ArticleScientific data2023
A high spatial resolution dataset of China's biomass resource potential.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- A high-resolution gridded dataset of biomass resource potentials in China with policy-aware land-use constraints for renewable energy planning.Scientific data · 2026Article
- Uneven renewable energy supply constrains the decarbonization effects of excessively deployed hydrogen-based DRI technology.Nature communications · 2025Article
- Reducing transition costs towards carbon neutrality of China's coal power plants.Nature communications · 2025Article
- Up-bottom assessments of nutrient supply and gaseous pollutant from Chinese wheat straw field management.Scientific data · 2024Article
- A high spatial resolution dataset of China's biomass resource potential.Scientific data · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
Assessing biomass resource potential is essential for China's ambitious goals of carbon neutrality, rural revitalization, and poverty eradication. To fill the data gap of high spatial resolution biomass resources in China, this study estimates the biomass resource potential for all types of lignocellulosic biomass feedstock at 1 km resolution in 2018, including 9 types of agricultural residues, 11 types of forestry residues, and 5 types of energy crops. By combining the statistical accounting method and the GIS-based method, this study develops a transparent and comprehensive assessment framework, which is in accordance with the principle of food security, forest land and pasture protection, and biodiversity protection. In the end, we organize and store the data in different formats (GeoTIFF, NetCDF, and Excel) for GIS users, integrated modelers, and policymakers. The reliability of this high spatial resolution dataset has been proved by comparing the aggregated data at the subnational and national levels with the existing literature. This dataset has numerous potential uses and is a crucial input to many bioenergy-related studies.
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Registered trials
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