Deep dive: Climate risk analytics & scenario modeling — what's working, what's not, and what's next

Climate risk analytics has crossed the threshold from niche consultancy service to enterprise infrastructure requirement. Regulators across the EU, US, UK, and Asia-Pacific now mandate that financial institutions, insurers, and publicly listed companies quantify their exposure to physical and transition risks under multiple warming scenarios. The global market for climate risk analytics platforms reached $4.2 billion in 2025, up from $1.8 billion in 2022, according to Verdantix. Yet behind this rapid growth lies a field still grappling with fundamental methodological challenges, data gaps, and a persistent disconnect between the precision that models suggest and the uncertainty that reality demands.

Why It Matters

The financial materiality of climate risk is no longer theoretical. Swiss Re estimated that global insured losses from weather-related catastrophes reached $140 billion in 2024, a 15% increase over the prior five-year average. Uninsured losses were roughly double that figure. These numbers represent the physical risk dimension alone. Transition risks, including stranded assets from fossil fuel phase-outs, regulatory compliance costs, and shifts in consumer demand, add another layer of financial exposure that McKinsey estimated at $4-6 trillion in cumulative asset value at risk across G20 economies through 2040.

Regulatory pressure has accelerated adoption dramatically. The EU's Corporate Sustainability Reporting Directive (CSRD), effective for large undertakings from fiscal year 2024, requires double materiality assessments that explicitly include climate scenario analysis. The International Sustainability Standards Board (ISSB) IFRS S2 standard mandates climate-related financial disclosures aligned with Task Force on Climate-related Financial Disclosures (TCFD) recommendations, and has been adopted or referenced by jurisdictions covering over 40% of global GDP. In the US, California's SB 261 requires companies with over $500 million in annual revenues to prepare and publish climate-related financial risk reports biennially, beginning in 2026. The SEC's climate disclosure rules, while narrowed from their original proposal, still require material climate risk disclosures from large accelerated filers.

For founders building in this space, the regulatory tailwinds are strong, but the competitive landscape is crowding fast. Understanding precisely where existing solutions fall short reveals where the next generation of products can deliver genuine differentiated value.

What's Working

Physical Risk Assessment for Real Assets

The most mature application of climate risk analytics involves quantifying physical hazards (flood, wildfire, wind, heat, drought) at the asset level. Platforms from Jupiter Intelligence, One Concern, and Moody's (through its RMS acquisition) now deliver hazard scores at resolutions of 90 meters or finer for most global locations, with projections spanning 2030 through 2100 under multiple Shared Socioeconomic Pathway (SSP) scenarios.

Financial institutions have integrated these tools into lending and underwriting workflows with measurable impact. JPMorgan Chase disclosed in its 2024 TCFD report that physical risk screening now covers 100% of its commercial real estate lending portfolio, with risk-adjusted pricing applied to properties in the highest hazard quintiles. European banks subject to the European Central Bank's 2022 climate stress test have built out internal physical risk assessment capabilities, with the ECB reporting in its 2025 supervisory review that 78% of significant institutions now have "adequate or better" physical risk quantification methodologies, up from 41% in 2022.

Insurance and reinsurance companies remain the most sophisticated users. Swiss Re's CatNet platform combines proprietary catastrophe models with climate projections to adjust risk premiums at the individual policy level. Munich Re's Location Risk Intelligence tool processes over 25 hazard layers to inform underwriting decisions across 130 countries. These deployments represent the gold standard because the financial feedback loop is immediate: mispriced risk shows up in loss ratios within years, not decades.

Transition Risk Screening for Investment Portfolios

Asset managers and asset owners have adopted climate scenario analysis to evaluate portfolio exposure to transition risks, primarily carbon pricing, technology disruption, and policy shifts. MSCI's Climate Value-at-Risk model, adopted by over 200 institutional investors managing a combined $40+ trillion, projects financial impacts under orderly transition (1.5C), disorderly transition (below 2C), and hot house world (3C+) scenarios.

The Paris Agreement Capital Transition Assessment (PACTA) tool, developed by the 2 Degrees Investing Initiative (now Theia Finance Labs), provides open-source portfolio alignment analysis for banks and investors. Over 5,000 financial institutions have used PACTA since its launch, including mandated assessments in France (Article 29 Energy-Climate Law) and Switzerland (FINMA guidance). PACTA's strength lies in its bottom-up approach: rather than applying top-down carbon intensity metrics, it maps individual company production plans against technology benchmarks derived from the International Energy Agency's Net Zero Emissions scenario.

