Multi-Criteria Decision Analysis towards promoting Waste-to-Energy Management Strategies: A critical review

TLDR: A critical review of 153 published papers addressing specific issues and questions can be found in this article, where the authors show that Asian and European countries are producing the most MCDAs studies on waste-to-energy management strategies.

Abstract: Waste production is constantly increasing worldwide and constitutes a problem that is expected to deteriorate in the future. In parallel, ensuring access to affordable and sustainable energy for all is crucial considering current environmental, economic, and social concerns. Waste-to-Energy (WtE) is an effective solution to address both issues, therefore it requires intense scientific attention. WtE management is characterized by different technologies, refers to various waste types, and needs multidisciplinary decision support. Thus, it is critical to include multiple criteria in the decision making process i.e. economic, technological, environmental, social, and political. These reflect different objectives that often come into conflict with each other. Multi-Criteria Decision Analysis (MCDA) is a tool that can effectively contribute to answer that challenge. The paper reviews the way, the scope, and the multi-criteria techniques that have been applied up-to-now to WtE Management Strategies (WtEMSs) in the globe. A critical review of 153 published papers addresses specific issues and questions. Asian and European countries are producing the most MCDAs studies on WtEMS. An increasing trend of papers commences from the year 2007. Results depict that Analytical Hierarchy Process is the most common approach, adopted in 62 real-life cases. Incineration and anaerobic digestion are mostly studied in MCDA frameworks. Emphasis is given on critical analysis and lessons that can be learnt from the available literature. Policy makers are motivated to: (i) adopt MCDA to holistically make WtEMSs decisions, (ii) adapt to local characteristics, (iii) encounter logistic problems, and (iv) efficiently promote implementation in real-life cases.