How Much Can TFA Boost Sustainable International Trade? --Evidence from RCEP and China
Bibliographic record
Abstract
In February 2017, as the first multilateral agreement on trade since the establishment of the WTO, the Trade Facilitation Agreement (TFA) came into effect.Hitherto, however, there is still very little evidence on the factual effect of trade facilitation on promoting international trade.Based on panel data from 2017-2021, using a trade gravity model and the least squares regression analysis, this paper explores the impact of the implementation of the TFA on the import and export values between China and five RCEP countries (Indonesia, Malaysia, Singapore, Thailand, and Vietnam).The results demonstrate that China's trade facilitation level is significantly and positively related to the trade flow of goods.In addition, the population size of RCEP countries is also positively correlated with the trade flows between the two sides. 1 per cent of Trade Facilitation Index (TFI) can increase trade flows by US$10,352.9million.It is recommended to actively promote the development of trade facilitation, including the formation of RCEP logistics industry alliance, the establishment of one-stop digital customs, the acceleration of Cross-border E-commerce, as well as the reasonable use of blockchain.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".