Environmental impact assessment: evidence-based policymaking in Brazil
Bibliographic record
Abstract
Environmental impact assessment (EIA) procedures aim prospectively to collect evidence about the environmental impacts of economic projects and to avoid or compensate for the costs incurred. This article asks whether such procedures have been effective in Latin America after many regional countries returned to some version of the developmental state after 2000. It does so by surveying the procedural effectiveness of Latin American regulations comparatively before turning to a deeper study of the Brazilian case. In Brazil, which has some of the strongest EIA procedures in the region, it finds that stakeholders make very different assessments of its effectiveness, not least because they define the standard differently. Economic actors in and out of the state criticise Brazilian EIA as ineffective from a transactive standpoint, which questions the time and cost associated with environmental licencing. Environmental and community activists see EIA as ineffective in achieving the substantive sustainability ends they value. Neither appreciates the procedural improvements offered by licencing professionals. The article concludes that EIA invites a broader set of stakeholders than did classic developmental states, but cannot on its own adjudicate among the resulting multiple visions of how to carry out development strategies.
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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 | 0.144 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".