Study on the behavioral decision of multiple subjects of agricultural green production under the double carbon target in China
Bibliographic record
Abstract
Excessive carbon emissions constitute a major driver of contemporary global warming. Achieving carbon neutrality in agriculture, particularly via carbon peaking, represents a critical strategy for emission reduction, wherein green agricultural production serves as a pivotal component. This study constructs a unified model encompassing the government, agricultural enterprises, and farms engaged in green agricultural production, utilizing a dynamic evolutionary game approach to examine the decision-making behaviors of these stakeholders. The findings indicate that green agricultural production entails the responsibilities of managers, users, and producers. The government fulfills a guiding and supervisory role, while agricultural enterprises actively produce low-carbon agricultural materials, and farms rigorously implement these materials. To facilitate this, the government should implement a reward and punishment mechanism, including increased carbon tax rebates for enterprises producing low-carbon materials and subsidies for farms utilizing them. Conversely, penalties should be levied on entities that fail to comply with low-carbon practices. The government must meticulously calibrate subsidies and fines within a reasonable range, appropriately reduce taxes, and effectively manage regulatory costs to mitigate financial strain. Under government incentives and penalties, agricultural enterprises should proactively respond by offering price concessions to farms utilizing low-carbon materials, balancing costs and benefits, and fostering a socially responsible corporate image. Farms should establish close collaboration with the government and enterprises to ensure the procurement, utilization, and production of low-carbon agricultural materials. This study provides valuable insights for advancing agricultural carbon neutrality through the perspective of green agricultural production.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".