Methodology - Location based financial metrics (1/2)

To provide a forward-looking and financially relevant assessment of climate risk, we use a two-step methodology to quantify the nuances of climate's financial impacts on different asset types:

1. Location-Based Physical Risk: First, we calculate a set of asset-agnostic metrics for a specific geographic location. These translate raw climate data (e.g., changes in temperature, precipitation) into standardized measures of baseline physical risk. This layer answers the question: "What is the baseline climate exposure at this location?"

2. Asset-Specific Vulnerability Adjustment: Second, we adjust the baseline geographic risk using evidence-based Sensitivity Coefficients to reflect the unique vulnerabilities of a specific asset class. This layer answers the question: "How does this specific asset (e.g., a data center vs. a residential building) actually experience the baseline risk at this location?"

The relationship between these layers forms the core of our analysis:

Final_Impact_Asset = Baseline_Impact_Geo × f(Sensitivity_Coefficient)

This page explains the formula, features and assumptions used to calculate location based financial metrics. The next page explains the asset-specific vulnerability adjustments for each metric.

1. Insurance Premium Impact

The insurance cost component calculates the expected annual increase in flood and fire insurance to help asset managers price in future increases in insurance premiums today.

As the frequency and intensity of acute climate hazards increase, the expected damage, or the Mean Damage Ratio increases over time, leading to an increase in an insurer's loss ratio (premiums earned that an insurance company pays out in claims and other expenses).

The increase in loss ratio is then translated into higher insurance premiums and will be borne directly by the customers. By understanding the risk of an asset today and the projected risk in the future, we can then calculate the estimated insurance premium at any future period with the following formula:

Where Pt is the projected premium in the future period t, L is the average Loss Ratio of insurance underwriters, and MDR is the projected mean damage ratio.

In practice, to avoid pricing in short term fluctuations due to volatilities in climate predictions, we take a 25-year interval, and we apply the method to wildfire, coastal flooding and inland flooding to obtain the annual increase of fire insurance and flood insurance for any location.

For property insurance, the loss ratio has historically been ranging from 68% to 78% in past 10 years according to the National Association of Insurance Commissioners (NAIC). For the future insurance calculations, we assume a fixed loss ratio of 70% which is close to the 10-year average.

Contributing Features:

2. Insurability Index

In certain high-risk locations, the probability and severity of climate-related damages may become so extreme that the risk is technically or economically uninsurable. This metric provides a simple binary flag to identify such locations by testing if their future risk level will surpass what is considered an extreme outlier in today's market.

To acheive this, a location is flagged as uninsurable if its future risk score exceeds the 99th percentile of the current risk distribution.

Uninsurable = 1 if OVERALL_IMPACT_2050 > P99(OVERALL_IMPACT_2025)

Contributing Features:

3. Utility Demand Impact

The utility demand component estimates the expected annual increase in heating and cooling costs experienced by an asset using the changes of cooling and heating degree days.

The cooling and heating requirement to keep occupants comfortable is expected over time due to climate change. By comparing the percentage increase (or decrease) between the cooling/heating demand of the baseline year (currently 2025) and future the future year (2050), we can then obtain the annual change of heat and cooling demand of a building.

3.1 Cooling Demand Calculation

The effective cooling demand of a location are calculated from Cooling Degree Days (CDD) which are a proxy to that measures the demand for energy to cool buildings to a comfortable temperature for its occupants. This is achieved using two different equations, with the maximum value taken as the final CDD value.

Eqn1 (Applicable in locations with CDD > 200): cooling energy demand kWh/m2 = cdd * 0.0193-2.913 
Eqn2 (Applicable for all locations): cooling energy demand kWh/m2 = cdd * 0.09 + 2.8

These equations reference findings from established literature and are designed to capture the increased energy demand for cooling as temperatures rise.

• Reference for equation 1: Home air conditioning in Europe – how much energy would we use if we became more like American households?

