Abstract
Purpose
Life cycle inventory (LCI) databases provide generic data on exchange values associated with unit processes. The “ecoinvent” LCI database estimates the uncertainty of all exchange values through the application of the so-called pedigree approach. In the first release of the database, the used uncertainty factors were based on experts’ judgments. In 2013, Ciroth et al. derived empirically based factors. These, however, assumed that the same uncertainty factors could be used for all industrial sectors and fell short of providing basic uncertainty factors. The work presented here aims to overcome these limitations.
Methods
The proposed methodological framework is based on the assessment of more than 60 data sources (23,200 data points) and the use of Bayesian inference. Using Bayesian inference allows an update of uncertainty factors by systematically combining experts’ judgments and other information we already have about the uncertainty factors with new data.
Results and discussion
The implementation of the methodology over the data sources results in the definition of new uncertainty factors for all additional uncertainty indicators and for some specific industrial sectors. It also results in the definition of some basic uncertainty factors. In general, the factors obtained are higher than the ones obtained in previous work, which suggests that the experts had initially underestimated uncertainty. Furthermore, the presented methodology can be applied to update uncertainty factors as new data become available.
Conclusions
In practice, these uncertainty factors can systematically be incorporated in LCI databases as estimates of exchange value uncertainty where more formal uncertainty information is not available. The use of Bayesian inference is applied here to update uncertainty factors but can also be used in other life cycle assessment developments in order to improve experts’ judgments or to update parameter values when new data can be accessed.
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References
Ben Letham CR (2012) 15.097 probabilistic modeling and Bayesian analysis 15.097 prediction: machine learning and statistics - spring 2012: MIT OpenCourseWare.
Björklund A (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7(2):64–72
Ciroth A, Muller S, Weidema B, Lesage P (2012) Refinig the pedigree matrix approach in ecoinvent (Version 7.1). Report for the ecoinvent database
Ciroth A, Muller S, Weidema B, Lesage P (2013) Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int J Life Cycle Assess. doi:10.1007/s11367-013-0670-5, 1–11
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
Funtowicz S, Ravetz, J (1990) Uncertainty and quality in science for policy. Kluwer Academic Publishers, Dordrecht
Heijungs R, Lenzen M (2014) Error propagation methods for LCA—a comparison. Int J Life Cycle Assess 19(7):1445–1461
Henriksson P, Guinée J, Heijungs R, de Koning A, Green D (2014) A protocol for horizontal averaging of unit process data—including estimates for uncertainty. Int J Life Cycle Assess 19(2):429–436
Hong J, Shaked S, Rosenbaum R, 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
Huijbregts M (1998) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3(5):273–280
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
INIES (2013) Base nationale de référence sur les impacts environnementaux et sanitaires des produits, équipements et services pour l'évaluation de la performance des ouvrages. INIES, France
Katz RW (2002) Techniques for estimating uncertainty in climate change scenarios and impact studies. Clim Res 20:167–185
Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11(1):161–179
Lo S-C, Ma H-W, Lo S-L (2005) Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. Sci Total Environ 340(1–3):23–33. doi:10.1016/j.scitotenv.2004.08.020
Miller SA, Moysey S, Sharp B, Alfaro J (2013) A stochastic approach to model dynamic systems in life cycle assessment. J Ind Ecol 17(3):352–362
Muller S, Lesage P, Ciroth A, Mutel C, Weidema B, Samson R (2014) The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int J Life Cycle Assess. doi:10.1007/s11367-014-0759-5
Qian SS, Stow CA, Borsuk ME (2003) On Monte Carlo methods for Bayesian inference. Ecol Model 159(2–3):269–277
Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment. Int J Life Cycle Assess 13(5):374–388
Ukidwe N, Bakshi BR, Parthasarathy G (2004) A multiscale Bayesian framework for designing efficient and sustainable industrial systems. Paper presented at the AIChE sustainability engineering conference proceedings, Austin
Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4(3–4):167–174
Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Vadenbo CO, Wernet G (2013) Overview and methodology. Data quality guidelines for the Ecoinvent database version 3. Ecoinvent report 1 (v3). The ecoinvent centre, St Gallen
Acknowledgements
The authors would like to acknowledge the financial support of the industrial partners of the International Life Cycle Chair, a research unit of the CIRAIG: ArcelorMittal, Bombardier, Desjardins Group, Hydro-Québec, LVMH, Michelin, Nestlé, RECYC-QUÉBEC, SAQ, Solvay, Total, Umicore, and Veolia. The authors would also like to acknowledge the support of the two Quebec ministries involved in the project (Ministère du développement durable, de l’environnement et des parcs—now MDELCC—and the Ministère du développement économique, de l’innovation et de l’exportation).
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Muller, S., Lesage, P. & Samson, R. Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information. Int J Life Cycle Assess 21, 1185–1196 (2016). https://doi.org/10.1007/s11367-016-1098-5
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DOI: https://doi.org/10.1007/s11367-016-1098-5