Parametric prediction study of global energy-related carbon dioxide emissions

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.