Are there infinitely many trucks in the technosphere, or exactly one? How independent sampling of instances of unit processes affects uncertainty analysis in LCA

  • Pascal LesageEmail author
  • Chris Mutel
  • Urs Schenker
  • Manuele Margni



Product systems use the same unit process models to represent distinct but similar activities. This notably applies to activities in cyclic dependency relationships (or “feedback loops”) that are required an infinite number of times in a product system. The study aims to test the sensitivity of uncertainty results on the assumption made concerning these different instances of the same activities. The default assumption assumes homogeneous production, and the same parameter values are sampled for all instances (e.g., there is one truck). The alternative assumption is that every instance is distinct, and parameter values are independently sampled for different instances of unit processes (e.g., there are infinitely many trucks). Intuitively, sampling the same values for each instance of a unit process should result in more uncertain results.


The results of uncertainty analyses carried out under either assumption are compared. To simulate models where each instance of a unit process is independent, we convert network models to acyclic LCI models (tree models). This is done three times: (1) for a very simple product system, to explain the methodology; (2) for a sample product system from the ecoinvent database, for illustrative purposes; and (3) for thousands of product systems from ecoinvent databases.

Results and discussion

The uncertainty of network models is indeed greater than that of corresponding tree models. This is shown mathematically for the analytical approximation method to uncertainty propagation and is observed for Monte Carlo simulations with very large numbers of iterations. However, the magnitude of the difference in indicators of dispersion is, for the ecoinvent product systems, often less than a factor of 1.5. In few extreme cases, indicators of dispersion are different by a factor of 4. Monte Carlo simulations with smaller numbers of iterations sometimes give the opposite result.


Given the small magnitude of the difference, we believe that breaking away from the default approach is generally not warranted. Indeed, (1) the alternative approach is not more robust, (2) the current default approach is conservative, and (3) there are more pressing challenges for the LCA community to meet. This being said, the study focused on ecoinvent, which should normally be used as a background database. The difference in dispersion between the two approaches may be important in some contexts, and calculating the uncertainty of tree models as a sensitivity analysis could be useful.


Ecoinvent LCA Tree model Uncertainty analysis 

Supplementary material

11367_2018_1519_MOESM1_ESM.docx (377 kb)
ESM 1 (DOCX 376 kb)
11367_2018_1519_MOESM2_ESM.xlsx (5.9 mb)
ESM 2 (XLSX 5990 kb)


