Climate risk analytics platform expansion

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗥𝗶𝘀𝗸 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 5 𝗻𝗲𝘄 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝗶𝗻 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗛3 𝗛𝘂𝗯 We've just expanded our Spatial H3 Hub with five new datasets that provide unprecedented insights for disaster planning, environmental monitoring, and climate resilience. 𝗧𝗵𝗲𝘀𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝘄𝗵𝗶𝗰𝗵 𝗮𝗿𝗲 𝗼𝘁𝗵𝗲𝗿𝘄𝗶𝘀𝗲 𝗼𝗻𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗶𝗻 𝗿𝗮𝘀𝘁𝗲𝗿 𝗳𝗼𝗿𝗺𝗮𝘁𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗶𝗻 𝘁𝗮𝗯𝘂𝗹𝗮𝗿 𝗳𝗼𝗿𝗺𝗮𝘁 𝗽𝗿𝗲-𝗶𝗻𝗱𝗲𝘅𝗲𝗱 𝘁𝗼 𝗛3 𝗰𝗲𝗹𝗹𝘀: 1. 𝗙𝗘𝗠𝗔 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗹𝗼𝗼𝗱 𝗛𝗮𝘇𝗮𝗿𝗱 𝗟𝗮𝘆𝗲𝗿 — Cover 90% of the U.S. population with comprehensive flood risk data. Perfect for insurance modeling, urban planning, and emergency preparedness. 2. 𝗨𝗦𝗔 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 — The nation's first complete inventory of structures >450 sq ft, including critical infrastructure like hospitals, schools, and emergency facilities. Essential for disaster response and homeland security planning. 3. 𝗛𝘂𝗺𝗮𝗻 𝗙𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 (100𝗺 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻) — Track human pressures on nature globally from 2017-2021. With standardized scores (0-50) covering everything from urbanization to infrastructure, this enables precise habitat risk assessments and biodiversity conservation planning. 4. 𝗚𝗹𝗼𝗯𝗮𝗹 𝗗𝗿𝗶𝘃𝗲𝗿𝘀 𝗼𝗳 𝗙𝗼𝗿𝗲𝘀𝘁 𝗟𝗼𝘀𝘀 — Powered by WRI and Google DeepMind's cutting-edge AI, this dataset reveals why we're losing forests globally (2001-2024). From agriculture to wildfires, understand the root causes at 1km resolution. 5. 𝗖𝗛𝗘𝗟𝗦𝗔 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 (𝗦𝗦𝗣370) — High-resolution climate projections through 2070 under an intermediate-pessimistic scenario. Critical for long-term infrastructure planning and climate adaptation strategies. 𝗔𝘁 𝗙𝗼𝘂𝗿𝘀𝗾𝘂𝗮𝗿𝗲, 𝘄𝗲 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗴𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗱𝗮𝘁𝗮 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲 𝘄𝗿𝗲𝘀𝘁𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮 𝗳𝗼𝗿𝗺𝗮𝘁𝘀. That's why we focus on making these critical datasets easily accessible and analysis-ready, so you can focus on building models benefitting from geospatial data. Checkout our 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗛3 𝗛𝘂𝗯 𝘁𝗼𝗱𝗮𝘆 (link in comments)!

NASA just trained a 3 billion parameter model on 100 million MODIS satellite images. Google released foundation models that reason across geospatial datasets. Yet most institutional investors still use Excel to assess physical climate risk. I met with a CRO of a $200B AUM fund last week. They were proud of their "advanced" climate risk system. It was a spreadsheet with color-coded cells. This gap between new technology and status quo is where revenue opportunity lives. Today's geospatial foundation models don't just find patterns. They understand causality across space and time. SatVision-TOA can predict the shape of objects in cloud-obscured images with 93% accuracy while spotting features for deeper analysis. Let's explore what this means for institutional investors: 1. Risk assessment is becoming multi-dimensional - models understand how risks compound across variables - demographic shifts, infrastructure resilience, economic activity, and climate patterns. 2. The speed of insight has accelerated exponentially - What used to take months of analysis can now be generated in minutes. 3. Power is now the only constraint, and space infra investment is now viable - Space solar power, orbital data centers, in-orbit manufacturing: geospatial AI can model the terrestrial economic impacts of these technologies years before deployment. (I've watched portfolio managers' eyes widen when we discussed how we can project the value of space-based solar transmission to specific grid-constrained regions) At Sust Global , we're embedding these foundation models into our geospatial AI platform. Not just layering data, but enabling true cross-domain reasoning. Last quarter, a client used our platform to identify real estate assets with both high climate resilience and proximity to emerging demographic booms. They executed a $300M allocation based on insights that didn't exist in any conventional dataset. That's the real breakthrough: finding opportunities others can't see by connecting domains others don't combine. Climate risk data can't exist in isolation. Neither can space technology. The future belongs to those who can reason across all these domains simultaneously. Curious how geospatial foundation models can unlock insights for your portfolio? Let's connect.

