Coupling dynamics of urban flood resilience in china from 2012 to 2022: A network-based approach
Bibliographic record
Abstract
• The Environment-Institution-Infrastructure-Agent (EIFA) framework was developed as the conceptual framework. • The commonly used network analysis methodology was refined to more accurately analyze correlation networks. • The first national-scale and long-term analysis of China's urban flood resilience coupling dynamics were provided. • 639 cities were evaluated for the multi-factor coupling effect of China's urban flood resilience. Urban flooding presents a significant challenge in Chinese cities, necessitating a deeper understanding of the coupling effects of China's urban flood resilience for effective resilience planning. This study introduces a four-component Environment-Institution-Infrastructure-Agent (EIFA) framework and utilizes an updated correlation network approach to analyze the temporal variation of coupling effects of urban flood resilience across 639 Chinese cities from 2012 to 2022. The findings indicate a decline in synergy and increased tradeoffs, primarily due to intensified competition within and between institutional and infrastructural sectors, marginal impacts of infrastructure investments, and socially excessive infrastructure. The study also highlights the agent component's strong internal and inter-component coupling effects, implying the effectiveness of China's people-centered resilience strategies, though risks of decoupling remain. Additionally, it notes a good match between societal urban flood resilience and natural flood risks, while natural vegetation loss due to urban expansion is noteworthy. The study further suggests that refining agent-focused deposit and insurance policies could coordinatively enhance urban flood resilience, as these elements are hubs within the network. The updated network-based framework and its findings offer insights for informing and optimizing urban flood resilience planning in China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".