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, revised annually for 2023, 2024, and 2026.
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: 2 hours
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.
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.
Usage of this tutorial is subject to the MIT License.
Spokoyny, D., Callaghan, M, & Schimanski, T. (2026). NLP Models for Climate Policy Analysis [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.21446699
@misc{spokoyny2026nlp,
title={NLP Models for Climate Policy Analysis},
author={Spokoyny, Daniel and Callaghan, Max and Schimanski, Tobias},
year={2026},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.21446699},
booktitle={Climate Change AI Summer School},
howpublished={\url{https://github.com/climatechange-ai-tutorials/nlp-policy-analysis}}
}