Climanomics
Methodology
S&P Global Sustainable1 – July 2024

Table of Contents
Terms and definitions ................................................................................... 3
Introduction and Context ............................................................................. 4
Data sources and collection ........................................................................ 4
Methodology Overview................................................................................. 7
Monitoring and review ................................................................................ 13
Assumptions and Limitations ................................................................... 19
Maintenance/updates ................................................................................. 20
Related Documentation/ References/Annexes ....................................... 20
Significant Updates..................................................................................... 20
S&P Global Sustainable1 Disclaimer ....................................................... 38

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Terms and Definitions
Term

Definition

Climate Hazard

Changes in temperature, precipitation and the occurrence of natural disasters
such as floods, wildfires and storms, that are driven by climate change.

CMIP6

The Coupled Model Intercomparsion Project (CMIP) is a collaborative initiative oftens of international climate change modelling groups which maintain over 100
distinct climate change models and deliver climate change projections in supportof the Intergovernmental Panel on Climate Change. CMIP6 is the sixth and mostrecent release of the CMIP initiative.

Asset

For the purposes of this dataset, an ‘Asset’ represents any structure or real asset
with accompanying critical information such as longitude/latitude coordinates orstreet address, which is uploaded by user of Climanomics®.

Shared Socioeconomic Pathway

A set of greenhouse gas emissions pathways driven by socioeconomic
assumptions which result in a range of climate change outcomes by the year 2100.

Representative Concentration Pathway

Representative Concentration Pathways describe four different 21st century
pathways of greenhouse gas (GHG) emissions and atmospheric concentrations, air
pollutants and land use.

Transition Risks

Transition risks comprises the climate-related policy and legal risks, technology
risks, market risks and reputational risks that can impose increasing costs oncompanies.

Financial Impact Function

Financial impact functions describe the estimated change in financial impact
arising from a unit change in hazard exposure for a given asset type.

MAAL

Climanomics® quantifies the direct financial impacts caused by climate change ina metric known as Modeled Average Annual Loss (MAAL). MAAL metrics represent
the costs of changing climate hazard exposure (which can include physical asset
damages, operating costs and productivity impacts), which is reported annuallyfor each decadal period.

Absolute Risk

Absolute risk (in USD$M) is a function of hazard, vulnerability, and asset value.
This reflects the expected financial impacts in dollar terms.

Relative Risk

Relative risk (in %) is a function of hazard and vulnerability, expressed as apercentage of the asset value.

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Introduction and Context
The release of the Taskforce on Climate Related Financial Disclosures (TCFD) report highlighted the importance of climate change as adriver of material financial risks for companies and investors that should be assessed, disclosed and managed.
The TCFD categorizes the financial risks posed by climate change as Physical Risk (both acute and chronic) and Transition Risks (including
policy and legal risks, technology risk, market risk and reputational risk).
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 for organizations such as damage to assets, interruption of operations anddisruption to supply chains.
S&P Global Sustainable1 launched a suite of Climate Change Physical Risk Analytics solutions to the market in 2019, offering an asset-based
approach to the assessment of physical risk at the real asset, company and portfolio level. This suite of solutions was enhanced in 2022with the integration of the latest available climate change models and data, and expansion of the range of hazards and scenarios covered.
This report describes the methodologies, models and datasets that underpin the Climanomics ® platform solution for real asset climate
risk analysis. Climanomics ® is a risk analytics platform that calculates the financial impact of climate risk on physical assets and aggregatesup to the portfolio level. Analysis spans across eight decades for four emissions scenarios globally.
Climanomics® is designed to support a range of use cases, including risk management, sustainable investing, strategic decisionmaking, as well as compliance and reporting.

Data Sources and Collection
This methodology is built upon two key sets of data sources:•

The CMIP6 Global Climate Models which provide the projections of temperature and precipitation under alternative climatechange scenarios and future time periods, which are foundational to Sustainable1’s climate hazard modelling framework.

•

Scientific research papers, reports and other resources which are utilized in the development and ongoing improvement of
Sustainable1’s impact function modelling framework for the quantification of the financial impacts of changing climate hazard
exposure.

•

The following section describe the datasets used to derive the projections for each of the nine climate change hazards coveredby the Climanomics® platform.

Extreme Heat, Drought and Wildfire
The extreme heat, drought, and wildfire hazards are modelled utilizing temperature and precipitation data from CMIP6 and downscaled
projections provided by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) project. The NEX-GDDP dataset
offers global downscaled climate change data at 25x25km resolution across a range of scenarios. Sustainable 1 integrates relevant
climate modelling parameters of relevance to each hazard across all available CMIP6 models.
The extreme heat, drought and wildfire hazards are calculated as follows:
Extreme Heat
There are three measures of extreme heat used in analysis of financial impact.
•

Tx95p, the percentage of days per year with a maximum temperature that exceeds the 95th percentile of the local historicalbaseline daily maximum temperature;
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•

Tx95pAbsChng representing the absolute change in 95th percentile maximum temperature vs. a historical baseline; and•Tx50pAbsChg representing the absolute change in median maximum temperature vs. a historical baseline.

