Evaluating the progress and identifying future improvement areas of mining's contribution to the sustainable development goals (SDGs)
Bibliographic record
Abstract
• Limited literature on examining the relationship between mining and SDGs. • The SDG framework is linked to ESG indicators for the mining sector. • Different levels of progress have been made in some ESG indicators. • We discuss mining's future contribution to SDGs beyond 2030. The intersection of Sustainable Development Goals (SDGs) and Environmental, Social and Governance (ESG) considerations in the mining sector is underexplored. This review aims to inform policymakers about the mining sector's experience and challenges in implementing SDGs and to encourage further discussions on the evolution of SDGs beyond 2030. It investigates how the mining sector adopts and integrates the SDGs framework into its current practices and matches the findings with an ESG lens. Firstly, we examine the mining sector's progress in achieving these goals based on refined literature. Secondly, we identify areas for improvement guided by the SDGs. Our results show that environmental progress has been made, particularly in renewable energy utilization and efficient water resources management. From a social and governance lens, higher progress has been observed in employment, inclusion, and policy implementation compared to moderate progress in other areas, such as gender equality, community engagement, and investment in local communities. Our study identifies three critical areas that must be prioritized by 2030: the intentional alignment of SDGs into mining operations, greater transparent disclosure of ESG data to all stakeholders, particularly mining communities, and protection of ecologically and culturally sensitive zones. Without them, ESG initiatives will remain fragmented and insufficient.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".