North American Natural Gas Model Impact of Cross-Border Trade with Mexico
Bibliographic record
Abstract
Natural gas as a source of energy has attracted a lot of interest as its emissions rate and price are lower than other fossil fuel energy sources. In the U.S., natural gas-fired power generation has been rising, as coal has declined as a share of the fuel mix. Likewise, Mexico recently launched its energy reform with focus on greatly expanding use of natural gas over other fossil fuels, primarily in the energy sector, by opening the market to private investors. These recent economic and policy changes, along with increasing gas production in the U.S. (shale gas boom) are likely to drive the natural gas market in North America in a new direction. For instance, the Annual Energy Outlook 2015 describes the U.S. for the first time as a net exporter of natural gas (via pipelines and LNG) by 2017. In order to study the current North American gas market with its new regulations like the Mexican energy reform, this paper presents the North American Natural Gas Market Model(NANGAM). We propose a long-term partial-equilibrium model of the United States, Mexican, and Canadian gas markets. NANGAM considers more granular details regarding market regions and pipelines in Mexico than other existing models, allows for endogenous infrastructure expansion, and is built in five year time-steps up to 2040, considering three seasons (low, high, and peak demand) for each time-step. NANGAM is calibrated using up-to-date data, which reflects current gas market trends, such as the increasing U.S. shale gas production. Using NANGAM, we assess the implications of the Mexican energy reform using a set of ad-hoc future scenarios. Results from the model show that, in the case of disappointing development of natural gas production in Mexico, the census region US7 (Texas and adjacent states) is the most affected, reaching an increase of natural gas production of up to 12% by 2040 compared to baseline projections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".