Physical Risk Scoresand Financial Impact Data
Methodology
Sustainable1-

July 2023

V3.0 - February 2023
July 2024: Document formatting and layout updates

Table of Contents
Introduction and Context ................................................................................................ 3
Exposure Scores and Financial Impact Metrics ...................................................................... 3
Indicators and Scenarios........................................................................................................ 4
CMIP6 Climate Hazard Modeling ............................................................................................ 6

Methodology Overview.................................................................................................. 10
1.
2.
3.
4.
5.
6.

Climate Hazard Mapping .......................................................................................................... 10
Physical Risk Exposure Quantification ..................................................................................... 10
Asset and Company Level Physical Hazard Exposure Scores .................................................. 14
Financial Impact Function Modelling ....................................................................................... 18
Asset and Company Level Physical Rick Financial Impact Calculation .................................... 20
Disaggregated Financial Impact Metrics .................................................................................. 21

Monitoring and Review ................................................................................................. 22
Assumptions and Limitations ....................................................................................... 22
Maintenance/Updates .................................................................................................. 23
Related Documentation ................................................................................................ 23
S&P Global Sustainable1 Disclaimer ............................................................................ 25

Introduction and Context
This document details the datasets, methods and assumptions that underpin the S&P Global
data and associated analytics for financial institutions and non-financial corporates.
The release of the Taskforce on Climate Related Financial Disclosures (TCFD) report highlighted theimportance of climate change as a driver of material financial risks for companies and investors thatshould be assessed, disclosed and managed.
The TCFD categorizes the financial risks posed by climate change as Transition Risks (including policy
and legal risks, technology risk, market risk and reputational risk) and Physical Risk (both acute andchronic).1
Physical risks resulting from climate change can be acute (driven by an event such as a flood or storm) or
chronic (arising from longer term shifts in climate patterns) and may have financial implications fororganizations such as damage to assets, interruption of operations and disruption to supply chains.
S&P Global Sustainable1 launched a suite of Climate Change Physical Risk Analytics solutions to themarket in 2019, offering an asset-based approach to the assessment of physical risk at the company andportfolio level. In 2022, Sustainable1 launched an enhanced physical risk analysis framework, leveraging
the expertise and intellectual property of The Climate Service (TCS), which was acquired by S&P Global in
January 2022. In 2024, the Climate Change Physical Risk Exposure Scores and Financial Impact dataset
was enhanced to incorporate a complete update of the asset level database underlying the dataset, anda series of methodology enhancements as describeddocument.

Exposure Scores and Financial Impact Metrics
Table 1 below describes the interpretation and user applications of physical risk exposure scores andfinancial impact metrics for financial institutions and non-financial corporate use cases.
Table 1: Exposure Scores and Financial Impact Metrics Explained

Physical Risk Exposure Scores

Physical Risk Financial Impact

What does
this metricrepresent?

Point in time exposure to climate
hazards relative to global conditions,
independent of the characteristics ofthe asset present at a given location

Financial consequences arising from thechange in climate hazard exposure vs a
baseline, specific to the asset present at agiven location

Advantages

•

•

•

•

Efficient and high throughput for
rapid screening of large asset
portfolios.
Offers an expansive view of climate
hazards present at a given location,
not limited to those hazards that are
assumed to be material.
Readily applicable where only
limited information (location only) isavailable on assets to be analyzed..

••

Deep dive analysis to quantify thefinancial impact of changing climate
hazard exposure based on the best
the most material impacts for each
asset type.
Granular analysis based on over 250
different asset type profiles andassociated financial impact pathways.
Ready integration into downstream
financial analysis such as valuation

1

TCFD. 2017. Recommendations of the Task Force on Climate-related Financial Disclosures. [Online]. Available: https://www.fsbtcfd.org/wp-content/uploads/2017/06/FINAL-2017-TCFD-Report-11052018.pdf

3Physical Risk Exposure Scores

Use Cases•

Valuable as proxy for risk in a given
location (or nearby locations) whenasset data is not available.

•

Risk screening exercises andportfolio analytics to understand:o Aggregate physical risk
exposure at the asset,
company or portfolio level,
and in comparison with
relevant benchmarks.
o Which climate hazards
represent the greatest
exposure.
o The assets or companies in a
portfolio which contributemost to portfolio level
exposure.
Inform initial TCFD disclosures andrisk screening initiatives.
Focus attention on the most
exposed assets, companies or
portfolio holdings to direct further
investigation to the areas withgreatest potential impact.

•
•

What
outputs areproduced?

Exposure Score: 1-100 score
representing the exposure to eachhazard relative to global conditions.

Physical Risk Financial Impact
models, credit risk models and thecreation of climate risk adjusted
financial accounts.
•

•
•

•

Deep dive physical risk analysis
focusing on the financial materiality ofclimate hazard exposures to specificasset types.
Inform detailed TCFD disclosures andreporting.
Integration of climate physical risk into
financial modelling, including thedevelopment of adjusted financial
accounts, credit risk modelling andequity valuation modelling.
Climate resilience strategy.

Financial Impact: Financial losses (e.g.,
CapEx, OpEx, and Business Interruption)reflected as a percentage of asset value
due to exposure to climate-related physicalhazards.
MAAL ($US): Modelled Average Annualized
Losses in absolute dollar terms. Available
for real assets analysis via Climanomics.

Assetclassescovered••••Equities
Fixed income
Real assets
Munis, sovereign and other asset
classes (planned in future)

•
•
•
•

Equities
Fixed income
Real assets (via Climanomics)
Munis, sovereign and other asset
classes (planned in future)

Indicators and Scenarios
Table 2 below presents the climate change physical hazards considered in the Sustainable1 dataset. All
hazards are evaluated globally at consistent spatial resolution, with the exception of coastal flood where
higher resolution is available for USA.

