Economic Valuation of Environmental Damages (EVED) to natural vegetation in scenarios of mining tailing dam breaks: case study of Brumadinho/MG
Bibliographic record
Abstract
In 2018, a report published by the United Nations Environment Programme (UNEP) identified dam failures as the leading cause of environmental disasters. The report highlighted that countries such as China, Canada, Chile, and the United States experienced the most significant issues related to dam safety in recent years. Brazil has also faced two major dam disasters: the 2015 collapse of the Samarco mining tailings dam in Fundão and the 2019 failure of the Vale S.A. mining tailings dam in Brumadinho, both of which occurred in the state of Minas Gerais. To hold the responsible parties accountable and facilitate compensation for damages resulting from dam failures, particularly in legal and criminal actions involving environmental harm, tools such as environmental damage valuation are crucial. The objective of this research was to establish an acronym to differentiate the well-known concept of Economic Value of Environmental Resources (VERA) from the proposed concept of Economic Valuation of Environmental Damages (EVED). This distinction aims to address a significant gap in the existing methodologies: the lack of a specific framework for evaluating environmental damages caused by the failure of mining tailings dams. In the first chapter, we reviewed the most prominent valuation approaches documented in globally recognized scientific databases. Our investigation identified 22 methodological approaches and specific methods that were frequently cited in the literature. Key approaches included the Economic Value of Environmental Resources (VERA), which utilizes methods such as the Replacement Cost Method, Avoided Costs Method, and Opportunity Cost Method. Another frequently approach was the Expected Total Environmental Costs (CATE I and CATE II), alongside other methods such as the Natural Resource Damage Assessment (DEPRN), Pecuniary Compensation Value (VCP), Almeida Method, and Estimated Reference Value for Environmental Degradation (VERD). For this research, EVED was applied to the three selected approaches - VERA, VCP, and CATE II - identified as among the most prevalent in the reviewed literature. The analysis concludes that the environmental damage to the natural vegetation of the Atlantic Forest biome resulting from the failure of the B1 dam complex in Brumadinho amounted to R$752,795,014.00. This figure equates to approximately USD 130,920,872.00, or R$4,323,883.62 per hectare. Hence, the damage to Atlantic Forest’s natural vegetation is estimated at R$ 4,986,388.18 per hectare. Keywords: mining tailings dams; methods for environmental damage; Economic Valuation of Environmental Damages (EVED).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".