Brightcon 2026, hackathon and courses

We present TRAILS, an open-source Python framework for time-explicit life cycle assessment (LCA) that enables deep temporalization of product systems. Rather than representing the technosphere as a collection of year-specific, independent two-dimensional matrices, TRAILS treats it as an interconnected temporal structure (i.e., a three-dimensional tensor) in which processes and exchanges can be distributed over time and linked consistently across scenario years (i.e., background databases). This allows temporal differentiation not only in foreground activities, but throughout the supply chain, so that background transformations in energy, materials, infrastructure, and other intermediate flows can be represented as part of a connected temporal system.

From a methodological point of view, TRAILS combines temporal routing of exchanges with sequential, year-by-year life-cycle inventory calculations. This approach makes it possible to construct scenario-consistent time series of inventories and impacts while avoiding a memory-straining single solve across the entire temporal system. The framework also supports coupling with climate emulators, allowing temporally distributed greenhouse gas emissions and removals to be translated into climate-relevant outputs such as radiative forcing and temperature response.

TRAILS is designed to be modular and transparent for researchers and practitioners working on prospective and dynamic LCA. Its open implementation supports reproducibility, facilitates integration with scenario-based background LCA databases, and enables users to study systems for which timing is likely to affect overall environmental performance substantially.

During the presentation, we will demonstrate the use of TRAILS live with the audience through a Jupyter notebook. This hands-on demonstration will show how: (i) deeply temporalised systems are constructed, (ii) prospective background scenarios are connected into a temporally explicit technosphere, and (iii) resulting inventories, impacts, and climate responses can be calculated and explored in a reproducible workflow.