S&P Global Sustainable1
Physical Risk: Municipal
DatasetMethodology
S&P Global Sustainable 1

March 2024

Table of Contents
Physical Risk: Municipal Climate Hazards and Exposures Introduction
and Context ................................................................................................ 3
Climate Hazard Metrics, Interpretation and Use Cases .......................... 4
Use Cases .......................................................................................................................... 5
Indicators and Scenarios ................................................................................................. 6
CMIP6 Climate Hazard Modeling and Enhancements .................................................... 8

Methodology Overview ............................................................................11
1.

Climate Hazard Mapping .................................................................................. 11

2.

Boundary Mapping ............................................................................................ 113.

GDP and Population Mapping ........................................................................... 12

4.

Exposure Metric Calculation ............................................................................ 12

5.

Linking to Muni Bond CUSIP Identifiers ........................................................... 14

Assumptions and Limitations .................................................................15
Dependencies ...........................................................................................16
Appendix ...................................................................................................17
Significant Updates ..................................................................................19
Related Documentation ............................................................................20
S&P Global Sustainable1 Disclaimer ......................................................21

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2

Physical Risk: Municipal Climate
Hazards and Exposures
Introduction and Context
Climate change is generating substantial and rising costs to regional and local economies, subnational
governments, and their resident populations. Given that the reporting of climate risk by state and local
authorities remains voluntary, there is a clear market imperative to provide information on the materialityof climate exposures for local geographic entities. This is crucial both for subnational authorities that seek
to understand and manage their climate risks, and for investors holding municipal instruments.
The S&P Global Sustainable1 (S1) Physical Risk: Municipal Dataset provides information on the climate
physical hazard exposure of US county and State issuers and municipal bond instruments. The dataset
covers both hazards arising from longer term shifts in climate patterns, namely extreme heat, extreme
cold, drought, and water stress, and acute event hazards such as tropical cyclone, wildfire and coastal,
fluvial and pluvial flood. The dataset provides insights into the levels, trajectories, and comparative
materiality of chronic and acute climate hazards faced by all 3,135 US counties and 50 US states underfour climate change scenarios and for all decades from the 2020s-2090s. The dataset enables users tounderstand the climate hazards projected to present material challenges to each geographic entity in each
decade; which counties face compound physical climate challenges, with the potential for impact
amplification; and which counties will likely face the greatest risks to property markets, supply chains,
tourism and other industries, and fiscal health in both the near- and medium-term.
The S1 Physical Risk: Municipal Dataset forms part of the broader S1 climate change physical risk
analytics suite, including solutions for real assets and corporate issuer analytics.
Key features of the new dataset include:•

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

•

Coverage of nine key climate change physical hazards at consistent resolution, globally: coastal
flood, fluvial flood, pluvial flood, extreme heat, extreme cold, tropical cyclone, wildfire, water stress,and drought.

•

Coverage of four climate change scenarios based on the Intergovernmental Panel on Climate Change(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.

•

Absolute hazard metrics describing the intensity of each physical hazard on average within theboundaries of each county or state, such as the frequency of extreme heat days or the frequency of a
1-in-100 flood event.

•

Physical risk exposure scores representing each lrelative to 1) exposures for all scenarios and time periods, globally; and 2) exposures for all US
counties for all scenarios during the 2020s-2050s.

•

Percent exposures of locality GDP and population for each hazard by scenario and decade;composite percent exposures of locality GDP and population.

•

Coverage of all US county and state issuers and associated general obligation municipal bonds
tracked in S&P Global databases.

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Climate Hazard Metrics, Interpretationand Use Cases
Table 1 below describes the interpretation and use cases of each climate metric.
Table 1 Climate Hazard Metrics and Exposure Scores

