Artificial Intelligence

New research across 11 operational microgrids reveals the conditions under which artificial intelligence delivers both cost savings and carbon reductions — and when it doesn’t. Drawing on two years of operational data from 11 microgrids across Europe, North America, and Australia, the study distinguishes between what the physical infrastructure achieves on its own and what algorithmic intelligence adds. Key finding — design matters more than code: before any AI enters the picture, a simple microgrid operating under basic self-consumption rules already delivers substantial environmental value across all 16 impact categories analyzed (climate change, resource depletion, water use, toxicity, etc.). Local photovoltaic generation displaces carbon-intensive grid imports, battery storage smooths demand, and reduced transmission losses compound the effect. Environmental performance begins with infrastructure design, not digital sophistication. The role of AI: AI amplifies the logic of the systems it serves — for better or worse. It can refine performance but cannot manufacture benefits that the system’s architecture does not already enable. The research challenges common assumptions about digital optimization and reveals a more nuanced picture of when AI genuinely serves both economic and environmental objectives in distributed energy systems.

Cette étude publie les résultats de l’Analyse du Cycle de Vie (ACV) multicritères évaluant l’ensemble des impacts environnementaux et sanitaires de l’intelligence artificielle (IA) à l’échelle mondiale en 2025 et 2030. Périmètre et méthodologie (ACV simplifiée type screening, conforme ISO 14040-44) : Fabriquer un serveur IA Utiliser ce serveur pendant 1 an Utiliser n serveurs pendant 1 an Pour chacune de ces unités fonctionnelles, 16 impacts environnementaux et sanitaires sont quantifiés aux 4 étapes du cycle de vie : fabrication, distribution, utilisation et fin de vie. L’évaluation est à l’échelle mondiale pour les années 2025 et 2030.

Ce rapport étudie une composante clé des infrastructures du numérique, la filière centre de données, et la manière dont elle se construit en interaction avec l’intelligence artificielle, principal déterminant de ses dynamiques aujourd’hui. Celle-ci trace le contour de la manière dont le déploiement généralisé de l’IA infléchit ces dynamiques déjà insoutenables. Il éclaire les pistes à suivre pour réorienter vers la soutenabilité énergie-carbone nos choix technologiques, qui sont de véritables choix politiques, économiques et stratégiques.

Although there are ways to estimate the global power demand of AI systems, it remains challenging to quantify the associated carbon and water footprints. The lack of distinction between AI and non-AI workloads in the environmental reports of data center operators makes it possible to assess the environmental impact of AI workloads only by approximating them through data centers’ general performance metrics. The environmental disclosure of tech companies is, however, often insufficient to determine even the total data center performance of these companies. The carbon footprint of AI systems alone could be between 32.6 and 79.7 million tons of CO2 emissions in 2025, while the water footprint could reach 312.5–764.6 billion liters. The shortcomings in the environmental disclosure of data center operators could be remedied with new policies mandating the disclosure of additional metrics. Because the environmental impact of data centers is growing rapidly, the urgency of transparency in the tech sector is also increasing.