Climate and Environmental Risk Analytics for Resilient Critical Infrastructure Finance

弹性关键基础设施融资的气候和环境风险分析

• 批准号: 2503081
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2503081
• 关键词: Climate; Environmental; Risk; Analytics; Resilient

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.

联合王国所有受监管的金融机构都要接受所谓的压力测试，根据测试，其资产负债表的具体内容要经受一系列繁重的情景，并评估其直接可行性的后果。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的孤立观察的经验。然而，英格兰银行审慎监管局等管理机构已将气候变化确定为对金融部门的直接稳定及其长期生存能力的最重大威胁之一。目前关于如何考虑气候风险的建议是幼稚的。目前还没有一种方法来研究（a）有害气候情景向金融体系的传导机制，以及（B）由此对资产负债表造成的压力的频率和幅度等。该项目的重点将是英国金融机构与大量库存的实物资产用作贷款抵押品。第1阶段：风险敞口的主要风险敞口的情况下，将考虑在这项研究中是洪水和地面不稳定。在大卫舒尔茨的监督下工作，学生将考虑一系列强加的（和可能的）未来气候情景，以及这些情景对英国河流和海平面的影响。这项工作将用于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叉点将用于确定具体的案例研究地点。第二阶段：响应将考虑三种不同类别的实物资产，并精确模拟其物理响应。本练习的目的是评估第一阶段所述的有形外部行为与模拟有形资产响应之间的直接联系。第三阶段：脆弱性第一阶段确定的风险将用于使用第二阶段开发的基本模型确定选定有形资产的脆弱性。在基于业绩的评估中，习惯上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风险强度的累积失败概率。在数学上，脆弱性曲线将以对数正态累积分布函数的形式表示。第四阶段：风险通过将问题视为随机过程，计算选定资产在给定暴露水平下的风险。这意味着将通过概率密度函数而不是其平均值来考虑脆弱性和脆弱性。此外，重要的是要对资产关键机械性能的固有异质性进行建模，例如建筑类型、建筑等级等，通过以平均值、标准差和相关长度定义的概率空间分布。由于这一过程的随机处理产生了大量的分析，因此将使用拉丁超立方法和蒙特-卡罗等抽样技术来减少总的计算时间。第五阶段：财务分析在项目合作伙伴JBA Risk Associates的参与下，通过案例研究，使用第一阶段至第四阶段新开发的损失数据，检查一个或两个资产负债表的弹性。将审查目前方法的乐观主义/保守主义。第一阶段至第五阶段将作为进一步开发更专业的气候依赖型财务分析的拟议框架。