Biodiversity ambition analysis with Large Language Models

AbstractThe 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.