This contribution presents an open-source Python workflow for variance-based global sensitivity analysis of upcycling life cycle assessment inventories, built on Brightway2 and the SALib library. The workflow enables practitioners to identify which foreground parameters drive result variability before committing to a full comparative study, addressing a common but under-supported step in LCA...

A Python Package for the Simultaneous Global Sensitivity Analysis of Foreground and Background Parameters in Life Cycle Assessment

Highlights/Discussion Points

• Existing Python packages can perform global sensitivity analysis (GSA) of foreground or background parameters, but not both at once.
• We propose a package based on Python and Brightway that simultaneously performs GSA on...

Highlights

• A coupling framework featuring a dual presampling strategy paired with a preaggregation step is developed to efficiently combine stochastic inputs with background stochasticity in LCA.
• The pelicun_2_brightway2 library is introduced to bridge pelicun and brightway2, open-source tools to perform asset performance assessment for the built environment and LCA, respectively....

Highlights/Discussion Points:
- Introduction of the problem
- How to go about assessing this issue with BW
- Results: Uncertainty due to price variance in economic allocation. Comparison to other sources of uncertainty in LCA.

Concise Description
Economic allocation is widely used in life cycle assessment (LCA) to distribute inputs and emissions among co-products, yet it...