A new hybrid short-term carbon emissions prediction model for aviation industry in China

Abstract

Given the China's construction of green aviation, a reliable carbon emissions prediction is essential to identify and mitigate the environmental impact of the aviation industry. In this regard, the current research aims at constructing “decomposition-prediction” to predict carbon emissions. To develop the above techniques, 1044 data points derived from the carbon emission accounts and datasets were used (70 % of the points for training and 30 % for validation). To solve the selection of mode number K and penalty factor α of variational mode decomposition (VMD) and calculate the matrix weight coefficient and kernel functions of extreme learning machine (ELM), optimized VMD and ELM is proposed. To begin with, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the experimental data and spatial-dependence recurrence sample entropy (SdrSampEn) is used to quantify the complexity of all intrinsic mode functions (IMFs) to obtain the high and low complexity IMFs respectively. Then, high complexity IMFs are integrated, and then VMD of gray wolf optimizer algorithm (GWO) is used for decomposition to obtain IMFs, named G-IMFs. Next, low complexity IMFs are predicted by optimization ELM of slime mould algorithm (SMA) and G-IMFs are predicted by optimization ELM of particle swarm optimization algorithm (PSO). Lastly, all prediction results are reconstructed to obtain the final prediction results. To validate the superiority of the proposed method, 9 comparative models are established and their prediction effectiveness is assessed by R2 and RMSE. The results show that the proposed method has the best prediction performance for domestic aviation (R2 = 0.9978, RMSE = 0.0015) and international aviation (R2 = 0.9942, RMSE = 0.0003) respectively.