Bibliographic record
Abstract
This article investigates emerging opportunities in climate change mitigation in South Asia through trade. Trade can mitigate the climate change issues of a country, region or the world as a whole. Through international agreement (or pressure), trade also creates the opportunity for green jobs that produce environment-friendly goods (EFG), which have less damaging impact on environment. This article examines possible potential trade opportunity of climate-friendly goods (CFG) in South Asia. Applying the gravity model, this article estimates potential trade of CFG in South Asia. It also measures the trade gap as to how well bilateral trade flow performs relative to the mean value of trade as predicted by the model. Here, ‘potential trade gap’ means the gap between actual trade and the predicted trade value. It suggests that there is a scope to improve the export of CFG with trading partners. This article suggests and also highlights an alternative possibility for a climate-friendly export-led growth model in South Asia. It also identifies the potential trade gap of CFG for each regional member and its partners within region and developed countries such as the European Union (EU) and North America (the USA and Canada). JEL Classifications: Q5, C23, F1
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".