The Ecological Footprint and Kuznets Environmental Curve in the USMCA Countries: A Method of Moments Quantile Regression Analysis
Bibliographic record
Abstract
This article examines the environmental Kuznets curve for the member countries of the United States–Mexico–Canada Agreement (USMCA), using the ecological footprint as a measure of environmental degradation during 1980–2016. Panel data econometric methods are applied in this research, such as the cross-section dependence, unit root, cointegration and causality tests, and the new method of moments quantile regression (MMQR). The results suggest that the variables are characterized by a cross-section dependence, integrated of order one, and cointegrated. The fully modified ordinary least squares (FMOLS) method shows that renewable energy reduces environmental degradation, and the environmental Kuznets curve is validated. In contrast, patents and trade openness do not show a statistically significant relationship. These results are confirmed with the MMQR, where renewable energy reduces environmental degradation in quantiles from 4 to 6, while the environmental Kuznets curve hypothesis is valid in quantiles from 3 to 9, and patents and trade openness do not show a statistically significant relationship in any quantile. Therefore, it is essential to promote renewable energies, cleaner technologies, and environmental regulations to reduce polluting emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".