These hazard variables are derived directly from each downscaled CMIP6 model, then averaged across models and years in a projected
decade.
Drought
The drought hazard is derived from the Standardized Precipitation and Evapotranspiration Index (SPEI), as computed by SPEIbase fromthe Spanish National Research Council (CSIC) which provides SPEI1 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 for a projected decade is the average proportion of months per annum where the SPEI is less than or equal to thehistorical local 10th percentile. 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 are favorable for wildfire development. The FWI is computed based on downscaled CMIP6 temperature,
precipitation, relative humidity, and surface wind speed projections. The hazard variable for wildfire is the average proportion of days
per annum where the FWI exceeds the historical local 90th percentile. The spatial resolution for wildfire is 0.25° (~25x25km) 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 derived satellite
imagery is sourced from the Copernicus Global Land Service dataset 2 . The land cover mask is applied at 300x300m resolution and sets
the wildfire hazard to zero in locations where less than 20% of the wildfire hazard pixel and its surrounding area, defined as 1.6 miles inlength, is covered with burnable vegetation.
Coastal Flooding
Climanomics® applies a statistical model of coastal flood depths in order to estimate the projected frequency of baseline 100-year flood
depth by decade to 2090s under four climate scenarios. Historical storm-tide (surge plus tide) levels at 9 return periods from the GTSR
global hydrodynamic system are combined with Sea-Level Rise (SLR) projections (Kopp et al) to model flood levels in coastal regions. A
flood-water path-finding algorithm is applied to determine 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 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 have applied an interpolation procedure to the
CMIP5-based SLR.
Fluvial Flooding
Climanomics® applies a statistical model of fluvial-basin flood volumes and depth that estimates changes in return period for thehistorical 100-year flood in each catchment basin. The 10-yr and 100-yr return-period (RP) river discharges are related statistically toseven variables (“covariates”) based on a published analysis of historical data over the USA. Four covariates are topographic in nature(river drainage basin area, basin-mean channel slope, basin water storage expressed as fractional area comprised of lakes and ponds, andbasin impervious surface fractional area) and three are climatological (5-day precipitation maxima and numbers of consecutive dry days
and frosts days). For future decades, the climate covariates are derived from the downscaled CMIP6 dataset. The projected 10-yr and100-yr discharges are interpolated to obtain the projected frequency (reciprocal of RP) corresponding to the baseline 100-yr discharge.

1

Svoboda et al.,2012. Standardized Precipitation Index User Guide. World meteorological organization. https://library.wmo.int/records/item/39629-standardized-precipitationindex-user-guide2
Copernicus Climate Data Store. 2019. Land cover classification gridded maps from 1992 to present derived from satellite observations. [Online]. Available:
https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview

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The grid for the analysis is comprised of the geometric intersection of the downscaled CMIP6 0.25° grid and the topographic-data grid of
irregular drainage-basin polygons (HydroAtlas level 12).
The fluvial-basin flooding model includes flooding from streams and natural water-flow networks within the basin in which an asset is
located.
Basin scale flood frequency projections are overlaid with projected flood extent data sourced from the WRI Aqueduct dataset3 . Floodextent data (100-year return period) at 0.0083° (~1x1km) resolution is used as a ‘mask’ to identify areas exposed to flood within eachbasin 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 torepresent 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 absence of better available data. WRI Aqueduct projections for five GCMs (MIROC-ESM-CHEM, IPSL-CM5A-LR, HadGEM2ES, GFDL-ESM2M, NorESM1-M) were used to create an ensemble flood extent ‘mask’, to identify pixels as floodable where at least threeof the five available GCMs projected flood within that pixel.
Tropical Cyclone
The Tropical Cyclone (TC) hazard is calculated via a statistical-stochastic model that simulates the lifecycle of TCs, trained on historical TC
track data in each of the world’s 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 infuture 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 made through the 2040s. Subsequent decades are held at the 2040s value.
Water Stress
In order to model water stress, Climanomics® uses location-specific data from WRI’s Aqueduct 4.0. 4 WRI Aqueduct 3.0 was utilized inprevious versions of Climanomics®. 5 Climanomics® incorporates the current water stress index metric as an indicator of competition forwater resources and is defined as the ratio of demand for water by human society divided by available water supply. WRI offers
projections only for the scenarios SSP3-7.0 (business-as-usual), SSP1-2.6 (optimistic), and SSP5-8.5 (pessimistic), and for the years 2030,
2050 and 2080. Climanomics® uses those three yearly values in each scenario to interpolate values for all years between 2030 and 2080,
and then creates decadal averages from the interpolated yearly values. Water stress is assumed to be constant from 2020 to 2030 andfrom 2080 to the end of the century. The scenario SSP2-4.5 is interpolated based on the water stress index projections for SSP1-2.6,
using the relative global mean surface air temperature (GSAT) as the scaling factor. The water stress index is a continuous variablecapped at a range of 0 to 1.
Pluvial Flooding
In order to model Pluvial flooding, a form of climate hazard which is driven by extreme precipitation, Climanomics® uses daily
precipitation data from an ensemble of NEX-GDDP-downscaled CMIP6 models (25-km resolution worldwide). Climanomics® applies thestatistical model of Generalized Extreme Value analysis to determine the Intensity of extreme rare events. The model uses a simplifying
assumption that topography and natural or artificial drainage capacity is constant in time thus avoiding the necessity of using highresolution 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 the pluvial hazard metric of annual frequency of 100-year flood depth.
Landslide
Landslide hazard is modelled based on a combination of the Antecedent Rainfall Index (ARI) and landslide susceptibility maps. The ARI
represents a weighted average of the most recent 7 days of rainfall, including the current date. The historical ARI distribution is