BlackRock's Aladdin Climate platform integrates transition and physical risk analytics into its broader portfolio management infrastructure, enabling portfolio managers to stress-test holdings under custom scenarios. The platform's 2025 update added supply chain transmission modeling, allowing users to evaluate how climate risks propagate from suppliers through to portfolio companies, a capability that addresses one of the most significant gaps in earlier generation tools.

Regulatory Compliance Automation

A third area of demonstrated success involves platforms that automate the production of regulatory disclosures. Persefoni, Watershed, and Plan A have built workflow tools that combine emissions accounting with scenario analysis outputs, generating TCFD-aligned, CSRD-compatible, and ISSB-formatted reports. Persefoni raised $100 million in Series C funding in 2024, reaching a $1 billion valuation, reflecting investor confidence in the compliance automation market.

These platforms work well for the specific problem they address: reducing the manual effort and consultant dependency required to produce standardized disclosures. Large enterprises that previously spent $500,000-2 million annually on climate disclosure consulting can now produce equivalent outputs for $100,000-300,000 in software and internal labor costs, with faster turnaround times and greater auditability.

What's Not Working

Scenario Precision That Outstrips Scientific Certainty

The most significant problem in climate risk analytics is the false precision embedded in many commercial models. Platforms routinely present physical risk scores with implied accuracy to two or three decimal places for specific locations 30-50 years in the future. The underlying climate models, however, produce outputs with substantial inter-model disagreement. The Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble shows a range of 2.5C to 4.0C for equilibrium climate sensitivity, meaning that the spread between "moderate" and "severe" outcomes remains enormous.

At regional and local scales, the uncertainty compounds. Precipitation projections for specific locations can differ by a factor of two or more across CMIP6 models, even under the same emissions scenario. A 2024 study published in Nature Climate Change found that flood risk projections for the same commercial real estate portfolio varied by 40-120% depending on which climate model, downscaling method, and hydrological model were used.

This is not merely an academic concern. When banks and insurers price risk based on model outputs that convey unwarranted certainty, they may systematically misprice assets, either creating false security or triggering premature capital flight from regions where actual risks are lower than modeled.

Transition Scenario Analysis Remains Largely Qualitative

While physical risk modeling benefits from established climate science, transition risk analysis depends on assumptions about policy trajectories, technology costs, and consumer behavior that resist quantification. The IEA's Net Zero Emissions scenario, widely used as a benchmark, assumes specific policy implementation timelines that may or may not materialize. When the same portfolio is assessed under the IEA NZE versus the Network for Greening the Financial System (NGFS) scenarios, differences in assumed carbon pricing trajectories alone can shift portfolio-level impacts by 15-30%.

The fundamental challenge is that transition risks are endogenous: the actions of market participants change the probability distribution of outcomes. If enough capital flows out of fossil fuels based on transition risk assessments, it accelerates the very transition those assessments projected. This reflexivity makes transition scenario analysis fundamentally different from physical risk modeling, yet most commercial platforms treat both with similar methodological frameworks.

Scope 3 and Supply Chain Risk Transmission

Climate risk does not stop at a company's direct operations. A manufacturer in a low-hazard location may depend critically on suppliers in high-flood-risk regions or on transportation corridors vulnerable to extreme heat. Yet fewer than 15% of climate risk assessments in 2025 incorporated supply chain risk transmission, according to a CDP analysis of corporate climate disclosures.

The data barriers are formidable. Mapping multi-tier supply chains with geographic specificity requires trade data, customs records, and supplier disclosure that most companies do not possess for anything beyond their Tier 1 suppliers. Several startups, including Altana AI and Resilinc, are building supply chain mapping capabilities, but integrating these with climate hazard models remains an early-stage effort.

Adaptation and Resilience Are Poorly Modeled

Current climate risk models excel at quantifying hazard exposure but struggle to incorporate adaptive capacity. A coastal property with robust flood defenses, elevated critical systems, and comprehensive insurance coverage faces fundamentally different residual risk than an unprotected property in the same hazard zone. Yet most commercial platforms apply identical or minimally differentiated risk scores to both, because granular adaptation data is rarely available at scale.

