On the underestimation of the significance of environmental impacts in Peru: an approach using homogeneous units
Bibliographic record
Abstract
This study examines the reliability of the Environmental Impact Statements (EISs) in Peru, focusing on projects predicted to have high negative impacts. It highlights the discrepancy between the expected and the estimated impacts in EISs, revealing a general trend of underestimating the environmental significance of projects, particularly in areas free from industrial activities. The study critiques the application of the Gomez Orea Method, originally successful under Spanish regulations but problematic in its Peruvian applications, as evidenced by inconsistencies in the methodologies and a lack of bibliographic and regulatory support for the indicators used. The analysis covers seven EISs involving hydroelectric and mineral exploitation projects. It identifies a recurrent pattern where despite the significant potential impacts of these projects on water quality, ecosystems, and biodiversity, the impacts are predominantly classified as ‘Compatible’ or low. This underestimation results in 97.37% of impacts being categorized as low, contradicting the projects’ initial high-risk classification. Moreover, the study compares the Gomez Orea Method with the Conesa Method, a more conservative approach, finding significant methodological differences affecting impact significance estimations. The study advocates for enhanced Environmental Impact Assessment practices in Peru, suggesting the adoption of more conservative assessment methods to accurately gauge and mitigate environmental impacts.
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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 | 0.034 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".