Assessing environmental liabilities of mining in Northern Australia: A case study of the McArthur River Mine
Bibliographic record
Abstract
• Highlights severe adverse effects on Indigenous communities from mining, including ecosystem service and well-being losses and opportunity costs, which are mostly unaccounted for in the environmental assessments. • Offers insights for improving environmental and mining legislation to protect ecosystems and Indigenous communities. • Fills a significant gap in assessing the true economic impacts of mining activities by using market and non-market valuation methods. Mining projects supposedly offer enormous economic benefits; however, they often involve serious environmental liabilities that extend far beyond the life of the mine, including perturbing the ecological balance and causing the loss of ecosystem services that are vital for sustaining human well-being. Understanding and assessing the environmental liabilities of mining is crucial for estimating the costs of restoring, replacing, or providing the equivalent of the damaged natural resources. This study estimates the market and non-market values of the mining impacts in the Northern Territory, Australia, particularly for the McArthur River Mine. We assess these costs by applying the Replacement Cost, Welfare Costs Savings, and Basic Value Transfer methods in terms of the loss of local Indigenous communities' well-being, loss of ecosystem services from native vegetation and freshwater, and the opportunity cost of the mine site none of which are not fully accounted for in the mining operator's environmental assessments and mitigation measures approved by the local governmental authorities. Our valuation analysis indicates that the market value of the environmental and social impacts of mining ascends to AUD 1.1 billion per year while the non-market value is AUD 20 million per year. Assessing mining-related environmental liabilities offers crucial insights for informed decision-making regarding mitigation and remediation efforts and strengthening environmental and mining legislation in the Northern Territory. In conclusion, our study contributes to developing a comprehensive understanding of the true economic impact of mining activities on ecosystems and local Indigenous communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".