Indicator Name

Interpretation

Use Cases

Absolute
Hazard• Absolute exposure to each hazard
within the geographic boundaries ofeach issuer, measured in physicalunits and weighted by the
distribution of GDP generation
within the issuer jurisdiction.
• Example: Fraction of extreme heatdays per annum• Exposure to each hazard within thegeographic boundaries of eachissuer relative to global conditions
and expressed as a 1 (least
exposed) to 100 (most exposed)
score. Exposure scores areweighted by the distribution of GDP
generation within the issuer
jurisdiction.
• Example: Exposure score of 100 for
extreme heat indicates areas
among the most exposed locations
to extreme heat globally• Exposure to each hazard within thegeographic boundaries of eachissuer relative to conditions in the
USA and expressed as a 1 (least
exposed) to 100 (most exposed)
score. Exposure scores areweighted by the distribution of GDP
generation within the issuer
jurisdiction.
• Example: Exposure score of 100 for
extreme heat indicates areas
among the most exposed locations
to extreme heat within the USA• Composite exposure scores arecalculated as an equally-weighted
additive combination of thephysical risk score of each hazard
for a locality for a given scenario
and year, which is then rescaled to•

Quantifying exposure to climate
physical hazards over time and acrossscenarios•

Screening of climate physical hazard
exposure to identify the most exposedbonds/issuers in a portfolio
Screening of climate physical hazard
exposure within mixed asset class
portfolios (e.g. real assets, equities,
corporate fixed income)Targeting of in-depth risk assessmentand engagement activities
Regulated and voluntary reporting

Exposure Score

US Exposure
Score

Composite
Exposure Score
and Composite
US Exposure
Score

•

•
•

•

•
•
•

•

•

Screening of climate physical hazard
exposure to identify the most exposedbonds/issuers in a US muni bondfocused portfolio
Ranking of US muni bond exposure toclimate physical hazards
Targeting of in-depth risk assessmentand engagement activities
Regulated and voluntary reporting

Screening of climate physical hazard
exposure to identify the most exposedbonds/issuers in a US muni bondfocused portfolio
Ranking of US muni bond exposure toclimate physical hazards

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•

GDP Exposed••

Population
Exposed••a 1-100 range using an exponential
scoring curve.
The Composite Exposure Scores fora given locality are generated using
all Exposure Scores for each hazard
for all scenarios for the 2020s2090s, and the US Composite
Exposure Scores for a given locality
are generated using all US Exposure
Scores for each hazard for all
scenarios for the 2020s-2050s.
Proportion of GDP generated within
the geographic boundaries of eachissuer that is exposed, as definedby pre-determined thresholds, tomaterial changes in each climate
physical hazard.
Example: 50% GDP exposed to
extreme heat by 2050 indicatesthat 50% of the GDP generated
within an issuer boundary is
projected to be exposed to at least
three months of extreme heat daysby 2050
Proportion of population within thegeographic boundaries of eachissuer which is exposed, as definedby pre-determined thresholds ,tomaterial changes in each climate
physical hazard.
Example: 50% population exposed
to extreme heat by 2050 indicatesthat 50% of the population within
an issuer boundary is projected to
be exposed to at least three monthsof extreme heat days by 2050

•
•

•

•

•
•

•

•

•
•

Targeting of in-depth risk assessmentand engagement activities
Regulated and voluntary reporting

Quantification of the significance/materiality of the exposure of eachissuer to each hazard from an
economic perspective
Ranking of issuers based on thesignificance/materiality of the
exposure to each hazard from an
economic perspective
Targeting of in-depth risk assessmentand engagement activities
Regulated and voluntary reporting

Quantification of the significance/materiality of the exposure of eachissuer to each hazard from a social /public welfare perspective
Ranking of issuers based on thesignificance/materiality of the
exposure to each hazard from asocial/public welfare perspective
Targeting of in-depth risk assessmentand engagement activities
Regulated and voluntary reporting

Use Cases
The S1 Physical Risk: Municipal dataset supports the following client use cases:
Risk Screening and Portfolio Analytics•

Identify localities and issuers which face the most material climate exposures to which climate
hazard(s) at any point in time and given scenario.

•

Evaluate the climate exposure trajectory for each locality and how it compares to other issuers.

•

Identify the issuers and climate hazards which contribute most to portfolio-level exposure.

•Calculate portfolio-level exposure metrics for comparison with relevant benchmarks.

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•financial capacity to support, and governance to implement.
Regulated and Voluntary Reporting•

Inform reporting on climate change physical risk exposure and materiality at the portfolio, fund ororganizational level.

•

Align with the guidelines of the Taskforce on Climate-Related Financial Disclosures, Sustainable
Finance Disclosures Regulation, and/or other reporting requirements.

