Contradictory impact of the natural forest closing policy and sanitary–phytosanitary measures on the export of non-timber forest products: a case study of Vietnam
Bibliographic record
Abstract
The gravity model was used in this paper to clarify the influencing factors of the export value of non-timber forest products (NTFPs) for Vietnam from 2003 to 2017. The estimates of this model indicate the importance of the size of economies, distance, common borders, exchange rates, average forest area, the natural forest closing (NFC) policy of the Vietnamese government, sanitary and phytosanitary (SPS) measures, and the interaction of SPS and importers’ GDP (SPS–GDP) as determinants of Vietnam’s NTFP exports. The main result of this study is the distinct and contradictory effects of the NFC policy and SPS measures. While the NFC policy increases the value of Vietnam’s NTFP exports, SPS measures significantly decrease the exportation. Using the SPS–GDP interaction variable yields a noticeable result: the negative impact of SPS on NTFP trade decreases with increasing income of NTFP importers. Furthermore, the NFC needs to continue to implement policies to increase investments in the NTFP trade and increase the quality of NTFPs from planting, to harvesting, to processing to meet the requirements of future importers. The findings offer several implications both in theory and in practice for trade policies and economic development theory based on Vietnam’s forest resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".