Sustainability Atlas
Climate Tech & Data·11 min read··...

Trend watch: climate risk analytics & scenario modeling in 2026

from pilots to scale: the operational playbook. Focus on an emerging standard shaping buyer requirements.

The climate risk analytics market reached $9.84 billion in 2024 and is projected to grow at 17.5% CAGR through 2033, with AI and machine learning now comprising 36% of new systems and improving predictive accuracy by 27% (Business Research Insights, 2024). As regulatory frameworks converge around TCFD-aligned disclosure requirements, product teams building climate risk solutions face an inflection point: the operational playbook for scaling from pilot to enterprise deployment is now crystallizing around emerging buyer standards that will define market winners through 2028.

Why It Matters

Climate-related economic losses exceeded $310 billion globally in 2024, intensifying demand for sophisticated risk analytics that can translate physical and transition hazards into actionable financial projections. For product and design teams in the European market, understanding the operational requirements for enterprise deployment is essential—52% of Fortune 100 companies now use multi-scenario planning for infrastructure investments, and this expectation is rapidly cascading to mid-market buyers.

The TCFD framework, though officially disbanded in October 2023, has been integrated into regulatory mandates across more than 60 nations. Oversight now sits with the IFRS Foundation, creating a convergence pathway between voluntary disclosure frameworks and mandatory reporting requirements like CSRD in Europe. This regulatory harmonization is reshaping buyer requirements: procurement teams increasingly demand platforms that can generate TCFD-aligned outputs natively, reducing the translation burden between analytics tools and disclosure documentation.

Financial services remain the dominant buyer segment, representing 39.83% of market share, with 61% of US banks having embedded climate risk analytics into ESG frameworks by 2024. However, the fastest growth is occurring in energy and utilities (33.5% CAGR) as operational planning increasingly requires climate-informed asset valuation and capital allocation decisions.

Key Concepts

Physical vs. Transition Risk Segmentation

Climate risk analytics bifurcates into two primary categories, each requiring distinct data architectures and modeling approaches:

Physical Risks (44.81% market share): Floods, hurricanes, wildfires, sea-level rise, and chronic heat stress. Physical risk modeling requires integration of geospatial data, climate projections (typically IPCC SSP scenarios), and asset-level exposure assessments. Real-time satellite data integration increased 31% during 2024 for flood and wildfire prediction capabilities.

Transition Risks (31.65% CAGR—fastest growing): Carbon pricing mechanisms, regulatory shifts, technology disruption, and market repricing of carbon-intensive assets. Transition risk modeling demands policy scenario libraries, sector-specific decarbonization pathways, and financial modeling of stranded asset potential.

Risk CategoryData RequirementsTypical Time HorizonsKey Output Metrics
Physical (Acute)Satellite imagery, weather models, asset coordinates1-10 yearsBusiness interruption days, repair costs
Physical (Chronic)Climate projections, sea-level data, temperature trends10-50 yearsAsset value impairment, operational cost increases
Transition (Policy)Carbon price scenarios, regulatory databases5-30 yearsStranded asset exposure, compliance costs
Transition (Market)Technology cost curves, demand elasticities5-20 yearsRevenue at risk, competitive positioning

The TCFD Scenario Analysis Framework

Product teams building for enterprise buyers must support the six-step TCFD scenario analysis workflow:

  1. Governance setup: Integration with strategic planning and risk management processes
  2. Exposure identification: Mapping organizational climate risks and opportunities across physical and transition dimensions
  3. Scenario selection: Supporting multiple climate futures (typically 1.5°C/2°C pathways vs. 4-5°C warming)
  4. Impact evaluation: Financial modeling of revenue, cost, asset value, and capital allocation effects
  5. Response identification: Enabling portfolio adjustments, capability development, and business model adaptation planning
  6. Disclosure documentation: Generating auditable outputs aligned with TCFD/ISSB requirements

Cloud-Native Architecture Requirements

Cloud-based platforms dominate with 49.79% market share and the fastest growth trajectory (32.58% CAGR). Enterprise buyers increasingly require SaaS delivery models that enable rapid deployment, automatic updates for evolving scenario libraries, and elastic compute capacity for portfolio-wide stress testing. On-premise deployment persists primarily for institutions with sensitive financial data governance requirements.