4

Table 2: Climate Hazard Coverage

Hazards

Analysis Metric

Indicator Definition

CoastalFloodFrequency of 100yr flood

Projected frequency of thehistorical baseline 100-yr
coastal flood depth

SpatialResolution

Data Sources

30x30m(USA)

GTSR hydrodynamic
surge model

90x90m(RoW)

Kopp et al SLR data
MERIT /US3DEPUSGS global
coastlines

River(Fluvial)
Flood

Frequency of 100yr flood

Projected return period of thehistorical 100-yr fluvial flood
depth within projected flood
extents~1x1 kmPluvial(Extreme
Rainfall)Flood
ExtremeHeat

Frequency of 100yr flood

Projected return period of thehistorical 100-yr pluvial flood
depth~25x25km

NEX-GDDP
downscaled CMIP6

Projected Tx95p

Annual percentage of days
with maximum temperature
warmer than the 95th
percentile local baseline daily
maximum temperature~25x25km

NEX-GDDP
downscaled CMIP6

Tx50pAbsChg

Hydro Atlas
WRI Aqueduct
NEX-GDDP
downscaled CMIP6

Absolute change in 50th
percentile temperature
Extreme
Cold

Projected Tx10p

Annual percentage of days
with minimum temperaturecolder than the 10th percentile
local baseline daily minimum
temperature~25x25km

NEX-GDDP
downscaled CMIP6

TropicalCycloneFrequency of
Cat3+ storms

Projected annual frequency of
category 3 and higher tropical
cyclones~25x25km

HURDAT

Wildfire

Water
StressFire Weather Index(FWI)

Water Stress Index

Financial Impact: Annual
percentage of days in which
the FWI exceeds the historical
local 90th percentile.
Exposure Scores: Annual
percentage of days classified
as high, very high or extreme
wildfire danger
Projected future ratio ofwater withdrawals to total

JTWC TC archives
CMIP5/6 SST~25x25km

NEX-GDDP
downscaled CMIP6

River Basin

WRI Aqueduct

5

Hazards

Analysis Metric

Indicator Definition

SpatialResolution

Data Sources

renewable water supply in agiven area.
Drought

Financial Impact: Annual
Standardized
percentage of months in which
Precipitation
Evapotranspiration the SPEI falls below thehistorical local 10th percentile. ~25x25km
Index (SPEI)
Exposure Scores: Annual
percentage of months classified
as severely dry or extremely dry

NEX-GDDP
downscaled CMIP6The Sustainable1 dataset focuses on four future climate change scenarios based on IPCC Representative
Concentration Pathways and Shared Socioeconomic Pathways and informed by the TCFD technicalguidelines (FSB, 2017)2:•

High Climate Change Scenario (SSP5-8.5): Low mitigation scenario in which total greenhouse gas
emissions triple by 2075 and global average temperatures rise by 3.3-5.7 °C by 2100.

•

Medium-High Climate Change Scenario (SSP3-7.0): Limited mitigation scenario in which total
greenhouse gas emissions double by 2100 and global average temperatures rise by 2.8-4.6 °C by
2100.

•

Medium Climate Change Scenario (SSP2-4.5): Strong mitigation scenario in which total greenhousegas emissions stabilize at current levels until 2050 and then decline to 2100. This scenario is
expected to result in global average temperatures rising by 2.1-3.5 °C by 2100.

•

Low Climate Change Scenario (SSP1-2.6): Aggressive mitigation scenario in which total greenhousegas emission reduce to net zero by 2050, resulting in global average temperatures rising by 1.3-2.4°C by 2100, consistent with the goals of the Paris Agreement.

The Sustainable1 dataset evaluates climate change physical risks for decadal averages from the 2020s to
the 2090s. Financial impact quantification pathways are not currently available for extreme cold but are
offered for all other climate hazards.

CMIP6 Climate Hazard Modeling
S&P Global Sustainable1 climate change hazard modeling utilizes the CMIP6 climate models, the latest
generation of global climate models informing the Intergovernmental Panel of Climate Change (IPCC)3.
Temperature and precipitation data from 35 CMIP6 models were recently downscaled from the varying
native spatial resolution of the models to a uniform 0.25° latitude-longitude grid, comprising the NEXGDDP4 downscaled CMIP6 dataset which forms the basis for the Sustainable1 hazard model. The NEXGDDP dataset was processed for a historical baseline plus four scenarios, SSP126, SSP245, SSP370, and
SSP585; however, not all of the 35 underlying CMIP6 models were available for all scenarios. The data
format, spatial maps at time slices, were first reprocessed to generate daily time series to year 2100 at
each grid cell. Model-mean time series were then generated for the daily precipitation and temperature
minimum and maximum, data which constitute the primary drivers for five of the nine hazards includedin the Sustainable1 model.

2

Ibid.
World Climate Research Programme. 2020. CMIP Phase 6 (CMIP6). [Online]. Available: https://www.wcrp-climate.org/wgcmcmip/wgcm-cmip6
4
NASA Center for Climate Simulation. 2022. NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). [Online].
Available: https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp
36

Driven by the downscaled CMIP6 data and other sources, the hazard models summarized below havevariables at the global scale. That is, hazard variable values are
generated and archived in decadal-mean values (historical baseline and the 2020s through 2090s) on
global grids for four climate scenarios. This preprocessed data is then accessed for subsequent scoring
and financial impact analysis. A brief description of the methodology utilized to calculate each of thenine hazards is provided in Table 3.
Table 3: Climate Change Hazard Modelling

HazardMethodology Description

Extreme
Heat

Extreme heat hazard is quantified based on three metrics:• Tx95p, the percentage of days per year with a maximum temperature that exceeds the90th percentile of the local historical baseline daily maximum temperature (Exposure
Scores and Financial Impact);•

