From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint

2026-05-06

AI Summary

This study addresses the fragmented understanding of environmental impacts across the full lifecycle of artificial intelligence systems, a gap marked by incomplete phase coverage, inconsistent metrics, and opaque methodologies in current “green AI” research. The authors propose a unified analytical framework encompassing eight stages—from hardware manufacturing and data processing to model training and deployment—and integrate life cycle assessment (LCA), systematic literature review, and multidimensional environmental indicators such as CO₂e emissions. Their analysis reveals that existing studies predominantly focus on training and inference while overlooking critical factors like water consumption, raw material extraction, and embodied carbon. To rectify these omissions, the paper advances a standardized, comparable, and policy-oriented pathway for assessing AI’s environmental footprint, thereby promoting more systematic and rigorous green AI research.

Abstract

The rapid growth in the deployment and scale of modern artificial intelligence (AI) systems has intensified concerns regarding their environmental impacts, yet we still lack a comprehensive view of where and how these impacts arise across the AI life cycle. In order to shed more light on this question, we conduct a structured, comprehensive literature review of scientific papers and technical reports that examine different aspects of AI's environmental footprint. Using an eight-stage life cycle framework, spanning hardware manufacturing, infrastructure construction, data gathering and preprocessing, model experimentation, training, post-training adaptation, deployment, inference, and end-of-life, we systematically map which stages are covered, the metrics reported at each stage, and the methodological choices made. We then draw conclusions about the information we gathered, finding that although life cycle language is increasingly common in discussions of "green" or "sustainable" AI, its definition remains unclear -- while some studies focus solely on model training and inference, others encompass broader measurements such as data collection, infrastructure, and embodied emissions. We also find that reporting practices rely predominantly on CO2e estimates derived from coarse proxies, with limited attention dedicated to water usage, materials manufacturing, and multi-impact life cycle assessment, making it difficult to compare and aggregate true results. Building on these findings, we propose measurement and reporting approaches to support more comprehensive, comparable and policy-relevant assessments of AI's environmental impacts.

Problem

• AI environmental footprint
• life cycle assessment
• sustainable AI
• CO2e estimation
• green AI

Innovation

• life cycle assessment
• AI environmental footprint
• sustainable AI
• CO2e estimation
• green AI