Freight shipper mode choice in the Quebec City-Windsor Corridor and its impact on carbon dioxide emissions
Bibliographic record
Abstract
The Quebec City - Windsor corridor is the busiest and most important trade and transportation corridor in Canada. The transportation sector is the second largest greenhouse gas (GHG) emission category in the country. Governments around the world, including Canada, are considering increased mode share by rail as a way to reduce transportation emissions. To understand whether freight mode shift is a realistic means to reduce transportation emissions, an analytical model is needed that can predict the effect of government policy on mode split. This thesis provides background on the freight transportation-GHG nexus in Canada and describes the development, implementation, reasoning behind, and results of, a Stated Preference shipper carrier choice survey for the Quebec City - Windsor corridor conducted during the fall of 2005. It then describes how the resulting carrier choice models are used to estimate the potential to displace truck traffic to rail (premium-intermodal) under current conditions, as well as to test the effectiveness of different possible future policy or service offering scenarios. The results show that premium-intermodal has the potential to capture a substantial share of traffic between the main destinations in the Quebec City - Windsor Corridor. However, its ability to contribute significantly to reducing CO2 emissions is limited. According to the analyses conducted, potential reductions are considered to be in the range of nil to 0.413 Mt---a fraction of what the federal government was hoping to be able to achieve through "further public-private collaboration to promote the use of intermodal freight opportunities and to increase the use of low-emission vehicles and modes" (Government of Canada 2002). At the same time, these potential reductions are based on a small proportion of total truck-related emissions and a few city-pairs. Extension of the current analysis to more city-pairs separated by longer distances might arrive at different conclusions.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".