Fiscal Incentives for Critical Mineral Development in Canada: An Empirical Analysis
Bibliographic record
Abstract
This capstone investigates empirically the effect of mining tax rate review on Nickel and Zinc production in Quebec, Ontario, and Manitoba. We employ the autoregressive distributed lag (ARDL) modelling technique to analyze the dynamic interactions between output of the two transition minerals and prices, mineral foreign direct investments, and relevant tax policy variables. Results show that long run relationship exist between Nickel production and the determinants for Quebec and Manitoba. However, no such relationship exists for Ontario. Zinc production is cointegrated with its determinants in Quebec, Ontario and Manitoba. The effect of mining tax policy is most discernible for Nickel in Manitoba, as a lower mining tax rate elicited improvement in Nickel production. Tax policy is not significant for Quebec and Ontario’s Nickel production, but price and foreign direct investments are prime for Ontario and Manitoba, while only foreign direct investments matter for Quebec. Zinc output in Quebec is significantly impacted by price and foreign direct investments, while the gradual upward tweak to Quebec’s mining tax rate coincides, curiously, with improvement in Zinc production. Possibly, the 2009/10 post-crisis growth momentum in Quebec’s mineral space overshadowed sensitivity to a mining tax hike. To boost critical mineral supply in the era of energy transition, both federal and provincial governments need to roll out more critical mineral-friendly tax and non-tax incentives, oriented toward growing the supply chain responsibly and sustainably.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".