This limitation creates perverse incentives. Property owners who invest in resilience measures may see no improvement in their risk scores or insurance pricing, weakening the financial case for adaptation investment. The Insurance Institute for Business and Home Safety (IBHS) has documented that FORTIFIED-rated buildings suffer 40-60% less damage from windstorms and hurricanes, yet this performance differential is inconsistently reflected in climate risk analytics outputs.

What's Next

AI-Native Analytics Platforms

The next generation of climate risk platforms will leverage large language models and foundation models trained on climate science literature, regulatory documents, and corporate disclosures to deliver more contextualized analysis. ClimateAI and Sust Global are building platforms that combine traditional climate modeling with machine learning to improve resolution and reduce latency. These approaches show promise for short-to-medium-term projections (1-10 years) where statistical downscaling and pattern recognition can supplement physics-based models.

Generative AI is also transforming the regulatory compliance layer. Tools that automatically interpret new regulatory requirements, map them to existing data holdings, and generate draft disclosures are reducing compliance cycles from months to weeks. Expect this capability to become table-stakes within 18-24 months.

Dynamic Risk Pricing and Parametric Products

The integration of real-time hazard monitoring with climate projections enables dynamic risk pricing that updates continuously rather than annually. Descartes Underwriting, which raised $120 million in Series B funding in 2024, uses satellite imagery and IoT sensor data to trigger parametric insurance payouts within days of qualifying events, bypassing traditional claims adjustment processes.

This model is expanding beyond insurance into lending (risk-adjusted interest rates that respond to changing physical exposure), real estate valuation (dynamic discount rates reflecting evolving hazard profiles), and supply chain finance (trade credit terms adjusted for supplier climate vulnerability). The convergence of real-time data, AI-driven analytics, and programmable financial products creates a market opportunity that several founders are pursuing.

Integrated Physical-Transition-Nature Risk Frameworks

Regulatory and investor expectations are converging toward integrated risk frameworks that assess physical climate risks, transition risks, and nature-related risks within unified analytical architectures. The Taskforce on Nature-related Financial Disclosures (TNFD), which published its final recommendations in September 2023, calls for nature-related scenario analysis that mirrors TCFD-style climate risk assessment. Platforms that can model the interdependencies between climate change, biodiversity loss, and ecosystem degradation will command premium positioning.

The NGFS published its first combined climate-nature scenarios in late 2025, providing a standardized framework for integrated assessment. Early movers including Iceberg Data Lab and NatureMetrics are building biodiversity analytics that complement existing climate risk platforms, creating opportunities for integration partnerships or acquisitions.

Localized Adaptation Intelligence

The gap between hazard assessment and adaptation planning represents perhaps the largest unaddressed market opportunity. Municipalities, utilities, and infrastructure operators need tools that go beyond "you are at risk" to provide actionable guidance on cost-effective resilience investments. Platforms that combine hazard modeling with engineering cost databases, building-level vulnerability assessments, and cost-benefit optimization could capture significant market share in a segment currently served primarily by engineering consultancies charging $200-500 per hour.

The US Federal Emergency Management Agency's updated Benefit-Cost Analysis toolkit, released in 2025, provides standardized methodologies for evaluating resilience investments that software platforms can operationalize at scale. First Street Foundation's risk data, now covering flood, fire, wind, and heat for every US property, provides the hazard layer upon which adaptation intelligence tools can be built.

Climate Risk Analytics KPIs: Benchmark Ranges

Metric	Below Average	Average	Above Average	Top Quartile
Physical Risk Coverage (% of portfolio)	<25%	25-60%	60-90%	>90%
Scenario Analysis Depth (# scenarios)	1	2-3	4-5	>5
Data Refresh Frequency	Annual	Quarterly	Monthly	Real-time
Asset-Level Resolution	Country	Subnational	1 km	<100 m
Supply Chain Risk Coverage	None	Tier 1	Tier 1-2	Multi-tier
Disclosure Automation (% automated)	<20%	20-50%	50-80%	>80%
Time to Regulatory Report	>6 months	3-6 months	1-3 months	<1 month

