Modeling the Impacts of Free Trade Agreements on Domestic Transportation Gateways, Corridors, and Ports
Bibliographic record
Abstract
Canada has recently made progress with several free trade agreements (FTAs), and although the government has carried out considerable analysis of their potential impact on the Canadian economy, little to no work has been done to assess the potential impact on Canada's transportation system. The objective of the research was to estimate the impacts of recent and forthcoming FTAs on Canada's domestic trade infrastructure. This study extended a typical computable general equilibrium simulation of an FTA by estimating high-level domestic supply chain characteristics (i.e., subnational region of origin or destination, sub-national region of exit or entry, international transportation mode, port of clearance) and by converting the resulting trade flows to freight flows measured in tonnage. The results indicate that the Comprehensive Economic and Trade Agreement (CETA) between Canada and the European Union (EU) may have had large impacts on Canada's Continental and Atlantic Gateways, especially at the Port of Montreal, Quebec, as a result of trade creation with the EU. CETA also has had impacts on various crossings at the U.S. border as a result of trade diversion with the United States. Simulations, however, suggested that the Canada–Korea Free Trade Agreement has had relatively small impacts, mostly concentrated in the Asia-Pacific Gateway, particularly at the Port of Vancouver, British Columbia. Although the impacts were FTA-specific, this research demonstrated the need to consider FTAs in commodity forecasting and freight transportation planning, because they could make sizable changes to future freight flows on domestic transportation infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".