2C FRAS: From severe disruption to innovation and learning in climate adaptation work

Context

Flooding in the form of torrential rain is an example of a severe disturbance that can affect the resilience of road infrastructure. Cloudbursts in urban environments are often complicated – the ground is largely impervious and runoff opportunities are limited. This type of severe disruption also requires multi-stakeholder cooperation, both private and public, as there is a fragmentation of responsibilities between state, municipal and private infrastructure owners and maintenance contractors.

Solution

Serious disruptions to the functioning of the infrastructure can, however, generate learning and adaptation, and lessons on how your own organisation prevents and manages disruptions are valuable in adapting the infrastructure to withstand future disruptions. Learning is important because it influences how actors seek, perceive and adopt new forms of interaction, decision-making processes and instruments to address climate change.
Since several different actors are responsible for the maintenance of road infrastructure, it is important to share learning between organisations. The aim of the project is to produce new implementable knowledge on how severe disruptions in the functioning of road infrastructure are translated into the policy and practice of maintenance organisations. What lessons at policy and administrative level have maintenance organisations developed as a result of severe disruptions? Is learning and possibly new methods, planning tools and practices disseminated between and within maintenance organisations?

Project goals

The project aims to deliver the following results:

1) Knowledge of the current policy and practice of maintenance organisations on how to maintain the functionality of the road infrastructure during severe disruptions;

2) Knowledge of how prevention of severe disturbances is prioritised in maintenance organisations;

3) Knowledge of maintenance organisations’ work with evaluation models and tools, both to calculate the effects of severe disruptions and to calculate the societal benefits of the measures that reduce the risk of severe disruptions in the road infrastructure;

4) Knowledge of how severe disruption involves distributive learning, which can generate new methods, planning tools and practices in maintenance organisations;

4) Recommendations on how intra- and inter-organisational learning can be ensured and disseminated after a severe disruption; and

5) Dissemination of knowledge by and between maintenance organisations on how they work to prevent and manage severe disruptions in road infrastructure.