  Growing awareness of the environmental impact of digital technologies has led
to several isolated initiatives to promote sustainable practices. However,
despite these efforts, the environmental footprint of generative AI,
particularly in terms of greenhouse gas emissions and water consumption,
remains considerable. This contribution first presents the components of this
environmental footprint, highlighting the massive CO2 emissions and water
consumption associated with training large language models, thus underlining
the need to rethink learning and inference methods. The paper also explores the
factors and characteristics of models that have an influence on their
environmental footprint and demonstrates the existence of solutions to reduce
it, such as using more efficient processors or optimising the energy
performance of data centres. The potentially harmful effects of AI on the
planet and its ecosystem have made environmental protection one of the founding
principles of AI ethics at international and European levels. However, this
recognition has not yet translated into concrete measures to address it.To
address this issue, our contribution puts forward twelve pragmatic
recommendations for public action to promote sustainable generative AI, in
particular by building a long-term strategy to achieve carbon neutrality for AI
models, encouraging international cooperation to set common standards,
supporting scientific research and developing appropriate legal and regulatory
frameworks.This paper seeks to inform the members of the Interministerial
Committee on Generative AI about the environmental challenges of this
technology by providing a brief review of the scientific literature on the
subject and proposing concrete recommendations of public policy actions to
reconcile technological innovation with the need to protect our environment.
