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 - 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 - 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 - JOUR A1 - Hintz, Marie Josefine A1 - Gross, Milena A1 - Creutzig, Felix A1 - Kaack, Lynn H. T1 - Practical implementation of artificial intelligence for climate change mitigation in cities – priorities, collaborations and challenges JF - Energy Research & Social Science N2 - European cities are increasingly exploring artificial intelligence (AI) applications to achieve their climate goals. Yet, how European city administrations implement AI-for-climate projects remains unclear. To address this gap, we interviewed city staff and urban innovation experts (n=15 interviewees) from Amsterdam, Berlin, Copenhagen, Greater Paris, Helsinki, and Vienna about their motivations, challenges, solutions, and partnerships when deploying AI tools. We found that city administrations were driven by different priorities that extend beyond accelerating climate action, such as improving decision-making, providing better services to residents, reducing costs, and showcasing innovation. We also identified implementation challenges for city administrations, for instance, socio-technical interoperability with existing systems or increasing AI literacy among city staff who work on climate action. We characterized three implementation arrangements through which cities deployed AI, highlighting the plural roles of city administrations in shaping AI deployment. Our analysis indicates that the European Commission, start-ups, researchers, and innovation labs were key partners for implementation, unlike civil society and large technology firms. Our study also reveals substantial challenges even for large, affluent cities, creating doubt about the applicability of AI projects for climate change mitigation in small and medium-sized cities. Y1 - 2026 U6 - https://doi.org/10.1016/j.erss.2025.104498 VL - 131 PB - Elsevier BV ER -