Introducing weights to life cycle sustainability assessment—how do decision-makers weight sustainability dimensions?

  • Peter Tarne
  • Annekatrin Lehmann
  • Matthias Finkbeiner



Decisions based on life cycle sustainability assessment (LCSA) pose a multi-criteria decision issue, as impacts on the three different sustainability dimensions have to be considered which themselves are often measured through several indicators. To support decision-making at companies, a method to interpret multi-criteria assessment and emerging trade-offs would be beneficial. This research aims at enabling decision-making within LCSA by introducing weights to the sustainability dimensions.


To derive weights, 54 decision-makers of different functions at a German automotive company were asked via limit conjoint analysis how they ranked the economic, environmental, and social performance of a vehicle component. Results were evaluated for the entire sample and by functional clusters. Additionally, sustainability respondents, i.e., respondents that dealt with sustainability in their daily business, were contrasted with non-sustainability respondents. As a last step, the impact of outliers was determined. From this analysis, practical implications for ensuring company-optimal decision-making in regard to product sustainability were derived.

Results and discussion

The results showed a large spread in weighting without clear clustering. On average, all sustainability dimensions were considered almost equally important: the economic dimension tallied at 33.5%, the environmental at 35.2%, and the social at 31.2%. Results were robust as adjusting for outliers changed weights on average by less than 10%. Results by function showed low consistency within clusters hinting that weighting was more of a personal than a functional issue. Sustainability respondents weighted the social before the environmental and economic dimension while non-sustainability respondents put the economic before the other two dimensions. Provided that the results of this research could be generalized, the retrieved weighting set was seen as a good way to introduce weights into an operationalized LCSA framework as it represented the quantification of the already existing decision process. Therefore, the acceptance of this weighting set within the respective company was expected to be increased.


It could be shown that conjoint analysis enabled decision-making within LCSA by introducing weights to solve a multi-criteria decision issue. Furthermore, implications for practitioners could be derived to ensure company-optimal decision-making related to product sustainability. Future research should look at expanding the sample size and geographical scope as well as investigating the weighting of indicators within sustainability dimensions and the drivers that influence personal decision-making in regard to weighting sustainability dimensions.


Automotive company Conjoint analysis LCSA Limit conjoint analysis MCDA Multi-criteria decision analysis Sustainability dimensions Weighting 



This paper is part of a PhD thesis sponsored by the BMW Group. The authors thank the valuable input of three anonymous reviewers that greatly contributed to the improvement of the manuscript.

Supplementary material

11367_2018_1468_MOESM1_ESM.xlsx (12 kb)
ESM 1 (XLSX 11 kb)


