Mapping regional disparities in the global shift toward decarbonized power systems

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.