1005 • governance oversight aligned with lifecycle decision‑making. This case illustrates that integrated management systems do not eliminate uncertainty in circular tailings strategies; rather, they enable its structured management, supporting risk‑informed decisions and reducing reliance on informal knowledge and ad‑hoc coordination. As such, they constitute a critical enabler for aligning circular economy initiatives with engineering rigor and governance expectations throughout the lifecycle of mining operations. 7. LIMITATION AND CRITICAL CONSIDERATIONS While digitalization and integrated data systems offer important support for implementing circular economy strategies in tailings management, their application is subject to a number of limitations and critical considerations that must be explicitly acknowledged. Failure to recognize these constraints may lead to overconfidence in technological solutions and an underestimation of residual risks. One of the primary limitations relates to data quality and completeness. Integrated systems are inherently dependent on the accuracy, consistency, and representativeness of the data they receive. Historical tailings facilities, legacy datasets, and long‑operating sites often suffer from incomplete material characterization, limited monitoring records, or undocumented operational changes. In such contexts, digital integration alone cannot compensate for data gaps, and decisions based on incomplete information may create a false sense of confidence rather than genuine risk reduction (Vick, 1990; Morgenstern et al., 2016). Another critical consideration concerns the maturity of organizational governance structures. Digital tools may facilitate documentation and traceability, but they do not inherently resolve issues related to unclear accountability, fragmented responsibilities, or inconsistent decision authority. Where governance roles and escalation pathways are poorly defined, even well‑structured information environments may fail to ensure that circular initiatives are evaluated, approved, and implemented within appropriate risk boundaries (Franks et al., 2021; ICMM, 2020). The increasing use of data analytics and artificial intelligence (AI) introduces additional limitations that warrant careful consideration. While AI‑based methods can support efficiency in data organization, trend detection, and documentation workflows, their outputs remain dependent on the assumptions embedded in models and on the training data used. In tailings management, where rare but high‑consequence failure modes are a central concern, there is a risk that data‑driven approaches may inadequately represent low‑probability, high‑impact scenarios if not complemented by conservative engineering judgment and qualitative risk assessment (Bugayev et al., 2022; Morgenstern et al., 2016). From a technical standpoint, circular economy strategies themselves introduce irreversible interventions in tailings systems. Reprocessing, re‑mining, or modifying disposal methods can permanently alter material properties, hydraulic behavior, and stability mechanisms. Once these changes are implemented, the ability to revert to previous configurations may be limited or nonexistent. This reinforces the need for cautious, stepwise decision‑making and for explicit consideration of uncertainty and long‑term consequences when evaluating circular alternatives (Lottermoser, 2010; Franks et al., 2021). Regulatory and institutional constraints also represent significant limiting factors. The reuse of tailings materials, for example, may be restricted by regulatory frameworks that were not designed to accommodate circular applications. Similarly, approval processes for changes to tailings management strategies may vary significantly across jurisdictions, introducing delays or uncertainties that must be incorporated into decision‑making (Chinchin, 2008; ICMM, 2020). Finally, there is a critical risk associated with the conceptual framing of circular economy initiatives. When circularity is promoted primarily as a sustainability or reputational objective, without equivalent emphasis on engineering feasibility and risk management, there is a danger that such initiatives may be prioritized despite unresolved technical or governance concerns. In the context of tailings management,
RkJQdWJsaXNoZXIy MTM0Mzk2