Mapping the Impact of Foundation Models on the UN Sustainable Development Goals

Understanding how Foundation Models contribute to sustainability-focused science is difficult due to the broad scope of AI applications and the diverse language used across disciplines. In this work, we analyze the use of Foundation Models in scientific publications aligned with the Sustainable Development Goals (SDGs). Starting from a large citation network of 269K papers, we isolate those that actively adopt Foundation Models and classify their relevance to SDGs using zero-shot language model classification. By linking citation intent to research themes and excluding methodological literature, we construct a filtered view of real-world AI applications in sciences relevant to the SDGs. Our findings reveal that Foundation Model usage is heavily concentrated on a few SDGs (most notably SDG 3 and SDG 9, while many are virtually unaddressed), highlighting uneven alignment and suggesting gaps where AI research could be directed to better support global sustainability efforts.