Tx90pAbsChng representing the absolute change in 90th percentile maximum
temperature vs. a historical baseline (Financial Impact); and•ExtremeCold

FluvialFlood

Tx50pAbsChg representing the absolute change in median maximum temperature vs. ahistorical baseline (Financial Impact).
The measure of extreme cold is Tn10p, the percentage of days per year with a minimum
temperature below the 10th percentile of the local historical baseline daily minimum
temperature. These hazard variables are derived directly from each downscaled CMIP6
model, then averaged across models and years in a projected decade.
The 10-yr and 100-yr return-period (RP) river discharges are related statistically to seven
A. Fourcovariates are topographic in nature and three are climatological (5-day precipitation
maxima and numbers of consecutive dry days and frosts days). For future decades, theclimate covariates are derived from the downscaled CMIP6 dataset. The projected 10-yr
and 100-yr discharges are interpolated to obtain the projected frequency (reciprocal ofRP) corresponding to the baseline 100-yr discharge. The grid for the analysis is comprisedof the geometric intersection of the downscaled CMIP6 0.25° grid and the topographicdata grid of irregular drainage-basin polygons (HydroAtlas level 12).
Basin scale flood frequency projections are overlaid with projected flood extent data
sourced from the WRI Aqueduct dataset.5 Flood extent data (100-year return period) at
each basin under two scenarios (RCP 4.5 and RCP 8.5) and three decades (2030, 2050,
2080). Flood extent data for RCP 4.5 was used to represent SSP1-2.6, RCP8.5 was used torepresent SSP3-7.0 and missing time-period projections were mapped to the nearest
available decade in the absence of better available data. WRI Aqueduct projections for five
GCMs (MIROC-ESM-CHEM, IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, NorESM1-M)
ing pixels as floodable whereat least three of the five available GCMs projected flood within that pixel.

Drought

The drought hazard is derived from the Standardized Precipitation and Evapotranspiration
Index (SPEI), as computed by SPEIbase from the Spanish National Research Council (CSIC)
which provides SPEI6 based on observational and reanalysis data. SPEI utilizes daily solar
radiation and daily surface wind as an input, in addition to temperature and precipitationfrom the downscaled CMIP6 models. For the financial impact metrics, the hazard variable
for a projected decade is the average proportion of months per annum where the SPEI isless than or equal to the historical local 10th percentile. For the exposure scores, thehazard variable is the average proportion of months per annum classified as severely dry

5 World Resources Institute. 2023. Aqueduct. [Online]. https://www.wri.org/applications/aqueduct/floods/
6 World Meteorological Organisation. 2012. Standardized Precipitation Index User Guide. [Online]. https://library.wmo.int/idurl/4/39629

7

Hazard

Methodology Description
or extremely dry based on SPEI. The spatial resolution for drought is 0.25° (~25x25km)
globally.

7

Wildfire

The wildfire hazard is defined based on the Fire Weather Index (FWI) of the Canadian
Forest Fire Danger Rating System and assesses if meteorological conditions are favorable
for wildfire development. The FWI is computed based on downscaled CMIP6 temperature,
precipitation, relative humidity, and surface wind speed projections. For the financial
impact metrics, the hazard variable for wildfire is the average proportion of days per
decade where the FWI exceeds the historical local 90th percentile. For the exposure
scores, the hazard variable is the average proportion of days per annum that are classified
as high, very high or extreme wildfire danger based on the FWI. The spatial resolution for
wildfire is 0.25° (~25x15km) globally.
Modelled wildfire conditions are overlaid with a land cover mask to differentiate pixels
containing burnable vegetation and thus susceptible to wildfire, from pixels that do notcontain burnable vegetation such as urban areas. The land cover mask derived satellite
imagery is sourced from the Copernicus Global Land Service dataset. 7 The land cover mask
is applied at 3 00x300m resolution and sets the wildfire hazard to zero in locations where
less than 20% of the wildfire hazard pixel and its surrounding area is covered withburnable vegetation.

TropicalCyclone

The Tropical Cyclone (TC) hazard is calculated via a statistical-stochastic model thatsimulates the lifecycle of TCs,
sustaining ocean basins. Included in the training are statistical relationships between TC
variability and sea-surface temperature (SST). For future decades, SST data directly from
10 CMIP6 models are used to drive new TC simulations in future climate states. The TC
metric derived from the simulations is annual rate of category 3 and higher TCs in 0.25°
grid cells globally. Due to rapidly increasing uncertainty, TCs projections are only madethrough the 2040s. Subsequent decades are held at the 2040s value.

CoastalFlood

Historical storm-tide (surge plus tide) levels at 9 return periods from the GTSR globalhydrodynamic system are combined with sea-level rise (SLR) projections (Kopp et al) tomodel flood levels in coastal regions. A flood-water path-finding algorithm is applied todetermine the interior points that are subject to flooding in response to different coastal
water levels. The algorithm makes use of topographic elevation data at 30-meter
resolution over the US and 90-meter resolution elsewhere, and this defines the resolutionof the coastal flood analysis. From the resulting flood depths at different return periods,
we compute the projected frequency of the historical baseline 100-yr flood depth as theprimary hazard variable. Note that components of the Kopp SLR data use CMIP5 inputs,
not CMIP6. To include certain CMIP6 scenarios, we have applied an interpolation
procedure to the CMIP5-based SLR.

