Mapping spatiotemporal variations of CO2 (carbon dioxide) emissions using nighttime light data in Guangdong Province

Abstract Industrialization and urbanization in China have resulted in a substantial increase in carbon emissions. Spatial and temporal analyses of the carbon emission inventoryhave become very necessary to policymaking and management. This study corrected inconsistency and uncontinuity of nighttime light (NTL) data based on stable light value pixels, and performed a distribute model of CO 2 emissionsin Guangdong province during the period of 2005–2013. The model results clearly showed the spatial and temporal variations of CO 2 emissionsduring 2005–2013 in Guangdong, with the Pearl River Deltaregion contributing the highest CO 2 emissions. These results are relevant for understanding the spatiotemporal CO 2 emissiondynamics at a county level and establishing policies for carbon emissionmitigation. This research highlights the importance of spatial spillovers, suggesting that future policies need to encourage inter-regional resource sharing, promote cooperation to ensure energy conservation and emissionreductions, and optimize the distribution of urban areas.