Time Series Prediction of CO2 Emissions in Saudi Arabia Using ARIMA, GM(1,1), and NGBM(1,1) Models

Year: 2021       Vol.: 70       No.: 2      

Authors: Z. F. Althobaiti, and A. Shabri

Abstract:

The investigation of economic aspects of gas emissions in terms of its volume and consequences is very important, given the current increasing trend. Therefore, the prediction of carbon dioxide emissions in Saudi Arabia becomes necessary. This study uses annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The study built the prediction model of CO2 emissions in Saudi Arabia by using Autoregressive Integrated Moving Average (ARIMA), Grey System GM and Nonlinear Grey Bernoulli Model (NGBM), and comparing their efficiency and accuracy based on MAPE metric. The results revealed that Nonlinear Grey Bernoulli Model (NGBM) is more accurate than the other prediction models. The results may be useful to Saudi Arabian government in the development of its future economic policies. As a result, five policy recommendations have been proposed, each of which could play a significant role in the development of acceptable Saudi Arabian climate policies.

Keywords: annual time series data, Autoregressive Integrated Moving Average (ARIMA), CO2 emissions, global warming, Grey Model (GM), Nonlinear Grey Bernoulli Model (NGBM), prediction, Saudi Arabia

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