期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 161, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2021.107611
关键词
Group decision-making (GDM); Consensus; Revenue and fairness; Carbon quota trading mechanism; Allocation scheme
资金
- National Natural Science Foundation of China [71971121]
- Major Project Plan of Philosophy and Social Sciences Research at Jiangsu University [2018SJZDA038, 2020SJZDA076]
- 2019 Jiangsu Province Policy Guidance Program (Soft Science Research) [BR2019064]
- impact of Weather Conditions on the Spread of Large-scale Influenza Virus [2020xtzx001]
- Spanish State Research Agency [PID2019-103880RB-I00/AEI/10.13039/501100011033]
- Andalusian Government [P20_00673]
- Graduate Research and Innovation Projects of Jiangsu Province [SJKY19_0958]
- China Scholarship Council [202008320537]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [20KJB410006]
- NUIST-UoR International Research Institute
This paper discusses the use of optimization consensus modeling to explore theoretical innovations regarding flexible carbon quota trading mechanisms and proposes practical strategies to deal with resulting unfairness within the trading system.
Consensus modeling aims to obtain collective agreement through group decision-making (GDM), generally by building mathematical programming models. This paper describes the use of optimization consensus modeling to explore theoretical innovations regarding flexible carbon quota trading mechanisms, with basic allocation schemes provided within a closed-loop trading system by simultaneously taking revenue and fairness into account. A series of optimization consensus models are constructed from the perspective of maximizing the corresponding revenue, resulting in optimal/fair carbon quota allocation schemes that include detailed trading information, e.g., participating individuals, transferred quantities, and unit transaction prices. To solve these models, a relaxation method based on particle swarm optimization is proposed. The inability to conduct real-life GDM usually stems from conflicts of interest based on the decision-makers' mutual competition, thus, two practical strategies are presented to deal with the resulting unfairness within the trading system. Finally, a numerical example incorporating five regions demonstrates the effectiveness of the proposed trading mechanisms. The results show that sufficient interactions among decision-makers are of great significance in achieving fairness within a trading system.
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