Sustainability assessment is a very complex appraisal method. It entails not only multidisciplinary aspects (environmental, economic and social), but also cultural and value-based elements. There have been various types of sustainability indicators for different types of systems and applications, and methodologies for indicator formulation strategy, data scaling, normalization, weighting, and aggregation. The practicing approaches for developing SA frameworks are: (a) a top-down approach – to enable experts/researchers to define and overall structure for achieving sustainability and subsequently indicators, (b) a bottom-up approach – to require participation of various stakeholders to understand the framework and propose key indicators, and (c) a mix of both. Each sustainability dimension (i.e., environmental, economic, or social) requires selection of a number of indicators to measure the performance of different aspects. This has posed a challenge: data/information uncertainty, which is pervasive in almost every phase of sustainability study. Examples include the available data about material or energy utilization, toxic/hazardous waste generation, and market fluctuation; the multifaceted makeup of the inter-entity dynamics, dependencies, and relationships; the prospect of forthcoming environmental policies, and the interrelationship among the triple-bottom-line aspects of sustainability, weighting methods, weights’ values and aggregation methods.
In this presentation, we will discuss the characteristics of two classes of uncertainties, i.e., the aleatory and epistemic uncertainties that appear in various types of industrial sustainability problems. We will also describe four uncertainty processing methods: the probability bounds analysis method, the fuzzy arithmetic method, the information gap theory based method, and the interval parameter based method. The applicability of these methods in sustainability assessment at the product and process level will be discussed, and an application of these methods to geographically distributed biofuel manufacturing will be presented.
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* All correspondence should be addressed to Prof. Yinlun Huang (Phone: (313) 577-3771; E-mail: yhuang@wayne.edu).
