Interview: practitioners on climate risk analytics & scenario modeling

The global climate risk analytics market reached $9.2 billion in 2024 and is projected to surge past $28 billion by 2030, representing a compound annual growth rate of 20.3%. North America commands roughly 38% of this market, driven by regulatory pressures from the SEC's climate disclosure requirements and an unprecedented wave of extreme weather events that cost the United States alone over $93 billion in insured losses during 2024. As practitioners across the startup-to-enterprise spectrum navigate this rapidly evolving landscape, their insights reveal critical patterns about what separates successful climate risk implementations from costly failures.

Why It Matters

Climate risk analytics and scenario modeling have transitioned from niche compliance exercises to board-level strategic imperatives within the past eighteen months. The convergence of regulatory mandates, investor pressure, and physical climate impacts has created an environment where organizations without robust climate risk capabilities face material financial and operational vulnerabilities.

In North America specifically, the regulatory landscape underwent seismic shifts in 2024-2025. California's climate disclosure laws (SB 253 and SB 261) now require over 10,000 companies doing business in the state to report comprehensive emissions data and climate-related financial risks. The SEC's final climate disclosure rule, while facing legal challenges, has catalyzed widespread voluntary adoption of TCFD-aligned reporting frameworks. Canadian regulators through OSFI have mandated climate scenario analysis for federally regulated financial institutions, with the first submissions completed in late 2024.

The physical risk dimension has become impossible to ignore. The 2024 Atlantic hurricane season produced 23 named storms, with Hurricane Milton causing an estimated $50-75 billion in damages across Florida alone. Western wildfires burned 7.8 million acres, while the Mississippi River experienced historic low water levels that disrupted $17 billion in annual barge traffic. These events have transformed climate risk from a long-horizon planning exercise into an immediate operational concern.

Practitioners report that enterprise adoption accelerated dramatically through 2024, with Fortune 500 companies increasing climate risk analytics spending by an average of 47% year-over-year. The startup ecosystem responded accordingly, with climate risk and resilience companies raising $2.1 billion in venture funding across North America during 2024, a 34% increase from 2023.

Key Concepts

Climate Resilience refers to the capacity of systems, communities, and organizations to anticipate, absorb, accommodate, and recover from climate-related hazards. In the analytics context, resilience modeling quantifies adaptive capacity across physical assets, supply chains, and operational processes. Practitioners emphasize that resilience is not merely about withstanding impacts but maintaining functional continuity under stress conditions. Leading frameworks now incorporate dynamic resilience metrics that account for cascading failures and interdependencies between critical infrastructure systems.

Battery Storage Risk Analytics has emerged as a critical subsegment as grid-scale battery installations in North America exceeded 25 GW of cumulative capacity by end of 2024. Analytics platforms now model thermal runaway risks, degradation curves under extreme temperature scenarios, and supply chain vulnerabilities for critical minerals. Practitioners note that battery performance under climate stress conditions—including sustained heat waves and grid instability events—requires scenario modeling that integrates both physical climate projections and energy system dynamics.

Water Risk Quantification encompasses the analytical frameworks used to assess exposure to water scarcity, flooding, and water quality degradation under various climate scenarios. North American water stress has intensified, with the Colorado River Basin operating under unprecedented shortage declarations and groundwater depletion accelerating across the Ogallala Aquifer. Modern water risk platforms integrate satellite-derived datasets, hydrological models, and economic impact assessments to quantify financial materiality across industrial operations, agricultural supply chains, and municipal infrastructure.

Grid Vulnerability Modeling addresses the intersection of climate impacts and electrical infrastructure reliability. The 2024 winter storm that caused rolling blackouts across the Texas Interconnection, combined with summer peak demand challenges in CAISO, highlighted the need for sophisticated analytics that model generation adequacy, transmission constraints, and demand response capacity under climate stress scenarios. Practitioners increasingly deploy probabilistic models that capture correlated failures across the grid topology.

Electrolyzer Performance Analytics has gained prominence as green hydrogen production capacity in North America scaled to 1.2 GW by 2024, with 15 GW in the project pipeline. Climate scenarios materially affect electrolyzer economics through impacts on renewable energy availability, water supply reliability, and thermal management requirements. Advanced analytics platforms now model hydrogen production curves under various climate pathways, integrating RCP scenarios with localized weather projections and infrastructure constraints.

