Assessing the robustness of accounting data for carbon neutrality plan

past

Master project
2023

Context and problem

Faced with the major challenges raised by climate change, a growing number of governments and organizations are committing to reducing their greenhouse gas emissions to reach carbon neutrality. But developing a decarbonization plan first requires accurate carbon footprinting of human activities. Several methods and data sources are available to perform these quantifications. However, the quality of accounting data and the impact of this quality on the robustness of the carbon quantifications is poorly known, despite existing mathematical tools to estimate this robustness. This uncertainty aspect is a major concern, as quantifications that are too uncertain could compromise the effectiveness of decarbonization plans and thus the ability to become carbon neutral as a society. The general objective of this project is to progress towards more reliable decarbonized plans to ensure humanity safety. Moreover, the project aims to map the main carbon footprinting databases, assess their quality per aggregated dataset, then the impact of their quality on the robustness of carbon footprint assessments on specific case studies.

Description of the project and tasks

To reach the objective of the project, the following tasks will be conducted:

  1. Analysing and comparing major carbon footprinting methods - at least the GHG Protocol standard, Life Cycle Assessment (LCA), Environmentally-Extended input output (EEIO), and national inventories.
  2. Mapping major accounting data and emission factor sources, including at least databases inventoried by the GHG Protocol, Ecoinvent, GaBi, Exiobase, OpenIO- Canada, StatCan data.
  3. Assessing the quality of these databases per aggregated dataset using the Pedigree matrix (or a more suitable method if existing).
  4. Performing a case study on one or more key sectors:
    1. Selecting one or more important production or consumption system for the GHG balance at Canada’s scale (a key sector such as energy (or electricity), transportation, or the economy as a whole).
    2. Assess the carbon footprint of the system using different sources of data.
    3. Compare the results and their robustness, for example using the Pedigree Matrix and Monte Carlo Simulations.
    4. Conclude about the best data sources for the system(s) assessed.
    5. Evaluate specific data quality issues, especially in Ecoinvent and OpenIO- Canada.
    6. Prioritize new data to develop, especially in Ecoinvent and OpenIO-Canada.

Deliverables

  • A descriptive report on the tasks conducted;
  • A presentation of the study at CIRAIG and potentially to industrial partner(s);
  • Modeling files if applicable;
  • Participation to the preparation of a scientific article, with co-authorship of the intern, if applicable.

Skills

Qualification implies having taken an EPFL LCA course or equivalent, as well as a good level in English.

Desired skills of the applicants are as follows:

  • Rigor
  • Analytic skills and ability to summarize
  • Large data analysis

Following knowledge is an asset:

  • Monte Carlo simulations
  • EEIO
  • Oracle Crystal Ball

Administrative

Supervision: the project is supervised by:

  • Manuele Margni, Ph.D., M.Sc. A., B. Ing. : Professor at Polytechnique Montreal, Professor HES-SO Valais Wallis, invited fellow at EPFL at IPESE with Pr. François Maréchal, EPFL-Energypolis, Sion. manuele.margni@hevs.ch.
  • Anne de Bortoli, Ph.D., M.Sc., B. Ing. : postdoctoral researcher at CIRAIG, Polytechnique Montréal, invited researcher at Ecole des Ponts ParisTech. anne.debortoli@polymtl.ca

Location: CIRAIG, Polytechnique Montréal, 3333 Queen Mary Road, Montreal, Canada.

Compensation: budget for the round-trip Europe-Montreal and accommodation in Montreal during the internship.

Application: Interested students must send an application file including a CV, a transcript (bachelor and master) and a cover letter to anne.debortoli@polymtl.ca and manuele.margni@hevs.ch. Applications will be reviewed in chronological order and positions remain open until suitable candidates are found.