Water loss and return flows matter for water stress mitigation in China
Bibliographic record
Abstract
Water is withdrawn, lost, consumed, polluted, returned, treated, reused, and traded between regions within the societal water cycle due to human activities, contributing to regional water stress. In this research, we aim to examine the impacts of the societal water cycle on water resources and explore strategies for reducing water stress in China. The results show that most provinces in China suffer from water quantity and quality stress. However, there is a significant potential to reduce water quantity stress by 36–79 % through reducing water loss and return flows. The return flows and water loss in the virtual export forms could be avoided to reduce virtual water export-induced quantity stress by 39–89 %. Agriculture and households’ return flows contribute 61–98 % to provincial water quality stress in China. The five sectors with the greatest potential to mitigate water quantity and quality stress are identified for each province, which could reduce quantity stress by 22–75 % and quality stress by 23–76 %. • Majority provinces in China suffer from both water quantity and quality stress • System thinking of societal water cycle is necessary for water stress assessment and mitigation • Water loss and return flows contribute to 36–79 % of water quantity stress • Agriculture and households' return flows contribute 61–98 % to provincial water quality stress • The top five sectors could mitigate quantity stress by 22–75 % and quality stress by 23–76 %
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".