What's Working and What Isn't

What's Working

Integrated Physical and Transition Risk Platforms: Practitioners consistently highlight the success of analytics solutions that unify physical climate risk assessment with transition risk modeling. Companies like Jupiter Intelligence and Moody's Analytics have developed platforms that allow users to stress-test portfolios against both acute physical events and policy transition scenarios simultaneously. A chief risk officer at a major Canadian pension fund reported that integrated modeling reduced their scenario analysis cycle time from six months to six weeks while improving the granularity of asset-level insights.

AI-Enhanced Extreme Event Attribution: Machine learning approaches to extreme weather attribution have achieved production-ready status. Practitioners at insurance carriers report that AI models now attribute individual event losses to climate change with sufficient confidence for pricing and underwriting decisions. One North American reinsurer noted that their ML attribution system processed over 4,000 loss events in 2024, enabling real-time premium adjustments that improved combined ratios by 3.2 percentage points compared to traditional actuarial approaches.

Supply Chain Climate Stress Testing: Enterprise adoption of supply chain-focused climate analytics has accelerated dramatically. Platforms that map multi-tier supplier networks and overlay climate hazard projections have demonstrated material value. A Fortune 100 consumer goods company reported that proactive supply chain stress testing identified $340 million in at-risk procurement spend, enabling supplier diversification and inventory adjustments that avoided $87 million in disruption costs during 2024's extreme weather events.

Standardized Scenario Frameworks: The convergence around NGFS climate scenarios has enabled meaningful comparability across institutions. Practitioners note that the 2024 NGFS scenario update, which incorporated improved physical risk modeling and more granular sectoral pathways, has become the de facto standard for North American financial institutions. This standardization has reduced implementation friction and enabled more productive supervisory dialogue.

What Isn't Working

Data Fragmentation and Quality Issues: Despite platform proliferation, practitioners consistently cite data challenges as the primary barrier to effective climate risk analytics. Physical asset location data often lacks the precision required for meaningful hazard overlay analysis—one energy company CRO noted that 23% of their facility coordinates were inaccurate by more than 500 meters, rendering flood risk assessments unreliable. Emissions data quality remains problematic, with Scope 3 estimates varying by factors of 2-3x across different provider methodologies.

Temporal Mismatch Between Risk Horizons and Decision Frameworks: Practitioners report ongoing difficulty translating 30-year climate projections into quarterly and annual planning cycles. A portfolio manager at a major asset manager observed that their investment committee struggles to act on risks that materialize beyond a 5-year horizon, even when the analytics clearly indicate material long-term exposure. Bridging this temporal disconnect requires not just better analytics but fundamental changes to governance and incentive structures.

Model Opacity and Validation Challenges: The complexity of climate scenario models creates accountability gaps. Practitioners across multiple sectors report difficulty validating vendor model outputs against observable outcomes, particularly for tail risk scenarios that lack historical precedent. One utility sector risk manager noted that three different vendor platforms produced flood risk estimates that varied by 400% for the same asset portfolio, with no clear basis for adjudicating between methodologies.

Integration with Enterprise Risk Management Systems: Climate risk analytics often remain siloed from core ERM platforms. Practitioners report that despite significant investment in climate-specific tools, outputs frequently fail to propagate into credit risk models, capital allocation frameworks, or operational planning systems. A banking sector CRO estimated that only 15% of their climate risk insights currently influence front-line business decisions.

Key Players

Established Leaders

Moody's Analytics has established a dominant position in climate risk analytics through its acquisition of Four Twenty Seven and subsequent integration with core credit risk platforms. Their physical risk scores now cover over 200 million global locations with meter-level resolution, and their transition risk pathways align with major regulatory frameworks including NGFS and ISSB standards.

S&P Global Sustainable1 offers comprehensive climate risk and ESG analytics serving over 2,000 institutional clients. Their Climate Credit Analytics platform, developed in partnership with Oliver Wyman, enables financial institutions to quantify climate-adjusted credit risk across lending portfolios with scenario flexibility aligned to regulatory requirements.

MSCI has built extensive climate risk analytics capabilities through both organic development and acquisitions, including Carbon Delta and Real Capital Analytics. Their Climate Value-at-Risk methodology has become widely adopted for portfolio-level climate risk quantification, with coverage spanning over 10,000 public companies and 800,000 real assets.

Jupiter Intelligence provides high-resolution physical climate risk analytics with particular strength in forward-looking hazard modeling. Their ClimateScore platform delivers asset-level risk scores across nine peril types with projections extending to 2100, serving enterprise clients across insurance, real estate, and infrastructure sectors.

