How to upgrade an enterprise's low-carbon technologies under a carbon tax: The trade-off between tax and upgrade fee

被引:40
作者
He, Senyu [1 ,2 ,3 ]
Yin, Jianhua [4 ]
Zhang, Bin [1 ,2 ,3 ,5 ]
Wang, Zhaohua [1 ,2 ,3 ,5 ]
机构
[1] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100081, Peoples R China
[3] Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China
[4] Univ Int Business & Econ, Business Sch, Beijing 100029, Peoples R China
[5] Sustainable Dev Res Inst Econ & Soc Beijing, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon tax; Production technology upgrade; Strategy optimisation; Multi-agent system; Genetic algorithm; ENERGY MANAGEMENT-SYSTEM; RESEARCH-AND-DEVELOPMENT; GENETIC ALGORITHM; ECONOMIC-IMPACT; CO2; EMISSIONS; POWER-PLANT; OPTIMIZATION; DESIGN; MODEL; DEMAND;
D O I
10.1016/j.apenergy.2017.07.015
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Reducing CO2 emissions is a hot topic, and an important policy to achieve this target is carbon tax. When an enterprise is subject to a carbon tax, it has to pay this extra fee for the long-term if it does not upgrade its production technology. It needs to pay a certain upgrade fee in the short-term if it chooses to upgrade its plant. Thus, it has been an important problem for enterprises seeking to balance the trade-off between the 'long-term tax fee' and the 'short-term upgrade fee'. This paper explores how to optimise an enterprise's production technology upgrade strategy based on existing low-carbon technologies, to minimise the total upgrade cost subject to an expected total cost per product. An integer programming model is proposed to formulate the problem, and a 'multi-agent system - genetic algorithm' method is presented for its solution. The model is applied to a numerical example and the results indicate that the proposed method is feasible. The impacts of carbon tax and enterprise's expected cost on its technology upgrade strategy are further discussed. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:564 / 573
页数:10
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