Screening green and low-carbon paste backfilling materials: Framework development and field application at an abandoned copper mine

Global increasing deep mining is coupled with a large number of goafs in abandoned mines, exacerbating land subsidence and environmental contamination risk. Adoption of paste backfilling in goafs is a viable technique to solve the challenges. However, screening suitable backfilling materials is complex because it is associated with multiple influencing factors, requiring a robust and systematic evaluation framework. This study proposed a systematic methodology integrating the fuzzy theory, life cycle assessment (LCA), analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) to select suitable paste backfilling materials. A hierarchical structure comprising performance parameters, mining conditions, economy, environmental risk, and life-cycle carbon emission was constructed using item objective congruence (IOC). Fuzzy AHP with Buckley’s method was applied to compute factor weights, followed by fuzzy TOPSIS to rank candidate materials. The proposed framework was employed in a case study at one abandoned copper mine. Results identified heavy metal contamination, mechanical performance, non-metallic contamination, and operation cost as the most influential factors. A mixture of steel slag, granulated blast-furnace slag, cement, gypsum, unclassified tailings, and Gobi sand emerged as the optimal paste backfilling material components. Sensitivity and engineering validation confirmed the approach's reliability and practicality.