Analysis of China’s Soybean Import: Based on the Perspective of Welfare Economics
Bibliographic record
Abstract
China’s soybean import meets the growing nutritional demand of Chinese residents, but the continuous increase in soybean import quantity has also caused disputes about over import. The continuous importation of soybean in China gradually forms a path dependence on some major exporting countries. On the analytic basis of soybean supply and demand in China, this paper employs CR index and HHI index to measure the import concentration of soybean, and analyzes the monopoly pricing mechanism of international soybean by adopting welfare economics method. The results show that the imported soybean has gained strengthened monopoly in China’s market, thus increasing the risk of consumers’ welfare in loss. From the demand perspective, the loss of consumer welfare can be reduced by lessening soybean demand and increasing demand elasticity. From the perspective of supply, the same objective can be also achieved by increasing market competition in diversification and reducing monopoly transaction costs. As a result, some policy suggestions can be thus put forward: firstly, it’s suggested to control the importation of soybean and augment the imported alternative products. Secondly, it’s recommended to reduce the transaction cost of the major exporters to China, and to promote an imported strategy in diversification by strengthening the cooperation with countries that join in the “belt and road initiative”.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".