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The escalating impacts of climate change, particularly intense urban flooding, demand a paradigm shift from static infrastructure management to dynamic, data-driven resilience governed by integrated policy. This chapter proposes a novel Digital Governance Framework for climate adaptation in deltaic mega-cities, specifically contextualised within the socio-institutional realities of Bangladesh. The framework integrates an Urban Digital Twin (UDT) for real-time risk simulation with Federated Learning (FL) as a privacy-preserving mechanism for data sharing. We argue that institutional fragmentation, data silos, and mistrust among key agencies (e.g., DWASA, DPDC/DESCO) are the primary barriers to effective resilience modelling, often rendering centralised UDTs ineffective. To address this, the chapter’s central contribution is the articulation of a multi-layered policy framework, advocating for regulatory instruments such as a Critical Infrastructure Resilience Act (CIRA) and the establishment of a Shared Funding Model. This policy structure mandates the sharing of collaborative intelligence and ensures data equity by prioritising vulnerable settlements through a Social Equity Index (SEI) and Community Sensor Nodes. By prioritising governance solutions that leverage advanced technological tools, this chapter outlines a scalable, institutionally sound, and equitable pathway toward achieving true environmental resilience and data-driven sustainability in the Global South.

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Digital Governance for Urban Resilience in Bangladesh: A Policy Framework Integrating Digital Twins and Federated Learning in Climate-Vulnerable Deltaic Cities

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  • 发表日期：

    2026年

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  • Pranab Kumar Panday

摘要

The escalating impacts of climate change, particularly intense urban flooding, demand a paradigm shift from static infrastructure management to dynamic, data-driven resilience governed by integrated policy. This chapter proposes a novel Digital Governance Framework for climate adaptation in deltaic mega-cities, specifically contextualised within the socio-institutional realities of Bangladesh. The framework integrates an Urban Digital Twin (UDT) for real-time risk simulation with Federated Learning (FL) as a privacy-preserving mechanism for data sharing. We argue that institutional fragmentation, data silos, and mistrust among key agencies (e.g., DWASA, DPDC/DESCO) are the primary barriers to effective resilience modelling, often rendering centralised UDTs ineffective. To address this, the chapter’s central contribution is the articulation of a multi-layered policy framework, advocating for regulatory instruments such as a Critical Infrastructure Resilience Act (CIRA) and the establishment of a Shared Funding Model. This policy structure mandates the sharing of collaborative intelligence and ensures data equity by prioritising vulnerable settlements through a Social Equity Index (SEI) and Community Sensor Nodes. By prioritising governance solutions that leverage advanced technological tools, this chapter outlines a scalable, institutionally sound, and equitable pathway toward achieving true environmental resilience and data-driven sustainability in the Global South.