Bibliographic record
Abstract
Mineral resources are vital to economic and social development, yet tailings generation and storage pose major sustainability challenges. In the context of addressing climate change and China's "dual carbon" goals, the country's increasing demand for copper, together with the lack of basic data on tailings, hampers effective management. This study develops a copper tailings generation accounting model integrated with dynamic material flow analysis to quantify provincial copper tailings generation in China from 1950 to 2060. It further examines the impacts of circular economy strategies and international trade on addressing the tailings crisis. The results show that approximately 5.56 Gt of copper tailings were cumulatively generated between 1950 and 2020, and under a carbon neutrality scenario, the total amount could reach 19.01 Gt between 2021 and 2060, posing challenges to "Zero-Waste city" development. The toxicity assessment of tailings storage reveals that Yunnan Province exhibits the highest freshwater ecotoxicity, creating obstacles to implementing the Kunming-Montreal Global Biodiversity Framework. Circular economy interventions are crucial for mitigating the crisis, while trade measures may shift environmental pressures across regions. Stronger collaboration among mining enterprises, policymakers, designers, consumers, and waste management entities is crucial for preventing a tailings crisis and advancing sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".