Forecasting Carbon Dioxide Emissions in China Using Optimization Grey Model

Abstract

Carbon dioxide (CO 2 ) is one of the most important anthropogenic greenhouse gases (GHG) that caused global environmental degradation and climate change. China has been the top one carbon dioxide emitter since 2007, surpassing the USA by an estimated 8%. So, forecasting future CO 2 emissions trend in China provides the basis for policy makers to draft scientific and rational energy and economic development policies. This paper presents an optimization GM (1, 1) model to forecast the carbon dioxide emissions in China. Considering the limitation of traditional GM (1, 1), the adaptive vector  is introduced instead of choosing constant value 0.5 to compute background value array. And the Harmony Search (HS) algorithm is adopted to determine the value of through optimizing the Mean Absolute Percentage Error (MAPE) function. The proposed HS optimization GM (1, 1) is applied to carbon dioxide emissions forecast in China. And the simulation results show that the HS optimization GM (1, 1) model gives better accuracy.