred because all parties have higher payoff, compared with the government-led mode; and (3) subsidies from the government has a greater impact for consumers' low-carbon behaviours. However, it has a less impact for the enterprise, which indicates the strategic action of the government is to establish a reasonable consumer subsidy system while reducing subsidies for the enterprise. This study offers a novel perspective on the effects of the carbon-inclusive policy on consumers' low-carbon behaviour, and enriches the practice of personal carbon trading.

碳包容政策下消费者低碳旅行行为的进化博弈研究
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来源期刊
Expert Systems
Expert Systems 工程技术-计算机:理论方法
CiteScore
7.40
自引率
6.10%
发文量
266
审稿时长
24 months
期刊介绍: Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.
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