Use cases

Resilience metrics for infrastructure investment

At the core of GRII is an infrastructure risk analytics model that allows financiers and other decision makers to assess the physical risks to individual infrastructure assets as well as the benefits of investing in resilient infrastructure systems. The model was developed by the Oxford Programme for Sustainable Infrastructure Systems (OPSIS), one of the technical contributors to GRII.

The following case study demonstrates an application of the infrastructure systems model (the ‘Systemic Risk Assessment Tool’, SRAT) underpinning GRII to an assessment of transport infrastructure resilience options across Kenya, Tanzania, Uganda, and Zambia.

The project seeks to understand the magnitudes and locations of exposures, damages, economic disruptions, and risks from climate related hazards to strategic road and railway network links. This in turn can inform financial decisions, for example to assess and price physical climate risk for new infrastructure investments and assess the economic costs and benefits of adaptation.

Use case developed by Oxford Programme for Sustainable Infrastructure Systems (OPSIS)

The analysis estimates a potential increase in cumulative direct damage risks for road and rail assets from flooding across all climate scenarios from US$ 41 million/year in the current baseline to about US$ 82-131 million/year by 2080. Further analysis shows that indirect economic risks to trade flows due to disruptions of key transport linkages might grow from US$ 0.16 million/day in the current baseline to about US$ 4.2 million/day by 2080 across all climate scenarios.

GRII allows users to access these physical risk and resilience metrics for infrastructure. These data, and the wider SRAT tool, can then inform analyses of the effectiveness of adaptation options; as illustrated below for river flooding of roads: (a) net present value (NPV) of maximum benefits due to avoided risks and the (b) NPV of maximum costs or investments needed over an implementation timeline, resulting in (c) a benefit-cost ratio (BCR) of optimal adaptation options. 


Source: Oxford Programme for Sustainable Infrastructure Systems, supported by the UK Foreign, Commonwealth, and Development Office project: ‘Decision Support Systems for Resilient Strategic Transport Networks’


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