Quantifying the Potential Impact of Autonomous Vehicle Adoption on Government Finances
Bibliographic record
Abstract
There is some understanding that autonomous vehicles will disrupt public sector policies and the existing transportation industry, but this disruption is often loosely defined and tends to ignore how it would affect governments financially. The primary objective of this paper is to quantify the short-term impact of introducing autonomous vehicles on government finances. The analysis focuses on eight Canadian governments, encompassing four government tiers. Public discourse and academic literature are used to generate nine predicted changes (forecast variables) in future adoption scenarios. Using the predicted rate of autonomous vehicle adoption, the remaining variables are converted into financial changes by combining them with government financial records, infrastructure inventory datasets, and project cost estimates. The results suggest that, while revenue impacts are fairly minimal, and mostly impact Canadian provinces, the cost of implementing the expected vehicle-to-infrastructure (V2I) communication upgrades could be expensive for governments with smaller populations, especially municipalities. The revenue analysis indicates the biggest shift is likely to be a loss in gas tax, which affects federal and provincial revenues, yet this share is relatively small compared with the size of these governments’ budgets. The expense analysis suggests that, although provinces have extensive road networks, the cost of upgrading all of their highways may not be unreasonable compared with their yearly revenue intake. On the other hand, municipalities would require substantial new funds to be able to make the same upgrades.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".