Research on collaborative R&D decision making of photovoltaic industry supply chain considering green preference under carbon target regulation
Bibliographic record
Abstract
Under the dual carbon target regulation, this article constructs a collaborative research and development carbon reduction model for the photovoltaic industry supply chain from the perspective of carbon tax and consumer green preferences, using differential game theory. Considering three different scenarios of no research and development carbon reduction, independent research and development carbon reduction, and collaborative research and development carbon reduction, the optimal factors and profit values are obtained, and case analysis and sensitivity analysis are conducted. Research has found that: 1) The optimal carbon reduction achieved by photovoltaic industry supply chain entities through cooperative research and development is higher than that achieved through independent research and development. 2) The increase in carbon tax rates has to some extent increased carbon emissions, but at the same time reduced the overall profit of the photovoltaic industry chain. 3) The higher the proportion of research and development costs borne by photovoltaic system manufacturers, the higher the carbon emission reduction of photovoltaic silicon wafer suppliers' research and development. 4) Consumer green preferences are beneficial for increasing carbon emissions reduction in the photovoltaic industry chain.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".