Many countries globally will undergo two major transitions over the next few decades: decarbonization and digitalization. Both transitions require significant technological change and strategic policy guidance. We use methods from statistics and machine learning to inform climate mitigation policy across the energy sector. We also work on climate-related AI policy, as well as governance issues that arise at the intersection of AI and climate action.
Recent publications on AI and climate technology policy
- From counting stations to city-wide estimates: data-driven bicycle volume extrapolation, Environmental Data Science, 2025 (Silke Kaiser, Nadja Klein, Lynn Kaack)
- European building stock characteristics in a common and open database for 200+ million individual buildings, Scientific Data, 2023 (Nikola Milojevic-Dupont, Felix Wagner, Lynn Kaack et al)
- Aligning artificial intelligence with climate change mitigation, Nature Climate Change, 2022 (Lynn Kaack, Priya L. Donti et al)
- Digitalization and the Anthropocene, Annual Review of Environment and Resources, 2022 (Felix Creutzig, Daron Acemoglu, Lynn Kaack et al)
- Tackling Climate Change with Machine Learning, ACM Computing Surveys, 2022 (David Rolnick, Priya L. Donti, Lynn Kaack et al)
- Climate Change and AI - Recommendations for Government Action, Global Partnership on AI Report, 2021 (Peter Clutton-Brock, David Rolnick, Priya L. Donti, Lynn Kaack)
- Machine learning enables global solar-panel detection, Nature, 2021 (Lynn Kaack)
- Digitizing a sustainable future, One Earth, 2021 (Lucia A. Reisch, Lucas Joppa, Lynn Kaack et al)
- Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals, Heinrich Böll Stiftung Ecology, 2020 (Lynn Kaack, Priya Donti, Emma Strubell, David Rolnick)
- Learning from urban form to predict building heights, PLOS One, 2020 (Nikola Milojevic-Dupont, Nicolai Hans, Lynn Kaack et al)
