README.md

---
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: ClimateSpecificity
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': non-specific
          '1': specific
  splits:
  - name: train
    num_bytes: 492077
    num_examples: 1000
  - name: test
    num_bytes: 174265
    num_examples: 320
  download_size: 373454
  dataset_size: 666342
---

# Dataset Card for climate_specificity

## Dataset Description

- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:[email protected])

### Dataset Summary

We introduce an expert-annotated dataset for classifying the climate-related specificity of climate-related paragraphs in corporate disclosures.

### Supported Tasks and Leaderboards

The dataset supports a binary classification task of whether a given climate-related paragraph is specific 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 climate-related paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> non-specific, 1 -> specific)

### Data Splits

The dataset is split into:
- train: 1,000
- test: 320

## 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](#citation-information)).

#### 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](#citation-information)).

#### 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](https://creativecommons.org/licenses/by-nc-sa/4.0/).

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

### Citation Information

```bibtex
@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](https://github.com/webersni) for adding this dataset.