Environmental Claim Detection

Stammbach D, Webersinke N, Bingler JA, Kraus M, Leippold M (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Pages Range: 1051-1066

Conference Proceedings Title: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Event location: Toronto CA

URI: https://aclanthology.org/2023.acl-short.91

Open Access Link: https://aclanthology.org/2023.acl-short.91

Abstract

To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.

Authors with CRIS profile

Nicolas Webersinke Lehrstuhl für Betriebswirtschaftslehre, insbesondere Finanzierung und Banken Mathias Kraus Juniorprofessur für Data Analytics

Involved external institutions

University of Zurich / Universität Zürich (UZH) CH Switzerland (CH) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH)

How to cite

APA:

Stammbach, D., Webersinke, N., Bingler, J.A., Kraus, M., & Leippold, M. (2023). Environmental Claim Detection. In Association for Computational Linguistics (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 1051-1066). Toronto, CA.

MLA:

Stammbach, Dominik, et al. "Environmental Claim Detection." Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto Ed. Association for Computational Linguistics, 2023. 1051-1066.

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