A novel time-delay multivariate grey model for impact analysis of CO2 emissions from China's transportation sectors

CO2 emissions from the transportation sector occupy an increasingly important proportion in China's carbon dioxide emissions. Measuring the accumulative impact of factors on carbon emissions over time is of great significance for formulating corresponding policies. This paper aims to propose a novel time-delay multivariate grey model to measure the CO2 emissions' accumulating impact of China's transportation sector. Firstly, the grey incidence model is used to identify time lags between the input and output variables, and also analyze the structure type of time-delay weights. Then, an accumulative time-delay multivariate grey prediction model is developed. In this model, a Gaussian formula is used to discretize the convolution integral of the time response function, and the particle swarm optimization algorithm is employed to determine the optimal weight coefficients. Finally, a case for CO2 emissions prediction is adopted to test the effectiveness and practicality of this model compared with the alternative models. The results show that the proposed model outperforms other six competing models in accordance with two measuring indices, and suggestions on emissions mitigation are proposed based on the prediction results. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

A novel time-delay multivariate grey model is proposed in this study to measure the accumulating impact of CO2 emissions from China's transportation sector. The model outperforms other competing models in predicting CO2 emissions and suggests mitigation strategies based on the prediction results.