Spatiotemporal analysis of CO ₂ emissions in Beijing from 2015 to 2020 based on NPP-VIIRS NTL images

China's new urbanization and rapid social economic development have led to a significant increase in carbon emissions, while their precise monitoring plays a crucial role in carbon emission reduction and assessment of sustainable development goals (SDGs). For the shortcomings of the temporal lag and insufficient spatial scale of statistical data on CO2 emissions, as well as the low estimation accuracy, this study considers the differences in CO2 emissions among different industries and sectors, designs a spatiotemporal fine monitoring method for Beijing's CO2 emissions from 2015 to 2020, combining with NPP-VIIRS NTL images, point of interesting (POI) data, grid population and residential land data. The distribution characteristics of energy consumption CO2 emissions in lit/unlit areas of 35 industries and residential areas, such as agriculture, forestry, mining and metal ore, are analyzed. The CO2 emissions in lit areas account for a larger proportion, up to 99%, while in unlit areas is relatively small at 1%. The density of population and residential areas in central urban areas will produce more CO2 emissions. This study can help to formulate appropriate emission reduction policies, optimize the energy structure, and build a green and low-CO2 economic development pattern.