Engagement•

Inform engagement with issuers to better understand mitigation, adaptation, and other management
strategies planned to address rising climate physical exposures, and opportunities to strengthen
climate resilience.

Indicators and Scenarios
Table 2 below presents the climate change physical hazards considered in the dataset. All hazards areevaluated globally at consistent spatial resolution, with the exception of coastal flood where higher
resolution is available.
Table 2 Climate Change Hazard Coverage, Metrics, Resolution and Data Sources
SpatialResolution

Hazards

Analysis Metric

Indicator Definition

ExtremeHeat

Projected Tx95p

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

NEX-GDDP
downscaled
CMIP6

Extreme
Cold

Projected Tn5p

Annual percentage of days withminimum temperature colder than
the 5th percentile local baseline
daily minimum temperature~25x25km

NEX-GDDP
downscaled
CMIP6

CoastalFloodFrequency of 100-yr
coastal flood

Projected annual frequency of thehistorical baseline 100-yr coastal
flood depth

30x30m(USA)

GTSR
hydrodynamic
surge model

90x90m(RoW)

Data Sources

Kopp et al SLR
data; Muis et al
2016
MERIT /US3DEPUSGS global
coastlines

Fluvial(River)Flood

Frequency of 100-yr
fluvial flood

Projected annual frequency of thehistorical baseline 100-yr flood
depth~1x1kmHydro Atlas

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Hazards

Analysis Metric

Indicator Definition

SpatialResolution

Data Sources
NEX-GDDP
downscaled
CMIP6
WRI Aqueduct

Pluvial(Rainfall)
Flood

Frequency ofrainfall event

100-yrProjected frequency of thehistorical baseline 100-yr daily
precipitation rate

~25x25km

NEX-GDDP
downscaled
CMIP6

TropicalCycloneFrequency
stormsCat3+

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

NASHM

Wildfire

Fire Weather Index (FWI)

Projected annual frequency ofdays classified as high, very high orextreme wildfire danger based on
the FWI. Adjusted for land
cover/presenceofburnablevegetation~25x25kmRiver BasinofHall et al 2015
NEX-GDDP
downscaled
CMIP6
ESA LULC

Water
StressWater Stress Index

Projected future ratio of water
withdrawals to total renewable
water supply in a given area

Drought

Standardized
PrecipitationEvapotranspiration
Index (SPEI)

Projected annual frequency of ~25x25km
months classified as severely dry or
extremely dry based on the SPEIWRI Aqueduct

NEX-GDDP
downscaled
CMIP6

The dataset focuses on four future climate change scenarios based on IPCC Representative Concentration
Pathways and Shared Socioeconomic Pathways and informed by the TCFD technical guidelines (FSB,
2017)1:•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.

1Financial Stability Board, 2017. Recommendations of the Task Force on Climate-Related Financial Disclosures.

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•

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 dataset evaluates climate change physical risks for decadal averages from the 2020s to the 2090s.

CMIP6 Climate Hazard Modeling and Enhancements
S1 climate change hazard modeling utilizes the CMIP6 climate models, the latest generation of global
climate models informing the IPCC 2 . Temperature and precipitation data from 35 CMIP6 models wererecently downscaled from the varying native spatial resolution of the models to a uniform 0.25° latitudelongitude grid, comprising the NEX-GDDP3 downscaled CMIP6 dataset which forms the basis for the S1
hazard model. The NEX-GDDP 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 timeseries 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 fiveof the eight hazards included in the S1 model.
Driven by the downscaled CMIP6 data and other sources, the hazard models summarized below have been
neratedand archived in decadal-mean values (historical baseline and the 2020s through 2090s) on global grids forfour 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 the eight hazards isprovided in Error! Reference source not found.Error! Reference source not found..
Table 3 Climate Change Hazard Modelling

HazardMethodology Description

Extreme Heat

The measure of extreme heat is Tx95p, the percentage of days per year with amaximum temperature that exceeds the 95th percentile of the local historical baseline
daily maximum temperature. The measure of extreme cold is Tn5p, the percentage ofdays per year with a minimum temperature below the 5th 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.
Basin Scale Frequency Projections: The 10-yr and 100-yr return-period (RP) river

Extreme Cold

Fluvial Floodpublished analysis of historical data over the USA. Four covariates are topographic innature and three are climatological (5-day precipitation maxima and numbers ofconsecutive dry days and frosts days). For future decades, the climate covariates arederived from the downscaled CMIP6 dataset. The projected 10-yr and 100-yr
discharges are interpolated to obtain the projected frequency (reciprocal of RP)
corresponding to the baseline 100-yr discharge. The grid for the analysis iscomprised of the geometric intersection of the downscaled CMIP6 0.25° grid and the
topographic-data grid of irregular drainage-basin polygons (HydroAtlas level 12).

