Unsustainable Development in Canada: Environmental Assessment, Cost-Benefit Analysis, and Environmental Justice in the Tar Sands
Bibliographic record
Abstract
Canada is on record as a strong supporter of sustainable development, yet environmental costs of projects like the oilsands are justified by the creation of economic wealth. Tar sands are the fastest growing source of greenhouse gases (GHGs) in Canada, contributing to climate change, which impacts the worlds most vulnerable populations the hardest. How have we reached the conclusion the tar sands create wealth? What kind of wealth? Wealth for whom?Projects like oil sands are assessed in Canada by means of environmental assessments (EAs). This paper tries to answer two questions in relation to using the tar sands as a case study. First, what is the standard to be reached in Canada, and what should it be? Secondly, how are decision makers assessing whether the economic benefits of projects justify the environmental costs? Though not expressly, they appear to be doing a kind of cost-benefit analysis (CBA) in EAs of proposed projects. Is this an appropriate approach, and if so, are they doing CBA in a complete, fair and transparent way? We argue CBA should not be the basis of environmental assessments, which must be guided by clear and legally forceable minimum standards. However, when performed in a way that attempt to include the full range and types of values in question. CBA can be one tool to identify, clarify and make more accessible the balancing of all competing interests and values at stake in projects like the tar sands, before final decisions are made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".