Possibility Theory: A New Approach to Uncertainty Analysis? (3 pp)

  • Enrico Benetto
  • Christiane Dujet
  • Patrick Rousseaux


Background, Aims and Scope

The problem of the evaluation of practitioner's belief and belief-related uncertainties on LCA results obtained from different methodological choices has been addressed so far by scenario modeling, Cultural Theory perspectives and probabilistic simulation. The direct evaluation of belief and related uncertainties could be of interest, e.g. when the information available (resulting from classical uncertainty analysis or the application of the precautionary principle) do not allow one to choose between methodological alternatives leading to different LCA results and conclusions. The difficulty of modeling belief arises from the additive nature of classical measures, e.g. probabilities. Since the 1960s, non-additive measures (e.g. possibilities) have been developed and applied to model belief in real world problems. The aim of this paper is to discuss the application of possibility measures in LCA for uncertainty analysis in complement to classical approaches.


The nature and the meaning of possibilities are briefly introduced by comparison with probabilities (subjective or not) in order to enlighten strengths, drawbacks and complementarities. A tentative possibilistic approach based on the evaluation of a posteriori possibilities of final LCA results depending on a priori possibilities of the methodological choices behind the calculations is described, also by means of an application example.


and Outlook. A new approach for the modeling of practitioner's belief and belief-related uncertainties in complement of classical methods of uncertainty analysis has been proposed for discussion. Uncertainty can be characterized by confidence intervals and indexes that could help practitioners in making methodological choices and could improve the interpretation and reliability of LCA results, still increasing its sophistication.

possibility theory methodological choices fuzzy sets belief uncertainty analysis probability theory 


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Copyright information

© Ecomed 2006

Authors and Affiliations

  • Enrico Benetto
    • 1
  • Christiane Dujet
    • 2
  • Patrick Rousseaux
    • 3
  1. 1.Dr. Enrico Benetto ECOINNOVA – Environmental innovation and assessment consultancy Turin Italy / Lyon France  
  2. 2.Christiane Dujet Laboratoire d'Analyse Environnementale des Procédés et Systèmes Industriels (LAEPSI) INSA-Lyon Villeurbanne France  
  3. 3.Dr. Patrick Rousseaux Laboratoire de Combustion et de Détonique (LCD, UPR 9028 CNRS) Ecole Nationale Supérieure de Mécanique et d'Aérotechnique (ENSMA) FUTUROSCOPE-CHASSENEUIL France  

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