2 World Climate Research Programme. 2020. CMIP Phase 6 (CMIP6). [Online]. Available: https://www.wcrp-climate.org/wgcm-cmip/wgcm-

cmip6
3 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

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Hazard

Methodology Description
Flood Extent Overlay: Basin scale flood frequency projections are overlaid withprojected flood extent data sourced from the WRI Aqueduct dataset 4. Flood extent
data (100-yea
identify areas exposed to flood within 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 wasused to represent SSP1-2.6, RCP8.5 was used to represent SSP3-7.0 and missing
time period projections were mapped to the nearest available decade in the absenceof better available data. WRI Aqueduct projections for five GCMs (MIROC-ESM-CHEM,
IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, NorESM1-M) were used to create an

Drought4

the five available GCMs projected flood within that pixel.
The drought hazard is derived from the Standardized Precipitation andEvapotranspiration Index (SPEI), as computed by SPEIbase from the Spanish National
Research Council (CSIC) which provides SPEI10 based on observational and reanalysis
data. SPEI utilizes daily solar radiation and daily surface wind as an input, in addition
to temperature and precipitation from the downscaled CMIP6 models. The hazard
variable in a projected year is the average proportion of months per annum classified
as severely dry or extremely dry based on SPEI. The spatial resolution for drought is
0.25° (~25x25km) globally.

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 arefavorable for wildfire development. The FWI is computed based on downscaled CMIP6
temperature, precipitation, relative humidity, and surface wind speed projections. 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
not contain burnable vegetation such as urban areas. The land cover mask is derived
satellite imagery sourced from the Copernicus Global Land Service 5 dataset. The land
cover mask is applied at 300x300m resolution and sets the wildfire hazard to zero inlocations where less than 20% of the wildfire hazard pixel and its surrounding iscovered with burnable vegetation.

TropicalCyclone

The Tropical Cyclone (TC) hazard is calculated via a statistical-stochastic model that

Coastal Flood

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.,TC-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 futureclimate 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 made through the 2040s. Subsequent decades are held at the2040s value.

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

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Hazard

Pluvial Flood

Water Stress

Methodology Description
20195) to model flood levels in coastal regions. A flood-water path-finding algorithm isapplied to determine the interior points that are subject to flooding in response todifferent coastal water levels. The algorithm makes use of topographic elevation data
at 30-meter resolution over the US and 90-meter resolution elsewhere, and thisdefines the resolution of 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 the primary hazard variable. Note that components of the Kopp
SLR data use CMIP5 inputs, not CMIP6. To include certain CMIP6 scenarios, we haveapplied an interpolation procedure to the CMIP5-based SLR.
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 isused to determine the intensity of extreme rare events. The model uses a simplifying
assumption that topography and natural or artificial drainage capacity is constant intime thus avoiding the requirement for high-resolution topographic or drainage data.
This generation of Pluvial hazard modelling is limited to projections of annual
frequency of the historical baseline 100-year precipitation rate which relates to thepluvial hazard metric of annual frequency of 100-year flood depth.
The Water Stress Index is the ratio of total water withdrawals within an area to theavailable water resources in surface and groundwater. The analysis covers water
consumptive and non-consumptive withdrawals for domestic, industrial, irrigationand livestock use. Water availability considers the impact of upstream consumptive
water users and dams. Higher values indicate more competition among users foravailable water resources. Water stress index data is sourced from the World
Resources Institute and classified into ten categories from lowest to highest water
stress.6

5Kopp, R. E., E. A. Gilmore, C. M. Little, J. Lorenzo Trueba, V. C. Ramenzoni, and W. V. Sweet (2019). Usable Science for Managing theRisks of Sea1269.
6

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

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Methodology Overview
The S1 Physical Risk: Municipal Dataset Absolute Hazards and Exposure Scores methodology is based onfour steps as shown in Error! Reference source not found.:
1.
2.
3.
4.

