  Climate change is a far-reaching, global phenomenon that will impact many
aspects of our society, including the global stock market
\cite{dietz2016climate}. In recent years, companies have increasingly been
aiming to both mitigate their environmental impact and adapt to the changing
climate context. This is reported via increasingly exhaustive reports, which
cover many types of climate risks and exposures under the umbrella of
Environmental, Social, and Governance (ESG). However, given this abundance of
data, sustainability analysts are obliged to comb through hundreds of pages of
reports in order to find relevant information. We leveraged recent progress in
Natural Language Processing (NLP) to create a custom model, ClimateQA, which
allows the analysis of financial reports in order to identify climate-relevant
sections based on a question answering approach. We present this tool and the
methodology that we used to develop it in the present article.
