metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license: cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids: []
pretty_name: ClimateTalkDetection
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
  splits:
    - name: train
      num_bytes: 638487
      num_examples: 1300
    - name: test
      num_bytes: 222330
      num_examples: 400
  download_size: 492038
  dataset_size: 860817

Dataset Card for climate_detection

Dataset Description

Homepage: climatebert.ai
Repository:
Paper: papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435
Leaderboard:
Point of Contact: Nicolas Webersinke

Dataset Summary

We introduce an expert-annotated dataset for detecting climate-related paragraphs in corporate disclosures.

Supported Tasks and Leaderboards

The dataset supports a binary classification task of whether a given paragraph is climate-related or not.

Languages

The text in the dataset is in English.

Dataset Structure

Data Instances

{
  'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
  'label': 1
}

Data Fields

text: a paragraph extracted from corporate annual reports and sustainability reports
label: the label (0 -> not climate-related, 1 -> climate-related)

Data Splits

The dataset is split into:

train: 1,300
test: 400

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Our dataset contains climate-related paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.

For more information regarding our sample selection, please refer to the Appendix of our paper (see citation).

Who are the source language producers?

Mainly large listed companies.

Annotations

Annotation process

For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see citation).

Who are the annotators?

The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.

Personal and Sensitive Information

Since our text sources contain public information, no personal and sensitive information should be included.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Julia Anna Bingler
Mathias Kraus
Markus Leippold
Nicolas Webersinke

Licensing Information

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit creativecommons.org/licenses/by-nc-sa/4.0.

If you are interested in commercial use of the dataset, please contact [email protected].

Citation Information

@techreport{bingler2023cheaptalk,
    title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
    author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
    type={Working paper},
    institution={Available at SSRN 3998435},
    year={2023}
}

Contributions

Thanks to @webersni for adding this dataset.