How does geopolitics affect energy law: North America—an illustrative example
Bibliographic record
Abstract
Abstract Global energy trade has changed over the last decade. Increased energy flows to Asia present a major shift in energy trade as Asia’s share of global energy demand rises. The shale revolution has enabled the United States (U.S.) to become the world’s largest oil and gas producer accounting for more than half of the world’s production. Renewable energy sources have become more important as countries are challenged to meet the climate goals set by the Paris Agreement. Nuclear Energy seems to be the energy source that can most dramatically help reducing carbon emissions rapidly. Gas demand will grow rapidly over the next 20 years as countries move to decarbonize their energy matrix, given it is a cleaner fuel for power generation than coal. The purpose of this article is to show the effects of global energy trade, its effects on the way countries interact with each other and how local energy law and policy are nonetheless affected by the shifts in the energy market. We will use North America (Canada, Mexico and the U.S.) as an example of the complexity of such interactions and the effects is having (or should have) in regional energy markets. The purpose then of this article is not to attempt to resolve these complex issues, but rather to motivate readers, energy lawyers and policymakers to start asking the right questions regarding global (and local) energy trade.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".