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As the largest emitter of greenhouse gases in the world, the peak values of Chinese CO2 emissions have attracted extensive attention at home and abroad. The carbon dioxide emissions of the Chinese transportation industry, accounting for 9.5% of total carbon dioxide emissions, is one of the high-emission industries, and its total carbon dioxide emissions continue to rise. Therefore, the accurate prediction of the peak values of carbon dioxide emissions from the Chinese transportation industry is helpful for China to formulate a reasonable policy of carbon dioxide emissions control. This paper, firstly, selects six major factors affecting the carbon dioxide emissions of the Chinese transportation industry. They are the Gross Domestic Product (GDP), population, urbanization rate, energy consumption structure, energy intensity, and industrial structure. Then, it builds a prediction model of carbon dioxide emissions based on Support Vector Regression (SVR). Finally, it analyses the sensitivity of each factor. The predicted results show that, under the baseline scenario, they will reach a peak of 1365.71 million tons in 2040; under the low-carbon scenario, the carbon dioxide emissions of Chinese transportation will peak at 1115.43 million tons in 2036; and in the high-carbon scenario, the peak value will occur in 2046 and the carbon dioxide emissions will be 1738.18 million tons. In order to promote the early peak of carbon dioxide emissions from the transportation industry, it is, firstly, necessary to change the mode of economic growth and appropriately reduce the speed of economic development. Secondly, the energy intensity of the transportation industry is reduced and the utilization rate of clean energy is improved. Thirdly, the industrial structure is optimized. Fourthly, the carbon dioxide emissions of the transportation industry caused by the increased urbanization rate are reasonably controlled.

Prediction on Peak Values of Carbon Dioxide Emissions from the Chinese Transportation Industry Based on the SVR Model and Scenario Analysis