Strategic Environmental Assessment (SEA) and provincial level expressway programme planning: an application framework and indicator system for China
Bibliographic record
Abstract
The thesis addresses the challenge of applying strategic environmental assessment (SEA) to provincial level expressway infrastructure planning in China. It first describes the evaluation process of EA (environmental impact assessment (EIA)) and SEA, then the current Chinese SEA application mechanism which was established by the Chinese EIA Law (2003) and subsequent laws and regulations. A comparative analysis is undertaken between the Chinese SEA system, the UK system and the Canadian SEA application systems. To clearly understand how SEA is applied in the road transport infrastructure development field, two SEA cases and one EIA case, and a questionnaire study, were carried out to obtain evidences of the actual situation of SEA application in this area. Conclusions were made to clearly describe those deficiencies existing in the current administration system, legal system and application framework and actual practices of SEA application in China. In the light of improving the quality of SEA application in provincial level expressway infrastructure development programme in China, an SEA application framework and indicator system which address these drawing on advanced experiences and principles of good SEA application from European countries, is proposed and tested through evaluation by experts. The thesis also makes recommendations for implementing the proposed SEA application framework and indicator system for expressway programming.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".