Understanding the energy transition: How models shape the future
Grant and Award Announcement
Updates every hour. Last Updated: 23-Jul-2025 09:10 ET (23-Jul-2025 13:10 GMT/UTC)
How does scientific model-building influence the energy transition – and with it our future? Models, and how they are presented, determine our thinking, but their foundations often remain invisible. The transdisciplinary research project Poetik der Modelle at the Karlsruhe Institute of Technology (KIT) is investigating how we shape the future with energy transition models, and how we can communicate about them in a more accessible way. Funded as a Reinhart Koselleck project by the German Research Foundation (DFG), it questions the practices of modeling and aims to improve transparency, participation, and inclusion in the transformation of our energy system.
Research team introduced CHASER, an incentive mechanism for blockchain-based EMCS systems. It ensures budget balance, truthfulness, rationality, and high social welfare. Simulations show a 42% increase in social welfare and high task completion rates.
Using data on more than 220,000 individuals on the Lyft rideshare platform, researchers report that drivers of color are significantly more likely to receive speeding tickets than white drivers, and to face steeper fines, even when traveling at identical speeds. Racial profiling by law enforcement is a pressing social issue in the United States. Previous research analyzing police and judicial records suggests that racial and ethnic minorities face disproportionately higher rates of searches, fines, force, detentions, and incarceration compared to white civilians. However, research on racial bias in policing has long been hindered by data limitations and challenging analyses. For example, to demonstrate racial bias in policing, researchers must compare officer treatment of minority and white civilians under identical circumstances, while also controlling for all other factors in a police-civilian encounter that might explain enforcement disparities. These so-called “all-else-equal” scenarios are scarce in policing research.
Leveraging high-frequency GPS location data from the rideshare platform Lyft, Pradhi Aggarwal and colleagues overcome some of these challenges and estimate the effect of racial profiling on citations and fines for speed violations. The analysis encompassed 222,838 Lyft drivers operating in Florida from 2017 to 2020. Lyft drivers use a smartphone application that transmits precise location and speed data to Lyft’s system at 10-second intervals, providing researchers with detailed, real-time driving information. Aggarwal et al. then matched this dataset with Florida’s government records for speeding violations, detailing traffic stops and driver’s license information for those involved. The authors found that minority drivers are significantly more likely to be cited for speeding and pay higher fines than white drivers, even after controlling for factors like driving speed, location, vehicle characteristics, and other relevant variables. The findings show that minority drivers are 24% to 33% more likely to be cited during a traffic stop and they pay 23% to 34% higher fines, compared to white drivers. Moreover, the analysis revealed no significant differences in accident or re-offense rates across White versus minority drivers, suggesting that policing bias – rather than driver behavior – drives these disparities. “Aggarwal et al. have provided a template for using recent technological advances to overcome some of the most challenging obstacles impeding policing research,” write Dean Knox and Jonathan Mummolo in a related Perspective.
For reporters interested in trends, a 2021 Science study led by Knox, and using a dataset on daily patrols of officers in the Chicago Police Department, reported that Black officers used force less often than white officers during a three-year period studied. https://science.sciencemag.org/cgi/doi/10.1126/science.abd8694