The effect of factors on CO <sub>2</sub> emissions in Thailand: New insights from VECM methodology
Bibliographic record
Abstract
The article examines the links between gross domestic product (GDP), fossil fuel consumption, foreign direct investment, trade openness, electricity consumption, renewable energy (REC), and carbon dioxide emissions (CO 2 ) in Thailand. The article utilizes time-series data from 1990 to 2023; the study investigates the impact of these parameters on Thailand's environmental pollution. This article investigates the determinants of CO 2 in Thailand. The Granger Causality Test method uses time-series analysis and the vector error correction model to explore how these factors interact and influence environmental pollution in Thailand. The results reveal significant interconnections, with fossil fuel consumption and electricity consumption (EC) positively correlated with CO 2 , while REC demonstrates a mitigating effect. The analysis also highlights the role of foreign direct investment and trade openness in shaping Thailand's environmental outcomes. The study concludes that transitioning to REC and implementing supportive policy measures are crucial for reducing CO 2 while maintaining GDP. The results indicate significant interconnections between these factors, highlighting the vital role of REC and policy measures in mitigating CO 2 while sustaining GDP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".