3

World Resources Institute. 2020. Aqueduct Floods Hazard Maps. [Online]. Available: https://www.wri.org/data/aqueduct-floods-hazard-maps
World Resources Institute. 2024. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators. [Online]. Available: https://www.wri.org/applications/aqueduct/waterrisk-atlas/#/?advanced=false&basemap=hydro&indicator=w_awr_def_tot_cat&lat=-14.445396942837744&lng=142.85354599620152&mapMode=view&month=1&opacity=0.5&ponderation=DEF&predefined=false&projection=absolute&scenario=optimistic&scope=baseline&timeScale=annual&year=baseline&zoom=2
5
The Exposure Score API added to Climanomics in the Q2 2024 release will use Aqueduct 3.0 on release but will be updated to Aqueduct 4.0 in a future release.
4

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calculated for the period 1950-2015 and the projected future ARI is calculated from projected daily rainfall from the CMIP6 models at
0.25 degree (~25x25km) resolution. The 95th percentile ARI is determined for the historical period and the frequency of days projected
to exceed this threshold was determined for the four scenarios and eight time periods considered in Climanomics®.
Landslide susceptibility maps consistent with ARI observations published by Kirschbaum et al (2018) were selected to represent landslide
susceptibility at 0.08 degree (~8x8km) resolution. Kirschbaum et al (2018) identify elevation changes (slope) and land cover-type as key
drivers of landslide susceptibility. Kirschbaum et al (2018) define a susceptibility rating system where a value of 1 indicates lowsusceptibility and increasing to 5 indicating very high-susceptibility. The landslide susceptibility layer was applied to the ARI projections
as an overlay, setting pixels with a susceptibility rating <3 as zero hazard.
Estimation of MAAL associated with landslide hazard is not currently available. Climanomics® currently offers hazard data only forlandslide. The hazard output for landslide represents the frequency of exceedance of the historical 95th percentile ARI for pixels with asusceptibility rating of greater than three (pixels with a susceptibility rating of less than 3 return zero for hazard).
The following section describe the datasets used model transition risk and opportunities covered by the Climanomics® platform.
Climanomics® utilizes carbon price projections from the underlying data of the Shared Socioeconomic Pathways (SSPs) models used by
the IPCC.
The latest SSP data (including the data for IPCC 1.5C) is specific for 5 different regions in each scenario. For a full list of the countries
included in each region, please see Appendix A.
•

OECD = Includes the OECD 90 and EU member states and candidates.

•

REF = Countries from the Reforming Economies of Eastern Europe and the Former Soviet Union.

•

ASIA = The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.

•

MAF = This region includes the countries of the Middle East and Africa.

•

LAM = This region includes the countries of Latin America and the Caribbean

The following section describes the datasets applied to quantify the financial impacts arising from changing exposure to climate change
hazards.
Climanomics® has developed an extensive library of detailed impact functions based on peer-reviewed published research and papers
published by government and industry sources; these are considered Tier 1, or specific functions. In order to cover the broadest possible
range of asset types, the impact function library also consists of Tier 2, or general functions which have been assigned by comparing agiven asset type with others and selecting the most appropriate function among those available or using a broader range of literature toestimate impacts.

Methodology Overview
S&P Global Sustainable 1 Climanomics ® platform enables users to quantify the future financial Impacts of climate change by:•Integrating terabytes of climate and socioeconomic data on climate-related hazards•

Driving econometric models with hazard inputs and business data•

Translating risk into financial terms to provide decision-relevant insights.

The Climanomics® physical risk methodology and outputs are fully aligned with the TCFD framework, shown in the figure below.
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Figure 1: TCFD Framework6
The purpose of this document is to provide a general introduction to the Climanomics® methodology. Core to this methodology are theconcepts of hazard, vulnerability, and risk:•

Hazard: Changes in environmental or economic conditions associated with climate change. These are expressed as specificmetrics that change through time.

•

Vulnerability: Responses of an asset or entity to changes in the climate-related hazards. These are sensitive to the levels of thehazard metrics.

•

Risk: Financial measures of impacts induced by the hazards via the vulnerabilities. This is based on the combination of thedegree of vulnerability (at a given hazard level) and the valuation of an asset.

These concepts, and their application in the Climanomics® framework, are described in detail in the following sections.

Principles of Hazard-Change Modeling
The Climanomics® hazard modeling reflects the climate-related change in the level of hazard exposure of an asset over time, relative to ahistorical baseline. Each hazard is associated with a specific metric, which defines how the hazard is measured and expressed. The data
underlying each hazard metric is sourced from a variety of climate models and other data sources.
The Climanomics® platform assumes complete adaptation to climate conditions prevalent in the last half of the 20th Century. Thisestablishes the reference level for risk and zero risk is assumed when hazards are at historical levels. Climanomics® thus estimates the
additional risk that is attributable to climate change, relative to a world without climate-related changes in hazard levels.

Physical Hazard Modeling
The Climanomics® platform processes and analyzes atmospheric data related to temperature, precipitation, drought, wildfire, as well asother data related to coastal flooding, tropical cyclones, water stress, and fluvial-basin flooding in order to provide a rigorous estimate ofrisk under various conditions. This section addresses each of the physical hazards modeled, as well as the data used.

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https://www.fsb-tcfd.org/publications/final-recommendations-report/

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Table 1: Climanomics® Physical Hazard Coverage
PHYSICAL
HAZARDHAZARDTYPE

BASELINEPERIODGEOGRAP
HIC
COVERAGERESOLUTION

UNDERLYING VARIABLES

SOURCE

Extreme
Heat

Chronic

19802000

Global25km*25km

Temperature

CMIP6 2; NEX-GDDP7

Drought

Chronic

19802000

Global25km*25km

Temperature, Precipitation, Surface windspeed, Incoming solar radiation

CMIP6; NEX-GDDP

Wildfire

Chronic

19802000

Global25km*25km

Temperature, Precipitation,
winds, Relative humidity

CMIP6; NEX-GDDP

Water Stress

Chronic

19502010

GlobalVariable(approx.
50-100km)

Withdrawal, Available Renewable Water
SupplyWorld
Resources
Institute (WRI)8

CoastalFlooding

Acute19862005

Global30*30mUSA

Sea level rise, storm tide

Kopp et al., 2014 9 ;Muis et al., 201610

Surface

90*90 RoW

FluvialFlooding

Acute19502000

Global

1km* 1km

Annual frost days, consecutive dry days,
maximum 5-day precipitation, basin area,
slope, impervious surface area, lakestorage area in basin, and projected flood
extent.

