Changes in the Trade Pattern in China Under the RCEP: An Analysis of Trade Creation and Diversion Using the SMART-WITS Model
Bibliographic record
Abstract
The Regional Comprehensive Economic Partnership (RCEP), one of the globe's most expansive free trade agreements (FTAs), has profoundly influenced its member countries' trading patterns. This fact is especially critical for a major economic powerhouse such as China. Understanding its trade creation and trade diversion within the RCEP context can facilitate successful formulation strategies and result in effective economic policies. In this study, we utilize the World Integrated Trade Solution Software for Market Analysis and Restrictions on Trade (WITS-SMART), a partial equilibrium modeling tool, on both state-level and industrial-tier tariff reductions under two distinct scenarios. Our findings confirm that China will benefit from impactful trade results across all RCEP members. Looking from industry point of view: machinery, chemicals, metals sector together with plastics and rubber production are projected to enjoy maximum rewards through increased trade creation and diversion opportunities from Japan and Korea. On the contrary, Australia and ASEAN have the greatest influence on the animal and vegetable sector. This crucial understanding creates strategic indicators that aid in evaluating the most appropriate alignments to harness the untapped potential in the RECP domain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".