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 - 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 - Milojevic-Dupont, Nikola A1 - Hans, Nicolai A1 - Kaack, Lynn A1 - Zumwald, Marius A1 - Andrieux, François A1 - de Barros Soares, Daniel A1 - Lohrey, Steffen A1 - Pichler, Peter-Paul A1 - Creutzig, Felix T1 - Learning from urban form to predict building heights JF - Plos one N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1371/journal.pone.0242010 VL - 15 IS - 12 ER -