Toward supply side incentive: The impact of government schemes on a vehicle manufacturer's adoption of electric vehicles
Bibliographic record
Abstract
Abstract Besides the consumer subsidy scheme, governments have recently implemented a hybrid scheme with an additional dual‐credit scheme on the supply side. It is important to understand the impact of this new practice on a vehicle manufacturer (VM) that provides gasoline vehicles (GVs) and/or electric vehicles (EVs), and the consumer and social welfare. Implementing the dual‐credit scheme leads to higher prices of GVs and EVs, but the effective price of EVs is actually lower. As the cost difference decreases and consumers’ low‐carbon awareness (LCA) increases, the VM prefers the product choice strategy including EVs under the pure subsidy scheme. Surprisingly, the hybrid scheme makes selling EVs feasible even though the cost difference is high and LCA is low. Although the additional dual‐credit scheme can improve the adoption of EVs, its parameter values should be carefully designed because otherwise it will damage the VM's profit and the consumer and social welfare.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".