Title: A prediction method of regional carbon emission peak based on energy consumption elasticity coefficient

Authors: Yingjie Zhang; Dongyuan Zhao

Addresses: China Energy Engineering Group, Guangdong Electric Power Design Institute Co., Ltd., Guangdong, Guanzhou, China ' Tsinghua University, Haidian District, Beijing, China

Abstract: In order to solve the shortcomings of the traditional methods in prediction accuracy and prediction efficiency, this paper proposes a regional carbon emission peak prediction method based on the elastic coefficient of energy consumption. First, carbon emission information is extracted directionally. Then, the elastic coefficient of energy consumption is calculated, and the carbon emissions are preliminarily calculated. After obtaining the carbon emissions in different paths, Lasso regression analysis method is used to analyse the impact of the elastic coefficient of energy consumption on the prediction results. By adjusting the harmonic parameter values to optimise the calculation results, the peak prediction results of carbon emissions are obtained after obtaining significant variables. Experimental results show that the prediction accuracy of this method is high, and the maximum kappa coefficient can reach 0.973. During the experiment, the method can complete 12 predictions, which shows that its prediction efficiency is relatively high.

Keywords: industrial carbon emissions; carbon emission performance; information extraction; elasticity coefficient of energy consumption; carbon emissions; lasso regression analysis; peak prediction.

DOI: 10.1504/IJGEI.2024.141926

International Journal of Global Energy Issues, 2024 Vol.46 No.6, pp.678 - 692

Received: 01 Nov 2022
Accepted: 27 Feb 2023
Published online: 03 Oct 2024 *

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