The Effect of New Energy Vehicle Policies on Traffic Congestion: Evidence from Beijing
Bibliographic record
Abstract
This paper systematically summarizes Beijing’s new energy vehicle (NEV) policies (including lottery policy and driving-restriction policy) and investigates their impacts on traffic congestion. We propose that although the current NEV policies might alleviate air pollution by reducing exhaust emission, they could worsen Beijing’s traffic condition by increasing congestion probability. We suggest that the lottery policy increases the proportion of NEVs in the total number of newly-added vehicles. Moreover, as the NEVs are not subjected to driving-restriction policy, the number of vehicles travelling on the road will increase and the average velocity will decrease. Hence, traffic congestion is more likely to happen. By adopting general traffic flow model and TTI congestion probability model, empirical findings based on Beijing’s traffic data show that the higher the proportion of newly added NEV is, the larger the congestion probability will be, which support our proposition. In addition, we also simulate NEV policies with different settings (% of NEVs and the degree of driving restrictions). Finally, policy implications and future research directions are also discussed.
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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 | 0.000 | 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".