Title: Prediction method of change trend of energy carbon emission intensity based on time series analysis

Authors: Yingjie Zhang; Dongyuan Zhao

Addresses: China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guanzhou, 510610, China ' Tsinghua University, Beijing, 100084, China

Abstract: Aiming at the problems of low prediction accuracy and long prediction time in the traditional prediction methods for the change trend of energy carbon emission intensity, a prediction method for the change trend of energy carbon emission intensity based on time series analysis is proposed. First, estimate the energy carbon emission intensity factor by analysing the impact of each major factor on the energy carbon emission intensity, then decompose the energy carbon emission intensity factor with the help of the expanded Kaya identity, and then use the difference method to stabilise the non-stationary sequence of the decomposed intensity factors. Finally, use the ARIMA model of differential autoregressive moving average in the time series method to predict the change trend of energy carbon emission intensity. The simulation results show that the proposed method has higher accuracy and shorter prediction time in predicting the change trend of energy carbon emission intensity.

Keywords: time series analysis; strong energy carbon emissions; strength change; trend prediction; differential autoregressive moving average model.

DOI: 10.1504/IJETM.2024.138201

International Journal of Environmental Technology and Management, 2024 Vol.27 No.3, pp.173 - 185

Received: 10 Nov 2022
Accepted: 27 Mar 2023
Published online: 30 Apr 2024 *

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