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Identifying Climate Targets in National Laws and Policies using Machine Learning (2404.02822v2)

Published 3 Apr 2024 in cs.CY, cs.CL, and cs.LG

Abstract: Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, which limits policymakers' and researchers' ability to (1) assess private and public sector alignment with global goals and (2) inform policy decisions. In this paper we present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target ('Net Zero', 'Reduction' and 'Other' (e.g. renewable energy targets)) and train a classifier to reliably identify them in text. We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features. Finally, we investigate the characteristics of the dataset produced by running this classifier on the Climate Policy Radar (CPR) dataset of global national climate laws and policies and UNFCCC submissions, highlighting the potential of automated and scalable data collection for existing climate policy databases and supporting further research. Our work represents a significant upgrade in the accessibility of these key climate policy elements for policymakers and researchers. We publish our model at https://huggingface.co/ClimatePolicyRadar/national-climate-targets and related dataset at https://huggingface.co/datasets/ClimatePolicyRadar/national-climate-targets.

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References (29)
  1. Defining ‘science-based targets’. 8(7), July 2021. doi: 10.1093/nsr/nwaa186. URL https://doi.org/10.1093/nsr/nwaa186.
  2. Machine learning for research on climate change adaptation policy integration: an exploratory uk case study. Regional Environmental Change, 20(3):85, 2020.
  3. Cheap talk and cherry-picking: What climatebert has to say on corporate climate risk disclosures. Finance Research Letters, 47:102776, 2022.
  4. Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature climate change, 11(11):966–972, 2021.
  5. Climate Action Tracker. Climate action tracker. https://climateactiontracker.org, 2021.
  6. Climate Policy Radar. Climate policy radar database. Online, 2024. URL https://app.climatepolicyradar.org.
  7. Climate change sentiment on twitter: An unsolicited public opinion poll. PLOS ONE, 10(8):1–18, 08 2015. doi: 10.1371/journal.pone.0136092. URL https://doi.org/10.1371/journal.pone.0136092.
  8. Taking stock of the implementation gap in climate policy. Nature Climate Change, 13(8):752–755, August 2023. ISSN 1758-6798. doi: 10.1038/s41558-023-01755-9. URL https://doi.org/10.1038/s41558-023-01755-9.
  9. Google. Google cloud translate api. https://cloud.google.com/translate. Accessed: January 24, 2024.
  10. Grantham Research Institute at the London School of Economics; Climate Policy Radar. Climate change laws of the world. Online, 2023. URL https://climate-laws.org. Sourced primarily from the Grantham Research Institute at the London School of Economics. Made available under the Creative Commons CC-BY licence.
  11. Maarten Grootendorst. Keybert: Minimal keyword extraction with bert., 2020. URL https://doi.org/10.5281/zenodo.4461265.
  12. Maarten Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794, 2022.
  13. H. Haarstad. Do Climate Targets Matter? The Accountability of Target-setting in Urban Climate and Energy Policy. Palgrave Pivot, Cham, 2020. doi: 10.1007/978-3-030-26891-6˙6. URL https://doi.org/10.1007/978-3-030-26891-6_6.
  14. IPCC. Summary for policymakers. pp.  1–34, 2023. doi: 10.59327/IPCC/AR6-9789291691647.001.
  15. Ask bert: How regulatory disclosure of transition and physical climate risks affects the cds term structure. Swiss Finance Institute Research Paper, (21-19), 2020.
  16. Net zero tracker, 2023.
  17. Roberta: A robustly optimized BERT pretraining approach. CoRR, abs/1907.11692, 2019. URL http://arxiv.org/abs/1907.11692.
  18. Analyzing sustainability reports using natural language processing. arXiv preprint arXiv:2011.08073, 2020.
  19. spaCy, October 2023. URL https://doi.org/10.5281/zenodo.10009823.
  20. Aligning national and international climate targets, 2018. Available at www.lse.ac.uk/GranthamInstitute/publications.
  21. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108, 2019.
  22. Climatebert-netzero: Detecting and assessing net zero and reduction targets. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp.  15745–15756, 2023.
  23. Progress on climate action: a multilingual machine learning analysis of the global stocktake. Climatic Change, 176(12):173, 2023.
  24. Towards answering climate questionnaires from unstructured climate reports. arXiv e-prints, pp.  arXiv–2301, 2023.
  25. A dataset for detecting real-world environmental claims. Center for Law & Economics Working Paper Series, 2022(07), 2022.
  26. The climate change debate and natural language processing. In Proceedings of the 1st Workshop on NLP for Positive Impact, pp.  8–18, 2021.
  27. Attention is all you need. CoRR, abs/1706.03762, 2017. URL http://arxiv.org/abs/1706.03762.
  28. ClimateBERT: A Pretrained Language Model for Climate-Related Text. 2022. doi: https://doi.org/10.48550/arXiv.2212.13631.
  29. World Resources Institute. Climate watch. https://www.climatewatchdata.org, 2022.

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