Lifetime assumptions lie at the heart of most dynamic stock models, yet they often represent the weakest link in these assessments. Despite having a significant impact on model results, our understanding of lifetimes remains limited, characterized by fragmented or missing data. Consequently, current sustainability and circularity assessments frequently rely on simplified or normative assumptions—such as how long a product is designed to last—rather than empirical reality.
This session aims to unite the scientific community to review the state of the art and identify opportunities for improvement. We will explore:
- Methodological advancements: Discussing various approaches to represent lifetimes in dynamic stock modelling.
- Factors influencing lifetimes: Investigating the drivers, such as external events, policies, and technological change.
- Data challenges: Addressing the time-intensive nature of collecting quality lifetime data.
- Technological solutions: Examining how AI-based methods can facilitate the automation of data collection for lifetime estimation.
Join us as we highlight recent developments in lifetime research and discuss future opportunities for the field of industrial ecology.
Program
Introduction (15 min)
- Kamila Krych, ETH Zurich – “Lifetimes in dynamic stock modelling – where do we stand?”
- Alessio Miatto, CSIRO – “How important are realistic lifespan assumptions for material stocks and outflows?”
Presentations (10 min each + 5 min Q&A)
- Wenjing Gong, University of Tokyo – “The phase-out of conventional vehicles: A multi-stock model of the ICEV to EV transition”
- Zoé Cord’homme, Norwegian University of Science and Technology – “Estimating building lifetimes in city centers”
- Nils Dittrich, Norwegian University of Science and Technology – “Generating building data from aerial orthophotos – use of machine learning in industrial ecology”
Joint discussion (30 min)
- How realistic are our current lifetime assumptions? How to improve our understanding of lifetimes? What developments are needed?
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