An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

Xiwen Cui, Shaojun E, Dongxiao Niu, Dongyu Wang and Mingyu Li
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Xiwen Cui: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Shaojun E: School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongyu Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Mingyu Li: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2021, vol. 13, issue 15, 1-21

Abstract: In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

Keywords: carbon peaking; energy consumption; whale algorithm; support vector machine regression; the 14th five-year plan (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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