Does regulation delay mines? A timeline and economic benefit audit of British Columbia mines
Bibliographic record
Abstract
Seeking to capitalize on a surge in global demand for critical minerals, the Canadian mining sector claims that regulatory processes like Environmental Assessment (EA) impede and delay mining’s economic benefits. This paper investigates whether regulation has delayed mining projects and how much economic benefit mines have delivered in British Columbia (BC), focusing the mines’ performance post-EA. We audit the 27 mines granted an EA certificate in BC since 1995 and projected to open by 2022, comparing each mine’s forecasted and actual timelines and economic benefits (production, employment, and taxes), and identifying publicly-stated reasons for any mine delays. Seven of the 27 mines opened on time: 13 remain non-operational, and of the 14 mines that have operated, seven were delayed. Regulation was cited as a factor in only three of the 20 delayed projects; economic factors like commodity prices were the most common cause of delay. Lack of data and transparency on economic benefits significantly constrained our benefit audit, but BC mines for which data are available are underperforming across production (−77%), employment (−82%), and tax revenue (−100%). These findings suggest economic underperformance and mine delays post-EA are common, with delays typically resulting from economic factors, not government regulations.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".