How to map upstream climate risks

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

Mapping ESG topics across the entire value chain is essential to understand where impacts occur and how they translate into financial exposure. The value chain mapping presented in Nestlé’s 2025 non financial disclosure is a strong example of how to approach this exercise in practice. It positions sustainability topics across upstream sourcing, own operations and downstream activities, making visible where specific exposures concentrate. Upstream agriculture carries a significant share of environmental and human rights exposure. Climate risk, deforestation, water stress, soil degradation and labor vulnerabilities are embedded in raw material production. These issues influence supply stability, input cost volatility, regulatory scrutiny and long term sourcing resilience. In own operations, the focus shifts to energy consumption, manufacturing emissions, workplace health and safety, diversity and governance controls. These areas affect operational efficiency, cost management and compliance. Performance at this level determines whether commitments translate into measurable outcomes. Downstream, packaging design, food loss, product formulation, marketing practices and data protection shape regulatory exposure, consumer expectations and competitive positioning. Circularity and nutrition influence market access and brand strength in tangible ways. This type of mapping shows how impact materiality and financial materiality intersect across different segments of the chain. A topic such as greenhouse gas emissions originates largely in agricultural sourcing, becomes an efficiency variable in manufacturing and evolves into a regulatory and reputational factor in consumer markets. It also clarifies ownership. Procurement manages sourcing practices. Operations manages energy and safety. R&D and marketing shape the product portfolio. Governance bodies oversee conduct and accountability. Without this alignment, ESG remains difficult to integrate into core decision making. For companies operating in complex global value chains, this visibility supports more precise prioritization, capital allocation and risk management across environmental, social and governance dimensions.

Their almond supplier faces 30% water cuts next season. Their risk assessment said: not material. (a due diligence story in 3 acts) I was assessing a food brand's climate risks for an investor. The brand had done the homework, or so it thought. A big consultancy had prepared its sustainability assessment. - Polished.  - Confident. - Professional. The conclusion? Water wasn't material. One small detail: a key ingredient was almonds from southern Spain. A water-intensive crop, grown in a region facing severe allocation cuts. 📌 Act 1: The gap I started where I always start - at sourcing regions. - Their assessment used generic industry benchmarks. - Nobody had mapped where ingredients actually came from. - Nobody had cross-referenced suppliers with climate projections. "Spanish almonds" was a line item.  It didn't show as a drought risk. 📌 Act 2: The conversation I flagged the water exposure in my assessment. The investor asked the brand directly: "Walk us through your water risk." Brand: "Our consultants said it wasn't material." Investor: "Your almond supplier is in a region with forecasted 30% allocation cuts. How is that not material?" Silence. 📌 Act 3: The lesson The deal didn't fall apart. But confidence took a hit. The brand had paid for an assessment that missed what a 10-minute sourcing conversation would have caught. Because generic templates don't ask "where do your almonds grow?" What I've learned from doing these assessments: Climate risk lives in the specifics. - Which seasons, not which years - Which regions, not which countries - Which suppliers, not which categories Before your next investor conversation, ask your team: → Do we know the climate risks in our top 5 sourcing regions? → Has anyone mapped our suppliers to water stress data? → Would our answers survive a 10-minute follow-up? Investors are starting to ask these questions. Better to discover the gaps yourself first. P.S. What's ONE climate risk you discovered hiding in your supply chain?