PluvialFlood

Pluvial flood hazard associated with extreme rainfall events is modelled using daily
precipitation data from an ensemble of NEX-GDDP-downscaled CMIP6 models (25-km
resolution worldwide). A statistical model of Generalized Extreme Value analysis is used todetermine the Intensity of extreme rare events. The model uses a simplifying assumptionthat topography and natural or artificial drainage capacity is constant in time thus
avoiding the requirement for high-resolution topographic or drainage data.
This generation of Pluvial hazard modelling is limited to projections of annual frequency ofthe historical baseline 100-year precipitation rate which relates to the pluvial hazard
metric of annual frequency of 100-year flood depth.

Water
StressThe Water Stress Index is the ratio of total water withdrawals within an area to theavailable water resources in surface and ground water. The analysis covers water

European Commission Joint Research Centre. 2023. Copernicus Global Land Service. [Online]. https://land.copernicus.eu/global/index.html

8

Hazard

Methodology Description
consumptive and non-consumptive withdrawals for domestic, industrial, irrigation andlivestock use. Water availability considers the impact of upstream consumptive water
users and dams. Higher values indicate more competition among users for available water
resources. Water stress index data is sourced from the World Resources Institute andclassified into ten categories from lowest to highest water stress. 8

8World Resources Institute. 2022. Aqueduct. [Online]. Available: https://www.wri.org/aqueduct

9Methodology Overview
The Sustainable1 Physical Risk Scores and Financial Impact methodology is based on five key analyticalsteps, as shown in Figure 1.
1.
2.
3.
4.
5.

Climate Hazard Modelling
Physical Risk Exposure Quantification
Asset and Company Level Physical Risk Exposure Score Calculation
Financial Impact Function Modelling
Asset and Company Level Physical Risk Financial Impact Calculation

Figure 1: Sustainable1 Physical Risk Analysis Methodology

1. Climate Hazard Mapping
Sustainable1 has assembled models and datasets representing projected absolute exposure to nine
discrete climate change hazards globally across four climate change scenarios and nine time
periods (See: Indicators and Scenarios section), to produce global climate change physical hazard
maps. Each indicator, scenario and time period is represented as a geospatial dataset with hazard
values assigned to location at a resolution deemed suitable to each hazard. This enables themodelling of exposure to each climate hazard at a given time period and the change in hazard
exposure over time and relative to a historical baseline.

2. Physical Risk Exposure Quantification
Exposure to climate change physical hazards is quantified by overlaying asset locations of interest
represent any structure or real asset owned or leased by a company covered by the Sustainable1
database of over 20,000 companies.
The Sustainable1 Climate Change Physical Risk dataset is generated based on an extensive
database of physical asset locations, linked to corporate owners (or lessees), developed and10maintained by S&P Global. An equivalent analysis can also be provided on a bespoke basis utilizing
asset data provided by clients, such as a real estate, mortgage, infrastructure or project financeportfolio.
Table 4 describes the asset level data sources and datapoints utilized in Sustainable1 Physical Risk
dataset. This includes a range of established S&P Global datasets focusing in energy, real estate,mining, telecommunications, technology, industrials and manufacturing, supplemented withexternal datasets including cement and steel production asset databases from the Spatial FinanceInitiative (McCarten et al. 2021a9; McCarten et al. 2021b10), other industrial asset data sourced from
Climate Trace,11 government regulatory datasets (DEFRA, 202212; European Environment Agency
202013; Government of Canada, 202214; Australian Department for Climate Change, Energy, The
Environment and Water 202215) and other sources. Each asset is mapped to a unique owneridentifier (KeyInstn and CIQ ID) enabling linking to other datasets within the S&P Global Capital IQ
database, and to the ultimate parent owner name and identifier, to enable aggregation of physicalrisk metrics at the owner and parent level. Attribute information for each asset, such as asset type,
sector, country, and other details, are also stored to inform the analysis.

9

McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D., Layman, C., Rossi, C., Scott, K., Tang,
K., Tkachenko, N., and Yoken, D. 2021. Global Database of Cement Production Assets. Spatial Finance Initiative
10
McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D., Layman, C., Rossi, C., Scott, K.,
Tang, K., Tkachenko, N., and Yoken, D., 2021. Global Database of Iron and Steel Production Assets. Spatial Finance Initiative11
Climate TRACE -Tracking Real-time Atmospheric Carbon Emissions (2022), Climate TRACE Emissions Inventory, https://climatetrace.org
12
UK Department for Environment, Food and Rural Affairs. 2022. UK Pollutant Release and Transfer Register (PRTR) data sets.
[Online]. Available: https://prtr.defra.gov.uk/facility-search
13
European Environment Agency. 2020. European pollutant release and transfer register (E-PRTR). [Online]. Available:
https://www.eea.europa.eu/archived/archived-content-water-topic/water-pollution/point-sources/eper
14
Government of Canada. 2022. National Pollutant Release Inventory. [Online]. Available:
https://www.canada.ca/en/services/environment/pollution-waste-management/national-pollutant-release-inventory.html
15
Australian Department for Climate Change, Energy, The Environment and Water. 2022. National Pollutant Inventory. [Online].
Available: http://www.npi.gov.au/npi-data/latest-data

11

Table 4: Sustainable1 Physical Risk Analytics: Asset Level Data Coverage and Sources
SectorAsset Database

