Explainer: Climate risk analytics & scenario modeling — the concepts, the economics, and the decision checklist

A practical primer: key concepts, the decision checklist, and the core economics. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.

In 2024, the global climate risk analytics market surpassed $9.8 billion, with projections indicating growth to over $50 billion by 2033 at a compound annual growth rate of 17.5% (Business Research Insights, 2024). More than 60 nations now mandate some form of climate risk disclosure, and the Network for Greening the Financial System (NGFS) Phase V scenarios released in November 2024 revealed that GDP losses under current policies could reach 15% by 2050—a figure two to four times greater than previous estimates. For financial institutions, asset managers, and corporations navigating the transition to a low-carbon economy, climate risk analytics and scenario modeling have evolved from voluntary exercises to strategic imperatives. Understanding the underlying concepts, the economic logic, and the decision framework is no longer optional for sustainability professionals.

Why It Matters

Climate risk analytics enables organizations to quantify, model, and stress-test their exposure to both physical risks (floods, wildfires, heat stress) and transition risks (carbon pricing, stranded assets, regulatory shifts). The Task Force on Climate-related Financial Disclosures (TCFD), which completed its mandate in 2023 and transferred monitoring to the IFRS Foundation, established scenario analysis as the gold standard for forward-looking climate risk assessment (TCFD, 2023). Nearly 5,000 organizations worldwide have publicly endorsed the TCFD recommendations, with European companies leading in comprehensive disclosure across all 11 recommended metrics.

The economics are stark. According to the Bank of England's Climate Biennial Exploratory Scenario, UK banks could face credit losses of up to £225 billion under a late and disorderly transition scenario, while insurers face escalating claims from acute physical events (Bank of England, 2022). The regulatory landscape has intensified: the EU Corporate Sustainability Reporting Directive (CSRD), the SEC's climate disclosure rules finalized in March 2024, and the European Central Bank's supervisory expectations now require financial institutions to integrate climate scenarios into their risk management frameworks.

Beyond compliance, climate risk analytics drives competitive advantage. Asset managers with robust climate risk capabilities can identify stranded asset exposure before it materializes, price risk more accurately in lending decisions, and construct portfolios aligned with net-zero pathways. Insurance carriers use predictive climate models to optimize underwriting, while corporate treasurers leverage scenario analysis to inform capital expenditure decisions on infrastructure resilience.

Key Concepts

Physical Risk vs. Transition Risk

Physical risks arise from the direct impacts of climate change: chronic hazards like rising sea levels, increasing average temperatures, and shifting precipitation patterns, alongside acute hazards such as hurricanes, floods, and wildfires. Physical risk modeling incorporates climate projections, geospatial asset data, and vulnerability functions to estimate potential damages.

Transition risks emerge from the societal shift toward a low-carbon economy. These include policy changes (carbon taxes, emissions regulations), technological disruption (renewable energy cost declines making fossil fuel assets uncompetitive), market shifts (changing consumer preferences), and reputational impacts. Transition risk modeling integrates policy scenarios, technology learning curves, and sector-specific decarbonization pathways.

RCP and SSP Scenarios

The Intergovernmental Panel on Climate Change (IPCC) developed Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) as foundational scenario frameworks. RCPs define greenhouse gas concentration trajectories based on radiative forcing levels, ranging from RCP 1.9 (Paris-aligned, <1.5°C) to RCP 8.5 (high emissions, >4°C warming). SSPs provide socioeconomic narratives describing different development futures, from SSP1 (sustainability, low challenges) to SSP5 (fossil-fueled development, high mitigation challenges).

These scenarios combine into matrices (e.g., SSP1-2.6, SSP5-8.5) that describe both the emissions pathway and the societal context shaping adaptation and mitigation capacity. Financial institutions translate these scientific scenarios into economic variables using Integrated Assessment Models (IAMs) like REMIND-MAgPIE and MESSAGE-GLOBIOM.

NGFS Scenarios

The Network for Greening the Financial System developed six standardized scenarios specifically designed for financial risk assessment:

Scenario	Type	Temperature Outcome	Key Characteristics
Net Zero 2050	Orderly	1.5°C	Immediate action, smooth transition
Below 2°C	Orderly	<2°C	Gradual policy tightening
Delayed Transition	Disorderly	~1.8°C	Action delayed until 2030
Fragmented World	Disorderly	~2.5°C	Divergent global policies
Current Policies	Hot House	3-4°C	Only existing policies
Low Demand	Orderly	1.5°C	Behavioral change emphasis

The NGFS Phase V release (November 2024) significantly revised physical risk estimates using updated damage functions, with GDP losses under current policies now projected at 15% by 2050 and potentially 30% by 2100 (NGFS, 2024).

