Internal PSI training on LCA, scenario-based LCA and prospective LCA

This repository holds the teaching material for the internal PSI LEA training.

Course objectives

The objective of this course is to provide an introduction to the LCA methodology and the tools used to perform it. The course will be divided in four parts:

1. Introduction to brightway and activity-browser.
2. Introduction to the wurst library.
3. Introduction to the premise library.
4. Practical session on scenario-based and prospective LCA.

Course description

This course will introduce participants to LCA, software to conduct it, LCA data manipulation, and prospective LCA. Hence, the course is divided in four parts.

The first part will be an introduction to brightway and activity-browser.

The second part will be an introduction to the wurst library, which is a python library used to operate large-scale modification on LCA databases.

The third part will be an introduction to the premise library, which is a python library used to create and operate prospective LCA database based on IAM scenarios.

The fourth part will be a practical session where the participants will be able to build their own prospective scenarios using the premise library.

Contact

Romain Sacchi

License

Unless otherwise specified, all material in this repository is licensed Creative Commons Attribution-NonCommercial 4.0 International.

Requirements

No special requirements are needed at the beginning of the course. We will install the required software during the course. We only ask the participants to download the following software before the course:

• Anaconda: https://www.anaconda.com/products/distribution
• Git: https://git-scm.com/downloads

Instructions

Install the libmamba solver in conda, for faster environment resolution:

  conda install -n base conda-libmamba-solver
  conda config --set solver libmamba