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.
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References
ADEME (2017) Agribalyse program. http://www.ademe.fr/en/expertise/alternative-approaches-toproduction/agribalyse-program. Accessed April 1, 2017
Besanko D, Dranove D, Shanley M (2004) Economics of strategy. Wiley, Hoboken
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 67–83
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 15:79–89
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):3–9
Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15(5):511–520. https://doi.org/10.1007/s11367-010-0158-5
Heijungs R, Kleijn R (2001) Numerical approaches towards life cycle interpretation five examples. Int J Life Cycle Assess 6(3):141–148. https://doi.org/10.1007/BF02978732
Heijungs R, Lenzen M (2014) Error propagation methods for LCA—a comparison. Int J Life Cycle Assess 19(7):1445–1461. https://doi.org/10.1007/s11367-014-0751-0
Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9900-9
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. https://doi.org/10.1007/s11367-017-1265-3
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. https://doi.org/10.1371/journal.pone.0121221
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. https://doi.org/10.1007/s11367-010-0175-4
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. https://doi.org/10.1021/es020971+
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. https://doi.org/10.1111/jiec.12001
Mattila T, Kujanpää M, Dahlbo H, Soukka R, Myllymaa T (2011) Uncertainty and sensitivity in the carbon footprint of shopping bags. J Ind Ecol 15:217–227
Mutel C (2017) Brightway: an open source framework for life cycle assessment. J Open Source Softw 2. https://doi.org/10.21105/joss.00236
Qin Y, Suh S (2017) What distribution function do life cycle inventories follow? Int J Life Cycle Assess 22(7):1138–1145. https://doi.org/10.1007/s11367-016-1224-4
Quantis (2017) World food life cycle assessment database. http://quantis-intl.com/tools/databases/wfldb-food/. Accessed 18 Dec 2017
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 19:882–890
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)
Selerant (2017) EcodEX ecodesign software. http://www.selerant.com/. Accessed 18 Dec 2017
Suh S, Qin Y (2017) Pre-calculated LCIs with uncertainties revisited. Int J Life Cycle Assess 22(5):827–831. https://doi.org/10.1007/s11367-017-1287-x
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.
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Simplest possible case (HTML rendition of Jupyter Notebook) (HTML 304 kb)
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Water bottle LCA example - code (HTML rendition of Jupyter Notebook) (HTML 981 kb)
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Water bottle LCA example, detailed results (Excel spreadsheet) (XLSX 11 kb)
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Correlation across datasets in ecoinvent code (HTML rendition of Jupyter Notebook) (HTML 932 kb)
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Correlation across pairs of datasets, ecoinvent 2.2 (Large zip files) (XLSX 10 kb)
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Correlation across pairs of datasets, ecoinvent 3.3 (Large zip files) (XLSX 10 kb)
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Burger LCA deterministic results (Excel spreadsheet) (XLSX 17 kb)
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Code for burger uncertainty analysis (HTML rendition of Jupyter Notebook) (HTML 852 kb)
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Lesage, P., Mutel, C., Schenker, U. et al. Uncertainty analysis in LCA using precalculated aggregated datasets. Int J Life Cycle Assess 23, 2248–2265 (2018). https://doi.org/10.1007/s11367-018-1444-x
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DOI: https://doi.org/10.1007/s11367-018-1444-x