Myth-busting Climate risk analytics & scenario modeling: 10 misconceptions holding teams back

By 2025, over 80% of the world's largest public companies are subject to mandatory climate-related financial disclosure requirements, yet a recent MSCI analysis reveals that fewer than 35% have implemented quantitative scenario analysis capable of meeting regulatory scrutiny. The global climate risk analytics market reached $4.2 billion in 2024 and is projected to exceed $12 billion by 2030, driven by TCFD-aligned mandates, central bank stress testing requirements, and investor pressure. Despite this explosive growth, persistent misconceptions about what climate risk analytics can and cannot deliver continue to hamper effective implementation across financial institutions, corporates, and asset managers.

Why It Matters

Climate risk analytics has transitioned from a voluntary sustainability exercise to a core component of enterprise risk management and regulatory compliance. As of Q4 2024, TCFD-aligned disclosures are mandatory or expected in 47 jurisdictions covering approximately 90% of global GDP. The European Union's Corporate Sustainability Reporting Directive (CSRD) requires over 50,000 companies to conduct climate scenario analysis by 2026. In Asia-Pacific, Japan's Prime Minister Office mandated TCFD reporting for listed companies in 2023, while Singapore's MAS requires climate stress testing for all major financial institutions.

The Network for Greening the Financial System (NGFS) reports that 134 central banks and supervisors are now incorporating climate scenarios into their prudential frameworks. Meanwhile, asset owners controlling over $130 trillion in AUM have committed to net-zero portfolio alignment, requiring granular climate risk metrics at the security level. Data providers have responded: Bloomberg, MSCI, S&P Trucost, Moody's, and specialized firms like Jupiter Intelligence and Cervest now offer competing climate analytics platforms, each with distinct methodologies, coverage, and limitations.

Yet adoption remains uneven. A 2024 survey by the Global Association of Risk Professionals found that 62% of risk managers consider their organization's climate risk capabilities "immature" or "developing." Common barriers include model opacity, scenario confusion, data fragmentation, and unrealistic expectations about precision. Understanding what climate risk analytics actually delivers—and what it does not—is essential for teams seeking to build defensible, decision-useful capabilities.

Key Concepts

Physical Risk refers to financial impacts from climate-driven hazards such as floods, wildfires, hurricanes, heat stress, and sea-level rise. Physical risk models combine geospatial asset data with climate projections to estimate expected losses, business interruption, and asset impairment under various warming scenarios. Acute risks represent discrete events; chronic risks represent gradual shifts like rising temperatures or changing precipitation patterns.

Transition Risk captures financial exposure to policy, technology, market, and reputational shifts associated with the low-carbon transition. Carbon pricing, stranded assets, demand destruction for fossil fuels, and litigation are core transition risk drivers. Transition risk modeling requires assumptions about decarbonization pathways, regulatory trajectories, and competitive dynamics.

Scenario Analysis is a forward-looking technique that explores how different climate futures—typically ranging from orderly 1.5°C transitions to disorderly 3°C+ warming—might affect portfolios, operations, or strategy. Scenarios are not forecasts; they are internally consistent narratives used to stress-test resilience and identify vulnerabilities.

Climate Value-at-Risk (Climate VaR) extends traditional VaR methodology to estimate potential portfolio losses attributable to physical and transition risks over defined time horizons. Climate VaR figures are highly sensitive to underlying assumptions and should be interpreted as indicative rather than precise.

Stranded Assets are capital investments that lose value prematurely due to climate-related factors, particularly fossil fuel reserves, carbon-intensive infrastructure, and real estate in high-risk zones. Identifying stranded asset exposure requires integrating physical and transition risk perspectives.