What's Working

AI-Enhanced Predictive Modeling

The integration of machine learning into climate risk systems has moved beyond experimentation to production deployment. AI-enhanced systems demonstrate 27% improvement in predictive accuracy for physical risk events compared to traditional statistical approaches. Key success patterns include:

Example: Jupiter Intelligence—The climate analytics platform processes petabytes of climate data through proprietary ML models to generate asset-level risk scores for more than 500 institutional clients. Their 2024 expansion included hyperlocation analysis at 90-meter resolution, enabling building-specific vulnerability assessments that insurers and real estate investors require for underwriting decisions.

Multi-Scenario Comparative Dashboards

Product teams that enable side-by-side scenario comparison with clear financial impact quantification are winning enterprise procurement processes. The most successful implementations surface:

  • Revenue/cost trajectories across 1.5°C, 2°C, and 4°C scenarios
  • Portfolio-level exposure heatmaps by geography and sector
  • Transition pathway alignment metrics with configurable benchmarks
  • Executive-ready visualization for board reporting

Example: MSCI Climate Solutions—Their Climate Value-at-Risk (CVaR) platform enables financial institutions to model portfolio impacts across multiple warming scenarios simultaneously. By 2024, the platform was deployed across >$40 trillion in assets under management globally, with European asset managers representing the fastest-growing customer segment driven by SFDR and CSRD requirements.

Regulatory-Native Reporting Modules

Platforms that generate disclosure-ready outputs—mapped to TCFD pillars, ISSB standards, and CSRD ESRS requirements—command significant pricing premiums (20-35% over analytics-only solutions). This reflects enterprise buyer recognition that the disclosure burden often exceeds the analytical challenge.

Example: Persefoni—The carbon accounting and climate disclosure platform expanded its scenario analysis module in 2024 to include pre-configured TCFD narrative templates. Financial services clients report 40% reduction in disclosure preparation time compared to manual compilation from separate analytics tools.

What's Not Working

Data Fragmentation and Quality Gaps

Despite market maturation, data infrastructure remains the primary implementation barrier. Organizations attempting climate risk analytics frequently encounter:

  • Scope 3 emissions data gaps: Supplier-level emissions data remains sparse, with most models relying on spend-based estimation that introduces 30-50% uncertainty
  • Asset geolocation deficiencies: Portfolio companies often lack precise coordinates for physical assets, forcing reliance on proxy locations that can mischaracterize exposure
  • Temporal mismatch: Climate projections operate on 2050-2100 timeframes while business planning typically extends only 5-10 years, creating scenario relevance challenges

Overreliance on Single Damage Functions

The Network for Greening the Financial System (NGFS) incorporated a new climate damage function (Kotz et al., Nature April 2024) in November 2024 that predicted 19% loss of global real income by 2050 and 60% by 2100. However, Nature issued a warning in late December 2024 that "reliability of data and methodology is currently in question," with academic critiques published in August 2025.

This controversy highlights a broader issue: enterprise buyers increasingly question platforms that rely on single methodological approaches without uncertainty quantification. Product teams must build for methodological pluralism—supporting alternative damage functions and clearly communicating assumption dependencies.

Integration Complexity with Core Systems

Climate risk analytics tools frequently operate as standalone applications without deep integration into ERM (Enterprise Risk Management), financial planning, and portfolio management systems. This creates workflow friction that limits adoption beyond sustainability teams to the CFO and CRO functions where climate risk should ultimately reside.

Key Players

Established Leaders

  • MSCI Climate Solutions: Dominant in financial services with CVaR and Implied Temperature Rise metrics. Covers 10,000+ companies with climate-adjusted valuations and scenario analysis.

  • S&P Global Sustainable1: Comprehensive data and analytics platform combining Trucost environmental data with scenario modeling capabilities. Strong CSRD alignment for European clients.

  • Moody's ESG Solutions: Acquired RMS climate models, offering integrated physical risk analytics with credit risk assessment. Particular strength in insurance sector applications.

  • Bloomberg Enterprise Access Point: Delivers climate data and scenario tools within existing terminal infrastructure, reducing integration burden for existing Bloomberg clients.

Emerging Startups

  • Jupiter Intelligence: Specialized in hyperlocal physical risk analytics with 90-meter resolution. Series D funding of $100M in 2024 valued the company at >$500M.

