How Does the Collaborative Economy Advance Better Product Lifetimes? A Case Study of Free-Floating Bike Sharing
Bibliographic record
Abstract
The collaborative economy is considered to have great potential in promoting the circular economy. However, there is little empirical research in this field. Taking the Beijing free-floating bike sharing (FFBS) program as an example, this study develops a system dynamics (SD) model based on the product lifetime extension business model (PLEBM) framework, and the business practices of FFBS. Combined with the dynamic evolution process of the FFBS market, the impact of FFBS on bicycle lifetime and the utilization efficiency of the urban bicycle system is explored. The results show that FFBS can reduce the required supply scale of the entire bicycle system by about 21%, and increase the average daily usage of bicycles by about 27%. In addition, FFBS also can increase the average lifecycle trip volume per bike in the entire urban bicycle system from approximately 900 to 1060, an increase of 16%. In particular, this study estimates that the optimal supply scale of the FFBS market in Beijing is about 800,000. It is worth noting that although enhancing the PLE strategy can increase the contribution of FFBS to PLE, it may also deteriorate the profitability of the FFBS platform. The authorities and FFBS operators should work together to continuously improve the profitability of the platform and strengthen its innovation capabilities to promote the healthy and sustainable development of FFBS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".