Identifying Climate Targets in National Laws and Policies using Machine Learning

TL;DR

This work addresses the challenge of scalable extraction of national climate targets from laws and policies by developing a paragraph-level, multi-label classifier based on ClimateBERT. It builds and annotates a dataset from the Climate Policy Radar corpus and UNFCCC submissions, achieving an overall F1 of 0.849 across Net Zero, Emissions Reductions, and Other targets. The authors examine biases and translation effects, and apply the model to produce a large, labeled target dataset (24,583 mentions) with topic analysis revealing sector-focused patterns in 'Other' targets. The approach enables automated, scalable data collection for climate policy databases and supports cross-jurisdictional analysis of ambition and implementation gaps, with release of the model and dataset to the research community.

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.