Africa stands at a pivotal moment in its development trajectory. The continent faces acute climate vulnerability manifested through droughts, floods, and extreme weather events that threaten livelihoods and economies, while simultaneously experiencing unprecedented development pressure to lift millions out of poverty. Amid this tension, a third force is rapidly reshaping the landscape: the digital revolution.

Across African nations, from Lagos to Nairobi, Accra to Cape Town, artificial intelligence (AI) adoption is accelerating. Governments are investing in smart city initiatives, agritech startups are deploying machine learning for crop prediction, and tech hubs are proliferating. Yet this digital transformation brings a fundamental question into sharp focus: Can AI drive sustainable development without worsening environmental and social inequalities? The answer is far from certain. The answer depends largely on the choices African nations make today regarding the kind of AI systems they adopt, deploy, and govern.
AI offers tangible benefits for addressing Africa’s most pressing challenges. Climate monitoring systems that integrate satellite data with machine-learning models can enhance early-warning capabilities for droughts and floods, improving disaster preparedness and resilience. In agriculture, precision-farming technologies support optimized irrigation, yield prediction, and early detection of crop diseases, contributing to climate-smart practices that enhance food security while conserving resources [3]. AI-enabled energy systems can assist with smart-grid management and improve the integration of variable renewable energy sources, particularly as solar and wind capacity expands [3]. In rapidly urbanizing cities, AI-based tools promise improvements in traffic management, land-use planning, and public service delivery [2]. While many of these applications are still emerging, pilot initiatives across the continent demonstrate that AI can deliver real developmental value when appropriately deployed.

Despite its promise, AI carries a substantial environmental footprint that is often overlooked in development discourse. Training and deploying modern AI models can be highly energy-intensive, leading to significant carbon emissions, particularly when powered by fossil-fuel-dominated energy systems. Data centers that support AI workloads also demand large amounts of electricity for computation and cooling, raising sustainability concerns in regions already facing energy and water stress. Moreover, as AI systems scale, the cumulative energy cost of inference can rival or exceed that of model training. These dynamics create the risk of “digital carbon leakage,” whereby energy-intensive AI infrastructure is relocated to regions with weaker environmental regulation. Without deliberate safeguards, Africa could import environmentally unsustainable AI systems that undermine, rather than support, long-term sustainability objectives.

Green AI offers a pathway to address these challenges by prioritizing energy efficiency and environmental responsibility across the AI lifecycle. Central strategies include data-centric and algorithmic approaches that reduce computational demand, such as active learning and efficient data selection [1]. Model-compression techniques, including pruning, quantization, and knowledge distillation, enable smaller models to achieve comparable performance at a fraction of the energy cost [2]. Additional measures include deploying energy-efficient hardware, powering digital infrastructure with renewable energy, and adopting lifecycle assessment frameworks that account for environmental impacts from manufacturing through end-of-life disposal [2]. For Africa, Green AI is not a luxury but a necessity. With constrained electricity systems and rapidly growing digital demand, the continent cannot afford energy-intensive AI that locks in carbon-heavy infrastructure. 

Africa has the potential to act as a strategic leader rather than a passive recipient of global AI technologies. The continent’s relatively nascent AI ecosystem creates opportunities to leapfrog directly into sustainable AI practices without the burden of legacy systems. Aligning AI infrastructure development with expanding renewable-energy capacity, such as co-locating data centers with solar or wind installations, can reduce emissions while strengthening energy resilience [2]. Furthermore, prioritizing AI applications that address Africa’s most urgent needs like supporting smallholder farmers, strengthening climate-resilience systems, and improving efficiency in water and energy use, ensures that technological advancement translates into inclusive development [3]. Sustainability-first AI policies, including energy-reporting requirements and procurement standards favoring efficient models, can further institutionalize Green AI principles [1,2].

The path forward faces real obstacles. Infrastructure gaps in electricity access, connectivity, and computing resources, especially in rural areas, limit the scalability of AI solutions [2]. Skills shortages constrain the local capacity needed to design, deploy, and maintain Green AI systems. Digital inequality risks concentrating AI benefits among urban elites while marginalizing vulnerable populations. In many countries, governance frameworks for AI and digital infrastructure remain underdeveloped, with limited integration of environmental considerations [2]. Yet these challenges also present opportunities: infrastructure gaps can encourage decentralized and edge-computing approaches; skills shortages can catalyze regional training partnerships; and governance gaps allow African states to craft forward-looking, sustainability-centered AI policies from the outset.

Sustainability must therefore be embedded into AI systems from the earliest stages of design and deployment, rather than retrofitted after scaling. Research-driven sustainability assessments that evaluate energy consumption, carbon emissions, and lifecycle impacts should become standard practice for AI initiatives [1]. Youth engagement is especially critical. Africa’s young innovators, equipped with education that integrates technical skills and sustainability principles, will shape the continent’s AI trajectory. Cross-sector collaboration among governments, academia, industry, and civil society is essential to ensure that AI advances multiple development goals simultaneously [1,2].

AI itself is neither inherently beneficial nor harmful; its impact depends on the values and priorities embedded in its deployment. Africa’s choices today will determine whether AI becomes a catalyst for climate-resilient development or an additional environmental burden that deepens existing inequalities. Policymakers must establish frameworks that mandate sustainability in AI systems. Researchers must generate context-specific evidence tailored to African realities. Innovators must prioritize efficiency, inclusivity, and social value over uncritical technology adoption. Young Africans, as digital natives confronting both the climate crisis and the AI revolution, hold a decisive role. With deliberate action, Africa can demonstrate that technological advancement and environmental stewardship are not competing objectives but complementary foundations for a just, resilient, and prosperous future. The question is not
whether Africa will adopt AI—it will. The question is what kind of AI Africa will choose.

Authors:  Luckman Aborah Yeboah, Samuel Osei-Amponsah.

References

[1] Salehi, S., & Schmeink, A. (2023). Data-centric green artificial intelligence: A survey. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/tai.2023.3315272

[2] Tabbakh, A., Aggarwal, D., Sharma, D., & Saxena, A. B. (2024). Green AI: Balancing model complexity and energy footprint in deep learning. Proceedings of the IEEE International Conference on Sustainable Computing and Data Science. https://doi.org/10.1109/icscds65426.2025.11167425

[3] Indra, E. W., Vasudevan, I., Arthy, M., et al. (2025). Green artificial intelligence and machine learning: Environmental solutions towards sustainable AI. https://doi.org/10.4018/979-8-3373-0766-4.ch014