  1. Acquaye AA, Wiedmann T, Feng K, Crawford RH, Barrett J, Kuylenstierna J, Duffy AP, Koh SCL, McQueen-Mason S (2011) Identification of “carbon hot-spots” and quantification of GHG intensities in the biodiesel supply chain using hybrid LCA and structural path analysis. Environ Sci Technol 45:2471–2478CrossRefGoogle Scholar
  2. Beloin-Saint-Pierre D, Heijungs R, Blanc I (2014) The ESPA (enhanced structural path analysis) method: a solution to an implementation challenge for dynamic life cycle assessment studies. Int J Life Cycle Assess 19:861–871CrossRefGoogle Scholar
  3. Besanko D, Dranove D, Shanley M (2004) Economics of strategy. Wiley, Hoboken, NJGoogle Scholar
  4. Bourgault G, Lesage P, Samson R (2012) Systematic disaggregation: a hybrid LCI computation algorithm enhancing interpretation phase in LCA. Int J Life Cycle Assess 17:774–786CrossRefGoogle Scholar
  5. Ciroth A, Muller S, Weidema B, Lesage P (2016) Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int J Life Cycle Assess 21:1338–1348.
  6. Frischknecht R, Jungbluth N, Althaus H-J, Doka G, Dones R, Heck T, Hellweg S, Hischier R, Nemecek T, Rebitzer G, Spielmann M (2005) The ecoinvent database: overview and methodological framework. Int J Life Cycle Assess 10(1):3–9CrossRefGoogle Scholar
  7. Groen EA, Heijungs R (2017) Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk? Environ Impact Assess Rev 62:98–109. CrossRefGoogle Scholar
  8. Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15:511–520CrossRefGoogle Scholar
  9. Heijungs R, Frischknecht R (2005) Representing statistical distributions for uncertain parameters in LCA. Int J Life Cycle Assess 10:248–254CrossRefGoogle Scholar
  10. Heijungs R, Huijbregts MAJ (2004) A review of approaches to treat uncertainty in LCA. iEMSs 2004 Int Congr 8.
  11. Heijungs R, Lenzen M (2014) Error propagation methods for LCA - a comparison. Int J Life Cycle Assess 19:1445–1461CrossRefGoogle Scholar
  12. Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Springer Netherlands, DordrechtCrossRefGoogle Scholar
  13. Henriksson PJG, Heijungs R, Dao HM, Phan LT, De Snoo GR, Guinée JB (2015) Product carbon footprints and their uncertainties in comparative decision contexts. PLoS One 10:1–11CrossRefGoogle Scholar
  14. Hong J, Shaked S, Rosenbaum RK, Jolliet O (2010) Analytical uncertainty propagation in life cycle inventory and impact assessment: application to an automobile front panel. Int J Life Cycle Assess 15:499–510CrossRefGoogle Scholar
  15. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. Part I: a general framework for the analysis of uncertainty and variability in life cycle assessment. Int J Life Cycle Assess 3:273–280CrossRefGoogle Scholar
  16. Imbeault-Tétreault H, Jolliet O, Deschênes L, Rosenbaum RK (2013) Analytical propagation of uncertainty in life cycle assessment using matrix formulation. J Ind Ecol 17:485–492CrossRefGoogle Scholar
  17. Kuczenski B (2015) Partial ordering of life cycle inventory databases. Int J Life Cycle Assess 20:1673–1683CrossRefGoogle Scholar
  18. Kuczenski B (2014) Life cycle fragments: computational methods for analyzing and sharing life cycle assessment models. In: Ames DP, Quinn NT, Rizzoli AE (eds) International environmental modelling and software society (iEMSs) 7th Intl. Congress on Env. Modelling and Software. San Diego, California, pp 107–113Google Scholar
  19. Lenzen M, Crawford R (2009) The path exchange method for hybrid LCA. Environ Sci Technol 43:8251–8256CrossRefGoogle Scholar
  20. Lenzen M, Murray J (2010) Conceptualising environmental responsibility. Ecol Econ 70:261–270CrossRefGoogle Scholar
  21. Lesage P, Mutel C, Schenker U, Margni M (2018) Uncertainty analysis in LCA using precalculated aggregated datasets. Int J Life Cycle Assess.
  22. Lloyd SM, Ries R (2008) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11:161–179CrossRefGoogle Scholar
  23. Muller S, Mutel C, Lesage P, Samson R (2018) Effects of distribution choice on the modeling of life cycle inventory uncertainty: an assessment on the ecoinvent v2.2 database. J Ind Ecol 22:300–313CrossRefGoogle Scholar
  24. Mutel C (2017) Brightway: an open source framework for life cycle assessment. J Open Source Softw 2.
  25. Norris G (2012) Scalable sourcing of benchmark models by TSC. Shonan, JapanGoogle Scholar
  26. Pinsonnault A, Lesage P, Levasseur A, Samson R (2014) Temporal differentiation of background systems in LCA: relevance of adding temporal information in LCI databases. Int J Life Cycle Assess 19:1843–1853CrossRefGoogle Scholar
  27. Suh S, Heijungs R (2007) Power series expansion and structural analysis for life cycle assessment. Int J Life Cycle Assess 12:381–390CrossRefGoogle Scholar
  28. Tillman AM (2000) Significance of decision-making for LCA methodology. Environ Impact Assess Rev 20:113–123CrossRefGoogle Scholar
  29. UNEP-SETAC (2011) Global guidance principles for life cycle assessment databases. Shonan, JapanGoogle Scholar
  30. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo CO, Wernet G (2013) Data quality guideline for the ecoinvent database version 3. St GallenGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Polytechnique MontrealCIRAIGMontrealCanada
  2. 2.Paul Scherrer InstituteVilligen PSISwitzerland
  3. 3.Nestlé Research CenterLausanne 26Switzerland

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