Analysis and forecast of China's energy consumption structure. (December 2021)

• Record Type: Journal Article
• Authors: Zeng, Sheng; Su, Bin; Zhang, Minglong; Gao, Yuan; Liu, Jun; Luo, Song; Tao, Qingmei
• Is Part Of: Energy policy. Volume 159(2021)
• Publication Date: 2021-12
• Subjects: Energy consumpxtion structure -- Advanced index -- Copula function model -- Multi-factor dynamic support vector machine model -- China; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2021.112630
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: In the context of the practice of high-quality social development gradually deepening, the optimization of energy structure is an important link to promote high-quality economic development. We used China's historical data from 1980 to 2019, and identified 17 influencing factors of its energy consumption structure. From four dimensions (economy, structure, technology, population and policy), Copula function was employed to establish a multi-factor dynamic support vector machine model to predict the advanced index of energy consumption structure in 2020–2030. The results show that (a) China's energy consumption structure is being optimized. An up-trend is found in the advanced index of China's energy consumption structure, and the proportion of its coal consumption shows a downward trend, but the decline is gradually decreasing. (b) Energy price adjustment, increased rural income, industry structure improvement, higher R&D expenses contribute to energy consumption structure optimization in China. (c) China is able to meet the carbon emission target set for 2030 on schedule. China is expected to reach carbon emission peak in 2030, and non-fossil energy will account for about 21% in 2026. The carbon emission target per unit of GDP is expected to be completed ahead of schedule. Highlights: Multi-factor dynamic support vector machine (MFD-SVR) model is built to predict China's energy consumption structure. Copula function is used to identify multi-dimensional influencing factors in China. China's energy consumption structure is being optimized. China is able to meet the 2030 emission targets ahead of schedule.