Solar and wind power plant site selection in China: Machine learning-based regional and temporal probability

Jiayue Chen, Jianxiang Shen, Shihui Zhang, Rui Wang, Zihan Zhen, Can Wang and Wenjia Cai

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017635

Abstract: Rapid renewable energy development under carbon neutrality targets encounters challenges in solar and wind power plant site selection due to the complex interplay of resource availability, economic conditions, and land use. It is critical to examine the preferred locations and the driving factors in China for both technologies from inter-regional and inter-temporal perspectives. We innovatively proposed a machine learning framework to evaluate the spatially explicit siting probability and identify the driving factors for solar and wind power plants across various regions and timeframes. Despite abundant resources, Tibet, Qinghai, and southern Xinjiang exhibit paradoxically low probability due to remote transportation and interconnection infrastructure, and lower GDP. Proximity to the nearest road and substation could significantly facilitate site selection, with probability declining sharply as the distances increase within around 5- and 30-km thresholds, respectively. By major driving factors in seven power grid subregions, solar power siting follows natural-geographical or socio-economic driven patterns. For example, the northwest and southwest grids are socio-economic driven. Wind power maintains the socio-economic driven in the northwest grid, while introducing a third, resource driven in the north and east grids. Over time, the high probability areas shifted from the resource-abundant west to the economically developed east, highlighting the growing importance of socio-economic drivers. This study indicates that future renewable power planning requires subregion-specific weighting of drivers and greater emphasis on socioeconomic factors.

Keywords: Renewable energy; Power plant site selection; Machine learning; Multi-scale predictions; Spatially explicit analysis (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.127033

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