• Reference for equation 2: Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach

3.2 Heating Demand Calculation

Heating Degree Days (HDD) are a proxy to that measures the demand for energy to heat buildings to a comfortable temperature for its occupants. The effective heating demand is calculated using a single equation derived from published literature. This equation accounts for the reduced need for heating as global temperatures rise. Studies have shown that HDDs will decrease as a result of global warming, leading to a reduction in heating energy demand.

heating energy demand kWh/m2 = hdd * 0.042 - 21.8

• Reference for equation: Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach

4. Maintenance Increase

Climate change accelerates the wear and tear on physical assets. This metric focuses on the cumulative stress from chronic climate conditions—such as prolonged heat, persistent humidity, and total annual rainfall—which drives the need for increased routine maintenance. It quantifies the required increase in an annual maintenance budget to counteract this accelerated degradation using the formula below where the coefficients for each feature adjusts the actual impact for each unit of increase of the contributing features, giving an overall maintance increase value.

Maint_Increase = ∑(C_i ⋅ ΔFeature_i)

Which translates to: Maint_Increase = (C_heat ⋅ ΔDays>35C) + (C_humidity ⋅ ΔHigh_Humidity_Days) + (C_precip ⋅ ΔTotal_Annual_Precip) + (C_wind ⋅ ΔHigh_Wind_Days) + (C_freeze ⋅ ΔFreezeThaw)

Contributing Features:

Coefficients:

5. Operational Downtime

Operational downtime is driven by the increased frequency of acute hazard events that force a temporary slowdown or complete shutdown of operations. This metric estimates the increase in the number of downtime days per year for a given location.

Formula: ΔDowntime_total = ∑(ΔHazard_Metric_i)

6. Operational Efficiency & Capacity Loss

Certain climate hazards do not cause shutdowns but directly reduce the efficiency or maximum output of productive assets. This metric quantifies that potential percentage loss at a location based on key climate drivers. For example, extreme heat can reduce human labor productivity due to health risks, while water scarcity can force a reduction in capacity for water-reliant assets.

7. Retrofit Recommendation (CapEx)

Remediation measures are crucial to mitigate the increasing risks posed by climate change. For example, retrofitting homes to improve insulation and ventilation can significantly reduce cooling demand and enhance thermal comfort, thereby reducing energy consumption. Similarly, implementing flood defenses and fire-resistant materials can mitigate damage and lower insurance premiums.

The retrofit recommendations provide a general guideline on the retrofit expenditure required for assets to adapt to a changing climate as CapEx denominated as percentage of annual net income.

For each relevant climate risk category (i.e. Heat Stress, Flood, Fire), we find the difference of the Mean Damage Ratio between 2025 and 2050 and translate the difference to estimated CapEx based on an exponential scale of 0 - 10% as shown in the following table. In practice, the exact breakpoint is calculated dynamically for each hazard type using a min-max scaling function. For each risk category, the maximum additional CapEx is capped at 10% of income. If the asset is exposed to multiple risk categories, the additional CapEx for each risk is summed together, and can exceed 10%.

CapEx Tiers:

The following histogram shows the total additional CapEx for 1 million randomly selected assets around the world across all hazards. With the heavy-tail nature of the distribution, 50% of assets would experience an additional CapEx below 3.2% while highly exposed assets would require heavy investments of up to 14% in additional CapEx.

Climate Discount Rate

Similar to green discounts that factor in potential long term stranded asset risk and green retrofits, the climate discount rate is a long-term discount rate that factors in uncertainties brought by climate risk.

The climate discount rate is a relative metric that is calculated by projecting the overall risk profile (total mean damage ratio from all 6 climate risk categories) on a global scale. The maximum possible discount rate varies between the applied time period. Based on current knowledge, for assets exiting before 2035, the maximum possible climate discount is 75bp (0.75%); before 2050, 125bp (1.25%); before 2100, 200bp (2%).

In practice, the period of 2035 chosen for most assets with exit windows below 10 years from today.

Climate-adjusted Valuation

All the metrics above can be easily incorporated into standard financial frameworks, including Discounted Cash Flow (DCF) models for climate-adjusted valuation.

This article captures how these metrics come together to form a climate-adjusted valuation in a DCF model.