Global efforts to transform power systems are accelerating,yet the localized patterns and trajectories of this transition-crucial for equitable and regionally tailored policy-making-remain insufficiently explored.This...Global efforts to transform power systems are accelerating,yet the localized patterns and trajectories of this transition-crucial for equitable and regionally tailored policy-making-remain insufficiently explored.This study introduces a comprehensive subnational dataset of global power plants,encompassing nine energy types and spanning the years 2015 to 2020.Through spatial statistics,clustering,and cross-regional comparisons,we identify distinct trajectories of power capacity change across energy types and regions.While decarbonization remains a clear global trend,structurally disadvantaged or over-averaged regions are still at risk of being overlooked.To better understand these transition dynamics,we conducted a machine learning-based driver analysis,which highlights the dominant influence of development-related factors such as electricity demand and economic growth.The openly accessible dataset fills a critical gap in global energy data and offers a standardized,robust framework for analyzing regional power infrastructure development.Its design enables fine-grained,dynamic assessments of transition pathways and facilitates interdisciplinary research across energy,climate,and policy domains.展开更多
基金supported by the Natural Science Foundation of China(71904125)the Shanghai Rising-Star Program(23QA1404900)+1 种基金the Natural Science Foundation of Shanghai(23ZR1434100)the Science and Technology Cooperation Program of Shanghai Jiao Tong in Inner Mongolia Autonomous Region——Action Plan of Shanghai Jiao Tong University for“Revitalizing Inner Mongolia through Science and Technology”(2025XYJG0001-01-08).
文摘Global efforts to transform power systems are accelerating,yet the localized patterns and trajectories of this transition-crucial for equitable and regionally tailored policy-making-remain insufficiently explored.This study introduces a comprehensive subnational dataset of global power plants,encompassing nine energy types and spanning the years 2015 to 2020.Through spatial statistics,clustering,and cross-regional comparisons,we identify distinct trajectories of power capacity change across energy types and regions.While decarbonization remains a clear global trend,structurally disadvantaged or over-averaged regions are still at risk of being overlooked.To better understand these transition dynamics,we conducted a machine learning-based driver analysis,which highlights the dominant influence of development-related factors such as electricity demand and economic growth.The openly accessible dataset fills a critical gap in global energy data and offers a standardized,robust framework for analyzing regional power infrastructure development.Its design enables fine-grained,dynamic assessments of transition pathways and facilitates interdisciplinary research across energy,climate,and policy domains.
