A framework for analyzing the market penetration of low-carbon road vehicles
Bibliographic record
Abstract
This study assesses pathways for transitioning to low-carbon energy in road transport in a fossil fuel-dependent jurisdiction. A novel assessment framework was developed and applied to road transport to analyze the transition in the sector considering sector activity, vehicle costs, and market shares to 2050. Seven fuel technologies and ten vehicle categories including hydrogen fuel cell and battery electric vehicles were examined across all sectors. Nine scenarios that incorporate three policy pathways – financial incentives, zero-emission vehicle (ZEV) mandates, and carbon prices – were evaluated. Different scenarios within these pathways were explored and their impact on vehicle costs and market shares to 2050 were evaluated. A case study focused on Alberta, a fossil fuel-intensive province in Canada, was performed using the developed framework. Results indicate carbon pricing and ZEV incentives alone are insufficient for significant ZEV adoption by 2050. Without a zero-emission vehicle policy, hydrogen fuel cell and battery electric passenger vehicles are projected to have market shares of 16 % and 31 % by 2050, respectively. With a 100 % zero-emission vehicle sales mandate by 2035, these shares rise to 36 % and 64 %, respectively, by 2050. The findings on the effectiveness of policy frameworks can be considered for policy development to mitigate greenhouse gases emissions from the road transportation sector and will inform infrastructure planners and other energy stakeholders. The developed framework can be applied internationally to assess the transport sector. • Market penetration potential of H 2 -FCEVs is lower than BEVs. • Carbon price policy and ZEV incentives alone do not significantly increase ZEVs. • ZEV sales mandate is the most effective policy to transition road transport sector. • Current policies leads to 69 % increase in ZEVs from REF scenario by 2050. • Under current policies, BEVs are expected to have the lowest TCO.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".