"Renewable Energy in Climate Change Adaptation: METRICS AND RISK ASSESSMENT FRAMEWORK INTRODUCTION" by International Renewable Energy Agency (IRENA) RISK ASSESSMENT IN THE ADAPTATION PROCESS 🔅Risk in the context of IPCC reports The IPCC defines climate risk as the intersection of: 📍Hazards: Physical climate events (e.g., drought, heatwaves). 📍Exposure: People and assets in harm's way. 📍Vulnerability: Susceptibility to damage and lack of coping capacity. 🔅 Risk assessment framework Risk assessments rely on tools like: 📍Vulnerability assessments (e.g., impact chain method). 📍Scenario analysis using climate pathways (e.g., RCP2.6, RCP8.5). 📍Cost-benefit analysis, GIS mapping, and participatory methods. The impact chain method, aligned with GIZ’s Vulnerability Sourcebook, is used to quantify risk by integrating hazard, exposure, and vulnerability indicators, normalized and weighted to produce a risk index. 🔅Introducing the Impact Chain Method 💠STEP 1: Identify Risk Risk: Increased energy demand and emissions due to higher reliance on desalination, driven by declining rainfall and water scarcity. Over 70% of water consumed on the Canary Islands is desalinated. Desalination already accounts for 10% of electricity demand in Gran Canaria. 💠STEP 2: Identify Hazards, Vulnerability, and Exposure Hazard: Decreasing rainfall, measured by SPEI (Standardised Precipitation Evapotranspiration Index). Exposure: 📍Population density: 0.39 (normalized score). 📍Tourist-to-resident ratio: 0.95 📍Tourism seasonality: 0.13 📍% of desalinated water: 0.40 Vulnerability: 📍Energy intensity: 0.20 📍Per capita electricity demand: 0.19 📍Purchasing power: 0.66 💠STEP 3: Develop Indicators Hazard indicator: SPEI values (drought index). Exposure indicators: Population, tourism volume, % desalinated water. Vulnerability indicators: Energy use per GDP, per capita demand, income. 💠STEP 4: Introduce Thresholds and Normalise Indicators Indicators are normalized on a 0–1 scale for comparability. Example: 📍Max tourist-to-resident ratio in EU: 4.42 = normalized score of 1. 📍Population density max: ~1373/km² (Malta) = normalized max. 📍Desalinated water share in some islands: 100% = score of 1. 💠STEP 5: Present Results Energy demand for desalination: 📍Present: 1,121.4 GWh/year 📍RCP2.6 (2046–2065): 1,749.4 GWh/year (+56%) 📍RCP8.5 (2046–2065): 2,063.4 GWh/year (+84%) Normalized hazard scores (based on SPEI): 📍Present: 0.00 📍RCP2.6: 0.56 📍RCP8.5: 0.84 Weighting of risk components (from correlation analysis): 📍Exposure: 35% 📍Vulnerability: 34% 📍Hazard: 31% Final risk scores: 📍Present climate: 0.29 (low) 📍Mid-century RCP2.6: 0.46 (medium) 📍Mid-century RCP8.5: 0.54 (medium–high) Risk scale interpretation: 📍0.0–0.2 = Very low 📍0.2–0.4 = Low 📍0.4–0.6 = Medium 📍0.6–0.8 = High 📍> 0.8 = Extremely high With 100% renewables in energy mix: 📍Risk under RCP2.6: drops from ~0.47 to ~0.34 📍Risk under RCP8.5: drops from ~0.55 to ~0.39

𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗚𝗔𝗥𝗣-𝗦𝗖𝗥 𝗶𝗻 𝟮𝟬𝟮𝟰 Clearing Sustainability & Climate Risk (SCR), wasn’t about memorizing risk jargon - it required thinking like a risk manager, understanding second-order effects, and identifying material risks over time. Here are a few of my learnings from this experience. 1. Don’t study in isolation - Inter-link all concepts Climate risk isn’t a checklist. Transition risks (policy, technology) cascade into financial risks - affecting valuation, credit ratings, and stranded assets. For example: Policy risk (carbon tax) → Higher cement production costs → Earnings decline → Stock price drops → Credit rating impact → Refinancing gets expensive. 2. Push beyond definitions - Think second-order effects A common mistake is stopping at “Transition Risk = policy, technology, market, and reputation risks.” See how you can connect risks to financial impact: For example: Hurricane risk for an insurer → Higher claims → Capital strain → Reinsurance costs surge → Policyholder churn. 3. It prioritizes solutions which are holistic, material & over a time horizon. For example, when you are asked on what’s the primary risk for an agriculture company exporting coffee? Consider the financial impact, look at cascade risks and cut this across time horizons - in the short-term, it is acute physical risk (droughts → lower yield → price spikes), while in the long-term, it is chronic physical risk (temp shift → reduces viable farmland). 4. You need to extract the focus of the analysis Phrases like “most likely,” “primary driver,” and “key challenge” change the final answer. For eg., when asked on what is most likely climate-related challenge for global food companies in the next 5 years? Think beyond carbon taxes and litigation (i.e. transition risks), as the literal water scarcity affecting crop yield (i.e. physical risk) will be a more immediate impact. 5. Mental hooks for frameworks & models, to ensure instant recall One thing that helped me was creating mental hooks for the key concepts. Create your own like the ones below. a) NGFS Scenarios = Orderly → Disorderly → Hot House b) Climate Models vs IAMs = Science vs Economics c) SBTi Target Classifications: Near-term → Long-term → Net Zero 6. Case studies follow predictable risk patterns Understanding how industries are affected by climate risk makes it easier to tackle case studies. Think about a few. a) Mining → Transition Risk (Carbon taxes, stranded coal reserves) b) Fisheries → Acute Risk (Ocean acidification reduces fish stocks, disrupting global seafood trade) c) Retail → Supply Chain Risk (Disruptions from floods or extreme weather affects logistics) These ideas did help me clear the SCR exam and grasp how climate risks translate into financial risks. If you’re preparing, focus on financial impact, cascading risks, and systemic effects - not just definitions and trivia. #GARP #SCR #ClimateRisk #RiskManagement #Sustainability

