@incollection{KaackChenGrangerMorgan, author = {Kaack, Lynn and Chen, George and Granger Morgan, M}, title = {Truck traffic monitoring with satellite images}, series = {Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies}, booktitle = {Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies}, publisher = {Association for Computing Machinery}, address = {New York}, doi = {10.1145/3314344}, publisher = {Hertie School}, pages = {155 -- 164}, abstract = {The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low-and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.}, language = {de} } @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} } @unpublished{KaackFriederichLuccionietal., author = {Kaack, Lynn and Friederich, David and Luccioni, Alexandra and Steffen, Bjarne}, title = {Automated Identification of Climate Risk Disclosures in Annual Corporate Reports}, abstract = {It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure.}, language = {en} } @techreport{KaackDontiStrubelletal., type = {Working Paper}, author = {Kaack, Lynn and Donti, Priya and Strubell, Emma and Rolnick, David}, title = {Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals}, abstract = {With the increasing deployment of artificial intelligence (AI) technologies across society, it is important to understand in which ways AI may accelerate or impede climate progress, and how various stakeholders can guide those developments. On the one hand, AI can facilitate climate change mitigation and adaptation strategies within a variety of sectors, such as energy, manufacturing, agriculture, forestry, and disaster management. On the other hand, AI can also contribute to rising greenhouse gas emissions through applications that benefit high-emitting sectors or drive increases in consumer demand, as well as via energy use associated with AI itself. Here, we provide a brief overview of AI's multi-faceted relationship with climate change, and recommend policy levers to align the use of AI with climate change mitigation and adaptation pathways.}, language = {en} } @techreport{SchmidCoroamăDumbravăetal., type = {Working Paper}, author = {Schmid, Nicolas and Coroamă, Vlad C. and Dumbravă, Oana and Eichler, Martin and Reisser, Moritz and Kaack, Lynn H. and Axenbeck, Janna and J{\"u}rg, F{\"u}ssler}, title = {Carbon leakage in AI-driven data center growth? An assessment of drivers and barriers to the localization of data center operations and investments with respect to carbon pricing policies}, number = {68/2025}, publisher = {German Environment Agency}, pages = {64}, abstract = {This study offers a comprehensive first assessment of the global carbon leakage potential associated with AI-driven data center operation and investment. The focus is on carbon leakage from costs imposed by emission trading systems (ETS) on data center electricity consumption. The study estimates AI's current and near-future electricity consumption and evaluates the technological feasibility as well as plausibility of shifting compute loads. Additionally, it maps global compute capacity against carbon intensities and the presence of ETS.}, language = {en} } @article{KaiserKleinKaack, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn H.}, title = {From counting stations to city-wide estimates: data-driven bicycle volume extrapolation}, series = {Environmental Data Science}, volume = {4}, journal = {Environmental Data Science}, publisher = {Cambridge University Press (CUP)}, issn = {2634-4602}, doi = {10.1017/eds.2025.5}, abstract = {Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Access to street-level data on bicycle traffic would assist cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate street-level bicycle counts for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, holiday data, and centrality measures. Our analysis reveals that crowdsourced cycling flow data from Strava in the area around the point of interest are most important for the prediction. To provide guidance for future data collection, we analyze how including short-term counts at predicted locations enhances model performance. By incorporating just 10 days of sample counts for each predicted location, we are able to almost halve the error and greatly reduce the variability in performance among predicted locations.}, language = {en} } @techreport{KaiserKleinKaack, type = {Working Paper}, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn}, title = {From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation}, doi = {10.48550/arXiv.2406.18454}, pages = {30}, abstract = {Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.}, language = {en} } @article{KaiserRodriguesAzevedoetal., author = {Kaiser, Silke K. and Rodrigues, Filipe and Azevedo, Carlos Lima and Kaack, Lynn H.}, title = {Spatio-temporal graph neural network for urban spaces: Interpolating citywide traffic volume}, series = {Expert Systems with Applications}, volume = {316}, journal = {Expert Systems with Applications}, publisher = {Elsevier BV}, doi = {10.1016/j.eswa.2026.131823}, abstract = {Graph Neural Networks have shown strong performance in traffic volume forecasting, particularly on highways and major arterial networks. Applying these models to urban street networks, however, presents unique challenges: urban networks are structurally more diverse, traffic volumes are highly overdispersed with many zeros, spatial dependencies are complex, and sensor coverage is often very sparse. To address these challenges, we introduce the Graph Neural Network for Urban Interpolation (GNNUI), a model designed specifically for citywide traffic volume interpolation. GNNUI employs a designated masking strategy to learn interpolation, integrates node features to capture the different functional roles across the street network, and uses a loss function tailored to zero-inflated traffic distributions. We evaluate GNNUI on two newly constructed, large-scale urban traffic volume benchmarks, covering different transportation modes: Strava cycling data from Berlin and New York City taxi data. Across multiple evaluation metrics, GNNUI outperforms both the state-of-the-art graph-based interpolation model IGNNK and the widely used machine-learning baseline XGBoost, reducing MAE by at least 13\% on Strava data and 7\% on Taxi data while better capturing the empirical traffic distribution, and improving the identification of zero-traffic streets. Additionally, the model remains robust under the realistic case of extremely scarce ground truth sensor data. When sensor coverage is reduced from 90\% to 1\%, the MAE increases by approximately 48\% on Strava and 76\% on the taxi data, despite the near-complete removal of sensor information. We also examine how graph connectivity choices influence model performance, and find that a simple and computationally efficient binary adjacency matrix outperforms distance or similarity based ones.}, language = {en} }