Standardization of Exchanged Water with Different Properties in China’s Water Rights Trading
Bibliographic record
Abstract
Water rights trading is an effective way to optimize the allocation of water resources. However, the existing practice of water rights trading in China lacks any consideration of the practical value of the exchanged water. This deficiency may lead to disputes between transferor and transferee during the implementation of the water rights trading contract. This paper puts forward the concept of Standard Water (SW). First, getting the original value of exchanged water by the shadow price model based on input-output table; Second, based on the original value, building the economic profits or costs model to obtain the practical value of exchanged water; Third, establishing SW quantity measurement model according to the principle of rewarding excellence and punishing inferiority, so as to convert the water quantity of exchanged water into SW quantity. With the standardization method, this paper takes the water rights transaction between Dongyang City and Yiwu City in 2000 as an example to carry out case study, and provides policy recommendations. The results show that when the contract requires the provision of 49.999 million m3 water of Class I the quality, if the exchanged water quality provided is in Class II-V, the corresponding SW will be decreased to 48.699–37.399 million m3. The application of this research will be conducive to ensuring the fairness and durability of the water rights trading processes.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".