Research on High-Resolution Urban Regional Carbon Emission Estimation Method from the Perspective of Functional Areas: A Case Study of Beijing

Rapid industrialization and urbanization have significantly accelerated carbon dioxide emissions, intensifying climate mitigation challenges. Accurate micro-scale assessment of urban carbon emissions is imperative for formulating effective reduction policies in China; however, current efforts are often constrained by a lack of high-resolution data, limiting the ability to capture fine-grained spatial heterogeneity. To address this gap, this study integrates the 1 m resolution national land cover product (SinoLC-1) with OpenStreetMap (OSM) networks and point of interest (POI) data to delineate urban functional zones in Beijing. We subsequently developed a method to estimate and spatially allocate carbon emissions at a 1 m resolution across these zones, categorized by industrial sector. Results for 2020 indicate that carbon sequestration by forests, water bodies, and grasslands totaled approximately 624,900 tons, while total emissions from cultivated land and energy consumption in built-up areas reached 107,692,300 tons. Built-up land was identified as the primary carbon source, whereas forestland and water bodies functioned as key sinks. Notably, the tertiary industry accounted for the largest share of energy-related emissions (41.80%), driven primarily by electricity and kerosene consumption. Spatially, emissions exhibited pronounced heterogeneity, with high-value clusters concentrated in the central urban core and specific suburban hubs. Cross-validation demonstrates that this functional-zone-based spatial allocation method significantly outperforms traditional nighttime light-based approaches in resolving micro-scale emission patterns. This high-resolution analysis improves the characterization of spatial variability in urban carbon cycles, offering robust data support for precision low-carbon planning and energy management.