Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA)

Abstract

Purpose

Comparative life-cycle assessments (LCAs) today lack robust methods of interpretation that help decision makers understand and identify tradeoffs in the selection process. Truncating the analysis at characterization is misleading and existing practices for normalization and weighting may unwittingly oversimplify important aspects of a comparison. This paper introduces a novel approach based on a multi-criteria decision analytic method known as stochastic multi-attribute analysis for life-cycle impact assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.

Methods

To contrast different valuation methods, this study performs a comparative LCA of liquid and powder laundry detergents using three approaches to normalization and weighting: (1) characterization with internal normalization and equal weighting, (2) typical valuation consisting of external normalization and weights, and (3) SMAA-LCIA using outranking normalization and stochastic weighting. Characterized results are often represented by LCA software with respect to their relative impacts normalized to 100 %. Typical valuation approaches rely on normalization references, single value weights, and utilizes discrete numbers throughout the calculation process to generate single scores. Alternatively, SMAA-LCIA is capable of exploring high uncertainty in the input parameters, normalizes internally by pair-wise comparisons (outranking) and allows for the stochastic exploration of weights. SMAA-LCIA yields probabilistic, rather than discrete comparisons that reflect uncertainty in the relative performance of alternatives.

Results and discussion

All methods favored liquid over powder detergent. However, each method results in different conclusions regarding the environmental tradeoffs. Graphical outputs at characterization of comparative assessments portray results in a way that is insensitive to magnitude and thus can be easily misinterpreted. Typical valuation generates results that are oversimplified and unintentionally biased towards a few impact categories due to the use of normalization references. Alternatively, SMAA-LCIA avoids the bias introduced by external normalization references, includes uncertainty in the performance of alternatives and weights, and focuses the analysis on identifying the mutual differences most important to the eventual rank ordering.

Conclusions

SMAA-LCIA is particularly appropriate for comparative LCAs because it evaluates mutual differences and weights stochastically. This allows for tradeoff identification and the ability to sample multiple perspectives simultaneously. SMAA-LCIA is a robust tool that can improve understanding of comparative LCA by decision or policy makers.

This is a preview of subscription content, access via your institution.

Fig 1
Fig 2
Fig 3
Fig 4
Fig 5
Fig 6
Fig 7

References

  1. Bare J, Gloria T (2006) Critical analysis of the mathematical relationships and comprehensiveness of life cycle impact assessment approaches. Environ Sci Technol 40(4):1104–1113

    CAS  Article  Google Scholar 

  2. Bare J, Gloria T, Norris G (2006) Development of the method and us normalization database for life cycle impact assessment and sustainability metrics. Environ Sci Technol 40:5108–5115

    CAS  Article  Google Scholar 

  3. Behzadian M, Kazemzadeh RB, Albadvi A, Aghdasi M (2010) PROMETHEE: a comprehensive literature review. Eur J Oper Res 200:198–215

    Article  Google Scholar 

  4. Boufateh I, Perwuelz A, Rabenasolo B (2011) Multiple criteria decision‐making for environmental impacts optimization. Int J of Business Performance and Supply Chain Modeling 3(1): 28–42

    Google Scholar 

  5. Brans JP, Mareschal B (2005) PROMETHEE methods. Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 163–186

  6. Census Bureau (2012) Industry Statistics NAICS 325611, U.S Department of Commerce. http://www.census.gov

  7. Ecoinvent v2.2 (2011) Swiss Centre for Life Cycle Inventories. www.ecoinvent.org

  8. Figueira J, Mousseau V, Roy B (2005) ELECTRE methods. Int Ser Oper Res Man 78(3):133–162

    Article  Google Scholar 

  9. Finnveden G, Hauschild MZ, Ekvall T et al (2009) Recent developments in life cycle assessment. J Environ Manage 9:1–21

    Article  Google Scholar 

  10. Gelderman J, Schobel A (2011) On the similarities of some multicriteria decision analysis methods. J MCDA 18(3–4):219–230

    Google Scholar 

  11. Heijungs R, Guinee J, Kleijn R et al (2007) Bias in normalization: causes, consequences, detection and remedies. Int J Life Cycle Assess 12(4):211–216

    Google Scholar 

  12. Hertwich EG, Hammit JK (2001) A decision analysis framework for impact assessment, part I: LCA and decision analysis. Int J Life Cycle Assess 6(1):5–12

    CAS  Article  Google Scholar 

  13. International Standardization Organization 14044 (2006) Environmental management—life cycle assessment—requirements and guidelines.

