Testing the Environmental Kuznets Curve Hypothesis in North America’s Free Trade Agreement (NAFTA) Countries
Bibliographic record
Abstract
In force since 1994, the North American Free Trade Agreement (NAFTA) is still the most comprehensive agreement ever developed, conforming to the world’s largest trade market. However, the environmental impacts cannot be neglected, particularly greenhouse gas emissions. The environmental Kuznets curve (EKC) hypothesis is revisited, studying Canada, Mexico, and the U.S.A. in relation to carbon dioxide (CO2) emissions, gross domestic product (GDP), energy, and exergy consumption. Ordinary least squares, vector autoregression, and Granger causality tests are conducted. Additionally, exergy indicators and the human development index (HDI) are proposed. Results for Mexico and the U.S.A. describe similar and interesting outcomes. In the search of the environmental Kuznets curve (EKC), the EKC hypothesis is confirmed for Mexico and the U.S.A. However, for Canada, the EKC hypothesis does not stand. The Granger causality test displays the existence of a uni-directional causality running from CO2 emissions to economic growth; a similar behavior was observed while testing the for the exergetic control variables. The most intriguing Granger causal results are those from the U.S.A. A bidirectional relation was observed between exergy intensity and CO2 emissions. Moreover, the EKC curve was plotted by both variables. Furthermore, Mexico’s outcomes reveal that increasing renewable exergy share will decrease CO2 emissions. On the contrary, increasing HDI will grow CO2 emissions. Policy implications arise for NAFTA countries to minimize CO2 emissions by means of the growing renewable energy share. Exergy tools offer an appealing insight into energetic and environmental strategies.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".