Parameterization in Life Cycle Assessment inventory data: review of current use and the representation of uncertainty

Uncertainties in LCA

Abstract

Purpose

Parameterization refers to the practice of presenting Life Cycle Assessment (LCA) data using raw data and formulas instead of computed numbers in unit process datasets within databases. This paper reviews parameterization methods in the European Reference Life Cycle Data System (ELCD), ecoinvent v3, and the US Department of Agriculture's Digital Commons with the intent of providing a basis for continued methodological and coding advances.

Methods

Parameterized data are reviewed and categorized with respect to the type (raw data and formulas) and what is being represented (e.g., consumption and emission rates and factors, physical or thermodynamic properties, process efficiencies, etc.). Parameterization of engineering relationships and uncertainty distributions using Smirnov transforms (a.k.a. inverse transform sampling), and ensuring uncertain individual fractions (e.g., market shares) sum to the total value of interest are presented.

Results

Seventeen categories of parameters (raw data and formulas) are identified. Thirteen ELCD unit process datasets use 975 parameters in 12 categories, with 124 as raw data points and 851 as formulas, and emission factors as the most common category of parameter. Five additional parameter categories are identified in the Digital Commons for the presentation and analysis of data with uncertainty information, through 146 parameters, of which 53 represent raw data and 93 are formulas with most being uncertainty parameters, percentages, and consumption parameters.

Conclusions

Parameterization is a powerful way to ensure transparency, usability, and transferability of LCI data. Its use is expected to increase in frequency, the categories of parameters used, and the types of computational methods employed.

Keywords

Data Databases LCA Parameterization Uncertainty 

Supplementary material

11367_2012_411_MOESM1_ESM.doc (478 kb)
ESM 1(DOC 477 kb)

References

  1. Birkved M, Hauschild M (2006) PestLCI—a new model for estimation of inventory data for pesticide applications. Ecol Model 198:433–451CrossRefGoogle Scholar
  2. Birkved M, Heijungs R (2011) Simplified fate modelling in respect to ecotoxicological and human toxicological characterisation of emissions of chemical compounds. Int J Life Cycle Assess 16(8):739–747CrossRefGoogle Scholar
  3. Björklund AE (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7(2):64–72CrossRefGoogle Scholar
  4. Bojacá CR, Schrevens E (2010) Parameter uncertainty in LCA: stochastic sampling under correlation. Int J Life Cycle Assess 15(3):238–246CrossRefGoogle Scholar
  5. Dawson FH (1975) Alternatives to the use of tabulated values of distributions in statistical programs. Nature 256:148CrossRefGoogle Scholar
  6. Gaver DP, Kafadar K (1984) A retrievable recipe for inverse t. Am Stat 38:308–311Google Scholar
  7. Gleason JR (2000) A note on a proposed student t approximation. Comput Stat Data An 34:63–66CrossRefGoogle Scholar
  8. Heijungs RR Frischknecht (2005) representing statistical distributions for uncertain parameters in LCA. Int J LCA 10(4):248–254Google Scholar
  9. Hill GW (1970) Algorithm 396 Student's t quantiles. Commun ACM 13(10):619–620CrossRefGoogle Scholar
  10. 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 Asses 15(5):499–510CrossRefGoogle Scholar
  11. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3:273–280CrossRefGoogle Scholar
  12. Ibáñez-Forés V, Bovea M, Simó A (2011) Life cycle assessment of ceramic tiles. Environmental and statistical analysis. Int J Life Cycle Assess 16(9):916–928CrossRefGoogle Scholar
  13. Joint Research Center (2010) International Reference Life Cycle Data System (ILCD). Documentation of LCA data sets. Ispra, Italy: European Commission. Retrieved from lct.jrc.ec.europa.eu/Google Scholar
  14. Kim CS, Hallahan C, Lindamood W, Schaible G, Payne J (2004) A note on the reliability tests of estimates from ARMS data. Agr Resource Econ Rev 33(2):293–297Google Scholar
  15. Koehler KJ (1983) A simple approximation for the percentiles of the t distribution. Technometrics 25(1):103–105Google Scholar
  16. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11:161–179CrossRefGoogle Scholar
  17. 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):374–388CrossRefGoogle Scholar
  18. Röös E, Sundberg C, Hansson P (2010) Uncertainties in the carbon footprint of food products: a case study on table potatoes. Int J Life Cycle Assess 15(5):478–488CrossRefGoogle Scholar
  19. Shaw WT (2006) Sampling Student's T distribution—use of the inverse cumulative distribution function. J Comput Finance 9(4):37–73Google Scholar
  20. Sommer JE, Hoppe RA, Green RC, Korb PJ (1998) Structural and financial characteristics of US farms, 1995: 20th Annual Family Farm Report to Congress. Retrieved from http://www.ers.usda.gov/publications/aib746/
  21. Spiegel MR, Schiller JJ, Srinivasan RA, Alu R (2009) Schaum's outlines—probability and statistics, 3rd edn. McGraw-Hill, New York, NYGoogle Scholar
  22. Ventura A (2011) Classification of chemicals into emission-based impact categories: a first approach for equiprobable and site-specific conceptual frames. Int J Life Cycle Assess 16(2):148–158CrossRefGoogle Scholar
  23. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Vadenbo CO, Wernet G (2011) Overview and methodology: data quality guideline for the ecoinvent database version 3 (final draft_revision 1). Retrieved from http://www.ecoinvent.org/fileadmin/documents/en/ecoinvent_v3_elements/01_DataQualityGuideline_FinalDraft_rev1.pdf
  24. Winitzki S (2003) Uniform approximations for transcendental functions. Proceedings of the ICCSA—2003, LNCS 2667 (p. 962). Presented at the International Conference on Computational Science and Its Applications—2003Google Scholar
  25. Winitzki S (2008) A handy approximation for the error function and its inverse. Retrieved from http://homepages.physik.uni-muenchen.de/~Winitzki/erf-approx.pdf

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Design for Environment LaboratoryUniversity of WashingtonSeattleUSA

Personalised recommendations