Abstract
Abstract Urban solid waste management is a complex system due to the many variables that compose it, which makes it difficult for managers to make decisions. Among the decision-making tools is system dynamics. To identify the gaps between the literature and the studies about urban solid waste management by using system dynamics method, a survey of published papers on the subject was made, which were later analyzed using some defined criteria, such as the level of the study, the software used for modeling, the variables included in the model, among others. The research identified that, among the stages of solid waste management, the collection process is still little incorporated in the models, being mentioned in only nine studies analyzed. Besides, the environmental, financial and institutional requirements were not adequately explored in the models. Future research should endeavor to include these elements in the models, which will allow the proposed system to be approximated to the complex reality of solid waste management.
Highlights
Globalization has provided new life perspectives and triggered behavioral changes due to the development achieved (Rosa & Andrade, 2016)
The legislation establishing the Brazilian Solid Waste Policy defines Urban Solid Waste (USW) as that coming from households and urban cleaning (Brasil, 2010)
The following criteria were adopted: (1) the level of application of the model; (2) the software used for simulation; (3) the nature of the data used in the model, which may be real or estimated; (4) how waste is addressed, that is, whether it is considered by its typology or in an aggregated way; (5) the variables simulated in the proposed scenarios; (6) and, if other methods were combined with DS
Summary
Globalization has provided new life perspectives and triggered behavioral changes due to the development achieved (Rosa & Andrade, 2016). A viable alternative for dealing with complex systems, which includes solid waste management planning, is the use of quantitative techniques. These tools enable the representation of reality to study their behavior and make decisions based on the conclusions obtained (Simonetto & Löbler, 2014). The simulation of different scenarios by the DS method allows users to accelerate the collective learning about possible behaviors and impacts of complex systems; model and test policy and program design options; analyze and improve business processes; design and test new strategies that can conduct to better results; and lead to realistic decisions and more likely to achieve your goals (Healthy London, 2018).
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