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
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related dataset at
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我们提出了一种从国家法律和政策中提取气候目标的方法，并利用分类器可靠地识别三类目标（“净零”，“减排” 和 “其他”）的文本提及，并调查了模型的偏见和公平性影响，最后通过在全球国家气候法律和政策以及联合国气候变化框架公约提交文件的 Climate Policy Radar 数据集上运行分类器产生的数据集的特征，突出了自动化和可扩展的数据收集对于现有气候政策数据库和进一步研究的潜力，为决策者和研究人员提供了更易获取的重要气候政策要素。

利用机器学习在国家法律和政策中识别气候目标

Identifying Climate Targets in National Laws and Policies using Machine  Learning

The number of standardized policy documents regarding climate policy and
their publication frequency is significantly increasing. The documents are long
and tedious for manual analysis, especially for policy experts, lawmakers, and
citizens who lack access or domain expertise to utilize data analytics tools.
Potential consequences of such a situation include reduced citizen governance
and involvement in climate policies and an overall surge in analytics costs,
rendering less accessibility for the public. In this work, we use a Latent
Dirichlet Allocation-based pipeline for the automatic summarization and
analysis of 10-years of national energy and climate plans (NECPs) for the
period from 2021 to 2030, established by 27 Member States of the European
Union. We focus on analyzing policy framing, the language used to describe
specific issues, to detect essential nuances in the way governments frame their
climate policies and achieve climate goals. The methods leverage topic modeling
and clustering for the comparative analysis of policy documents across
different countries. It allows for easier integration in potential
user-friendly applications for the development of theories and processes of
climate policy. This would further lead to better citizen governance and
engagement over climate policies and public policy research.

本研究使用基于潜在狄利克雷分配的自动摘要和分析管道，对欧盟 27 个成员国在 2021 年至 2030 年期间制定的 10 年期国家能源和气候计划（NECPs）进行分析，旨在分析政策框架以便实现更好的公民治理和气候政策参与。