Optimal subsidy decisions for building electric vehicle charging piles: unit subsidy vs percentage subsidy
Bibliographic record
Abstract
The shortage of electric vehicle charging piles (EVCPs) has seriously restricted the adoption of electric vehicles (EVs). To support the EV industry, policymakers such as the Chinese government propose two schemes to encourage automakers to build EVCPs: unit subsidy (US) where the policymakers provide a certain subsidy per EVCP, and percentage subsidy (PS) where the policymakers provide a percentage of funds based on the construction cost of EVCPs. Our study shows that the PS scheme leads to more EVCPs and higher EV adoption and subsidy efficiency than the US scheme when the policymaker’s objective is to maximize subsidy efficiency, defined as EV adoption minus subsidy expenditure. However, when the policymaker pursues social welfare maximization, the policies are equivalent. Basically, the automaker makes less profit under the US scheme than the PS scheme only if the policymaker’s EV adoption target is low and the automaker’s unit EV production cost is small. Nevertheless, when we consider market competition or social welfare, the automaker’s profit under the US scheme is always higher than under the PS scheme. Finally, if the government raises its EV adoption target, it will need to increase subsidies, so automakers will gain more profits, but the government’s subsidy efficiency will be reduced.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".