Evaluating the Potential of ChatGPT to Support Climate Risk and Adaptation Assessment

Robert L. Wilby

Abstract

Adaptation to climate change is increasingly urgent, as efforts to curb greenhouse gas emissions falter. Scaling up adaptation finance is essential to address climate risks, but no adaptation inventory covers all sectors and regions globally, especially for vulnerable, information-scarce communities. Large language models (LLMs) like ChatGPT could help bridge these gaps through rapid scoping of climate risks, adaptation options, programme costs and potential maladaptation. This paper uses structured conversations with ChatGPT to explore adaptations to climate hazards in the United Kingdom (for a national perspective), Bangladesh (for an education sector) and Ghana (for vulnerable communities). Queries were run multiple times to test consistency of outputs and contextual awareness. Early results are promising when compared with published information and expert insight. Nonetheless, practical steps can be taken for more effective use of LLMs, and these are captured in a checklist for users. Further research is needed to compare ChatGPT with other LLMs in giving reliable, domain-specific information about climate risks and priority adaptations.

Relevance to scouting

Scouts around the world have actively engaged in climate action through initiatives like the World Scouting Environment Programme and Earth Tribe, which focus on sustainability, climate change education, and community resilience. This article is relevant to Scouting because it explores how tools like ChatGPT can support climate adaptation by identifying local risks and solutions, especially in underserved areas. Scouts working on climate-related badges or community projects can use such tools to better understand environmental challenges and plan effective responses.