Brightcon 2026, hackathon and courses

Highlights

1. Showcases MyLCA, a Python modelling framework for generic, parametric and modular LCA with Brightway25
2. Enables reusable, composable, parametric LCA models through grouped containers and activities, and associated parameters
3. Demonstrates a reproducible workflow to rebuild and compare published battery LCA studies
4. Identifies and quantifies drivers of impact differences
5. Takeaway: a parametric and modular approach to explain discrepancies in complex LCA models

Concise Description

In Life Cycle Assessment (LCA), practitioners typically model systems as fixed process graphs, where exchanges are explicitly defined between datasets. While some parametrization is possible, these models often remain static and tightly coupled to specific background data, limiting genericity, modularity and reusability. At the same time, the growing need to compare complex systems is hindered by the difficulty of explaining differences between studies that rely on varying assumptions and structures.

This session introduces MyLCA, a Python library built on top of Brightway25, designed to enable generic, parametric, and modular LCA modelling. The approach extends traditional activity representations with “ports” that abstract the activities that will be used as inputs during the calculation. Processes can be encapsulated in “containers”, enabling sub-modelling, encapsulation of alternatives delivering the same products, templating, and reuse across the model. A hierarchical parameter system (namespaces, inheritance, and parameter transfer) combined with a built-in solver enables efficient and consistent model evaluation. Evaluated models are then converted into Brightway objects for LCA calculation, including sensitivity and uncertainty analyses across foreground and background systems.

The capabilities of MyLCA are illustrated through a battery LCA use case. Several published studies are reconstructed within a unified parametric model using the new flexible descriptions enabled by MyLCA. This allows controlled comparison from differences in product design (e.g., cell chemistry, performance assumptions), supply chain configurations (e.g., energy mix, sourcing), or methodological choices such as functional units, system boundaries, and databases. By systematically varying parameters (design, supply chain, and methodological choices), causal drivers of impact differences can be identified more easily and natively.

The session will include: (1) presentation of core concepts, (2) integration with Brightway25, (3) application to comparison of battery LCA and (4) discussion to gather feedback and assess the community interest.

In the perspective of an open-source release of MyLCA, inputs regarding the development, potential overlapping or integration with other tools will be very-much appreciated.