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<p>Growth in solar photovoltaic capacity supports grid decarbonization but can result in land transformation. Quantifying land–solar interactions is hampered by inconsistent methods and data. We develop a&#xa0;consistent,&#xa0;replicable framework to quantify land-solar interactions&#xa0;and apply it to annotated aerial imagery covering 719 solar photovoltaic projects (13,272 megawatts of installed capacity) connected to the Western Interconnection&#xa0;in the United States. We train a deep-learning convolutional neural network to characterize solar photovoltaic land&#xa0;footprints, post-process outputs with geospatial land-cover overlays, and compute land-use efficiency and&#xa0;energy-normalized land transformation per project. Across the sample, mean&#xa0;capacity-based land-use efficiency is 24.7 ± 15.2 watts per square meter and mean lifetime land transformation is 0.846 ± 0.722 square meters per megawatt-hour; regional differences&#xa0;and engineering choices explain project-level variability. Our open-source&#xa0;inventory and method enable more consistent large-scale assessments of planning, life&#xa0;cycle impacts, and ecological trade-offs of solar expansion.</p>

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Quantifying land-use metrics for solar photovoltaic projects in the western United States

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

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  • Siyuan Hu,
  • Yinong Sun,
  • Rebecca R. Hernandez,
  • Jeya Maria Jose Valanarasu,
  • Vishal M. Patel,
  • Sarah M. Jordaan

摘要

Growth in solar photovoltaic capacity supports grid decarbonization but can result in land transformation. Quantifying land–solar interactions is hampered by inconsistent methods and data. We develop a consistent, replicable framework to quantify land-solar interactions and apply it to annotated aerial imagery covering 719 solar photovoltaic projects (13,272 megawatts of installed capacity) connected to the Western Interconnection in the United States. We train a deep-learning convolutional neural network to characterize solar photovoltaic land footprints, post-process outputs with geospatial land-cover overlays, and compute land-use efficiency and energy-normalized land transformation per project. Across the sample, mean capacity-based land-use efficiency is 24.7 ± 15.2 watts per square meter and mean lifetime land transformation is 0.846 ± 0.722 square meters per megawatt-hour; regional differences and engineering choices explain project-level variability. Our open-source inventory and method enable more consistent large-scale assessments of planning, life cycle impacts, and ecological trade-offs of solar expansion.