Revenue and Policy Implications from Emerging Fuel Sale Trends in Metro Vancouver, British Columbia, Canada
Bibliographic record
Abstract
Taxes generated from fuel sales represent an important source of revenue throughout North America and are broadly used with other sources to maintain and develop transportation networks and associated infrastructure. Historically, these taxes have performed well in providing a secure source of revenue. However, recent factors such as improved vehicle fleet efficiency and declining vehicle kilometers traveled (VKT) on a per vehicle and per capita basis affect the volume of fuel sold and threaten the security of existing sources of revenue generated from fuel taxes. Notwithstanding the obvious environmental benefits to which these factors contribute, a need remains to ensure that revenue streams are able to keep pace with funding requirements, given that auto ownership continues to rise, which places pressure on total VKT. The result is a decoupling of road usage and the main source of road user payments. The traditional solution to maintaining or growing revenue streams in the region of Metro Vancouver, British Columbia, Canada, has been to increase the fuel tax. However, the decoupling problem is intensified in the region because of the proximity to the border with the United States, where fuel price differentials are placing pressure on such solutions. This paper explores a number of recent trends that affect fuel sales in Metro Vancouver. Revenue implications are outlined, and policy recommendations are made to strengthen the link between road usage and road user payments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".