A novel optimized grey model and its application in forecasting CO2 emissions

Abstract

Carbon dioxide emissions are the main cause of global warming. At present, how to reduce carbon dioxide emissions while promoting energy savings and emission reduction is a hot research topic. Hence, China’s carbon dioxide emissions must be reasonably and accurately predicted because it is very important for the Chinese government to formulate energy and environmental policies. In this study, the classical optimization theory of the Fibonacci sequence and golden ratio were applied to the grey prediction model of an approximately inhomogeneous exponential series. Then, a new optimization model was established, and the properties of the optimization model were studied. The purpose is to reduce the parameter estimation errors of the model and improve the simulation and prediction accuracy of the model. Next, the novel model was applied to the simulation and prediction of CO2 emissions in China. The experimental results show that the effectiveness of the novel model was much better than that of the other models, which confirms the effectiveness of the new model. Based on this, China’s carbon dioxide emissions were predicted and analysed. The results show that China’s carbon dioxide emissions will still be on the rise over the next five years, and carbon dioxide emissions remain a serious problem.

Keywords

• Forecasting
• Grey forecasting theory
• Background value
• CO2 emissions