Mapping Vehicle Diffusion Dynamics in the United States
Bibliographic record
Abstract
Household vehicle fleet composition is an important dimension of transportation policy and planning. Vehicle choice and diffusion may be considered as a function of two network dimensions. First, spatial proximity may be associated with diffusion of a new vehicle powertrain, category, or model. A household purchases a vehicle based on local incentives or seeing the vehicle on the road. Second, social proximity as measured by similarity in demographics. We use a historical county-level vehicle registrations database for the United States to examine these factors. Results of the Moran I coefficient of correlation confirm the spatial diffusion phenomena for plugin electric vehicles (PEV)—including battery and plug-in hybrid electric, hybrid electric vehicles (HEV), hydrogen fuel cell electric vehicles (FCEVs), and US brand vehicles. The spatial autoregressive (SAR) model is estimated to measure the spatial diffusion effect after accounting for demographic variables associated with social diffusion. Larger county populations are linked to higher PEV and HEV adoption rates but lower US brand vehicle ownership. Elevated state grid eqCO 2 intensity is associated with higher adoption rates for both PEV and HEV. Centrally located states maintain high US brand vehicle ownership rates, while West and East Coast states show consistently low rates, with declining diffusing into neighboring regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".