@inproceedings{troost-etal-2025-biodiversity,
    title = "Biodiversity ambition analysis with Large Language Models",
    author = "Troost, Stefan  and
      Immerzeel, Roos  and
      Krueger, Christoph",
    editor = "Dutia, Kalyan  and
      Henderson, Peter  and
      Leippold, Markus  and
      Manning, Christoper  and
      Morio, Gaku  and
      Muccione, Veruska  and
      Ni, Jingwei  and
      Schimanski, Tobias  and
      Stammbach, Dominik  and
      Singh, Alok  and
      Su, Alba (Ruiran)  and
      A. Vaghefi, Saeid",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.climatenlp-1.7/",
    doi = "10.18653/v1/2025.climatenlp-1.7",
    pages = "99--112",
    ISBN = "979-8-89176-259-6",
    abstract = "The Kunming-Montreal Global Biodiversity Framework (GBF) has 23 action-oriented global targets for urgent action over the decade to 2030. Parties committing themselves to the targets set by the GBF are required to share their national targets and biodiversity plans. In a case study on the GBF target to reduce pollution risks, we analyze the commitments of 110 different Parties, in 6 different languages. Obtaining satisfactory results for this target, we argue that using Generative AI can be very helpful under certain conditions, and it is a relatively small step to scale up such an analysis for other GBF targets."
}