  The topic of Climate Change (CC) has received limited attention in NLP
despite its urgency. Activists and policymakers need NLP tools to effectively
process the vast and rapidly growing unstructured textual climate reports into
structured form. To tackle this challenge we introduce two new large-scale
climate questionnaire datasets and use their existing structure to train
self-supervised models. We conduct experiments to show that these models can
learn to generalize to climate disclosures of different organizations types
than seen during training. We then use these models to help align texts from
unstructured climate documents to the semi-structured questionnaires in a human
pilot study. Finally, to support further NLP research in the climate domain we
introduce a benchmark of existing climate text classification datasets to
better evaluate and compare existing models.