  1. Adepoju JA, Ipinyomi RA (2016) Construction of asymmetric fractional factorial designs. Int J Eng Appl Sci 3:88–91Google Scholar
  2. Alfares HK, Duffuaa SO (2008) Assigning cardinal weights in multi-criteria decision making based on ordinal ranking. J Multi-Criteria Decis Anal 15:125–133CrossRefGoogle Scholar
  3. Alriksson S, Öberg T (2008) Conjoint analysis for environmental evaluation. Environ Sci Pollut Res 15:244–257CrossRefGoogle Scholar
  4. Backhaus K, Erichson B, Plinke W, Weiber R (2011a) Multivariate Analysemethoden. Eine anwendungsorientierte Einführung, 13th edn. Springer-Verlag, BerlinGoogle Scholar
  5. Backhaus K, Erichson B, Weiber R (2011b) Fortgeschrittene Multivariate Analysemethoden. Eine anwendungsorientierte Einführung., 1st edn. Springer-Verlag, BerlinCrossRefGoogle Scholar
  6. Baier D, Brusch M (eds) (2009) Conjointanalyse. Springer Berlin Heidelberg, BerlinGoogle Scholar
  7. Bond A, Morrison-Saunders A, Pope J (2012) Sustainability assessment: the state of the art. Impact Assess Proj Apprais 30:53–62CrossRefGoogle Scholar
  8. Cattin P, Wittink DR (1982) Commercial use of conjoint analysis: a survey. J Mark 46:44–53CrossRefGoogle Scholar
  9. Choo EU, Schoner B, Wedley WC (1999) Interpretation of criteria weights in multicriteria decision making. Comput Ind Eng 37:527–541CrossRefGoogle Scholar
  10. Cortés-Borda D, Guillén-Gosálbez G, Esteller LJ (2013) On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming. Int J Life Cycle Assess 18:948–957CrossRefGoogle Scholar
  11. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res 22:763–770CrossRefGoogle Scholar
  12. Figueira J, Roy B (2002) Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur J Oper Res 139:317–326CrossRefGoogle Scholar
  13. Finkbeiner M, Reimann K, Ackermann R (2008) Life cycle sustainability assessment (LCSA) for products and processes. In: SETAC Europe 18th annual meeting, 25–29 May. Warsaw, PolandGoogle Scholar
  14. Finkbeiner M, Schau EM, Lehmann A, Traverso M (2010) Towards life cycle sustainability assessment. Sustainability 2:3309–3322CrossRefGoogle Scholar
  15. Finnveden G (1997) Valuation methods within LCA—where are the values? Int J Life Cycle Assess 2:163–169CrossRefGoogle Scholar
  16. Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S, Koehler A, Pennington D, Suh S (2009) Recent developments in life cycle assessment. J Environ Manag 91:1–21CrossRefGoogle Scholar
  17. Green PE, Srinivasan V (1978) Conjoint analysis in consumer research: issues and outlook. J Consum Res 5:103CrossRefGoogle Scholar
  18. Hamlin RP (2005) The rise and fall of the Latin Square in marketing: a cautionary tale. Eur J Mark 39:328–350CrossRefGoogle Scholar
  19. Heijungs R, Huppes G, Guinée JB (2010) Life cycle assessment and sustainability analysis of products, materials and technologies. Toward a scientific framework for sustainability life cycle analysis. Polym Degrad Stab 95:422–428CrossRefGoogle Scholar
  20. Hofstetter P, Braunschweig A, Mettier T, Müller-Wenk R, Tietje O (1999) The mixing triangle: correlation and graphical decision support for LCA-based comparisons. J Ind Ecol 3:97–115CrossRefGoogle Scholar
  21. Itsubo N, Inaba A (2003) A new LCIA method: LIME has been completed. Int J Life Cycle Assess 8:305CrossRefGoogle Scholar
  22. Itsubo N, Sakagami M, Washida T, Kokubu K, Inaba A (2004) Weighting across safeguard subjects for LCIA through the application of conjoint analysis. Int J Life Cycle Assess 9:196–205CrossRefGoogle Scholar
  23. Itsubo N, Sakagami M, Kuriyama K, Inaba A (2012) Statistical analysis for the development of national average weighting factors—visualization of the variability between each individual’s environmental thoughts. Int J Life Cycle Assess 17:488–498CrossRefGoogle Scholar
  24. Johnson R, Orme B (1996) How many questions should you ask in choice-based conjoint studies? Sawtooth Softw Research P:23Google Scholar
  25. JRC (2012) Towards a life-cycle based European sustainability footprint framework. Publications Office of the European Union, LuxembourgGoogle Scholar
  26. Kerkow U, Martens J, Müller A (2012) Vom Erz zum Auto - Abbaubedingungen und Lieferketten im Rohstoffsektor und die Verantwortung der deutschen AutomobilindustrieGoogle Scholar
  27. Klöpffer W (2003) Life-cycle based methods for sustainable product development. Int J Life Cycle Assess 8:157–159CrossRefGoogle Scholar
  28. Klöpffer W (2008) Life cycle sustainability assessment of products. Int J Life Cycle Assess 13:89–95CrossRefGoogle Scholar
