Policy Brief – AVs in the Pacific Northwest: Reducing Greenhouse Gas Emissions in a Time of Automation
Bibliographic record
Abstract
The transportation sector accounts for the largest portion of greenhouse gas (GHG) emissions compared to all other sectors, and GHGs are once again on the rise. At the same time, new mobility technologies are being introduced and fully autonomous vehicles (AVs) are anticipated to be deployed, at least to varying extents, within 5-10 years. (Waymo, Google’s self-driving project, is already operating a limited robotaxi service in Phoenix, AZ with a fleet of AVs.) AVs have the potential to improve safety, reduce congestion, and increase mobility— but they could also increase congestion, increase vehicle miles/ kilometers traveled (VMT/VKT), and erode transit, walk, and bike mode share, exacerbating existing conditions. The cities of Portland, OR; Seattle, WA; and Vancouver, BC have adopted climate action plans with the goal of dramatically reducing GHG emissions. This policy brief is intended to help the three cities better understand how AVs may help or hinder them in achieving their goals, and what recommended actions to take at this critical moment in time.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".