Chinese cities contributes a large amount of CO2emissions. Reducing CO2emissions through urban governance is an important issue. Despite the increasing attention paid on the CO2emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO2emissions through comprehensive urban governance elements, this paper use the random forest model through the data from 1903 Chinese county-level cities in 2010, 2012 and 2015, and establish a CO2forecasting platform based on the effects of urban governance elements. The results are as follows: (1) The municipal utility facilities element, the economic development & industrial structure element, and the city size &structure and road traffic facilities elements are crucial for residential, industrial and transportation CO2emissions, respectively; (2) Governance elements have nonlinear relationship with CO2emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO2emissions more accurately than can other predictive models. These findings can be used to conducts the CO2scenario simulation and help government formulate active governance measurements.