BERT Classification of Paris Agreement Climate Action Plans

Tom Corringham, Daniel Spokoyny

Abstract:

As the volume of text-based information on climate policy increases, natural language processing (NLP) tools can distill information from text
to better inform decision making on climate policy. We investigate how large pretrained transformers based on the BERT architecture classify
sentences on a data set of climate action plans
which countries submitted to the United Nations
following the 2015 Paris Agreement. We use the
document header structure to assign noisy policy relevant labels such as mitigation, adaptation, energy, and land use to text elements. Our models
provide an improvement in out-of-sample classification over simple heuristics though fall short
of the consistency observed between human annotators. We hope to extend this framework to a
wider class of textual climate change data such
as climate legislation and corporate social responsibility filings and build tools to streamline the
extraction of information from these documents
for climate change researchers.