Holistic asset-level climate analysis

Physical climate risk data: the more we learn, the less we know? Khalid Azizuddin's recent piece in *Responsible Investor captures well what many practitioners are grappling with today: - asset-level data that remain incomplete or hard to interpret; - physical hazard exposure often disconnected from financial materiality; - little visibility on supply chains or customers; - adaptation and resilience efforts largely ignored; - and a risk of over-simplifying complex realities into a single “score.” Some three years ago, EDHEC Business School set out to address exactly these challenges, working to advance climate risk modelling and make decision-useful for investors, companies, and public authorities. In this work, we have developed: 🔹 a blueprint for a new generation of probabilistic climate scenarios; 🔹 high-resolution geospatial modeling capabilities to allow for geographic and sectoral downscaling, consistent with each scenario; 🔹 an open database of decarbonisation and resilience technologies through the #ClimaTech project, which officially launched this week. While the research is public, the new EDHEC Climate Institute has also been assisting a school-backed venture, Scientific Climate Ratings (SCR), which integrates this research to deliver forward-looking quantification of the #financialmateriality of climate risks for infrastructure companies and investors worldwide. While SCR provides a rating scale for comparability, it avoids the trap of over-simplification. Each rating is backed by probabilistic scenario modelling, analysis of physical and transition risk exposures, and explicit accounting for adaptation measures. The result is a synthesis that remains transparent, interpretable, and anchored in scientific rigour. Together, these initiatives aim to move the discussion from data abundance to decision relevance, equipping practitioners with tools that connect climate science, finance, and strategy.

Is physical climate risk financially material? 🌍 This whitepaper from MSCI is an event-based analysis of how physical climate hazards translate into equity market outcomes. Using asset-level geospatial data and hurricane events between 2022 and 2024, the study examines whether localized exposure to physical risk has a measurable financial impact after controlling for market, sector and style factors. 👉 Equity performance is affected after impact. Companies with assets located in hurricane paths showed cumulative excess-return declines that continued for at least 30 business days following the event. 👉 Exposure is widespread at the portfolio level. During peak storm months, more than half of MSCI ACWI constituents had assets exposed to hurricanes, representing around three-quarters of index weight. 👉 Downside risk increases. Hurricane-exposed firms exhibited weaker tail outcomes, with fifth-percentile returns continuing to deteriorate across the event window. 👉 Concentration matters. Companies with a higher share of assets or revenues in affected areas underperformed more than those with more diversified footprints. Storm category alone did not explain results. 👉 Sector labels are insufficient. Utilities and real estate showed greater sensitivity due to fixed, infrastructure-heavy assets. In IT and industrials, portfolio impact depended on asset location and operational relevance. Physical climate risk is observable, measurable and financially relevant. Asset location and exposure concentration are factors that markets increasingly reflect in performance. Source: MSCI ESG Research – “Is Physical Risk Financially Material? A hurricane-event study” (September 2025)

One of the biggest criticisms we hear about climate scenario analysis is that it is not decision-useful. The scenarios used often miss key factors, understate tail risk and ignore tipping points, creating long-term uncertainty that is too large to make any meaningful decisions.   The BoE has today published a bulletin exploring how financial institutions can use scenario analysis to quantify climate change risks. The bulletin is clear on this point, noting that standard climate scenarios “generally do not provide the level of detail end users need to undertake asset-level financial risk analysis”; however it offers that the solution lies with the end-users of scenarios, and encourages firms to extend scenarios to improve spatial granularity and resolution for physical risks, derive and interpret relevant related variables, determine the extent to which long-term risks are likely to be “priced in” to asset prices over time and understand the interconnectedness of different sectors in the economy.   The key requirement of this solution is data. For one of the BoE’s examples, residential mortgages, this means: Data on individual assets (for example, using Energy Performance Certificates (EPC) ratings to assess the impacts of an aggregate energy price shock on individual households). Data on macroeconomic variables (household disposable income, default rates, household debt-solvency ratios). Data on macroclimate variables (granular flood-risk data and other risks outside the UK).   Scenario analysis can be powerful, even when it has limitations. Any refinement to the scenario and reduction to the limitations only makes it more powerful, and it is fair to conclude that investing in improved data capture and sourcing will be a key step for many firms. In any case, to be decision-useful is sometimes about helping users keep in mind the limitations in models alongside the results, adhering to the "all models are wrong, some are useful" maxim. https://lnkd.in/evbyk3PU

