Speaker
Description
The treatment of uncertainty is a central issue in life cycle assessment (LCA), especially when results are used to support decision-making processes. However, many LCA studies still skip this step because it requires additional knowledge, effort and computation resources. MOCA is a Python package that aims to lower these barriers by making uncertainty analysis more time-efficient and easier to integrate into existing LCA workflows. It is built on top of Brightway2 and helps users to explore uncertainty within their data with only minimal changes to their existing calculation setup. At present, it focuses on high-speed Monte Carlo simulation with parallelised computation.
This contribution will briefly introduce the package, its current capabilities and the ideas behind its implementation. The package is still under development and one of its goals is to support additional uncertainty methods in the future. Therefore, the goal is to show what it can already do, discuss where it may be useful and gather feedback for further development.
- MOCA: a new Python package built to work with Brightway2
- Parallelised Monte Carlo simulation for uncertainty analysis in LCA
- Designed to fit into existing Brightway2 workflows and for users without prior uncertainty expertise
- Planned to be extended towards more uncertainty analysis methods
| How much time do you ideally wish for your contribution? | 20 min (Presentation, slides; Presentation, with notebook) |
|---|