Yangtze River Delta County-Level Sustainable Developmet Index

Land use datasets from the ESA(European Space Agency) at 300-meter resolution in the WGS84 geographic coordinate system. The CCI global land characterization utilizes FAO's LCCS framework to establish 22 categorical divisions. To align with analytical requirements, the land cover classes were reclassified into a simplified framework. The CO2 emission data were obtained from CGER (Center for Global Environmental Research)as 1km×1km resolution raster data and aggregated to county-level annual CO₂ emissions (units, metric tons) for the YRD (2000–2022). Spatial resolution of PM2.5 concentration data was standardized through resampling to 1km×1km grid cells, and aggregated to county-level annual PM2.5 concentrations (2000–2022) using zonal statistics. Population data sourced from Landscan Global Population Grids (Oak Ridge National Laboratory, USA), which integrates census data, land cover, nighttime lights, and remote sensing imagery at 1km×1km resolution. Annual population data (2000–2022) for counties in the YRD were extracted via zonal statistics. Annual maximum NDVI values were from Google Earth Engine (GEE) and aggregated to county-level data (2000–2022) using zonal statistics (Yang, et al. 2019). The National Geographic Information Public Service Platform of China provided county-level administrative boundary vectors covering the YRD region (Approval No. GS (2024) 0650). Key socioeconomic indicators (GDP, industrial output, employment rates) originated from China's official statistical repositories, specifically the Urban Statistical Yearbook and National Annual Database. Data deficiencies were supplemented via referencing regional statistical yearbooks, with residual gaps imputed through spatial kriging interpolation, ensuring comprehensive coverage for multivariate regression analysis.