Game analysis on the choice of emission trading among industrial enterprises driven by data. (15th January 2022)

• Record Type: Journal Article
• Authors: Hong, Zitao; Peng, Zhen; Zhang, Liumei
• Is Part Of: Energy. Volume 239:Part E(2022)
• Publication Date: 2022-01-15
• Subjects: Emission trading -- Dynamic bargaining game -- Forward reasoning -- Support vector regression (SVR); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.122447
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The construction and promotion of emission trading information platform makes it possible for enterprises to collect and use emission rights and other data. How to conduct game analysis for industrial enterprises' emission trading under data driven has become an effective basis and inevitable trend to assist enterprises to achieve emission reduction and optimal decision-making. However, existing game methods are not used for comprehensive optimal decision for enterprises based on these data. Therefore, this paper integrates dynamic game and data to effectively solve optimal choice in the process of emission trading among industrial enterprises. The bargaining dynamic game model and forward reasoning method are proposed to realize the game analysis of emission trading among enterprises in the secondary market based on the data mining or evaluation of pollutant emissions, market price and marginal revenue of emission rights and initial emission rights by Support Vector Regression (SVR), Linear Regression (LR) and Analytical Hierarchy Process (AHP). Taking six industrial enterprises in Tianjin as an example, this paper analyzes the optimal trading price, trading volume and object of emission trading among different enterprises under different loss factors. Highlights: A bargaining game model of emission trading based on SVR, LR, AHP was proposed. Prediction or evaluation methods on emission were established and implemented. A forward reasoning of emission trading game among enterprises was completed. Taking actual emission data from Tianjin, the game process of trading was analyzed. Comprehensive optimal decision of emission trading under loss factors was provided.