OECD Case Studies of Integrated Regional and Strategic Impact Assessment: What Does ‘Integration’ Look Like in Practice?
Bibliographic record
Abstract
Increasingly, protocols for assessing the impacts of land-uses and major resource development projects focus not only on environmental impacts, but also social and human health impacts. Regional and Strategic Environmental Assessment (RSEAs) are one innovation that hold promise at better integrating these diverse land-use values into planning, assessment, and decision-making. In this contribution, a realist review methodology is utilized to identify case studies of "integrated RSEA"-those which are strategic, have a regional assessment approach, and seek to integrate environmental, community and health impacts into a singular assessment architecture. The results of a systematic literature review are described and six RSEA-like case studies are identified: Kimberly Browse LNG SEA; HS2 Appraisal of Sustainability; Lisbon International Airport SEA; Beaufort Regional Environmental Assessment; Nordstream 2 Transboundary EIA; and the Portland Harbour Sustainability Project. The case studies are examined according to their unique contexts, mechanisms and outcomes of their assessment protocols to determine the degree to which they consider more than environmental valued components, and the means by which they were included. Findings suggest that RSEA has a contentious relationship with the integration of more than environmental values, but that there are significant lessons to be learned to support project planning, especially for assessment contexts characterized by large, transboundary projects.
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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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".