Evaluation and prediction of water security levels in Northwest China based on the DPSIR model
Bibliographic record
Abstract
• A water security evaluation framework was established based on the DPSIR. • Five-element connection degree method was used to evaluate the water security. • Northwest China’s water security developed to a very high level. • Five dimensions of water security showed a steadily improving trend. Since the the dawn of the 21st century, water security has occupied a pivotal position in fostering sustainable development. However, frequency of extreme weather events due to climate change and higher intensity of anthropogenic events, water security problems in Northwest China are becoming increasingly prominent. This paper elucidates water security levels and predicts future trends of Northwest China. Firstly, the Driving Force-Pressure-State-Impact-Response (DPSIR) conceptual framework served as the foundation for the establishment of the water security evaluation index system. Then used the entropy weight method to calculate the weight of indicators and the five-element connection degree method was used to assess the water security levels. The period for the data used in the study was from 2010 to 2019. Finally, a grey prediction model was employed to forecast the water security levels from 2023 to 2030. The findings showed that: (1) From 2010 to 2019, the water security situation gradually developed to a very high level in Northwest China: Shaanxi Province mostly attained a high level, and the water security grades of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region were mostly at a very low level. (2) Generally, from 2023 to 2030, the water security level will continue to improve. The water security grades of Shaanxi, Gansu, and Qinghai will mostly be at the medium level, while that of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region will be at the low level. (3) The five dimensions of water security in Northwest China demonstrated a fluctuating yet consistently upward trend. (4) Based on the results, suggestions were put forward regarding water security and sustainable development in Northwest China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".