Challenges of Quantifying Climate Risk Narratives

📢 Research Alert: A Probabilistic Framework for Climate Scenario Analysis 🌍 "Median global warming expected at 2.7°C - well above the #ParisAgreement" As climate risks become central to #financial and #regulatory decision-making, one challenge remains critically unmet: most climate scenarios lack probabilistic grounding. To address this, the EDHEC Climate Institute with Lionel Melin, Riccardo Rebonato, FANGYUAN ZHANG has released a groundbreaking study: 📘 "How to Assign Probabilities to Climate Scenarios" This research proposes an innovative framework to quantify the likelihood of long-term temperature outcomes, enriching narrative-based scenarios with a probabilistic layer essential for asset pricing, risk management, and policy planning. ✅ Key contributions: • Based on 5,900+ Social Cost of Carbon estimates from 207 academic sources • Uses two rigorous methods: an elicitation-based approach and a maximum-entropy framework • Integrates real-world policy constraints and macroeconomic data 🔍 Findings: • 35–40% chance of >3°C warming by 2100 • The 1.5°C target is technologically feasible, but highly improbable • Median expected warming: 2.7°C - well above the Paris Agreement • Physical climate damages outweigh the cost of transition, emphasizing urgent financial realignment 🔗 The study also maps #probabilities onto Oxford Economics’ scenario framework, assigning over 90% likelihood to pathways involving limited or delayed emissions cuts: Climate Catastrophe, Climate Distress, and Baseline. 👉 A must-read for those in climate finance, regulatory strategy, and risk modeling. This research pushes the frontier in integrating uncertainty and feasibility into climate scenario analysis. #ClimateChange and #Mitigation remains both the greatest source of risk and of opportunity of our time. Let’s prepare! radicant bank #InvestInSolutionsNotProblems

🌍Typology of climate scenarios by MSCI Inc. and United Nations Environment Programme Finance Initiative (UNEP FI) 🔹 1. Fully Narrative Scenarios These scenarios are qualitative descriptions of potential climate futures. ✅ Strengths: - Easily customizable - Useful for high-level strategic discussions - Can capture complex risks that are difficult to quantify ⚠️ Limitations: - Subjective and vulnerable to bias - Lack of numerical outputs makes them hard to integrate into risk models 🔍 Example Providers: - University of Exeter & Universities Superannuation Scheme 🔹 2. Quantified Narrative Scenarios This type builds on fully narrative scenarios by adding expert-driven quantitative estimates (macroeconomic forecasts, asset class returns, regional physical risks). ✅ Strengths: - Balances qualitative storytelling with numerical data - Allows for scenario comparisons without requiring sophisticated models - Easier to communicate results with clear quantitative insights ⚠️ Limitations: - Can give a false sense of precision if assumptions are weak - Still dependent on subjective expert input, leading to potential biases 🔍 Example Providers: - MSCI Sustainability Institute & University of Exeter – Estimating physical climate risks based on expert-defined damage functions. - IEA - WEO 🔹 3. Model-Driven Scenarios These scenarios rely on integrated quantitative models to project how climate change and transition risks might evolve under different policy and economic conditions, using macroeconomic models, IAMs, and energy system models. ✅ Strengths: Highly structured and data-driven, reducing subjectivity. Can produce detailed, sector-specific outputs useful for investment decisions. Widely used by regulators and financial institutions for stress testing. ⚠️ Limitations: - Expensive and time-consuming to develop and maintain - “Black box” nature of complex models makes interpretation difficult - Results are only as good as underlying assumptions and data inputs 🔍 Example Providers: - NGFS – Climate scenarios for central banks and financial supervisors - IEA – Net-Zero Emissions by 2050, STEPS & APS scenarios - IPCC – SSPs & RCPs 🔹 4. Probabilistic Scenarios Probabilistic models go beyond single-scenario forecasting by assigning probabilities, variance, and uncertainty estimates to different climate outcomes. ✅ Strengths: - Models uncertainty, improving risk management - Enables sophisticated stress testing for asset prices, portfolios, and corporate exposure - Valuable for insurance, catastrophe modeling, and financial risk assessments ⚠️ Limitations: - Highly complex and computationally demanding - Requires strong assumptions about uncertainty - Limited research on how climate change affects probability distributions 🔍 Example Providers: - NGFS & IPCC Probabilistic Models #ClimateFinance #ScenarioAnalysis #SustainableInvesting #RiskManagement #climatescenarios

