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Record W6903410459 · doi:10.11575/prism/47695

The Transition to Net-Zero of Heavy-Duty Road Freight in Alberta: A Scenario Model

2024· other· en· W6903410459 on OpenAlexfundaboutno aff

Bibliographic record

VenueOpen MIND · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaIndian Institute of Science
KeywordsGreenhouse gasHydrogen vehicleSoftware deploymentStock (firearms)Government (linguistics)Alternative fuel vehicleScope (computer science)Green vehicleElectrification

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.031
GPT teacher head0.300
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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