Water Pollution and Environmental Governance of the Tai and Chao Lake Basins in China in an International Perspective
Bibliographic record
Abstract
The Tai and Chao Lake basins are currently facing a serious water pollution crisis associated with the absence of an effective environmental governance system. The water pollution and the water governance system of the two basins will be compared. The reasons for water pollution in both basins are similar, namely the weak current water environmental governance system cannot deal with the consequences of the rapidly growing economy. China’s water governance system is a complicated combination of basin management with both departmental management and regional management. There is an absence of legal support and sound coordination mechanisms, resulting in fragmented management practices in the existing water environmental governance system. A comparison is made for the Tai and Chao Lake basins and Canada, France, the United Kingdom and the United States. Based on China’s present central-local governance structure and departmental system, an integrated reform of basin level and water environmental governance in China should learn from international experiences. The reforms could consist of improved governance structures, rebuilding authoritative and powerful agencies for basin management, strengthening the organizational structure of the basin administrations, improving legislation and regulatory systems for basin management and enhancing public participation mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".