Game analysis on the choice of emission trading among industrial enterprises driven by data

Zitao Hong, Zhen Peng and Liumei Zhang

Energy, 2022, vol. 239, issue PE

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.

Keywords: Emission trading; Dynamic bargaining game; Forward reasoning; Support vector regression (SVR) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221026967

DOI: 10.1016/j.energy.2021.122447

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