Project Description
Financial regulators and central banks are increasingly integrating sustainability aspects into their operations. At the same time, regulatory developments aimed at harmonizing company-level disclosures on sustainability issues notwithstanding, significant data gaps remain. One particular challenge is that companies communicate their sustainability information through unstructured reports that contain both numerical and textual data. To make this information amenable to comparative research, GIST applies Natural Language Processing (NLP) and Large Language Models (LLMs) for data extraction. By doing so, the project aims to make methodological contributions to the NLP field and produce substantial insights on companies’ sustainability performance by means of systematically extracting data for established and new indicators.
Project Team
| Name | Organization unit | |
|---|---|---|
| Dimmelmeier, Andreas | a.dimmelmeier@stat.uni-muenchen.de | LMU |
| Beck, Jacob | jacob.beck@stat.uni-muenchen.de | LMU |
| Schierholz, Malte | malte.schierholz@stat.uni-muenchen.de | LMU |
| Kreuter, Frauke | soda@stat.uni-muenchen.de | LMU |
| Kormanyos, Emily (Deutsche Bundesbank) | Deutsche Bundesbank | |
| Fraser, Alex | alexander.fraser@tum.de | TUM |
| Fehr, Maurice | Deutsche Bundesbank | |
| Domenech Burin, Laia | LMU | |
| Reichenbach, Lisa | Deutsche Bundesbank | |
| Oehler, Simon | Deutsche Bundesbank | |
| Sommer, Felicitas | felicitas.sommer@tum.de | TUM |
Publications
- Andreas Dimmelmeier, Hendrik Doll, Malte Schierholz, Emily Kormanyos, Maurice Fehr, Bolei Ma, Jacob Beck, Alexander Fraser, and Frauke Kreuter. 2024. Informing climate risk analysis using textual information - A research agenda. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 12–26, Bangkok, Thailand. Association for Computational Linguistics.