Climate and Environmental Risk Analytics for Resilient Critical Infrastructure Finance

英国所有受监管的金融机构都要接受所谓的压力测试，即对其资产负债表中的特定部分进行一系列繁重的情景，并评估其近期生存能力的后果。在目前的方法中，所使用的压力的大小和来源是基于对金融部门发生的事情进行孤立观察的经验。然而，英国央行审慎监管局(Prudential Regulatory Authority)等管理机构已将气候变化视为对金融业短期稳定及其长期生存能力的最重大威胁之一。目前关于如何将气候风险考虑在内的建议是天真的。目前还没有关于(A)有害气候情景向金融系统的传导机制和(B)由此对资产负债表造成压力的频率和程度等方面的方法。为了解决这个问题，需要制定一种方法，将关键金融机构的资产负债表与气候情景明确挂钩。该项目的重点将是拥有大量实物资产用作贷款抵押的英国金融机构。阶段1：风险敞口本研究将考虑的主要风险敞口情景是洪水和地面不稳定。在大卫·舒尔茨的指导下，这名学生将考虑一系列强加的(和可能的)未来气候情景，以及这些情景将对整个英国的河流和海平面产生的影响。这项工作将被用来确定洪水和地面不稳定风险最大的地区。这些地点与实物资产的交集将被用来确定具体的案例研究地点。阶段2：响应将考虑三种不同类别的实物资产，并对其实物响应进行精确建模。这项工作的目的是评估第1阶段所述的外部实物行动与所建模型实物资产反应之间的直接联系。第3阶段：脆弱性第1阶段确定的风险敞口将用第2阶段开发的基本模型来确定选定实物资产的脆弱性。在基于业绩的评估中，习惯于用脆弱性曲线来表示有形资产的脆弱性，脆弱性曲线表示给定暴露强度下的累积失效概率。在数学上，脆性曲线将以对数正态累积分布函数的形式表示。阶段4：风险将通过将问题视为随机过程来计算选定资产对给定风险水平的风险。这意味着安全性和脆弱性都将通过它们的概率密度函数来考虑，而不是通过它们的平均值来考虑。此外，重要的是通过以平均值、标准差和相关长度定义的概率空间分布来模拟资产的关键力学属性的内在异质性，例如建筑类型、建筑类别等。由于这一过程的随机处理产生了大量的分析，将使用拉丁-超立方体法和蒙特卡罗等抽样技术来减少总体计算时间。估计的风险将用四种未来气候情景不可接受业绩的累积分布来表示。阶段5：财务分析在项目合作伙伴JBA Risk Associates的参与下，将使用项目合作伙伴JBA Risk Associates参与的案例研究，使用新开发的第1至4阶段损失数据，检查一到两个资产负债表的弹性。将审查当前方法的乐观/保守。第1至5阶段将作为一个拟议的框架提出，以进一步发展更加专业化的气候相关金融分析。

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: ExposureThe 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: ResponseThree 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: VulnerabilityThe 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: RiskThe 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 analyticsA 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.