135 of SDGs in mining projects (RQ1–RQ3). While SDG alignment is increasingly prevalent in corporate disclosures and policy narratives, integration into project-level planning, risk management, operations, and closure decisions remains inconsistent and is highly contingent on governance capacity and the availability of decision-ready methodologies. The evidence indicates that SDGs are primarily utilized as tools for reporting and strategic framing, with particular focus on goals that are straightforward to benchmark and communicate, such as SDG 8, SDG 9, SDG 12, SDG 13, and, increasingly, SDG 7. Conversely, SDGs associated with locally significant impacts—including health and safety (SDG 3), water (SDG 6), poverty and food security (SDG 1 and 2), inequality and inclusion (SDG 5 and 10), and biodiversity and ecosystems (SDG 15)—are less consistently integrated into operational practices, even though they are often central to community experiences and project risk. The findings further demonstrate that SDG localization generates practical value when goals are translated into measurable indicators and incorporated into structured tools such as multi-criteria appraisal, spatial monitoring, lifecycle assessment, and closure or post-mining land-use planning. In contexts where institutional capacity and enforcement are limited, particularly in artisanal and small-scale mining (ASM), localization tends to serve as a diagnostic tool that reveals governance and data limitations rather than delivering immediate improvements. To achieve credible SDG performance in mining, it is necessary to move beyond basic SDG mapping toward lifecycle-integrated, accountable decision-making systems. This includes the use of sitelevel indicators, transparent monitoring and reporting of both positive and negative impacts, participatory co-design with communities and Indigenous rights-holders, and closure planning that frames post-mining land use as a development transition. When implemented in this manner, SDGs can serve as a robust decision-support framework that enhances risk management, regulatory preparedness, and long-term social and environmental outcomes. Future research should focus on developing decision-integrated SDG appraisal frameworks that incorporate measurable indicators into early project evaluation, rather than relying on retrospective reporting. Advances in machine learning, natural language processing, and spatial analytics have the potential to improve site-level data quality and alignment with SDG indicators. These approaches should be validated through case studies in diverse mining contexts to assess governance sensitivity, integration across project lifecycle stages, and to enhance participatory, locally co-designed indicator systems that link global SDGs with community needs and project decision-making. REFERENCES Agusdinata, D. B., Liu, W., Sulistyo, S., LeBillon, P., & Wegner, J. (2023a). Evaluating sustainability impacts of critical mineral extractions: Integration of life cycle sustainability assessment and SDGs frameworks. Journal of Industrial Ecology, 27(3), 746–759. https://doi.org/10.1111/jiec.13317 Agusdinata, D. B., Liu, W., Sulistyo, S., LeBillon, P., & Wegner, J. (2023b). Evaluating sustainability impacts of critical mineral extractions: Integration of life cycle sustainability assessment and SDGs frameworks. Journal of Industrial Ecology, 27(3), 746–759. https://doi.org/10.1111/jiec.13317
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