Development Strategy of New Energy Business for Traditional Car Manufacturers under the Dual-Credit Policy
Bibliographic record
Abstract
With the track of new energy vehicles continuously booming and accelerating, more and more automobile manufacturers are seeking effective methods to help companies stand out. Some of them attempt to split the new energy vehicle (NEV) business module in order to boost brand competitiveness for the top spot in an increasingly competitive market. The dual-credit policy, however, makes Chinese automakers consider whether the benefits of adopting the splitting strategy exceed the drawbacks, yet few domestic studies can offer assistance. As a result, the study develops split strategy and integrative development strategy’s optimal decision models under the dual-credit policy. The paper also addresses which scenario the Integrative development approach or the splitting strategy is more advantageous and effective under using model comparison and numerical analysis. According to the study, manufacturers of fuel vehicles frequently reduce their output and raise their prices in response to the dual-credit policy. The credit price should receive significant attention from both traditional car manufacturers and NEV manufacturers due to its profound impact on both. Whether traditional car manufacturers should split the NEV business module independently depends on the pricing of NEV credits as well as the supply and demand for NEV credits. Traditional car manufacturers can only use a splitting strategy when the price of NEV credits is within a particular range and the NEV credit is surplus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".