Sector-Specific KPIs for Climate Risk Analytics

Sector	Key KPI	Benchmark Range	Data Source
Banking	Climate VaR (% of portfolio)	2-8% stressed loss	NGFS scenarios
Insurance	Probable Maximum Loss increase	15-40% by 2050	CAT modeling
Real Estate	Properties in high-risk zones (%)	<10% target	Geospatial overlays
Energy	Stranded asset ratio	20-50% fossil reserves	IEA Net Zero
Agriculture	Yield volatility coefficient	±15-30% variance	CMIP6 projections
Manufacturing	Scope 3 transition exposure	40-70% of emissions	LCA databases

What's Working and What Isn't

What's Working

Regulatory momentum is driving adoption. The combination of CSRD in Europe, SEC climate rules in the United States, and central bank stress testing requirements has created consistent demand signals. Financial institutions representing 39% of global climate risk analytics users now conduct regular portfolio stress testing, with cloud-based analytics adoption growing 44% between 2023 and 2025.

Geospatial analytics has matured. High-resolution climate models combined with satellite imagery and asset-level geocoding enable granular physical risk assessment. Jupiter Intelligence's ClimateScore Global platform, for example, provides parcel-level flood, heat, wind, and fire risk scores that integrate directly into underwriting and lending workflows.

Standardization improves comparability. The NGFS scenarios provide a common reference framework that enables regulators, investors, and corporations to conduct comparable analyses. The Phase V release, accessible through the IIASA database, provides country-level granularity across 40+ sectors with 5-year time steps through 2100.

AI-powered analytics accelerate insights. Thirty-six percent of new climate risk systems incorporate machine learning, improving forward-looking accuracy by an estimated 27% compared to traditional statistical approaches (Business Research Insights, 2024).

What Isn't Working

Data harmonization remains a bottleneck. Thirty-five percent of organizations report significant challenges in merging physical and transition risk metrics, particularly when integrating proprietary datasets with standardized scenario outputs. Asset-level data gaps, especially in emerging markets and private companies, constrain analytical precision.

Internal expertise is scarce. Forty-six percent of organizations cite lack of internal expertise as a primary barrier to effective climate risk analytics implementation. The intersection of climate science, financial modeling, and data engineering requires specialized talent that is in short supply.

Time horizon mismatches create governance challenges. Climate scenarios project 30-80 year horizons, while most financial risk frameworks operate on 1-5 year timeframes. This disconnect complicates integration with credit risk models, capital allocation processes, and board reporting cadences.

Model uncertainty is underappreciated. The academic debate surrounding the Kotz et al. (2024) damage function underlying NGFS Phase V physical risk estimates highlights the sensitivity of outputs to methodological choices. Organizations often treat scenario results as precise forecasts rather than exploratory projections.

Key Players

Established Leaders

Moody's Analytics provides climate risk solutions integrating physical and transition risk modeling with credit analytics, serving global banks and asset managers with scenario-based stress testing capabilities.

S&P Global offers TCFD-aligned climate risk assessments through its Sustainable1 division, combining ESG data with scenario analysis tools used by institutional investors managing over $100 trillion in assets.

MSCI delivers climate value-at-risk analytics and implied temperature rise metrics, enabling portfolio managers to assess alignment with Paris Agreement pathways across equity and fixed income holdings.

Bloomberg integrates climate scenario data into its Terminal platform, providing portfolio-level physical and transition risk dashboards used by over 350,000 financial professionals globally.

Emerging Startups

Jupiter Intelligence (San Mateo, CA) has raised approximately $100 million to date and provides asset-level climate risk scores for flood, heat, wind, and fire hazards, serving clients across insurance, banking, and infrastructure sectors.

Mitiga Solutions acquired London-based Cervest in 2024, combining EarthScan's AI-powered climate intelligence platform with expanded physical risk modeling capabilities for enterprise climate adaptation planning.

Sust Global offers climate risk APIs that enable programmatic integration of physical risk data into enterprise applications, targeting real estate, lending, and infrastructure investment workflows.

Key Investors & Funders

Breakthrough Energy Ventures (Bill Gates) has invested in multiple climate analytics and data infrastructure companies, recognizing the enabling role of risk quantification in capital allocation decisions.

Lowercarbon Capital participates in climate risk analytics funding rounds, including investments in platforms addressing physical risk modeling and climate data infrastructure.

CDPQ (Caisse de dépôt et placement du Québec) and other institutional investors have backed climate analytics platforms as part of broader climate tech allocation strategies, with CDPQ participating in Jupiter Intelligence's Series C round.