Data Source

Urban Environment

Company Headquarters

S&P Global

757,892

Company Offices

S&P Global

45,626

Retail Sites

S&P Global

137,243

Real Estate Investment Trust Properties

S&P Global

87,291

Bank Branches

S&P Global

478,574

Oil and Gas Production and Industrial

S&P Global

33,462

Pipelines

S&P Global

634,164

Gas Storage Facilities

S&P Global

366

LNG Assets

S&P Global

41

Automobile Plants

S&P Global

900

Cement Plants

Spatial Finance Initiative

708

Iron and Steel Plants

Spatial Finance Initiative

473

Paper and Pulp Assets

Spatial Finance Initiative

123

Petrochemical Assets

Spatial Finance Initiative

112

Beef Cattle Assets

Spatial Finance Initiative

110

Waste Management Assets

Spatial Finance Initiative

79

Other Industrial Assets

Climate Trace

3,028

Regulated Industrial Assets

Government Databases

9,768Power Plants

S&P Global

41,501

Transmissions Lines

S&P Global

644,588

Metals & Mining

Metals and Mining Assets

S&P Global

6,685

Tech & Telecom

Data Centers

S&P Global

4,272

Broadcast Stations

S&P Global

2,712

Cell Towers

S&P Global

181,979

Other Assets

S&P Global

2,106Energy, Industrials
and Agriculture

Power & Utilities

Asset Count

Total

3,073,803

12

Assets are mapped to corporate owners (or lessees) and ultimate parent identifiers in the S&P Capital IQ
database using string matching techniques to enable efficient linking to financial and other market
datasets in the S&P Global databases. The Sustainable1 asset database will be continually expanded tointegrate new asset level datasets sourced within S&P Global and externally.
Figure 2 presents coverage of selected S&P Global Dow Jones Indices with asset based and revenue
exposure based physical risk scores as of February 2024. As shown, asset level data is available forcompanies representing over almost 100% of index weight in the S&P500, S&P Europe 350, ASX 200 and
S&P Global LargeMidCap Index. Coverage of asset level data is expected to increase as additional asset
datasets are incorporated.
100%

Share of Index Weight (%)

90%
80%
70%
60%
50%
40%
30%
20%
10%

Asset Based Assessment

Revenue Exposure Based Assessment

Country Average Assessment

S&P Emerging BMI

S&P Asia BMI

S&P Global BMI

S&P Europe 350

S&P Global LargeMidCap

S&P Japan 500

S&P Global 1200

S&P ASX 200

S&P5000%

Not Covered

Figure 2: Sustainable1 Physical Risk Analytics: Coverage Summary

13

3.

Asset and Company Level Physical Hazard Exposure Scores
Figure 3 presents an overview of the methodology applied to calculate asset and company level
physical risk exposure scores.
The Sustainable1 physical risk exposure score model assigns risk scores from 1 (lowest risk) to 100(highest risk) to each asset in the database based on location within the climate change hazard
maps described in Step 1. The exposure score is intended to represent the relative level of exposure
to each hazard at each location relative to global conditions across all scenarios and time periods. A
score of 100 indicates an asset location at the highest level of exposure to a given hazard globally,
and a score of 1 indicates the lowest level of exposure. Physical risk metric values are normalized toscores based on the formula described in Equation 1.

Figure 3: Company and Asset Level Physical Risk Score Calculation Overview

Equation 1: Climate Change Physical Risk Score Normalization

14

Asset level physical risk exposure scores are aggregated to company level scores as a weighted average of
all assets mapped to the company of interest, based on assumed asset values for each asset type. Assumed
asset values were derived from a literature review and are intended to be indicative of the relative value ofeach asset type (see examples in Table 5). Companies evaluated using asset level data are assessed using
the ‘Asset Level Data’ analysis methodology.
Table 5: Example Assumed Asset Values per Asset Type
Asset Type

Assumed Asset Value ($US Million)

Light Manufacturing - Owner/Occupier (Urban)

150

Cement Manufacturing - Owner/Occupier (Urban)

150Power Generation (General) - Owner/Operator

1,200

Natural Gas-Fired Power Plant - Owner/Operator

640

Data Center - Owner/Occupier

300

Hotel - Owner/Occupier (Urban)

75

Office - Owner/Occupier25

For some companies in the Sustainable1 CorePlus universe, insufficient asset level data is available tocalculate physical risk exposure scores or financial impact metrics. In these instances, a two-tier methodology
is applied as described below in descending order or preference:-

Revenue Exposure Based Methodology: Where both the location of the company headquarters and ageographic revenue exposure breakdown is available, physical risk exposure scores are estimatedbased on a combination of physical risk exposure score at the company headquarters location (20%weight), and a revenue weighted average of the country average physical risk exposure scores in thosecountries where the company generates revenues (80% weight). Country physical risk profiles arecalculated as a GDP weighted average within the country boundaries, drawing on the climate hazard
data described at step 1, and downscaled spatial GDP data sourced from Kummu et al. (2018).16
Physical risk financial impact metrics are estimated based on the financial impact metrics for thecompany headquarters location and country / GICS Industry financial impact profiles based on arepresentative asset type for each GICS Industry and GDP weighted average hazard profiles for eachcountry in which the company generates revenue. The company level financial impact metrics arecalculated as a weighted average of the headquarters financial impact metrics (20% weight) and ageographic revenue weighted average of the country / GICS Industry financial impact profiles describedabove (80% weight).

-

Country Average Methodology: Where only the headquarters location for a company is known, physicalrisk exposure scores are estimated based on the exposure scores for the headquarters location (20%weight) and the country average physical risk exposure scores for the country in which the company isheadquartered. Country physical risk profiles are calculated as a GDP weighted average within the
country boundaries, drawing on the climate hazard data described at step 1, and downscaled spatial
GDP data sourced from Kummu et al. (2018).
Physical risk financial impact metrics are estimated based on the financial impact metrics for thecompany headquarters and the country / GICS Industry financial impact profile for the company GICS
Industry and headquarters country.

A data field is included in the dataset designating the analysis methodology used to calculate the physical risk
metrics i.e. Asset Level Data, Revenue Exposure and Country Average.

16

Kummu, M., Taka, M., Guillaume, J.H.A. 2018. Gridded global datasets for Gross Domestic Product and Human Development Index
over 1990–2015. Scientific Data volume 5, Article number: 180004 (2018).

