Optimization consensus modeling of a closed-loop carbon quota trading mechanism regarding revenue and fairness

Abstract

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.