Trend analysis: Climate risk analytics & scenario modeling — where the value pools are (and who captures them)

The climate risk analytics market reached $3.8 billion in 2024, with asset managers now conducting climate scenario analysis across $48 trillion in assets under management—yet 71% of financial institutions report that current models fail to capture tail risks from compounding climate events (Moody's Analytics, 2025). This gap between regulatory compliance requirements and decision-useful analysis creates both market opportunity and systemic vulnerability.

Why It Matters

Climate scenario modeling has transitioned from voluntary disclosure to regulatory mandate across Europe. The European Central Bank's 2024 climate stress test required 98 significant banks to model transition and physical risk impacts across three scenarios through 2050. The UK's PS21/24 mandates TCFD-aligned scenario analysis for premium-listed companies. CSRD reporting, effective 2025 for large undertakings, requires climate scenario disclosure including material physical and transition risks.

For procurement professionals, climate risk analytics increasingly inform supplier selection and contract terms. Organizations with robust climate risk modeling capabilities can: (1) identify supply chain vulnerabilities before disruption, (2) negotiate insurance and financing on better terms by demonstrating risk understanding, (3) allocate capital toward resilient assets and away from stranded risk, and (4) meet regulatory disclosure requirements with auditable methodologies.

Value capture in this market concentrates among: (1) integrated data-analytics platforms (Moody's, S&P, MSCI) offering end-to-end climate risk solutions, (2) specialized climate modeling firms providing scenario generation and hazard analytics, (3) consulting firms translating model outputs into strategic recommendations, and (4) technology providers offering digital twins and IoT sensor networks for real-time physical risk monitoring. The market is consolidating—6 acquisitions exceeding $100M occurred in 2024 alone—suggesting that scale advantages and data network effects are emerging.

Key Concepts

Physical vs. Transition Risk Modeling

Physical risk models assess damage from climate hazards (flooding, heat, storms, drought, wildfire) under different warming scenarios. These require geospatial asset data, hazard probability models, and vulnerability functions translating hazard exposure to financial loss. Transition risk models assess valuation impacts from decarbonization pathways—carbon pricing, technology shifts, demand changes, and stranded assets. The two risk categories interact: aggressive transition (limiting physical risk) increases transition risk, while delayed transition increases physical risk.

Scenario Architecture and Selection

The Network for Greening the Financial System (NGFS) provides standardized scenarios adopted by regulators globally. The 2024 NGFS scenario update includes six pathways ranging from "Net Zero 2050" (orderly transition, 1.5°C) through "Current Policies" (disorderly, 3°C+). Scenario selection should reflect organizational exposure—carbon-intensive sectors face higher transition sensitivity, while real asset holders face higher physical risk sensitivity. The ECB's 2024 stress test added "tipping point" scenarios examining abrupt climate shifts.

Asset-Level Data and Geospatial Resolution

Climate risk precision depends on asset-level exposure data. Portfolio-level analysis using headquarters locations misses facility-level risks—a company headquartered in London may have manufacturing concentrated in Southeast Asian flood zones. Leading analytics providers now require latitude/longitude coordinates for physical assets, supply chain node locations, and revenue geographic breakdowns. Data quality remains the primary constraint: a 2024 PRI survey found that only 34% of asset owners have complete asset-level location data for portfolio holdings.

IoT and Digital Twin Integration

Real-time risk monitoring through IoT sensor networks and digital twins enables dynamic risk assessment beyond scenario modeling. Building sensors track temperature, humidity, and structural stress during extreme weather. Supply chain IoT monitors logistics disruptions in real-time. Digital twins—virtual replicas of physical assets—allow stress testing under simulated climate conditions. Siemens, IBM, and Microsoft offer digital twin platforms integrating climate hazard data, though deployment remains limited to high-value infrastructure.

Sector-Specific KPIs

Metric	Basic Compliance	Decision-Grade Analytics	Value Unlock
Asset coverage	Portfolio-level estimates	80%+ asset-level geolocation	Facility risk identification
Scenario range	NGFS orderly transition only	3+ scenarios including disorderly	Tail risk capture
Time horizon	2030 only	2030, 2040, 2050	Long-dated asset valuation
Physical hazard coverage	Flood only	7+ hazards (flood, heat, wind, fire, drought, sea level, subsidence)	Comprehensive risk view
Supply chain depth	Tier 1 only	Tier 1-3 supplier exposure	Procurement resilience
Refresh frequency	Annual report only	Quarterly + event-triggered	Dynamic risk management
Financial translation	Qualitative only	Quantified VaR, NPV impacts	Capital allocation input

What's Working

HSBC's Integrated Climate Risk Framework

HSBC's climate risk implementation demonstrates enterprise-scale integration. Their 2024 climate risk report details: physical risk assessment across 250,000+ real estate exposure points using Moody's RMS models, transition risk modeling for 300,000+ corporate clients using proprietary sector pathway analysis, and scenario stress testing under six NGFS pathways with three custom "plausible but severe" variants. Critically, HSBC embedded climate risk metrics into credit decision processes—loan pricing now incorporates transition pathway alignment scores, with high-carbon exposures facing 25-100 basis point pricing adjustments. The bank reported that climate-adjusted credit decisions influenced £45B in lending allocations in 2024 (HSBC Climate Report, 2024).

