The Implementation and Welfare Effect of Vehicle Quantity Regulation Policy: A Case Study of Beijing Vehicle Quota System
Bibliographic record
Abstract
The quantity regulation of license plates for small passenger cars is a typical public policy of government intervention in transportation market. The goal of the policy is to control the number of vehicles in a region, with the main purpose of controlling the growth rate of small passenger cars and reduce traffic congestion. This paper takes Beijing as an example to analyze the implementation effect and welfare effect of the vehicle quantity regulation. The analysis results show that the policy implementation is different from Singapore. It can control the rapid growth of small passenger cars in a city from a macro perspective, but it cannot control the growth of vehicles on the road for a city, so it is limited to reduce traffic congestion. On the other hand, the policy of controlling the number of small passenger cars will bring a series of welfare losses. In this regard, this paper puts forward suggestions of improving policy design, increasing supporting policy measures, strengthening urban public transport construction and changing residents’ travel mode to enhance the implementation effect of the policy.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".