补贴
双头垄断
业务
消费(社会学)
碳排放税
政府(语言学)
电动汽车
燃料效率
燃油税
消费税
经济
微观经济学
产业组织
环境经济学
税制改革
公共经济学
温室气体
从价税
汽车工程
工程类
财务
功率(物理)
市场经济
古诺竞争
社会学
哲学
物理
生物
量子力学
收入
语言学
社会科学
生态学
作者
Junhai Ma,Yaming Hou,Zongxian Wang,Wenhui Yang
出处
期刊:Energy Policy
[Elsevier]
日期:2021-01-01
卷期号:148: 111919-111919
被引量:92
标识
DOI:10.1016/j.enpol.2020.111919
摘要
The game model of the duopoly automobile manufacturers established in this paper takes the carbon emission reduction policy constraint as the research background, and discusses how the electric vehicle and the fuel vehicle compete in the performance of the product in the delay pricing decision under the strategic consumer, which is seldom considered in other related studies. The government gives electric vehicle consumers preferential policies on consumption subsidies and exemption from consumption tax. Additionally, he levies consumption tax and sales carbon tax from fuel vehicle consumers and manufacturers, respectively. The government also supports the development of electric vehicles by establishing charging piles. This paper cast light on how different market structures and different adjusting speed of price work on the vehicle manufacturers' operation and the system's stability. At first, we consider a single period game to derive the optimal solutions, finding that government intervention policies can maximize the social welfare. Additionally, the impacts of tax and subsidy on social welfare are different when fuel vehicle manufacturer is leader. Then, we consider a repeated game where players make decision based on bounded rationality. We compare the optimal solutions and give a numerical simulation for the dynamic process. For fuel vehicle manufacturer, too much energy consumption triggers high emission tax and also lowers consumers' surplus. We also find that fuel vehicle manufacturer acting as the leader enables the system to be more stable than the scenario in the current vehicle industry condition, where the electric vehicle manufacturer acts as the leader.
科研通智能强力驱动
Strongly Powered by AbleSci AI