Variation in Total Factor Productivity of Corn in 19 Main Producing Areas under the Constraint of Carbon Emissions
Bibliographic record
Abstract
To realize the sustainable development of the corn industry, the key lies in improving the total factor productivity (TFP) of corn under the constraint of carbon emissions.Based on the panel data of 19 main corn producing areas in China, this paper creates a corn TFP measurement model, applies the model to measure the corn TFPs in each main producing area from 2008 to 2018, and analyzes the features and causes of the variation in corn TFP in China with constraint of carbon emissions.The results show that: After 2015, the corn TFP in China was on the rise with constraint of carbon emissions, and the corn production was moving towards low-carbon mode, but exhibited huge regional difference; The policies on corn structure adjustment in the Sickle Band areas have effectively promoted the low-carbon production of corn in these areas, and improved the corn TFP; The growth of corn TFP in China is mainly bottlenecked by the slow technical progress.Finally, several policy suggestions were put forward to promote the low-carbon production and TFP of corn and other crops.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".