Abstract
Forecasting is the process of making predictions based on past and present data, with the most common method being trend analysis. Forecasting models are becoming increasingly crucial in uncovering the intricate linkages between large amounts of imprecise data and uncontrollable variables. The main purpose of this article is to compare CO2 emission forecasts in Gulf countries. In this study, the autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and holt-Winters exponential smoothing (HWES) forecasting models are used to anticipate CO2 emissions in the Gulf countries on an annual basis. This study attempts to predict time series data on CO2 emissions in the Gulf countries using statistical tools. The current analysis relied on secondary data gathered from the United States Energy Information Administration (EIA). The study’s findings show that the ARIMA (1,1,1), Holt-Winters exponential smoothing, ARIMA (1,1,2), and ARIMA (2,1,2) models do not outperform the artificial neural network model in estimating CO2 emissions in the Gulf countries. This study gives information on the current state of CO2 emission forecasts. This study will aid the researcher’s understanding of CO2 emissions forecasts. In addition, government agencies can use the findings of this study to develop strategic plans.
Highlights
Introduction is research aims to forecastCO2 emissions in Gulf countries
autoregressive integrated moving average (ARIMA) (1,0,0), ARIMA (0,1,1), ARIMA (1,1,2), and artificial neural network (ANN) suitable models were used for predicting the total revenue and expenditure of Saudi Arabia [2]. e expected growth of CO2 emissions of China has suddenly increased throughout the selected period of the study [3]. e regression analyses had been employed for 25 countries, and the statistical analyses indicated that eleven countries had a significant trend [4]. e gray prediction method was used to forecast the future CO2 emissions for the period of 2010–2012 in Taiwan, and the study showed that CO2 emissions would increase over the three years [5]. e CO2 data of 1999–2009 had been used to predict the future trend by using gray method (GM) [6]
Autoregressive integrated moving average, Holt-Winters exponential smoothing, and artificial neural network models are the finest models with any changing pattern to predict the amount of any time series data. ey are appropriate for at least fifty observations
Summary
Gulf countries have long dominated the oil and gas industry. CO2 is the most significant greenhouse gas emitted by human activity. According to Saudi government plans, multiple measures have been taken towards predicting the country’s future. E expected growth of CO2 emissions of China has suddenly increased throughout the selected period of the study [3]. E gray prediction method was used to forecast the future CO2 emissions for the period of 2010–2012 in Taiwan, and the study showed that CO2 emissions would increase over the three years [5]. E CO2 data of 1999–2009 had been used to predict the future trend by using gray method (GM) [6] ARIMA (1,0,0), ARIMA (0,1,1), ARIMA (1,1,2), and ANN suitable models were used for predicting the total revenue and expenditure of Saudi Arabia [2]. e expected growth of CO2 emissions of China has suddenly increased throughout the selected period of the study [3]. e regression analyses had been employed for 25 countries, and the statistical analyses indicated that eleven countries had a significant trend [4]. e gray prediction method was used to forecast the future CO2 emissions for the period of 2010–2012 in Taiwan, and the study showed that CO2 emissions would increase over the three years [5]. e CO2 data of 1999–2009 had been used to predict the future trend by using gray method (GM) [6]
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