This paper uses a stochastic frontier model combining with an adjustment factor of rebound effect to measure the carbon rebound effect and investigate its driving factors. Furthermore, we adopt the Kernel density to explore the spatial-temporal evolution characteristics of carbon rebound effect. The results are as follows. China’s carbon rebound effect remains at a high level of 70%, and presents a spatial distribution pattern of the largest in northeast and decreasing from east to west. Rapid economic development is the primary driving factor of carbon rebound effect. Carbon intensity constraint policy exerts a significant mitigation effect on the rebound, but the effect is unsustainable. Carbon rebound effect has the obvious characteristics of path-dependence without considering the spatial conditions. When considering the spatial conditions, the spatial agglomeration mode is characterized by the polarization effect, which is strengthened under dynamic conditions. Meanwhile, the spatial correlation decreases gradually over time. We for the first time measure the carbon rebound effect in urban China and empirically investigate its driving factors. It helps to improve the effectiveness of carbon reduction policies through technological innovation and efficiency improvement, providing policy references for achieving the carbon emission peak and carbon neutralization.