The International Journal of Life Cycle Assessment

, Volume 21, Issue 9, pp 1327–1337 | Cite as

The application of the pedigree approach to the distributions foreseen in ecoinvent v3

  • Stéphanie Muller
  • Pascal Lesage
  • Andreas Ciroth
  • Christopher Mutel
  • Bo P. Weidema
  • Réjean Samson



Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.


A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.

Results and discussion

In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.


Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.


Data quality Life cycle inventory database Pedigree matrix Probability density functions Uncertainty 



The authors would like to acknowledge the financial support of the industrial partners of the International Chair in Life Cycle Assessment and the International Life Cycle Chair (research units of CIRAIG): ArcelorMittal, Bell Canada, Bombardier, Cascades, Eco Entreprises Québec, Groupe EDF, GDF-SUEZ, Hydro-Québec, Johnson&Johnson, LVMH, Michelin, Mouvement Desjardins, Nestlé, Rio Tinto Alcan, RECYC-QUÉBEC, RONA, SAQ, Solvay, Total, Umicore, and Veolia Environnement. The industrial partners were in no way involved with the study design, the collection, analysis, and interpretation of the data, the writing of the paper, or the decision to submit the paper for publication.

The authors would also like to acknowledge the work of two anonymous reviewers that helped clarify and improve this paper.

Supplementary material

11367_2014_759_MOESM1_ESM.docx (17 kb)
ESM 1 (DOCX 17 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stéphanie Muller
    • 1
  • Pascal Lesage
    • 1
  • Andreas Ciroth
    • 2
  • Christopher Mutel
    • 3
  • Bo P. Weidema
    • 4
  • Réjean Samson
    • 1
  1. 1.CIRAIG, Department of Chemical EngineeringPolytechnique MontréalMontréalCanada
  2. 2.GreenDelta GmbHBerlinGermany
  3. 3.ETH ZurichInstitute of Environmental EngineeringZurichSwitzerland
  4. 4.Aalborg UniversityAalborgDenmark

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