INTEGRATING CUMULATIVE EFFECTS IN REGIONAL STRATEGIC ENVIRONMENTAL ASSESSMENT FRAMEWORKS: LESSONS FROM PRACTICE
Bibliographic record
Abstract
The need to advance the assessment of cumulative environmental effects beyond the individual project, to the broader regional scale and strategic tier, is well argued. However, regional strategic environmental assessment (SEA) frameworks that facilitate cumulative effects assessment (CEA) at this scale and tier have been slow to evolve. The need for such frameworks is now at the forefront of Canadian environmental assessment. This paper examines current and recent attempts at regional, and strategic-type assessment frameworks to integrate and assess cumulative environmental effects. Based on lessons from practice and interviews with practitioners and administrators, we observe that assessing cumulative effects in a regional SEA context is most effective when there is a shared regional vision about the future state of environment and development; the development of regional environmental targets, thresholds and indicators takes precedent over cumulative impact prediction; strategies can be translated into operational terms and mandates; the assessment is sensitive to key decision windows; and CEA is recognised to be more than simply the "adding up" of environmental effects. Regional SEA is the most appropriate framework within which to address cumulative effects, if the primary goal is to influence the nature and pace of conservation and development in support of regional sustainability.
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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 | 0.117 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".