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Record W2351168338

The Green Barrier of Trade Effect of EU's Environmental Regulations on Textile and Clothing Exported from China Based on Panel Data Analysis on China's Four Types of Textile and Clothing Exported to Eleven of the EU Member Countries: 1990-2006

2009· article· en· W2351168338 on OpenAlexaboutno aff
Jiang Bing-bing

Bibliographic record

VenueChina Industrial Economy · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsClothingChinaTextileEuropean unionInternational tradePanel dataQuarter (Canadian coin)BusinessEconomicsGeographyEconometrics
DOInot available

Abstract

fetched live from OpenAlex

本文采用引力模型,对1990—2006年我国短纤混纺布、棉制男衬衫、合成纤维制男式西服套装、棉制其他袜等四种纺织品和服装对欧盟11国的年度出口值进行面板数据分析,合并考察欧盟有关纺织品与服装的一揽子环境规制措施对我国纺织品与服装出口是否产生壁垒效应。本文拓展出环境规制严度变量的纵向赋值法.按规制措施对纺织品与服装出口影响强弱对环境规制严度变量赋予不同的增幅.在时间序列数据中对先后实施多项不同种类的环境规制措施如何进行计量分析的问题取得突破。本文指出.引力模型的变量设定要反映所考察产品的特殊性.并设定两个虚拟变量以反映我国纺织品与服装出口所特有的受配额限制的三个阶段。结果显示:欧盟有关纺织品与服装的一揽子环境规制措施对我国四种纺织品与服装的出口已构成绿色贸易壁垒。这一结果为欧盟环境规制构成绿色壁垒的论断提供了经验分析的证据。

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)0.001
metaresearch head score (Gemma)0.002
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.218
Teacher spread0.181 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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