A Game-Theoretic Analysis of Canada’s Entry for LNG Exports in the Asia-Pacific Market
Bibliographic record
Abstract
The import demand for energy resources, including liquefied natural gas (LNG), has been steadily increasing in the Asia-Pacific region. Australia, the Middle East (Qatar), the Russian Federation, and the U.S. are the major players who compete strategically to capture this ever-growing market for LNG. The objective of this paper is to examine the potential for Canada’s entry into this market as another LNG exporter and what impact that can have on the existing suppliers. Using a game-theoretic LNG export competition model, we explore the conditions under which Canada can make a profitable entry. We also investigate the effect of Canada’s entry on the profitability of the four incumbent exporters. Employing a multi-leader Stackelberg model, we found that Canada’s entry could be a Pareto superior outcome under certain conditions because it benefits all competing firms and consumers. Further, Canada’s entry into the LNG export market always helps the low-cost incumbent firms by increasing their output and profit. However, the high-cost incumbent firms’ output falls, while their profit may increase or decrease depending on the unit cost and market size parameters. With differential export costs between Canada and the U.S., the latter has an incentive to act strategically to affect the entrance of the former.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".