Impact of Transport Cost and Travel Time on Trade under China-Pakistan Economic Corridor (CPEC)
Bibliographic record
Abstract
China is the second biggest economy in the world and almost 40% of its trade in 2016 is transported through the South China Sea. China needs a small, secure, and low-cost path to trade with Europe and the Middle East and China-Pakistan Economic Corridor (CPEC) is a feasible solution to this requirement. This research analyzes the effect of CPEC on trade in terms of transport cost and travel time. In addition, the study compares the existing routes and the new CPEC route. The research methodology consists of qualitative and descriptive statistical methods. The variables (transport cost and travel time) are calculated and compared for both the existing route and new CPEC route. The results show that transport cost for 40-foot container between Kashgar and destination ports in the Middle East is decreased by about $1450 dollars and for destination ports in Europe is decreased by $1350 dollars. Additionally, travel time is decreased by 21 to 24 days for destination ports in the Middle East and 21 days for destination ports in Europe. The distance from Kashgar to destination ports in the Middle East and Europe is decreased by 11,000 to 13,000 km.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".