CMIP6; NEX-GDDP;World Wildlife Fund(WWF)
HydroBASINS 11 ; WRI
Aqueduct12

TropicalCycloneAcute19501980

Global25km*25km

Sea surface temperature

NASHM 13 , Hall et al(2021)

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Further information on the NEX-GDDP dataset is available at https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp .
The Aqueduct Water Stress Projections database is licensed under a Creative Commons Attribution International 4.0 License and is based upon analysis from Luck, M., M. Landis,
F. Gassert. 2015. “Aqueduct Water Stress Projections: Decadal projections of water supply and demand using CMIP5 GCMs.” Washington, DC: World Resources Institute. See also
Hofste, R., S. Kuzma, S. Walker, E.H. Sutanudjaja, et. al. 2019. “Aqueduct 3.0: Updated Decision- Relevant Global Water Risk Indicators.”
https://www.wri.org/resources/maps/aqueduct-water-risk-atlas
9Kopp, R. E., R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer, D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (201 4), Probabilistic 21st and 22nd century sea-levelprojections at a global network of tide-gauge sites, Earth’s Future, 2, 383–406, doi:10.1002/2014EF000239.
10
S. Muis, et al, “A global reanalysis of storm surges and extreme sea levels”, Nature Comm., DOI: 10.1038/ncomms11969, 2016.
11https://hydrosheds.org/images/inpages/HydroBASINS_TechDoc_v1c.pdf
12WRI. 2019. Aqueduct V3.0. [Online]. Available: https://www.wri.org/data/aqueduct-floods-hazard-maps
13Hall and Jewson, Statistical modeling of North Atlantic tropical cyclone tracks. Tellus, 59A, 485-498 (2007); Hall and Yonekura, North-American tropical cyclone landfall and SST:A statistical model study. J. Climate, 26, 8422-8439 (2013). Hall, Kossin, Thompson and McMahon, U.S. tropical cyclone activity in the 2030s based on projected changes in tropicalsea surface temperature, J. Climate., 34, 1321-1335 (2021).
8

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Pluvial
Flooding

Acute19502000

Global25km

Precipitation

CMIP6; NEX-GDDP

LandslideAcute19502015

Global8km

Precipitation, landslide susceptibility

CMIP6; NEX-GDDP;
Kirschbaum et al200814INTRODUCTION TO THE CMIP6 CLIMATE MODELSAs described in Table 1, the majority of the climate data underpinning Climanomics® is derived from the Coupled Model Intercomparison
Project (CMIP) run by the World Climate Research Programme. The latest version of Climanomics®, released in 2023, is based on CMIP6,
which integrates many of the latest advances in climate change science. The CMIP6 models were developed in support of the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). Climanomics® also leverages downscaled CMIP6
datasets provided by the NASA Earth Exchange (NEX), enabling an enhancement of the resolution of analysis for many hazards from
100x100km to ~25x25km spatial resolution.
The following sections explain some of the most important advantages of the CMIP6 climate models and the differences compared to
CMIP5 which are relevant to Climanomics®.
Leveraging an Increased Number of Climate Models
A total of 49 modeling institutions contributed a total of 132 climate models to CMIP6, compared to 28 institutions contributing 61 models
in CMIP5. Integration of a broader range of modelling institutions and models, each with their own strengths and weaknesses, helps toimprove the performance of the project as a whole and helps scientists identify which trends in the data are due to model biases and which
are not. In the aggregate, this allows for more robust modelling of climate change hazards and their potential impacts on real assets.
Higher Climate Sensitivity
Climate sensitivity is a measure of the relationship between changes in global CO2 concentrations and changes in global temperatures.
Most previous climate model data, including data from CMIP5, found that a doubling of atmospheric CO2 would result in a 1.5-4.5°C risein global temperatures. However, more recent research that feeds into CMIP6 finds that a doubling of atmospheric greenhouse gas
emissions would result in larger changes in global temperatures, ranging from 1.8-5.6°C. Integration of the best available science on climate
sensitivity is important to ensure that future climate change impacts are not underestimated.
New Climate Scenarios
The CMIP5 climate modelling framework was structured around four Representative Concentration Pathway (RCP) scenarios. The RCP
scenarios are driven primarily by projections of changes in factors such as greenhouse gas emissions and land use change, which directly
impact radiative forcing, or the amount of excess energy in the Earth’s system. Since these scenarios centered around radiative forcing,
they were named for the amount of forcing (in the standard units of W/m2) projected for the year 2100 (RCP2.6, RCP4.5, RCP6.0, and
RCP8.5).
In the CMIP6 framework, a complementary set of scenarios focused on projecting socioeconomic changes was developed to be usedalongside the RCPs. These new scenarios, Shared Socioeconomic Pathways (SSPs), are based on five distinct narratives for futuresocioeconomic development. The narratives describe alternative futures for socio-economic development using a consistent logic for thequalitative projections of land use, energy use, population, emissions, and other factors, embedded within the scenario. Climanomics®
incorporates four climate-change scenarios which are described in Table 2.

