Climate Change and Development: Trade Opportunities of Climate Smart Goods and Technologies in Asia
Bibliographic record
Abstract
This study focuses on trade opportunities of climate smart goods and technologies (CSGT) in Asia. Paper mainly highlights the export gaps for climate smart goods and technologies (CSGT) in Asia and identifies the trade opportunities among trade partners in intraregional and interregional. Applying the gravity model we estimate the export gap for the CSGT as the difference between the actual bilateral export flow and the mean value predicted by the model. In other words, ‘export gap’ is the difference between the actual and predicted export value. There is a scope to increase the export of climate smart goods and technologies with trading partners when the actual trade is below the predicted value ( i.e., negative value of the export gap). This gap actually provides the opportunity to raise the trade and attracting investment in CSGT sector and thereby development takes place. This paper also identifies the export gaps in CSGT for each regional member in its trade with partners within the region, EU, and North America (i.e., the US and Canada). This study contributes to the empirical literature in terms of measuring and identifying the potential trade opportunity of CGST in Asia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".