In face of the crisis of survival and the complicated situations of environmental governance both caused by climate changes, the key solution to reduce the intensity of carbon emissions is to study the dynamic monitoring and early warning control of the spatiotemporal evolution of the carbon intensity in China. This paper estimated the spatial panel data of the carbon intensity in 30 provinces in China during 1997-2015,and did spatial statistical analysis of the spatial correlation,agglomeration characteristics and space-time transition of the carbon intensity by the means of exploratory spatio-temporal data analysis ( ESTDA). It also adopted quantile regression and the space-time transition nested models to reveal the spatiotemporal transition mechanism of the carbon intensity in the Chinese provinces under the dual functions of time and space. The results show that: ①The carbon intensity of the 30 provinces in China is not completely random in the spatial and temporal distribution. There are distinct spatial correlation characteristics between the carbon intensity of the provinces,and the change trend of the carbon intensity in the various provinces is affected by that in the adjacent provinces. The carbon intensity among provinces embodies the spatio-temporal evolution characteristics,bothagglomeration' anddifferentiation'in the spatial distribution.②The spatial agglomeration of the carbon intensity in China is on the increase,with high solidification and low liquidity. The stability of the 10 provinces with high carbon intensity will critically restrict the overall transition of carbon intensity in China. In the contrast,the transition of related provinces will critically drive the overall transition of carbon intensity in China. ③There are driving modes and control modes of the space-time transition in the process of spatial agglomeration of the carbon intensity in the different provinces. The quantile regression model can well explain the driving mechanism caused by different driving factors in the space-time transition of carbon intensity. There are strong nesting between the quantile of the driving factors and the different types of space-time transition about the carbon intensity in the different stages of the response. ④ According to the results of carbon intensity evolution and transition mechanism in the different provinces,we should put forward further different control measures to reduce the carbon emissions,such as strengthening the effective monitoring and management of key provincial carbon intensity and further restricting the carbon emissions.