Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts
It addresses environmental sustainability challenges in AI for researchers, practitioners, and policymakers, offering a comprehensive framework to improve transparency and reduce impacts, but it is incremental as it builds on existing lifecycle and measurement concepts.
This article tackles the problem of environmental burdens across the AI lifecycle by establishing a unified definition of Green AI, formalizing a five-phase lifecycle model, and defining a calibrated measurement framework to enable reproducible comparisons. It provides actionable guidance for reducing energy, carbon, water, and embodied impacts through systematic strategies and governance.
Across the Artificial Intelligence (AI) lifecycle - from hardware to development, deployment, and reuse - burdens span energy, carbon, water, and embodied impacts. Cloud provider tools improve transparency but remain heterogeneous and often omit water and value chain effects, limiting comparability and reproducibility. Addressing these multi dimensional burdens requires a lifecycle approach linking phase explicit mapping with system levers (hardware, placement, energy mix, cooling, scheduling) and calibrated measurement across facility, system, device, and workload levels. This article (i) establishes a unified, operational definition of Green AI distinct from Sustainable AI; (ii) formalizes a five phase lifecycle mapped to Life Cycle Assessment (LCA) stages, making energy, carbon, water, and embodied impacts first class; (iii) specifies governance via Plan Do Check Act (PDCA) cycles with decision gateways; (iv) systematizes hardware and system level strategies across the edge cloud continuum to reduce embodied burdens; and (v) defines a calibrated measurement framework combining estimator models with direct metering to enable reproducible, provider agnostic comparisons. Combining definition, lifecycle processes, hardware strategies, and calibrated measurement, this article offers actionable, evidence based guidance for researchers, practitioners, and policymakers.