NLP Models for Climate Policy Analysis

Explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature using a supervised learning approach and leverage a state of the art Large Language Model (LLM) to classify climate policy documents.

Author(s):

• Daniel Spokoyny, Carnegie Mellon University, dspokoyn@cs.cmu.edu
• Max Callaghan, Mercator Research Institute on Global Commons and Climate - Berlin, callaghan@mcc-berlin.net
• Tobias Schimanski, University of Zurich, tobias.schimanski@df.uzh.ch

Originally presented at Climate Change AI Summer School 2022

Access this tutorial

We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.

Part 1:

Part 2:

Estimated time to execute end-to-end: 15 minutes

Contribute to this tutorial

Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.

Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.

Climate Change AI Tutorials

Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.

License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Spokoyny, D., Callaghan, M, & Schimanski, T. (2024). NLP Models for Climate Policy Analysis [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.12533572

BibTeX

@misc{spokoyny2024nlp,
  title={NLP Models for Climate Policy Analysis},
  author={Spokoyny, Daniel and Callaghan, Max and Schimanski, Tobias},
  year={2024},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.12533572},
  booktitle={Climate Change AI Summer School},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/nlp-policy-analysis}}
}