International Practice of Compensation for Damage Caused by Environmental Violations in the Management of Tailings Storage Facilities and Waste Dumps
Bibliographic record
Abstract
The purpose of the study is to examine and standardise international practices for compensating for environmental damage caused by violations in the management of tailings storage facilities and waste dumps. The study identifies effective compensation mechanisms used in Brazil, Canada, Hungary, the United States, Australia, and European Union countries and analyses various approaches to damage assessment and compensation. It reviews key frameworks such as the Comprehensive Environmental Response, Compensation, and Liability Act (Superfund), the FY 2022–2026 Strategic Plan of the Environmental Protection Agency (EPA), Directive 2008/99/EC of the European Parliament and Council on the Criminal Law Protection of the Environment, the Canadian Environmental Assessment Act, the Canadian Environmental Protection Act, and the principles of the International Council on Mining and Metals (ICMM). According to the study, Brazil, Canada, Hungary, the United States of America, Australia, and European Union countries have implemented stringent regulations for the design, management, and oversight of tailings storage facilities, which reduces the risk of accidents. The global adoption of these regulations could mitigate ecological disasters and optimise damage compensation procedures. Companies violating environmental standards are legally required to compensate for the harm they cause, including both ecological restoration and compensation for damage suffered by victims. The necessity of global cooperation and experience-sharing in preventing environmental violations and addressing their consequences is a critical factor. The studied countries have established procedures for prosecuting enterprises that violate environmental legislation, particularly in the management of tailings storage facilities. Precedents in such cases underscore the importance of creating clear procedures for determining legal liability and providing compensation to victims. The practice of environmental insurance enables mining companies to create reserves for covering potential damages, thereby reducing the financial risks faced by the state and encouraging enterprises to take greater responsibility.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".