‘I study how unstructured, ‘messy’ data, especially text, can be transformed into measurable economic information. Using modern language models, I build methods that identify and quantify firms’ expectations and actions concerning climate risks and opportunities. I then integrate these text-derived indicators into traditional financial and asset-pricing models. My aim is to capture forward-looking climate risk signals that conventional datasets often overlook, and to understand how these risks shape financial markets and financing conditions.

Economic implications

I have a background in both AI and econometrics, and I am fascinated by how advances in language models open entirely new ways of measuring economic concepts. It’s an exciting intersection! The climate transition has economic implications that traditional data have difficulty capturing. Modern AI-based methods help us to turn the vast amount of ‘messy’ information into actionable insights for policymakers.

Support for decision-making

I want to make climate risk visible and measurable in ways that genuinely support decision-making and policies. My research aims to build tools that turn the enormous flow of unstructured data into insight. That way, researchers, policymakers, and investors can better anticipate how the climate transition shapes our economies and financial systems.’