Using the Reliability Theory for Assessing the Decision Confidence Probability for Comparative Life Cycle Assessments

• Published in: Environmental science & technology 2016-03, Vol.50 (5), p.2272-2280
• Main Authors: Wei, Wei, Larrey-Lassalle, Pyrène, Faure, Thierry, Dumoulin, Nicolas, Roux, Philippe, Mathias, Jean-Denis
• Format: Article
• Language: English
• Subjects: Decision Making; Environment; Environmental impact; Environmental Sciences; Life cycles; Models, Theoretical; Monte Carlo Method; Probability; Reliability; Reproducibility of Results; Stochastic Processes; Uncertainty
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.5b03683

Comparative decision making process is widely used to identify which option (system, product, service, etc.) has smaller environmental footprints and for providing recommendations that help stakeholders take future decisions. However, the uncertainty problem complicates the comparison and the decision making. Probability-based decision support in LCA is a way to help stakeholders in their decision-making process. It calculates the decision confidence probability which expresses the probability of a option to have a smaller environmental impact than the one of another option. Here we apply the reliability theory to approximate the decision confidence probability. We compare the traditional Monte Carlo method with a reliability method called FORM method. The Monte Carlo method needs high computational time to calculate the decision confidence probability. The FORM method enables us to approximate the decision confidence probability with fewer simulations than the Monte Carlo method by approximating the response surface. Moreover, the FORM method calculates the associated importance factors that correspond to a sensitivity analysis in relation to the probability. The importance factors allow stakeholders to determine which factors influence their decision. Our results clearly show that the reliability method provides additional useful information to stakeholders as well as it reduces the computational time.