Climate Risk Analytics KPIs

Metric	Definition	Benchmark Range	Data Source Examples
Portfolio Physical Risk Score	Aggregate exposure to acute and chronic climate hazards	0-100 (low-high)	Jupiter, Moody's RMS, Munich Re
Transition Risk Intensity	Carbon footprint weighted by transition scenario impact	0-500+ tCO2e/$M revenue	MSCI, S&P Trucost, ISS ESG
Climate VaR (Physical)	Estimated portfolio loss under physical risk scenarios	2-15% (2050 horizon, 3°C scenario)	MSCI, BlackRock Aladdin
Climate VaR (Transition)	Estimated portfolio loss under transition scenarios	5-40% (disorderly transition)	MSCI, Ortec Finance
Implied Temperature Rise (ITR)	Portfolio alignment to warming pathways	1.5°C - 4°C+	MSCI, S&P, right. based science
Asset-Level Hazard Exposure	Percentage of assets in high-risk zones	Varies by hazard (e.g., 5-25% flood exposure)	Cervest, Jupiter, Four Twenty Seven
Scenario Coverage Ratio	Share of portfolio with quantitative scenario analysis	Target: >80% by AUM	Internal tracking
Data Completeness Score	Percentage of holdings with climate data coverage	70-95% (large cap higher)	Provider-specific

What's Working and What Isn't

What's Working

Improved Climate Models and Downscaling: Climate science has advanced significantly, with CMIP6 models offering higher resolution and better representation of extreme events. Commercial providers now offer localized hazard projections at the 100-meter scale, enabling asset-level physical risk assessment for real estate, infrastructure, and supply chain nodes.

Portfolio Stress Testing Integration: Leading financial institutions have integrated climate scenarios into existing enterprise risk frameworks. Banks in Europe and Asia-Pacific routinely run climate stress tests aligned with NGFS scenarios, generating capital adequacy insights and identifying concentration risks. This normalization of climate stress testing has accelerated capability building.

Regulatory Standardization: The proliferation of TCFD-aligned mandates, coupled with ISSB sustainability standards (IFRS S1 and S2), is driving methodological convergence. While significant variation remains, common scenario sets (NGFS, IEA Net Zero) and disclosure frameworks reduce the "wild west" dynamic that characterized early climate risk efforts.

Sector-Specific Materiality Frameworks: SASB materiality maps and TCFD sector guidance have clarified which climate risks matter most for specific industries. This allows teams to prioritize analytics investments and avoid boiling-the-ocean approaches.

What Isn't Working

Model Uncertainty and Comparability: Climate VaR figures from different providers can vary by 3-5x for the same portfolio due to divergent methodologies, scenario assumptions, and hazard models. This undermines comparability and creates confusion for boards and regulators expecting precise figures.

Data Gaps and Quality Issues: Scope 3 emissions data—critical for transition risk assessment—remains incomplete and often based on sector averages rather than company-specific measurement. Physical risk models require geocoded asset data that many companies cannot provide, forcing reliance on headquarters-based proxies.

Short-Termism and Discount Rate Debates: Climate risks materialize over decades, but financial decision-making often operates on 1-5 year horizons. Discounting future climate damages to present value can make catastrophic long-term risks appear trivial, distorting capital allocation.

Scenario Misinterpretation: Non-technical stakeholders frequently confuse scenarios with predictions, leading to inappropriate conclusions. A 2°C scenario is not a forecast of likely outcomes; it is a conditional "what if" analysis that requires careful communication.

Key Players

Established Leaders

MSCI offers the Climate Value-at-Risk suite covering physical and transition risks across global equities and fixed income. MSCI's Implied Temperature Rise metric is widely used for portfolio alignment assessments.

S&P Global Trucost provides granular carbon and environmental data feeding into transition risk models. Trucost's physical risk analytics cover floods, heat stress, water scarcity, and hurricanes at the asset level.

Moody's acquired RMS and Four Twenty Seven, combining catastrophe modeling expertise with climate analytics. Moody's now offers integrated physical risk scores and scenario-based loss estimates for banks and insurers.

Bloomberg integrates climate data into the Bloomberg Terminal, enabling portfolio-level physical and transition risk screening. Bloomberg's partnership with MSCI and in-house data science team supports growing institutional demand.

