Impacts of Reverse Global Value Chain (GVC) Factors on Global Trade and Energy Market
Bibliographic record
Abstract
Since the outbreak of COVID-19 and the American decoupling policy, the global value chains (GVCs) have been switched to regional GVCs, and, in the worst case, are subject to a potential alteration of reversing the GVCs, ultimately entailing a severe impact on international trade and the global energy market. This paper applies a quantitative approach using a computational general equilibrium (CGE) model to estimate the effects of the reverse GVC factors on the global economy, trade, and energy market. These reverse GVC factors will decrease the global GDP, and such effect will bring a greater influence on both China as well as the United States, which is pursuing decoupling. The increased trade costs due to these factors will reduce the GVC indices, mostly in ASEAN by 0.2~1.15%, followed by Korea, Japan and China. Surprisingly, the GVC index in the United States is expected to be strengthened due to the enhanced GVC with its allies such as Canada and Mexico. In China, the use of oil, gas and petroleum is expected to decrease by around 10%, and similar effects are expected in Korea and the EU. Among the world’s major energy producers, it is estimated that the US will reduce energy exports by 16–62% depending on the energy source, and the Middle East and Russia will significantly reduce their gas exports. The global energy market is shrinking, but in particular, the international gas market is expected to decrease by 27.3~38.6%.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".