Climate Hazard Modelling
Shapefile Mapping
GDP and Population Mapping
Exposure Metric Calculation

Figure 1: S1 Physical Risk: Municipal Methodology

1. Climate Hazard Mapping
S1 has assembled models and datasets representing projected absolute exposure to nine discrete climatechange hazards globally across four climate change scenarios and eight time periods (See: Indicators andScenarios section), to produce global climate change physical hazard maps. Each indicator, scenario andtime period is represented as a geospatial dataset with hazard values assigned to location at a resolution
deemed suitable to each hazard. This enables the modelling 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. Boundary Mapping
Geographic boundaries for all US counties and states were mapped based on the Database of Global
Administrative Areas (GADM), licensed from Rastera LLC7. This dataset provides geographic boundary data
for all administrative divisions globally.

7

https://gadm.org

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3. GDP and Population Mapping
Data on the spatial distribution of GDP and population globally was derived from the following sources:•

GDP: Kummu et al. 20188 provides data on GDP PPP generation at 0.0083° (~1x1 km) resolution
globally for the year 2015.

•

Population: The SEDAC Gridded Population of the World (GPW) v49 dataset provides data on
population spatial distribution globally at a resolution of 0.0083° (~1x1 km) for the year 2010.

Spatial distribution of GDP and population were overlaid with climate hazard data for use as a weight inthe calculation of muni/state averages, and in the calculation of GDP and population exposed metrics.

4. Exposure Metric Calculation
The S1 Physical Risk: Municipal dataset incorporates four key metrics which are calculated as follows:
Weighted Average Absolute Hazard Metrics
For each hazard, scenario and time period combination, hazard data is overlaid with spatial GDP data andthe GADM administrative boundary dataset. For each administrative unit (county or state), the hazard and
GDP pixels are multiplied together, summed and then divided by the total sum of GDP pixels within theadministrative boundary, to calculate a weighted average hazard metric. This approach overweights thehazard within GDP-generating areas of the county or state, and underweights non-economically
productive areas. Adjustments are made to account for GDP or hazard pixels that span the boundary of acounty or state. See Appendix for further detail.
Weighted Average Exposure Scores
The S1 Physical Risk Exposure Score model assigns risk scores from 1 (lowest exposure) to 100 (highest
exposure) to each hazard pixel, and the pixel level scores can be aggregated to county or state level
exposure scores as GDP-weighted averages for each scenario and time period (as described for theweighted average absolute hazard metrics above). 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 (above a threshold defined by S&P Global for each hazard), and a score of 1 indicates the lowest
level of exposure (below a threshold defined by S&P Global for each hazard). Physical risk metric values arenormalized to scores based on the formula described in Figure 2.
Figure 2 Climate Change Physical Risk Score Normalization for (global) Exposure Scores

8 Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci Data 5, 180004 (2018).

https://doi.org/10.1038/sdata.2018.4
9 Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).

http://dx.doi.org/10.7927/H41V5BX1.

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12For US Exposure Scores, the Rmax and Rmin reflect US maxima and minima (without thresholds) for the US
for each hazard across all scenarios for the 2020s to 2050s, as described in Figure 3.
Figure 3 Climate Change Physical Risk Score Normalization for US Exposure Scores

The composite score is intended to provide a combined measure of county exposure to nine climatechange physical hazards. The composite exposure score is calculated as an equally-weighted additive
combination of the county physical risk score on each hazard for a given scenario and year, which is thenrescaled to a 1-100 range using an exponential scoring curve. The scoring curve is designed to ensurethat counties with high exposure to one hazard, but low exposure to all others, will be assigned amoderate 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 county
exposure to climate change.
The Composite Physical Exposure Score is calculated as described in Figure Error! Reference source notfound..
Figure 4 Composite Physical Risk Score Calculation

