Grey prediction of civil aviation carbon emission intensity based on fractional order and Markov optimization

As a typical univariate grey predication model, the GM(1,1) model is widely used in predication research in many fields. Based on the GM(1,1) model, the paper introduces the idea of fractional order to construct the FGM model. In the current research, the particle swarm algorithm is used to optimize the fractional order, and then the FGMM model is constructed by combining the FGM model and the Markov model. According to the calculation standard of civil aviation carbon emissions issued by IPCC, the current research collects data related to civil aviation carbon emissions and total turnover to verify the effectiveness of the model improvement. In addition, the current research uses an optimization model to predict the future carbon emission intensity of civil aviation. The example shows that the prediction accuracy of the grey model after multiple optimization is significantly improved than the original model. The prediction accuracy of the FGMM model is as high as 97.28%, which is 0.98% higher than that of the FGM model. Compared with the GM model, the accuracy of the FGM model is improved by 2.2%. This proves the effectiveness of the model improvement. At the same time, it is predicted that the carbon emission intensity of China's civil aviation in 2030 will be 0.59 kg/ton-km, a decrease of 44.02% compared with 1.06 kg/ton-km in 2005. Compared with the “13th Five-Year Plan”, the five-year average has dropped by 29.80%. Accordingly, the current research provides policy recommendations that are helpful for civil aviation to save energy and reduce emissions, which can be referenced by relevant departments for decision-making.