Machine Learning based Geospatial Sensitivity Analysis Tool (ML-GeoSAT) for Gross Exposure to Climate-Related Risks

ML-GeoSAT是一种独特的工具，它将允许承销商测量物理和过渡风险对交易对手整个价值链的直接和间接影响，以确定暴露或脆弱性风险/水平。特别是，ML-GeoSAT适用于直接交易对手投资，如采掘和采矿公司（涉及钴和其他矿产资源开采等直接开采过程），以及间接交易对手，如具有跨大陆供应链网络的电池和电动汽车制造商。对于直接交易对手，承销商感兴趣的是了解天气模式和当地气候相关政策如何影响或对这些投资造成损害，无论是否有缓解（总损失或净损失），而对于间接交易对手，承销商感兴趣的是了解供应链基础设施的物理和过渡风险如何影响交易对手偿还债务或履行其他财务义务的能力。ML-GeoSAT是一种评估风险的颠覆性方法，特别是对交易对手而言，因为它在风险评估中采用了自然资本分析模型。ML-GeoSAT假定自然为资本存量，并据此评估自然退化如何对金融机构产生负面影响。这采取了投资组合级别评估的形式，帮助金融机构确定其对自然资本资产的依赖。它通常分四个步骤进行，确定：(1)相关地区、部门、借款人和/或资产；(2)与交易对手直接或间接关联的相关自然资本资产（如矿产、水、橡胶等）；(3)可能发生的潜在自然和过渡相关的中断；(4)风险最大的地区、行业、借款人和/或资产及其对承销商投资组合风险的总体影响。ML-GeoSAT将寻求实现以下目标：(1)通过创建从来源到最终产品的基于物流的输入途径，动态离散交易对手的投资组合；(2)利用地理空间数据和先进的图像识别技术，为每个材料中心发展物理风险框架（用于实时和预测风险评估）；(3)利用先进的自然语言处理技术，为每个材料中心（特定区域）发展实时知识框架（用于数字化实时气候政策和编纂过渡风险评估协议）；(4)将各材料枢纽的物理和过渡风险评估纳入综合气候风险评估工具（CRAT），进行实时敏感性分析；(5)提供一个直观的可视化平台，提供创新的财务指标，以实时和基于未来的各种场景的减值。

ML-GeoSAT is a unique tool that will allow underwriters to measure the direct and indirect impacts of physical and transition risks on the entire value chain of a counterparty to determine exposure or vulnerability risks/levels. Particularly, ML-GeoSAT is suited for direct counterparty investments like extractive and mining companies (involved in direct extraction processes like cobalt and other mineral resources mining) and indirect counterparties like battery and electric vehicles manufacturers with transcontinental supply chain networks. For the direct counterparties, underwriters are interested in understanding how weather patterns and local climate-related policies can influence or inflict damage on these investments with or without mitigation (gross or net losses) while for the indirect counterparties, underwriters are interested in understanding how physical and transition risks on supply chain infrastructure can affect a counterparty's ability to service debts or meet other financial obligations.ML-GeoSAT is a disruptive approach to evaluating risks, especially for counterparties as it adopts a Natural Capital Analysis model in risk evaluation. ML-GeoSAT posits nature as capital stock and accordingly assesses how natural degradation negatively impacts a financial institution. This takes the form of a portfolio-level assessment that helps financial institutions identify their natural capital asset dependencies. It is usually carried out in four steps, identifying: (1) relevant geographies, sectors, borrowers and/or assets; (2) relevant natural capital assets (e.g., minerals, water, rubber etc.) that their directly or indirectly tied to counterparties; (3) potential natural and transition-related disruptions that could occur; and (4) geographies, sectors, borrowers and/or assets most at risk and its overall impact on the underwriters' portfolio risk.ML-GeoSAT will seek to achieve the following: (1) dynamically discretise counterparties portfolios by creating a logistics-based pathway of inputs from source to final products; (2) utilise geospatial data and advanced image recognition in evolving a physical risk framework (for real-time and predictive risk assessments) for each material hub; (3) leverage advanced natural language processing techniques in evolving a real-time knowledge-based framework (for digitising real-time climate policies and codifying a transition-risk assessment protocol) for each material-hub (region-specific); (4) integrating physical and transition risk assessments for each material hub into a comprehensive climate-risk assessment tool (CRAT) for real-time sensitivity analysis; (5) providing an intuitive visualisation platform with innovative financial metrics to convey real-time and future-based impairment for varying scenarios.