This study examines how artificial intelligence (AI)-enhanced dynamic supply chains can accelerate renewable energy deployment in Africa's frontier markets, addressing critical gaps in both adoption strategies and policy frameworks for sustainable energy transitions. The research employs a mixed-methods approach combining dynamic panel generalized method of moments estimation (20 countries, 2015–2024), machine learning analysis (XGBoost with SHAP values on 5214 firm-quarter observations), and policy simulation modeling. Robustness is ensured through seven validation procedures including alternative measurement approaches, subsample analyses, and placebo tests. The theoretical framework integrates Dynamic Capabilities Theory with the Technology-Organization-Environment model. Results demonstrate a 42.8% increase in renewable capacity per unit AI adoption, with optimal outcomes at 70% AI penetration and 90% digital infrastructure coverage. Supply chain disruptions reduce by 46% under coordinated implementation. The study identifies mobile broadband penetration and regulatory quality as critical enablers, while revealing asymmetric effects where positive AI shocks have 1.9× greater impact than negative ones. This research makes three novel contributions: (1) Quantification of non-linear thresholds for AI adoption in frontier energy markets, (2) empirical validation of the AI-governance-digital infrastructure nexus through advanced machine learning techniques, and (3) development of a policy simulation framework that accounts for spatial and temporal heterogeneities specific to African renewable supply chains. The study bridges theoretical rigor with practical implementation insights for sustainable energy transitions.
Suleman Bawa (2025) studied this question.