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Artificial Intelligence and Environmental Sustainability in Nigerian Small and Medium Enterprises: a Mixed-method Review

Year: 2026
Type: article
Abstract: Small and medium-sized enterprises (SMEs) form the backbone of Nigeria's economy, comprising over 80% of registered businesses and contributing approximately 48% of national GDP. Yet, despite this strategic importance, artificial intelligence (AI) adoption for environmental sustainability among Nigerian SMEs remains limited, with scarce empirical evidence of outcomes. This study examines the pathways through which AI tools influence green practices in Nigerian SMEs, identifies structural and contextual barriers to adoption, and proposes policy and institutional interventions to accelerate environmentally beneficial AI uptake. Using a concurrent mixed-methods design, quantitative data were gathered from 144 SMEs across Nigeria's six geopolitical zones, complemented by qualitative case studies of three agri-tech firms: AirSmat Inc. (Lagos), Green Eden (Plateau State), and Anatsor (Plateau State). Analysis was grounded in the Socio-Technical-Environmental (STE) model. Five key AI-enabled sustainability pathways were identified: resource optimisation, climate adaptation planning, demand forecasting for waste reduction, real-time anomaly detection, and automated ESG reporting. SMEs applying AI in precision agriculture recorded eco-metric gains of 22–35%. Overall adoption stands at just 32.6%, constrained by high technology costs (81%), inadequate digital skills (71%), unreliable infrastructure (69–77%), and insufficient regulatory incentives (67%). Access to external partnerships emerged as the strongest predictor of positive AI-sustainability outcomes. Edge computing shows promise in low-connectivity contexts. Policy recommendations include stable energy infrastructure, targeted AI financing, and capacity-building programmes. This study offers the first systematic empirical assessment of AI's ecological impact within Nigerian SMEs, with implications for business owners, policymakers, financial institutions, and development organisations.
DOI: 10.46654/zqcb4v17
Language: English
Open Access status: diamond
OpenAlex ID: W7224962944
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