Forecasting Carbon Dioxide Emissions from Industrial Processes in Sudan: A Time Series Approach

Maria H Mohamed, Altaiyb Omer Ahmed Mohmmed, Mohammedelameen Eissa Qurashi, Mubarak H. Elhafian

Abstract: This study analyzes the long-term behavior of carbon dioxide emissions from Sudan?s industrial sector using time series forecasting techniques. Data spanning from 1970 to 2023 were evaluated to identify the most appropriate model for prediction. Preliminary analysis showed that the series was non-linear and non-stationary, but first-order differencing yielded stationarity. Based on model selection criteria, ARIMA (2, 1, 0) was identified as the best fit. Diagnostic checks confirmed the model?s validity, with residuals behaving randomly. Forecasts generated from this model offer valuable insights for policymakers, enabling more effective planning and mitigation strategies to reduce industrial carbon emissions.

Keywords: ARIMA, carbon emissions, Sudan industry, time series forecasting, environmental policy

How to Cite?: Maria H Mohamed, Altaiyb Omer Ahmed Mohmmed, Mohammedelameen Eissa Qurashi, Mubarak H. Elhafian, "Forecasting Carbon Dioxide Emissions from Industrial Processes in Sudan: A Time Series Approach", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 1396-1402, https://www.ijsr.net/getabstract.php?paperid=SR25701180634, DOI: https://dx.doi.org/10.21275/SR25701180634