Evaluation and Drivers of Green Agricultural Water Use Efficiency in Yangtze River Economic Zone
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.000 |
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 it