A Dataset for Assessing and Predicting Resource and Environmental Carrying Capacity in Xuzhou City, China (2011-2023)

This dataset supports the research presented in the manuscript entitled "Analysis and Prediction of Resource and Environmental Carrying Capacity Differences in Xuzhou Based on Panel PVAR and GWO-SVM Models". It comprises a comprehensive panel dataset from 2011 to 2023 for Xuzhou City, Jiangsu Province, China, and its subordinate counties/districts, designed for evaluating the Resource and Environmental Carrying Capacity (RECC) and conducting predictive modeling.The data encompasses <b>27 indicators</b> across five core dimensions:<b>Water Resource Development Scale and Intensity:</b> Including water supply modulus, water consumption per 10,000 CNY GDP, and average groundwater depth.<b>Water Use Structure and Efficiency:</b> Including proportions of domestic, productive, and ecological water use, and effective irrigation coefficient of farmland.<b>Resource Base Supporting Capacity:</b> Including per capita water resources, annual precipitation, surface water resources, and water-land matching coefficient.<b>Ecological Environment Buffering Capacity:</b> Including compliance rate of water functional zones, forest and grass coverage, vegetation carbon sequestration, and soil erosion modulus.<b>Socio-Economic Pressure:</b> Including population density, urbanization rate, proportion of construction land, per capita GDP, etc.Data were compiled from authoritative sources such as the <i>Xuzhou Statistical Yearbook</i>, <i>Jiangsu Statistical Yearbook</i>, <i>Xuzhou Water Resources Bulletin</i>, MODIS satellite products (e.g., NPP), and the National Tibetan Plateau Data Center. The data has undergone rigorous cleaning and standardization procedures.This dataset is ideally suited for research in <b>spatiotemporal analysis, regional disparity decomposition (e.g., using the Dagum Gini coefficient), econometric modeling (e.g., Panel VAR), and machine learning prediction (e.g., SVM optimized by GWO)</b> concerning resource and environmental sustainability.<b>File Format:</b> Microsoft Excel (.xlsx)