@article{MinxCreutzigAgostonetal., author = {Minx, Jan C. and Creutzig, Felix and Agoston, Peter and al., et.}, title = {Urban infrastructure choices structure climate solutions}, series = {Nature Climate Change}, volume = {6}, journal = {Nature Climate Change}, issn = {1758-678X}, doi = {10.1038/nclimate3169}, pages = {1054 -- 1056}, abstract = {Cities are becoming increasingly important in combatting climate change, but their overall role in global solution pathways remains unclear. Here we suggest structuring urban climate solutions along the use of existing and newly built infrastructures, providing estimates of the mitigation potential.}, language = {en} } @techreport{KhannaBaiocchiCallaghanetal., type = {Working Paper}, author = {Khanna, Tarun and Baiocchi, Giovanni and Callaghan, Max W. and Creutzig, Felix and Bogdan Guias, Horia and Haddaway, Neal and Hirth, Lion and Javaid, Aneeque and Koch, Nicolas and Laukemper, Sonja and Loeschel, Andreas and Del Mar Zamora, Maria and Minx, Jan C.}, title = {Reducing carbon emissions of households through monetary incentives and behavioral interventions: a meta-analysis}, doi = {10.21203/rs.3.rs-124386/v1}, abstract = {Despite the importance of evaluating all mitigation options so as to inform policy decisions addressing climate change, a systematic analysis of household-scale interventions to reduce carbon emissions is missing. Here, we address this gap through a state-of-the-art machine-learning assisted meta-analysis to comparatively assess the effectiveness of a range of monetary and behavioral interventions in energy demand of residential buildings. We identify 122 studies and extract 360 effect sizes representing trials on 1.2 million households in 25 countries. We find that all the studied interventions reduce energy consumption of households. Our meta-regression evidences that monetary incentives are on an average more effective than behavioral interventions, but deploying the right combinations of interventions together can increase overall effectiveness. We estimate global cumulative emissions reduction of 8.64 Gt CO2 by 2040, though deploying the most effective packages and interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation with energy demand reductions and realizing substantial co-benefits. }, language = {de} } @techreport{HintzMilojevicDupontCreutzigetal., type = {Working Paper}, author = {Hintz, Marie Josefine and Milojevic-Dupont, Nikola and Creutzig, Felix and Kaack, Lynn}, title = {A systematic map of machine learning in urban climate change mitigation}, publisher = {Research Square}, doi = {10.21203/rs.3.rs-4242075/v1}, abstract = {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.}, language = {en} } @article{HintzMilojevicDupontCreutzigetal., author = {Hintz, Marie Josefine and Milojevic-Dupont, Nikola and Creutzig, Felix and Repke, Tim and Kaack, Lynn H.}, title = {A systematic map of machine learning for urban climate change mitigation}, series = {Nature Cities}, journal = {Nature Cities}, publisher = {Springer Science and Business Media LLC}, doi = {10.1038/s44284-025-00328-5}, abstract = {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.}, language = {en} } @article{KaackDontiStrubelletal., author = {Kaack, Lynn and Donti, Priya L. and Strubell, Emma and Kamiya, George and Creutzig, Felix and Rolnick, David}, title = {Aligning artificial intelligence with climate change mitigation}, series = {Nature Climate Change}, volume = {12}, journal = {Nature Climate Change}, doi = {10.1038/s41558-022-01377-7}, pages = {518 -- 527}, abstract = {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.}, language = {en} } @article{KaackRolnickDontietal., author = {Kaack, Lynn and Rolnick, David and Donti, Priya L. and Kochanski, Kelly and Lacoste, Alexandre and Sankaran, Kris and Ross, Andrew S. and Milojevic-Dupont, Nikola and Jaques, Natasha and Waldman-Brown, Anna and Luccioni, Alexandra S. and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, Karthik and Kording, Konrad P. and Gomes, Carla P. and Ng, Andrew Y. and Hassabis, Demis and Platt, John C. and Creutzig, Felix and Chayes, Jennifer and Bengio, Yoshua}, title = {Tackling Climate Change with Machine Learning}, series = {ACM Computing Surveys}, volume = {55}, journal = {ACM Computing Surveys}, number = {2}, doi = {10.1145/3485128}, pages = {1 -- 96}, abstract = {Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.}, language = {en} } @article{HintzGrossCreutzigetal., author = {Hintz, Marie Josefine and Gross, Milena and Creutzig, Felix and Kaack, Lynn H.}, title = {Practical implementation of artificial intelligence for climate change mitigation in cities - priorities, collaborations and challenges}, series = {Energy Research \& Social Science}, volume = {131}, journal = {Energy Research \& Social Science}, publisher = {Elsevier BV}, doi = {10.1016/j.erss.2025.104498}, abstract = {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.}, language = {en} } @article{MilojevicDupontHansKaacketal., author = {Milojevic-Dupont, Nikola and Hans, Nicolai and Kaack, Lynn and Zumwald, Marius and Andrieux, Fran{\c{c}}ois and de Barros Soares, Daniel and Lohrey, Steffen and Pichler, Peter-Paul and Creutzig, Felix}, title = {Learning from urban form to predict building heights}, series = {Plos one}, volume = {15}, journal = {Plos one}, number = {12}, doi = {10.1371/journal.pone.0242010}, abstract = {Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.}, language = {en} } @article{KhannaBaiocchiCallaghanetal., author = {Khanna, Tarun and Baiocchi, Giovanni and Callaghan, Max and Creutzig, Felix and Guias, Horia and Haddaway, Neal R. and Hirth, Lion and Javaid, Aneeque and Koch, Nicolas and Laukemper, Sonja and L{\"o}schel, Andreas and del Mar Zamora Dominguez, Maria and Minx, Jan C.}, title = {A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings}, series = {Nature Energy}, volume = {6}, journal = {Nature Energy}, doi = {10.1038/s41560-021-00866-x}, pages = {925 -- 932}, abstract = {Despite the importance of evaluating all mitigation options to inform policy decisions addressing climate change, a comprehensive analysis of household-scale interventions and their emissions reduction potential is missing. Here, we address this gap for interventions aimed at changing individual households' use of existing equipment, such as monetary incentives or feedback. We have performed a machine learning-assisted systematic review and meta-analysis to comparatively assess the effectiveness of these interventions in reducing energy demand in residential buildings. We extracted 360 individual effect sizes from 122 studies representing trials in 25 countries. Our meta-regression confirms that both monetary and non-monetary interventions reduce the energy consumption of households, but monetary incentives, of the sizes reported in the literature, tend to show on average a more pronounced effect. Deploying the right combinations of interventions increases the overall effectiveness. We have estimated a global carbon emissions reduction potential of 0.35 GtCO2 yr-1, although deploying the most effective packages of interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation pathways with energy-demand reductions.}, language = {en} }