Abstract: As the concentratareas of carbon emissions, cities are meanwhile the most suitable to deeply participate and effectively implement nationwide emission reduction targets and emission reduction policies numerous city administrations that have strong jurisdictions. ased on the data of 108 cities in the Yangtze River Economic Beltestimate the carbon intensity of energy consumption and analye its evaluation of spatial-temporal features and influencing mechanisms kernel density analysis, STIRPAT model, and quantile regression panel model. We fthat: (1) The cities’ average carbon intensity energy in the Yangtze River Economic Belt hamaintained a downward trend from 2003 to 2020, decreasing from 1.87 yuan in 2003 to 0.51 t/ 10 000 yuan in 2020 with an annual average growth rate of -7.34%. (2) For the spatial evaluation, the cities’ average carbon intensity energy showa continuously increasing spatial feature, which shiftfrom downstream regions with scarce fossil fuels to midstream and upstream regions with abundant fossil fuels. Besides, the cities’ carbon intensity energy s highest in small cities, followed by medium-sized cities, and lowest in large cities “small cities>medium-sized cities>large cities”. (3) Economic growth, population size, government intervention, and industrial structure significantly can reduce the carbon intensity of energy consumption, but the human capital level and energy intensity were the main contributing factorshe positive promotion effect of human capital level no significant for low energy intensity cities. e suggested that government take different carbon intensity levels into full consideration before policy making.