Pole of Excellence


The Tools Pole of Excellence aims to create life cycle assessment (LCA) software tools specifically tailored to the needs of clients, to operationalize LCA research in computational tools and to develop a suite of open-source modules that can be assembled to create cutting-edge LCA software tools when combined. 


  • Develop industry-specific LCA software for a client
  • Implement LCA research as computational packages
  • Develop "model explorer" software to allow a client of an LCA to evaluate scenarios beyond the ones initially included in the static LCA report
  • Provide precalculated LCI samples for inclusion in software tools


lead analyst

Pascal Lesage is a research officer at CIRAIG. His duties include research, tool development, supervision of graduate students, teaching, carrying out LCA studies and participation in LCA critical review panels.


Canadian Analytical Framework for the Environmental Evaluation of Electricity: Software tool developed for Environment and Climate Change Canada, allowing probabilist LCA assessments of electricity production scenarios.   Not publicly available.
Presamples is used to write, load, manage and verify presample arrays. Presample arrays refer to arrays of values that specific parameters or matrix elements can take on. The presamples package allows these arrays to be generated ahead of their use in a particular model. This is useful if:
  • Generating these values is computationally expensive and there is no need to recalculate them with each model run;
  • We want to reuse the same values every time a model is solved.
Presamples was initially built specifically for parameters and matrix elements used in life cycle assessment (LCA), and hence has many methods specifically geared at making the integration of presamples in LCA models easy. However, it can be used in any other type of model.
bw2preagg is used to generate dependently sampled LCI and LCIA arrays for whole databases. It uses Brightway2 framework and presamples. The arrays are stored as numpy.ndarray files, and can be integrated in Brightway2 models using other modules (brightway2-aggregatedpresamples). The resulting result arrays allow the use of aggregated LCI or LCIA results (also known as cradle-to-gate results) in LCA while also correctly integrating the uncertainty of LCI data, see Lesage et al. 2018. Generating samples of cradle-to-gate results for all activities in a database can be a lengthy process, especially when dealing with large LCI databases like ecoinvent. The functions are therefore geared towards large, bulk calculations, by e.g. facilitating multiprocessing and breaking down the task in multiple “batches” that can run on different systems.