Uncertainty analysis in LCA using precalculated aggregated datasets

  • Pascal Lesage
  • Chris Mutel
  • Urs Schenker
  • Manuele Margni



Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.


The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.


Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.


Aggregated datasets ecoinvent Uncertainty analysis 



The authors would like to acknowledge the financial support of the following CIRAIG industrial partners: Arcelor-Mittal, Bombardier, Mouvement ́des caisses Desjardins, Hydro Québec, RECYC-QUÉBEC, LVMH, Michelin, Nestlé, SAQ, Solvay, Total, Umicore and Veolia. The authors would also like to acknowledge the participation of Yohan Marfoq in early investigations into the topics discussed in this paper.

Supplementary material

11367_2018_1444_MOESM1_ESM.html (304 kb)
SI 1 Simplest possible case (HTML rendition of Jupyter Notebook) (HTML 304 kb)
11367_2018_1444_MOESM2_ESM.html (981 kb)
SI 2 Water bottle LCA example - code (HTML rendition of Jupyter Notebook) (HTML 981 kb)
11367_2018_1444_MOESM3_ESM.xlsx (12 kb)
SI 3 Water bottle LCA example, detailed results (Excel spreadsheet) (XLSX 11 kb)
11367_2018_1444_MOESM4_ESM.html (933 kb)
SI 4 Correlation across datasets in ecoinvent code (HTML rendition of Jupyter Notebook) (HTML 932 kb)
11367_2018_1444_MOESM5_ESM.xlsx (10 kb)
SI 5 Correlation across pairs of datasets, ecoinvent 2.2 (Large zip files) (XLSX 10 kb)
11367_2018_1444_MOESM6_ESM.xlsx (10 kb)
SI 6 Correlation across pairs of datasets, ecoinvent 3.3 (Large zip files) (XLSX 10 kb)
11367_2018_1444_MOESM7_ESM.xlsx (18 kb)
SI 7 Burger LCA deterministic results (Excel spreadsheet) (XLSX 17 kb)
11367_2018_1444_MOESM8_ESM.html (853 kb)
SI 8 Code for burger uncertainty analysis (HTML rendition of Jupyter Notebook) (HTML 852 kb)


  1. Besanko D, Dranove D, Shanley M (2004) Economics of strategy. Wiley, HobokenGoogle Scholar
  2. Broadbent C, Stevenson M, Caldiera-Pires A, Cockburn D, Lesage P, Martchek K, Réthoré O, Frischknecht R (2011) Aggregated data development. Global guidance principles for life cycle assessment databases—a basis for greener processes and products. G. Sonnemann and B. Vigon. Shonan, Japan, UNEP-SETAC Life Cycle Initiative, pp 67–83Google Scholar
  3. De Koning A, Schowanek D, Dewaele J, Weisbrod A, Guinée JB (2010) Uncertainties in a carbon footprint model for detergents; quantifying the confidence in a comparative result. Int J Life Cycle Assess 15:79–89CrossRefGoogle Scholar
  4. 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
  5. Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15(5):511–520. CrossRefGoogle Scholar
  6. Heijungs R, Kleijn R (2001) Numerical approaches towards life cycle interpretation five examples. Int J Life Cycle Assess 6(3):141–148. CrossRefGoogle Scholar
  7. Heijungs R, Lenzen M (2014) Error propagation methods for LCA—a comparison. Int J Life Cycle Assess 19(7):1445–1461. CrossRefGoogle Scholar
  8. Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Springer, Dordrecht. CrossRefGoogle Scholar
  9. Heijungs R, Henriksson PJG, Guinée JB (2017) Pre-calculated LCI systems with uncertainties cannot be used in comparative LCA. Int J Life Cycle Assess 22(3):461–461. CrossRefGoogle Scholar
  10. 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(3):e0121221. CrossRefGoogle Scholar
  11. 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(5):499–510. CrossRefGoogle Scholar
  12. Huijbregts MAJ, Gilijamse W, Ragas AMJ, Reijnders L (2003) Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling evaluating uncertainty in environmental life-cycle assessment. Environ Sci Technol 37(11):2600–2608. CrossRefGoogle Scholar
  13. 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(4):485–492. CrossRefGoogle Scholar
  14. Mattila T, Kujanpää M, Dahlbo H, Soukka R, Myllymaa T (2011) Uncertainty and sensitivity in the carbon footprint of shopping bags. J Ind Ecol 15:217–227CrossRefGoogle Scholar
  15. Mutel C (2017) Brightway: an open source framework for life cycle assessment. J Open Source Softw 2.
  16. Qin Y, Suh S (2017) What distribution function do life cycle inventories follow? Int J Life Cycle Assess 22(7):1138–1145. CrossRefGoogle Scholar
  17. Quantis (2017) World food life cycle assessment database. Accessed 18 Dec 2017
  18. Roy PO, Deschênes L, Margni M (2014) Uncertainty and spatial variability in characterization factors for aquatic acidification at the global scale. Int J Life Cycle Assess 19:882–890CrossRefGoogle Scholar
  19. Schenker U, Espinoza-Orias N, Popovic D (2014) EcodEX: a simplified ecodesign tool to improve the environmental performance of product development in the food industry. 9th International Conference on Life Cycle Assessment in the Agri-Food Sector R. Schenck and D. Huizenga. San Francisco (US)Google Scholar
  20. Selerant (2017) EcodEX ecodesign software. Accessed 18 Dec 2017
  21. Suh S, Qin Y (2017) Pre-calculated LCIs with uncertainties revisited. Int J Life Cycle Assess 22(5):827–831. CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Polytechnique Montreal, CIRAIGMontréalCanada
  2. 2.Paul Scherrer InstituteVilligen PSISwitzerland
  3. 3.Nestlé Research CenterLausanneSwitzerland

Personalised recommendations