A review of the current state, challenges, and emerging trends for sustainable tailings remediation in South Africa: transforming mine tailings dumps into bioenergy hotspots
Bibliographic record
Abstract
In alignment with the objectives of the 2030 Climate and Energy Framework, renewable energy sources have been identified as a key driver in global efforts to mitigate climate change. For Africa, renewable energy represents a pathway toward achieving low-carbon energy self-sufficiency. Biomass derived from energy crops forms part of this renewable portfolio; however, competition between land for food and energy production remains a major constraint. To ensure meaningful contributions to energy sustainability, the cultivation of energy crops should prioritize marginal lands, particularly tailings storage facilities (TSFs). The use of such crops for phytoremediation presents a multifunctional approach that simultaneously facilitates soil decontamination and biomass production. Transforming mining waste into secondary resources is central to advancing circular economy principles in the extractive sector. Effective management of these waste streams requires an integrated framework that promotes reduction, reprocessing, upcycling, and responsible disposal to achieve long-term environmental and economic sustainability. Repurposing TSFs for bioenergy production offers a dual benefit: valorizing mining residues while reducing land-use conflicts between food and energy systems. In South Africa, where land availability is highly contested, extensive tailings deposits present a unique opportunity to convert degraded sites into bioenergy hotspots. However, realizing this potential necessitates reforming existing regulatory frameworks that prioritize biodiversity conservation in remediation practices. Differentiating between ecological restoration of footprint areas, such as former mining sites and waste storage zones, and resource-oriented remediation of TSFs is critical for sustainable transformation. This perspective advocates for the strategic cultivation of suitable bioenergy crops, including indigenous species and noninvasive alien plants such as Chrysopogon zizanioides (vetiver grass), on stabilized tailings. This review synthesizes current practices, challenges, and emerging trends in sustainable tailings remediation in South Africa. It highlights the potential to reframe mine TSFs as valuable bioenergy resources, aligning environmental rehabilitation with national energy security goals and supporting the transition toward a circular and low-carbon mining economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".