Spatiotemporal dynamics of energy-related CO2 emissions in China based on nighttime imagery and land use data

Abstract

It is difficult to accurately estimate the spatial distribution of carbon dioxide (CO2) at the grid scale because of the lack of city-level statistical data. In this paper, CO2 emission regions were devided into urban area, industrial area and rural area in order to accurately calculate CO2 emissions. In addition, a more accurate calculation model for energy-related CO2 emissions was proposed through integrating nighttime light datasets and land use data. The maps of estimated CO2 emissions in different regions were compared and examined through using spatial dependence and grid overlay methods to understand the spatial distribution characteristics and spatiotemporal dynamics of CO2 emissions in China. The results showed that the model proposed in this study was appropriate and reliable for CO2 emissions not only in urban and rural areas but also in industrial areas. CO2 emissions in China were mainly concentrated in the coastal areas and the northern area, and the amount of carbon emissions increased rapidly from 2000 to 2018 in the Middle Yellow River. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at gride scale, and provide a scientific reference for the formulation of energy conservation and emission reduction policies by the local government.