The Impact of Purchase Restriction Policy on Car Ownership in China’s Four Major Cities
Bibliographic record
Abstract
With the improvement of living standards, the demand for residents’ travel has grown rapidly. At present, China has surpassed the U.S. to become the world’s largest vehicle sales country. By the end of 2018, there had been over 200 million private passenger cars in China. Meanwhile, the increase in the number of cars has also brought a series of other problems: energy consumption, air pollution, traffic congestion, etc. Therefore, some first-tier cities have successively introduced motor vehicle purchase restriction policies to constrain the surge of local private cars. However, existing researches have overemphasized the factors that promote the development of China’s motor vehicle market and ignored the importance of the purchase restriction policies. In this study, policies in Beijing, Tianjin, Shanghai, and Guangzhou are introduced, and their impacts on local private passenger car stock are analyzed. The results indicate that purchase restriction policies kept the car ownership per thousand people in these cities in a relatively stable level with growing economy. Therefore, as the number of cities with restriction policies increases, it is necessary to take those policies into consideration in the forecast of possession. Meanwhile, the local governments should still think over policy contents from more aspects, like number of issued plates every year, special measures for new energy vehicles, and travel demand of residents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".