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<p>The global mining sector faces mounting pressure to balance industrial efficiency with sustainable development, particularly during transitions from open-pit to underground methods. This study evaluates such a shift at China’s Sijiaying Iron Mine, employing a hybrid framework that combines expert-driven Multi-Criteria Decision-Making (MCDM) and Machine learning (ML) to assess sustainability outcomes aligned with the United Nations Sustainable Development Goals (SDGs). Through Fuzzy AHP-TOPSIS analysis, social license to operate (96% impact) and real-time environmental monitoring (94% impact) emerged as critical factors, emphasizing the need for community trust (SDG 16) and ecological transparency (SDG 13) in mining practices. Environmental benefits included a 34% reduction in land disturbance (SDG 15) and 27% higher energy efficiency (SDG 7), achieved through renewable energy adoption and spatial footprint minimization. However, challenges like economic volatility (82% impact) and geotechnical risks (84% impact) highlighted tensions between profitability (SDG 8) and long-term sustainability. Machine learning validation confirmed the robustness of results, particularly for water management (90% impact, SDG 6) and post-mining rehabilitation (82% impact, SDG 11). While biodiversity preservation scored lower (68% impact), the study underscores the importance of adapting global targets like SDG 15 to local contexts. The framework integrates expertise and analytics for SDG-aligned mining, promoting responsible resource use. Policymakers should prioritize monitoring, engagement, and innovation to align growth with planetary health. This research fills the gap between technical feasibility and sustainability needs, offering practical research on the mining industry’s role in achieving the 2030 Agenda.</p>

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SDG-Aligned Sustainability Assessment of Open-Pit to Underground Mining Transition: A Multi-Criteria Decision-Making and Machine Learning Analysis at Sijiaying Iron Mine, China

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Aboubakar Siddique,
  • Zhuo ying Tan,
  • Naigen Tan,
  • Jiang Li,
  • Hilal Ahmad,
  • Jiang Yanshui,
  • Muhammad Zeshan,
  • Dhanesh Kumar

摘要

The global mining sector faces mounting pressure to balance industrial efficiency with sustainable development, particularly during transitions from open-pit to underground methods. This study evaluates such a shift at China’s Sijiaying Iron Mine, employing a hybrid framework that combines expert-driven Multi-Criteria Decision-Making (MCDM) and Machine learning (ML) to assess sustainability outcomes aligned with the United Nations Sustainable Development Goals (SDGs). Through Fuzzy AHP-TOPSIS analysis, social license to operate (96% impact) and real-time environmental monitoring (94% impact) emerged as critical factors, emphasizing the need for community trust (SDG 16) and ecological transparency (SDG 13) in mining practices. Environmental benefits included a 34% reduction in land disturbance (SDG 15) and 27% higher energy efficiency (SDG 7), achieved through renewable energy adoption and spatial footprint minimization. However, challenges like economic volatility (82% impact) and geotechnical risks (84% impact) highlighted tensions between profitability (SDG 8) and long-term sustainability. Machine learning validation confirmed the robustness of results, particularly for water management (90% impact, SDG 6) and post-mining rehabilitation (82% impact, SDG 11). While biodiversity preservation scored lower (68% impact), the study underscores the importance of adapting global targets like SDG 15 to local contexts. The framework integrates expertise and analytics for SDG-aligned mining, promoting responsible resource use. Policymakers should prioritize monitoring, engagement, and innovation to align growth with planetary health. This research fills the gap between technical feasibility and sustainability needs, offering practical research on the mining industry’s role in achieving the 2030 Agenda.