Research on collaborative innovation decision making of new energy vehicle industry chain considering carbon quota sharing contract
Bibliographic record
Abstract
This article constructs a collaborative innovation decision-making model for the new energy vehicle industry chain under decentralized and carbon quota sharing contracts, and obtains the optimal parameter values and profit values of the new energy vehicle industry entities under two different scenarios. Taking BYD's new energy vehicle industry as a case study, the beneficial effect of carbon sharing contracts on the collaborative decision-making of the new energy vehicle industry system is empirically analyzed. Research has found that although carbon sharing contracts may weaken the willingness of new energy vehicle battery suppliers to innovate in carbon reduction, they will effectively improve their innovation in the range of new energy vehicles. The market price of new energy vehicle manufacturers under carbon sharing contracts decreases with the increase of the carbon sharing coefficient. Carbon sharing contracts can significantly increase the profits of the main players in the new energy vehicle industry system, and are directly proportional to the carbon sharing coefficient of the contract.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".