Mapping and quantifying engagement levels of governments in IPCC approval meetings by topic from text

We introduce a text-as-data approach from natural language processing that allows mapping and quantifying topic-level variation in engagement levels of member governments in intergovernmental meetings. Building on research that characterizes the Intergovernmental Panel on Climate Change (IPCC) approval process as a key site for legitimating knowledge input into negotiations under the United Nations Framework Convention on Climate Change (UNFCCC), we illustrate this method for two key topics from the IPCC’s Sixth Assessment Report (AR6) that became identified policy approaches for national mitigation and adaptation plans in the outcome of the first Global Stocktake of the Paris Agreement: “carbon management” and “nature-based solutions.” Our analysis provides robust evidence that governments’ engagement levels vary by topic, which allows us to nuance existing findings that IPCC meetings are dominated by a few large country delegations across all agenda items. Our proposed method can be applied to other intergovernmental meetings and complements qualitative research methods.