arXiv subjects

Felix Creutzig

Publications and source records attributed to Felix Creutzig.

10 recordsLinked to original sources

arXiv2026

Hyperlocal monitoring of urban activity reveals responses to heat exposure

Rising temperatures create new challenges for local heat adaptation. Yet, it remains unclear how urban activity changes during hot periods and which urban environments people concentrate in as temperatures rise. Here, we perform a hyperlocal spatiotemporal analysis of urban activity across 10 German cities over a two-month period in 2024 with different levels of heat exposure. To monitor urban activity, we use fine-grained telecommunication data to map locations of people with high spatio-temporal resolution (i.e., hourly at 100m x 155m grid cells), yielding more than 100 million data points. We then link activity counts with hourly weather records and point-of-interest data. We find that sustained periods of hot weather, defined as at least three consecutive days with daily maximum temperatures $\geq 25${\deg}C, are characterized by below-expected city-wide presence, with activity counts that are 1.5 percentage points below regular urban activity. During hot periods, urban activity concentrates more strongly around leisure- and culture-oriented amenities (e.g., caf\'es or swimming pools), with an increase of up to around 10 percentage points relative to cooler days, while public-service environments (e.g., educational and health facilities) show weaker or negative shifts. Our study provides policy-makers with fine-grained monitoring of which urban areas attract citizens during heat exposure, which can enable evidence-based, spatially-targeted urban heat adaptation plans.

physics.soc-ph↗

arXiv2026

The economic alignment problem of artificial intelligence

Artificial intelligence (AI) is advancing exponentially and is likely to have profound impacts on human wellbeing, social equity, and environmental sustainability. Here we argue that the "alignment problem" in AI research is also an economic alignment problem, as developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. We show that post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. We propose governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. Finally, we argue that the development of artificial general intelligence (AGI) requires new economic theories and models, for which post-growth scholarship provides a strong foundation.

econ.GN↗

arXiv2025

Algorithmic Control Improves Residential Building Energy and EV Management when PV Capacity is High but Battery Capacity is Low

Efficient energy management in prosumer households is key to alleviating grid stress in an energy transition marked by electric vehicles (EV), renewable energies and battery storage. However, it is unclear how households optimize prosumer EV charging. Here we study real-world data from 90 households on fixed-rate electricity tariffs in German-speaking countries to investigate the potential of Deep Reinforcement Learning (DRL) and other control approaches (Rule-Based, Model Predictive Control) to manage the dynamic and uncertain environment of Home Energy Management (HEM) and optimize household charging patterns. The DRL agent efficiently aligns charging of EV and battery storage with photovoltaic (PV) surplus. We find that frequent EV charging transactions, early EV connections and PV surplus increase optimization potential. A detailed analysis of nine households (1 hour resolution, 1 year) demonstrates that high battery capacity facilitates self optimization; in this case further algorithmic control shows little value. In cases with relatively low battery capacity, algorithmic control with DRL improves energy management and cost savings by a relevant margin. This result is further corroborated by our simulation of a synthetic household. We conclude that prosumer households with optimization potential would profit from DRL, thus benefiting also the full electricity system and its decarbonization.

eess.SY↗

arXiv2025

An Assessment of the CO$_2$ Emission Reduction Potential of Residential Load Management in Developing and Developed Countries

Intermittent renewable energies are increasingly dominating electricity grids and are forecasted to be the main force driving out fossil fuels from the grid in most major economies until 2040. However, grids based on intermittent renewables are challenged by diurnal and seasonal mismatch between supply of sun and wind and demand for electricity, including for heat pumps and electric two and four wheelers. Load management and demand response measures promise to adjust for this mismatch, utilizing information- and price-based approaches to steer demand towards times with high supply of intermittent renewables. Here, we systematically review the literature estimating CO$_2$ savings from residential load management in developing and developed nations. We find that load management holds high potential, locally differentiated with energy mix (including the respective share of renewables and fossils), climate zone, and the regulatory environment and price mechanism. Most identified studies suggest a mitigation potential between 1 and 20%. Load management becomes more relevant with higher shares of intermittent renewables, and when electricity prices are high. Importantly, load management aligns consumers' financial incentives with climate change mitigation, thus rendering accompanying strategies politically feasible. We summarize key regulatory steps to facilitate load management in economies and to realize relevant consumer surplus and mitigation potential.