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Kirschbaum, D., & Stanley, T. (2018). Satellite‐based assessment of rainfall‐triggered landslide hazard for situational awareness. Earth's Future, 6(3), 505-523.

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Table 2: Climanomics® climate change scenarios
SSP5-8.5

High Climate Change Scenario
Low mitigation scenario in which total greenhouse gas emissions triple by 2075 and global averagetemperatures rise by 3.3-5.7 °C by 2100

SSP3-7.0

Medium-High Climate Change Scenario
Limited mitigation scenario in which total greenhouse gas emissions double by 2100 and global averagetemperatures rise by 2.8-4.6 °C by 2100

SSP2-4.5

Medium Climate Change Scenario
Strong mitigation scenario in which total greenhouse gas emissions stabilize at current levels until 2050 andthen decline to 2100. This scenario is expected to result in global average temperatures rising by 2.1-3.5 °C
by 2100

SSP1-2.6

Low Climate Change Scenario
Aggressive mitigation scenario in which total greenhouse gas 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

CMIP6 also adds a new RCP, RCP7.0, which falls between RCP4.5 and RCP8.5 and offers a plausible projection of the outcome of current
efforts to mitigate climate change. In addition, RCP6.0 has been replaced with SSP3-7.0 in the new CMIP6 version of Climanomics®. The
SSPs and RCPs are combined to create a final set of scenarios for input into the CMIP6 models. The combined SSP/RCP scenarios capture
both physical and socioeconomic factors (Figure 2). For example, SSP1-2.6 simulates a global society that focuses on sustainable
development (SSP1) and is able to hold radiative forcing levels to 2.6 W/m2 in 2100 (RCP2.6).

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Figure 2: CMIP6 SSP and RCP Combinations15 16

15Adapted from O'Neill et al., 2016 , the matrix shows how the SSP narratives and RCP emission scenarios are combined in CMIP6. The dark blue boxes show scenarios that wereprioritized in CMIP6. These are the scenarios used in the updated Climanomics®. The light blue boxes show other scenarios considered important, but lower priority in CMIP6.
The white boxes show the SSP/RCP combinations that are possible (some forcing targets were found to be impossible for some SSP narratives).
16 Tier 1 or dark blue boxes are scenarios that are available in Climanomics. Tier 2 or light blue boxes are additional material scenarios of interest to scientific community as wellas white boxes are other possible scenarios

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TRANSITION HAZARD MODELING
Carbon Pricing
This transition risk is related to policies and regulations that may impose a carbon price through such mechanisms as carbon taxes or
emissions trading17.
Climanomics® links each asset to the appropriate country and then to the region. "Climanomics® maintains at least three levels ofgeographic specificity for each asset. For instance, in the case of a U.S.-specific asset, it includes city, state, and country information. This
framework allows for additional granularity as carbon-price projections become more detailed over time (e.g., UK vice France, orCalifornia vice Montana). Specially tailored price projections could also be used upon request, if there are issues that a customer would
like to explore.
For the platform’s RCP8.5 scenario, the system uses a carbon price model based on the Shared Socioeconomic Pathways (SSP) scenario
SSP3-60 , which assumes high challenges to both adaptation and mitigation. The price varies across the five regions and through time,with the range of values being from approximately $8/ton to approximately $82/ton by 2100. Mid-century prices are approximately$29/ton. For each location, the system assigns a likely carbon price; combined with GHG emissions data for each asset, Climanomics®
calculates the carbon pricing risk.
For RCP6.0, Climanomics uses interpolated carbon prices based on scenarios SSP3-60 and SSP3-45. Prices range from approximately$8/ton to $180/ton by 2100. Mid-century prices are around $50/ton.
For the platform’s RCP4.5 scenario, the system uses a carbon price model based on the SSP scenario SSP3-45. Prices range fromapproximately $8/ton to approximately $440/ton by 2100. Mid-century prices are approximately $65/ton.
Lastly, the RCP2.6 scenario is based on carbon prices from SSP3-34. Prices range from approximately $8/ton to approximately $880/ton
by 2100. Mid-century prices are approximately $180/ton.
Climanomics® employs temperature extremes as a surrogate for future transition risks, specifically focusing on the localized frequency ofdaily maximum temperatures exceeding the 90th percentile (only for Transition Hazard Modelling) in comparison to the baseline period(1950-1999). Additionally, impact functions are applied to reflect high, medium, and low levels of transition impacts.
Ongoing research and development also explore the incorporation of supplementary metrics that may influence these risks. The
following outlines each risk category:1.

Litigation: Litigation risks involve corporations face increasing costs to defend against climate-related litigation. Claims include
failure to mitigate, adapt, and disclose risks in reference to various local and sovereign laws and regulations.

2.

Reputational damage: Analyzing reputational risk requires consideration of multiple stakeholders. Perception of anorganization's "social license to operate" can affect supplier prices, employee costs, consumer demand, and shareholder value.

3.

New Technology: Technology risks stem from advancements accompanying the transition to a lower-carbon economy. Theseadvancements may have financial implications, potentially affecting competitiveness, production efficiency, or demand,
leading to the impairment or stranding of assets.

4.

Market: Market risks encompass the impacts of the transition to a lower-carbon economy, where both the supply and demandfor products and services can be affected.

17See, for example, The World Bank, “Pricing Carbon”, at https://www.worldbank.org/en/programs/pricing-carbon .

