Agent-based modeling in solid waste management: Advantages, progress, challenges and prospects

Abstract

The growing issue of solid waste management (SWM) is recognized as a significant challenge to ecosystem preservation. Agent-based modeling (ABM) has received significant attention for its capability to address complex systems and simulate the outcomes of strategic implementation. This review compares ABM with other methods and provides a comprehensive overview of research on ABM in SWM from 2000 to 2023, emphasizing its advantages, progress, challenges, and future directions. Results indicate that: 1) ABM possesses 8 key advantages in simulating individual behavior, responses to environmental changes across time and spatial scales, and decision-making processes, namely interactivity, heterogeneity, dynamism, traceability, spatiality, scalability, complexity, and adaptability. 2) Current research primarily focuses on simulating behavioral and strategic effects of SWM (accounting for 45.5 %), while multi-model coupling is becoming a new trend. 3) ABM encounters challenges in its research, including a lack of standardized research steps, high data dependency, limited computing resources, and difficulties in algorithm explanation. Therefore, this study introduces a set of normative steps that provide clear guidance for research and help ensure the reproducibility and accuracy of studies. Future research should incorporate big data and emerging technologies to enhance computational efficiency and processing capabilities of models. To better utilize ABM for achieving environmental protection and sustainable development goals, prioritizing integration of multi-model coupling, interdisciplinary collaboration, visualization, and open-source code sharing as key strategies is essential.