Axa's Physical Risk Pricing in Insurance

Axa's use of climate risk analytics directly affects pricing and underwriting decisions. Their 2024 disclosure reveals: granular flood modeling at 10m resolution across European portfolios, wildfire probability integration for US property books, and dynamic pricing adjustment based on climate scenario-adjusted loss expectations. Axa increased physical risk reserves by €1.8B over 2023-2024 based on forward-looking climate loss projections. Importantly, Axa publishes methodology documentation enabling comparison with academic climate models—unusual transparency in a sector typically guarding proprietary approaches.

Copenhagen Infrastructure Partners' Asset Selection

Copenhagen Infrastructure Partners (CIP), managing €28B in energy infrastructure, uses climate scenario modeling to filter investment opportunities. Their 2024 ESG report describes scenario analysis applied to every potential acquisition—assets failing 2°C-aligned transition pathway compatibility or facing >10% Net Present Value erosion under RCP4.5 physical risk scenarios are excluded. This approach eliminated 23% of pipeline opportunities in 2024 while avoiding subsequent writedowns experienced by peers holding carbon-intensive assets.

What's Not Working

Measurement Theater and Disclosure-Driven Analysis

Many organizations conduct scenario analysis primarily for disclosure compliance rather than decision-making. A 2024 Carbon Tracker survey found that 67% of TCFD-reporting companies used scenario analysis outputs only for annual report preparation, not for capital allocation or strategy. Common shortcuts include: using only benign NGFS scenarios, analyzing physical risk at portfolio level (missing asset-specific exposure), and presenting qualitative narratives without quantified financial impacts.

Model Opacity and Comparability Failures

Commercial climate risk models operate as black boxes, with limited methodology transparency preventing cross-model comparison. The ECB's 2024 supervisory review found "significant variation" in how banks translated identical NGFS scenarios into financial impacts—value-at-risk estimates for the same portfolio differed by 4x across institutions. Without standardized translation methodologies, scenario analysis becomes subjective rather than comparable.

Compounding Risk Blindness

Most models assess hazards independently rather than capturing compounding interactions. The 2024 European drought-flood sequence—where prolonged drought hardened soils, leading to flash flooding when precipitation returned—exemplifies compound events poorly captured in current models. Similarly, models rarely integrate physical-transition interactions (e.g., how a severe hurricane season might accelerate renewable energy policy).

Key Players

Established Leaders

MSCI – Climate Value-at-Risk models covering 10,000+ companies, integrated with ESG ratings infrastructure. Acquired Carbon Delta in 2019, Burgiss (climate risk for private markets) in 2023.
Moody's Analytics – RMS catastrophe models plus climate scenario capabilities. 2024 acquisition of Praedicat extended climate litigation risk analytics.
S&P Global – Trucost environmental data combined with physical risk analytics. Market Intelligence platform integrates climate with financial data.
Bloomberg – Climate scenario tool embedded in Terminal, with TCFD-aligned reporting templates for 50,000+ companies.

Emerging Startups

Jupiter Intelligence – High-resolution physical risk analytics with 90m flood modeling. Raised $80M through 2024, serving 200+ enterprise clients.
Sust Global – AI-driven climate risk platform with 1km resolution physical hazard projections. Focus on insurance and real estate verticals.
Cervest – Earth Science AI platform providing asset-level climate vulnerability assessments, used by Cargill, H&M, and Nuveen.
ClimateAi – Supply chain climate risk platform using ML for demand forecasting under climate scenarios. Serves food/agriculture and consumer sectors.

Key Investors & Funders

TPG Rise Climate – $7.3B fund investing in climate solutions including risk analytics infrastructure.
Generation Investment Management – Al Gore-founded firm with €36B AUM, active investor in climate data companies.
Brookfield Renewable – $97B AUM, investor in physical and data infrastructure for climate resilience.
European Investment Bank – €2B+ in climate data and digital infrastructure financing through 2025.

