AI Climate Risk Modeling for Physical and Transition Risks

Imagine you manage a portfolio of 500 industrial assets across diverse climate zones. Regulators require disclosure of physical and transition risks under the TCFD. Insurers demand Expected Annual Loss (EAL) for each asset. Manual analysis across 4 IPCC scenarios takes weeks of routine work, and off-the-shelf black boxes like MSCI don't let you peek under the hood. Our AI climate risk modeling translates this challenge into a quantitative assessment tied to specific assets, time horizons, and emission scenarios. With over 5 projects completed for industrial and financial companies, we guarantee accurate and relevant results. Compared to manual calculation, our solution cuts assessment time by 80% and costs by 30–50% through automation and model reuse.

Climate risks split into physical (floods, heat waves, wildfires) and transition (carbon taxes, regulatory shifts). We build separate models for each type and aggregate them into a unified ClimateVaR metric. Here's how.

Physical Climate Risks

Classification by time horizon:

• Short-term (<5 years): extreme precipitation, heat waves, floods
• Medium-term (5–30 years): changes in hurricane frequency, droughts, sea-level rise
• Long-term (>30 years): structural shifts in climate zones, desertification

IPCC scenarios:

For each scenario, we use a set of climate projections (CMIP6 models) as a basis for regional downscaling.

How We Build the Physical Risk Model

Step 1: Asset Geocoding and Location Intelligence

assets = { 'asset_id': 'plant_001', 'lat': 55.7522, 'lon': 37.6156, 'asset_type': 'manufacturing', 'replacement_value': 50_000_000, # USD 'operational_lifespan': 30 # years } 

Geospatial joining with climate layers (GADM, OpenStreetMap elevation).

Step 2: Hazard Modeling

Flood risk: we use global flood models (FATHOM, JBA Risk, AQUEDUCT). For each point: flood depth × probability distribution.

Heat stress: WBGT (Wet Bulb Globe Temperature) from NOAA, ERA5, CMIP6 projections. We calculate the change in number of days exceeding 35°C.

Wildfire: FWI (Fire Weather Index) based on CMIP6 — increase in high-risk fire days.

Step 3: Vulnerability Assessment

Damage function based on curves from Huizinga (Huizinga, 2017). Example for flood:

depth_damage_curve = { 0.0: 0.00, 0.5: 0.15, 1.0: 0.30, 2.0: 0.55, 3.0: 0.75, 5.0: 0.95 } 

Step 4: Expected Annual Loss (EAL)

Integration over return periods (RP10 … RP500):

def expected_annual_loss(damage_by_return_period): return_periods = [10, 50, 100, 250, 500] probs = [1/rp for rp in return_periods] damages = [damage_by_return_period[rp] for rp in return_periods] return np.trapz(damages, probs) 

Why Our Model Is More Accurate Than Competitors

Instead of commercial black boxes (MSCI, Jupiter), we calibrate on open records (ERA5, AQUEDUCT) and use ensemble methods for uncertainty quantification. Our average flood-depth error on historical events is 15% — twice as good as standard global models. Furthermore, we incorporate Bayesian updating as new climate data becomes available, reducing the p95 error of long-term projections by 25%.

How We Assess Forecast Uncertainty

For each asset we calculate confidence intervals for EAL using bootstrap (1000 iterations). We report P50, P75, P95 — allowing clients to set reserves according to their risk appetite. This transparency is not available in standard models.

Transition Risks

Carbon cost: for companies with high Scope 1+2 emissions.

def carbon_cost_impact(scope12_emissions_tCO2, carbon_price_scenarios): return {scenario: emissions * price for scenario, price in carbon_price_scenarios.items()} 

Stranded Assets: DCF at different carbon prices — risk of depreciation of coal/oil/gas assets.

Policy risk: NLP analysis of legislation (EU ETS, CBAM, Russian ECO levy) — assessing the likelihood of introduction of restrictions for industries.

Project Phases