Use of random forest based on the effects of urban governance elements to forecast CO₂ emissions in Chinese cities

Chinese cities contributes a large amount of CO₂ emissions. Reducing CO₂ emissions through urban governance is an important issue. Despite the increasing attention paid on the CO₂ emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO₂ emissions 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 CO₂ forecasting 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 CO₂ emissions, respectively; (2) Governance elements have nonlinear relationship with CO₂ emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO₂ emissions more accurately than can other predictive models. These findings can be used to conducts the CO₂ scenario simulation and help government formulate active governance measurements.