Bibliographic record
Abstract
Prior to Mexico's entry to the North American Free Trade Agreement (NAFTA), predictions of the consequent impact on the environment in that country ranged from the dire to very optimistic. This article investigates NAFTA's outcomes in terms of energy use and the emission of atmospheric pollutants. Specifically, has entry into NAFTA led to a convergence or divergence in indicators of emissions, environmental efficiency, and emissions‐specific technology in Mexico, the United States, and Canada? A battery of tests is applied to these indicators for energy use and carbon, sulfur, and NOx emissions in the three countries. The results show that the extreme predictions of the outcomes of NAFTA have not materialized. Rather, trends that were already present before the introduction of NAFTA continue and, in some cases, improve post‐NAFTA, but not yet in a dramatic way. There is strong evidence of convergence across the three countries toward a lower intensity of energy use and emissions per unit of GDP. Although intensity is rising initially for some variables in Mexico, it eventually begins to fall post‐NAFTA. Per capita emissions of sulfur and NOx also show convergence, but this is not the case for energy and carbon, and the latter variables also drift moderately upwards. The state of technology in energy efficiency and sulfur abatement is improving in all countries, although there is little, if any, sign of convergence and NAFTA has no effect on the rate of technology diffusion. However, total energy use and carbon emissions increase both pre‐ and post‐NAFTA and total NOx emissions increase in Mexico. Only total sulfur emissions are stable and falling in all three NAFTA partners.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".