Input data for Yang et al., "Intra-settlement scaling across the urban–rural continuum: material stocks, service provisioning, and greenhouse gas emissions in the contiguous United States"

Input data for the article Yiwei Yang, Benedikt Grammer, Rafael Prieto-Curiel, David Frantz, Helmut Haberl, and Dominik Wiedenhofer, Intra-settlement scaling across the urban–rural continuum: material stocks, service provisioning, and greenhouse gas emissions in the contiguous United States, Journal of Industrial Ecology (forthcoming). All input datasets were transformed into aligned 1 km raster maps. Use this in conjunction with the SocioMetabolicScaling software repository (https://doi.org/10.5281/zenodo.20600537) to reproduce the analysis and figures of the article. In case of reuse of this data, please cite the original authors of the work: Material Stock Map of the CONUS Haberl, H., Baumgart, A., Zeidler, J., Schug, F., Frantz, D., Palacios-Lopez, D., Fishman, T., Peled, Y., Cai, B., Virág, D., Hostert, P., Wiedenhofer, D., Esch, T. (2025). Weighing the global built environment: High-resolution mapping and quantification of material stocks in buildings. Journal of Industrial Ecology, 29(1), 159–172. [https://doi.org/10.1111/jiec.13585] U.S. Census Bureau — Census Block Population Data (2020) Census-block-level population counts for the contiguous United States in seamless polygon shapefile format (TIGER/Line 2020). U.S. Census Bureau (2020). TIGER/Line Shapefiles: Tabulation Block (2020). U.S. Department of Commerce ((https://www2.census.gov/geo/tiger/TIGER2020/TABBLOCK20/) DARTE — On-Road CO₂ Emissions Annual on-road CO₂ emissions for the United States at 1 km resolution (1980–2017). We used the 2017 data. Gately, C., Hutyra L.R., Wing, I.S. (2019). DARTE Annual On-road CO2 Emissions on a 1-km Grid, Conterminous USA, V2, 1980-2017. ORNL DAAC, Oak Ridge, Tennessee, USA. [https://doi.org/10.3334/ORNLDAAC/1735] Vulcan — Building and Industry GHG Emissions Annual gridded GHG emissions at 1 km resolution for the contiguous United States (2010–2015). We used the 2015 residential and commercial/industrial data. Gurney, K.R., Liang J., Patarasuk R., Song Y., Huang J., Roest G. (2020). The Vulcan Version 3.0 High-Resolution Fossil Fuel CO₂ Emissions for the United States. Journal of Geophysical Research: Atmospheres, 125. [https://doi.org/10.1029/2020JD032974] GHS-UCDB — Global Human Settlement Urban Centre Database Urban centre boundaries based on population thresholds and built-up surface areas (minimum population 1,500 people). Marí Rivero I., Melchiorri M., Florio P., Schiavina M., Krasnodębska K., Politis P., Uhl J., Pesaresi M., Maffenini L., Sulis P., Crippa M., Guizzardi D., Pisoni E., Belis C., Oom D., Branco A., Mwaniki D., Kochulem E., Githira D., Carioli A., Ehrlich D., Tommasi P., Kemper T., Dijkstra L.,Joint Research Centre (JRC). [https://doi.org/10.2905/1A338BE6-7EAF-480C-9664-3A8ADE88CBCD] GHS-SMOD — Global Human Settlement Settlement Model Grid Raster classification of settlement types (urban centre, dense/semi-dense urban cluster, suburban, rural cluster, low/very-low density rural). Schiavina M., Melchiorri M., Pesaresi M. (2023) GHS-SMOD R2023A - GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) [https://doi.org/10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA] USCB Urban Areas Dataset (USCB-UADS) Densely developed urban areas (minimum population 5,000) as defined by the U.S. Census Bureau. U.S. Census Bureau (2020). Urban Areas. Bureau of Transportation Statistics. ((https://geodata.bts.gov/datasets/usdot::urban-areas/about) Morphological Urban Areas Contiguous city-boundary polygons used for the city-based scaling analysis only (not rasterized). Taubenböck H., Weigand M., Esch T., Staab J., Wurm M., Mast J., Dech S. (2019). A new ranking of the world's largest cities—Do administrative units obscure morphological realities?, Remote Sensing of Environment, 232 [https://doi.org/10.1016/j.rse.2019.111353]