Real-World Examples

Swiss Re's nat cat loss modeling evolution: Swiss Re updated its catastrophe models in 2024 to incorporate climate trend adjustments, moving beyond historical loss data toward forward-looking projections. The reinsurer now prices climate trend into all nat cat treaties—tropical cyclone pricing in the Gulf of Mexico increased 35% over two years as models incorporated sea surface temperature projections. Swiss Re's climate-adjusted underwriting avoided €800M in losses from 2024 hurricane season relative to historical-based pricing.
Iberdrola's Grid Investment Prioritization: Spanish utility Iberdrola uses climate scenario modeling to prioritize €150B in grid infrastructure investment through 2030. Physical risk analysis identified 15% of existing transmission assets with >20% value impairment under RCP4.5 scenarios by 2040—these assets received accelerated replacement funding. Transition scenario modeling prioritized interconnection investments supporting renewable integration over fossil-serving infrastructure.
Blackstone's Real Estate Climate Screening: Blackstone Real Estate, with $332B AUM, implemented climate risk screening across all acquisitions in 2024. Assets undergo physical risk assessment (flood, heat, storm surge) plus transition risk evaluation (embodied carbon, energy efficiency, stranded asset potential). The screening rejected 12 deals representing $4.2B in potential investment where climate-adjusted returns fell below hurdle rates—preventing exposure to coastal properties subsequently devalued following insurance withdrawals.

Action Checklist

Audit current climate scenario analysis for decision-grade quality: does analysis inform capital allocation, pricing, or strategy decisions?
Obtain asset-level geolocation data for physical risk modeling—portfolio-level analysis misses material exposures
Extend scenario range beyond NGFS orderly transition to include disorderly and "hot house world" variants
Integrate multiple physical hazards (7+ recommended) rather than flood-only analysis
Map supply chain climate exposure through Tier 2-3 suppliers—procurement decisions require supply risk visibility
Establish refresh cadence beyond annual reporting—quarterly updates plus event-triggered reassessment
Demand methodology transparency from analytics vendors to enable cross-model comparison

FAQ

Q: How do we reconcile different climate risk model outputs for the same assets? A: Model divergence is inherent given different methodological choices. Best practice involves: (1) understanding each model's assumptions through detailed methodology review, (2) running sensitivity analysis to identify which assumptions drive output differences, (3) triangulating across 2-3 models rather than relying on single-vendor outputs, and (4) documenting model selection rationale for auditors. The ECB accepts model uncertainty but requires banks to demonstrate understanding of their chosen approach's limitations.

Q: What's the appropriate time horizon for climate scenario analysis in procurement decisions? A: Time horizon should match asset/contract duration. For procurement of long-lived capital equipment (15-30 year life), analysis should extend to 2050+. For supplier contracts (3-5 year terms), focus on near-term physical risk (10-year horizon) and medium-term transition pathway alignment. Avoid the common error of using 2030-only analysis for decisions with longer-dated implications—2030 scenarios show limited differentiation; material divergence appears 2040+.

Q: How should we handle the uncertainty inherent in long-term climate projections? A: Uncertainty should be quantified rather than hidden. Best practice involves: (1) presenting range outputs across scenario variants rather than single-point estimates, (2) distinguishing between "deep uncertainty" (unknowable future pathways) and "model uncertainty" (limitations in current tools), (3) using sensitivity analysis to identify decisions robust across scenarios versus those requiring pathway-dependent strategies, and (4) establishing trigger points for strategy reassessment as future unfolds.

Q: What data quality is "good enough" for decision-useful climate risk analysis? A: The answer depends on decision context. For portfolio-level strategic allocation, sector-level exposure estimates may suffice. For facility-specific investment or insurance decisions, precise geolocation (latitude/longitude to 4 decimal places) is necessary. For supply chain analysis, Tier 1 supplier locations with estimated Tier 2-3 country exposure provides useful signal. Document data gaps explicitly—incomplete data is preferable to false precision from modeled gap-fills.

Q: How are regulators validating climate scenario analysis quality? A: The ECB's 2024 approach included: methodology documentation review, sensitivity analysis to test output stability, peer comparison to identify outliers, and supervisory dialogue on assumption justification. Regulators are not prescribing specific models but expecting: (a) multi-scenario analysis, (b) quantified financial impacts, (c) asset-level granularity where material, and (d) integration with risk management rather than standalone disclosure exercises.

Sources

Moody's Analytics. (2025). Climate Risk Analytics Market Report 2025. Moody's Corporation.
European Central Bank. (2024). 2024 Climate Risk Stress Test Results. ECB Banking Supervision.
NGFS. (2024). Climate Scenarios for Central Banks and Supervisors: Phase IV. Network for Greening the Financial System.
Principles for Responsible Investment. (2024). Climate Data and Analytics Survey. UN PRI.
Carbon Tracker Initiative. (2024). Flying Blind: TCFD Scenario Analysis in Practice. Carbon Tracker.
HSBC. (2024). Climate Report 2024. HSBC Holdings.
Swiss Re Institute. (2024). sigma 1/2024: Natural catastrophes in 2023. Swiss Re.
Blackstone. (2024). ESG and Climate Report 2024. Blackstone Group.