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<p>The pursuit of carbon neutrality in China demands a rapid, spatially informed scale-up of renewable energy, including biomass, yet high-resolution, policy-aware data for site-specific planning remain scarce. To bridge this gap, we develop China’s high-resolution spatially explicit biomass resource potential dataset, which integrates five biomass categories (agricultural residues, forestry residues, energy crops, animal manure, and municipal waste) at 1 km resolution for 2020, with projections to 2050. This dataset incorporates key constraints such as food security, ecological conservation, and land use suitability. It provides heat value potential distribution maps in GeoTIFF and PDF formats, and heat value potential data in Excel format. By combining multi-source geospatial data, statistical downscaling, and machine learning, this dataset enables precise assessment of resource conditions and provides forward-looking planning for biomass power deployment, rural revitalization, and carbon reduction strategies, thereby meeting China’s critical need for integrated, location-aware open data in energy and land-use decision-making.</p>

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A high-resolution gridded dataset of biomass resource potentials in China with policy-aware land-use constraints for renewable energy planning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Li Huang,
  • Di Fan,
  • Ke Wang

摘要

The pursuit of carbon neutrality in China demands a rapid, spatially informed scale-up of renewable energy, including biomass, yet high-resolution, policy-aware data for site-specific planning remain scarce. To bridge this gap, we develop China’s high-resolution spatially explicit biomass resource potential dataset, which integrates five biomass categories (agricultural residues, forestry residues, energy crops, animal manure, and municipal waste) at 1 km resolution for 2020, with projections to 2050. This dataset incorporates key constraints such as food security, ecological conservation, and land use suitability. It provides heat value potential distribution maps in GeoTIFF and PDF formats, and heat value potential data in Excel format. By combining multi-source geospatial data, statistical downscaling, and machine learning, this dataset enables precise assessment of resource conditions and provides forward-looking planning for biomass power deployment, rural revitalization, and carbon reduction strategies, thereby meeting China’s critical need for integrated, location-aware open data in energy and land-use decision-making.