Action Checklist

• Audit current climate risk assessment coverage across all asset classes and geographies in your portfolio
• Evaluate at least three commercial platforms using standardized test portfolios to compare outputs and identify model divergence
• Require vendors to disclose underlying climate models, downscaling methods, and uncertainty ranges alongside headline risk scores
• Integrate physical and transition risk assessments into a unified framework rather than treating them as separate exercises
• Map Tier 1 and Tier 2 supplier locations and overlay with physical hazard data to identify supply chain concentration risks
• Establish governance processes for scenario selection, assumption documentation, and results interpretation
• Build internal capacity for critical evaluation of model outputs rather than relying solely on vendor-provided scores
• Plan for regulatory convergence by aligning disclosure processes with ISSB, CSRD, and TNFD frameworks simultaneously

FAQ

Q: How should organizations choose between competing climate risk analytics platforms? A: Start by defining your primary use case: regulatory compliance, investment decision support, or operational risk management. Request that vendors run identical test portfolios and compare not just headline risk scores but uncertainty ranges. Evaluate data update frequency, geographic coverage depth, and integration capabilities with existing enterprise systems. For regulated financial institutions, verify that the platform's methodology aligns with supervisory expectations (ECB, PRA, or relevant local regulator guidance).

Q: What level of internal expertise is needed to implement climate risk analytics effectively? A: At minimum, organizations need a climate risk lead with quantitative background (climate science, actuarial science, or quantitative finance) who can critically evaluate model outputs and communicate uncertainty to decision-makers. Larger organizations benefit from dedicated teams of 3-5 professionals combining climate science, data engineering, and risk management expertise. Outsourcing entirely to vendors or consultants creates dependency risk and limits the organization's ability to challenge model assumptions.

Q: How reliable are long-term (2050-2100) physical risk projections for specific locations? A: Long-term projections are useful for identifying directional trends and relative risk rankings, but should not be treated as precise forecasts. Inter-model uncertainty increases substantially beyond 2040, particularly for precipitation, extreme events, and regional temperature patterns. Best practice is to present results as ranges across multiple scenarios and models rather than single point estimates, and to focus decision-making on robust strategies that perform well across multiple plausible futures.

Q: Can smaller companies afford climate risk analytics, or is this only for large enterprises? A: The market has diversified significantly. Open-source tools like PACTA and OS-Climate provide free portfolio alignment analysis. First Street Foundation offers property-level risk scores at consumer price points. Mid-market SaaS platforms from providers like Cervest and Climanomics offer annual subscriptions starting at $25,000-50,000 for portfolio screening. The key constraint for smaller companies is typically not software cost but internal capacity to interpret and act on results.

Q: How are climate risk analytics evolving to address nature and biodiversity risks? A: The TNFD framework is driving rapid development of integrated climate-nature risk tools. Platforms are beginning to overlay biodiversity data (species richness, ecosystem integrity, deforestation risk) with physical climate hazard layers. The NGFS combined climate-nature scenarios published in 2025 provide standardized analytical pathways. Expect nature-related risk analytics to follow a trajectory similar to climate risk analytics circa 2019-2021: regulatory drivers creating demand that outstrips current analytical capabilities, with rapid product development following.

Sources

• Verdantix. (2025). Global Market Sizing: Climate Risk Analytics Platforms 2022-2027. London: Verdantix Ltd.
• Swiss Re Institute. (2025). Natural Catastrophes in 2024: Global Insured Losses Review. Zurich: Swiss Re.
• European Central Bank. (2025). Supervisory Assessment of Climate Risk Management Practices. Frankfurt: ECB Banking Supervision.
• Network for Greening the Financial System. (2025). NGFS Climate-Nature Scenarios: Technical Documentation. Paris: Banque de France.
• Intergovernmental Panel on Climate Change. (2023). AR6 Synthesis Report: Climate Change 2023. Geneva: IPCC Secretariat.
• Nature Climate Change. (2024). Quantifying Uncertainty in Asset-Level Climate Risk Assessment. Springer Nature.
• CDP. (2025). Global Climate Disclosure Analysis: Supply Chain Risk Coverage. London: CDP Worldwide.
• International Sustainability Standards Board. (2024). IFRS S2 Implementation Guide: Climate-Related Disclosures. London: IFRS Foundation.