Performance evaluation of China's agricultural water rights markets (2002–2020)
Bibliographic record
Abstract
Abstract The water rights market has been promoted in China since 2000. The lack of data and suitable evaluation methods impeded efforts to evaluate the market performance systematically. This research examines the characteristics, performance, and variation of China's agricultural water rights market (AWRM) on the basis of data from the field investigation, China Water Exchange, academic literature, and policy documents. We construct a comprehensive evaluation index system from the aspects of efficiency, fairness, and sustainability and quantitatively evaluate the performance of seven typical agricultural water markets in China. From 2002 to 2020, there were 1,752 cases of agricultural water transactions in China, with a total trading volume of 10.09 million m3. The market scale is increasing and the development of AWRM can be divided into three stages. Most agricultural water rights transactions happened in the west and the north. The average performance of typical AWRMs was poor. Typical water markets had the highest score for fairness and the lowest score for sustainability. Water markets in the humid areas performed the best, whereas the markets in the higher transaction level performed better. Water markets in areas with higher economic development had better performance.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".