The Environmental Footprint of Artificial Intelligence: A Theoretical Framework of Energy, Water, Minerals and Waste in the Sustainability Assessment of Digital Transformation in Africa
DOI:
https://doi.org/10.68854/r032hp31Keywords:
sustainability, artificial intelligence, data centres, energy consumption, water use, critical minerals, e-waste, life-cycle assessment, green AI, AfricaAbstract
Artificial intelligence (AI) is promoted as an instrument of sustainability, yet the infrastructure that runs it draws electricity and water, embodies critical minerals and ends its life as electronic waste. Sustainability assessments of digital transformation in Africa rarely count that infrastructure, even though the continent supplies many of the minerals, is courted as a host for data centres, receives used and discarded electronics, and arrives late to the benefits. This theoretical paper develops a framework for assessing the net sustainability of AI-driven digital transformation in Africa. Methodologically, it is an integrative conceptual review leading to theory-building. The corpus comprises 31 peer-reviewed journal articles published between 2023 and 2026, purposively selected from Crossref and PubMed searches using stated inclusion criteria, with every record checked against Crossref metadata. The analysis combined thematic synthesis with abductive theory-building, drawing on life-cycle assessment, industrial ecology, environmental justice and green-AI scholarship. The paper distinguishes four footprint components (energy, water, minerals and waste), locates each across the life cycle of AI hardware and maps each to its African geography: Copperbelt extraction in the Democratic Republic of the Congo and Zambia, data-centre clusters in coastal cities such as Lagos, and informal recycling economies in Accra, Lagos and Ibadan. Eight propositions follow. They specify when AI's sustainability benefits exceed its costs (additional and firm low-carbon supply, basin-appropriate cooling, high environmental value per unit of computation, and limited rebound), how costs and benefits are distributed between places that compute and places that extract and dispose, and why single-component policy shifts burdens between components. The paper argues that sustainability assessment of digital transformation must include the digital infrastructure itself. It recommends that African states link data-centre siting, energy and water rules to mineral governance, extended producer responsibility for electronic waste and public procurement standards for AI services, and that researchers test the propositions with place-based life-cycle inventories.
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