Sustainability Atlas
Climate Tech & Data·13 min read··...

Interview: the builder's playbook for Climate risk analytics & scenario modeling — hard-earned lessons

A practitioner conversation: what surprised them, what failed, and what they'd do differently. Focus on data quality, standards alignment, and how to avoid measurement theater.

The climate risk analytics market reached $9.84 billion in 2024 and is projected to grow at 17.5% CAGR to $50.62 billion by 2033—yet the Federal Reserve's 2024 pilot exercise with six major US banks revealed that even the world's largest financial institutions struggle with fundamental data quality and modelling challenges. When JPMorgan Chase, Citigroup, Bank of America, Goldman Sachs, Morgan Stanley, and Wells Fargo collectively cannot produce consistent climate risk estimates, it signals that the gap between regulatory ambition and operational reality remains dangerously wide.

We spoke with practitioners across risk management, sustainability reporting, and climate analytics to understand what's working, what's failing, and how to avoid the "measurement theater" that plagues this rapidly evolving field.

Why It Matters

The UK's adoption of ISSB-aligned Sustainability Reporting Standards (UK SRS) makes climate scenario analysis mandatory for listed companies and large financial institutions, with first mandatory reporting expected for financial year 2026. The transition from voluntary TCFD frameworks to regulatory requirements fundamentally changes the stakes: what was once a reputational exercise now carries legal and financial consequences.

For UK sustainability leads, three forces are converging simultaneously. First, over 60 nations have adopted or announced mandatory climate risk disclosure regulations based on IFRS S1 and S2 standards. Second, AI-powered predictive models now constitute 36% of all new climate risk systems, improving accuracy by 27% but introducing new complexity and opacity. Third, a 2024 Nature study revealed that single-country data errors can bias global climate impact predictions by up to three times—demonstrating how fragile the underlying data infrastructure remains.

The Bank of England's 2024 guidance on climate scenario analysis explicitly notes that damage function calibration remains "an area of ongoing research" and that current scenarios capture only a subset of chronic and acute physical risks. Practitioners building systems today must navigate genuine scientific uncertainty while meeting increasingly specific regulatory requirements.

Key Concepts

Physical vs. Transition Risk Modelling

Climate risk analytics bifurcates into two domains that require fundamentally different methodologies. Physical risk modelling—covering floods, hurricanes, wildfires, and temperature extremes—commands 44.81% of market focus and requires geospatial precision. MSCI's platform covers over 2 million corporate asset locations across 14 hazard types. Transition risk modelling addresses policy shifts, carbon pricing, and stranded assets, projected to grow at 31.65% CAGR as net-zero commitments tighten.

The Bank of England found that most institutions treat these as separate workstreams, yet real-world impacts are interconnected. A disorderly transition (rapid policy action after delayed response) amplifies both: physical damages continue accumulating while transition costs spike suddenly.

Scenario Frameworks and Their Limitations

The Network for Greening the Financial System (NGFS) scenarios have become the de facto standard for regulatory compliance, with Version 5.0 released in November 2024 reflecting post-pandemic net-zero commitments and Ukraine war energy impacts. However, NGFS scenarios still do not account for climate tipping points that could dramatically accelerate impacts—a gap that practitioners must acknowledge in their disclosures.

Common scenario types include orderly transition (Paris-aligned 1.5°C/2°C pathways), disorderly transition (delayed action followed by rapid decarbonisation), and hot house world (physical risks dominate with minimal transition). The TCFD recommends organisations describe strategy resilience under "a 2°C or lower pathway"—but translating macro-level scenarios into asset-specific financial impacts requires significant modelling assumptions.

Data Quality Hierarchy

Practitioners consistently emphasise a data quality hierarchy: measured data (Scope 1 and 2 emissions from direct operations) at the top, modelled estimates (Scope 3 and supply chain exposures) in the middle, and proxy-based approximations at the bottom. The Federal Reserve pilot found that banks were "forced to rely on vendor models and proxy estimates to fill gaps"—a reality that should be transparently disclosed rather than obscured.

