A Decision-Support Digital Twin Framework for Sustainable Urban Waste Collection Planning

Municipal solid waste systems are under increasing pressure as urban populations continue to grow. This growth often results in container overflow, inefficient collection routes, and avoidable greenhouse gas emissions. To deal with these challenges, city authorities increasingly rely on decision-support tools that help them evaluate different options before changes are introduced in real-world operations. In this context, this study proposes a decision-support digital twin framework for sustainable urban waste collection planning. The framework brings together short-term predictions of container fill levels, long-term waste generation forecasts, scenario-based simulations, and route planning tools. By combining these elements, municipal managers can test alternative collection strategies and better understand their potential impacts ahead of implementation. Rather than operating as an autonomous system, the digital twin is designed for use in a human-in-the-loop planning setting, where decision-makers can explore “what-if” scenarios related to collection thresholds, operational choices, and environmental outcomes. The approach is demonstrated using real operational and spatial data from the municipality of Cascais, Portugal. The results show that predictive, threshold-based collection strategies can reduce unnecessary travel distances, lower estimated distance-based emissions, and decrease the likelihood of container overflow when compared with static collection practices. In addition, the framework supports informed infrastructure planning decisions, even in situations where historical data are limited. Overall, this work demonstrates how digital twin concepts can be translated into a practical environmental management tool that supports evidence-based decision-making for urban waste collection planning.