Master's thesis

Prof. Johanna Arlinghaus is an applied environmental economist with a focus on fiscal and climate policy. She can support you with your thesis in the fields  of price-based environmental and climate policy instruments, e.g. carbon and energy taxes, interactions between tax and environmental policies, financial markets and climate policy, policies to support greenhouse gas removal, and topics related to environmental justice. While she is happy to supervise theses outside of her specific research topics and methods, she looks to supervise theses that apply quantitative research methods. For more information, please see Professor Arlinghaus’s Master’s Thesis Supervision Plan. 

Prof. Christian Flachsland is an interdisciplinary social scientist employing mostly qualitative methods but often engaging in quantitative projects, too. He can support you with writing a thesis on institutional analysis and design of climate policy instrument mixes; political economy of climate and energy policy; governance of climate policy, employing e.g. policy integration frameworks; the role of discourse and narratives in climate and energy policy, in particular in the German Energiewende; international climate policy, in particular EU climate and energy policy, the UNFCCC regime, international flexibility mechanisms; and the science-policy interface. For more information, please see Professor Flachlsand’s Master’s Thesis Supervision Plan. 

Prof. Lion Hirth is an economist by training and primarily works quantitatively. He can support you with your thesis in the field of the economics of renewable energy; energy policy – mostly in the European context; electricity system modeling; electricity markets; power grids; open science - including open data, open source software and re-use of scientific methods. For more information, please see Professor Hirth’s Master’s Thesis Supervision Plan. 

Prof. Lynn Kaack is trained in the fields of engineering and public policy. She can support you with your thesis in fields that focus on applications of data science and machine learning, more theoretical machine learning method development, method development in policy analysis, as well as public policy more generally if it is related to her research interests. Possible topics include the greenhouse gas emissions impacts of machine learning, machine learning approaches for improving bike infrastructure, data-driven approaches for assessing charging infrastructure needs, machine learning methods for public policy, municipal governance models of urban AI for sustainability, social and environmental impacts of mineral deep-sea mining. For more information, please see Professor Kaack’s Master’s Thesis Supervision Plan.