The optimal design of differentiated subsidy policies for new energy vehicle firms by considering the difference in market share and endurance mileage
Bibliographic record
Abstract
To promote the development of the new energy vehicle industry, China has introduced many subsidy policies. Existing policies rarely consider the difference in market share of new energy vehicle enterprises, but the difference in market share will directly affect consumer demand, and then affect the development of the new energy vehicle industry. Therefore, we consider the difference of the market share and endurance of the new energy vehicle firms at the same time, establishing a Stackelberg model and considering three most common subsidy forms, which are quantity-based subsidies, price-based subsidies, and endurance-based subsidies to derive the optimal differentiated subsidies of the government. The analysis of this paper shows that for the new energy vehicle firms with different market share and endurance, differentiated subsidies can achieve higher social welfare. In addition, for any subsidy form of the sale-based subsidies, price-based subsidies, and endurance-based subsidies, the final cost for consumers will be reduced, and the total sales quantity of the new energy vehicles in the market will increase, but the profits of firms and the sales quantity of one firm could increase or decrease. Lastly, under the assumption of this paper, the optimal price of both firms and social welfare are the same under the three aforementioned subsidy forms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".