Implementation of Sendai Framework for DRR: Role of Science and Technology in strengthening implementation at local level

One of the key aspects of advancing the Sendai Framework for Disaster Risk Reduction (SFDRR) locally is the development of robust early warning systems. Leveraging Science and Technology (S&T), such as satellite imagery, AI-based predictive modelling, and Geographic Information Systems (GIS), enables accurate risk assessment and timely disaster alerts. These technologies, when tailored to local contexts, can significantly enhance preparedness and response.

This session seeks to address the Implementation of Sendai Framework for DRR, focusing on the Role of Science and Technology in advancing implementation at the local level.c

Session objectives

1. Evaluate the status of advancing Science & Technology (S&T) in Disaster Risk
2. Evaluate and explore the strategies that member states are adopting to leverage, promote, and integrate local knowledge with existing/emerging S&T-driven innovations for DRM/DRR or implementing the SFDRR.
3. Discuss how to strengthen collaboration, identify opportunities for research and knowledge-sharing, as well as develop practical solutions to integrate S&TI into disaster risk governance at all levels.
4. Explore appropriate investment strategies that  could be mobilized to procure modern science and technology innovations to strengthen SFDRR priority, targets, and goals through the effective implementation of local knowledge
5. Analyse some of the recent reports on global multi-hazard early warning systems, MHEW4ALL, the Global and Regional Risk Index reports, etc,  to see how member states leverage science and technology innovations to address the identified gaps.

Outcomes

1. An enhanced understanding of the needed commitment to catalyse local knowledge geared towards the realization of SFDRR through innovative Science and Technology strategies.
2. Enriched ideas, information, and new experiences gained from other participants on the progress, gaps, and recommendations on leveraging science & tech-driven innovation in DRR local knowledge,