Economic and trade determinants of carbon emissions in the American region
Bibliographic record
Abstract
• This study draws attention to the challenging job of striking a balance between economic expansion and carbon emissions in the American region. • Findings highlights Antigua and Barbuda, Bolivia, Brazil, Chile, and Guatemala, all of which have continued economic expansion, have a substantial impact on regional carbon emissions. • Developed countries like the U.S. and Canada demonstrate GDP growth decoupled from emissions, supporting the Environmental Kuznets Curve. Balancing economic growth with sustainability has been a significant challenge over the past decades, largely due to the environmental damage caused by carbon emissions. This study investigates the relationship between energy consumption, gross domestic product (GDP), and trade openness and their impact on carbon emissions in 28 countries in the American region from 2000 to 2022. Using a multiple linear regression model for country-level analysis, the findings reveal diverse trends across the region. For instance, countries such as Antigua and Barbuda, Bolivia, Brazil, Chile, and Guatemala demonstrate a strong link between economic growth and increased carbon emissions. In contrast, developed nations such as the United States and Canada show signs of decoupling GDP growth from emissions, supporting the Environmental Kuznets Curve hypothesis, which suggests that higher income levels lead to reduced environmental degradation. The study highlights the importance of tailored, country-specific strategies to reduce emissions while promoting sustainable economic growth. A thorough understanding of the complex relationships between gross domestic product, energy consumption, trade openness, and carbon emissions will enable policymakers to devise strategies that balance ecological sustainability with socio-economic objectives.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".