In a post earlier this week I outlined the problem with today’s physical climate risk models. Essentially, they boil down building vulnerability to a single loss number, primarily stemming from collected claims data — a top-down approach that doesn't provide asset-level traceability. But buildings are comprised of hundreds or thousands of components and systems — mechanical, electrical, plumbing, architectural, and structural elements — many of which are interdependent. That’s why we need to model building vulnerability and physical damage at the building component level, using a bottom-up, first principles of engineering-based approach. With component-based modeling, we can trace: • the hazard intensity each component is likely to experience • the likelihood a component is damaged, and to what severity • what specific repair actions would be required • how those repairs translate into time and cost With this type of approach to vulnerability modeling, we can explain so much more: Why is the risk high? What’s driving the loss? And what specific interventions (and I don't mean flood walls!) would change the outcome? When you can connect hazard → component damage → repair actions → business impact, the analysis becomes technically defensible — and stakeholders can make retrofit, acquisition, design, and resilience decisions with far more confidence. Importantly, this approach to quantifying credible asset-level behavior scales naturally to portfolios of hundreds or thousands of buildings.

Physical climate risk is now an investment risk—not a future one 1. Increasing frequency and intensity of floods, heatwaves, storms and droughts are directly affecting asset valuations, operating expenses, insurance costs and creditworthiness. Investors can no longer treat climate impacts as long-term externalities; they are material risks today. 2. Asset-level vulnerability is becoming clearer—and more costly High-resolution climate modelling (like First Street) now shows how specific facilities, supply chain nodes, retail sites, and infrastructure assets are exposed. Even resilient manufacturing facilities can face regional ecosystem collapse, supply disruption, or infrastructure failure that interrupts operations. Climate events may damage assets temporarily, but valuation recovery lags, creating long-term financial drag. 3. Risk assessment is shifting from qualitative to data-driven Institutions are increasingly integrating top-down portfolio exposure models (sector, region, hazard) with bottom-up company-level scoring (critical assets, revenue dependencies, adaptive capacity). Although data remains patchy and non-standardised, investors are adopting third-party analytics, scenario models, and geospatial hazard data to build credible assessments. 4. Climate resilience is now a driver of competitive advantage Companies with strong adaptation measures—storm-resistant design, raised electrical systems, water-resilient floors, strengthened local infrastructure—show: Lower downtime Lower insurance costs More stable revenues Higher long-term asset values Investors increasingly reward issuers that quantify adaptation capex, demonstrate climate-ready supply chains, and disclose how they safeguard critical assets. 5. Identifying adaptation solutions unlocks new investment opportunities Moving from risk avoidance to opportunity spotting: Growing demand for green bonds focused on adaptation, climate-resilient infrastructure, water systems, flood defenses, resilient agriculture, and nature-based solutions. Adaptation-enabling businesses—those producing resilient materials, sensors, weatherproof systems—can outperform during climate volatility. Infrastructure, real assets, and utilities are well positioned to benefit from adaptation finance flows. 6. Integration into investment decision making is becoming standard practice Investors are embedding climate risk in: Due diligence and investment committees Discount rate adjustments for hazard probability Portfolio construction and manager mandates Engagement and stewardship dialogues Physical Climate Risk Assessments (PCRAs) increasingly influence design, structuring, and operations of investments—especially in real assets and infrastructure. Continued in the comment section below

This article is a small gem because it sets out practical questions to ask when reviewing a physical climate risk assessment. The questions may sound trivial to climate scientists, but they are exactly the kinds of checks that can help to "translate" scientific knowledge into operational guidelines for climate risk professionals in financial institutions. My main key points: 1. Match the model/data resolution to the hazard. The right spatial scale depends on the physical processes behind the hazard (some hazards are driven by large-scale weather patterns, synoptic scale others by local processes). 2. Be careful with asset level (“postcode-scale”) insights. Downscaling and bias correction can look precise, but at that scale internal variability can dominate, so the result may not be reliable. 3. Don’t rely on the ensemble mean only. For risk management, it matters to look at the spread across models and scenarios, not just an average result. 4. Check the observations behind any correction. If a tool uses gridded observational products, it is important to know how many local weather stations are actually included. The article 👉https://lnkd.in/ePbFwGfQ

