Abstract: Risks and Demand of LNG Supply in the China and USA West Coast Markets
Bibliographic record
Abstract
Abstract Demand for natural gas is growing in what can be seen as the world two highest profile economies, China and the USA. In both cases domestic supply is projected to fall short of this demand growth. While China is not yet currently an importer of natural gas, the USA imports are forecast to grow quickly as domestic supply shows signs of decline. With significant reductions being seen in natural gas liquefaction and transportation costs, the opportunity presented by these two new markets for previously stranded gas resources looks attractive. However, in order to capture consumers in these markets LNG needs to be delivered at a competitive price to domestic gas or alternative fuel sources such as coal. The consequence of this for natural gas producers particularly in Asia is near term downward pressure on the wellhead value of gas for conventional large scale LNG projects. In some instances this may make alternative commercialization options more attractive and could conversely constrain the availability of gas for these markets. This paper will look at the demand profiles for the US West Coast and China and comment on the demand volume and timing, what uncertainties there are in these factors and, the risks that the supply to these markets can be executed. Import projects in China have already been launched but the size and number of import projects to the US West Coast will be a determining factor in LNG price setting with strong competition between potential suppliers. Economic incentives to import LNG are growing in the US because of rapidly rising gas prices in Canada and the US since 2000 and the rising F&D costs of domestic production coupled with accelerating decline rates. Presented at: 2005 South East Asia Petroleum Exploration Society (SEAPEX) Conference, Singapore, 2005
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".