Harnessing network science for urban resilience: the CASA model’s approach to social and environmental challenges
Resilience in social systems is crucial for mitigating the impacts of crises, which threaten communities worldwide. As disasters grow in frequency and intensity, strengthening urban resilience has become an urgent necessity. This study applies the Complex Analysis for Socio-environmental Adaptation (CASA) model to Puchuncaví, a Chilean town facing significant environmental and social challenges as a recognized “Sacrifice Zone.” Our approach redefines traditional resilience models by transforming their un-integrated structure into a complex network that integrates global scientific knowledge and advanced network methodologies. Our findings underscore the model’s effectiveness in identifying vulnerabilities and informing policies to strengthen climate resilience and urban adaptation. The analysis reveals that Puchuncaví faces substantial resilience gaps in emergency management, sustainable livelihoods, and access to sanitation and potable water. The CASA model assigns a topological resilience score (TR) of 0.82, indicating an 18% higher vulnerability compared to ideal resilience conditions. While key sectors such as food security and public health demonstrate relative strengths, governance and infrastructure require targeted interventions to enhance resilience outcomes.
Authors
- Miguel Fuentes (ORCID: https://orcid.org/0000-0003-0833-1245)
- Juan Pablo Cárdenas (ORCID: https://orcid.org/0000-0001-9324-1898)
- Eric Rasmussen (ORCID: https://orcid.org/0000-0001-7148-2180)
- Carolina Urbina (ORCID: https://orcid.org/0000-0001-7051-4654)
- Gerardo Ramírez Vidal (ORCID: https://orcid.org/0000-0002-7019-3376)
- Gastón Olivares (ORCID: https://orcid.org/0000-0003-3655-7831)
- Soledad Salazar
Institutions
- Pontificia Universidad Católica de Valparaíso (CL)
- Santa Fe Institute (US)
- Universidad de Flores (AR)
- Viña del Mar University (CL)
- International Humanitarian University (UA)
- Complex Engineering System Institute (CL)
Publication Details
- Journal
- Applied Network Science
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1007/s41109-025-00703-0
- Citations
- 4
- Primary Topic
- Smart Cities and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 4.54
Funders
- Office of Naval Research
- Fondo Nacional de Desarrollo Científico y Tecnológico