ANALYZING EIA IN PARANÁ, BRAZIL AND CALIFORNIA, UNITED STATES WITH FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS AND THE ANALYTICAL HIERARCHY PROCESS
Bibliographic record
Abstract
Since its introduction in the US, environmental impact assessment (EIA) has become one of the most widespread environmental policy instruments, which has evolved from solely conservation aims to serve as a tool for sustainable development.Despite its history and dissemination, EIA is routinely criticized for being ineffective at impacting decision-making or promoting more sustainable development.This study performed a comparative case study using the effectiveness dimensions from the EIA evaluative literature and two methodologies.Two states in federalist systems were chosen, Paraná, Brazil and California, United States.This comparative case study formats the cases into contextual conditions using the fuzzy-set Qualitative Comparative Analysis (fsQCA) methodology in order to identify the necessary and sufficient conditions that foster effective outcomes.These effectiveness outcomes and criteria are then ranked by EIA stakeholders via the analytical hierarchy process (AHP) in order to identify stakeholder priorities and to improve stakeholder management.The results show that in Paraná stakeholders identified normative effectiveness as the most important dimension for an ideal effective EIA outcome, and stakeholders in California identified this dimension as the second-most important following substantive effectiveness.For normative effectiveness outcome early project definition and public participation were found to be necessary conditions and stakeholder coordination was found to be a sufficient condition.Following normative effectiveness, Paraná stakeholders identified procedural effectiveness as the second most important.While transactive effectiveness was ranked lowest overall in both case studies, improving procedural effectiveness has been shown to be connected to the transactive effectiveness.Finally, transformative effectiveness ranked third and fourth in California and Paraná respectively, which also had the lowest set membership in fsQCA.This study advances EIA evaluatory literature by assessing various effectiveness dimensions through two complementary methodologies.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".