Happy to share a recent working paper, "Differentiated Impacts of Climate Physical Risks on the Indian Power Sector," led by Dr Abhinav Jindal and Ruben Kerkhofs. This is part of our research in the India Transition Finance (https://lnkd.in/gPiXXi77) and Environmental Stress Testing & Scenarios (https://lnkd.in/gQBjGpiv) projects at the Oxford Sustainable Finance Group at the Smith School of Enterprise and the Environment - University of Oxford and the UK Centre for Greening Finance and Investment (CGFI). Background: - Climate change presents significant challenges for businesses globally, particularly in India. - This paper offers a novel, data-driven evaluation of flood-related physical climate risks in the Indian power sector. - The research employs a forward-looking Monte Carlo simulation framework to assess financial impacts of physical climate risks. Findings: - First, the Indian power sector faces substantial exposure to physical climate risks from flooding. - Second, while insurance alone offers limited protection from physical damage or service disruptions, targeted adaptation strategies can substantially reduce these losses. - Third, financial impact of flood risk varies sharply across different types of power generation technologies, with coal and gas assets being most vulnerable. - Fourth, there exist sharp geographic disparities in flood-related risks, with Kerala, Puducherry, Gujarat, and Odisha being the most exposed. - Fifth, there is a need for fine-grained, asset-level risk analysis to accurately assess and manage climate risk. Implications: - First, regulators should require asset-level climate risk disclosures for critical infrastructure. - Second, utilities and investors must adopt climate-adjusted valuation frameworks and align disclosures with global standards like the TCFD. - Third, infrastructure planning must be tailored to specific context, and climate resilience must become a core element of energy planning. #india #physicalrisk #acuterisk #floodrisk #adaptation #resilience #insurance #power #coalpower #gaspower #renewables #disclosure The executive summary is here: https://lnkd.in/gaJtrMmB The working paper is here: https://lnkd.in/gpuPn6FM

𝗧𝗵𝗲 𝗡𝗚𝗙𝗦 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴— for the first time, we now have 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 tailored for 𝘀𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁𝗶𝗻𝗴, 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗻𝗲𝗮𝗿-𝘁𝗲𝗿𝗺 𝗺𝗮𝗰𝗿𝗼 𝗿𝗶𝘀𝗸. 🔸 This isn't about 2050. It's the next five years, i.e. 𝟮𝟬𝟮𝟱–𝟮𝟬𝟯𝟬. 🔸 This isn't abstract. It's 𝗚𝗗𝗣 𝘀𝗵𝗼𝗰𝗸𝘀, 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗶𝘀𝗸, 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗲𝗺𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 1.  A smooth transition ("Highway to Paris") 2.  A delayed, abrupt policy shift ("Sudden Wake-Up Call") 3.  Physical risk disasters without transition ("Disasters & Policy Stagnation") 4.  A fragmented world with climate chaos and policy misalignment ("Diverging Realities") These scenarios are a wake-up call for taking short-term climate risks seriously. ➤ Delaying climate action could increase global 𝗚𝗗𝗣 𝗹𝗼𝘀𝘀𝗲𝘀 𝗯𝘆 𝗼𝘃𝗲𝗿 𝟯𝘅, and unemployment spikes by 1.3 percentage points (Sudden Wake-Up Call vs Highway to Paris). ➤ Climate disasters aren’t just regional anymore. Floods, fires and droughts in Asia or Africa can cut European 𝗚𝗗𝗣 𝗯𝘆 𝟭.𝟳%, driven by supply chain exposure. ➤ Credit risk spreads explode in carbon-intensive sectors. In some cases, default probabilities jump by 20–30 percentage points, stressing banks and insurers alike. ➤ Green sectors could lose out if the transition is abrupt, fragmented, or disrupted by physical shocks. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 ➤ For the first time, compound hazards—droughts, floods, wildfires—are modelled together, showing how climate risk can become systemic through trade, finance, and supply chains. ➤ Monetary policy is now integrated, so climate shocks affect interest rate paths, inflation dynamics, and macroeconomic volatility. ➤ Financial contagion is now factored in. Using advanced modelling, the framework maps how climate-related losses feed into default risk, cost of capital, and sectoral investment flows. ➤ Sector-by-sector and region-by-region outcomes now include asset-level exposure, probability of default, and sovereign bond repricing, offering tools fit for risk management. 𝗠𝘆 𝘁𝗮𝗸𝗲 This release is a step-change in how we understand and model climate risk. These scenarios are critical because they model economic and financial impacts on business over the next five years. A timeline relevant for senior management, boards and shareholders. Because these scenarios capture dynamic feedback loops, sector-specific capital costs, and second-round effects that ripple through the financial system, the risk science is taken to a whole new level. These real-world complexities have been missing from science to date, which is why these scenarios are so critical. #NGFS #NetZero #ClimateRisk _____________ For updates, follow me on LinkedIn: Scott Kelly

Climate risk assessment and impact modeling are meaningful when done bottom-up, starting at the asset - property - factory level, then scaling to the portfolio- , entity- and company level. Relying on sector-average or industry-average proxies for climate-related losses is misleading and inaccurate. If your company isn’t reporting these numbers, neither are your competitors, suppliers and customers. Example. The average flood risk exposure for automobile factories does not reflect the risk for your specific factory. Such an 'average' is not actionable information for a corporate risk manager or an investor.