Geographic information systems for sustainable municipal solid waste management: A systematic review and bibliometric analysis

Municipal solid waste management (MSWM) remains one of the most pressing environmental challenges, particularly in developing countries, where rapid urbanization and population growth intensify waste generation. The advancement of Geographic Information Systems (GIS) has significantly improved waste management planning by enabling efficient data capture, integration and spatial analysis. This study presents a systematic literature review of 473 peer-reviewed articles published between 2000 and 2024, focusing on the applications of GIS in MSWM. The review examines GIS models and methodologies across key domains, including landfill and treatment facility siting, route optimization for waste collection, transfer station planning and integrated waste management strategies. The systematic literature review followed three-step process, which includes: bibliometric search, scientometric analysis and qualitative analysis. These helped in the identification of the foremost and effective articles, journals, researchers and countries that actively participate in the field of research study. The review reveals that landfill siting and collection optimization dominate GIS-based research, with India, Iran, Turkey and the United States contributing significantly, whereas recycling, waste-to-energy and transfer station planning remain underexplored. Most studies employ GIS in combination with multi-criteria decision-making (MCDM) techniques such as analytic hierarchy process, analytic network process and technique for order preference by similarity to ideal solution, and fuzzy logic, although newer approaches integrating hybrid MCDM frameworks, artificial intelligence and machine learning are gaining momentum. This review highlights these research gaps while underscoring the advantages of GIS in integrating large spatial datasets and supporting transparent decision-making. Future directions include embedding real-time data, AI-driven predictive modelling and sustainability-oriented frameworks to improve the scalability and applicability of GIS-based waste management systems, thereby supporting the achievement of the Sustainable Development Goals.