A Reduction In Canada’s Freight Transportation Greenhouse Gas Emissions By 2030 And 2050 A Scenario Analysis
Bibliographic record
Abstract
Greenhouse gases (GHG) from Canada’s freight transportation must be reduced by 30% to meet 2030 climate change commitments and by 80% to meet Canada’s 2050 targets. Despite the importance of this sector to Canada’s economy, there is an absence of cost-effective, low carbon options and the pathways to a low carbon future remain undefined. To explore this challenge, the historical emissions profile for rail and road transport in Canada are deconstructed and insights are used to scenario model a low carbon future with a greater share of freight shifted to rail and the energy intensity of road transportation improved. While reducing emissions by 18 Mt CO2e/yr relative to a reference scenario in 2030, the low carbon scenario failed to meet Canada’s reduction targets. The results demonstrate that for Canada to meet its long-term economic and climate change goals, development in disruptive technology, such as alternative fuel systems, is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".