85 production of approximately 5.41 Mt in 2025, peaking around 2027 and stabilizing near 6.0 Mt toward the end of the 2025–2034 period (Cochilco, 2025), reinforcing the need for tools that support robust decisions under multiple pressures. In line with the above, this study proposes the use of the Analytic Hierarchy Process (AHP) to structure, prioritize, and weight socio-environmental variables relevant to transition decisions, based on expert-driven pairwise comparisons with consistency verification. As a core contribution, the study provides a transparent and replicable methodological basis to derive relative weights and a final ranking of socio-environmental criteria, supporting the construction of an integrated socio-environmental risk indicator as an input for strategic assessments in mine planning. 2.- STATE-OF-THE-ART TRANSITION MODELS AND KEY DECISION VARIABLES The decision to transition from open-pit mining to underground mining has historically been addressed through models aimed at determining the optimal switching point under profitability and technical feasibility criteria. Broadly, the literature can be grouped into: (i) simplified cost-based economic models, (ii) economic models based on block valuation, (iii) mathematical optimization models (integer and mixed-integer programming), and (iv) extensions incorporating uncertainty and additional constraints such as operational and geotechnical conditions. Early approaches framed the transition mainly as an economic problem. Abdollahisharif et al. (2008) introduced BEV/NPV-based comparisons between continuing as open pit versus switching to underground mining, aligning the assessment with block-based valuation practices. In parallel, geotechnical feasibility became a binding condition through crown pillar design requirements; Bakhtavar et al. (2010), proposed formulations to estimate crown pillar thickness based on rock mass and geometric variables. More recent state-of-the-art research has consolidated the problem as one of optimization with discrete decisions, where the objective is typically to maximize NPV subject to economic, operational, and geotechnical constraints. Chung et al. (2016) address the transition using binary integer programming (BIP) to determine the optimal switching point, incorporating constraints such as pit slope angle, crown pillar stability, and operational extraction conditions. These models enable the simultaneous evaluation of open-pit and underground alternatives within an integrated framework, scaling from 2D representations to 3D models with a large number of blocks, thereby approaching realistic planning scenarios. As an extension, Chung et al. (2022) move toward mixed-integer programming (MIP) with simultaneous production optimization for both methods and explicit incorporation of transition timing, including scenarios with production delays. A distinctive feature is that the model not only assumes the crown pillar’s existence but optimizes its location and size, and it adds typical underground development investments (ramps, drifts/tunnels, and accesses) together with period-based extraction constraints. Similarly, Bakhtavar, (2017) incorporate
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