The Impact of Car-Sharing on the Willingness to Postpone a Car Purchase: A Case Study in Hangzhou, China
Bibliographic record
Abstract
This paper aims to explore the potential of car-sharing in reducing car ownership and what are the impact factors. Based on the data of Hangzhou “Fun Car-sharing” system, a discrete choice model was established to study the willingness to postpone car purchase with participation in car-sharing. Compared with previous studies, this study included the variables involved in the questionnaire and those related to the usage characteristics extracted from rental data and GPS data. The questionnaire data indicate that about 50% of respondents will postpone car purchase by participating in car-sharing. The discrete choice model indicates that car-sharing in China can play a role in delaying car purchase. The results further suggest that respondents who use car-sharing more frequently, travel to work by car, or have an activity anchor in their trip are more likely to postpone car purchase. Moreover, respondents whose most common travel purpose of car-sharing is work-related or car purchase plan is definite are less likely to postpone car purchase after participating in car-sharing. The insights gained in this study can help cities and car-sharing operators to formulate relevant policies and regulation that optimally integrate car-sharing services into the overall urban transport systems.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".