Brightcon 2026, hackathon and courses

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.
• The case studies underline the ability of the coupling framework in generating large numbers of stochastic LCA results, without enforcing simplifications to the inputs or the LCA itself.

Description

LCA is increasingly leveraged as part of a series of simulation-intensive assessments, as in asset performance assessments. Asset performance assessments strive to estimate natural hazard effects on the sustainability of the built environment but require large numbers of stochastic samples to characterize the resulting environmental impacts associated with repair activities and collateral downtime effects. General-purpose LCA software struggle with interoperability issues when provided with large input samples, prompting for simplifications and limiting LCA uncertainty propagation. This project proposes a novel coupling framework that leverages a dual presampling strategy paired with a preaggregation step on background environmental datasets, expediting in minutes the computation of hundreds of thousands of stochastic LCA results. The Python implementation of the coupling framework bridges the open-source software pelicun and brightway2 via the GitHub-available library pelicun_2_brightway2. The coupling framework is investigated through two case studies exploring the influence of hurricane winds on the LCA of an individual building and a portfolio of buildings in Halifax, Canada (also available on GitHub). The case studies underline how the coupling framework can assist engineering-level and urban-scale decision-making in developing sustainable and resilient assets. The coupling framework and its underlying methodology aim to foster new developments in LCA for simulation-intensive assessments, a necessary feature in optimizing the mitigation and adaptation strategies of a changing climate.