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IMPACT MODELING FOR PHYSICAL HAZARDS
The following section describes the methodologies and methods applied to quantify the financial impacts arising from changing exposure
to climate change hazards.
Impact Functions
The Climanomics® vulnerability methodology models direct financial impacts that each hazard is expected to have on each asset type.
Each asset type’s vulnerability is characterized based on the specific ways (“impact pathways”) in which a particular asset type is
impacted by a given climate hazard. Finally, impact functions, composed of impact pathways, are assigned to model the risk based on thehazard and vulnerability.
Impact functions estimate the financial losses - including revenue, operating expenses, and capital expenditures - that a hazard of varying
intensity would cause to a specific class of asset. A hazard might cause harm via diverse impacts, which would require multiple impact
pathways to characterize. For example, high maximum daily temperatures at a manufacturing facility could drive up cooling costs,
degrade the HVAC system, and reduce the productivity of employees working inside.
Asset Structure
Impact functions may be applied to a point, spatial, or linear asset:•

Point: Assets whose locations are associated with a specific latitude and longitude coordinate.

•Spatial: Assets that typically cover a large area and are assessed through a number of sampled points. Climate risk for theseassets is the sum of climate risk at each point.

•

Linear: Assets assessed through a series of latitudinal and longitudinal points. Climate risk for these assets is the sum of climate
risk at each point.

For spatial and linear assets, the risk to the asset itself is calculated as a sum of risks across sampled points. Sample points are points
selected by Climanomics® clients to represent the area or path of a non-point asset for example, a 10km power transmission line canpotentially be divided into 10 point locations. Weighting of sample points can be applied by entering each point as a separate asset in
Climanomics®. For example, when assessing a railroad (linear asset), impact functions would be assigned to sampled points ranging fromtrack, stations, and junctions; varying asset values / importance weighting would be applied to stations and junctions in contrast to track
points.
Financial Materiality Perspectives
Within the Climanomics® platform, impact functions are modeled from four financial perspectives, based on ownership of assets andinvestment structure of financial instruments:•

Investor-Owner: This perspective reflects a situation in which an investment manager owns the asset and leases it out toothers. The implication is that the investment manager is financially impacted by direct damage to the building and rental
income, but not for damage to tenants’ equipment/inventory or revenue losses. Investor-Owner impact functions include
operating expenses related to facility maintenance or utilities to account for the potential impact on investors, despite the factthat tenants are typically responsible for these expenses.

•

Owner-Occupier: This perspective captures impacts that accrue to a company that owns and uses the building. The implicationis that Owner-Occupiers are responsible for financial impacts spanning revenues, operating expenses, and capital expenditures
related to employee productivity, cooling costs, and direct building damages.

•

Tenant: This perspective reflects tenants of buildings, who lease either an entire building or a space from Investor-Owners. The
implication is that Tenants are responsible for financial impacts related to revenue and operating expenses, but not directbuilding damage (which would be paid for by the Investor-Owner).

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•

Manufacturer-Supplier: This perspective reflects the manufacturer who produces goods in a facility and supplies them to a
Company; however, the Company does not own or lease the building. The main financial impacts stem from a disruption in
operations at the facility, resulting in a reduction of goods to be sold for profit.

In some cases, impact functions from multiple perspectives are included in analyses to capture impacts to both income and investments.
For real estate investors, for example, both the Investor-Owner and Tenant perspectives are often relevant. While real estate investors
themselves function as investor-owners who are not responsible for operating expenses related to facility maintenance or utilities, which
are passed through to tenants, understanding the tenant perspective sheds light on potential vulnerability tipping points which impact
rental income and, via net operating income, overall return on investment.
Location and Geographic Considerations
Some impact functions are specified by locational and geographic considerations of an asset, such as whether the asset is located in a
tropical or temperate region.
For some crops, impact functions have been developed that distinguish between the physiological response of crops in tropical versus
temperate climates. While this is not currently available for all crops, Climanomics® will continue to incorporate such geographic
considerations over time and as literature becomes available. For analysis within the platform, tropical and temperate are defined as:•

Tropical: The area of the tropics surrounding the equator from 23°N to 23°S.

•

Temperate: The region extending north from 23°N and south from 23°S.

Impact Function Coverage
For investment portfolios, Climanomics® begins the impact function modeling process (outlined in the diagram below) by identifying theunderlying entity of each financial instrument. For a corporate bond, for example, Climanomics® requires inputs on the company ofinterest, information about company’s assets (offices, data centers, manufacturing facilities, etc) and, wherever possible, information onwhether the company owns or leases each asset. Impact functions are then assigned based on asset and ownership type: for each hazard
Climanomics® currently covers about 290 asset types as described in Table 5 (Appendix B) Impact function whitepapers are available
upon request.

Figure 3: Impact function modeling process for financial instruments
Adaptation Impact Functions
These impact functions incorporate a more precise analysis of assets, taking into consideration adaptation measures commonly
integrated into specific asset types. In this phase, adaptation functions are limited to hazards such as flooding, water stress, tropical
cyclones, and temperature extremes. The selection of impact functions is based on criteria including a focus on commonly applied
adaptation measures. An expanded suite of adaptation impact functions is anticipated in the future.

