Yunsong Yang , Tian Jing , Huihui Wang , Yuhao Zhong , Weijun Yu , Hui Zhou
{"title":"Causal network of high-quality development and urban resilience in Chinese cities based on transfer entropy: Structure and determinants","authors":"Yunsong Yang , Tian Jing , Huihui Wang , Yuhao Zhong , Weijun Yu , Hui Zhou","doi":"10.1016/j.scs.2025.106875","DOIUrl":null,"url":null,"abstract":"<div><div>As the core engines of China’s economy, cities have driven four decades of rapid global growth. Yet their expansion under the traditional model has intensified risks such as environmental degradation, resource shortages, and socioeconomic imbalances. The shift to high-quality development (HQ) prioritizes innovation, but cities now face dual pressures—internal structural vulnerabilities and external shocks such as trade frictions and climate crises—necessitating an urban resilience (UR) framework. However, by neglecting cities’ interconnectedness and the complexity of cross-regional networks, current HQ strategies often operate in silos, which address resilience and development in isolation, fail to capture inter-city causal relationships, leading to fragmented policies and missed opportunities for integrated development management. This study addresses this gap by developing a comprehensive framework to map causal influence networks across 283 Chinese cities. It integrates multi-dimensional evaluations of UR and HQ with transfer entropy—a nonparametric, information-theoretic technique—to identify bidirectional causal information flows. By combining social network analysis with an exponential random graph model, this research illuminates the structural characteristics and determinants of UR and HQ networks, offering insights into urban influence patterns under uncertainty. Key findings highlight that high concentrations of UR and HQ emerged in eastern coastal agglomerations. All networks exhibited cross-regional influences, with HQ interactions occurring predominantly in adjacent areas. The UR network demonstrated sensitivity to extreme events, while the HQ network was more responsive to routine events. Network analysis further revealed weak agglomeration patterns and key node cities driving causal influence linkages. Economic resilience and social resilience influenced the formation of the urban causal networks. This study unravels the spatial patterns and determinants of UR and HQ networks, providing empirical support for policies that enhance inter-city influence, bridge regional divides, and foster resilient high-quality growth. The research contributes to global sustainability agendas by offering a scalable framework for integrating UR and development planning.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"133 ","pages":"Article 106875"},"PeriodicalIF":12.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725007486","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
As the core engines of China’s economy, cities have driven four decades of rapid global growth. Yet their expansion under the traditional model has intensified risks such as environmental degradation, resource shortages, and socioeconomic imbalances. The shift to high-quality development (HQ) prioritizes innovation, but cities now face dual pressures—internal structural vulnerabilities and external shocks such as trade frictions and climate crises—necessitating an urban resilience (UR) framework. However, by neglecting cities’ interconnectedness and the complexity of cross-regional networks, current HQ strategies often operate in silos, which address resilience and development in isolation, fail to capture inter-city causal relationships, leading to fragmented policies and missed opportunities for integrated development management. This study addresses this gap by developing a comprehensive framework to map causal influence networks across 283 Chinese cities. It integrates multi-dimensional evaluations of UR and HQ with transfer entropy—a nonparametric, information-theoretic technique—to identify bidirectional causal information flows. By combining social network analysis with an exponential random graph model, this research illuminates the structural characteristics and determinants of UR and HQ networks, offering insights into urban influence patterns under uncertainty. Key findings highlight that high concentrations of UR and HQ emerged in eastern coastal agglomerations. All networks exhibited cross-regional influences, with HQ interactions occurring predominantly in adjacent areas. The UR network demonstrated sensitivity to extreme events, while the HQ network was more responsive to routine events. Network analysis further revealed weak agglomeration patterns and key node cities driving causal influence linkages. Economic resilience and social resilience influenced the formation of the urban causal networks. This study unravels the spatial patterns and determinants of UR and HQ networks, providing empirical support for policies that enhance inter-city influence, bridge regional divides, and foster resilient high-quality growth. The research contributes to global sustainability agendas by offering a scalable framework for integrating UR and development planning.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;