Although it's well known that the carbon intensity from passenger transport of cities varies widely, few studies assessed the disparities of that in city-level and its underlying factors due to the limited availability of data, and thus developed effective strategies for different types of cities. This study is the first to present a comprehensive inventory of emissions from passenger transport on road for 360 cities in mainland China for 2018, based on the data from 5 transport modes and evaluated by combining distance-based and top-down fuel-based methods. In 2018, passenger transport on road in China emitted 1076 MtC. A large portion of CO2 emissions was identified in the southern and eastern coastal areas and capital cities. GDP, population, and policy were the major factors determining the total CO2 emissions, but not carbon intensity. Clustering analysis of carbon intensity and 9 socio-economic predictors, using a tree-based regression model, clustered the 360 cities into 6 groups and showed that higher carbon intensities occurred in both affluent city groups with a high active population share and less affluent city groups with a low population density but high density of trip destinations. Forward-and-backward stepwise multiple regression analysis indicated that constructing a compact city is more effective for city groups with a high income and high active population share. Enhancing land-use mixed degree is more critical for city groups with a high income and low active population share, while shortening travel distance by intensifying infrastructure construction is more important for the less affluent city groups.