摘要 : Based on the IPAT (I = human impact, P = population, A = affluence, and T = technology) framework, we used the temporal and spatial Logarithmic Mean Divisia Index decomposition approach to investigate whether the temporal and spat... 展开 Based on the IPAT (I = human impact, P = population, A = affluence, and T = technology) framework, we used the temporal and spatial Logarithmic Mean Divisia Index decomposition approach to investigate whether the temporal and spatial impacts of human activities on carbon emissions are synergistic in 330 prefecture-level cities in China from 2003 to 2017. The results showed that carbon emissions in Chinese cities increased to different degrees during the study period, and a spatial distribution pattern could be recognized, with high levels in the East and South, and low levels in the West and North. Carbon emissions in cities in North China and in the eastern coastal areas displayed a cluster pattern higher than the national average level, although in most cities were lower than the national average. With respect to temporal effects, we found that although the decrease in carbon intensity played an important role in reducing carbon emissions in cities, it was not sufficient to offset the incremental gains associated with economic growth. The effect of population change was not apparent. With respect to spatial effects, we found that the effect of carbon intensity in most cities enhanced the difference with the national average carbon emission, whereas the effect of economic development reduced it. The effect of population size on spatial differences in carbon emission was bound by the Heihe-Tengchong line, forming a distribution pattern in which the difference in emissions was enhanced in the Southeast and reduced in the Northwest. The temporal and spatial impacts of human activities on carbon emissions in most cities were not synergistic. These findings can provide a reference for formulating differentiated carbon emission reduction policies among cities. 收起