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Record W2995581391 · doi:10.1017/s1474745620000075

Baptists and Bootleggers in the Biodiesel Trade: <i>EU–Biodiesel (Indonesia)</i>

2020· article· en· W2995581391 on OpenAlexaff
Carolyn Fischer, Timothy Meyer

Bibliographic record

VenueWorld Trade Review · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProtectionismInternational tradeBiodieselSubsidyBusinessDomestic marketInternational economicsEconomicsMarket economy

Abstract

fetched live from OpenAlex

Abstract EU–Biodiesel (Indonesia) is the latest in two lines of cases. On the one hand, the case offers yet another example of the Dispute Settlement Body striking down creative interpretations of antidumping rules by developed countries. Applying the Appellate Body's decision in EU–Biodiesel (Argentina) , the panel found that the EU could not use antidumping duties to counteract the effects of Indonesia's export tax on palm oil. On the other hand, the decision is another chapter in the battle over renewable energy markets. Both the EU and Indonesia had intervened in their markets to promote the development of domestic biodiesel industries. The panel's decision prevents the EU from using antidumping duties to preserve market opportunities created by its Renewable Energy Directive for its domestic biodiesel producers. The EU has responded in two ways. First, through regulations that disfavor palm-based biodiesel, but not biodiesel made from from other foodstocks, such as rapeseed oil commonly produced in the EU. Second, the EU has imposed countervailing duties on Indonesian biodiesel, finding that Indonesia's export tax on crude palm oil constitutes a subsidy to Indonesian biodiesel producers. The EU's apparently inelastic demand for protection raises two questions: First, when domestic political bargains rest on both protectionist and non-protectionist motives and policies have both protectionist and non-protectonist effects, what are the welfare consequences of restraining only overt protectionism? Second, under what circumstances may regulatory approaches be even less desirable than duties for addressing combined protectionist and environmental interests, and would the WTO have the right powers to discipline them in an environmentally sound way?

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2020
Admission routes1
Has abstractyes

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