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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".