  In this study, we propose a methodology to extract, index, and visualize
``climate change narratives'' (stories about the connection between causal and
consequential events related to climate change). We use two natural language
processing methods, BERT (Bidirectional Encoder Representations from
Transformers) and causal extraction, to textually analyze newspaper articles on
climate change to extract ``climate change narratives.'' The novelty of the
methodology could extract and quantify the causal relationships assumed by the
newspaper's writers. Looking at the extracted climate change narratives over
time, we find that since 2018, an increasing number of narratives suggest the
impact of the development of climate change policy discussion and the
implementation of climate change-related policies on corporate behaviors,
macroeconomics, and price dynamics. We also observed the recent emergence of
narratives focusing on the linkages between climate change-related policies and
monetary policy. Furthermore, there is a growing awareness of the negative
impacts of natural disasters (e.g., abnormal weather and severe floods) related
to climate change on economic activities, and this issue might be perceived as
a new challenge for companies and governments. The methodology of this study is
expected to be applied to a wide range of fields, as it can analyze causal
relationships among various economic topics, including analysis of inflation
expectation or monetary policy communication strategy.
