Climate and Environmental Risk Analytics for Resilient Critical Infrastructure Finance

All regulated financial institutions in the United Kingdom are subject to so-called stress tests, whereby specific elements of their balance sheets are subjected to a range of onerous scenarios and the consequences for their immediate viability assessed. In the present approach, the magnitude and source of the stresses used is based upon experience from insular observations of what happens in the financial sector. However governing bodies, such as the Bank of England's Prudential Regulatory Authority, have identified climate change as being one of the most significant threats to the immediate stability of the financial sector, as well as its long-term viability. Current proposals for how climate risks are to be factored in are naïve. There is no methodology surrounding (a) the transmission mechanism of deleterious climate scenarios into the financial system and (b) the frequency and magnitude of the resulting stresses on balance sheets etc. To resolve this, an approach that can explicitly link the balance sheets of critical financial institutions to climate scenarios needs to be developed. The focus of this project will be UK financial institutions with large inventories of physical assets used as lending collateral.
Stage 1: Exposure
The primary exposure scenario that will be considered in this study is flooding and ground instability. Working under the supervision of David Schultz, the student will consider a range of imposed (and probable) future climate scenarios and the consequences these will have for river and sea levels across the UK. This exercise will be used to identify areas at greatest risk from flooding and ground instability. The intersection of these locations with the presence of physical assets will then be used to determine specific case study locations.
Stage 2: Response
Three different classes of physical asset will be considered and their physical responses modelled with precision. The purpose of this exercise is to evaluate the direct link between the physical external actions described in Stage 1 and the response of the modelled physical assets.
Stage 3: Vulnerability
The exposures determined in Stage-1 will be used to determine the vulnerability of the selected physical assets using the fundamental models developed in stage-2. In performance-based assessments, it has become customary to express the vulnerability of physical assets in terms of fragility curves, which express the cumulative probability of failure for a given exposure intensity. Mathematically, the fragility curves will be expressed in the form of lognormal cumulative distribution functions.
Stage 4: Risk
The risk of the selected assets to a given level of exposure will be computed by treating the problem as a stochastic process. This implies that both esposure and vulnerability will be considered through their probability density function, rather than their mean values. Furthermore, it will be important to model the inherent heterogeneity of key mechanical properties of the assets, eg, construction typology, building class etc., through probabilistic spatial distributions defined in terms of mean, standard deviation and correlation lengths. As this stochastic treatment of the process produce a large number of analyses, sampling techniques such as Latin-hypercube method and Monte-Carlo will be used to reduce the overall computation time. The risk estimated will be expressed in terms of cumulative distribution of probability of unacceptable performance for four future climate scenarios.
Stage 5: Financial analytics
A case study will be used, with the involvement of project partner JBA Risk Associates, to examine the resilience of one or two balance sheets using the newly developed loss data in Stages 1 to 4. The optimism/conservatism of current approaches will be examined. Stages 1 to 5 will be presented as a proposed framework for developing further, more specialised climate dependent financial analytics.