An integrated decision framework for landfill mining site selection using GIS, multi-criteria analysis, and optimization models

Most studies on landfill management focus on siting new landfills (LSS), while the selection of landfill mining sites (LMSS) in environmentally unsuitable areas remains underexplored. To address this gap, this study proposes a novel three-stage decision-making framework that integrates multiple-criteria decision-making (MCDM), geographic information systems (GIS), hesitant fuzzy linguistic term sets (HFLTS), K-means clustering, and a capacitated plant location model (CPLM). Unlike previous research that relies on case-specific analyses or subjective expert judgment, this framework provides a systematic, data-driven approach to optimize landfill mining decisions while minimizing costs. In Stage 1, GIS-based MCDM evaluates landfill suitability. Stage 2 incorporates HFLTS to account for decision-makers' hesitation in criteria weighting, while K-means clustering categorizes landfills into three types: A (lifespan extension), B (do nothing), and C (priority for mining and removal). Finally, in Stage 3, CPLM optimizes excavation, transportation, and disposal costs, integrating enhanced landfill mining (ELFM) efficiency levels (50 %–90 %). Applied in Taiwan, this framework identified 17 landfills for removal, reclaiming 66 ha of land, with an estimated economic benefit of NT$3.96–13.2 G. Sensitivity analysis highlights the cost-reduction potential of ELFM efficiency improvements, demonstrating the framework's adaptability across different policy contexts. The findings provide policymakers with a scientifically grounded tool to advance sustainable waste management and zero-waste initiatives.