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗷𝘂𝘀𝘁 𝗰𝗵𝗮𝘁 𝘄𝗶𝘁𝗵 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘂𝗻𝗰𝗼𝘃𝗲𝗿 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸𝘀? 𝗡𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘄𝗶𝘁𝗵 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁. The most critical climate datasets — heatwave projections, precipitation models, land surface temperature, drought indices — live as 𝗿𝗮𝘀𝘁𝗲𝗿 𝗱𝗮𝘁𝗮: dense grids of pixel values derived from satellite sensors and climate models. Turning them into actionable intelligence requires specialized GIS tooling, resampling pipelines, CRS transformations, and significant engineering overhead before joining them with contextual data like population density or land use. This friction is why climate risk analysis has historically been slow, expensive, and inaccessible outside specialist teams. 𝗙𝗦𝗤 𝗛3 𝗛𝘂𝗯 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗮𝘁 𝗲𝗾𝘂𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆. 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁 𝗽𝘂𝘁𝘀 𝗶𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸. FSQ H3 Hub's proprietary indexing pipeline converts raw raster datasets into 𝗛3 𝗵𝗲𝘅𝗮𝗴𝗼𝗻𝗮𝗹 𝗰𝗲𝗹𝗹𝘀 — making satellite-derived climate data available in clean, tabular form at a consistent spatial resolution. Every dataset shares the same H3 grid, so joining a Copernicus heatwave projection with a CHELSA precipitation model, a wildfire risk layer, and population density becomes a simple SQL join on a cell ID. No resampling. No CRS headaches. No bespoke ETL. 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁, built on this foundation, lets you converse with that unified data layer to surface climate insights at scale. 𝗦𝗼 𝘄𝗲 𝗽𝘂𝘁 𝗶𝘁 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝘀𝘁: "Do a temporal climate risk analysis for Europe — pick an area with the most interesting future climate impacts." The agent selected 𝗔𝗻𝗱𝗮𝗹𝘂𝘀𝗶𝗮, 𝗦𝗼𝘂𝘁𝗵𝗲𝗿𝗻 𝗦𝗽𝗮𝗶𝗻 — citing Mediterranean climate sensitivity, agricultural economy, water scarcity, and dense coastal populations. It tessellated the region into 113,437 𝗛3 𝗰𝗲𝗹𝗹𝘀 at resolution 8 (~460m), drawing on five datasets spanning climate projections (Copernicus RCP 8.5, CHELSA SSP370), environmental risk (Drivers of Forest Loss), population exposure (Global Population 2020), and land use (MODIS Land Cover). 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗿𝗲𝘃𝗲𝗮𝗹𝗲𝗱: By late century, Andalusia faces a compounding climate trajectory: +23.7 heatwave days/year in extreme risk zones (32% of the region); +2.13°C average warming by 2070–2100; 53% projected "Extremely Drier" with over 600mm precipitation loss; 5,519 high-risk cells with significant population exposure; and a wildfire-climate feedback loop accelerating vegetation loss and further warming. 𝗥𝗮𝘀𝘁𝗲𝗿 𝗱𝗮𝘁𝗮 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗽𝗲𝗲𝗱 𝗼𝗳 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. The world's most detailed climate record is largely trapped in formats accessible only to specialists. FSQ H3 Hub and FSQ Spatial Agent change that — delivering climate risk intelligence that scales as fast as the questions you can ask. Download Foursquare 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 to get started.

Supply chain risks don’t just show up. They hide in plain sight. Most companies wait for disruptions to expose the weak links. Smart companies identify risks before they become problems. Here’s how: — 1. Map Your Supply Chain Do you know all your suppliers, partners, and processes? Most risks come from areas you can’t see. — 2. Analyze Historical Data What disruptions have impacted you before? Past events often signal patterns or vulnerabilities. — 3. Assess Supplier Stability Are your suppliers financially sound and operationally reliable? A single failure upstream can cripple your operations. — 4. Evaluate Environmental Factors Natural disasters, climate change, or geopolitical tensions. Are you prepared for location-specific risks? — 5. Use Risk Modeling Tools AI and analytics can help simulate potential disruptions and pinpoint where you’re most vulnerable. — 6. Collaborate Across Teams Your logistics, procurement, and operations teams hold key insights. Bring them together to uncover hidden risks. — Risk identification isn’t a one-time task—it’s a continuous process. The more proactive you are, the fewer surprises you’ll face. Where are the blind spots in your supply chain?

Your CFO doesn't care about heat maps. I've sat in enough meetings to know exactly when a sustainability team loses the room. It's the moment someone says "high risk" or "medium exposure" without a dollar sign attached. CFOs don't budget for colors. They budget for numbers. That's why we built an Impact Calculator into Beehive. You define your own risk parameters—downtime costs, revenue exposure, asset replacement values—and the system spits out dollar figures your finance team can actually use. Not "this facility has elevated flood risk." But "a flood at this facility could cost $4.2M in downtime and $800K in inventory loss, based on your inputs." And because your audit team will inevitably ask "where did these numbers come from?"—everything is fully auditable. Every assumption, every input, every calculation. Documented and traceable before you put it in a report. This is what it looks like when climate risk stops being a sustainability problem and starts being an enterprise risk problem. I walked through the whole feature in last week's Beekly. Here's a clip showing how it works.

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?