Optimizing the environmental performance of integrated waste management scenarios by means of linear programming: a case study

Abstract

When municipal waste scenarios are compared by using Life Cycle Assessment, the comparison is usually carried out among a limited number of alternative scenarios identified in advance. Therefore, however accurate and broad the scenario definition may be, the scenario actually generating the lowest environmental impacts might just not be included among the alternatives proposed and analysed. To overcome this limitation, this paper proposes linear programming models developed to identify, among all the potential scenarios, the waste management scenario that minimises one particular environmental impact or a set of impacts at the same time, using environmental data from Life Cycle Assessment. Besides describing the proposed models, a concise overview of solution methods for multi-objective linear programming is provided.These models were tested in a case study and the results obtained are here presented and analysed. As a case-study, a suitable waste management system in the Abruzzo Region, Italy, was identified. In addition, a further hypothetical waste management context, also including an incinerator plant, was considered. Moreover, a sensitivity analysis was carried out to identify how changing distances to plants may affect optimal scenarios.As a result, different best-performing scenarios for the analysed waste management system were obtained, one for each single impact category considered, and one for each solution method adopted. Furthermore, the analysis of the hypothetical context shows how the introduction of an additional treatment plant could affect the system. Both distances and the solution methods used affect the results.The models developed could be used in decision-making processes to identify the best-performing scenario of a waste management system from the environmental point of view. The models are easy to apply and flexible, since they can be modelled according to the context to be analysed by introducing new factors.