Carbon emission accounting and spatial distribution of industrial entities in Beijing—Combining nighttime light data and urban functional areas

Quantifying current carbon emissions their fine scale spatial distribution is necessary to improve carbon emission management, requirements, and emission reduction strategies of key industries. This study established an entity-level model to estimate carbon emissions by combining geographic information of points of interest (POIs) and nighttime light data from Beijing in 2018. The model accounted for the carbon emissions of Beijing's key entities and industries and simulated their spatial distribution. The results showed a good fit between the carbon emissions of the entities and nighttime light brightness values. The 130-m resolution of the urban carbon emission distribution data had a higher spatial simulation accuracy than that of the 1-km Open-Data inventory for anthropogenic carbon dioxide (ODIAC) data. Through the lens of urban functional areas, the average value of carbon emissions was highest in commercial areas and lowest in public management and service areas, at 78,840.11 tC/km 2 and 6844.79 tC/km 2 , respectively. In terms of the industrial sector, the transportation industry had the highest carbon emissions, with a total of 31.86 Mt., while non-metal mining and oil and gas extraction had almost no energy consumption, with total carbon emissions of 1.38 Mt. The spatial clustering results showed that the distribution of carbon emissions in Beijing had a significant positive spatial correlation; forming high-high aggregation clusters dominated by the city center and major business districts and a low-low aggregation clusters dominated by the city's suburban areas. The simulation model clearly reflected the fine scale characteristics of carbon emissions, in terms of their quantity and spatial distribution. Results obtained in this study can aid relevant departments to formulate appropriate strategies for collectively guiding industrial enterprises towards carbon neutrality. • We developed an entity-level model to estimate Beijing's CO 2 emission. • POIs data are spatially overlaid with night-light data. • The spatial resolution of CO 2 emission simulations is improved to 130 m. • The spatial distribution of CO 2 emissions displayed an expected pattern among functional areas.