March 8, 2022, Menlo Park, CA— The demand for weather and climate resilience analytics to support analyzing property, infrastructure, and business operations resilience to climate-related risks for financial stability is proliferating. As such, One Concern, a resilience analytics firm, published today a white paper that presents new Machine-Learning (ML)-powered analytics for assessing climate-change-generated exposure and vulnerability. The company’s white paper, Estimating the One Concern Downtime Statistic (1CDS™), introduces a new metric for evaluating climate resilience by measuring the degree of physical risk to commercial properties from flooding, high wind, and earthquake. In addition to common vulnerabilities, One Concern’s resilience statistics reflect the ratio of building damage and the operational downtime due to a hazard’s direct and indirect impacts on functional lifeline dependencies, such as power, transportation networks, and people.
“It’s taken a generation to wake up to how our rapidly changing climate creates risk faster than we can plan for or price it,” said Dr. Jeffrey Bohn, Chief Strategy Officer, One Concern. “Awareness of our increasing vulnerabilities to disruptions of power, transportation networks, and people have been amplified by the pandemic, global supply chain issues, and more extreme natural catastrophes. However, without the ability to observe and consistently measure these vulnerabilities, businesses and governments cannot effectively assess, mitigate and report climate risk, which is especially important amid imminent regulatory requirements regarding physical risk disclosure.”
One Concern’s new resilience statistics measure resilience to increasingly frequent and severe climate-change-driven catastrophic events. These resilience statistics can also create benchmarks for analyses and monitoring. These developments will enable banks, for example, to improve their commercial real estate mortgage models. These statistics can also help reinsurers improve underwriting and risk management for a range of covers, such as business interruption, contingent business interruption, and non-damage business interruption. The power of this methodology lies in providing sufficiently granular data on both properties and the networks they depend on to modify existing valuation and risk models.
“Going forward, relying on historical climate and natural catastrophe data is insufficient for the insurance and finance industries; we need a new way to account for climate change and the effects it has on properties, communities, and infrastructure,” said Dr. Bohn. “With the One Concern Downtime Statistic™, we are taking an important step toward understanding how climate change impacts property and infrastructure risk and resilience while enabling more accurate valuation assessments that account for climate-related events.”
One Concern’s methodologies utilize the latest in physics and statistics-based models to make damage predictions and apply machine learning to impute missing data to assess the resilience of underlying networks of dependencies (e.g., water and power utilities). One Concern’s approach relies on a mix of physics-based (e.g., fragility functions) and machine learning models to generate resilience analytics at the building level in a way that facilitates comparisons across buildings, across geographies, and over time.
About One Concern
One Concern, a climate resilience technology company, enables organizations to focus on adaptation and resilience strategies by using newly developed resilience analytics for supporting risk selection, mitigation, pricing and risk management. Applying machine learning and state-of-the-art resilience modeling, One Concern helps organizations better understand and prepare for physical climate risks with the mission of making disasters less disastrous. A 2019 Technology Pioneer, One Concern is part of the World Economic Forum’s Global Innovators community. oneconcern.com
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