Green artificial intelligence (AI) requires a dual approach: making AI systems themselves more efficient while simultaneously using AI to solve environmental challenges. This framework emerged as the central focus at the Mobile World Congress (MWC) Barcelona in March 2026, where UNESCO, the World Bank, and Smart Africa outlined how governments and industry can embed sustainability into AI development from the ground up. What Are the Two Dimensions of Green AI? The concept of green AI splits into two complementary but distinct imperatives. Leona Verdadero, Programme Specialist for Digital Policies and Transformation at UNESCO, introduced this framework at MWC Barcelona: "greening AI" focuses on reducing the resource footprint of AI systems themselves across energy, water, and materials, while "greening with AI" leverages AI as a tool to advance climate resilience, environmental monitoring, and sustainable development goals. The stakes are substantial. Data center operation accounts for 85.5% of AI-related emissions and 91% of its water consumption. Yet the encouraging news is that targeted design choices can deliver immediate impact. According to the UNESCO and UCL report "Smarter, Smaller, Stronger: Resource-Efficient AI and the Future of Digital Transformation," model compression and optimized inference can reduce AI energy consumption by up to 90%, and smaller, task-specific models are not only more energy-efficient but also more resilient and better suited to resource-constrained environments across the Global South. How Are Governments and Industry Driving Green AI Solutions? - Spain's Green Algorithm Programme: Spain's Ministry of Digital Transformation launched one of the most concrete government-led initiatives globally, using public procurement standards and regulatory incentives to systematically reduce the computational and environmental footprint of public sector AI. - Nigeria's National AI Strategy: Nigeria's National AI Strategy 2025 stands out as an African leader, acknowledging energy instability as a barrier to AI investment while setting targets for clean energy use and hosting a national competition for green AI solutions that prioritize efficiency from the ground up. - Industry-Led Efficiency Gains: Telecommunications companies are reducing the energy consumption of their own networks and shifting toward leaner, more resilient models designed for low-resource environments, while companies like FarmerChat reduced the cost of reaching farmers from 35 dollars to just 35 cents through a smaller, locally-adapted model. - Predictive AI for Renewable Energy: Husk Power Systems achieved a 40% reduction in diesel use through predictive AI for solar mini-grids, demonstrating how AI can optimize energy systems in resource-constrained settings. Xavier Decoster, Senior Digital Specialist at the World Bank, presented a policy framework for governments seeking to integrate resilience and efficiency into their AI strategies. After reviewing 75 national AI strategies published as of 2025, he noted that while many address AI inclusion, very few explicitly address AI's resource footprint. "Digital policies and sector policies are developed in silos," Decoster observed. "We need to work across those silos to manage AI sustainably". Why Is Academic Dialogue on AI and Environment Critical? Beyond policy and industry initiatives, universities are fostering balanced public conversation about AI's environmental impact. Students in UPEI's Environmental Studies program organized a public symposium titled "AI and the Environment: Impact and Innovation" in March 2026, bringing together academic perspectives from computer science and ethics. The event was designed to encourage thoughtful dialogue that examines both the potential benefits of AI and its environmental impacts, rather than presenting the technology as entirely positive or entirely negative. The symposium featured three speakers addressing different dimensions of the challenge. Dr. Dania Tamayo-Vera from UPEI's School of Mathematical and Computational Sciences discussed how AI can help address complex environmental challenges and support sustainable development, including the use of machine learning to improve crop modeling and optimize agricultural strategies in the context of climate change. Dr. Tushar Sharma from Dalhousie University's Faculty of Computer Science explored the environmental costs associated with AI, including water and energy consumption, while discussing ways to make AI technologies more efficient through his research in sustainable AI and software engineering. Dr. Pamela Courtenay-Hall from UPEI's Department of Philosophy examined the social and ethical dimensions of AI development, considering how significant funding directed toward AI technologies can result in unevenly distributed benefits and environmental costs. What Does the Path Forward Look Like? The alignment visible at MWC Barcelona between governments, international organizations, and the private sector signals that momentum is building. Lacina Koné, CEO and Director-General of Smart Africa, framed green AI as a foundational governance priority requiring coordinated action across governments, international organizations, and industry. The frameworks, tools, and examples for resilient and efficient AI now exist, but what is needed is political commitment to embed both dimensions of the green AI agenda into national AI strategies, data center licensing, energy planning, and public procurement before infrastructure choices lock in unsustainable patterns. The key insight emerging from both academic symposiums and international policy forums is that green AI is not a single solution but a comprehensive approach. When AI is designed for the hardest environments first, it scales faster, costs less, and reaches the people who need it most. This principle applies equally to developing nations with limited energy infrastructure and to wealthy nations seeking to reduce their technological carbon footprint.