Cervest delivers AI-powered climate intelligence through their EarthScan platform, which provides asset-level physical risk assessments with full transparency into underlying science and methodologies. Their focus on decision-grade analytics has attracted significant enterprise adoption across agriculture, food and beverage, and financial services sectors.

Emerging Startups

One Concern has developed digital twin technology for infrastructure resilience, enabling municipalities and utilities to model cascading failures during extreme events. Their platform saw significant adoption following 2024's major hurricane season, with deployments across Florida and Gulf Coast municipalities.

Sust Global offers climate risk analytics specifically designed for financial institutions, with particular strength in portfolio-level aggregation and regulatory reporting automation. Their Series A funding in 2024 supported expansion across North American banking and asset management markets.

ClimateAi applies machine learning to agricultural climate risk, providing crop-specific yield projections and supply chain resilience analytics. Their platform now covers over 150 million acres of North American farmland, serving agricultural commodity traders and food and beverage companies.

Climavision combines proprietary weather radar networks with AI-enhanced forecasting to deliver high-resolution climate risk analytics. Their gap-filling radar installations across the southeastern United States have enabled unprecedented extreme weather detection capabilities.

Riskthinking.AI provides scenario modeling and stress testing tools with particular focus on financial sector applications. Their open-source approach to climate scenario generation has attracted significant adoption from institutions seeking greater model transparency.

Key Investors & Funders

Congruent Ventures has emerged as a leading climate risk analytics investor, with portfolio companies spanning physical risk modeling, transition risk analytics, and climate data infrastructure. Their 2024 fund deployment included significant allocation to climate intelligence platforms.

Energy Impact Partners invests across the climate risk value chain, with particular focus on grid resilience analytics and infrastructure adaptation technologies. Their utility LP base provides valuable customer development support for portfolio companies.

Breakthrough Energy Ventures has backed multiple climate analytics platforms, with investments focused on technologies that enable faster decarbonization decision-making across enterprise and government sectors.

The Nature Conservancy's NatureVest has deployed capital into climate risk analytics with emphasis on nature-based solution quantification and natural capital accounting platforms.

DCVC (Data Collective) focuses on data-intensive climate technologies, including risk analytics platforms that leverage alternative data sources and advanced machine learning for climate intelligence.

Examples

Duke Energy's Integrated Grid Resilience Program: Duke Energy implemented a comprehensive climate risk analytics platform across its 54,000 MW generation portfolio and 300,000 miles of transmission and distribution infrastructure in 2024. The platform integrates NOAA climate projections with asset-level vulnerability assessments and cascading failure models. Initial deployment identified $2.3 billion in at-risk infrastructure across the Carolinas and Florida service territories. Proactive hardening investments of $890 million, prioritized through the analytics platform, reduced projected outage costs by 62% during Hurricane Milton, avoiding an estimated $1.4 billion in customer outage costs and restoration expenses compared to pre-hardening baselines.

Ontario Teachers' Pension Plan Portfolio Stress Testing: OTPP deployed climate scenario analytics across its $250 billion portfolio in response to OSFI regulatory requirements. The implementation covered over 4,000 distinct holdings spanning real estate, infrastructure, private equity, and public equities. Analysis under the NGFS Delayed Transition scenario identified $8.7 billion in transition-risk-exposed assets, enabling proactive engagement with portfolio companies and strategic reallocation that reduced projected value-at-risk by 31% while maintaining return targets. The analytics platform now integrates with quarterly portfolio review processes, with climate metrics incorporated into investment committee reporting.

Cargill North American Supply Chain Resilience: Cargill implemented ClimateAi's agricultural risk platform across its North American grain origination network, covering over 2,000 supplier relationships and 45 million acres of sourced production. The platform models yield variability under climate scenarios, identifies water stress exposure, and quantifies logistics disruption risk across rail and barge networks. During 2024's Mississippi River low-water event, the platform's early warning capabilities enabled Cargill to pre-position inventory and secure alternative logistics capacity, reducing disruption costs by an estimated $156 million compared to the 2022 low-water event when the platform was not yet deployed.