15

Equation 2: Revenue Exposure Based Physical Risk Exposure Score Estimation

Equation 3: Revenue Exposure Based Physical Risk Financial Impact Estimation

Composite Exposure Score Calculation
The composite score is intended to provide a combined measure of company exposure to all nine climatechange physical hazards. Two forms of the composite score are presented in the Sustainable1 Climate
Change Physical Risk analysis:
Composite Physical Risk Score: An equal weighted additive combination of the company physical risk
score on each hazard for a given scenario and year, and then rescaled to a 1-100 range using a logarithmic
scoring curve. The scoring curve is designed to ensure that assets or companies with high exposure to one
hazard, but low exposure to all others, will be assigned a moderate to high composite physical risk exposure
score. Alternative approaches, such as a simple average of hazard exposure scores within a given scenario
and time period, risk understating the exposure of an asset or company to climate change physical risk incases such as this. The Composite Physical Risk Score is calculated as described in Equation 4.

16

Equation 4: Composite Physical Risk Score Calculation

Sensitivity Adjusted Composite Physical Risk Exposure Score: The Sensitivity Adjusted Physical Risk
Exposure Score is intended to account for both exposure to climate hazards, and the expected sensitivity ofcompanies and assets to each hazard. The sensitivity adjustment is applied based on a set of sensitivity
scores calculated as follows at the company level (and applied to all assets mapped to each company):•

Water Stress and Drought Sensitivity Score: Water intensity is assumed to be an indicator ofsensitivity to water stress since companies with high water demands are more likely to be impacted byconstrained water supply or increased water costs. Water intensity (direct and purchased water
consumption (m3) / revenue ($US Million) is calculated for all companies in the Sustainable1 Core Plus
universe of 20,000+ companies, trimmed to exclude outliers beyond the 5th and 95th percentile, and thennormalized to a 1-100 score using the normalization formula shown below and included in themethodology report at Equation 1. This score reflects the relative water intensity of each company
compared to all other companies in the Sustainable1 Core Plus universe.

•

Extreme Heat and Extreme Cold Sensitivity Score: Labour intensity is assumed to be an indicator ofsensitivity to extreme heat and cold since temperature extremes can impact on labour force productivity,
and therefore labour intensive companies may be more severely affected. Labour intensity (total
employees (count) / revenue ($US Million)) is calculated for all companies in the Sustainable1 Core Plus
universe, trimmed to exclude outliers beyond the 5th and 95th percentile, and then normalized to a 1-100
score using the normalization formula shown at Equation 1. This score reflects the relative labour
intensity of each company compared to all other companies in the Sustainable1 Core Plus universe.

•

Wildfire, Fluvial Flood, Pluvial Flood, Hurricane and Coastal Flood Sensitivity Score: Tangible
asset intensity is assumed to be an indicator of sensitivity to physically destructive hazards sincecompanies with large investments in fixed assets and inventories which can be damaged, are more
likely to be severely impacted. Tangible asset intensity ((Plant and Equipment ($US Million) +Inventories ($US Million)) / Total Assets ($US Million)) is calculated for all companies in the
Sustainable1 Core Plus universe, trimmed to exclude outliers beyond the 5th and 95th percentile, andthen normalized to a 1-100 score using the normalization formula shown at Equation 1. This scorereflects the relative tangible asset intensity of each company compared to all other companies in the
Sustainable1 Core Plus universe.

Using the sensitivity scores above, the sensitivity adjusted hazard and composite scores are calculated asfollows:•

Sensitivity adjusted hazard scores (e.g., for wildfire) are calculated by multiplying the physical risk
exposure score for a given hazard/scenario/year with the corresponding sensitivity score and then dividing
by 100.

•Sensitivity adjusted composite scores are calculated by multiplying each hazard physical risk exposure
score with the corresponding sensitivity score, summing the results for a given scenario and year, andrescaling to 1-100 based on a logarithmic scoring curve. The Sensitivity Adjusted Composite PhysicalRisk Exposure Score is calculated as described in Equation 4.

17

Equation 5: Sensitivity Adjusted Composite Physical Risk Score Calculation

4. Financial Impact Function Modelling
The Sustainable1 physical risk model quantifies the expected financial consequences of changes inphysical risk exposure at both the asset and company level. This model is based on a library of ‘Impact
Functions’ developed by S&P Global which describe the relationship between the degree of change inclimate hazard exposure and the financial impact on a given asset type across time and climate change
scenarios. Impact functions have been developed for over 250 unique asset types, each focusing on aset of pathways by which climate change hazards may impact on the value, revenues, operations orother value drivers for that asset type. The impact function database has been developed over several
years through extensive literature research and analytical development.
At the asset level, Financial Impact is quantified as the projected financial costs associated withchanging climate hazard exposure, expressed as a percentage of the asset value. The Financial Impact
metric is calculated at the asset level for each hazard and can be summed to produce a composite
Financial Impact metric, and aggregated to the company level as a weighted average based onassumed asset value weights (see Table 5). Financial Impact is expressed as a relative metric because
accurate data or estimates of the actual value of each asset is currently not available. Negative financial
impact results, indicating potentially reduced costs due to a reduction in hazard exposure, are capped at–5% due to uncertainty around the ability of companies to capture the financial benefit of reducing
hazard exposure.
The following example describes the process applied to developing impact functions for a single hazard
and asset type combination.
Step 1. Identify Material Impacts
S&P Global has developed over 1,200 impact functions linked to over 250 asset types for application in
the physical risk dataset and related tools (e.g., the Climanomics platform). The following example
shows the extreme heat impact function for the office building asset type from the owner/occupierperspective. The temperature hazard metric used in this impact function is projected Tx50pAbsChg,
measuring the absolute change in the annual 50th-percentile local daily maximum temperature (degree
Celsius), relative to the historical value (1950-1999). To analyze the impact of increasing maximum
temperature on owned/occupied office properties, a review of available research literature wasconducted to identify a range of impact pathways, or avenues by which the operations and value of an
office building may be impacted by increasing temperature. The following impact pathways wereidentified as material to the office building asset type:•

Cooling Costs: Excess operating expenses associated with increased use of coolingequipment/systems to maintain optimal temperatures for employees and plant/equipment in thecontext of rising temperatures.

