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Environmental Claim Detection

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arxiv 2209.00507 v4 pith:SE7433YX submitted 2022-09-01 cs.CL

Dominik Stammbach , Nicolas Webersinke , Julia Anna Bingler , Mathias Kraus , Markus Leippold This is my paper · ORCID
classification cs.CL
keywords environmentalclaimsmodelsclaimdatasetdetectdetectionmade
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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.

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Cited by 1 Pith paper

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  1. Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change

    cs.CL 2025-05 conditional novelty 5.0 of 10

    ClimateEval unifies 25 climate-related NLP tasks into one benchmark and shows that open-source LLMs gain from few-shot examples but lag on misinformation and fine-grained entity recognition.

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