Uncertainty analysis in LCA using precalculated aggregated datasets

  • Pascal Lesage
  • Chris Mutel
  • Urs Schenker
  • Manuele Margni
UNCERTAINTIES IN LCA
  • 130 Downloads

Abstract

Purpose

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.

Methods

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.

Conclusions

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.

Keywords

Aggregated datasets ecoinvent Uncertainty analysis 

Notes

Acknowledgements

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)

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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

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