TY - JOUR A1 - Kaack, Lynn A1 - Rolnick, David A1 - Reisch, Lucia A. A1 - Joppa, Lucas A1 - Howson, Peter A1 - Gil, Artur A1 - Alevizou, Panayiota A1 - Michaelidou, Nina A1 - Appiah-Campbell, Ruby A1 - Santarius, Tilman A1 - Köhler, Susanne A1 - Pizzol, Massimo A1 - Schweizer, Pia-Johanna A1 - Srinivasan, Dipti A1 - Kaack, Lynn A1 - Donti, Priya L. T1 - Digitizing a sustainable future JF - One Earth N2 - Digital technologies have a crucial role in facilitating transitions toward a sustainable future. Yet there remain challenges to overcome and pitfalls to avoid. This Voices asks: how do we leverage the digital transformation to successfully support a sustainability transition? Y1 - 2021 U6 - https://doi.org/10.1016/j.oneear.2021.05.012 VL - 4 IS - 6 SP - 768 EP - 771 ER - TY - RPRT A1 - Hintz, Marie Josefine A1 - Milojevic-Dupont, Nikola A1 - Creutzig, Felix A1 - Kaack, Lynn T1 - A systematic map of machine learning in urban climate change mitigation N2 - To tackle the climate crisis, cities must reduce greenhouse gas (GHG) emissions rapidly. To aid these efforts, many cities are interested in leveraging artificial intelligence and machine learning (ML). Researchers and practitioners, however, only begin to understand how ML can contribute to achieving climate targets in urban contexts. To provide an overview of application areas, and the potential leverage of ML to reduce GHG emissions, we systematically map research conducted over the past three decades. We identify 1,206 relevant peer-reviewed records, and discover that research involving ML is expanding more rapidly than the literature on urban climate mitigation more broadly. The research focus largely aligns with urban mitigation options that the Intergovernmental Panel on Climate Change assessed as having high impact. We also find that research concentrates on the ML-strong regions Eastern Asia, Europe, and Northern America. This regional focus can influence research agendas, and we observed signs that this can lead to bias regarding which ML applications are pursued in urban climate action. Y1 - 2024 U6 - https://doi.org/10.21203/rs.3.rs-4242075/v1 PB - Research Square ER - TY - JOUR A1 - Chevance, Guillaume A1 - Nieuwenhuijsen, Mark A1 - Braga, Kaue A1 - Clifton, Kelly A1 - Hoadley, Suzanne A1 - Kaack, Lynn H. A1 - Kaiser, Silke K. A1 - Lampkowski, Marcelo A1 - Lupu, Iuliana A1 - Radics, Miklós A1 - Velázquez-Cortés, Daniel A1 - Williams, Sarah A1 - Woodcock, James A1 - Tonne, Cathryn T1 - Data gaps in transport behavior are bottleneck for tracking progress towards healthy sustainable transport in European cities JF - Environmental Research Letters Y1 - 2024 U6 - https://doi.org/10.1088/1748-9326/ad42b3 VL - 19 IS - 5 ER - TY - JOUR A1 - Creutzig, Felix A1 - Acemoglu, Daron A1 - Bai, Xuemei A1 - Edwards, Paul N. A1 - Hintz, Marie Josefine A1 - Kaack, Lynn A1 - Kilkis, Siir A1 - Kunkel, Stefanie A1 - Luers, Amy A1 - Milojevic-Dupont, Nikola A1 - Rejeski, Dave A1 - Renn, Jürgen A1 - Rolnick, David A1 - Rosol, Christoph A1 - Russ, Daniela A1 - Turnbull, Thomas A1 - Verdolini, Elena A1 - Wagner, Felix A1 - Wilson, Charlie A1 - Zekar, Aicha A1 - Zumwald, Marius T1 - Digitalization and the Anthropocene JF - Annual Review of Environment and Resources N2 - Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: (a) planetary boundaries and stability, (b) equity within and between countries, and (c) human agency and governance, mediated via (i) increasing resource efficiency, (ii) accelerating consumption and scale effects, (iii) expanding political and economic control, and (iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability. Y1 - 2022 U6 - https://doi.org/10.1146/annurev-environ-120920-100056 VL - 47 SP - 479 EP - 509 ER - TY - RPRT A1 - Clutton-Brock, Peter A1 - Rolnick, David A1 - Donti, Priya L. A1 - Kaack, Lynn T1 - Climate Change and AI. Recommendations for Government Action BT - Global Partnership on AI Report. In collaboration with Climate Change AI and the Centre for AI & Climate N2 - The report, Climate Change and AI: Recommendations for Government, highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses. Y1 - 2021 UR - https://www.gpai.ai/projects/responsible-ai/environment/climate-change-and-ai.pdf ER - TY - JOUR A1 - Hintz, Marie Josefine A1 - Milojevic-Dupont, Nikola A1 - Creutzig, Felix A1 - Repke, Tim A1 - Kaack, Lynn H. T1 - A systematic map of machine learning for urban climate change mitigation JF - Nature Cities N2 - Many cities are interested in leveraging artificial intelligence and machine learning (ML) to help urban climate change mitigation (UCCM). Researchers and practitioners, however, are only beginning to understand how ML can contribute to achieving climate targets in cities. Here, we systematically map 2,300 peer-reviewed articles published between 1994 and 2024 that explore the use of ML in UCCM. We find that, despite fast growth in this research area, the use of generative artificial intelligence and large language models remains