•

Flood Walls: Floodwalls are vertical structures anchored into the ground that are designed to withstand flooding from eitherrivers or storm surge and prevent areas behind the wall from flooding. Flood walls are walls built along a shoreline or bank toprotect from flooding. These engineered structures temporarily constrain the waters of a river or other waterway as it rises

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15due to extreme weather events. Floodwalls sometimes have gates to allow access for a roadway or other right-of-way, which
can be closed in advance of a flood event 18•

Wet Flood-Proofing: Wet-flood proofing involves use of flood damage resistant materials that do not need to be replaced if
flooded, including pressure-treated plywood, concrete, and cement board. Flood vents are installed in the walls of theenclosure to let flood waters enter and leave by gravity, which allows forces on either side of the structure’s walls to equalize.
This prevents the structure and foundation from collapsing in the event of a flood.19•

Elevating Structures: Elevating a building to prevent floodwaters from reaching living areas is an effective retrofitting method.
The goal of the elevation process is to raise the lowest floor to or above the flood protection elevation (FPE). This can be doneby elevating the entire building, including the floor, or by leaving the building in its existing position and constructing a new,elevated floor within the building. The method used depends largely on construction type, foundation type, and flooding
conditions.20,21•

Dry Flood-Proofing: Dry floodproofing includes measures that make a structure watertight below the level that needs flood
protection to prevent floodwaters from entering. It involves sealing the exterior of a building to prevent the entry of flood
waters. This technique can only be used when the walls are strong enough to withstand the hydrostatic force of the water.
Shields may be installed to seal off doors, windows, and other openings22•

Cool Roofs: Cool roofs are roofs made up of highly reflective and emissive materials that can keep roofs 50 to 60 degrees (°F)
cooler than roofs made from traditional materials during warmer days. They can be made from a variety of different lightcolored materials including coatings, asphalt shingles, metal, clay tiles, and concrete tiles, all known to reflect heat. As abuilding adaptation, cool roofs can reduce cooling costs by decreasing air conditioning needs and subsequently reducing
energy bills.

•

Green Roofs: Green roofs, roofs inclusive of a vegetative overgrown layer, are a type of cool roof that can keep buildings cooler
during the summer and warmer during the winter. Green roofs provide more energy savings than cool roofs as they act as anadditional barrier between a building’s interior and exterior environment, both hot and cold.

•

Impact-Resistant Glass Windows and Reinforced Concrete Foundation: Impact-resistant glass can prevent shattering andbreaking of windows and doors during conditions of high winds and flying debris. For example, in an office property recently
developed in Miami, Florida, the curtain wall, comprised of laminated tempered glass, is nine-sixteenths of an inch thick and isstrengthened by heavy bolts, thick aluminum framing, and silicone. This means that the structure should be able to withstandCategory 5 hurricane-strength winds.

•

Rainwater Harvesting: During the rainy season, a rain harvesting system (water tank design) is used to catch and collectrainwater. The rain harvesting system design uses gravity for water flow, reducing the electricity that would otherwise beneeded for pumping. This water is then available for people to use and consume during the dry season when there is a
shortage of clean water. Likewise, a rain harvesting system (absorption well design) can also capture rainwater and funnel it
directly into the ground in order to conserve ground water in the future.

•

Greywater Recycling: Greywater recycling capture water from showers, washing machines, bathroom and kitchen sinks.
Greywater does not include water from toilets or wash water with fecal material (e.g., soiled diapers)•

Greywater Recycling & Rainwater Harvesting: Combined rainwater harvesting and greywater recycling.

18

Federal Emergency Management Agency, 2012, Engineering Principles and Practices for Retrofitting Flood-Prone Residential Structures (Third Edition).
https://www.fema.gov/sites/default/files/2020-08/fema259_complete_rev.pdf
19
Federal Emergency Management Agency, 2012, Engineering Principles and Practices for Retrofitting Flood-Prone Residential Structures (Third Edition).
https://www.fema.gov/sites/default/files/2020-08/fema259_complete_rev.pdf
20
ibid
21
Mile High Flood District, Flood Risk Management, 2016: Urban Drainage and Flood Control District, Urban Storm Drainage Criteria Manual Volume 1
22
STC Planning, 2019: Dry Floodproofing. Southern Tier Central Regional Planning and Development Board, https://www.stcplanning.org/wpcontent/uploads/2020/09/FProof_04_Dry_Floodproof.pdf.

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BUSINESS DATA AND RISK MODELLING
For corporate climate risk analyses, Climanomics® gathers business data from customers to inform the vulnerability modeling and risk
calculation portions of the analysis.
Business Data
Climanomics® ingests business data regarding operations (Corporate customers) or holdings (Investment Managers).
In general, there are four characteristics of business data that are critical to informing the analysis.
•

Asset Type: Asset type determines which impact function is selected to model vulnerability. Impact functions are composed of
impact pathways that are governed by asset characteristics such as structure and building codes; requirements for heating andcooling; and annual revenue.

•

Asset Ownership: For the purposes of selecting the correct impact functions, it is best to determine whether an asset is leasedor owned, or, if appropriate, percentage ownership. This distinction determines the extent to which, for example, a customer’sbalance sheet would be impacted by climate risk.

•

Asset Location: During the data intake process, Climanomics® provides the flexibility to a customer to enter longitude/latitude
coordinate or a street address which will be geocoded to longitude/latitude automatically.

•

Asset Value: Several measures of asset value can be assessed through the Climanomics® platform, depending on what is most
relevant to each customer. During the data collection phase, Climanomics® works with each customer to determine whichmeasure of value will be most useful e.g., replacement value, total insured value or market value.

Risk Calculations
Climanomics® quantifies the direct financial impacts caused by climate change in a metric known as Modeled Average Annual Loss
(MAAL). MAAL is the sum of climate-related expenses, decreased revenue, and/or business interruption costs. MAAL is reported annuallyfor each decadal period.
Calculation of MAAL is explained in detail in the Physical Risk Exposure Scores and Financial Impact Dataset Methodology report,
referenced to as Financial Impact calculation.
For each hazard metric, MAAL is calculated from the ensemble mean of daily values averaged over an entire decade. For example, thevalue listed as “2030” represents the average of the daily values for the 2030s (2030-2039) for all available models.
Other notable features of Climanomics® outputs include:
1.

