Prediction of CO2 emissions in Saudi Arabia using Nonlinear Grey Bernoulli Model NGBM (1,1) compared with GM (1,1) model

Abstract

One of the most critical solution for tackling the challenges of global warming and climate change is to study and know the accurate prediction of carbon dioxide (CO2) emissions. Thus, aid to develop appropriate strategic plans that will reduce future damages caused by these emissions into the atmosphere. This study utilizes annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The goal of this study is to predict CO2 emissions using the Nonlinear Grey Bernoulli model NGBM (1,1), and compared with the GM (1,1) model based on MAPE metrics to achieve a high-accuracy prediction. The NGBM (1,1) is a newly created grey model with wide ranging applications in diverse fields due to its precision in handling small time-series datasets with nonlinear variations. The NGBM (1,1) with power γ is a nonlinear differential equation that can control the predicted result and adjust the solution to fit the 1-AGO of previous raw data. Thus, the findings show that at sample sizes of N=10 and N=5, the Nonlinear Grey Bernoulli Model (NGBM) is more precise than the Grey Model GM (1, 1). The findings could help the government develop future economic policies.