A MULTIPLE ACCOUNT BENEFIT-COST ANALYSIS OF COAL MINING IN ALBERTA
Bibliographic record
Abstract
We examine the positive and negative effects of coal mining in Alberta from a social perspective — that of the province of Alberta rather than the project proponent — using benefit-cost analysis. We provide estimates of the economic, social and environmental impacts (benefits and costs associated with the development, construction, operation and reclamation) of an illustrative coal mine in the Eastern Foothills of Alberta’s Rocky Mountains. Our analysis is meant to inform the public on the potential trade-offs associated with additional coal development, and support and inform Alberta’s current coal policy review. Our analytical framework relies on the method of multiple account benefit-cost analysis. We find small economic benefits in the form of incremental tax revenues ($671 million, nominal dollars) and employment earnings by mineworkers ($35 million, nominal dollars). Given any individual mine’s small size relative to Alberta’s overall economy, there is unlikely to be any material increase in economic activity relative to the absence of mine development. In contrast, costs to Alberta are likely to be significant. These costs come from displacing other economic activity (primarily ranching and tourism); significant and adverse environmental impacts on water, wildlife, vegetation and air; a non-zero probability the province will be responsible for reclamation liabilities; negative social impacts on nearby communities; and interference with Indigenous Peoples’ interests and rights. Overall, we conclude that coal mine development is not likely to be a net benefit to Alberta, and the costs are likely to outweigh the benefits.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".