💸 $𝟭𝟮.𝟱 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝗰𝗹𝗶𝗺𝗮𝘁𝗲-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗹𝗼𝘀𝘀𝗲𝘀 𝗯𝘆 𝟮𝟬𝟱𝟬, 𝗮𝗿𝗲 𝘄𝗲 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗿𝗶𝘀𝗸 𝗶𝗻𝘁𝗼 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀? The new PCRAM (Physical Climate Risk Appraisal Methodology) framework and tool from Institutional Investors Group on Climate Change (IIGCC) gives investors a clear, practical way to assess and act on physical climate risk. Here’s why it matters: 🔹𝗦𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝘀𝗰𝗼𝗽𝗲: Goes beyond individual assets to evaluate risks across funds and portfolios, including interdependencies with surrounding systems. 🔹𝗠𝘂𝗹𝘁𝗶𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗮𝗿𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Brings together climate science, engineering, and finance into one replicable and practical framework. 🔹𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗮𝘀 𝘃𝗮𝗹𝘂𝗲: Shifts the lens from cost and loss to resilience premiums like stable returns, stronger credit quality, and reduced lifecycle costs. 🔹𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲𝗱, 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀: Follows a 4-step approach: scoping, materiality, resilience building, and financial analysis, scalable across geographies and sectors. 🔹𝐁𝐫𝐨𝐚𝐝𝐞𝐫 𝐚𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: Incorporates nature-based solutions and explores insurability and credit-strengthening opportunities. 𝘊𝘭𝘪𝘮𝘢𝘵𝘦 𝘳𝘪𝘴𝘬 𝘪𝘴 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵 𝘳𝘪𝘴𝘬. We need to act not just to climate-proof portfolios, but to future-proof capital. Read the report and explore the tool → link in comments. #ClimateRisk #ClimateFinance #Investors #PhysicalRisk #RealAssets #ESG #NetZero #IIGCC #AdaptationFinance #ResilienceInvesting

One year of building Beehive. Four product milestones that matter: September 2024: Launched wildfire risk assessments for US offices February 2025: Introduced comprehensive transition risk analysis April 2025: Released AI-powered TCFD report drafting August 2025: Climate risk quantification goes live Not the typical startup victory lap. No "we raised X million" or "we hit Y users." Just four product releases that actually help companies deal with climate risk. The wildfire assessments came first because California was burning and companies needed answers fast. Not in 3 months. Not after hiring consultants. Right now. Transition risk analysis followed because our customers kept asking: "Great, we know our physical risks. But what about carbon pricing? Stranded assets? Market shifts?" The AI-powered TCFD drafting was born from frustration. Watching consultants charge $200k to write reports that follow the same template every time. We automated the boring parts so they could focus on strategy. Climate risk quantification launched last month. Turns a vague "we have flood risk" into "$40.5M potential impact to our Dallas data center." CFOs finally started paying attention. Four releases. Each solving a real problem our customers actually had. Not what we thought they needed. Year two started this week. The compliance deadlines are getting closer. The storms aren't getting weaker. And we're just getting started. Building climate risk software isn't sexy. But neither is losing millions when your warehouse floods. We'll take useful over sexy every time.

WOULD YOU BET YOUR CAPITAL REQUIREMENTS ON YOUR EXISTING CLIMATE MODELS - WOULD YOU DO IT WITHOUT BENCHMARKING? The Bank of England (BoE) is effectively operationalizing what RiskThinking.ai has been predicting: the era of "climate as CSR" is over, and the era of "climate as solvency" has begun. Here is how the RiskThinking.ai (RTAI) platform is the exact technical answer to the specific pain points identified in your post. 1. "Climate Risk is Credit Risk" The BoE Demand: Climate must be treated as a core financial risk, indistinguishable from credit or market risk. The RTAI Solution: This validates RTAI’s move away from "Climate Scores" (0-100) to Financial Metrics like Value at Risk (VaR) and CVaR. A credit risk officer cannot calculate a capital buffer with a "High Risk" score, but they can with a "17% VaR" figure . The Platform Advantage: RTAI integrates these financial metrics directly into the bank's credit models via API, treating climate strictly as a driver of Probability of Default (PD) and Loss Given Default (LGD). 2. "Boards are Personally Accountable" The BoE Demand: Directors can no longer claim ignorance or rely on vague sustainability reports. The RTAI Solution: The Downside Likelihood metric (e.g., "85% chance conditions will be worse than history") is designed specifically for the Board . It cuts through the noise and provides a single, defensible metric that defines the strategic risk environment, fulfilling their fiduciary duty to monitor material risks . 3. "Lack of Data = Hold More Capital" The BoE Demand: If you can't quantify the risk, the regulator will assume the worst and force you to hold expensive capital against it. The RTAI Solution: This addresses the Data Gap explicitly. The Climate Digital Twin (CDT) fills this void with over 140 billion forward-looking projections. The Platform Advantage: By using RTAI’s stochastic data, a bank can prove to the regulator that its risk is measured and managed, potentially lowering the capital surcharge compared to a bank using "black box" AI or generic data . 4. "The Connected System" (Banks + Insurers) The BoE Demand: Regulators see the system as one unit. If insurers exit, bank collateral collapses. The RTAI Solution: This is exactly what OUR MULTIFACTOR CLIMATE STRESS TESTING MODELS: Systemic Amplification. Scenario: A bank might think its mortgage portfolio is safe. RTAI UNCOVERS TAIL RISK WITH ITS STOCHASTIC METHODOLOGY(modeled as part of the ecosystem) The platform then recalculates the bank’s LGD based on uninsured collateral, revealing the true "systemic" credit risk that a siloed, DETERMINISTIC model would miss . #CLIMATERISK #MODELRISK #STOCHASTIC #RISKTHINKING