Speaker
Description
Traditional Life Cycle Impact Assessment (LCIA) methods assign characterization factors (CFs) uniformly to flows (nodes), neglecting the contextual richness of each exchange in a product system. In this session, we introduce edges, an open-source Python library that expands the Brightway2/25 LCA framework by shifting impact characterization from nodes to edges—the exchanges between processes.
This novel approach allows CFs to reflect critical contextual attributes, such as the geographic locations of suppliers and consumers, the magnitude of exchanged flows, and scenario-based parameters (e.g., atmospheric CO₂ concentration).
Built for flexibility and scalability, edges integrates seamlessly into existing Brightway workflows. It supports regionally differentiated CFs for over 340 regions, composite and relative region resolution (e.g., RoW, RER), and symbolic CF expressions that respond to user-defined scenarios. A wide range of matching criteria allows fine-grained mapping of exchanges to CF rules, enabling some control in prospective and context-sensitive impact modeling. edges already includes regionalized indicators for AWARE, ImpactWorld+, and GeoPolRisk.
This 45-minute interactive session will combine a technical introduction with hands-on exercises using Jupyter notebooks. Participants will:
- Explore the concepts and capabilities of edge-based LCIA.
- Learn how to load and apply dynamic CFs based on scenario variables.
- Perform impact assessment using regionally and temporally adaptive CFs.
- Experiment with a simplified, user-editable LCIA method to model custom scenarios.
By the end of the session, attendees will understand how edges empowers researchers and practitioners to perform transparent, parameter-driven, and geographically explicit LCIA, pushing the boundaries of open-source sustainability modeling in Brightway.
| How much time do you ideally wish for your contribution? | 45 minutes |
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