1240 while reducing waste generation (Stahel, 2016). In mining systems, circularity is implemented through changes to extraction, processing, water management, and downstream reuse pathways, including crossindustry exchanges that resemble industrial symbiosis and eco-industrial development (Chertow, 2000; Geetha, 2025). From a systems perspective, these strategies create technical opportunities but also introduce implementation challenges related to integration, risk, compliance, and performance stability. Although research on mining circularity has expanded rapidly since 2000, it remains fragmented across waste streams, process routes, and performance definitions. Many studies report strong process-level results, but comparison across studies is difficult because papers often use different metrics and units (for example, recovery versus removal efficiency), define performance over different system scopes (single unit operation versus an integrated process train), and report different sets of indicators (for example, technical recovery without consistent operational or economic metrics). Typical reporting is strongest at the process level, where studies commonly quantify recovery yield, selectivity, reagent and energy intensity, process time and productivity, operational stability, and by-product handling (Kaya et al., 2020; Mourdikoudis & Dominguez-Benetton, 2025). These indicators are useful for demonstrating technical feasibility, but they do not, by themselves, show whether a pathway can be integrated into an operating plant and maintained under realistic variability. As a result, traceability from technical improvements to operational outcomes, such as reliability under variable feed conditions or integration into existing processing flowsheets, is uneven, and traceability to economic outcomes such as operating cost per ton, margin per ton, or risk-adjusted viability is even less consistent (Khan & Magweregwede, 2025; Mulopo, 2022). This creates a gap between process evidence and decision-ready evidence for adoption and scale-up. As a result, the literature contains many strong technical contributions, but fewer studies that make it clear how a recovery route becomes a stable plant-level practice with repeatable performance. A key reason for this gap is that moving to plant-scale implementation is both a technical and an organizational challenge. Beyond proving that a recovery mechanism works, implementation requires coordination across functions, disciplined execution, monitoring and control routines, knowledge capture across shifts and contractors, and learning cycles that diagnose failures and institutionalize improvements. These dynamics align with organizational learning theory, which explains how practices become embedded in routines over time (Crossan et al., 1999), and with capability perspectives that stress a firm’s ability to absorb external knowledge and reconfigure routines as conditions change (Cohen & Levinthal, 1990; Teece, 2007). Culture also matters because it shapes whether standardization, continuous improvement, and crossfunctional problem-solving become normal operating behavior, which is why culture frameworks are often used to assess readiness for disciplined execution and change (Cameron & Quinn, 2011). In the mining circularity literature, however, organizational culture and learning are rarely operationalized as explicit constructs; they tend to appear indirectly through adoption barriers, governance requirements, collaboration needs, and integration constraints (Khan & Magweregwede, 2025). 2. OBJECTIVES AND SCOPE This paper addresses: Technology (mapping recovery and circularity pathways), Trust (improving traceability and comparability of performance claims), and Transformation (proposing an implementable framework and measurement agenda that supports adoption and scale-up analysis). The objectives are: - Map the research landscape (2000–2025) on circularity and recovery in mining using bibliometric performance analysis and science mapping. - Analyze a targeted full-text set using a traceability logic that links waste stream families, recovery mechanism families, and deployment conditions.
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