| Summary: | Different cities have significant variations in terms of economic development, population size, geographical features, energy endowment, and total amount and structure of carbon dioxide (CO2) emission from transport. It is essential to understand the types and characteristics of urban transport CO2 emissions and propose differentiated CO2 emission reduction measures and formulate renewable energy use strategy, with a view to sequentially achieving the peak of urban transport CO2 emissions. Based on the analysis of driving factors of urban transport CO2 emission, this paper establishes a classified index system and then adopts the Gaussian mixture model (GMM) and expectation–maximization (EM) algorithm to cluster the trends of transport CO2 emissions peak in 672 municipal cities in China. The results show that the model can effectively identify the differences between different cities and divide them into five types, namely the types of public transport demonstration, emission pressure, low carbon potential, population loss, and high carbon pressure, the proportion of urban transportation carbon emission in these five cities is 44.39%, 22.19%, 18.21%, 7.66% and 7.55%. Moreover, by analyzing different energy endowment characteristics and geographical features, optimization suggestions are put forward for the transportation energy consumption of different cities to realize the efficient utilization of renewable energy.
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