Volume 6 , Issue 1 , PP: 13–22, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Saad Metawea 1 * , Maha Metawea 2
Doi: https://doi.org/10.54216/JSDGT.060102
Renewable electricity growth is central to sustainable development, decarbonization, and green-technology planning. However, much of the forecasting literature remains focused on plant-level or narrow-horizon technical prediction, with limited attention to country-level decision support for investment screening, transition monitoring, and strategic benchmarking. This study develops a business analytics framework to forecast the renewable share of electricity generation and classify countries by renewable-transition level using a cross-country panel based on the Our World in Data Energy database. The empirical sample comprises 5,162 country-year observations from 213 countries over the period 2000–2025 and includes measures of electricity demand, electricity generation, primary energy use, greenhouse-gas emissions, and energy-system structure. Three regression models and three classification models were evaluated using a fixed train–test de sign. The random-forest regressor achieved the best continuous forecasting performance, with MAE = 3.536, RMSE = 6.466, and R2 = 0.960, while the random-forest classifier delivered the best tier-classification performance, with 93.998% accuracy and macro-F1 = 0.940. Feature-importance analysis identified greenhouse-gas emissions, energy intensity, electricity generation, electricity demand, and per-capita electricity consumption as the most influential predictors. The findings indicate that renewable-transition benchmarking can be framed as a managerial analytics problem, extending sustainability research beyond descriptive monitoring toward practical decision support for business and policy planning.
Renewable electricity , Green technology , Sustainable development , Business analytics , Machine learning , Energy transition , Explainable forecasting
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