1684 (C5, 16.64%), distance between water resources and population centers (C6, 15.51%), and mine-to-waterresources distance (C4, 12.70%), while surface disturbance (C1, 9.84%) has the lowest weight. Taken together, these weights are the direct outcome of the aggregated expert judgment and provide an explicit prioritization of the relative importance of each dimension within the proposed socio-environmental framework. As a contribution, this work provides a replicable and transparent basis for constructing an integrated socio-environmental risk indicator by defining its structure as a weighted aggregation of normalized criterion scores (Q%. As future work, it is necessary to operationalize the estimation of (Q%, including the definition of scales, assignment criteria, and normalization procedures, and to assess the stability of the ranking through sensitivity analyses and/or by expanding the expert panel. In addition, a comparative analysis using other multi-criteria decision-making approaches, such as the Analytic Network Process (ANP), ELimination Et Choice Translating REality (ELECTRE), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), is recommended to further examine the robustness of the prioritization and explore potential variations associated with the aggregation and ranking procedures. ACKNOWLEDGEMENTS LFO and FE acknowledge financial support from ANID through grant CIA250010 (Advanced Mining Technology Center, AMTC) and from the ANID National Doctoral Scholarship (Folio No. 21250527). REFERENCES 1. Abdollahisharif, J., Bakhtavar, E., & Shahriar, K. (2008). Open-Pit to Underground Mining − Where Is the Optimum Transition Depth? 2. Arboleda, M. (2017). La naturaleza como modo de existencia del capital: Organización territorial y disolución del campesinado en el superciclo de materias primas de América Latina. Anthropologica, 35(38), 145-176. https://doi.org/10.18800/anthropologica.201701.006 3. Azapagic, A. (2004). Developing a framework for sustainable development indicators for the mining and minerals industry. Journal of Cleaner Production, 12(6), 639-662. https://doi.org/10.1016/S0959-6526(03)00075-1 4. Badakhshan, N. (2023). Determining the environmental costs of mining projects: A comprehensive quantitative assessment. Resources Policy. 5. Bakhtavar, E. (2017). A stochastic mathematical model for determination of transition time in the non-simultaneous case of surface and underground mining. Journal of the Southern African Institute of Mining and Metallurgy, 117(12), 1045-1053. https://doi.org/10.17159/24119717/2017/v117n12a9 6. Bakhtavar, E., Oraee, K., & Shahriar, K. (2010). 29th International Conference on Ground Control in Mining. 7. Bastidas-Orrego, L. M., Ramírez-Valverde, B., Cesín-Vargas, A., Juárez-Sánchez, J. P., MartínezCarrera, D., & Vaquera-Huerta, H. (2018). Conflictos socioambientales y minería a cielo abierto en la Sierra Norte de Puebla, México. Textual, (72), 35-66. https://doi.org/10.5154/r.textual.2017.72.003 8. Chung, J. (2016). Where to make the transition from open-pit to underground? Using integer programming. Journal of the Southern African Institute of Mining and Metallurgy, 116(8), 801808. https://doi.org/10.17159/2411-9717/2016/v116n8a13
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