Efectos del crecimiento económico en las emisiones de CO2 en América del Norte
Bibliographic record
Abstract
This paper examines the heterogeneity in the environmental field, specifically in the emission of the main of the greenhouse gases, CO2, within the 3 countries composing the North American region. On the one hand, the United States is a large emitter of CO2 (19% of global emissions accumulated in 1990-2020), only surpassed by China as of 2005. On the other hand, Canada and Mexico are relatively small emitters on a global scale (1.8% and 1.4% of global emissions accumulated in 1990-2020, respectively), but with large economic and technological differences. Under these conditions, the objective of this research is to find differences and similarities between the three countries regarding CO2 emissions trends and their driving structural factors: scale, technological and composition effects, based on annual data from 2001 to 2014. With a structural decomposition analysis, we found that, in general, the scale effect was polluting and dominating in the period, while the technological effect was anti-polluting, in addition to a small and ambivalent composition effect (positive in some years and negative in others). It is concluded that there is a process of convergence in the intensity of emissions per GDP value and with respect to the three mentioned effects. It is also concluded that the characteristics of the three effects in Mexico were like those of the United States, while Canada approached along the analysis period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".