Study of carbon dioxide emissions prediction in Hebei province, China using a BPNN based on GA

With the deterioration of the global greenhouse effect, the study of carbon dioxide emissions has received more and more international attention and accurate prediction of carbon dioxide emissions is also important for the formulation of reasonable energy-saving emission reduction measures. In this paper, the genetic algorithm is used to optimize the initial connection weights and thresholds of the traditional back propagation neural network (BPNN) which can give full play to the advantages of the genetic algorithm's global search capacity and BPNN's local search. The data of Hebei province in China during the period 1978-2012 are selected to carry out the carbon dioxide emissions prediction with the established model. In the view of the choice of input variables, the coal consumption, gross domestic product, total population, and urbanization level are examined by Pearson coefficient test. Auto correlation and partial correlation are applied to analyze the inner relationships between the historic carbon dioxide emissions, thus to select the input variables of BPNN. Besides, in order to verify the validity of the built model, the residual auto correlation and partial correlation are done upon the training set. The prediction results suggest the proposed model outperforms the compared models. Published by AIP Publishing.