Analyzing the Effectiveness of Environmental Impact Assessment in Paraná, Brazil and California, United States With Fuzzy-set Qualitative Comparative Analysis and the Analytical Hierarchy Process
Bibliographic record
Abstract
Abstract Environmental impact assessment (EIA) has become one of the most widespread environmental management instruments. Despite this, EIA is routinely criticized for being ineffective at impacting decision-making. This study compared the EIA systems of Paraná, Brazil and California, United States using the effectiveness dimensions from the EIA literature. This study formats the cases into contextual conditions using the fuzzy-set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions that cause effective outcomes. These effectiveness outcomes are then ranked by EIA stakeholders via the analytical hierarchy process (AHP) to identify stakeholder priorities and to improve stakeholder management. The results show that in Paraná stakeholders identified normative effectiveness as the most important dimension, while stakeholders in California identified this dimension as the second-most important following substantive effectiveness. Public participation was found to be a necessary condition for both substantive and normative effectiveness to occur. Early project definition was found to be sufficient for substantive effectiveness and necessary for normative effectiveness, for which stakeholder coordination was a sufficient condition. This suggests that in order for EIA to influence decision-making and foster sustainable development, greater care needs to be taken to actively engage stakeholders in public participation, with clear roles and project design communicated early on, and a clear role for regulatory authority to promote stakeholder coordination for acceptable outcomes. These findings suggest that some effectiveness dimensions are caused by similar conditions, which could help focus stakeholder management efforts and point to new avenues for future EIA effectiveness research.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".