edges is a Python Library for exchange-based Impact Assessment in
Life Cycle Analysis (LCA) for the brightway2 LCA framework.
The edges Python library offers a novel approach to applying characterization factors
(CFs) during the impact assessment phase of Life Cycle Assessment (LCA).
Unlike conventional methods that uniformly assign CFs to nodes (e.g., processes
like Water, from well in the brightway2 ecosystem), edges shifts the focus to the
edges—the exchanges or relationships between nodes. This allows CFs to be conditioned
based on the specific context of each exchange. Essentially, edges introduces unique
values in the characterization matrix tailored to the characteristics of each edge.
By focusing on edges, the library incorporates contextual information such as the attributes of both the supplier and the consumer (e.g., geographic location, ISIC classification, amount exchanged, etc.). This enables a more detailed and flexible impact characterization, accommodating parameters like the location of the consumer and the magnitude of the exchange.
Furthermore, edges supports the calculation of weighted CFs for both static regions
(e.g., RER) and dynamic regions (e.g., RoW), enhancing its ability to model complex
and region-specific scenarios.
- National characterization factors for water-related impacts.
- Seamless integration with the Brightway LCA framework.
- Implements national and sub-national characterization factors of:
- the AWARE method 1.2c.
- Future updates will include additional impact categories.
You can install the library using pip:
pip install git+https://github.com/romainsacchi/edges.gitCheck out the examples' notebook.
from edges import get_available_methods
# Get the available methods
methods = get_available_methods()
print(methods)import bw2data
from edges import EdgeLCIA
# Select an activity from the LCA database
act = bw2data.Database("ecoinvent-3.10-cutoff").random()
# Define a method
method = ('AWARE 1.2c', 'Country', 'unspecified', 'yearly')
# Initialize the LCA object
LCA = EdgeLCIA({act: 1}, method)
LCA.lci()
# Perform the LCAI calculation
LCA.lcia()
LCA.score
# Print a dataframe with the characterization factors used
LCA.generate_cf_table()- AWARE: The AWARE factors are adapted from peer-reviewed sources and tailored to provide precise country-specific data for environmental modeling. Refer to the AWARE website https://wulca-waterlca.org/ for more information.
If you use the AWARE method, please cite the following publication:
@article{boulay2018aware,
title={The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE).},
author={Anne-Marie Boulay, Jane Bare, Lorenzo Benini, Markus Berger, Michael J. Lathuillière, Alessandro Manzardo, Manuele Margni, Masaharu Motoshita, Montserrat Núñez, Amandine Valerie Pastor, Bradley Ridoutt, Taikan Oki, Sebastien Worbe & Stephan Pfister },
journal={Int J Life Cycle Assess},
volume={23},
pages={368–378},
year={2018},
publisher={Springer}
}-
edgesintroduces edge-specific characterization factors in the characterization matrix ofbw2calcbefore performing the LCA calculation. The characterization factors are stored in thedatafolder of the library. Currently,edgesprovides characterization factors for 346 national and sub-national regions, based on the AWARE method, based on the location of edge consumers. -
For specific
ecoinventregions (e.g., RER, Canada without Quebec, etc.),edgescomputes the weighted average of the characterization factors for the countries included in the region, based either on population or GDP. The weighting key can be selected by the user (weighting by population size by default). -
For relative regions (e.g., RoW, RoE, etc.),
edgesdynamically defines the locations included in the region based on the mathing activities in the LCA database. The weighted average of the characterization factor of the geographies containd in the region is then computed accordingly.
-
Off-Diagonal Targeting:
- The library identifies specific exchanges between suppliers and consumers in the technosphere (A matrix).
- Characterization factors are applied selectively based on these relationships, introducing off-diagonal terms in the characterization matrix (C matrix).
- For example, an activity in Region A using resources from Region B can have a distinct characterization factor that reflects the inter-regional transfer's environmental consequences.
-
Conditional Characterization:
- The characterization factors are conditioned on the location (or other attributes) of the activities.
- For instance, water scarcity impacts might depend on both the supplier's and consumer's geographical context, assigning higher weights to regions with water stress.
-
Matrix Adjustment:
- The enriched C matrix now accounts for interactions between different regions and flows.
- Off-diagonal elements (C_ij, where i ≠ j) capture interdependencies, such as the environmental cost of resource transport or upstream emissions.
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch for your feature or fix.
- Commit your changes.
- Submit a pull request.
This project is licensed under the MIT License. See the LICENSE.md file for more information.
For any questions or inquiries, please contact the project maintainer at [email protected].
The development of this library was entirely supported by the French agency for Energy ADEME, via the financing of the HySPI project. The HySPI project aims to provide a methodological framework to analyze and quantify, in a systemic and prospective manner, the environmental impacts of the decarbonization strategy of H2 production used by the industry in France.