Bibliographic record
Abstract
Abstract This paper analyses natural gas demand in the United States of America, Canada and Mexico for the period 1980–2001. Visual inspections of the historical trends for natural gas demand, GDP and natural gas prices provide an initial assessment of the relationship between these variables. The paper then estimates various regressions, using the Almon polynomial distributed lag model. Several regressions, utilizing different combinations of explanatory variables, were run with or without an autoregressive scheme of various orders. The best‐fitting model in each country was selected, and this was later used to generate natural gas demand forecasts for the respective countries to 2020. The results show that the income elasticity for Canada is more than unity (1.4), compared with less then unity for the USA (0.8) and Mexico (0.65). Changes in natural gas consumption generally lag by a few years, before the full impact of the changes in gas prices is realised. The length of lags varies between countries, but generally the effect of a price change on natural gas consumption dissipates after three‐to‐four years. Population is one of the important explanatory variables for Mexico, while it is not significant for the USA and Canada. The model predicts that natural gas demand in the USA (base case) will increase at an annualised growth rate of 1.9 per cent and, by 2020, should reach 919 billion cubic metres. This is not very different to the forecast of the US Department of Energy's Energy Information Administration (EIA) (900 bcm). Similarly, the base case forecast for Canada to 2020 is 128 bcm, compared with that of the EIA at 125 bcm. The forecast for Mexico (92 bcm) is significantly different to that of the EIA (120), perhaps due to different economic growth assumptions. The paper concludes that there is an urgent need to enhance exploration efforts and investment in liquefied natural gas (LNG) terminals, to be able to receive LNG from multiple sources, particularly in the USA and Mexico.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".