What's Working

MSCI's Climate Value-at-Risk Integration

MSCI's Climate Value-at-Risk methodology has become the benchmark for institutional investors, quantifying portfolio-level financial impacts across NGFS scenarios. Their December 2024 guidance, "How Can I Use Climate Scenarios?", provides a practical typology covering narrative, quantified, model-driven, and probabilistic approaches. MSCI's Asia-Pacific analysis found that loan books show a 33% increase in probability of default at year 10 under a 2°C disorderly transition—compared to 22% for Europe and 19% for the Americas—demonstrating how scenario analysis can reveal geographic concentration risks.

The key to MSCI's adoption is interoperability: their Implied Temperature Rise (ITR) metric translates company emissions into temperature alignment scores that map directly to portfolio construction decisions. This bridges the gap between climate science and financial decision-making in a way that sustainability leads can communicate to investment committees.

Moody's Climate Pathways Platform

Launched in June 2024, Moody's Climate Pathways translates NGFS parameters into full macroeconomic forecasts across 70+ countries and 18,000 variables. The platform explicitly models how carbon price trajectories flow through inflation and central bank reaction functions—a level of economic integration that enables stress testing against existing credit risk frameworks.

Moody's 2024 Catastrophe Review documented approximately $135-140 billion in global insured losses—the fifth consecutive year exceeding $100 billion—providing empirical grounding for physical risk scenarios. Their stochastic scenario generation supports ORSA (Own Risk and Solvency Assessment) requirements for insurance companies, demonstrating how climate analytics can integrate with existing regulatory frameworks rather than creating parallel compliance burdens.

Risilience and the Cambridge Connection

Risilience, a Cambridge University spin-out, has pioneered academic-rigour-meets-commercial-application in climate risk. Their enterprise platform supports TCFD, CSRD, and SFDR disclosure requirements while maintaining scientific credibility. The academic pedigree matters: practitioners report that boards and auditors respond more favourably to methodologies with peer-reviewed foundations.

What's Not Working

The "Scenario Analysis Competition" Problem

A 2024 ScienceDirect study identified what researchers termed the "climate scenario analysis competition myth"—where companies compete to display numerous scenario analyses in their reports without genuine risk assessment. The symptoms are predictable: firms copy each other's approaches, avoid crucial issues, and omit truly important information in favour of standardised templates.

This produces what practitioners call "measurement theater"—sophisticated-looking disclosures that satisfy regulatory form while failing to inform strategic decisions. The tell-tale signs include generic scenarios without business-specific analysis, lack of transparency on assumptions and methodologies, missing discussion of data limitations, and no clear linkage to capital allocation or strategic planning.

Data Fragmentation and Inconsistency

The Federal Reserve's 2024 pilot revealed that even the six largest US banks took "vastly different approaches driven by business models, risk appetite, and foreign jurisdiction experience." Real estate exposures, insurance coverage information, counterparty emissions data, and infrastructure data remain incomplete or inconsistent across the industry.

A particularly concerning finding: GHG emissions data often lacks the granularity needed for portfolio-level analysis. Banks rely on third-party vendor models that may use different methodologies, creating comparability problems that the TCFD Hub acknowledges will be "a very real challenge" to resolve. Climate data volume increased 48% between 2022-2025, creating computational challenges that exceed the capacity of many analytics teams.

Tipping Points and Non-Linearities

Current scenario frameworks assume relationships between climate variables and economic outcomes that may not hold under unprecedented conditions. The NGFS Version 5.0 explicitly notes that tipping points—irreversible shifts in ice sheet dynamics, permafrost carbon release, or ocean circulation patterns—are not incorporated. Damage functions derived from historical temperature variations may dramatically underestimate impacts at higher warming levels where historical experience provides no guide.

