Dynamic assessment and influencing factors analysis of water environmental carrying capacity in the Yangtze River Economic Belt, China
Bibliographic record
Abstract
Improving water environmental carrying capacity (WECC) is pivotal to support water sustainability and continued economic development. The improvement pathways of WECC based on both subsystem coupling and driving factors have not yet been identified. Therefore, taking the Yangtze River Economic Belt (YREB) as a case study, this paper firstly constructed a set of scientific comprehensive evaluation index system of WECC based on the coupling system of water resources-water environment-society-economy. Secondly, the improved catastrophe progression method was properly introduced to dynamically evaluate the level of provincial WECC from 2010 to 2018. Finally, the coupling coordination degree (CCD) and obstacle factor diagnosis model were creatively combined to identify the main influencing factors of WECC. The main findings were as follows: (1) The WECC of the YREB was generally low at the provincial and subsystem levels without any obvious upward trend. In comparison, the WECC was higher in Shanghai City, Zhejiang Province and the lower reaches of the YREB. (2) The CCD among the four subsystems was generally low in the YREB. Meanwhile, there was a significant positive correlation between the CCD and WECC in each provincial area, and thus the low CCD was a vital reason for the low WECC in the YREB. (3) Overall, the economic subsystem had the highest restriction on WECC improvement in the YREB, while that of the water resources subsystem had the least. Seen from the mean, the top five obstacle factors of WECC were proportion of ecological environment water consumption, urbanization rate, per capita GDP, proportion of tertiary industry and water consumption per unit of GDP respectively. Furthermore, the rankings of main obstacle factors showed a certain time fluctuation. In general, it could be found out that the CCD of subsystems and main obstacle factors should be focused on simultaneously when improving regional WECC. Additionally, this study has provided a new analytical framework for identifying the pathways of improving WECC, which is also applicable to find the improvement pathways for stability and safety of other complex systems.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".