🤖 AI Summary
Current environmental impact assessments of the AI lifecycle suffer from tool heterogeneity, insufficient coverage of water usage and value-chain stages, and poor cross-study comparability. To address these gaps, this paper proposes a unified operational definition of Green AI and introduces a five-stage lifecycle framework—encompassing hardware manufacturing, model development, training, deployment, and reuse. It explicitly distinguishes Green AI (energy-efficiency–focused) from Sustainable AI (holistically sustainable across environmental, social, and economic dimensions). Methodologically, the framework integrates Life Cycle Assessment (LCA), PDCA-based governance, edge–cloud co-optimized hardware strategies, and a calibrated multi-level metrics system combining estimation and empirical measurement. The resulting methodology enables vendor-agnostic, reproducible quantification of energy consumption, carbon emissions, water use, and embodied impacts. This significantly enhances transparency and comparability, providing researchers, engineers, and policymakers with an evidence-driven, actionable guide for sustainable AI development and deployment.
📝 Abstract
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.