- Este evento ha pasado.
Preventing cascading failures of critical assets: Using the Open-Source Critical Asset Management System (CAMS) to help build resilience
abril 12 @ 3:00 am - 4:00 am
As a result of ARISE-US’s work to support the ARISE global priorities, the Critical Asset Management System (CAMS) is a collaborative, open-source, and data-centric tool that helps cities to identify their critical assets and the cascading failure chains should one of them fail. The tool is currently being piloted in collaboration between ARISE US and ARISE Dominica.
Climate change is increasing the risk of extreme weather events all over the world. Often it is not those responsible for greenhouse emissions who suffer the worst consequences. Island nations, cities, and communities run the risk of devastation from natural hazards, stresses, and shocks. Governing bodies need to be resilient to these hazards to reduce their impacts, rebuild better and efficiently and save lives.
Protecting critical assets is essential to ensure city’s resilience. While a city may be able to identify many of its critical assets, there are still several major gaps to be addressed. For example:
- Do you know which critical assets could affect the city’s disaster and climate resilience?
- Do you understand the risk that each asset faces and how well placed it is to deal with that risk?
- Do you understand how assets are interconnected and the chained consequences that may result?
Without understanding, these gaps, shocks and stresses can lead to failure chains which can significantly weaken resilience. The goal of CAMS is to help cities plan, protect critical assets, and respond to inevitable disasters to save lives and rebuild faster.
This webinar will be divided into two parts: the introduction to the CAMS tool and the demo of its usage. The CAMS tool is available for MCR2030 cities on the MCR2030 dashboard at https://mcr2030dashboard.undrr.org/directory/service/153
For more information about the event: https://www.undrr.org/event/MCR2030-CAMS-webinar