Parameter uncertainty in LCA: stochastic sampling under correlation

UNCERTAINTIES IN LCA

Abstract

Purpose

At the parameter level, data inaccuracy, data gaps, and the use of unrepresentative data have been recognized as sources of uncertainty in life cycle assessment (LCA). In many LCA uncertainty studies, parameter distributions were created based on the measured variability or on “rules of thumb,” but the possible existence of correlation was not explored. The correlation between parameters may alter the sampling space and, thus, yield unrepresentative results. The objective of this article is to describe the effect of correlation between input parameters (and the final product) on the outcome of an uncertainty analysis, carried out for an LCA of an agricultural product.

Methods

After a theoretical discussion about the statistical concepts on the creation of multivariate random distributions for a Monte Carlo simulation, a LCA case study for potatoes was performed. LCA followed the International Standards Organization guidelines, and the CML baseline characterization method was applied. The functional unit was 1 t of potatoes, while the inputs were restricted to inorganic fertilizers and pesticides. Differences among the two ways to assess uncertainty (with or without correlation) were analyzed through Monte Carlo methodologies, based on the respective estimated probability density functions. In order to demonstrate the effect of correlation on the final outcome, only global warming potential, acidification, and eutrophication impact categories are presented.

Results and discussion

The LCA outcome evidenced the highest environmental impact for N-based fertilizers. Environmental impact of the pesticides to the categories considered was minimum, while its contribution in the characterization phase was lower than 10%. Different degrees of correlation were found between the input factors analyzed and also in relation with yield. Uncertainty analysis results indicated a lower uncertainty level for abiotic depletion and global warming when correlation was taken into account, and the Monte Carlo simulations were based on a multivariate sampling space. The results presented allowed the inclusion of the existence of such correlation within the sampling space for a Monte Carlo simulation. Multivariate sampling spaces can be included in LCA uncertainty analysis but only if sensitivity analysis are done previously in order to identify the input factors with the highest contribution to the output uncertainty.

Conclusions

The results of an LCA uncertainty analysis at the parameter level may lead to the wrong conclusions when the input parameters are correlated. Under a Monte Carlo procedure, the sampling space derived from univariate or multivariate normal distributions exert a varying degree of error propagation leading to different responses in the uncertainty analysis.

Keywords

Agricultural LCA Error propagation Input parameters correlation Monte Carlo simulation Multivariate sampling distribution Uncertainty analysis 

References

  1. Baruah DC, Dutta PK (2007) An investigation into the energy use in relation to yield of rice (Oryza sativa) in Assam, India. Agric Ecosyst Environ 120:185–191CrossRefGoogle Scholar
  2. Björklund AE (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7(2):64–72CrossRefGoogle Scholar
  3. Ciroth A, Fleischer G, Steinbach J (2005) Uncertainty calculation in life cycle assessments a combined model of simulation and approximation. Int J Life Cycle Assess 9(4):216–226CrossRefGoogle Scholar
  4. R Development Core Team (2008) R—a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. Available from http://www.r-project.org
  5. Ecoinvent centre (2007) Ecoinvent data v2.0. Swiss centre for life cycle inventories. Available from: http://www.ecoinvent.org
  6. Everitt BS, Dunn G (1991) Applied multivariate data analysis. Edward Arnold, BristolGoogle Scholar
  7. Ferret R, Mendoza G, Castilla M (2004) The influence of agricultural data uncertainty in the life cycle assessment of biodegradable hydraulic lubricants. In: Pahl-Wostl C, Schmidt S, Rizzoli A, Jakeman A (eds) Complexity and integrated resources management—transactions of the 2nd biennial meeting of the international environmental modelling and software society, vol 1. International Environmental Modelling and Software Society, Manno, Switzerland, pp 301–307Google Scholar
  8. Guinée J, Gorrée M, Heijungs R, Huppes G, Kleijn R, de Koning A, van Oers L, Wegener Sleeswijk A, Suh S, Udo de Haes H.A, de Bruijn H, van Duin R, Huijbregts MAJ, Lindeijer E, Roorda AAH, van der Ven BL, Weidema BP (eds) (2002) Life cycle assessment. An operational guide to the ISO standards. VROM & CML, Leiden University, The NetherlandsGoogle Scholar
  9. Heijungs R, Huijbregts MAJ (2004) A review of approaches to treat uncertainty in LCA. In: Pahl-Wostl C, Schmidt S, Rizzoli A, Jakeman A (eds) Complexity and integrated resources management—transactions of the 2nd biennial meeting of the international environmental modelling and software society, vol 1. International Environmental Modelling and Software Society, Manno, Switzerland, pp 332–340Google Scholar
  10. Heijungs R, Suh S, Kleijn R (2004) Numerical approaches to life cycle interpretation: the case of the ecoinvent '96 database. Int J Life Cycle Assess 10(2):103–112CrossRefGoogle Scholar
  11. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. Part I: general framework for the analysis of uncertainty and variability in life cycle assessment. Int J Life Cycle Assess 3(5):273–280CrossRefGoogle Scholar
  12. Huijbregts MAJ, Norris G, Bretz R, Ciroth A, Maurice B, von Bahr B, Weidema B, de Beaufort ASH (2001) Framework for modelling data uncertainty in life cycle inventories. Int J Life Cycle Assess 6(3):127–132CrossRefGoogle Scholar
  13. Huijbregts MAJ, Gilijamse W, Ragas MJ, Reijnders L (2003) Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling. Environ Sci Technol 37(11):2600–2608CrossRefGoogle Scholar
  14. ISO (2006a) ISO 14040: Environmental management—life cycle assessment-principles and framework. ISO 14040:2006(E). International Standards OrganizationGoogle Scholar
  15. ISO (2006b) ISO 14044: Environmental management—life cycle assessment-requirements and guidelines. ISO 14044:2006(E). International Standards OrganizationGoogle Scholar
  16. Johnson RA, Wichern DW (1998) Applied multivariate statistical analysis, 4th edn. Prentice Hall, Upper Saddle River, New JerseyGoogle Scholar
  17. Karkacier O, Gokalp Goktolga Z, Cicek A (2006) A regression analysis of the effect of energy use in agriculture. Energy Policy 34:3796–3800CrossRefGoogle Scholar
  18. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment. A survey of quantitative approaches. J Ind Ecol 11(1):161–179CrossRefGoogle Scholar
  19. May JR, Brennan DJ (2003) Application of data quality assessment methods to an LCA of electricity generation. Int J Life Cycle Assess 8(4):215–225CrossRefGoogle Scholar
  20. Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment. Part 2: impact assessment and interpretation. Int J Life Cycle Assess 13(5):374–388CrossRefGoogle Scholar
  21. Sonnemann GW, Schuhmacher M, Castells F (2003) Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. J Cleaner Prod 11:279–292CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Centro de Investigaciones y Asesorías Agroindustriales, Facultad de Ciencias NaturalesUniversidad de Bogotá Jorge Tadeo LozanoChíaColombia
  2. 2.Department of Biosystems, Faculty of Applied Bioscience EngineeringKatholieke Universiteit Leuven, Geo-InstituteHeverleeBelgium

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