Economic Cost Analysis of New Energy Vehicle Policy -Empirical Research Based on Beijing’s Data
Bibliographic record
Abstract
In recent years, in order to improve Beijing's air quality and reduce vehicle emissions, the Beijing Municipal Government promotes the popularization of new energy vehicles through purchase subsidies, plate lottery, and driving restriction policy. However, the increase in the number of new energy vehicles and the increase in the number of vehicles travelling on roads have intensified the traffic pressure in Beijing. Traffic congestion has increased the emissions of motor vehicle exhaust in turn, resulting in higher socio-economic costs. Based on the actual data of Beijing, this paper quantitatively analyzes the economic cost of new energy vehicle policies by discussing the impact of current new energy vehicle policies on time, energy consumption and tail gas cost. Empirical results show that during the implementation period of the new energy vehicle policy, time cost and tail gas cost increase, energy consumption cost decreases, and the overall economic cost of the policy implementation period increases from 50 million yuan to 321 million yuan.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".