Given the recent emergence of the Low Energy Zone (LEZ) concept in academic literature, there is a pressing need for a consistent, high-resolution, and accurate spatial dataset to support carbon-related studies. To bridge this data gap, we developed a systematic framework for delineating LEZs across China. This dataset, produced via the Google Earth Engine (GEE) platform, integrates multi-source remote sensing imagery and geospatial big data. It leverages a customized sample collection strategy, domain-specific feature engineering, and a robust classification algorithm implemented on GEE. The resulting product is the first 100 m resolution LEZ map of China for the year 2020. This dataset is designed to facilitate fine-grained carbon emission modeling, policy assessment, and cross-regional comparisons, thereby supporting spatially explicit climate mitigation strategies and urban energy-carbon research.