mlsanityData to 14 Oct 2026

Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures

arXiv 2312.17337 ↗First posted 28 Dec 2023cs.CL econ.GN q-fin.ECcited by 1

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Abstract (arXiv)

Nature is an amorphous concept. Yet, it is essential for the planet's well-being to understand how the economy interacts with it. To address the growing demand for information on corporate nature disclosure, we provide datasets and classifiers to detect nature communication by companies. We ground our approach in the guidelines of the Taskforce on Nature-related Financial Disclosures (TNFD). Particularly, we focus on the specific dimensions of water, forest, and biodiversity. For each dimension, we create an expert-annotated dataset with 2,200 text samples and train classifier models. Furthermore, we show that nature communication is more prevalent in hotspot areas and directly effected industries like agriculture and utilities. Our approach is the first to respond to calls to assess corporate nature communication on a large scale.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

6 of 19 references.

References · 6

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  3. 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 90
  4. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  5. 2020ClimaText: A Dataset for Climate Change Topic Detectioncited by 22
  6. 2020Analyzing Sustainability Reports Using Natural Language Processingcited by 18

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