negligible, which contrasts to their increasing adoption in other urban domains. Among 40 identified application areas, ML research focuses predominantly on high-impact mitigation options denoted by the Intergovernmental Panel on Climate Change. This trend may partly be driven by data availability and commercial interest, which risk perpetuating geographic inequities and diverting efforts toward less impactful mitigation options. We therefore offer recommendations to guide the impactful deployment of ML solutions in UCCM. Y1 - 2025 U6 - https://doi.org/10.1038/s44284-025-00328-5 PB - Springer Science and Business Media LLC ER - TY - RPRT A1 - Kaiser, Silke K. A1 - Rodrigues, Filipe A1 - Lima Azevedo, Carlos A1 - Kaack, Lynn H. T1 - Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume N2 - Reliable street-level traffic volume data, covering multiple modes of transportation, helps urban planning by informing decisions on infrastructure improvements, traffic management, and public transportation. Yet, traffic sensors measuring traffic volume are typically scarcely located, due to their high deployment and maintenance costs. To address this, interpolation methods can estimate traffic volumes at unobserved locations using available data. Graph Neural Networks have shown strong performance in traffic volume forecasting, particularly on highways and major arterial networks. Applying them to urban settings, however, presents unique challenges: urban networks exhibit greater structural diversity, traffic volumes are highly overdispersed with many zeros, the best way to account for spatial dependencies remains unclear, and sensor coverage is often very sparse. We introduce the Graph Neural Network for Urban Interpolation (GNNUI), a novel urban traffic volume estimation approach. GNNUI employs a masking algorithm to learn interpolation, integrates node features to capture functional roles, and uses a loss function tailored to zero-inflated traffic distributions. In addition to the model, we introduce two new open, large-scale urban traffic volume benchmarks, covering different transportation modes: Strava cycling data from Berlin and New York City taxi data. GNNUI outperforms recent, some graph-based, interpolation methods across metrics (MAE, RMSE, true-zero rate, Kullback-Leibler divergence) and remains robust from 90% to 1% sensor coverage. On Strava, for instance, MAE rises only from 7.1 to 10.5, on Taxi from 23.0 to 40.4, demonstrating strong performance under extreme data scarcity, common in real-world urban settings. We also examine how graph connectivity choices influence model accuracy. Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2505.06292 PB - arXiv ER - TY - JOUR A1 - Milojevic-Dupont, Nikola A1 - Wagner, Felix A1 - Nachtigall, Florian A1 - Hu, Jiawei A1 - Brüser, Geza Boi A1 - Zumwald, Marius A1 - Biljecki, Filip A1 - Heeren, Niko A1 - Kaack, Lynn A1 - Pichler, Peter-Paul A1 - Creutzig, Felix T1 - EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings JF - Scientific Data N2 - Building stock management is becoming a global societal and political issue, inter alia because of growing sustainability concerns. Comprehensive and openly accessible building stock data can enable impactful research exploring the most effective policy options. In Europe, efforts from citizen and governments generated numerous relevant datasets but these are fragmented and heterogeneous, thus hindering their usability. Here, we present EUBUCCO v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland. Three main attributes – building height, construction year and type – are included for respectively 73%, 24% and 46% of the buildings. We identify, collect and harmonize 50 open government datasets and OpenStreetMap, and perform extensive validation analyses to assess the quality, consistency and completeness of the data in every country. EUBUCCO v0.1 provides the basis for high-resolution urban sustainability studies across scales – continental, comparative or local studies – using a centralized source and is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments. Y1 - 2023 U6 - https://doi.org/10.1038/s41597-023-02040-2 VL - 10 ER - TY - JOUR A1 - Kaack, Lynn A1 - Donti, Priya L. A1 - Strubell, Emma A1 - Kamiya, George A1 - Creutzig, Felix A1 - Rolnick, David T1 - Aligning artificial intelligence with climate change mitigation JF - Nature Climate Change N2 - There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. Y1 - 2022 U6 - https://doi.org/10.1038/s41558-022-01377-7 VL - 12 SP - 518 EP - 527 ER - TY - JOUR A1 - Kaack, Lynn T1 - Machine learning enables global solar-panel detection JF - Nature N2 - An inventory of the world’s solar-panel installations has been produced with the help of machine learning, revealing many more than had previously been recorded. The results will inform efforts to meet global targets for solar-energy use. Y1 - 2021 U6 - https://doi.org/10.1038/d41586-021-02875-y VL - 598 IS - 7882 SP - 567 EP - 568 ER -