Building the energy policy picture Researchers at ETH Zurich are preparing a training dataset on design characteristics of policies which affect the deployment of renewable energy technologies. This could help in the development of algorithms to automatically identify the design characteristics of more of these policies, which would be an invaluable tool in formulating climate policy, as Dr Sebastian Sewerin and Dr Lynn Kaack explain. A lot of attention and financial resources have been focused on the development of renewable sources of energy over recent years, part of the wider goal of mitigating climate change. Many governments have designed policies to encourage this transition and have committed themselves to ratcheting-up their ambitions in line with the Paris Agreement, yet currently researchers lack a clear picture of what measures have been adopted. “We don’t really have a comprehensive understanding of what countries are doing in terms of their policy interventions,” explains Dr Sebastian Sewerin, a senior researcher and lecturer in the Energy Politics Group at ETH Zurich. This is an issue Dr Sewerin and his colleagues in a new interdisciplinary research project based at ETH Zurich are working to address. “Each country has hundreds of policies that are potentially relevant to the deployment Uncovering policy designs: A training dataset for future automated text analysis Sebastian Sewerin & Lynn Kaack, Energy Politics Group, Swiss Federal Institute of Technology (ETH) Zurich, Haldeneggsteig 4, 8092 Zürich, Switzerland. T: +41 44 632 47 22 E: sebastian.sewerin@gess.ethz.ch E: lynn.kaack@gess.ethz.ch W: https://epg.ethz.ch/
Sebastian Sewerin is a Senior Researcher and Lecturer at ETH Zürich’s Energy Politics Group. He is interested in policy design and in assessing long-term policy change in the energy sector. His research brings together theories and concepts from political science and innovation studies to understand the co-evolution of policy and technology. Lynn Kaack is a Postdoctoral Researcher at ETH Zürich’s Energy Politics Group, and a chair of the organization Climate Change AI. Her research applies methods from statistics and machine learning to inform climate mitigation policy. She obtained a PhD in Engineering and Public Policy and a Master’s in Machine Learning from Carnegie Mellon University.
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of renewable energy technologies,” he says. “It’s important to have more data on the design characteristics of all of the relevant policies if we are to understand how effective these policies are in inducing technological change in the energy sector.” His colleague in this project, Dr. Lynn Kaack, emphasizes, “Currently, students in our research group spend weeks annotating texts to create policy data. This doesn’t allow us to go much beyond analysing small selections of policies. With the help of computerized text analysis, we can speed up this process. This project is a first step in that direction.”
Design characteristics that are relevant for a policy’s effectiveness are, for example, an ambitious goal and clear implementation rules. A second set of indicators is more specifically related to the energy technologies themselves. “We’re also interested in how technology-specific certain policies are. Are they targeting renewable energy technology overall? Or are they targeted more towards specific renewable technologies, like solar PV or wind?” explains Dr Sewerin. A variety of policy design options are available to help stimulate the development of renewable energy, and Dr Sewerin believes they all have a
Currently, students in our research group spend weeks annotating texts to create policy data. With the help of computerized text analysis, we can speed up this process. Policy mix The challenge here is to create relevant data from the large number of policies which affect the renewable energy sector. These policies could mean subsidies and pricing strategies designed to support renewable energy projects for example, as well as more small-scale initiatives. “We’re looking at a very broad range of policy instruments, including things like complex feed-in tariffs or simple information campaigns. You will find different types of policies in a policy mix,” outlines Dr Sewerin. The first part of the project’s work centres around building a text corpus of energy policies. “We started with federal renewable energy policies in the US, and also include a selection of EU policies,” continues Dr Sewerin. “The next step then would be to label these individual policies according to the policy design characteristics that we’re interested in and start building up the training dataset.”
role to play in terms of meeting the emissions reduction goals set out in the Paris Climate Agreement of 2016. “The idea is that policies should be well-designed. Unfortunately, political discussions often focus on the perceived merits of policy instrument types, such as carbon pricing. This is not helpful at all, we should be talking about good design,” he stresses. The wider aim in the project is to use the training dataset that has been gathered to develop algorithms that can then pick up these policy design characteristics in other texts. “If a future algorithm is able to pick up on these policy design characteristics, we will have a much better understanding of the differences between countries and over time. This would help us tremendously to identify those policy mixes that are effective and thus keep a chance to limit climate change to 1.5°,” explains Dr Sewerin.
The 21st session of the UN Conference on Climate Change in Paris 2015.
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