Assessing Clean Energy Transition Readiness in Resource-Constrained Economics with Predictive Machine Learning Techniques

Nigeria faces a persistent electricity crisis, with only 45% of its population accessing grid-supplied power for less than four hours daily, despite a central generation capacity of 15 GW that often drops to 3.5–5.0 GW against a 20 GW demand. This shortfall, costing the economy USD 28–29 billion annually (approximately 2% of GDP), has driven over 22 million households and enterprises to rely on costly and unreliable diesel or petrol generators. However, Nigeria’s abundant renewable energy potential, particularly in solar, offers a pathway to address this crisis through decentralised solutions like solar hybrid microgrids and standalone solar home systems. The Nigeria Electrification Project (NEP) has deployed over 125 mini-grids and 1.4 million solar home systems, electrifying over 5.5 million people and creating 5,000 green-sector jobs by 2023. The 2022 Energy Transition Plan (ETP) aims for a 30% renewable energy share by 2030 and net zero emissions by 2060, supported by the Climate Change Act (2021) and Paris Agreement commitments. Yet, unclear policy frameworks and infrastructural limitations hinder progress, presenting an opportunity for Nigeria to leapfrog carbon-intensive pathways by adopting distributed renewable technologies.