Impact of Carsharing on Household Vehicle Holdings: Results from North American Shared-Use Vehicle Survey
Bibliographic record
Abstract
Carsharing has grown considerably in North America during the past decade and has flourished within metropolitan regions across the United States and Canada. The result has been a new transportation landscape, which offers urban residents an alternative to automobility without carownership. As carsharing has expanded, there has been a growing demand to understand its environmental impacts. This paper presents the results of a North American carsharing member survey (N = 6,281). The authors establish a “before-and-after” analytical design with a focus oncarsharing’s impacts on household vehicle holdings and the aggregate vehicle population. The results show that carsharing members reduce their vehicle holdings to a degree that is statistically significant. The average vehicles per household of the sample drops from 0.47 to 0.24. Most ofthis shift constitutes one-car households becoming carless. The average fuel economy of carsharing vehicles used most often by respondents is 10 miles per gallon (mpg) more efficient than the average vehicle shed by respondents. The median age of vehicles shed by carsharinghouseholds is 11 years, but the distribution covers a considerable range. An aggregate analysis suggests that carsharing has taken between 90,000 to 130,000 vehicles off the road. This equates to 9 to 13 vehicles (including shed and postponed auto purchases) for each carsharing vehicle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".