Machine learning to foster climate action - examples from research (EN)
10-01, 14:30–15:30 (Europe/Berlin), feige
Language: English

In this workshop, we discuss with you how machine learning approaches can foster climate action. Computational approaches help to better understand what, when and how climate policies work and how public and political debates enable or hinder their implementation. The workshop first showcases some of our research before engaging in a discussion on how to further develop methods and applications.

In this workshop, we present and discuss ideas and concrete research projects that use machine learning approaches to facilitate ambitious climate policies. There are already various case studies on how these techniques can be used: One the one hand, they help to synthesize knowledge and thus further our understanding of what, when and how climate policies work under which conditions. On the other hand, they allow for the analysis of large text collections capturing public and political debates that might enable or hinder the implementation of ambitious climate policies. The workshop first showcases some of these case studies, including examples from our own work, and then wants to engage with participants in a discussion on how to further develop these methods and come up with impactful applications to foster climate action.

Finn Müller-Hansen does research on how to apply machine learning and bibliometric analysis on the scientific literature and public as well as political discourse about energy transitions.

Researcher at MCC Berlin (PhD candidate) with a focus on empirical evaluations of climate mitigation policies and evidence synthesis

(Source of picture: Matti Hillig)