Are present-day infrastructure disaster risk models still applicable in a post Covid-19 world?
Wednesday, Dec 02, UTC 12:00 to 12:55
Organizer: Vrije Universiteit Amsterdam, Lloyds Register Foundation, Resilience Shift, University of Oxford
|Critical infrastructure systems, such as energy, transportation, water, waste, and digital communications, are the backbone of modern economies and societies. Failures of these systems can result in large-scale economic losses and social disruptions.|
As presented last year in the World Bank Lifelines report, the direct damages from natural hazards to power generation and transport alone cost $18 billion a year, cutting into the already scarce budget of road agencies and power utilities. But the main impact of natural shocks on infrastructure is through the disruptions they impose on people and communities, for instance, businesses unable to keep factories running or use the internet to take orders and process payments; or on the households that don’t have the water they need to prepare meals or on people unable to go to work, send children to school, or get to a hospital.
To quantify these impacts, many data and models have been developed over the past years for critical infrastructure risks assessment at the level of local, national, and even global scales. Models look both at infrastructure assets individually and at the networks within they exist. They have been applied to assess both damage and risks, but also to help in long-term planning for infrastructure investment and adaptation.
However, there are important lessons from Covid-19 related to long term infrastructure investments, in particular in relation to the complexity and uncertainty of global dynamics. What have we learnt are the limitations of our models? What is the future for risk models following this large scale crisis?
In this session, we’ll discuss whether developed infrastructure risk models are still fit-for-purpose and whether we can use them to further “mainstream” resilience action? And how do we turn these (often academic) risk-based models into useful tools for practitioners and decision makers?