Environmental Impact

Le Baromètre Green IT 2025 présente un état des lieux complet des pratiques numériques responsables dans les organisations françaises. Cette étude analyse les tendances et l’évolution des pratiques Green IT, mesurant les progrès réalisés et identifiant les axes d’amélioration pour un numérique plus durable. Le rapport fournit des indicateurs clés et des recommandations concrètes pour améliorer la maturité des organisations en matière de numérique responsable.

This comprehensive study analyzes the real carbon footprint of training large neural network models, taking into account multiple often-overlooked factors. The research provides a detailed methodology for calculating CO2 emissions and demonstrates how the choice of data center location and timing can significantly impact the environmental cost of AI training. The authors show that thoughtful choices about where and when to train models can reduce CO2 emissions by up to 100x compared to random choices.

This research introduces new methodologies for measuring and reducing the carbon footprint of AI computations across different computing environments. The study presents tools and techniques for accurate carbon impact assessment of AI workloads, considering factors such as hardware efficiency, datacenter location, and time-of-day energy mix. The authors provide practical recommendations for implementing carbon-aware computing practices in AI development and deployment.

This pioneering study examines the carbon footprint of training natural language processing models. The authors quantify the financial and environmental costs of training various NLP models. The study reveals that training a single BERT model can emit as much CO2 as a trans-Atlantic flight, and that the computational costs of NLP models double every 3-4 months. The authors provide concrete recommendations to reduce environmental impact, particularly by prioritizing energy efficiency in model design and using renewable energy sources for training.

The last decade saw the acceleration of new technologies adoption, shaping the digital landscape in terms of speed, quality and connectivity for multimedia contents and communication tools. While many activities have been able to benefit from the numerous innovations (4.0 industry, e-commerce, telecommunications, etc.) to develop, this growth has always been coupled with a significant increase of pressures on the environment and natural resources. The French Agency for Ecological Transition (ADEME) has proposed four scenarios for achieving carbon neutrality by 2050 in its study “Transition 2050, Choisir maintenant, Agir pour le Climat”. They aim to link the technical and economic dimensions with thoughts on the transformations in society that they imply or that they give rise to. This is the context for the study entitled “Assessment of the environmental impact of digital technology in France and prospective analysis”. The present report on the “prospective analysis for 2030 and 2050 and medium- and long-term courses of action”, is a continuation of Task 1 on the “state of play and courses of action” and Task 2 on the “environmental assessment of digital services in France”. Specifically, Task 3 aims to assess the environmental impact of the digital sector in France for the 2030 and 2050 timeframes according to a trend scenario and different scenarios for the mitigation of this impact. Like Task 2, it consists of an evaluation of the impacts using the Life Cycle Assessment (LCA) methodology. This methodology focuses on the three thirds of the digital world: user terminals, networks and data centers. The results are presented at the France-wide scale and are detailed under several levels of analysis in order to get a more acute interpretation and a better comprehension of direct environmental stakes related to digital technologies in France.

This influential paper introduces the concept of Green AI, which encourages AI research that yields better results while consuming less computing power and thus lower environmental impact. The authors contrast Green AI with what they call Red AI: research that seeks to improve accuracy through massive computational power, regardless of the environmental cost. The paper proposes new evaluation criteria for AI research that include computational efficiency alongside accuracy, encouraging more sustainable approaches to AI development.

Ce rapport intermédiaire du Shift Project examine les implications environnementales des technologies d’intelligence artificielle. L’étude analyse la consommation d’énergie, les émissions de carbone et les ressources nécessaires à l’entraînement et au déploiement des modèles d’IA. Le rapport formule des recommandations pour développer et utiliser l’IA en accord avec les objectifs de durabilité écologique et les principes de sobriété numérique.

This paper presents a methodology for accurately measuring the carbon emissions of AI workloads running in cloud environments. The research provides detailed measurements across different cloud providers and regions, showing how carbon intensity can vary significantly based on location and time of day. The authors also release tools and best practices for researchers and practitioners to measure and reduce the carbon footprint of their AI applications.

This paper provides a comprehensive analysis of the environmental impact of AI systems throughout their lifecycle, from development to deployment and maintenance. The authors examine various strategies for reducing the carbon footprint of AI, including efficient model architectures, green computing practices, and renewable energy usage. The research also presents concrete recommendations for developing and deploying AI systems in an environmentally responsible manner.

This comprehensive survey examines the environmental impact of artificial intelligence throughout its lifecycle, from development to deployment and maintenance. The paper provides a systematic analysis of the challenges in making AI more sustainable, including hardware efficiency, algorithm design, and operational practices. The authors identify key opportunities for reducing AI’s environmental footprint and propose a research agenda for sustainable AI development.