Water Security Assessment of China’s One Belt and One Road Region
Bibliographic record
Abstract
The sustainable development of socioeconomic and environmental systems are highly dependent on water capital and water utilization efficiency. Nowadays, a significant portion of the world is facing water security issues due to a combination of various factors. As a result, socioeconomic and environmental systems are threatened. China is also currently experiencing problems. Water security assessment helps to identify key determining factors for optimal water utilization, so the authors present the Driving Forces-Pressures-Carrying Capacity-State-Impacts-Responses (DPSCIR) water security assessment framework. Unlike previous methods, the proposed framework incorporates the carrying capacity of the environment, and as a result, yields assessment results that are more realistic. As a case study, the proposed framework coupled with the entropy method is applied to assess the water security status of the One Belt and One Road (B&R) region in China. In addition, the water security level of the provinces and municipalities in this region are simulated for the time period from 2017 to 2022 using the Grey Prediction Model. The results show that Responses, State, Pressures, and Carrying Capacity Subsystems greatly influence water security of the region. According to the assessment, water security of the area improved from 2011 to 2016. The results portray the following trend among the three subregions of the study area, the water security of the 21st Maritime Silk Road (One Road) area is better than Silk Road Economic Belt (One Belt) and the Strategy Support and Pivotal Gateway (SSPG) of B&R areas. Generally, from the evaluation results it can be concluded that only focusing on the subsystem of Responses cannot entirely address the water security problems within the B&R area. Therefore, to ensure sustainable water security in the region and in the country, the government needs to design water resource management mechanisms that take all the subsystems into account.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".