Research on the driving effect of Eco-economic Zones on urban coordinated development and its effect mechanism
Bibliographic record
Abstract
Abstract The core goal of Eco-economic Zone planning is to enhance the coordinated development of cities in the basin in terms of main function realization and livelihood development and achieve coordinated and high-quality development in the basin. Using a double-difference approach, this paper uses panel data from 2004 to 2019 for 49 prefecture-level cities in Jiangxi, Hunan, Hubei, and Jiangsu provinces to test the proposed theoretical hypotheses and empirically examine the driving effects and mechanisms of the three Eco-economic Zones on the coordinated development of cities along the river basin. The study finds that the three zones significantly enhance the coordinated development of cities along the zones. The dimension of livelihood development is more significant in strengthening cities' coordinated development along the three zones. At the same time, there is substantial regional individual heterogeneity and temporal dynamic heterogeneity in the driving effect of the plan. The Eco-economic Zone Plan is designed to enhance the coordinated development of cities along the Great Lakes Basin through green economic development and ecological environment improvement. The above findings help explore new experiences in the protection, management, and development of the Great Lakes Basin. They have important policy implications for cities in the Eco-economic Zone to develop a “new path” of collaborative ecological and economic development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".