  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.
