Effect of Forest Certification on International Trade in Forest Products
Bibliographic record
Abstract
Forest certification plays an important role in the global trade of legal, sustainably harvested timber. There is no accurate definition of how international forest certification systems impact international trade from a global perspective. This paper is intended to evaluate the influence of forest certification on international trade, so that it can provide a scientific basis for the improvement of the international forest certification systems and for the development of relevant forestry industries in different countries. First, the influence of forest certification on international trade of forest products is explained in the economic model; hence, four hypotheses are put forward. Second, to test these hypotheses, we verify the panel data of bilateral trade and forest certification of all forest products among 67 economies from 2009 to 2018 by incorporating forest certifications into the gravity model. Finally, tests by country groups and product groups were further analyzed, respectively. The results show that: (1) The extended Poisson pseudo maximum likelihood (PPML) estimation solves the problem of the heteroscedasticity and zero trade value problems of the gravity model well in the forest industry. (2) Forest certification has an export competitive effect, a trade barrier effect, as well as common language effect. (3) Forest certification has asymmetric trade effects. The export competitive effect of forest certification in developing countries is greater than that in developed countries. Forest certification has become a trade barrier for developing countries, especially in the process of trade with developed countries. The common language effect is higher during the trade between developed and developing countries. The export competitive effect of wood products is higher than that of furniture products. Forest certification has trade barrier effect on wood products in developing countries, while it has trade barrier effect on furniture products in developed countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".