Hybrid modelling applied in favour of the performance of the Brazilian plastic waste management system

Abstract

This empirical study aims to suggest improvements to the performance of plastic waste management (PWM) systems. It uses a hybrid modeling integrating Partial Least Squares Structural Equation Modeling (PLS-SEM), Interpretive Structural Modeling (ISM), and Matrix of Impact Cross-Multiplication Applied to Classification (MICMAC). The data for the modeling were collected through a structured questionnaire and a semi-structured questionnaire targeting the recycling stakeholders of this type of waste. The first questionnaire asked about the importance of various indicators from the literature; on the other hand, the second questionnaire asked about the relationships between constructs. Regarding the results, socioeconomic factors and organizational features correlate with the system effectiveness and are driving constructs influencing the dependent constructs: system efficiency, system effectiveness, and system performance. The results also say that efficiency correlates with performance, highlighting the importance of high productivity and cost-effective recycling processes. So, when deciding about PWM, the local public service should observe population density, per-capita income, and popular consciousness about sorting waste at the source; in turn, organizations should focus on personnel training, advertising, and incentive-based schemes to enhance the performance of PWM systems. Also considering the results, an application framework proposal for systems assessment was developed, merging them with ones from another framework evaluating PWM systems. This framework proposal is structured into the basic and subsequent application levels. The basic level includes six constructs: socioeconomic factors, organizational features, policies, technological status, recovery value, and supply chain coordination. The subsequent level comprises three constructs: efficiency, effectiveness, and performance. As for theoretical implications, this study presents a comprehensive hybrid modeling considering technical, socioeconomic, and environmental variables for a higher-performance plastic waste management system. Regarding the practical implications, the framework proposal provides practitioners with a measurement and tracking tool to help interventions for improving PWM systems. Furthermore, this study can contribute to plans like Planares (the new national solid waste plan) in Brazil—the world's fourth-largest plastic waste generator—a country requiring a systematic improvement to reach, by 2040, the target of 48.1% Municipal Solid Waste (MSW) yearly recovered. Regarding the social implications, improved PWM systems foster economic opportunities through job creation, reducing environmental pollution, enhancing public health, and promoting community engagement in recycling efforts. This study goes directly towards the Sustainable Development Goals for sustainable cities (SDG 11) and responsible consumption and production (SDG 12).