Reflections on the Consideration of Greenhouse Gas Emissions in Environmental Impact Assessment
Bibliographic record
Abstract
Abstract Environmental impact assessment (EIA) involves assessing the implications of proposed activities on the environment to inform decisions about whether those actions should proceed and under what conditions. Efforts are being made to incorporate climate change considerations into EIA internationally, but the assessment of greenhouse gas (GHG) emissions poses particular challenges. This article compares the incorporation of GHG emissions into EIA in two jurisdictions: Canada (under the Impact Assessment Act 2019) and Western Australia (under the Environmental Protection Act 1986). Four questions are considered, relating to screening and scoping; information requirements; decision-making and condition setting; and post-approval activities. Key differences between the two jurisdictions were found in relation to screening and scoping (Western Australia applies an emissions threshold while Canada utilizes a project list coupled with tailored guidelines); decision-making (Western Australia generally considers a straight line trajectory to net zero by 2050 as acceptable whereas Canada considers emissions in the context of international commitments and against other sustainability considerations); and post-approval activities (a strength of the Western Australian system is mechanisms enabling review and tightening of GHG conditions over time). It will be important to continue to review the effectiveness of EIA as a tool for climate mitigation as practice evolves.
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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 | 0.161 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".