Integrating the sustainable development goals into post-mining land use selection
Bibliographic record
Abstract
Active mines regularly commit to post-mining land uses (PMLUs) several decades before their planned closure, setting closure outcomes and commitments in regulatory instruments during the initial, pre-mining phases. Considering the mounting global challenges of water, energy, food, and livelihood security, as well as climate change, many of these PMLUs may be considered sub-optimal for a future context. Whether new land uses are being defined or existing land uses are being refined, options for PMLUs should be selected using various planning lenses. In this paper, three of these lenses are considered to demonstrate how post-mining landscapes could contribute to addressing complex global challenges through effective mine closure transitions. These lenses are: (i) safe, stable, and non-polluting; (ii) suitable, practicable, and aligned with land capability and local/regional needs, supported by a comprehensive knowledge base; and (iii) integration of the Sustainable Development Goals (SDGs) and the water-energy-food (WEF) nexus. This approach is presented as a proposed conceptual framework, demonstrating how the SDGs can be utilised as a lens for selecting PMLUs, and which of these PMLUs are aligned with addressing water, energy, food, and/or livelihood security, as well as climate change mitigation. Selected case studies are highlighted, after which regulatory considerations and policy recommendations are made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".