The study models annual (1970-2023) percentages of energy-related CO2 emissions in Sudan using ARIMA for the mean process and ARCH for conditional variance. After differencing to attain stationarity, several ARIMA ranks are compared; residual heteroskedasticity motivates an ARCH extension. The authors finally adopt an ARCH (2) specification and generate 10-year forecasts. They conclude that the ARCH (2) model better captures volatility and yields more reliable predictions than the tested ARIMA alternatives.
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