Forecast of urban traffic carbon emission and analysis of influencing factors

China's transportation industry is entering a stage of high-quality development, and the construction of green and low-carbon transportation system has begun to meet new challenges. In order to reduce CO2 emissions, it is necessary to study the influencing factors and predictions of urban traffic CO2 emissions. This article first calculates the urban traffic CO2 emissions from 1995 to 2010, and then uses the gray model, cubic exponential smoothing, and gray cubic exponential smoothing combined model to forecast the traffic CO2 emissions. The gray model is established based on the data from 1995 to 2010. The carbon dioxide emissions from 2011 to 2017 are predicted and compared with the real value. The results show that the cubic exponential smoothing predictions have the highest degree of fit with the real value. Then, 13 influencing factors were selected and binary correlation analysis and linear regression analysis were conducted on the 13 pre-selected influencing factors and traffic CO2 emission. In order to obtain some potential commonalities among the influencing factors, 13 influencing factors were divided into 4 categories, and then factor analysis was carried out for each category to obtain 4 potential factors. The results show that the four factors have a significant impact on carbon dioxide emissions of transportation. Finally, based on the analysis of four influencing factors, policy recommendations are made for the CO2 emission reduction path of the transportation sector.