Consideration of climate change in Nova Scotian environmental assessments: A critical review and recommendations for improvement
Bibliographic record
Abstract
This study evaluates how climate change (CC) is considered in recent environmental assessments (EA) in Nova Scotia, Canada. We assessed 38 EA reports covering six project types: wind farms, road expansions/utility corridors, quarries, mines, green hydrogen plants, and waste treatment facilities. Reports were scored based on 4 or 5 questions in each of four categories: coverage of basic CC science, greenhouse gas (GHG) accounting, greenhouse gas mitigation, and CC adaptation. Wind farms and road expansion/utility corridor projects scored the highest across most categories, particularly in GHG accounting and mitigation. Quarry expansions and waste treatment facilities scored poorly, with quarry projects receiving the lowest scores in GHG accounting and adaptation. Common weaknesses included inadequate enforcement of mitigation measures and a lack of consideration for carbon sequestration in GHG accounting. Green hydrogen production plants demonstrated strengths in renewable energy sourcing but lacked comprehensive GHG accounting and basic CC science. Mines, though reporting well on basic climate-change science and CC adaptation, had inadequate GHG accounting and mitigation. Environmental assessment practices have improved slightly but can be better aligned with Nova Scotia’s climate action goals. Planners need better integration of sequestration, more consistent accounting, and more consistent enforcement of mitigation strategies. Keywords: Greenhouse gas accounting; climate mitigation; climate adaptation; carbon sequestration
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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 | 0.099 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".