The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders.
Keywords: Benchmark; Catalog of data; Optimization; Review; Uncertainty analysis; Waste management.
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