  • Cervest: UK-based platform focused on asset-level physical risk with particular strength in agricultural and real estate sectors. Expanded European operations in 2024.

  • Sust Global: Climate risk API provider enabling integration of physical risk data into existing enterprise applications. Developer-focused go-to-market differentiates from enterprise-sales competitors.

  • ClimateAi: Agricultural-specific climate risk platform using AI to predict supply chain disruptions. Series B closed in 2024 with >$20M raised.

Key Investors & Funders

  • TPG Rise Climate: Climate-focused fund with significant investments across climate analytics and data infrastructure companies.

  • Blackstone Energy Partners: Investing in climate risk tools supporting portfolio company resilience and disclosure capabilities.

  • European Investment Bank: Provides catalytic capital for climate data infrastructure through InvestEU Climate stream.

  • Microsoft Climate Innovation Fund: Strategic investor in platforms building on Azure sustainability infrastructure.

Sector-Specific KPI Benchmarks

MetricPoorAdequateGoodExcellent
Asset coverage (% portfolio)<50%50-75%75-90%>90%
Scenario granularity (warming pathways)2 scenarios3-4 scenarios5-6 scenarios>6 with variations
Physical risk resolutionCountry-levelRegionalMunicipalAsset-level
Update frequencyAnnualQuarterlyMonthlyReal-time
Integration depthStandalone exportAPI availableERM integrationFull stack integration
Disclosure alignmentManual mappingPartial automationTCFD-nativeMulti-framework native

Action Checklist

  • Audit current data architecture for geolocation precision and Scope 3 emissions coverage gaps before platform selection
  • Require vendor demonstration of multi-scenario comparison capabilities with configurable damage functions
  • Prioritize platforms with native CSRD/ESRS output modules for European disclosure requirements
  • Establish integration requirements with existing ERM and financial planning systems upfront in procurement
  • Build internal capacity for scenario interpretation—tools generate data, humans make decisions
  • Plan for 18-24 month implementation cycles for full enterprise deployment with adequate change management

FAQ

Q: How do TCFD and ISSB requirements differ, and which should product teams prioritize? A: TCFD provides recommendations that have now been subsumed into ISSB standards (specifically IFRS S2). Product teams should build primarily for ISSB/IFRS S2 alignment, which incorporates TCFD recommendations while adding specificity around climate-related metrics and targets. European clients additionally require ESRS E1 (Climate) alignment under CSRD—ensure platforms support mapping across both frameworks.

Q: What scenario time horizons do enterprise buyers most commonly require? A: Financial services buyers typically require 2030, 2040, and 2050 horizons for regulatory stress testing and portfolio alignment. Corporate buyers often focus on 2030-2035 for transition planning aligned with SBTi targets. Physical risk assessments increasingly extend to 2100 for long-lived infrastructure assets. Product teams should support configurable time horizon selection rather than hard-coding specific endpoints.

Q: How should platforms handle uncertainty in climate projections? A: Best practice involves presenting probability distributions rather than point estimates, clearly documenting assumptions and their sources, supporting sensitivity analysis across key parameters, and enabling comparison across alternative methodological approaches. Platforms that present single-value outputs without uncertainty ranges are increasingly rejected by sophisticated buyers.

Q: What is the appropriate pricing model for climate risk analytics platforms? A: The market is segmenting between asset-count-based pricing (dominant for physical risk tools), AUM-based pricing (common for financial services applications), and user-seat licensing (typical for disclosure-focused tools). Hybrid models combining base platform fees with consumption-based components for compute-intensive scenario runs are emerging as the sustainable approach for enterprise relationships.

Q: How are emerging markets approaching climate risk analytics adoption? A: Asia-Pacific adoption is accelerating, though with different priorities than European markets. Physical risk analytics—particularly for tropical cyclone, flood, and extreme heat exposure—dominate over transition risk tools. Local regulatory alignment (e.g., MAS guidelines in Singapore, RBI expectations in India) drives adoption patterns distinct from TCFD/ISSB frameworks. Product teams should anticipate regional scenario libraries and localized physical hazard models for these markets.

Sources

Topics

MRVcircularitytraceabilitybatteryCSRD

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