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

TL;DR

This paper reviews the entire AI life cycle to understand and improve how environmental impacts are measured and reported, highlighting gaps and proposing better assessment methods.

Contribution

It provides a structured review of AI's environmental footprint across all life cycle stages and suggests improved measurement and reporting practices.

Findings

Life cycle language in AI is increasing but remains ill-defined.

Most studies focus on model training and inference, neglecting other stages.

Reporting mainly relies on CO2e proxies, with limited coverage of water and materials.

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.