The Potential for Premium-intermodal Services to Reduce Freight CO2 Emissions in the Quebec City - Windsor Corridor: A Stated Preference Application
Bibliographic record
Abstract
The Quebec City-Windsor corridor (the 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. This paper develops estimates of the potential for CO2 emission reductions in the freight transportation sector through the use of premium-intermodal services between the main Corridor destinations. CO2 reduction estimates are arrived at using a Stated Choice methodology. The basis of the analysis is a recently administered stated-preference carrier-choice survey of shippers in the Corridor. Survey data were used to develop mode share models for five different categories of shipments between eighteen city-pairs. A railyard-catchment approach was taken to arrive at estimates of contestable intercity truck traffic using a subset of the Ontario Ministry of Transportations Commercial Vehicle Survey. CO2 emissions were based on current truck traffic estimates, and emissions factors obtained from MOBILE6.2C. The results show that premium-intermodal has the potential to capture a significant share of traffic between the main Corridor destinations with potential CO2 emissions reductions considered to be in the range of nil to 0.4 Mt.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".