Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

TL;DR

This paper provides a comprehensive framework for defining, assessing, and reducing the environmental impacts of AI across its entire lifecycle, emphasizing standardized measurement and system strategies.

Contribution

It establishes a unified Green AI definition, formalizes a lifecycle model, and develops a calibrated measurement framework for environmental impact assessment.

Findings

Unified Green AI definition distinct from Sustainable AI

Lifecycle model aligned with LCA stages for impact assessment

Calibrated measurement framework enabling reproducible comparisons

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.