Abstract: It can provide theoretical guidance for the development of low-carbon policies to research and forecast the growth rate of carbon emissions since carbon emissions have become an important reason for global warming. The growth rate of China's carbon emissions was decomposed into short-term volatility and trend term these two sequences by the use of empirical mode decomposition method, and the national policy, the domestic macroeconomic changes and the financial crisis influence to the short-term volatility and trend term were analyzed respectively. On this basis, using dynamic neural network to forecast the short-term volatility and trend term, and sum the two predicted values as the final growth rate of carbon emissions. Finally, from the error sequence of absolute value maximum, minimum, mean and standard deviation of the four angles to compare this prediction method with neural network model that separate input variables of carbon emissions or the growth rate of carbon emission, and found that the model presented in this paper can predict the growth rate effectively.

Key words: the growth rate of carbon emissions, empirical mode decomposition (EMD), prediction, neural network