Before the storm: Using data to predict and manage climate risk

In the first five months of 2023 alone, the United States experienced 11 climate disaster events with confirmed losses of over $1 billion each. With June’s severe weather in the south, that number climbed to 12. Last year, there were 18 such disasters, costing more than $175 billion and 474 lives.

Business and government leaders are past the point of just taking notice. The World Economic Forum’s 2023 Global Risk Report, based on polling of business and country-level leaders, listed natural disasters and extreme weather as a top short-term global risk. The failure to mitigate climate risk and implement adaptation strategies for climate change, natural disasters and extreme weather are the top three global risks over the next decade, according to the report.

Despite this broadly recognized need to act, it is not yet mainstream to approach extreme weather risk systematically using projections of future climate conditions. They instead generally rely on historical data, like the above-mentioned disaster information or anecdotal evidence, to design and evaluate the benefits of individual projects in a vacuum.

There is already a strong case for action from multiple studies, such as research from the National Institute of Building Sciences, which found that every dollar spent on resilience saves between $4 and $11. As the frequency and intensity of climate-related shocks increase, that figure should increase proportionally.

FEMA announced in May the selection of more than 400 more resilience projects nationwide through the Building Resilient Infrastructure and Communities and Flood Mitigation Assistance programs. Since the Bipartisan Infrastructure Law was signed more than a year ago, FEMA has awarded $370 million through these programs.

By taking a future-focused, more data-driven policy view, government leaders can better manage risk, optimize newly available infrastructure spending, and potentially achieve longer-term resilience against inevitable disaster considering this three-part approach:

Develop a consistent and objective understanding of risk to assets and operations.

In this age of big data, AI, and machine learning, there is more than enough information for insightful predictive modeling—leveraging climate science, actuarial data, and financial analysis—that can be used to understand organizational vulnerability to different climate-driven physical risks.

Yet many organizations often don’t know where to start. For example, following Superstorm Sandy, public and private sector organizations spent billions of dollars protecting impacted assets from future events without considering adjacent assets not impacted by the storm but possibly more critical and likely to falter in the future. Past storms do not necessarily predict future risk, particularly in light of changing weather patterns, which are becoming more frequent and more extreme. There are existing tools that can identify and map critical assets and assess their vulnerability (economic and operational) to multiple present and projected future climate risks.

Assign a dollar value to avoided risk.

When disaster strikes, organizations and their insurers invariably work to calculate how much repairing the damage will cost. But not many organizations assign a value to the benefits gained from averting disaster. Why? Because it’s hard to do.

A credible cost-benefit assessment to justify the investment in risk reduction may be worth it, however. And there are tools and methods available to do this today.

An objective and data-driven approach enables community leaders to project the annual likelihood and economic impact of different climate events, like flooding or extreme winds. This quantifies the annualized cost of inaction. For example, if leaders knew up front that a 10-year storm (that is, one with a 10% annual chance of occurring) would cause $2 million in losses through repairs and unplanned facility downtime, they may find it easier to justify spending much less to flood-proof that same building.

Prioritize and strategically plan investments toward resilience.

No one has unlimited resources, so it is imperative that agencies align their climate resilience investments to where those investments can have the greatest benefit. This means evaluating the cost of inaction against the cascading impacts of inevitable disruption, with existing asset management and capital planning inputs.

This can be accomplished using a multifactor, decision-support tool to help stress test different capital investments, which evaluates the funding and sequencing of climate resilience projects to achieve the greatest value.

The combined power of these portfolio analysis tools, decision support capabilities and predictive analytics can help leaders make their communities and organizations stronger and more resilient. Humanity faces a potential economic impact of at least $178 trillion globally by 2070 if the world can’t efficiently transition to a low-carbon future and build resilience across geographies and global supply chains, according to a recent Turning Point report from Deloitte.

The time for action is now. Leaders at all levels can take advantage of new tools and technical capabilities to understand their vulnerabilities to changing climate and act before disaster strikes.

Jerry Johnston is a managing director for Deloitte & Touche LLP and advises government clients on the use of geospatial technology. He is a former director of Information and Technology Development for the U.S. Department of the Interior and served as the geospatial information officer for Interior and the U.S. Environmental Protection Agency.

Matt Gentile is a principal with Deloitte Transactions and Business Analytics LLP and leads Risk & Financial Advisory for Deloitte’s Government & Public Services practice. He was an entrepreneur and former tech CEO in the analytics community and serves on the board of the Homeland Security and Defense Business Council.

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