Emerging Startups

Jupiter Intelligence specializes in high-resolution physical risk analytics, using machine learning to downscale climate projections and model compound hazards. Jupiter's platform serves real estate, infrastructure, and corporate supply chain use cases.

Cervest offers an asset-level climate intelligence platform with forward-looking ratings for physical climate risk. The company's EarthScan product provides transparent, science-based hazard assessments.

Sust Global provides API-driven physical risk data for integration into financial workflows, focusing on scalability and developer-friendly deployment.

Key Investors and Funders

Generation Investment Management, co-founded by Al Gore, has backed multiple climate data and analytics companies. Breakthrough Energy Ventures invests in climate tech infrastructure, including risk analytics platforms. Government-backed initiatives like the UK's Green Finance Institute and Singapore's Project Greenprint fund climate data standardization efforts.

10 Misconceptions About Climate Risk Analytics

Misconception 1: Climate VaR Provides Precise Loss Estimates

Reality: Climate VaR figures are indicative ranges, not precise predictions. A portfolio showing 8% Climate VaR under a 3°C scenario might reasonably range from 4-15% depending on model assumptions. The value lies in relative comparisons and trend identification, not absolute precision.

Misconception 2: Physical and Transition Risks Are Separate Problems

Reality: Physical and transition risks interact dynamically. A company heavily invested in coastal infrastructure (physical risk) may face compounding exposure if carbon pricing increases operating costs (transition risk). Integrated scenario analysis captures these dependencies; siloed approaches miss material interactions.

Misconception 3: Scenario Analysis Is a One-Time Exercise

Reality: Climate science evolves, regulations shift, and portfolio compositions change. Leading practitioners refresh scenario analysis annually, update hazard data as new climate models emerge, and stress-test against emerging scenarios (e.g., delayed transition, carbon border adjustments). Static analysis becomes stale within 18-24 months.

Misconception 4: More Scenarios Mean Better Analysis

Reality: Analyzing dozens of scenarios can overwhelm decision-makers without improving insight quality. Most institutions benefit from 3-5 well-chosen scenarios spanning orderly transition, disorderly transition, and hot house world outcomes. Depth of analysis matters more than breadth of scenario count.

Misconception 5: Climate Risk Analytics Requires Perfect Data

Reality: Waiting for perfect data delays action indefinitely. Successful implementations use materiality-based prioritization, starting with high-impact sectors and asset classes where data quality is adequate. Progressive data improvement strategies address gaps over time rather than demanding perfection upfront.

Misconception 6: Off-the-Shelf Solutions Meet All Needs

Reality: Commercial climate risk platforms provide valuable starting points but rarely address institution-specific portfolios, risk appetite, or strategic context without customization. Effective implementations combine vendor data with internal expertise, sector knowledge, and governance integration.

Misconception 7: Climate Risk Is Only Material for Long Horizons

Reality: Physical climate impacts are already affecting asset values, insurance costs, and operational continuity. The 2024 hurricane season caused over $100 billion in insured losses. Heat stress reduced labor productivity by 3-5% in exposed sectors during 2023-2024 summers. Near-term materiality is demonstrable, not speculative.

Misconception 8: Alignment Metrics Like ITR Are Sufficient for Risk Management

Reality: Implied Temperature Rise and portfolio alignment metrics measure directional compatibility with climate goals but do not capture financial risk. A portfolio aligned to 2°C may still face significant physical risk exposure or transition risk concentration. Alignment and risk metrics serve complementary purposes.

Misconception 9: Central Bank Stress Tests Set the Standard

Reality: Regulatory stress tests (e.g., ECB, BoE, APRA) establish minimum compliance thresholds but are not designed for strategic decision-making. Prudential exercises often use simplified assumptions and standardized scenarios that may not reflect institution-specific risk profiles. Internal capabilities should exceed regulatory minimums.

Misconception 10: Climate Risk Analytics Is Purely a Risk Function Responsibility

Reality: Climate risk insights inform strategy, capital allocation, product development, and stakeholder engagement. Effective implementations involve cross-functional teams including sustainability, finance, operations, and business units. Risk functions provide methodology and governance; impact requires enterprise-wide integration.