  14. Koehler A, Wildbolz C (2009) Comparing the environmental footprints of home-care and personal-hygiene products: the relevance of different life-cycle phases. Environ Sci Technol 43(22):8643–8651

    CAS  Article  Google Scholar 

  15. Lahdelma RU, Salminen P (2001) SMAA-2: stochastic multicriteria acceptability analysis for group decision making. Oper Res 49(3):444–454

    Article  Google Scholar 

  16. Lahdelma RU, Hokkanen J, Salminen P (1998) SMAA—stochastic multiobjective acceptability analysis. Eur J Oper Res 106:137–143

    Article  Google Scholar 

  17. Lautier A, Rosenbaum RK, Margni M et al (2010) Development of normalization factors for Canada and the United States and comparison with European factors. Sci Total Environ 409(1):33–42

    CAS  Article  Google Scholar 

  18. Le Téno JF (1999) Visual data analysis and decision support methods for nondeterministic LCA. Int J of Life Cycle Assess 4(1):41–47

  19. Linkov I, Satterstrom FK, Yatsalo B et al (2007) Comparative assessment of several multi-criteria decision analysis tools for management of contaminated sediments. Environmental Security in Harbors and Coastal Areas. Springer, Amsterdam, pp 195–215

  20. Norris GA (2001) The requirement for congruence in normalization. Int J Life Cycle Assess 6(2):85–88

    CAS  Google Scholar 

  21. Novozymes (2010) Liquid Enzyme Product LCI Dataset. Granulated Enzyme Product LCI Dataset. Novozymes

  22. Prado V, Rogers K, Seager TP (2012) Integration of MCDA tools in valuation of comparative life cycle assessment. In: Curran MA (ed) Life cycle assessment handbook: a guide for environmentally sustainable products. John Wiley & Sons, Inc., Hoboken, NJ, USA. doi:10.1002/9781118528372.ch19

  23. Rogers M, Bruen M (1998) Choosing realistic values of indifference, preference and veto thresholds for use with environmental criteria within ELECTRE. Eur J Oper Res 107:542–551

    Article  Google Scholar 

  24. Rogers K, Seager TP (2009) Environmental decision-making using life cycle impact assessment and stochastic multi-attribute decision analysis: a case study on alternative transportation fuels. Environ Sci Technol 43(6):1718–1723

    CAS  Article  Google Scholar 

  25. Schmidt WP, Sullivan J (2002) Weighting in life cycle assessments in a global context. Int J Life Cycle Assess 7(1):5–10

    Article  Google Scholar 

  26. Showell EMS (2006) Handbook of detergents, part D: formulation. CRC Press, Boca Raton

    Google Scholar 

  27. The Sustainability Consortium (2011) Product Category Life Cycle Assessment (PCLCA) Laundry Detergent. The Sustainability Consortium "Sustainability Measurement and Reporting System" pilot project

  28. Tylock SM, Seager TP, Snell J et al (2012) Energy management under policy and technology uncertainty. Energ Policy 47:156–163

    Article  Google Scholar 

  29. US Environmental Protection Agency (2011) Municipal solid waste generation, recycling, and disposal in the United States—Table and Figures for 2010

  30. Wang M (2011) GREET 1.0 Software. Center for Transportation Research, Energy Systems Division, Argonne National Laboratory. Copyright © 1999 U Chicago Argonne, LLC

  31. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories — an example of using data quality indicators. J Clean Prod 4:167–174

    Article  Google Scholar 

  32. White P, Carty M (2010) Reducing bias through process inventory dataset normalization. Int J Life Cycle Assess 15:994–1013

    Article  Google Scholar 

Download references

Acknowledgments

A previous version of this paper was presented at the 2013 International Symposium on Sustainable Systems and Technologies. This draft has benefited from the constructive comments made by the audience. In addition, the authors would like to thank the Sustainable Energy & Environmental Decision Science studio at Arizona State University for support throughout the preparation of this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Valentina Prado-Lopez.

Additional information

Responsible editor: Adriana Del Borghi

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 141 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Prado-Lopez, V., Seager, T.P., Chester, M. et al. Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA). Int J Life Cycle Assess 19, 405–416 (2014). https://doi.org/10.1007/s11367-013-0641-x

Download citation

Keywords

  • Comparative life-cycle assessment
  • Decision analysis
  • Normalization
  • Outranking
  • Valuation