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<p>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.</p>

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Mapping regional disparities in the global shift toward decarbonized power systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Binbin Ju,
  • Zhujun Chen,
  • Yong Geng,
  • Peixue Xing,
  • Linbo Han,
  • Lili Wang,
  • Wendong Wei

摘要

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.