  29. Klöpffer W, Grahl B (2014) From LCA to sustainability assessment. In: Life Cycle Assessment (LCA). Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 357–374Google Scholar
  30. Kohne F, Totz C, Wehmeyer K (2005) Consumer preferences for location-based service attributes: a conjoint analysis. Int J Manag Decis Mak 6:16Google Scholar
  31. Mettier TM, Hofstetter P (2004) Survey insights into weighting environmental damages: influence of context and group. J Ind Ecol 8:189–209CrossRefGoogle Scholar
  32. Mettier T, Scholz RW (2008) Measuring preferences on environmental damages in LCIA. Part 2: choice and allocation questions in panel methods. Int J Life Cycle Assess 13:468–476CrossRefGoogle Scholar
  33. Mettier T, Scholz R, Tietje O (2006) Measuring preferences on environmental damages in LCIA. Part 1: cognitive limits in panel surveys (9 pp). Int J Life Cycle Assess 11:394–402CrossRefGoogle Scholar
  34. Orme B (2010) Sample size issues for conjoint analysis. In: Getting started with conjoint analysis: strategies for product design and pricing research, second Edi. Research Publishers LLC, Madison, pp 57–66Google Scholar
  35. Ostermeyer Y, Wallbaum H, Reuter F (2013) Multidimensional Pareto optimization as an approach for site-specific building refurbishment solutions applicable for life cycle sustainability assessment. Int J Life Cycle Assess 18:1762–1779CrossRefGoogle Scholar
  36. Rao VR (2014) Theory and design of conjoint studies (ratings based methods). In: Applied conjoint analysis. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 37–78Google Scholar
  37. Schmidt W-P, Sullivan J (2002) Weighting in life cycle assessments in a global context. Int J Life Cycle Assess 7:5–10CrossRefGoogle Scholar
  38. Scholl A, Manthey L, Helm R, Steiner M (2005) Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: an empirical comparison. Eur J Oper Res 164:760–777CrossRefGoogle Scholar
  39. Sichtmann C, Stingel S (2007) Limit conjoint analysis and Vickrey auction as methods to elicit consumers’ willingness-to-pay. Eur J Mark 41:1359–1374CrossRefGoogle Scholar
  40. Singh RK, Murty HR, Gupta SK, Dikshit AK (2009) An overview of sustainability assessment methodologies. Ecol Indic 15:281–299CrossRefGoogle Scholar
  41. Skiera B, Gensler S (2002) Berechnung von Nutzenfunktionen und Marktsimulationen mit Hilfe der Conjoint-Analyse, Teil I. Wirtschaftswissenschaftliches Stud 31(4):200–206CrossRefGoogle Scholar
  42. Tarne P, Traverso M, Finkbeiner M (2017) Review of life cycle sustainability assessment and potential for its adoption at an automotive company. Sustainability 9:670CrossRefGoogle Scholar
  43. Traverso M, Finkbeiner M, Jørgensen A, Schneider L (2012) Life cycle sustainability dashboard. J Ind Ecol 16:680–688CrossRefGoogle Scholar
  44. Traverso M, Tarne P, Wagner V (2015) Towards a comprehensive approach for the sustainability assessment of a product: product social impact assessment. In: Pfeffer P (ed) 6th International Munich Chassis Symposium 2015. Springer-Verlag, pp 161–174Google Scholar
  45. UNEP/SETAC Life Cycle Initiative (2011) Towards a life cycle sustainability assessment - making informed choices on products. UNEP/SETAC Life Cycle Initiative, FranceGoogle Scholar
  46. Voeth M (2000) Nutzenmessung in der Kaufverhaltensforschung. Deutscher Universitätsverlag, WiesbadenCrossRefGoogle Scholar
  47. Voeth M, Hahn C (1998) Limit conjoint-analyse. Mark Zeitschrift für Forsch und Prax 20:119–132Google Scholar
  48. Wittink DR, Cattin P (1989) Commercial use of conjoint analysis: an update. J Mark 53:91–96CrossRefGoogle Scholar
  49. Wolf J (2011) Sustainable supply chain management integration: a qualitative analysis of the German manufacturing industry. J Bus Ethics 102:221–235CrossRefGoogle Scholar
  50. Yang G, Yang J-B, Xu D-L, Khoveyni M (2017) A three-stage hybrid approach for weight assignment in MADM. Omega 71:93–105CrossRefGoogle Scholar
  51. Zimmer K, Fröhling M, Breun P, Schultmann F (2017) Assessing social risks of global supply chains: a quantitative analytical approach and its application to supplier selection in the German automotive industry. J Clean Prod 149:96–109CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chair of Sustainable Engineering, Institute of Environmental TechnologyTechnische Universität BerlinBerlinGermany
  2. 2.Product SustainabilityBMW GroupMunichGermany

Personalised recommendations