Catching the Brass Ring: Oil Market Diversification Potential for Canada
Bibliographic record
Abstract
This paper examines the nature and structure of the Canadian oil export market in the context of world prices for heavy crude oil and the potential price differential available to Canadian producers gaining access to new overseas markets. Success in this arena will allow Canada to reap incredible economic benefits. For example, the near term benefits for increased access to Gulf Coast markets after mid-continent bottlenecks are removed, are significant, representing nearly 10$ US per barrel for Canadian producers. On the Pacific Coast, the world market is represented by growing capacity for heavy crude products in emerging Asian markets including Japan, Korea and China and existing heavy crude facilities in California and the west coast. Here, in the reference scenario for California and Asia the benefits are assumed to begin in 2020. The differential value range in California in 2020 is estimated at $7.20US per barrel and escalates to $8.77US by 2030. In Asia, the benefit range is estimated to grow from $11.15US per barrel in 2020 to $13.60US in 2030. Those higher prices for Canadian heavy oil would translate into significant increases in profits, jobs and government revenues. With better access and new pipeline capacity, oil producers will see more efficient access to international markets which can add up to $131 billion to Canada’s GDP between 2016 and 2030 in the reference scenario. This amounts to over $27 billion in federal, provincial and municipal tax receipts, along with an estimated 649,000 person-years of employment. Alberta will be the principal but not sole beneficiary from increased access to world market pricing. Most provinces and territories will realize fiscal and economic gains from the distribution and sale of products reflecting reduced costs and increased access to refineries for heavy oil. The key to this change is the elimination of current bottlenecks in transport and the expansion of a network of pipelines that can move Canadian crude oil to locations reflecting minimal discounts from world market prices. As this paper demonstrates with hard facts and figures, the rewards are too great to ignore.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".