US Exposure Scores
The US Exposure Scores are calculated using the same approach as the Exposure Scores described above,
except that the scoring range is set against the hazard exposure range for US counties and states only, as
opposed to all locations globally, for the 2020s, 2030s, 2040s and 2050s decades. The US Exposure Scores
help to simplify the comparison of exposure levels among US states and counties since the full range of
exposure scores is contained within the boundaries of the USA, in contrast with the Exposure Scores where
the most and least exposed locations may be outside of the USA. The analysis time horizon is also limited
to the 2020s-2050s as this is more representative of the duration of bonds issued by municipal bondissuers.
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13

GDP Exposed and Population Exposed Metrics
The GDP and population exposed metrics quantify the proportion of GDP or population located withinpixels in which the hazard is greater than or equal to the materiality thresholds detailed in Error! Not avalid bookmark self-reference.4.
Table 4 Climate hazard materiality thresholds for computing percent GDP and population exposed

Climate Hazard

HazardMateriality
ThresholdRationale

Extreme Heat

0.246

Equivalent to three months of extreme heat days

Extreme Cold

0.1

Equivalent to 36 days of extreme cold

Drought

0.246

Equivalent to three months of high drought likelihood days

Wildfire

0.246

Equivalent to three months of high wildfire likelihood days

Fluvial Flood0.01

A 1% annual probability of the one-in-100-year flood depth, inflood-exposed areas

Coastal Flood0.01

A 1% annual probability of the one-in-100-year flood depth, inflood-exposed areas

Pluvial Flood0.02

A 2% annual probability of the one-in-100-year flood depth

Tropical Cyclone0

All exposure to category 3+ tropical cyclones is consideredmaterial

Water Stress

0.4

High water stress as defined by the WRI Aqueduct dataset

The sum of GDP or population located in pixels exceeding the thresholds above within each county or stateboundary is divided by the total GDP or population within the county or state boundary to calculate thepercentage of GDP or population exposed (respectively) to each hazard under each scenario and time
period. See Appendix for further detail.
The composite GDP and population exposed metrics are calculated as the sum of the GDP or population
exposed to all nine hazards under a given scenario and time period. The composite GDP and population
exposed metrics are capped at 100%.

5. Linking to Muni Bond CUSIP Identifiers
Muni bond CUSIPs were retrieved via R search in S&P Capital IQ on county name and potential variants,
complemented by manual review of missing entries. Bond name was further filtered for General
Obligation/GO/G.O. to isolate general obligation instruments.

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Assumptions and Limitations
Key limitations of S1

Physical Risk: Municipal Dataset analytics include:•

Modelling Uncertainty: The climate models underpinning the physical risk analysis are complex andsubject to uncertainty. S1 assumes physical risk assessment based on averages of the output of allavailable CMIP6 GCMs is broadly representative of the future paths of hazard values. Future analysis
could consider the range of GCM hazard values and other approaches for uncertainty assessment.

•

Focus on Productive Areas: Regional hazard metrics are calculated using component cell (1km x 1km)
GDP to weight cell hazard inputs for computing representative regional averages. Future iterations
may consider using cell population for weighting hazard inputs, as an alternate approach for gauging
socioeconomic exposure.

•

Materiality: S1 currently defines hazard metrics using climate extremes and recognizes hazard
thresholds of major magnitude in the measurement of GDP and population exposed, to ensure thecapture of significant climate trend developments beyond natural variability. Differences in thevulnerability of specific locales could mean significant impacts exist at hazard levels far below theextremes and thresholds defined.

•

Threshold Materiality: Expert judgment is the basis for the hazard thresholds used to calculate
percent GDP and population exposed (Table 4), which were chosen to reflect a significant level ofhazard beyond year-to-year variation. Our review of external sources did not identify any literaturethat would define the basis for the US thresholds.

•

Spatial Resolution: S1 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 frequency of associated wind risks
while coastal flooding hazard metric independently includes storm surge flooding, likely capturing
flooding associated with tropical cyclone.
Further analysis could consider compound or concurrent hazard extremes, as data becomes
available.

•

Data Timelines: The datasets used to represent the distribution of population and GDP are historicaland infrequently updated due to their reliance on country census collections. The datasets chosencurrently are the definitive sources, and any future updates (or new datasets made available thatsupersede these in data quality) will be incorporated into the modeling in a timely fashion. In
addition, population and GDP distributions are held constant in the future scenario projections. The
distribution of population and the production of GDP is expected to change with time as economies
and communities develop, and these changes will not be reflected in the metrics presented in thisdataset.

