As the power and scale of AI systems expand, so too does their environmental footprint. Training large models demands vast computational resources. Data centres consume significant amounts of electricity and water. Supply chains for hardware rely on extractive processes with tangible ecological costs. AI’s environmental implications are no longer peripheral concerns. They are central to the future of both digital innovation and planetary sustainability.
In this context, the emergence of green artificial intelligence represents not a niche subfield but a necessary evolution in how artificial intelligence is conceived, designed, and governed. The future of technological progress cannot be measured solely in terms of model performance, accuracy benchmarks, or market valuation. It must also be measured in terms of environmental impact.
With chapters written by globally recognized experts, Green AI is a forward-looking exploration of how AI can be designed, developed, and deployed with environmental sustainability at its core. It blends technical insight with ethical urgency, outlining both the environmental costs of today’s AI systems, especially in compute and carbon emissions, and practical strategies to reduce those impacts without sacrificing innovation. From energy-efficient algorithms and hardware to lifecycle-wide sustainability frameworks, the book shows how researchers, engineers, and policymakers can balance AI’s transformative potential with ecological responsibility. Real-world case studies, policy analysis, and calls to action make the book a foundational text for anyone interested in making the next wave of AI both powerful and planet friendly.