Scientific shortcomings in environmental impact statements internationally
Bibliographic record
Abstract
Abstract Governments around the world rely on environmental impact assessment (EIA) to understand the environmental risks of proposed developments. To examine the basis for these appraisals, we examine the output of EIA processes in jurisdictions within seven countries, focusing on scope (spatial and temporal), mitigation actions and whether impacts were identified as ‘significant’. We find that the number of impacts characterized as significant is generally low. While this finding may indicate that EIA is successful at promoting environmentally sustainable development, it may also indicate that the methods used to assess impact are biased against findings of significance. To explore the methods used, we investigate the EIA process leading to significance determination. We find that EIA reports could be more transparent with regard to the spatial scale they use to assess impacts to wildlife. We also find that few reports on mining projects consider temporal scales that are precautionary with regard to the effects of mines on water resources. Across our sample of reports, we find that few EIAs meaningfully consider the different ways that cumulative impacts can interact. Across countries, we find that proposed mitigation measures are often characterized as effective without transparent justification, and sometimes are described in ways that render the mitigation measure proposal ambiguous. Across the reports in our sample, professional judgement is overwhelmingly the determinant of impact significance, with little transparency around the reasoning process involved or input by stakeholders. We argue that the credibility and accuracy of the EIA process could be improved by adopting more rigorous assessment methodologies and empowering regulators to enforce their use. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.379 | 0.643 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".