Regionalised Life Cycle Assessment with the Brightway 2.5 LCA software

An introduction to the use of the Brightway 2.5 Open Source LCA software with a particular focus on applying it to regionalised assessments. The matrix foundations of LCA, their adaptation for different types of regionalisation, and its use for uncertainty analysis. Adapting background databases for regionalised assessments, and creating and using regionalised foreground activity datasets and regionalised impact assessment methods. Students apply these methods on a complete case study. Calculations will be done in the cloud to avoid GIS library installation hassles.

The course

Course outline:

From data to matrices
Introduction to the mental and computational model behind Brightway from the ground up, from raw data to data packages to matrices. Building matrices and solving linear systems.

Matrices in space and uncertainty
Theory of regionalisation in life cycle assessment. How to incorporate spatial data in matrices at different levels of resolution. Using uncertain data in matrix systems. Application to case study system.

Regionalisation with real world data
How to regionalize inventory datasets given available data, and with the inclusion of additional data. Using MRIO tables to disaggregate background databases. Handling “Rest-of-World” locations. How to import and use regionalized impact assessment methods.

GIS to support weighted calculations
How to use detailed raster maps of specific flows in GIS to make weighted calculations of site-specific characterization factors. Dispatching calculations to the cloud. Interpretation of results. Introduction of case study and beginning of case study work.

Case study work and feedback
Individual case study work with support and specific tasks. Feedback on data flow workflows and suggestions for future Brightway development.

Form and academic recognition:

Learning outcomes:

  • Knowing the different components of Brightway, and their application in LCA calculations.
  • Ability to manage data in Brightway, including data import and modification.
  • Understanding the way that LCA data is stored in data packages and used in Brightway, and how the data schema of Brightway is translated into matrices for (regionalised) LCA calculations
  • Ability to apply and interpret basic LCA calculations in Brightway
  • Ability to describe and appraise how Monte Carlo sampling is implemented in Brightway
  • Ability to explain the differences between different regionalization calculation methods
  • Ability to plan data collection for regionalized LCA studies
  • Ability to calculate regionalized LCA scores of a case study
  • Ability to interpret the calculated scores

Course development