Evolution and Equilibrium of Collaborative Innovation System of Low-Carbon Technology: Simulation of a Multi-stakeholders Game Model
Bibliographic record
Abstract
Low-carbon technology innovation is different from ordinary technology research, which has high investment, high risk and great uncertainty. It is very hard for enterprises and research institutions to succeed independently, and almost impossible for them to cooperate actively. Due to the different objective of participants, the expectation of innovation is reflected in the initial collaborative intention, which is a pivotal factor influencing the stability of collaborative innovation. On the premise of bounded rationality, this paper constructs multiple stakeholders evolutionary game model involving government, enterprises and scientific institutions. The influence of initial strategy probabilities of three participants is analysed in detail through simulation. The findings are as follows: (1) The evolution of government strategy is not affected by the initial collaboration probabilities of enterprises and research institutions. Eventually government strategies evolve into stimulation and support. (2) The strategy evolution of enterprises and research institutions is significantly affected by the initial strategy probabilities of three participants. The higher the initial probability of government support, the higher the possibility of enterprises and scientific institutions participating in collaboration. At the same time, the initial collaboration probabilities of enterprises and research institutions have a significant impact on each other, and the higher initial collaboration probability of one participant, the higher the probability of the other participating in collaboration. (3) Through the scenario simulation of two extreme probabilities, it is found that enterprises, compared with research institutions, play a more decisive role in collaborative low-carbon technology innovation under the support of the government. Therefore, if the government wants to realize the low-carbon technology collaborative innovation, the essential point is to stimulate collaboration enthusiasm of enterprises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".