Parametric prediction study of global energy-related carbon dioxide emissions

摘要

In this work, we develop an estimation model for the future tendency of worldwide energy-associated carbon dioxide emissions. The presented model is based on the past statistics of the energy-related carbon dioxide emissions worldwide from 1975 to 2018. We employ the auto-regressive moving average (ARMA) model with the best possible estimation model order which provides the highest level of confidence in generating and estimating the time-series of worldwide energy-related carbon dioxide emissions. Thus, ARMA (3, 3) model has been successfully used to model and estimate the short-term future values for the worldwide energy-related carbon dioxide emissions for the next five years 2019–2023. The simulation results showed that ARMA (3, 3) has was the optimal estimation model order since it recorded the minimum estimation error with highest level of confidence listed as 86.1%. Finally, in 2018, the energy-related CO2 emissions reached 33.14 gigatonnes then the estimation results showed a linear increasing trend in the short future of annual released numbers of the energy-associated carbon dioxide emissions (in gigatonnes) which is expected to reach 35 gigatonnes in 2023.