Dynamic effects of foreign direct investment, globalization, economic growth, and energy consumption on carbon emissions in Mexico: An ARDL approach
Bibliographic record
Abstract
Due to the global threat of climate change, investigating the interplay between environmental factors and environmental quality is crucial for effective policy implementation to achieve environmental sustainability. This study examines the influence of economic growth, energy utilization, foreign direct investment (FDI), and globalization on carbon dioxide (CO2) emissions in Mexico from 1970 to 2022. By adopting the Autoregressive Distributed Lag (ARDL) method, it has been observed that a 1% boost in GDP and energy use contributes to a 1.05% and 1.41% surge in Mexico's carbon emissions in the long run, while 1.81% and 1.85% increase in the near term. Instead, a 1% rise in FDI and globalization have a favorable implication on Mexico's ecosystem level by reducing 0.5% and 0.03% carbon emissions in the long run while 0.28% and 0.01% fall in the near term. The results would help policymakers put the right policies into place to achieve the Sustainable Development Goals (SDGs) through the development of renewable energy, environmental investments, a low-carbon economy, and international cooperation for the transfer of green technologies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".