Multi-actor multi-criteria sustainability assessment framework for energy and industrial systems in life cycle perspective under uncertainties. Part 2: improved extension theory

Abstract

The concept of sustainability and sustainable development has been widely incorporated in energy and industrial systems. This paper is the second part of a two-paper series dealing with multi-actor multi-criteria sustainability assessment of alternative energy and industrial systems in life cycle perspective under uncertainties. The criteria system including four macroscopic aspects (environmental, safety, social and economic aspects) has been developed for sustainability assessment of energy and industrial systems. An improved extension theory which can address interval decision-making matrix has been developed for determining the sustainability degree of the energy and industrial systems. The weights of the criteria for sustainability assessment are the first part of the two-paper series. An illustrative case has been studied by the proposed multi-criteria decision-making method, and the sustainability of six alternative options for the production of a 1-t product was investigated. The sustainability degree of these six alternative options can be determined by the proposed method. A methodology for multi-actor multi-criteria sustainability assessment of energy and industrial options has been developed in this study, the traditional extension theory has been modified to deal with the uncertainty problems and the proposed method can rank the alternative energy and industrial systems with the decision-making matrix in which the data of the alternatives with respect to the evaluation criteria are intervals. In the improved extension theory, sustainability has been dived into five grades: excellent, good, satisfied, barely adequate and fail. According to the method for calculating the weights of the criteria for sustainability assessment proposed in part 1, these weights were used to calculate the integrated dependent degree which is a measure of what degree an alternative belongs to the classical fields. An optimal programming model for maximizing the satisfied degree has been developed to rank the sustainability sequence of the alternative options and determine the sustainability degree of each alternative.