econ.GN↗

arXiv2024

Ride-pooling adoption model for $\mathrm{CO_2}$ emission estimation

With the climate emergency and growing challenges ranging from pollution to congestion, ride-pooling (rp) has been floated as a potential solution for less congested, low-carbon and more space-efficient urban transportation. However, it is unclear which system configurations will enable an economically viable case for shared pooled mobility. To develop a more profound comprehension of the mechanisms underlying this subject, we here develop a simplified model to analyze the switching potential and \coo emissions of ride-pooling systems for a specified number of transport users, road network topology, and other system parameter values. This analysis is conducted across a broad range of switching probability functions (defined as the probability that a car or public transport user switches to ride-pooling) between an upper and lower bound of switching behaviour assumptions. Based on current Berlin parameters and the basic switching probability function, we find that ride-pooling can reduce the carbon emissions resulting from local transportation in Berlin by approximately 39\%. Policies that reduce the time factor - such as the provision of priority lanes - have the greatest effect in encouraging ride-pooling. For the system to be efficient and achieve measurable reductions in carbon emissions, the fleet size must be large enough. Across the range of switching probability functions, our results demonstrate that a fleet of 6,000 to 23,000 minibuses would be optimal to serve Berlin and reduce system-wide emissions.

physics.soc-ph↗

arXiv2023

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents. We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. Our results demonstrate that destination accessibility matters most overall, while low density and low connectivity also sharply increase CO$_2$ emissions. These general trends are similar across cities but we find idiosyncratic effects that can lead to substantially different recommendations. In more monocentric cities, we identify spatial corridors -- about 10--50 km from the city center -- where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.

cs.LG↗

arXiv2023

The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection

Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifying housing locations that minimize travel demand and induced CO2 emissions is crucial to mitigate climate change. While the built environment plays an important role, the precise impact on travel behavior is obfuscated by residential self-selection. To address this issue, we propose a double machine learning approach to obtain unbiased, spatially-explicit estimates of the effect of the built environment on travel-related CO2 emissions for each neighborhood by controlling for residential self-selection. We examine how socio-demographics and travel-related attitudes moderate the effect and how it decomposes across the 5Ds of the built environment. Based on a case study for Berlin and the travel diaries of 32,000 residents, we find that the built environment causes household travel-related CO2 emissions to differ by a factor of almost two between central and suburban neighborhoods in Berlin. To highlight the practical importance for urban climate mitigation, we evaluate current plans for 64,000 new residential units in terms of total induced transport CO2 emissions. Our findings underscore the significance of spatially differentiated compact development to decarbonize the transport sector.

cs.LG↗

arXiv2023

Shared Mobility in Berlin: An Analysis of Ride-Pooling with Car Mobility Data

In face of the threat of a climate catastrophe and the resulting urgent need for decarbonization together with the widespread emergence of the sharing economy, shared pooled mobility has been suggested as an alternative to private vehicle use. However, until now all of its real-life implementations have served a niche market, adjacent to taxi services. To better understand this discrepancy, as well as the potential of pooled mobility, we have here simulated and analyzed pooled mobility on the street network of Berlin with car trip data as input for ride requests. We measure the rate of sharable trips, the relative travel time of passengers, the average occupancy of the vehicles, the relatively driven distance compared to driving with a private vehicle. We observe that for requests in the city center of Berlin it is possible to serve all mobility requests currently done by car, with around 4700 vehicles. The travel time is around 1.34 higher than with a private vehicle, the vehicle's occupancy increases to 2.6. The driven distance is reduced by 65%. In the whole area of Berlin we observe that a ride-pooling system with 10000 vehicles can serve 60% of the trips. The travel time is 1.4 times higher than with a private vehicle, the occupancy gets three and the driven distance is reduced by 40%.

stat.AP↗

arXiv2021

Open government geospatial data on buildings for planning sustainable and resilient cities

As buildings are central to the social and environmental sustainability of human settlements, high-quality geospatial data are necessary to support their management and planning. Authorities around the world are increasingly collecting and releasing such data openly, but these are mostly disconnected initiatives, making it challenging for users to fully leverage their potential for urban sustainability. We conduct a global study of 2D geospatial data on buildings that are released by governments for free access, ranging from individual cities to whole countries. We identify and benchmark more than 140 releases from 28 countries containing above 100 million buildings, based on five dimensions: accessibility, richness, data quality, harmonisation, and relationships with other actors. We find that much building data released by governments is valuable for spatial analyses, but there are large disparities among them and not all instances are of high quality, harmonised, and rich in descriptive information. Our study also compares authoritative data to OpenStreetMap, a crowdsourced counterpart, suggesting a mutually beneficial and complementary relationship.

cs.CY↗

arXiv2019

Tackling Climate Change with Machine Learning

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.

cs.CY↗