Bibliographic record
Abstract
Purpose: The purpose of this paper is to measure the implications of the EU’s CBAM for Korea’s GVC exports, since Korea is ranked fifth in GVC participation and is the EU’s major trading partner. Research design, data, and methodology: This paper uses data from the OECD’s “Carbon dioxide emissions embodied in international trade” (TECO2), the IEA-EDGAR CO2, and the OECD’s Trade in Value Added (TiVA). It also uses the total probability formula and Bayes’ theorem to analyze much of the statistical data. Results: The world’s top 10 carbon emitters are China, the U.S.A., Russia, India, Japan, Germany, Canada, Korea, the U.K., and Iran, accounting for 66% of global CO2 emissions between 1990 and 2020. The top 10 CO2 emitters are also ranked highest by their share of GVC participation, with a 22.09% influence on CO2 emissions from GVC trade. China and the U.S.A. affect most of the countries they trade with, including Korea, when it comes to CO2 embodied in intermediate and final GVC goods. Conclusions: China, the U.S.A., Japan, and India are the closest trading partners to Korea under the GVC structure. These four countries, as top CO2 emitters, are highly interconnected with other trading partners, which means damage from the EU CBAM can have ripple effects on their partners, especially Korea. Therefore, Korea needs to develop and apply carbonreducing technology to achieve export competitiveness in response to potential EU CBAM implications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".