Bibliographic record
Abstract
The step change in liquefied natural gas (LNG) demand, given Europe’s pivot from Russian gas, has led to a scramble for volumes and a return to a long-term view on LNG supply. But even with the near-term focus on Europe, the sustained opportunity for LNG continues to remain in Asia, where we expect more than 440 Mtpa of demand in 2030. Given the urgency, Asian buyers and portfolio players with positions in Asia have rushed to the country with the scale of over 100 Mtpa of new supply available within this decade: the United States, setting the scene for the rising influence of Henry Hub prices in Asian LNG. We discuss how LNG pricing and liquidity in Asia continues to develop through 2030, with oil-indexation waning slowly and the increasing presence of Henry Hub-indexed volumes, which could make up to a third of Asian LNG imports in that year. We also anticipate the advent of the Permian’s Waha and Canada’s AECO in Asian LNG as Mexico and Canada begin exports to Asia later this decade. We conclude that the virtuous cycle of liquidity required to create an Asian LNG hub has likely been delayed, with short-term prices governed by European fundamentals and long-term prices increasingly by US fundamentals. For Australian LNG producers, this means both spot and term prices present substantial upside potential through 2030, with the deficit in Europe providing short-term upside and the geographical distance of alternative US supply providing long-term upside.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".