bw_timex makes your LCA use the right data for the right point in time. It's a python package for time-explicit Life Cycle Assessment, built on top of the Brightway LCA framework.
⏳ The problem
An LCA normally evaluates your entire supply chain with data for a single point in time. But an electric car built in 2025 has a battery made in 2024, is driven on an evolving electricity mix until 2040, and is recycled with 2040 technologies. Anything long-lived, or in a system that changes over time, ends up being assessed with data that doesn't match when processes actually happen.
You can work around this by hand: split each exchange into one copy per point in time, work out which year every process upstream lands in — a delay at one level shifts every other process connected to it — and wire each copy to a different time-specific background database. It's tedious, easy to get wrong, and you redo it every time the model changes.
✨ What bw_timex does
You bring your normal Brightway model, plus:
- temporal distributions, specifying when each exchange happens relative to the process consuming it — either a single shift ("2 years earlier") or spread over time ("30% two years earlier, 50% now, 20% four years later")
- time-specific process data, like background databases representing different points in time (e.g. from
premise) and, for your own foreground processes, how they change over time (e.g. efficiency gains)
bw_timex figures out when every process in the supply chain actually happens, relinks each one to the background database matching that date (interpolating between databases in between), and applies your time-specific amounts.
You get a time-explicit inventory: each process assessed with data from the time it actually occurs, and each emission tagged with when it happens — so you can characterize it with dynamic characterization functions instead of static factors.
Most useful for long-lived products, strongly evolving production systems, and biogenic carbon. Still unsure whether you need time-explicit LCA? Check out our decision tree for some guidance.
👩💻 Getting Started
📚 Citation
If bw_timex supports your scientific work, please consider citing our companion publications:
- The conceptual framework and formalization of time-explicit LCA are described in our methodology paper
- The implementation of this methodology in
bw_timexis covered by our JOSS paper
🤝 Contributing
We welcome contributions! If you have suggestions or want to fix a bug, please:
💬 Support
If you have any questions or need help, do not hesitate to contact us:
- Timo Diepers (timo.diepers@ltt.rwth-aachen.de)
- Amelie Müller (a.muller@cml.leidenuniv.nl)
- Arthur Jakobs (artos.jakobs@psi.ch)
1 maintainer
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