Mapping local energy zones in China to support urban-scale energy-carbon research

• Development of a robust LULC classification model on GEE using multi-source features. • Evaluation of model robustness across samples, features, and geographical regions. • Validation via independent samples, benchmark datasets, and sector-specific emission data. • Creation of a novel 100 m-resolution LEZ map for China (2020) for urban carbon research. The concept of Local Energy Zones (LEZs) has recently been proposed as a novel landscape classification system designed specifically for urban carbon accounting, spatial characterization, and predictive modeling. Unlike conventional land use and land cover products that primarily focus on physical morphology or administrative boundaries, LEZs categorize urban landscapes based on their dominant carbon emission or absorption characteristics, offering a functionally oriented framework for assessing urban carbon neutrality. Despite its theoretical advantages, the absence of a standardized, high-resolution LEZ map has hindered its broader adoption. To address this critical data gap, this study presents the first national-scale, high-resolution (100 m) LEZ map of China for the year 2020, developed through an intelligent classification model implemented on the Google Earth Engine platform. Methodologically, this work introduces several innovations: (1) a specialized LEZ sample archive constructed through hybrid manual collection and stratified random sampling to ensure representativeness across diverse geographical contexts; (2) a comprehensive feature set integrating 47 variables from multi-source geospatial big data, including spectral, textural, and semantic information, which captures the functional heterogeneity of energy-related land uses; and (3) a rigorous modeling framework with repeated cross-validation and robustness testing across seven major geographical regions of China. The resulting LEZ map achieves an overall accuracy of 91.49% and demonstrates strong correlations (R² > 0.81) with benchmark datasets. More importantly, when validated against sector-specific carbon emission inventories, the LEZ map significantly outperforms existing land use and land cover products in explaining spatial variations in carbon emissions, highlighting its superior capability for urban carbon research. This publicly available, standardized LEZ dataset can serve as a useful spatial reference for mapping urban carbon footprints, supporting carbon neutrality pathways development, and assisting interdisciplinary low-carbon research across Chinese cities.