Practitioners report frustration that regulatory scenarios provide false precision. A scenario showing 2.1% GDP impact by 2050 implies accuracy that the underlying models cannot support. Honest disclosure requires acknowledging that results are directional indicators, not predictions.

Insurance Coverage Assumptions

The Federal Reserve pilot highlighted that insurance market dynamics are "particularly difficult to model due to data limitations." Assumptions about future insurance availability and pricing dramatically affect physical risk calculations—yet these assumptions are often buried in methodology appendices rather than highlighted as key sensitivities.

In flood-prone regions, insurers are already withdrawing coverage or pricing it out of reach. Scenario analyses that assume continuation of current insurance markets may significantly underestimate tail risks for asset owners in exposed locations.

Key Players

Established Leaders

  • MSCI — Market leader in portfolio climate risk analytics. Climate Value-at-Risk covers public and private assets; Implied Temperature Rise metric enables portfolio temperature alignment. December 2024 scenario guidance sets industry standard.

  • Moody's Analytics — Climate Pathways platform (June 2024) delivers macroeconomic scenarios across 70+ countries. Strong integration with credit risk models and regulatory frameworks (Bank of England, ECB, Federal Reserve).

  • S&P Global — Comprehensive ESG and climate data provider. TCFD Report 2024 demonstrates enterprise-level implementation. Trucost acquisition provides carbon accounting foundation.

  • Bloomberg — Climate risk data integrated into Terminal workflows. Physical risk scores and transition pathway analytics for listed companies and sovereign bonds.

Emerging Startups

  • Cervest — UK-based climate intelligence platform with $45.3 million total funding. EarthScan analyses climate risk at asset level across 50 years historical and 80 years forward projections. Backed by Lowercarbon Capital and TIME Ventures.

  • Persefoni — Carbon accounting platform with January 2024 partnership embedding First Street physical risk data for SEC disclosure compliance. Strong enterprise adoption.

  • Jupiter Intelligence — ClimateScore platform provides high-resolution physical risk analytics at property/asset level covering flood, wildfire, heat, and wind perils. Focus on real estate and infrastructure investors.

  • Climate X — Asset-level physical risk platform with granular hazard modelling. Strong UK presence and regulatory expertise.

Key Investors & Funders

  • Lowercarbon Capital — Chris Sacca's climate-focused fund backing Cervest and other climate analytics providers.

  • Breakthrough Energy Ventures — Bill Gates-backed fund investing across climate data and analytics infrastructure.

  • TIME Ventures — Marc Benioff's investment arm active in climate intelligence platforms.

  • EU Innovation Fund — Major public funding source for climate data infrastructure and analytics projects.

Action Checklist

  1. Audit your data supply chain: Map every data input to your climate risk models, identifying which are measured, modelled, or proxy-based. Document the methodology and assumptions behind third-party vendor data. The Federal Reserve pilot found this basic inventory was often incomplete even at major banks.

  2. Align with UK SRS timeline: Review the UK Sustainability Reporting Standards consultation (closing September 2025) and plan for mandatory reporting in financial year 2026. Start voluntary adoption of ISSB standards now to identify gaps before compliance becomes mandatory.

  3. Test multiple scenarios: Use at least three NGFS scenarios (orderly, disorderly, and hot house) to demonstrate strategic resilience across pathways. Document why specific scenarios were selected and how they relate to your business model.

  4. Integrate with existing risk frameworks: Climate scenario analysis should connect to ICAAP, ORSA, and enterprise risk management rather than operating as a standalone compliance exercise. Moody's Climate Pathways approach—flowing climate variables through macroeconomic models—provides a template.

  5. Acknowledge limitations transparently: Disclose data gaps, modelling assumptions, and uncertainties explicitly. The TCFD Hub guidance emphasises that honest discussion of limitations builds credibility more than false precision.

