Integrated life cycle sustainability assessment using fuzzy inference: A novel FELICITA model

Abstract

This paper presents a new FELICITA (Fuzzy Evaluation for Life Cycle Integrated Sustainability Assessment) model which integrates life cycle assessment (LCA), life cycle costing (LCC) and social LCA (SLCA) into a fuzzy inference framework. The input data for the model are sustainability indicators estimated through LCA, LCC and SLCA. These are first normalised, then fuzzified, processed using the Mamdani fuzzy inference system and finally defuzzified using the centre-of-area method. This results in three composite indicators, one each for LCA, LCC and SLCA, providing crisp numerical values. This process is then repeated, using the composite indicators as the inputs to obtain an overall life cycle sustainability indicator for each alternative being considered in the decision-making process. These results are used to rank alternatives of interest and identify the most sustainable option(s). The application of the model is illustrated by a case study considering different electricity technologies. FELICITA is particularly useful in helping deal with imprecise (fuzzy) information often encountered in sustainability assessments and it can be used as a practical tool for decision making and policy development related to sustainable development.