The sustainability assessment of industrial systems is always a very challenging task due to the existence of various types of uncertainties that are associated with the available data, assessable information, possessed knowledge, and problem understanding, etc. This paper introduces a fuzzy logic based approach for the effective short- and long-term assessment of industrial sustainability under the restrictions of aleatory and epistemic uncertainties. A comprehensive study on a metal-finishing-centered industrial zone is illustrated to show the efficacy of the introduced methodology.

Industrial sustainability is a vital issue in pursuing the long-term development of industrial systems. This paper utilizes the existing Ecological Input−Output Analysis (EIOA) method, in combination with known and established environmental (mass intensity) and economic (gross profit) sustainability metrics, to (i) create a systematic analysis methodology capable of evaluating various decisions, which are made by individual plant management for the benefit of their own company, and (ii) determine which option results in the best route for improving sustainable development, at the plant, industry, and regional levels. Such information will be valuable for the synergistic sustainability improvements of both individual entities as well as industrial regions. To demonstrate the efficacy of this methodology, a case study involving a network of different industries is presented. Sustainability of the base case and two modified cases is assessed. The one with the highest positive environmental and economic sustainability impact is recommended.

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