Narratives matter. We know this. What you are about to witness now is not said in the open and certainly not visible for most of the people. But its effect is tremendous. The financial sector doesn’t openly deny climate change. Instead, it manages the narrative into financialised language: risk, opportunity, transition, disclosure. This allows the sector to look responsible, keep fossil fuel pipelines open, and delay real system change—while still making money on both “green” and “brown” sides of the economy. Framing Climate Risk as a Financial Stability Issue (not an ecological one) Banks, insurers, and asset managers adopt the language of risk—stranded assets, physical risk, transition risk. This reduces climate change to portfolio exposure, not planetary collapse. Example: Central banks (through the Network for Greening the Financial System, NGFS) frame climate as a “macro-financial risk” rather than a call for systemic degrowth. Climate becomes a reporting exercise: ESG scores, net-zero pledges, climate-risk disclosures. This shifts focus to metrics & transparency rather than actual decarbonisation. Result: Oil majors still attract trillions in financing under “transition” labels. Example: In 2022–23, the world’s 60 biggest banks pumped over $670 billion into fossil fuels while touting net-zero goals. Heavy lobbying against hard regulation (like fossil phase-out, carbon caps), while supporting lighter-touch “market-based solutions.” Example: Finance industry helped water down EU sustainable finance taxonomy, allowing gas & nuclear as “green.” US: Wall Street firms lobby both sides—supporting climate risk disclosure rules (SEC) but resisting binding divestment. Delay through “Transition Finance” Pitch narrative: “We’re not funding fossil fuels, we’re funding the transition.” Keeps capital flowing to oil/gas under the guise of “bridging energy security.” Example: JPMorgan, Citi, Barclays all promote “transition finance” frameworks while remaining top fossil funders.   Asset managers issue glossy reports: “$50 trillion climate opportunity by 2030.” This rebrands climate from existential crisis into investment theme. Climate activism framed as “risk to returns” instead of “risk to life.” Narrative tools: “Energy security first” → Argue that fossil fuel financing is necessary for stability and growth. “Transition finance” → Loans to oil/gas framed as bridging capital toward renewables. “Client-driven” excuse → “We can’t tell clients what to do — we just finance their needs.” “Market choice” rhetoric → Suggest regulation should be light because markets will allocate capital efficiently. Reality: Between 2016–2023, the 60 biggest banks poured over $5.5 trillion into fossil fuels while promoting their own “net-zero alliances.”

One of the wicked problems in climate risk disclosures whether to comply with TCFD, ISSB, ESRS or for strategic reasons, is quantification of climate transition risks. While the financial sector and some companies themselves have developed methodologies for transition risk quantification, these methodologies have often not been publicly disclosed, are not based on any peer-reviewed methodology and are not standardized across the economy or industry or sector. Bringing some standardization to transition risk quantification will be imperative to make risk disclosures comparable and meaningful. In our new paper, myself, David Carlin, Edward Byers and Keywan Riahi propose fundamental principles that are based in the science of climate scenarios, and should be followed when doing company level climate transition risk quantifications. These principles include;  1. Climate risks are more than just carbon emissions and won’t be mitigated by emissions reduction only. 2. At least two scenarios should be used for one risk disclosure statement. Risk has to be measured against a ‘business-as-usual’ scenario. 3. There should be transparency around which transition risks are assessed quantitatively and qualitatively, and which are excluded. 4. Lack of extreme events coverage should be acknowledged in the disclosures. 5. Risk model assumptions should not differ from underlying climate scenario assumptions. It would be important to build fora to standardize risk quantification and expand on this set of principles and methodologies. If you want to contribute to this important exercise please don’t hesitate to reach out. You can read the complete paper at https://lnkd.in/dHWsAyER

The Network for Greening the Financial System (NGFS) has been working with leading climate modelers to develop reference scenarios for the financial sector. These scenarios are commonly used by supervisors in climate scenario exercises, and financial actors for climate risk reporting and assessment. However, many challenges exist in translating the outputs of these scenarios into decision-useful financial information. The NGFS sought feedback from financial actors with a survey earlier this year. Last month they published the results. In United Nations Environment Programme Finance Initiative (UNEP FI)'s climate risk program, we've identified a series of questions that IAM-generated scenarios need to address to be used effectively by financial actors. They are: -Time horizons: how can we apply long-term scenarios with multiyear timesteps to our financial decision-making? -Impacts: are these scenarios really exploring stressful transition scenarios or just constrained best-case scenarios? Are economic and business cycle impacts being considered? -Variables: does the model produce the variables we need? Do we trust the outputs of the models for variables that we need for financial assessments? (e.g., commodity prices, demand variables) -Validation: how do we assess or validate the outputs we are seeing from these climate scenario models? What assumptions are being used to generate the pathways we see, especially around policies and technologies? -Understanding: how do we explain these scenarios to our businesspeople and our executives? Do we fully understand the implications and appropriate use of these scenarios? https://lnkd.in/eTSdtHS4 #climate #climatescenarios #ngfs #climatestresstesting #sustainability #tcfd #issb #esg #netzero #climatemodels #iams #ipcc #physicalrisk #transitionrisk #risk #climatefinance #environment #finance

