Assessing Greenhouse Gas Emissions in the Oil Sands: Legislative or Administrative (in)Action?
Bibliographic record
Abstract
The development of the oil sands in Alberta has become a focal point in Canada’s response to global climate change. Before an oil sands project can proceed, it must first undergo environmental assessment. This process frequently engages both provincial and federal environmental assessment legislation, which are implemented by administrative tribunals. The purpose of the paper is two-fold: firstly, to determine whether environmental assessment legislation provides regulators with the tools to assess the impact of an oil sands project's greenhouse gas emissions on the environment, and secondly, to examine how tribunals have been enforcing those standards during assessments. This paper finds that federal and provincial statutes provide panels with the scope to consider GHG emissions at the assessment stage; however, the relevant legislation places no demands on tribunals to specifically address the issue. The permissive language affords tribunals with latitude in downplaying the environmental effects of climate change. As such, climate change issues rarely surface in environmental assessments of oil sands projects, and when they do, the effects of climate change do not hinder regulatory approval. After reviewing policy responses, the paper concludes that any change in environmental assessment is only possible when either tribunals or governments prioritize the effects of climate change in environmental assessment.
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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 | 0.054 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".