Unveiling characteristics and determinants of China's wind power geographies towards low-carbon transition. (1st April 2023)

• Record Type: Journal Article
• Authors: Han, Mengyao; Sun, Ruyi; Feng, Ping; Hua, Ershi
• Is Part Of: Journal of environmental management. Volume 331(2023)
• Publication Date: 2023-04-01
• Subjects: Geographic patterns -- Spatial autocorrelation -- Low-carbon transition -- Emission reduction potentials; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2023.117215
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: The temporal and spatial patterns of wind power installation and the evaluation of carbon emission reduction potentials are of great significance to promoting China's wind power development planning and dual carbon targets achievement. This study analyzes the temporal and spatial characteristics, identifies main driving factors, and measures carbon emission reduction potentials of China's wind power installation by province based on spatial autocorrelation analysis and spatial econometric model. Overall, China's wind power installed capacity increased rapidly from 346 MW in 2000 to 279, 550 MW in 2020, basically showing a significant positive spatial correlation during 2000 and 2020. Regarding driving factors of wind power installation, the technological factors and environmental factors were the main positive factors for wind power installation, and the economic factors and resource endowments showed positive spatial spillover effects. Regarding carbon emission reduction potentials, the carbon emission reduction potentials of China's wind power installation increased by year, among which Northwest China gradually accelerated Northeast China after 2015. Based on China's wind power evolution characteristics and carbon emission reduction potentials, this study attempts to provide quantitative supports and policy implications to promote sustainable development of wind power industry and the achievement of carbon peak and carbon neutrality within China. Highlights: The spatial-temporal evolution patterns of wind power installation are analyzed. Spatial autocorrelation analysis and spatial econometric modeling are applied. Inner Mongolia, Jilin, Liaoning, and Gansu are mainly high-high agglomeration. Carbon emission reduction potentials of wind power installation are assessed. Policy implications regarding multi-regional wind power installation are proposed.