Advancing Sustainable Operational Efficiency in the Mining Industry: Trends, Innovations, Frameworks, and Future Research Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This review highlights the urgent need for sustainable operational efficiency (SOE) in the mining industry (MI) as it confronts escalating environmental, social, and economic pressures. Although considerable progress has been achieved in areas such as life cycle assessment, renewable energy integration, and data‐driven decision‐making, significant gaps persist, particularly in integrating cultural sustainability, stakeholder participation, and dynamic operational frameworks. This shift to quadruple bottom line (QBL) thinking provides a more inclusive and holistic approach for aligning mining operations with the United Nations Sustainable Development Goals. The SWOT analysis and scientometric insights reveal that technological innovations, such as artificial intelligence, the internet of things, and circular economy models, hold transformative potential. However, their practical implementation remains hindered by infrastructural, financial, and institutional barriers. Addressing these challenges requires not only the development of sector‐specific eco‐efficiency models and context‐sensitive LCA tools but also the adoption of participatory governance frameworks that embed community trust and cultural relevance into operational planning. To advance SOE in the mining industry, future research should focus on interdisciplinary, adaptive strategies that bridge technological innovation with inclusive policy design. By operationalizing the QBL approach, fostering stakeholder engagement, and scaling renewable integration in remote contexts, the mining industry can transition from reactive compliance to proactive leadership in sustainability. This review provides a critical foundation for that transition, guiding academia, industry, and policymakers toward a more equitable and resilient future for the mining sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it