•

HVAC Degradation: Annualized costs of reduced operating life and early replacement of HVAC
systems due to increased operation in response to rising temperatures.

18

•

Employee Productivity: Costs associated with reduced employee productivity and associatedexpenses caused by increasing ambient temperatures (including employees working indoors).
Step 2. Model Impact Pathway

For each impact pathway a series of relevant research studies and data sources are assembled toquantify the impact of a unit change in hazard on relevant financial performance metrics, as described
below:•

Cooling Costs: Excess energy consumption associated with higher temperatures were estimatedbased on trends identified in a series of papers focusing on changes in energy demand and power
generation17, and estimated economic damages arising from climate change in the USA.18 Basedon this data, cooling energy demand is projected to increase by 5% per one-degree Celsius
increase in average maximum temperature.

•

HVAC Degradation: Excess costs associated with reduced operating lifespan for HVAC systems perunit change in temperature were estimated from a series of studies including Fenaughty and Parker(2018). 19 Based on this data, HVAC lifespan is projected to decrease by 6.76% per one-degree
Celsius increase in average maximum temperature.

•

Employee Productivity: Reductions in employee productivity were estimated based on a global
study of the effects of heat on working populations.20 Based on this data, workforce productivity is
projected to decrease by 1.14% per one-degree Celsius increase in average maximum temperature.
Step 3. Quantify Financial Impact
To quantify the total financial impact on asset value, the impact pathways described in the priorsection are weighted based on a set of financial ratios reflecting the proportion of the total value of agiven asset type that is represented by the value driver impacted by temperature change for eachpathway. The asset value metric for the owned/occupied office building asset type is thereplacement value, and the financial ratios applied to each impact function described below (Theseassumptions are based on literature review and analysis by S&P Global):•••

Cooling Costs: 1.19% of asset value
HVAC Degradation: 13.29% of asset value
Employee Productivity: 7.84% of asset value

The financial impact (%) for each impact pathway is multiplied by the corresponding financial ratio
and summed to quantify the aggregated financial impact (%) on the asset value of an owneroccupied office building per one-degree Celsius increase in average maximum temperature, andextrapolated across the range of projected future temperature increases. Figure 4 presents theimpact function curve for the owner-occupied office building asset type, showing the residual asset
value remaining as the change in average maximum temperature increases.

17

Larsen, Kate et al., 2017: Assessing the Effect of Rising Temperatures: The Cost of Climate Change to the U.S. Power Sector.
Rhodium Group, LLC, https://rhg.com/wp-content/uploads/2017/01/RHG_PowerSectorImpactsOfClimateChange_Jan2017-1.pdf
18
Hsiang, Solomon et al., 2017: Estimating economic damage from climate change in the United States. Science 356, 1362–1369,
https://www.science.org/doi/10.1126/science.aal4369.
19
Fenaughty, Karen and Parker, Danny, 2018: Evaluation of Air Conditioning Performance Degradation: Opportunities from Diagnostic
Methods. University of Central Florida, Florida Solar Energy Center, accessed April 2020, http://publications.energyresearch.ucf.edu/wpcontent/uploads/2018/09/FSEC-PF-474-18.pdf.
20
Kjellstrom, Tord et al., 2009: The Direct Impact of Climate Change on Regional Labor Productivity. Archives of Environmental &Occupational Health, 64, 4, pp. 217–227, https://doi.org/10.1080/19338240903352776.

19Figure 4: Impact Function Curve for the Effect of Temperature Change on Asset Value for Owner Occupied Office Buildings

In Figure 4, the x-axis represents the extreme heat hazard metric (tx50pAbsChg) and the y-axis
represents the change in asset value, represented as a multiplier. For example, for a 2-degree Celsius
absolute temperature change relative to the historical 50th percentile of daily maximum temperature, theaggregated impact on an owned-occupied office building would be 2.16% of asset value.
Financial impact may be negative in instances where exposure to a hazard is projected to decrease
compared to the baseline under a given scenario and time period. This implies that the cost associatedwith exposure to that hazard is projected to decrease relative to the baseline. Negative financial impact
is capped at -5% in recognition of the uncertainty regarding the ability of an asset owner or company torealize the financial benefits of reduced hazard exposure.

5. Asset and Company Level Physical Rick Financial Impact Calculation
Figure 5Figure 5 presents an overview of the methodology applied to calculate asset and company level

physical risk financial impact metrics.
The Sustainable1 physical risk model quantifies financial impact for each asset based on:
A. The change in climate change physical hazard under a given scenario and time period relative to ahistorical baseline.
B. The asset type classification, and associated impact functions, for the asset located at a given
location.
Asset level Financial Impact is aggregated to company level as a weighted average of all assets
mapped to the company of interest, based on assumed asset values for each asset type. Assumed
asset values were derived from a literature review and are intended to be indicative of the relative valueof each asset type (see examples in Table 5.). Where insufficient asset level data is available for a given
company, financial impact metrics are estimated based on the ‘Revenue Exposure’ and ‘Country
Average’ methodology as described earlier in the document.

20

Asset and company level Financial Impact is calculated for each climate hazard under eachscenario and time period and are summed to a combined Financial Impact metric covering all
hazards.

