Spatiotemporal dynamics and driving forces of city-level CO2 emissions in China from 2000 to 2019

Climate issues caused by energy consumption and related carbon dioxide (CO2) emissions from urbanization and industrialization have attracted growing attention worldwide. Accurate estimation of urban CO2 emissions and exploration of main driving factors behind are of great importance for achieving low-carbon development. In order to identify the spatiotemporal heterogeneity of CO2 emissions among Chinese cities, we integrate two kinds of nighttime light datasets and estimate CO2 emissions for 286 prefecture-level cities in China from 2000 to 2019, then characterize the spatiotemporal dynamics and their decoupling types with economic growth through Tapio decoupling model. Moreover, the random forest model and the partial dependence method are employed to explore the correlation between the urban CO2 emissions and thirteen socioeconomic driving factors, from which the key influencing factors are extracted. The results show that from 2000 to 2019, the total CO2 emissions of 286 prefecture-level cities increased and then fluctuated; meanwhile, the CO2 emissions intensity decreased, indi-cating that energy efficiency has been improved. Most cities with high emissions intensity and high per capita emissions agglomerated in the north China. The number of decoupling cities has increased, while that of coupling cities and negative decoupling cities has decreased. Regarding driving factors, economic development, indus-trialization, transportation, land urbanization, and the production and distribution of electricity, gas, and water were the leading socioeconomic factors driving the increase in CO2 emissions. This study has great theoretical and practical significance. It helps us not only to deeply understand the coupling relationship among resources, environment, and economic development but also to design effective low-carbon development pathways and improve urbanization quality.