Evaluation and Drivers of Green Agricultural Water Use Efficiency in Yangtze River Economic Zone
Bibliographic record
Abstract
The efficient use of agricultural water is the key for Yangtze River Economic Zone (YREZ) to realize ecological green development. Taking the panel data on 11 YREZ regions in 2011-2018 as the object, this paper establishes an evaluation indicator system for green agricultural water use efficiency (GAWUE) containing undesired output, and adopts the epsilon-based measure (EBM) model to evaluate YREZ’s GAWUE. After analyzing the regional differences in YREZ’s GAWUE, the Tobit model was introduced to verify the drivers of GAWUE. The results show that: In the study period, YREZ’s GAWUE exhibits some regional differences. The mean GAWUEs of Shanghai, Jiangsu, Zhejiang, and Sichuan were optimized; those of Guizhou, Yunnan, Chongqing, and Hubei were relatively desirable, leaving a small room for improvement, the mean GAWUEs of Hunan, Jiangxi, and Anhui were undesirable, waiting for major improvement in future. Overall, the lower reaches had the highest GAWUE, followed by the upper reaches, while the middle reaches had the minimum GAWUE. The Tobit model shows that agricultural technological growth (ATG) and agricultural water intensity (AWI) greatly promote GAWUE, while farmer income level (FIL), water resources endowment (WRE), agricultural planting structure (APS), and farmland irrigation area (FIA) significantly suppress GAWUE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".