Blockchain Adoption in Agricultural Supply Chain for Better Sustainability: A Game Theory Perspective
Bibliographic record
Abstract
Within the context of the rise of the Internet of Things, blockchain, and other new technologies, telecommunications operators are committed to applying technologies to promote business transformation and upgrading. The government also actively applies technologies to traditional fields to promote social progress. In agriculture, the agricultural supply chain has a low information level and low degree of digitization. The application of blockchain technology in agriculture offers exceptional advantages because of its decentralization, openness, and transparency. Based on the application of blockchain in an agricultural scenario, an evolutionary game model made up of governments, telecom operators, and agricultural enterprises was established to analyze the model’s equilibrium stability and evolutionary stable strategy. Then, numerical simulation was carried out to study the influence of the initial green level, equipment deployment cost, technology operation cost, and other core factors on the tripartite evolution behaviour. The results show that each factor influences the behaviour of a third party in different ways. Finally, according to the simulation results, this paper puts forward practical suggestions, explores the long-term impact of the application cost and sustainable income of blockchain technology on cooperation, and provides new ideas for the governance of China’s traditional fields from the perspective of new technology application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".