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

Author

Listed:
  • Tobias Schimanski

    (University of Zurich)

  • Chiara Colesanti Senni

    (University of Zurich - Department of Finance)

  • Glen Gostlow

    (University of Zurich - Department Finance)

  • Jingwei Ni

    (ETH Zurich)

  • Tingyu Yu

    (University of Zurich - Department Finance)

  • Markus Leippold

    (University of Zurich; Swiss Finance Institute)

Abstract

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.

Suggested Citation

  • Tobias Schimanski & Chiara Colesanti Senni & Glen Gostlow & Jingwei Ni & Tingyu Yu & Markus Leippold, 2024. "Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures," Swiss Finance Institute Research Paper Series 24-95, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2495
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    Keywords

    Nature-related risks; TNFD; Natural Language Processing; Disclosure;
    All these keywords.

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