Action Checklist

FAQ

Q: How should organizations prioritize between physical and transition risk analytics investments? A: The appropriate prioritization depends on sector exposure and time horizon. Asset-intensive industries with long-lived physical infrastructure—utilities, real estate, transportation—should typically prioritize physical risk analytics given the immediacy of acute weather impacts and the long planning horizons for infrastructure adaptation. Carbon-intensive sectors facing regulatory and market pressure—fossil fuels, heavy industry, automotive—should emphasize transition risk modeling to inform strategic pivots and capital reallocation. Most enterprises require both capabilities, and practitioners recommend starting with the risk category most material to near-term financial performance while building toward integrated assessment. Financial institutions with diversified exposure should pursue parallel development of both capabilities from the outset.

Q: What level of spatial and temporal resolution is actually decision-useful for enterprise climate risk analytics? A: Resolution requirements vary significantly by application. For real estate and infrastructure portfolios, practitioners report that 30-meter horizontal resolution and daily temporal granularity represent the minimum threshold for meaningful asset-level risk differentiation. Supply chain applications often require lower spatial resolution (county or watershed level) but benefit from higher temporal precision for logistics planning. The key insight from practitioners is that excessive resolution can introduce false precision when underlying hazard models contain substantial uncertainty. Effective implementations match resolution to decision requirements and communicate uncertainty ranges alongside point estimates. For most enterprise applications, decadal time steps (2030, 2040, 2050) under multiple scenarios provide more actionable insight than year-by-year projections with spurious precision.

Q: How are leading organizations handling the challenge of Scope 3 emissions data quality in transition risk models? A: Practitioners have converged on a tiered approach that acknowledges data quality heterogeneity. For material Scope 3 categories, leading organizations combine multiple estimation methodologies—spend-based, activity-based, and supplier-reported—and model uncertainty ranges rather than point estimates. The emerging best practice involves engaging priority suppliers directly for measured data while using modeled estimates for the long tail. Transition risk models increasingly incorporate data quality confidence levels that propagate through scenario outputs, enabling decision-makers to understand which insights are robust versus contingent on data improvements. Several practitioners noted that regulatory pressure is accelerating supplier data availability, with material improvements in Scope 3 data quality expected through 2025-2026 as disclosure requirements cascade through supply chains.

Q: What are the key indicators that a climate risk analytics implementation is failing to deliver value? A: Practitioners identify several warning signs. First, if climate risk outputs remain confined to sustainability reports without influencing operational or financial decisions, the implementation has failed to achieve integration. Second, if scenario results produce uniform risk ratings across diverse asset portfolios, the analytics lack meaningful discrimination. Third, if business units cannot explain how climate risk insights affect their planning, communication and governance have broken down. Fourth, if analytics outputs diverge dramatically from observable loss experience during extreme events, model validation is inadequate. Successful implementations demonstrate clear linkages between analytics outputs and resource allocation decisions, with traceable impact on capital expenditure priorities, supplier selection, and risk transfer strategies.

Q: How should startups approach enterprise sales in the climate risk analytics space given long procurement cycles? A: Practitioners from successful climate analytics startups emphasize several strategies. First, target specific use cases rather than enterprise-wide platform sales—regulatory compliance deadlines, specific asset portfolio assessments, or supply chain mapping projects provide defined scope and shorter decision timelines. Second, cultivate relationships with sustainability teams while building credibility with risk and finance functions who control larger budgets. Third, leverage industry consortia and pilot programs that provide proof points without requiring individual enterprise procurement. Fourth, structure initial engagements as time-bounded projects with defined deliverables rather than SaaS subscriptions, reducing procurement complexity. Fifth, prepare for 12-18 month sales cycles in financial services and 6-12 months in corporate sectors, with working capital requirements planned accordingly.

Sources

Network for Greening the Financial System (NGFS). "NGFS Climate Scenarios for Central Banks and Supervisors - Phase IV." December 2024.
National Oceanic and Atmospheric Administration (NOAA). "U.S. Billion-Dollar Weather and Climate Disasters: 2024 Annual Summary." January 2025.
BloombergNEF. "Climate Risk Analytics Market Outlook 2025." December 2024.
Task Force on Climate-related Financial Disclosures. "2024 Status Report: Progress and Challenges in Climate Risk Disclosure." October 2024.
California Air Resources Board. "Implementation Guidance for SB 253 Climate Corporate Data Accountability Act." September 2024.
Office of the Superintendent of Financial Institutions Canada (OSFI). "Climate Risk Returns: 2024 Submission Analysis and Supervisory Findings." November 2024.
Energy Information Administration (EIA). "Battery Storage in the United States: An Update Through 2024." January 2025.
PitchBook. "Climate Tech Venture Funding Report: North America Q4 2024." January 2025.