Collaborative Governance in Strengthening the Early Warning System (EWS) and the Impact of Floods on Communities in Special Areas of Jakarta
DOI:
https://doi.org/10.59261/jequi.v8i5.460Keywords:
Collaborative Governance, Early Warning System, Flooding, Digital Governance, Data Interoperability, Special Region of JakartaAbstract
Background: Flooding remains a recurring hydrometeorological disaster in Jakarta, causing significant social, economic, and environmental impacts. Strengthening flood early warning systems (EWS) requires technological infrastructure and effective stakeholder collaboration.
Objective: This study examines collaborative governance in strengthening flood EWS and proposes a collaborative model to mitigate flood impacts on communities.
Methods: A descriptive qualitative approach was employed through in-depth interviews, observations, and document analysis. Data were analyzed using an interactive model supported by NVivo 15. Informants represented government agencies, BPBD, BMKG, the Water Resources Agency, communities, volunteers, the private sector, and academia.
Results: Institutional Design emerged as the strongest dimension, supported by regulations, standard operating procedures, task allocation, coordination mechanisms, memoranda of understanding, command structures, and information dissemination. Conversely, Starting Conditions represented the weakest dimension due to resource disparities, capacity limitations, misaligned incentives, and insufficient operational trust among stakeholders. Although Jakarta possesses EWS infrastructure, including sensors, geospatial dashboards, command centers, and flood information applications, data integration and utilization remain suboptimal.
Conclusion: This study proposes a Digital Collaborative Governance model encompassing data interoperability, evidence-based collaborative decision-making, adaptive collaborative learning, and enabling factors. The model emphasizes digital technology as collaborative infrastructure supporting an integrated, adaptive, data-driven flood EWS to strengthen community resilience.
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