Action Checklist

Audit current climate risk data sources and identify coverage gaps across physical risk, transition risk, and Scope 3 emissions
Establish a cross-functional climate risk working group including risk, sustainability, finance, and business unit representatives
Select 3-5 core scenarios aligned with NGFS and TCFD guidance, documenting assumptions and narrative rationale
Pilot asset-level physical risk assessment on highest-exposure portfolio segments before full-scale rollout
Develop internal capability to interpret and challenge vendor model outputs rather than treating them as black-box oracles
Integrate climate scenario results into existing risk appetite statements and capital planning processes
Establish annual refresh cycles for scenario analysis with triggers for interim updates based on material developments
Create board-level reporting that contextualizes Climate VaR and physical risk metrics with appropriate uncertainty ranges

FAQ

Q: How should we communicate Climate VaR uncertainty to boards and regulators? A: Present Climate VaR as a range rather than a point estimate, explicitly noting key assumptions and model limitations. Use sensitivity analysis to show how results change under different parameter choices. Emphasize that Climate VaR supports relative prioritization and trend monitoring rather than precise loss forecasting. Regulators increasingly expect transparency about methodology and uncertainty; confident precision signals unsophisticated analysis.

Q: Which climate scenarios should we prioritize for initial analysis? A: Start with the NGFS core scenarios: Orderly (Net Zero 2050), Disorderly (Delayed Transition), and Hot House World (Current Policies). These span the plausible outcome space and align with regulatory expectations. Add sector-specific scenarios (e.g., IEA Sustainable Development Scenario for energy companies) as capabilities mature. Avoid scenario proliferation that dilutes analytical depth.

Q: How do we address Scope 3 data gaps that undermine transition risk analysis? A: Use a tiered approach: apply company-reported data where available, supplement with modeled estimates from providers like CDP and S&P Trucost, and use sector averages for remaining gaps with appropriate disclosure. Engage portfolio companies on data improvement and prioritize high-emitting sectors for enhanced due diligence. Accept that Scope 3 precision will remain limited and focus on materiality-weighted estimates.

Q: What is the appropriate time horizon for climate risk analysis? A: Climate risk analysis typically spans multiple horizons: near-term (2025-2030) for operational and regulatory planning, medium-term (2030-2040) for capital investment decisions, and long-term (2040-2050+) for strategic resilience assessment. Physical risks require longer horizons to capture chronic changes; transition risks may materialize faster under disorderly policy scenarios. Match horizons to decision contexts rather than applying a single default.

Q: How do we reconcile conflicting outputs from different climate risk vendors? A: Expect divergence and treat it as informative rather than problematic. Map methodological differences: which hazards are covered, what climate models underpin projections, how transition pathways translate to financial impacts. Use multiple vendors as ensemble inputs rather than selecting a single "correct" answer. Document reconciliation approaches and present boards with range-based conclusions that acknowledge legitimate methodological uncertainty.

Sources

Task Force on Climate-related Financial Disclosures. (2024). 2024 Status Report: TCFD Recommendations Implementation. TCFD Secretariat.
Network for Greening the Financial System. (2024). NGFS Climate Scenarios for Central Banks and Supervisors: Technical Documentation. NGFS.
MSCI ESG Research. (2024). Climate Value-at-Risk Methodology: Physical and Transition Risk Modeling. MSCI Inc.
Intergovernmental Panel on Climate Change. (2023). AR6 Synthesis Report: Climate Change 2023. IPCC.
Global Association of Risk Professionals. (2024). Climate Risk Management Survey: State of Practice. GARP.
International Sustainability Standards Board. (2024). IFRS S2: Climate-related Disclosures. IFRS Foundation.
S&P Global Trucost. (2024). Physical Risk Analytics: Methodology and Data Guide. S&P Global.
Jupiter Intelligence. (2024). ClimateScore Global: Technical Documentation for Physical Risk Assessment. Jupiter Intelligence Inc.