This week we are joined by two PhD researchers, Silke Kaiser and Chiara Fusar Bassini, from the Hertie School in Berlin. With host Chris Adams they discuss their use of data science and machine learning and how they are using them to tackle some of today’s most pressing environmental challenges. Silke shares insights into her research on predicting cycling traffic in cities to better inform urban planning and promote sustainable transport, while Chiara discusses her work on analyzing European energy data to support the renewable energy transition. Together, they explore the intersection of technology, data, and policy, highlighting the importance of data-driven decision-making in achieving sustainability goals.Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteSilke Kaiser: LinkedIn | WebsiteChiara Fusar Bassini: LinkedInFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:From counting stations to to city wide estimates: data driven bicycle volume extrapolation | Silke Kaiser [09:46] Pedalling Towards a Greener Future: The Impact of Cycling and Active Transport on Climate Change and Public Health - Catalyse [12:46]Chapter 10: Transport | IPCC [14:10]Estimating Coal Power Plant Operation From Satellite Images with Computer Vision [24:11]Does the EU AI Act really call for tracking inference as well as training in AI models? [38:27]What is the methodology used to measure the carbon footprint of training Llama 3.1? [41:12]Climate policies that achieved major emission reductions: Global evidence from two decades | Science [44:27]Microsoft employees spent years fighting the tech giant's oil ties. Now, they’re speaking out. | Grist [46:51]A review of the ENTSO-E Transparency Platform Resources:How does AI and ML Impact Climate Change? | EV Ep 5 [05:38]French Revolution: Cyclists Now Outnumber