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June 8, 2026Humanities and Social Sciences CommunicationsOpen Access

Exploring the driving mechanism and scenario prediction of the spatialtemporal pattern of resilience of urban agglomerations in the middle reaches of the Yangtze River

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Authors

SFShufei FuYLYishen LiaoTLTiangui Lv

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Overview

Randomized trial explores urban resilience dynamics in an urban agglomeration, highlighting improvement strategies.

Key Points

  • This study aims to evaluate urban resilience (UR) in the Middle Reaches of the Yangtze River and identify key factors influencing its development.
  • Constructed a five-dimensional framework for urban resilience assessment.
  • Conducted spatiotemporal evolution analysis and identified spatially vulnerable areas.
  • Developed an interpretable machine learning model to simulate UR development pathways.
  • From 2008 to 2023, UR level exhibited an average annual growth rate of 10.02%.
  • Social resilience grew fastest at 11.68%, while ecological resilience grew by only 1.02%.
  • Under the innovation-driven scenario, UR levels are projected to significantly improve.

Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/6a265ce5ad53cfb9357c628fhttps://doi.org/10.1057/s41599-026-07867-9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Spatiotemporal Evolution and Driving Factors of Synergistic Development Between Urban Resilience and Urban Land Use Efficiency in the Yangtze River Economic Belt2026
  2. 2Identification of Key Determinants Influencing the Spatiotemporal Heterogeneity of Urban Resilience2024
  3. 3The Evaluation and Analysis of Spatial and Temporal Evolution of Urban Resilience in the Yangtze River Economic Belt2025
  4. 4Exploring the Nonlinear Response Patterns and Interaction Effects of Urban Resilience Using the XGBoost-SHAP Model: Evidence from the Yangtze River Economic Belt2026
  5. 5Nonlinear attribution and precision governance of urban resilience in Henan: An integrated machine learning and Geographically Weighted Random Forest approach2026