Nexus Between Economic Growth, Renewable Energy, Industry Value Added and CO2 Emissions in ASEAN
Bibliographic record
Abstract
This study aims to identify the relationship between economic growth, renewable energy, and industrial value added to CO2 emissions in ASEAN. The data used is panel data of 10 ASEAN countries from 2001-2020. This study uses the vector error correction model (VECM) for analysis. The estimation results show that CO2 emissions are only influenced by the CO2 variable itself in the previous period in the short term. In addition, economic growth and renewable energy significantly negatively affect CO2 emissions in the long term. Economic growth has the largest contribution to reducing CO2 emissions. The empirical findings also support the existence of the environmental Kuznets curve (EKC) in ASEAN. However, industrial value added has no significant effect on CO2 emissions. This study has several policy implications. The government needs to 1) strengthen energy transition regulations to encourage the use of renewable energy, 2) increase investment in R&D to stimulate green technology innovation, and 3) protect the environment to mitigate negative externalities of economic activity.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".