Has the sharing economy changed our lives? Unveiling the effects of car-sharing on urban public transportation use
Bibliographic record
Abstract
Over the past decade, the car-sharing sector has emerged as a disruptive service innovation, reshaping urban mobility and influencing public transportation ecosystems. Positioned at the intersection of convenience, affordability, and flexibility, car-sharing increasingly functions as a key component in multimodal transport strategies. However, existing research on its substitutive versus complementary effects has been largely inconclusive due to reliance on perception-based methods such as surveys and interviews. Addressing this gap, our study offers a robust empirical analysis based on 659,305 real-world reservations across 67 avenues over a 12-month period in South Korea. To address potential endogeneity and selection bias, we adopt a two-stage least squares (2SLS) regression model using instrumental variables. Results indicate that a 1% increase in car-sharing reservations is associated with an 18.25% reduction in public transportation usage, highlighting a significant substitutive relationship. This effect is context-dependent, varying by population density and public holiday status. By quantifying car-sharing’s operational and societal impact, our findings provide actionable insights for urban mobility planners, transportation service providers, and policymakers aiming to balance innovation with public infrastructure sustainability.
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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 | 0.001 |
| 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".