  6. Build internal capability: 46% of organisations cite lack of internal expertise as an adoption barrier. Invest in training risk managers on climate science basics and sustainability teams on financial modelling fundamentals.

  7. Establish governance ownership: Designate board-level accountability for climate scenario analysis quality and ensure integration with audit committee oversight. The Federal Reserve found that most banks adapted existing governance frameworks—make this explicit.

  8. Benchmark against peers: Use industry reports and disclosed methodologies to understand how comparable organisations approach scenario analysis. Avoid the "scenario competition" trap of copying form without substance.

FAQ

Q: How do we balance regulatory compliance with scientific uncertainty in our climate disclosures?

A: The most effective approach is radical transparency about what you know and don't know. IFRS S2 explicitly requires disclosure of "significant assumptions and judgements" used in climate-related assessments. Document your data sources, explain why you selected specific scenarios, acknowledge where proxy data was necessary, and describe how results would change under different assumptions. Regulators and investors increasingly value honest disclosure of limitations over false precision. The Bank of England's 2024 guidance specifically notes that climate scenario analysis remains "an area of ongoing research"—your disclosures should reflect this reality.

Q: Our organisation lacks the budget for enterprise-grade climate analytics platforms. What's a viable starting point?

A: Begin with the NGFS Scenarios Portal (free access) and the TCFD Knowledge Hub's scenario analysis guidance. Use publicly available tools like the WRI Aqueduct platform for water risk and the IPCC Data Distribution Centre for climate projections. For Scope 1 and 2 emissions, start with measured data from your own operations before attempting complex supply chain modelling. Many organisations find that qualitative scenario narratives—describing how different climate pathways would affect strategy—provide more immediate value than quantitative models built on weak data foundations. Build the data infrastructure first; sophisticated analytics can follow.

Q: How do we avoid "measurement theater" while still meeting disclosure requirements?

A: The antidote to measurement theater is strategic integration. Ask whether your scenario analysis informs actual decisions: capital allocation, geographic expansion, supplier diversification, product development. If the answer is no, you're likely engaged in compliance theater. The 2024 ScienceDirect study recommends industry-specific Standard Operating Procedures and customised scenario production tailored to business needs—generic approaches copied across sectors are the hallmark of performative disclosure. Test your disclosure by asking: would a sophisticated investor learn something decision-relevant from this analysis? If not, revise until the answer is yes.

Q: What role should third-party vendors play in our climate risk analytics?

A: Third-party vendors like MSCI, Moody's, and specialist providers like Cervest offer capabilities most organisations cannot build internally—global asset databases, satellite data integration, and validated modelling frameworks. However, vendors cannot substitute for internal understanding. Ensure your team can explain the methodology behind vendor outputs, identify which assumptions most affect results, and translate findings into business-relevant insights. The Federal Reserve pilot found that banks using vendor models still struggled with data consistency—outsourcing calculation does not outsource responsibility for quality.

Q: How should we think about the timeframe mismatch between climate scenarios and business planning cycles?

A: This is one of the field's most persistent challenges. Climate scenarios typically project to 2050 or beyond, while business planning rarely extends past five years. The most effective practitioners work backward: identify which climate-related changes are likely to materialise within the planning horizon, then use longer-term scenarios to stress-test assumptions about future conditions. For example, regulatory transition risks (carbon pricing, disclosure requirements) often crystallise within 3-5 years even if physical impacts are decades away. Focus scenario analysis on the intersection between climate pathways and business-relevant decision points.

Sources

Climate risk analytics is maturing rapidly, but maturation does not mean the hard problems are solved. The practitioners who succeed will be those who embrace uncertainty transparently, integrate climate analysis with strategic decision-making, and resist the temptation to substitute sophisticated-looking outputs for genuine insight. The regulatory framework is settling; the challenge now is building analytical capability that creates value beyond compliance.

Topics

riskanalyticsIoT sensorsstandardstransition planuncertaintyverification

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