Research on Multilevel Nonlinear Optimization Path of Agricultural Low-Carbon Development Based on Double Constraints of Government Regulation and Agricultural Insurance
Bibliographic record
Abstract
The study uses a multilevel nonlinear optimization algorithm to optimize the low-carbon development path of agriculture with the dual constraints of government regulation and agricultural insurance. The algorithm solves the development path optimally through convergence analysis, parameter setting and constraint problem modeling. In addition, the study establishes an index system for evaluating agricultural low-carbon development, and assesses the effectiveness of low-carbon development through field application. The algorithmic path optimization in this paper has better performance in terms of solution quality, iteration number and solution time. At iterations 17, 43, 62 and 82, the algorithm of this paper found feasible solutions for path optimization. By 2023, the annual increase in pollutant emissions from agricultural production, total carbon emissions, carbon emission intensity of 10,000 yuan output value, and comprehensive energy consumption of 10,000 yuan output value are projected to be reduced to 42696.39(tons), 21141.5(10,000 tons), 1017.9(tons), and 6422.6(tons), respectively. The evaluation indicators Agricultural activity average carbon intensity, Reduction of carbon intensity and other indicators have relatively high weights, which is the main reason for the differences in low carbon development.The correlation between the effectiveness of agricultural low carbon development and the optimal sequence in 2023 is 0.9981, which demonstrates that the role of government regulation and agricultural insurance in promoting agricultural low carbon development.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".