Figure 5: Company Level Physical Risk Financial Impact Calculation Overview

6. Disaggregated Financial Impact Metrics
Financial impact metrics at the asset and company level, disaggregated by impact pathway, are now
available to Physical Risk Exposure Score and Financial Impact dataset clients upon request. Thisdataset supplement disaggregates asset level financial impact into the financial impact associated witheach underlying impact pathway for a given asset type. For example, in the case shown in Section 4 –extreme heat impacts on an owner-occupied office building – the financial impact is presented for eachscenario and time period for three impact pathways: Cooling Costs; HVAC Degradation; and Employee
Productivity.
Disaggregated financial impact is also aggregated from asset to company level based on the same
assumptions as the standard financial impact metrics – as a weighted average based on the assumed
asset value for each asset type.
Disaggregated financial impact metrics may be useful for clients seeking to undertake an adjusted
financial analysis for an asset or company, since the financial impact for each pathway can be assigned
to the relevant line item in the financial statements. For example, excess cooling costs are associatedwith increased operating expenditure.
Please contact your S&P Global relationship manager to arrange access to the disaggregated financial
impact data supplement.

21

Monitoring and Review
All new methodologies and any material changes to existing methodologies are reviewed and approvedby an independent methodology governance committee.

Assumptions and Limitations
Key limitations of Sustainable1

physical risk analytics include:•

Modelling Uncertainty: The climate models underpinning the physical risk analysis are complex andsubject to uncertainty. Sustainable1 has sought to mitigate this uncertainty by basing the physicalrisk assessment on averages of the output of all available CMIP6 GCMs.

•

Asset Location Uncertainty: The Sustainable1 physical risk assessment incorporates a range ofasset location datasets, some of which are actively managed and updated regularly, whereas others
are updated less frequently. Consequently, it is possible that the database does not reflect changes
in asset ownership and activity that have occurred in the recent past. Sustainable1 has sought tomitigate this uncertainty by limiting data sourced from historical datasets to the past three years.

•

Company Asset Coverage:
material asset locations that are covered in the Sustainable1 asset database for most sectors.
Sustainable1 is exploring opportunities to calculate or estimate an asset level coverage confidence
measure for future releases.

•

Spatial Resolution: Sustainable1 has sought to integrate climate modelling at sufficient spatial
resolution to enable a robust estimation of the physical risk exposure, however this analysis could
be enhanced in the future.

•

Hazard Correlation: All hazards are modeled independently, and correlation or vulnerability
associated with the co-occurrence of multiple hazards is not currently specifically modelled. For
example, the tropical cyclone hazard metric encompasses the wind risks associated with the event,while the coastal flooding hazard metric includes storm surge flooding, so the flooding associatedwith the tropical cyclone would be captured in that metric. Finally, the precipitation models andtopographic variables in each basin included in the fluvial flooding hazard metric would integrate
any fluvial flooding associated with a tropical cyclone event.

•Sensitivity Framework: The sensitivity weighting framework is designed to weight the nine physicalrisk indicators based on the expected sensitivity of individual companies to each indicator. The
framework will be enhanced in the future to better reflect the financial materiality of different formsof physical risk to companies across sectors and regions.

22

Maintenance/Updates
S1 periodically reviews the methodology as appropriate.

Related Documentation
There are several supporting documents specific to Physical Risk Scores and Financial Impact Data on
the Support Center website:•

Physical Risk User Guide This user guide provides an overview of Physical Risk Scores andFinancial Impact Data, including package description, database schema, and specific details about
working with the data.

•

Physical Risk Scores and Financial Impact Data Guide: This spreadsheet provides information on all
the columns and data Item IDs in every single table by package, along with their corresponding
definitions.

•Physical Risk Scores and Financial Impact Data Item List: This spreadsheet provides details on thedata items, including dataItemId, dataItemName, and dataItemDefinition.

•

Physical Risk Scores and Financial Impact Release Notes: The Release Notes describe the PhysicalRisk Scores and Financial Impact data, by providing an overview of the dataset, data coverage,
history, the new packages, and the new tables by package.

•

Physical Risk Scores and Financial Impact Xpressfeed File Format: This spreadsheet contains theXpressfeed package description, zip file prefixes, text file names, and database column details,
such as column name, data type, filed size, primary keys, and encoding format.

23

Significant Updates
Version2.0

Date

31/01/2024

Updates• Q2 release enhancement info•

Q1 release enhancement info•

Robust and science-based climate change physical hazard
characterization methodology, leveraging the latest available climatechange models (CMIP6) and proprietary methodologies.

•

Coverage of nine key climate change physical hazards at variable
resolution, globally: coastal flood, fluvial flood, extreme heat, extreme
cold, tropical cyclone, wildfire, water stress, drought and pluvial flood. The
2024 release incorporated one new hazard (pluvial flood) and modellingenhancements for fluvial flood, wildfire, drought, extreme heat andextreme cold.

•

Coverage of four climate change scenarios based on the IPCC Shared
Socioeconomic Pathway (SSP) and Representative Concentration Pathway(RCP) scenarios, and offering annualized decadal averages for all hazards
from the 2020s to the 2090s.

•

Physical risk exposure scores representing point in time exposure toclimate hazards, and physical risk financial impact metrics describing thefinancial consequences arising from changing climate hazard exposure forover 250 unique asset types.

•

Built upon a proprietary database of over 3 million asset locations linked
to corporate entities and ultimate parent entities based on S&P Market
Intelligence, S&P Commodity Insights, and Sustainable1-assembled
datasets and with flexibility to rapidly analyze client provided asset
datasets.

•

Physical risk analytics for over 20,000 companies representing over 98% of
global market capitalization, ensuring high levels of coverage for equity
and fixed income portfolios across all markets.

•

1.0

09/01/2022

Enhanced modelling of companies with low asset counts based on
geographic revenue data and country physical risk profiles.
Initial version

24

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