Profiling and Analyzing Climate Change Statements in IPCC Reports

Ruiqi Li, Paige Reeves, Alasdair Tran, Jing Jiang, Lexing Xie

Keywords: climate science, information extraction, IPCC reports, natural language processing

TL;DR: This paper presents a NLP based method to extract and profile 10,393 climate change statements from the IPCC's Sixth Assessment Reports, focusing on their distribution, uncertainty levels, and related glossary terms, with several case studies.

Abstract: We propose new methods to extract and profile the climate change statements from the Sixth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). We represent the 10,393 statements from the latest IPCC reports (AR6) with associated uncertainty levels and glossary terms. We profile their distributions across different parts of the 6000+ page AR6 reports. We also present a few case studies centered around the glossary term ``wetland'', namely linking related statements across summary sections and chapter content, finding and profiling supporting references, and comparing them with large language models for statement summarization. We believe this work marks an initial step towards in-depth information extraction regarding climate change. It lays the groundwork for more advanced automated analysis of climate-related statements and broader integrative scientific assessments.

Archival Option: The authors of this submission do *not* want it to appear in the archival proceedings.

Submission Number: 25