Evaluation of the Sustainable Development of the Social-Economic-Natural Compound Ecosystem in the Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration (China): Based on Complex Network Analysis
Bibliographic record
Abstract
In the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a series of natural environmental, economic, and social issues have emerged sequentially in the process of rapid economic and social development. Therefore, for the sustainable development of the GBA, how to closely integrate nature protection with economic and social development to improve the sustainable development level of the social-economic-natural compound ecosystem, and realize the coordinated development of the system is particularly important. Based on the perspective of complex network and the theory of compound ecosystem, this study proposes a set of sustainable development evaluation model based on complex network modeling to evaluate the sustainable development level of compound ecosystem in GBA from 2014 to 2018, and further analyze the coupling coordination degree. The major findings include: 1) For the sustainable development in the GBA, the development of the natural subsystem is an important foundation, and the synchronous development of the social and economic subsystems are the main driving force. 2) The sustainable development level in the GBA shows an overall steady upward trend; the average level of the compound ecosystem’s coupling coordination development is in a “good” state, and it shows an evident upward trend. 3) Whether it is within the GBA or the GBA and its surrounding regions, there are problems of unbalanced and insufficient regional development. Policy recommendations include increasing the emphasis on the sustainable development of the natural subsystem, promoting the coordinated development of the economic, social, and natural subsystems, and promoting the balanced development of cities within the GBA, as well as the GBA and surrounding regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".