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

Abstract

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.

Interpretation of comparative LCAs: external normalization and a method of mutual differences

“…As shown here, the overlap area uses quantitative uncertainty information, which can derive from inventory, characterization, and/or methodological choices (Mendoza Beltran et al 2016). We can identify relevant aspects of a comparison with the overlap area between probability distributions or other approaches that evaluate mutual differences (Heijungs and Kleijn 2001;Prado-Lopez et al 2014;Henriksson et al 2015;Gregory et al 2016;Wei et al 2016). These results do not suffer from the systematic biases of external normalization, as the outputs vary for each LCA application (Fig.…”

“…Another example can be found in Henriksson et al (2015), where authors perform dependent sampling in uncertainty analysis and test hypotheses to evaluate comparative performances. Finally, the use of reliability theory from engineering science has also been incorporated to LCA for the purposes of evaluating the superiority of an alternative with respect to another given probabilistic results (Wei et al 2016). However, illustrations of these methods are limited to two alternatives and implementation of these methods to comparisons involving additional alternatives generates increasingly complicated results.…”

Systems Thinking for Life Cycle Sustainability Assessment: A Review of Recent Developments, Applications, and Future Perspectives

“…These additional uncertainties are mostly related to formalization of the nexus between social, economic, and environmental sub-systems interacting with the system-of-interest (the system assessed). Although methods such as Exploratory Modeling Analysis [18,157] and reliability theory [158], and viability theory [159] might be helpful to address uncertainties, the major source of uncertainties are the assumptions made when mathematically defining dynamic relationships among system parameters (especially in further parts of the system). Overcoming this challenge requires further attention from the scientific community and in-depth research about how things affect each other.…”

Entity Normalization in Life Cycle Assessment: Hybrid Schemes Applied to a Transportation Agency Case Study

“…; Wei et al. ). Additionally, applying category weights on top of the externally normalized results might allow for additional influence of entity goals and ensure that the intrinsic importance of each impact type is appropriately considered in resulting decisions.…”

“…It is important to note that the case-study data did not include uncertainty parameters that might have impacted the apparent degree of differentiation between alternative oil systems. In a rigorous comparison, it would be encouraged to characterize uncertainty in both the product data and normalization references, such as through Monte Carlo simulation, fuzzy methods, or the FORM method (Lloyd and Ries 2007;Finnveden et al 2009;Wei et al 2016). Additionally, applying category weights on top of the externally normalized results might allow for additional influence of entity goals and ensure that the intrinsic importance of each impact type is appropriately considered in resulting decisions.…”