China is committed to developing a low-carbon economy that will contribute to achieving the national strategic target of carbon peak and carbon neutrality. However, changes in energy-based carbon dioxide (CO2) emissions at both long-term and global-local scales remain poorly revealed. This study explored the temporal-spatial distribution characteristics of energy-based CO2 emissions calculated by the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method during the period of 2004, 2010 and 2019, covering 30 provinces in China. Then, this presented study examined the impact degree of socio-economic factors concerning energy-based CO2 emissions at the global and local levels using the expanded Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and geographically weighted regression (GWR) model, respectively. The results indicated that the total CO2 emissions have significant spatial spillover effect in northeast region, where the cluster pattern of high total CO2 emissions and high CO2 emissions from coal mainly occur. Moreover, both urbanization and Gross Domestic Product (GDP) are significantly responsible for the increase in CO2 emissions. This proposed research framework can be promoted to explore the temporal-spatial characteristics of energy-based CO2 emissions in the city, the county, and even town levels to successfully realize CO2 emissions reduction. Display omitted •This study accounted CO2 emission in China for the long time series 2004–2019 based on IPCC method.•We discovered that urbanization and GDP have a greater positive impact on driving CO2 emission.•The influence of driving factors on CO2 emissions had spatial heterogeneity and agglomeration.•The temporal-spatial characteristics of provincial CO2 emission could promote carbon emission research.