High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China

The accurate examination of the spatial distribution of carbon emissions is critical for carbon reduction strategies. Large uncertainties still exist for previous studies which tried to simulate carbon emissions in spatial, and the resolution needs to be improved to a large extent. At a city level, this study collected various sources of big data and designed a new methodology to examine carbon emissions in Nanjing city at a high resolution of 300 m. In addition, regional differences were compared, and influence factors were analyzed. This study found, the core urban area in Nanjing presented an obvious intensity variation, but the emission intensities were much lower than in those from the peripheral region where industrial land was mainly distributed. Broad areas away from urban areas, where cropland and rural residential land were distributed, presented low carbon emission intensities. Regionally, the districts in the core urban area always presented high emission intensities. The characteristics of land usage and social-economic development were key factors in determining carbon emissions. An increase in ecological land and a decrease in developed land will help carbon reduction strategies greatly. For social and economic development, adjustments in the structure of industry and energy use efficiency improvements will play key roles in the reduction of carbon emissions. [Display omitted] •Big data can well support high resolution carbon emissions simulation•90% of the carbon emissions were from industry•Carbon emissions intensities of industry were much higher than from other sources•Core urban area always faces greater pressure to reduce carbon emissions