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form
requires S&Pprior written consent.

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Dependencies
While the S1 Physical Risk: Municipal Dataset is a standalone product, known dependencies are:-corporate physical risk dataset will use US state- or county-level hazard exposures as aproxy where asset geolocation is not specified, yet information exists to specify state or county.
Such hazard metrics will be used in calculations of financial impact to an asset based on specificimpact functions.

-

S&P Global Ratings Public Finance will use US state- and county-level hazard exposures in due
diligence engagement with municipal issuers, in evaluating the materiality of climate physicalrisk exposure for government issuers in the credit ratings process, and providing commentary on
issuer-relevant climate physical risks.

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form
requires S&Pprior written consent.

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Appendix
Details the calculation of GDP-weighted absolute hazard metric, and GDP/population exposed metric.
The GDP-weighted average absolute hazard is intended to give more weight to hazard cells within a county
that provide a higher share of GDP, to reflect the risk to economic activity. The GDP values for each hazard
cell are adjusted to account for area contribution of the cell to the county by multiplying cell GDP by cellarea weight. This adjusted GDP is used as a weight to calculate the GDP-weighted average hazard values.
If Figure 1a is the aggregated GDP (USD) contribution of each 25-km hazard cell in Miami-Dade, withhazard levels indicated by color; Figure 1b is the GDP value adjusted to reflect the area of each cell withrespect to county boundaries; and Figure 1c is the contribution of each component cell to overall GDP:

Figure 1a. Aggregate GDP
associated with 25-km hazard
cells

Figure 1b. GDP associated withcells with respect to county
boundaries

Figure 1c. Contribution ofcomponent cells to overall GDP

final regional hazard level.
For GDP and population exposed, the metric represents GDP or population within a county which havematerial exposure to a hazard, for a given scenario and decade
derived by expert judgment. The metric, a percentage, is derived by summing the GDP (or population) of
hazard cells that are above the threshold, and dividing by the total GDP (or population) of the county.
For example, in Figure 2 Coastal flood cells above threshold, the GDP of cells which are above the defined
threshold - in green would be summed and then divided by overall GDP, to generate the % GDP exposed
to coastal flooding.

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form
requires S&Pprior written consent.

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Figure 2 Coastal flood cells above threshold (Miami-Dade county)

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form
requires S&Pprior written consent.

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Significant Updates
S&P Global Sustainable1 applies a rigorous quality assurance process to the development and ongoing
maintenance and enhancement of the S1 Physical Risk: Municipal Dataset based on input data validation,model unit testing, output data validation and benchmarking against current solutions (Physical Risk Exposure
Scores and Financial Impact Dataset), and delivery channel validation.
The changes made to this document include the following:
Version

Date

Changes

1.0
1.1

11/10/2023
12/07/2023

Initial version
Revised to add QA

1.2

01/15/2023

Revised to enhance discussion of assumptions,add Sec 7 Dependencies

1.3

01/17/2023

Revised to reflect discussion of assumptions anddependencies, add Appendix.

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of thecontent in any form requires S&Pprior written consent.

Related Documentation
There are several supporting documents specific to the Physical Risk: Municipal Dataset on the Support Center website(https://www.marketplace.spglobal.com/en/datasets/physical-risk-municipal-(1707421548)):•

Physical Risk User Guide This user guide provides an overview of Physical Risk: Municipal Dataset,including package description, database schema, and specific details about working with the data.

•

Physical Risk: Municipal Dataset 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: Municipal Dataset Data Dictionary: This spreadsheet provides details on the data items,
including dataItemId, dataItemName, and dataItemDefinition.

•

Physical Risk: Municipal Dataset FAQ Document: This FAQ Document provides quick orientation to thedata set, including assumptions, usage, and limitations.

•Physical Risk: Municipal Dataset Release Note: The Release Note describe the Physical Risk Scoresdata, by providing an overview of the dataset, data coverage, history, the new packages, and the newtables by package.

•

Physical Risk: Municipal Dataset Xpressfeed File Format: This spreadsheet contains the Xpressfeed
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.

If you are linking to other S&P Global Market Intelligence data sets, supporting documents are available on
the Support Center website.

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