Examples

Bank of England Climate Biennial Exploratory Scenario (2021-2022): The Bank of England tested the UK's largest banks and insurers against NGFS-derived scenarios, finding that climate-related losses could reach tens of billions of pounds under adverse transition pathways. The exercise identified data gaps, modeling limitations, and the need for enhanced physical risk granularity, informing subsequent supervisory expectations (Bank of England, 2022).
BlackRock Climate Risk Integration: The world's largest asset manager has integrated climate scenario analysis across its investment platform, using proprietary physical and transition risk models to inform portfolio construction and engagement strategies. BlackRock's 2024 TCFD report details scenario testing of equity and fixed income portfolios against Net Zero and Current Policies pathways, identifying sector-level exposures and informing stewardship priorities (BlackRock, 2024).
Zurich Insurance Climate Risk Analytics: Zurich partnered with Microsoft to develop an enhanced climate risk assessment capability leveraging Azure's computing infrastructure and geospatial tools. The initiative applies machine learning to catastrophe modeling, improving underwriting accuracy for flood and windstorm perils while informing reinsurance purchasing decisions (Zurich Insurance, 2023).

Action Checklist

Conduct a materiality assessment to identify which physical and transition risks are most relevant to your organization's assets, operations, and value chain.
Select an appropriate scenario framework: NGFS for financial institutions, RCP/SSP combinations for longer-term strategic planning, or custom scenarios for sector-specific analysis.
Map asset-level geolocation data to enable granular physical risk assessment; address data gaps in Scope 3 supply chains and emerging market exposures.
Establish governance structures that bridge the time horizon gap between climate scenarios (30-80 years) and traditional risk management cycles (1-5 years).
Build internal expertise through hiring, training, or partnerships with specialized climate risk analytics providers; avoid treating scenario outputs as precise forecasts.
Integrate climate risk metrics into existing risk management processes, investment decision frameworks, and board reporting cadences.
Document assumptions and methodological choices to support regulatory compliance and stakeholder communication.

FAQ

Q: How do physical and transition risks interact, and can they be modeled together? A: Physical and transition risks exhibit complex interdependencies. Aggressive decarbonization (Net Zero scenarios) reduces long-term physical risks but increases near-term transition costs; delayed action (Current Policies) minimizes transition disruption but amplifies physical damages. Integrated assessment models attempt to capture these trade-offs, but most financial institutions model physical and transition risks separately before combining results at the portfolio level. Emerging approaches use dynamic interaction models, but data and computational constraints limit practical implementation.

Q: Which scenarios should we prioritize for stress testing and strategic planning? A: Regulatory stress tests typically require testing against adverse transition scenarios (Delayed Transition, Fragmented World) and high-physical-risk scenarios (Current Policies). For strategic planning, organizations should test against at least three contrasting scenarios: an orderly transition (Net Zero 2050), a disorderly transition (Delayed Transition), and a hot house world (Current Policies). Avoid anchoring on a single scenario; the value of scenario analysis lies in exploring a range of plausible futures.

Q: How should we handle the uncertainty in climate scenario outputs? A: Treat scenario outputs as exploratory projections rather than probabilistic forecasts. Document the sensitivity of results to key assumptions (discount rates, damage functions, technology learning curves). Use scenario ranges rather than point estimates in decision-making. Communicate uncertainty to boards and stakeholders transparently, emphasizing that scenario analysis informs strategic optionality rather than providing precise predictions.

Q: What is the relationship between TCFD and ISSB climate disclosures? A: The TCFD disbanded in October 2023 after completing its mandate, with monitoring transferred to the IFRS Foundation. The International Sustainability Standards Board (ISSB) IFRS S2 standard incorporates the core elements of TCFD recommendations, ensuring continuity. Organizations currently reporting under TCFD can leverage their existing frameworks for ISSB compliance, with additional granularity required on Scope 3 emissions and transition plan disclosures.

Q: How do we address data gaps in climate risk analytics? A: Prioritize data acquisition based on materiality: focus first on assets and supply chain exposures with the greatest climate sensitivity. Use proxy methodologies (sector averages, regional data) where asset-level information is unavailable, documenting assumptions explicitly. Engage with data providers (CDP, industry associations, geospatial vendors) to fill gaps iteratively. Recognize that perfect data is unattainable; the goal is decision-useful insight, not spurious precision.

Sources

Business Research Insights. (2024). Climate Risk Analytics Market Size, Share - Forecast To 2033. https://www.businessresearchinsights.com/market-reports/climate-risk-analytics-market-118511
TCFD. (2023). Task Force on Climate-related Financial Disclosures: 2023 Status Report. Financial Stability Board. https://www.fsb-tcfd.org/publications/
NGFS. (2024). NGFS Climate Scenarios for Central Banks and Supervisors - Phase V. Network for Greening the Financial System. https://www.ngfs.net/en/publications-and-statistics/publications/ngfs-climate-scenarios-central-banks-and-supervisors-phase-v
Bank of England. (2022). Results of the 2021 Climate Biennial Exploratory Scenario (CBES). https://www.bankofengland.co.uk/stress-testing/2022/results-of-the-2021-climate-biennial-exploratory-scenario
BlackRock. (2024). 2024 Climate Report Aligned with the Task Force on Climate-related Financial Disclosures. https://www.blackrock.com/corporate/literature/continuous-disclosure-and-important-information/climate-report-blkinc.pdf
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report. Cambridge University Press.