Antecedents and Forecasting of Carbon Emissions Using Machine Learning Algorithms: Insights from the Top Ten Carbon-Emitting Nations

Mousa Gowfal Selmey (), Bassam A. El Bialy (), Ahmed Hassanein (), Abdalqader Ahmed Baker (), Wael Mohamed Ali (), Nagi Rashed Aboushadi (), Abdullah Abdulaziz Alhumud () and Elsayed Farrag Elsaid Mohamad ()
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Mousa Gowfal Selmey: Department of Economics, Faculty of Commerce, Mansoura University, Mansoura, Egypt
Bassam A. El Bialy: Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Ahmed Hassanein: Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait; & Mansoura University, Mansoura, Egypt
Abdalqader Ahmed Baker: Department of Economics, College of Law and Economics, Islamic University of Madinah, Medina, Saudi Arabia
Wael Mohamed Ali: Department of Basic Sciences, Higher Future Institute for Specialized Technological Studies, Obour, Egypt
Nagi Rashed Aboushadi: Department of Economics, Faculty of Commerce, Mansoura University, Mansoura, Egypt
Abdullah Abdulaziz Alhumud: Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Elsayed Farrag Elsaid Mohamad: Department of Economics, Faculty of Commerce, Damietta University, New Damietta, Egypt; & Department of Economics, College of Law and Economics, Islamic University of Madinah, Saudi Arabia

International Journal of Energy Economics and Policy, 2025, vol. 15, issue 4, 511-524

Abstract: This paper presents an analysis of predicting annual carbon emissions (CO? emissions) from 1990 to 2023 in the top ten high-carbon emission source countries using machine learning algorithms. The research employed a random forest algorithm, logistic regression, support vector machines (SVM), K-nearest neighbours (KNN), and gradient boosting to predict CO? emissions based on a set of selected features. The performance of these models was evaluated using root mean square error (RMSE), mean absolute error (MAE), R-squared, accuracy, precision, recall, F1 score, area under the curve (ROC AUC), and confusion matrix accuracy. The paper finds that primary energy consumption is the main antecedent and most influential factor, followed by population size and gross domestic product (GDP). The results also reveal that trade openness, urbanisation, and renewable energy consumption have relatively minor impacts on the model's predictions. Furthermore, the results indicate that the Random Forest algorithm achieves near-perfect performance across all evaluation metrics for the prediction of carbon emission. The paper provides significant implications for policymakers and scholars in reducing CO? emissions.

Keywords: Carbon Emissions; Primary Energy Consumption; Top Ten Carbon-Emitting Nations; Machine Learning Algorithms (search for similar items in EconPapers)
Date: 2025
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DOI: 10.32479/ijeep.19622

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