As we’ve discussed in previous posts, regulators expect climate risk to be integrated into ECL frameworks. ECL requires scenario weighting. But on the climate side, we don’t have these weights ready. This is where EDHEC’s research offers a practical breakthrough (https://lnkd.in/e3bNRgQM). It developed a way to introduce scenario probabilities for climate risk. This is a great starting point for aligning climate risk with ECL-style calculations. While there are many useful outputs from this research, here I want to focus on Step 1: assigning scenario probabilities and comparing this to what we intuitively do in ECL. In typical ECL modelling: - The baseline scenario gets most of the probability weight (often 70-80%), - With downside and upside scenarios splitting the rest. But in the climate space, this intuition breaks down. According to EDHEC scenario probabilities: -“Climate Catastrophe” receives 57.5% probability. -The “Baseline” scenario receives only 5%. In other words, climate catastrophe becomes the “new baseline”. The whole risk modelling approach needs to change. Currently, we are modelling climate risk as if catastrophe is a tail event, while sleepwalking into catastrophe being the most probable scenario. Other scenarios, e.g. net zero, delayed transition, climate distress, should be modelled in comparison to this “new baseline” of catastrophe, not the other way around. This is not just a technical nuance. If we are serious about integrating climate risk into ECL, this reframing is necessary.

𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱: 𝗨𝗡𝗘𝗣 𝗙𝗜 & 𝗚𝗹𝗼𝗯𝗮𝗹 𝗖𝗿𝗲𝗱𝗶𝘁 𝗗𝗮𝘁𝗮 𝗦𝘂𝗿𝘃𝗲𝘆 𝘀𝗵𝗼𝘄𝘀 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸 𝗶𝘀 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗶𝘀𝗸. 𝗕𝗮𝗻𝗸𝘀 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗻𝗼𝘁 𝗱𝗼𝗶𝗻𝗴 𝗲𝗻𝗼𝘂𝗴𝗵. Regulators have raised the bar for climate disclosure, but banks are still a long way from embedding climate risks into their business. 🔸  Collateral value adjustment remains low. Just 12% of banks adjust collateral for physical risk, and only 4% for transition risk. 🔸  ESG integration is fragmented. Over half of banks have internal ESG scoring, but there's no consensus. Few banks tie ESG directly into credit decisions, methods vary and full integration into ratings is rare. 🔸  Scenario analysis is widespread, but validation is not. 85% of banks use NGFS climate scenarios, but fewer than 5% regularly backtest climate impacts in credit models. 🔸  Incorporating climate into key credit metrics is lagging. Metrics like Probability of Default (PD), Loss Given Default (LGD) and Internal ratings-based (IRB) models remain inconsistently or only partially integrated with climate risk considerations. 🔸  Adjustments to ECL (Expected Credit Loss), RWA (Risk-Weighted Assets) and Economic Capital remain low and still in early, exploratory stages. Most banks report financial impact for climate risks between 0-2.5%. For transition risks, this increases to 5-10% but this is not reflected in key metrics. There remains a significant gap in quantification and adoption for capital impact. 🔸 Many banks still rely on expert judgement over data-driven models. While climate risk is assessed across major portfolios, most banks depend on judgement, due to data and methodological constraints. 🔸 Data quality & granularity are key obstacles. Obstacles to robust, forward-looking climate data (especially Scope 3) push banks toward proxies and general averages. 𝗠𝘆 𝗧𝗮𝗸𝗲 The UNEP report shows the banking sector still struggles to consistently quantify and integrate climate risk in credit portfolios, capital models, and client processes. Most banks remain reliant on expert judgment and qualitative overlays, mainly due to the lack of granular, forward-looking data and practical scenario analytics. Scenario analysis exists but is rarely deeply embedded in major decisions, and backtesting is the exception. This is where data-driven platforms are critical. Delivering granular scenario analysis, data harmonisation, and dynamic simulation enables banks to move beyond overlays to defensible, auditable climate risk insights. The leaders will be the banks who industrialise scenario analytics and make regulatory pressure a driver of real competitive advantage. #ClimateRisk #CreditRisk #Banking #ESG #RiskManagement #SustainableFinance Source: https://lnkd.in/eC4S8mRN ___________ 𝘛𝘩𝘦𝘴𝘦 𝘷𝘪𝘦𝘸𝘴 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯. 𝘍𝘰𝘭𝘭𝘰𝘸 𝘮𝘦 𝘰𝘯 𝘓𝘪𝘯𝘬𝘦𝘥𝘐𝘯: Scott Kelly