Scientific shortcomings in environmental impact statements internationally
Bibliographic record
Abstract
Governments around the world rely on environmental impact assessment (EIA) to provide rigorous analyses and an accurate appraisal of the risks and benefits of development. But how rigorous are the analyses conducted in EIAs, and how do they compare across nations? We evaluate the output from EIAs for jurisdictions in seven countries, focusing on scope (temporal and spatial), mitigation actions, and impact significance determination, which is integral for decision-making. We find that in all jurisdictions, the number of identified significant adverse impacts was consistently small (or nonexistent), regardless of context. Likely contributing to this uniformity, we find that the scopes of analyses are consistently narrower than warranted ecologically and toxicologically, many proposed mitigation measures are assumed to be effective with little to no justification,and that the professional judgement of developer-paid consultants is overwhelmingly the determinant of impact significance, with no transparent account of the reasoning processes involved. EIA can be salvaged as a rigorous, credible decision-aiding tool if rigor is enforced in assessment methodologies, regulators are empowered to enforce rigor, and pro-development conflict of interest is avoided.
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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 | 0.333 | 0.572 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.034 | 0.030 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".