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

Assessing Clean Energy Transition Readiness in Resource-Constrained Economics with Predictive Machine Learning Techniques | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Clean Energy Transition Readiness in Resource-Constrained Economics with Predictive Machine Learning Techniques Thauban O. Omooseti, Olalekan J. Akintande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8081231/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Renewable Resources Artificial Intelligence and Machine Learning Energy Engineering Clean Energy Transition Readiness (CETR) Machine Learning Energy Access Governance Full Text Additional Declarations The authors declare no competing interests. Supplementary Files DescriptiveStatisticswithaKeyvariablesbyCluster.docx Summary Statistcs Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8081231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543000079,"identity":"2efcc773-935c-47e9-aadf-c33b4b3c9bb2","order_by":0,"name":"Thauban O. 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