Effectiveness of China's Organic Food Certification Policy: Consumer Preferences for Infant Milk Formula with Different Organic Certification Labels
Bibliographic record
Abstract
China's current organic certification policy prohibits distribution of food in the Chinese market that has only obtained foreign organic certification but has not obtained Chinese organic certification, and prohibits the independent operation of foreign organic certification bodies in China. In this study, we use consumers’ willingness to pay (WTP) as a criterion for judging the effectiveness of China's organic certification policy. A choice experiment infant milk formula (IMF) with four attributes, including organic certification label, brand, country of origin, and price was conducted in Shandong province of China. Estimation with a mixed logit model revealed that consumers’ WTP for IMF with an American or European organic certification label was higher than IMF with a Chinese label. Moreover, consumers’ knowledge of organic food and food safety risk perceptions had an impact on their WTP . Results suggest scope for policy failure in that allowing independent certification in China by European and American organic certification bodies, or legal sale in the Chinese mainland market of IMF certified by American or European organic certification bodies, could increase consumer surplus beyond the status quo under the present policy regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".