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 inventory have 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 CO2 emissions in Guangdong province during the period of 2005–2013. The model results clearly showed the spatial and temporal variations of CO2 emissions during 2005–2013 in Guangdong, with the Pearl River Delta region contributing the highest CO2 emissions. These results are relevant for understanding the spatiotemporal CO2 emission dynamics at a county level and establishing policies for carbon emission mitigation. 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 emission reductions, and optimize the distribution of urban areas.