China's carbon emissions prediction model based on support vector regression

Six influnce factors including population, urbanization rate, per capita GDP, added value proportion of service industry, per GDP energy consumption and coal consumption ratio were seleted as independent variables, and a model based on support vector regression (SVR) was established for predicting carbon emissions of China. Using the data of carbon emissions and influence factors from the year 1980 to 2009 as samples, the SVR model with good learning and generalization ability was established through training and testing. According to the 12th five-year program, prediction values of influence facors under different situations were set, and the carbon emissions of China from the year 2010 to 2015 were predicted. The results show that China can appropriately reduce GDP growth speed and constantly optimize energy structure so as to achieve carbon reduction target efficiently.