Advancing Sustainable Development Through Exports of Clean Energy Products From Emerging Asian Economies: A Poisson Pseudo Maximum Likelihood Estimation Approach
Bibliographic record
Abstract
ABSTRACT The promotion of clean energy is critical to achieving sustainable development. This study investigates the factors that affect the exports, trade potential, and revealed comparative advantage (RCA) of clean energy products (CEPs) in the emerging Asian economies (EAEs). The export data of the CEPs was collected from UN Comtrade based on six‐digit HSN codes and for other variables, the world development indicators database was used for the period 2000–2020. The Augmented Gravity Model and Poisson Pseudo Maximum Likelihood Estimation (PPMLE) were applied for the analysis and the results show that exporting countries' economic growth is significantly promoting the exports of CEPs in all countries except Indonesia and Saudi Arabia. Surprisingly, it was found that distance is playing a positive role in export growth in most countries, a sign of a globalized world. However, these economies, especially India and Pakistan should resolve their border disputes and enhance regional cooperation to increase CEPs exports, which will help them achieve sustainable economic growth.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".