Why waiting for climate data is a risk

🏠🌪 As climate disasters intensify, the hidden fault lines in our financial system are starting to crack—especially in the mortgage and insurance sectors. For decades, mortgage lenders have relied on homeowners insurance as a shield against loan losses. But today, that shield is weakening. Skyrocketing premiums, insurer withdrawals, and flood insurance gaps are leaving millions of households—and their mortgages—vulnerable. 📉 A new national analysis from First Street shows that #climaterisk has become the 6th “C” of credit, joining character, capacity, capital, collateral, and conditions. Why? Because physical climate risk is now driving mortgage defaults—especially from floods—and conventional credit models are failing to capture these losses. 💰 In fact, climate-driven credit losses could cost U.S. banks: $1.2 billion by 2025, and $5.4 billion by 2035, even without accounting for indirect economic shocks like housing downturns. 🌊 Floods are the leading peril, particularly damaging in areas outside FEMA flood zones, where insurance isn’t mandatory. Following disasters like Hurricane Sandy, banks faced tens of millions in hidden losses—unforeseen and unmodeled. 📉 Rising insurance premiums are also forcing borrowers to absorb more risk. For every 1% increase in insurance costs, the foreclosure rate ticks up by over 1%. At the same time, household savings have shrunk to just 4.6% of disposable income. 👉 The message is clear: climate risk is credit risk. If lenders don’t integrate high-resolution climate data into their risk models, they risk being blindsided by the next disaster—not just physically, but financially. Read the report here 👇 https://lnkd.in/edmCrQY8

If I’m running a manufacturing business, I don’t need a sustainability report. I need foresight on financial risk. That means we don’t start with disclosures or frameworks — we start with real questions. What will carbon taxes do to my margins in priority markets? Where are my most emissions-intensive suppliers — and how fast can I reduce that exposure? What routes, processes, or inputs could be optimized today to protect profitability tomorrow? Because here’s the reality: Margins don’t erode overnight. They erode because no one was looking ahead. If I had that emissions and cost data two years earlier, I could have: • Shifted transport from road to rail • Repriced contracts to account for carbon exposure • Rethought asset investments before regulation forced my hand That’s not “compliance.” That’s protecting enterprise value. The companies getting this right aren’t using generic estimates or siloed reports. They’re using decision-grade data — tied to real levers: operations, supply chain, procurement, finance. What matters isn’t just tonnes of carbon avoided. It’s the business case behind every number: • Cost per unit improvement • Margin protection • Exposure reduction In 2025, climate risk won’t just sit in your sustainability report. It will show up on your income statement. The question is whether you’ll see it coming — or explain it after the fact.

𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗥𝗶𝘀𝗸 𝗶𝗻 𝗕𝗮𝗻𝗸𝗶𝗻𝗴: 𝗪𝗲 𝗡𝗲𝗲𝗱 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗧𝗿𝗶𝗮𝗹 𝗥𝘂𝗻𝘀 Glaciologists know the world can change fast—and financial systems are hardly immune. While early ice core data hints at abrupt temperature shifts, today’s climate data shows we are experiencing those shifts at a rapid, nonlinear pace. Banks, however, are still treating climate risk as a hypothetical. Last year, the Federal Reserve released a cautious summary of a climate "scenario exercise" by six major U.S. banks, gauging their ability to handle extreme climate risks. The result? Not much assurance. This scenario exercise was intended as an “early exploration,” yet the process exposed glaring gaps. Banks struggled to evaluate risks independently, leaning heavily on third-party vendors, and faced issues of “limited data, lack of back-testing, nonlinear risks, and time constraints." Their models, in short, are black boxes—and the data? Insufficient. What’s missing is also the heart of the matter: data on building locations, construction, insurance costs, and climate shocks on communities. This exercise isn’t about financial losses, but about our thin readiness for climate upheaval. Our federal leadership must insist on sharper, rigorous assessments. The climate doesn’t wait for comfort zones; neither should we. This is the beginning of a massive, multi-layered effort, requiring immediate action to make sure financial models can withstand a climate reality that science tells us is coming quickly. As glaciologists would remind us, societies have been swept away by climate changes before—without the capacity to plan. Today, we do have that capacity. It’s up to us to use it.

Unpopular opinion: We don’t have a data problem when dealing with climate change. Of course, there’s lots we don’t know and we need to continue to invest in data and science to sharpen and improve policy solutions - particularly when it comes to resilience and adaptation. But on the other hand, we already have a very good idea what the risks are. There are many no regrets decisions we can already make today based on the data we already have available. For example - the state of NSW has world leading data on flood risk in the Hawkesbury Napean Valley. The science and data is impeccable. And yet over the last ten years we continue to knowingly build homes in one of the highest risk areas in all of Australia. The data isn’t the problem. Waiting for the perfect data is a problem. #unpopularopinion

We’ve reached the point where reality is outpacing fiction… again. In the past few weeks alone, the abrupt dismantling of USAID programs has sparked geopolitical whiplash, the CIA 'accidentally' doxxed itself as it complied with an exec order (which, if you’ve seen Mission: Impossible, the list of CIA agents was literally the 1996 movie's plot), and now NOAA, the backbone of climate and weather modeling data, is being restructured in ways that could compromise access to critical data. Climate and weather data aren’t just for scientists. They guide insurance pricing, infrastructure planning, and financial markets. If that data becomes unreliable, restricted, or disappears, the ripple effects will be massive—insurers pulling out of high-risk areas, mortgages becoming harder to secure, property markets reshuffling based on uncertainty rather than foresight, and municipalities scrambling to cover budget gaps. To be clear, this isn’t a political argument, it’s a crack in the systemic foundation of modern markets, including the U.S. property market. When data goes dark, risk increases, and markets react accordingly. In an era where climate-driven economic shifts are accelerating, pulling the plug on NOAA’s data would be like shutting off the headlights while driving at full speed. I share more in my latest write-up, and thanks to Gopal Erinjippurath, Cool Climate Collective LP and co-founder of Sust Global, for his insights into how climate data is shaping financial and risk models. His background in geospatial analytics and climate risk has given him a front-row seat to how industries, from real estate to insurance, are integrating climate intelligence into decision-making. As foundational datasets like NOAA’s face uncertainty, it raises bigger questions about how businesses and markets will adapt. In venture, there’s a saying: the best investments are both contrarian and right. Some might look at the current headwinds and say climate is falling out of vogue. But if the signals are clear, the opportunity isn’t contrarian… it’s inevitable. 𝐌𝐚𝐫𝐤𝐞𝐭𝐬 𝐝𝐨𝐧’𝐭 𝐞𝐫𝐚𝐬𝐞 𝐫𝐢𝐬𝐤; 𝐭𝐡𝐞𝐲 𝐫𝐞𝐩𝐫𝐢𝐜𝐞 𝐢𝐭. And as these structural shifts accelerate, the price will be paid: by insurers, by lenders, by property owners, by governments. The question is, who is positioned to absorb the shock and who is prepared to channel it into transformation? Private markets will correct for the gaps left behind, because they always do. The only real uncertainty is who will step in to shape that future, and whether they see the gains not just as profit, but as a lever to reduce the broader cost to society. I’ve put together a deeper dive into what this means for climate models, insurance markets, and financial stability. 👇 Read the full write-up below (linked in the first comment).