Dynamic optimization model of carbon emission allocation in agricultural product supply chain based on differential game theory
Bibliographic record
Abstract
To effectively balance emission reduction and preservation in the agricultural product sales system, this study models the carbon emission allocation of the agricultural product supply chain based on differential game theory. Starting from decentralized and centralized strategies without subsidies, research and modeling are conducted on the influencing factors of emission reduction, publicity, and preservation. Secondly, considering the scenario of government subsidies, the research analyzes the dynamic impact of different emission reduction, publicity, and preservation measures on the operation of the system. In the unsubsidized analysis model, the trajectory of goodwill shows variability, while the trajectory of freshness and emission reduction is mainly monotonic. In addition, freshness preference can enhance emission reduction decisions, and centralized strategies are superior to decentralized strategies in emission reduction. The increase in goodwill preference enhances the preservation, emission reduction, and sales status variables of agricultural products, but the centralized strategy is better than the decentralized strategy. Under the bilateral coordination mechanism, the maximum profit of the centralized strategy and bidirectional cost coordination mechanism system is 4350. In addition, under the two-way cost coordination mechanism, both retailers and suppliers have the highest profits, which are 1950 and 2352 respectively. In subsidy analysis, when the profit ratio is 0.5, the benefits of retailers and sellers reach equilibrium. In addition, bilateral coordination mechanisms have better system economic benefits, environmental benefits, and social welfare. This study is beneficial for improving carbon emissions from agricultural products and enhancing the effectiveness of agricultural product market development. This study provides technical support for agricultural emissions reduction and optimization of agricultural product supply chains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".