Toward sustainable energy production: a comparative machine learning framework for predicting green hydrogen cost across the african continent

Abstract

= 0.9710 ± 0.032). SHAP analysis revealed that renewable energy capacity, electrolyser capacity, and the energy security index contributed most to predicted LCOH variability within the dataset. The framework provides a transferable screening pipeline for prioritizing investment and data collection in data-scarce settings, with explicit linkages to Sustainable Development Goal (SDG) relevant indicators, including energy security, climate mitigation, and water stress. This approach complements deterministic techno-economic appraisal by enabling rapid cross-country comparison during early-stage planning Fig.S1.