Bibliographic record
Abstract
In 2001, the newly elected Paris Mayor started to implement an active probicycle policy, mainly based on investments in bike paths and on the launching of a bike rental system in July 2007 (Vélibs). This article aims at appraising such a public policy by measuring its welfare impact, expressed as the Net Present Value (NPV) of the overall changes generated over 2007-2010. Faster bikes perform more kilometres in Paris. Some people shift from cars, buses, and subway to bikes and Vélibs. Switching from a passive mode of transportation to bicycle has a positive health impact. The amounts of C02, local pollutants, noise and congestion externalities generated by each mode are also changing. By contrast, the speed of car is slightly reduced, partially because of a narrowed road capacity. Public finance is affected by the change in fiscal revenues collected whereas the bike rental system's operator (Decaux) realizes some profits. Finally, the pro-bicycle policy has an initial investment cost and a residual value. All these changes are calculated and computed in a same monetary unit. This policy is (slightly) beneficial for society (a total NPV of +136 M€) even if the cost for public finance (-704 M€) is close to the bikers' benefit (+859 M€). Vélibers are highly subsidized by the city. This policy is also working at the expense of the cars' drivers (- 286 M€) while positive externalities are not very important (+101 M€) and Decaux's profits are moderated (+166 M€). Several sensitivity analyses are conducted to identify the key drivers of the policy's success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".