The Transition to Net-Zero of Heavy-Duty Road Freight in Alberta: A Scenario Model
Bibliographic record
Abstract
The global climate crisis has prompted Canada’s commitment to achieving net-zero greenhouse-gas (GHG) emissions by 2050. The transportation sector, responsible for ~25% of Canada's GHG emissions, faces challenges in decarbonizing heavy-duty vehicles (HDVs), which make up ~20% of transportation emissions. Alberta’s heavy-duty trucking industry, a significant emissions contributor, encounters challenging conditions with strict range and vehicle weight requirements, complicating efforts to decarbonize. This thesis models the transition of Alberta’s heavy-duty trucking sector to net-zero GHG emissions, evaluating the feasibility of meeting Canada’s federal targets of 35% zero-emission vehicle (ZEV) sales by 2030 and nearly 100% by 2040. A comprehensive stock and flow model for hydrogen fuel-cell electric vehicles (FCEVs) and battery electric vehicles is developed, integrating vehicle projections, kilometers traveled, energy use, and GHG emissions under different decarbonization scenarios. The study also explores the development of a hydrogen-based value chain for Alberta's long-haul trucking industry, addressing the economic, logistical, and technical challenges of building infrastructure to support FCEVs. The economic analysis compares the total cost of ownership (TCO) for FCEVs and internal combustion engine vehicles (ICEVs) and examines the role of government policies, particularly the carbon tax, in supporting the transition. Key findings indicate that meeting the 2030 sales target is unlikely due to infrastructure and deployment challenges, while the 2040 target, though challenging, remains feasible. The extended timeline allows for the development of zero-emission vehicle technologies and hydrogen infrastructure, providing substantial GHG emission reduction benefits of at least 87% across all scenarios. FCEVs initially have a higher TCO than ICEVs, but as production scales and technology improves, the TCO is projected to fall below ICEVs by 2045. Incremental costs are projected to peak at CAD 500 million annually by 2035, achieving cost parity by 2040, and resulting in total costs of CAD 4 billion, with potential savings of up to CAD 2.5 billion annually by 2050. The projected carbon tax revenue covers the incremental costs, and even if doubled, would require only 75% of the revenue, demonstrating the strong economic feasibility of this beneficial and essential transition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".