Context-dependent carbon mitigation potential using long-term onshore wind turbine datasets in China

Abstract

Wind power is essential for achieving carbon neutrality, yet forecasting its future distribution-related mitigation potential is challenging due to the often-overlooked development mechanisms marked by spatial heterogeneity and path dependence. To address this, we first harmonized the China Onshore Wind Turbine (COWT) dataset, which comprises 153,722 turbines installed from 1984 to 2023. Using explainable machine learning methods, we find that the long-term national wind farm expansion was guided by wind resource availability, while infrastructure drove short-term agglomeration. Based on projected wind turbine distribution in the future, scenario analyses suggest that wind power could reduce emissions by 938–995 Mt CO₂ in 2060, with the northern regions contributing 45%–52% of the total reduction. Context-dependent simulations highlight the best mitigation benefits when prioritizing high-quality wind resources in future layouts. Our framework for systematically understanding China’s onshore wind power development provides a transferable paradigm for similar studies.