In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO₂ emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO₂ emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO₂ emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO₂ emission distributions. The changes of CO₂ emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO₂ reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO₂ emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO₂ emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO₂ emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO₂ emissions and provide information for policymakers to make targeted policies across different areas within the city.

This research constructs a 1 km × 1 km Shanghai energy consumption and carbon emission spatial grid through a bottom-up approach. First, we locate all energy consumption locations in Shanghai via GIS. Second, we calculate energy consumption and associated CO₂ emissions by energy type, by usage type, and by facilities. Finally, we use a spatial grid to represent the energy consumption and CO₂ emissions. The grid shows CO₂ emissions in Shanghai are highly spatially correlated with energy types and volumes of consumption. This research also finds out that high energy consumption and carbon emission locations in Shanghai display significant spatial aggregation. In 7209 spatial energy consumption cells, the top 10 grids of emissions account for 52.8% of total CO₂ emissions in Shanghai; the top 20 grids account for 64.5% and the top 50 grids account for 76.5%. The most critical point emission sources are coal-fired power plants and iron and steel plants. The most important line emission sources are the Yan’an Road and Inner Ring viaducts. The area emission sources that account for the most future-projected growth are commercial and residential natural gas. After this spatial analysis, this paper makes policy suggestions and solutions to conserve energy consumption and mitigate carbon emissions in Shanghai.