Towards sustainable waste management: A systematic PRISMA review of environmentally responsible landfill siting

Global municipal solid waste (MSW) generation is increasing rapidly, driven by population growth expected to reach 10 billion by 2050. Currently, 2.01 billion tonnes of MSW are generated annually, with 33% unmanaged, particularly in developing countries where open dumping persists. Proper landfill siting is therefore essential to minimize environmental and public health impacts. This systematic review evaluated 50 landfill site selection studies (2020-2025) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Multi-criteria decision-making (MCDM) techniques dominate, with the analytical hierarchy process applied in 12 studies (24%), while only 8 studies (16%) adopted hybrid models integrating geographic information systems, fuzzy logic, clustering or optimization. A total of 81 criteria were analysed across the studies: topographic factors were used in 84%, hydrological in 78%, geological in 72%, infrastructural in 68%, and socio-economic 56%. However, critical parameters such as soil pH and air quality indices were not applied in any of the studies, and predictive modelling for land-use, population, and household density changes was not implemented. Likewise, remote sensing (RS) applications were rarely used, with Normalized Differential Vegetation Indices and Normalized Difference Built-up Index dominating, while advanced indices such as Modified Normalized Water Index, Modified Soil Adjusted Vegetation Index, and Topographic Wetness Index were used in fewer studies. Furthermore, 84% of reviewed works relied on single models, reflecting methodological conservatism and limited innovation. The findings highlight overdependence on expert judgement, inconsistent regional guidelines and inadequate integration of sustainability-oriented indicators. To strengthen alignment with United Nations Sustainable Development Goals, future research should prioritize explainable artificial intelligence, machine learning-based MCDM, predictive modelling and expanded RS datasets, enabling scientifically rigorous and more socially acceptable landfill siting.