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Record W3110597610 · doi:10.3390/f11121270

Effect of Forest Certification on International Trade in Forest Products

2020· article· en· W3110597610 on OpenAlexaff
Jiaojiao Chen, Lanhui Wang, Lingchao Li, Juliana Magalhães, Song Wei-ming, Wenming Lu, Lichun Xiong, Wei-Yew Chang, Yujun Sun

Bibliographic record

VenueForests · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCertified woodCertificationForest productBusinessGravity model of tradeSustainabilityDeveloping countryTrade barrierInternational tradeForest managementEconomicsForestryEconomic growthGeographyEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.024
GPT teacher head0.274
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2020
Admission routes1
Has abstractyes

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