High-resolution mapping reveals spatiotemporal dynamics and scaling law of industrial carbon emissions in China

This dataset contains high-resolution industrial carbon emissions data for China from 2000 to 2020, generated using a machine learning framework based on land-use probability and geospatial datasets. The data is provided at a 30-meter resolution and covers industrial carbon emissions across different regions, enabling detailed spatiotemporal analysis of emission patterns. The dataset was modeled using multiple machine learning techniques, including Random Forest, XGBoost, and the TabPFN model, to ensure high accuracy and reliability.