Results are presented in absolute and relative terms
Absolute risk (in USD$M) is a function of hazard, vulnerability, and asset value. This reflects the expected financial impacts indollar terms. A very valuable asset with low hazard exposure and vulnerability could still hold substantial risk due to the highasset value.
Relative risk (in %) is a function of hazard and vulnerability. Reported as a percent of asset value (calculated as MAAL/asset
value), it provides a perspective on exposure and vulnerability across assets, independent of their value.

2.

MAAL relative to the baseline (or historical) period
Climanomics® outputs convey the expected delta in financial risk due to climate change. Thus, all outputs are reported relative
to a historical baseline, which is specific to each hazard. The historical baselines for each hazard are described earlier in thisdocument.
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3.

Reported for a decadal period
Climanomics® MAAL outputs are applicable for a specific decadal period, e.g., the 2020s, defined as 2020-2029 (applicable forevery decade). To calculate risk for an entire decade, one would multiply the reported MAAL for a particular decade by 10.

OTHER CLIMATE HAZARDS AND DELIVERYThe Climanomics® platform now offers two additional forms of climate hazard metrics as described below.
Hazard Data Export
Climanomics® now offers functionality to allow for the export of baseline and projected hazard data by scenario and time period for
assets and portfolios uploaded to the platform. The hazard metrics are defined as per Table 1 and representative of the hazard data usedin the calculation of risk / MAAL outputs in Climanomics®. Hazard data can be exported using the ‘Export’ page in the Climanomics®
platform.
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 that underpin Climanomics®. The exposure score is intended torepresent 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 to scores based on the formula described in Equation 1.
Equation 1: Climate Change Physical Risk Score Normalization

The exposures scores are intended to aid comparisons between the climate hazards, which are each defined in different physical units,by presenting all hazards on a 1-100 scale.
The composite exposure score is intended to provide a combined measure of exposure to all eight-climate change physical hazards. The
composite exposure score is an equal weighted additive combination of the exposure scores for each hazard for a given scenario andtime period, and then rescaled to a 1-100 range using an exponential scoring curve. The scoring curve is designed to ensure that assets
with high exposure to one hazard, but low exposure to all others, will be assigned a moderate to high composite 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 in cases such as this. The composite exposure score is calculated as
described in Equation 2.

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Exposure scores can be exported using the ‘Export’ page in the Climanomics® platform. Currently, the platform provides extreme cold
exposure scores, which is a hazard metric not offered in Climanomics®. These extreme cold scores are included in derivation ofcomposite exposure scores. Conversely, the platform doesn’t provide landslide exposure scores, a hazard metric available in
Climanomics®. Water stress exposure scores currently utilize WRI Aqueduct 3.0; however, they will soon be updated to WRI Aqueduct
4.0 in line with the hazard metrics in Climanomics®.
The methodology and assumptions used to calculate all exposure scores, including extreme cold exposure scores and WRI Aqueduct 3.0
data is described in detail in the Physical Risk Exposure Scores and Financial Impact Dataset methodology report.

Monitoring and Review
Sustainable 1 applies a rigorous quality assurance process to the development and ongoing maintenance and enhancement of the
Climanomics® dataset and outputs, including input data validation, model unit testing, output data validation, delivery channel validationand continuous improvement efforts. The methodology and modelling process is overseen by a dedicated team including I IPCC-affiliatedclimate scientist, technologists, economists, data scientists, and finance professionals.

Assumptions and Limitations
Key limitations and assumptions of Climanomics® include:•

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

•

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.

•

Impact of Varying Baseline Values: In context of Climanomics® physical hazard modelling, the data sources used do not presently
allow for complete alignment of baseline values, and this results in some minor inconsistencies in the modelling of some hazards.
Essentially, each grid cell in our data has a different starting point or baseline value, even though all grid cells are aligned on the same
threshold. This means that while the thresholds for triggering alerts or identifying hazards are consistent across all grid cells, the initialconditions from which these thresholds are measured vary slightly from one grid cell to another. This is not considered to be significant
for screening-level analysis.

•

Data Limitations Impacting Transition Hazard Modeling: The primary data limitation is that the carbon prices used are nominal
values, which means they have not been discounted to reflect their present value. Additionally, data limitations present a challenge,
particularly regarding robust transition-risk-related data encompassing litigation, reputation, technology, and market risks. To address
this, Climanomics® employs temperature extremes as a surrogate for future transition risks, specifically focusing on the localized
frequency of daily maximum temperatures exceeding the 90th percentile (only for Transition Hazard Modelling) in comparison to thebaseline period (1950-1999).

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•

Discount Rate Uncertainty: Climanomics® quantifies the direct financial impacts caused by climate change in a metric known as
Modeled Average Annual Loss (MAAL). A limitation that may impact interpretation of results is that MAAL outputs are currently notdiscounted. Since myriad factors may impact the discount rate, customers have a preference to receive the raw outputs from
Climanomics® and then discount in their own models if they so choose.

Maintenance/Updates
We periodically review our methodology as appropriate.

Significant Updates
The last significant update was in June 2023. Those enhancements were primarily driven by the transition from CMIP5 to CMIP6 as thebasis for hazard modeling, availability of improved data and estimation models, and efforts to achieve consistent scenario and time period
coverage for all hazards. See Appendix B below for insight into 2024 material updates.

Related Documentation/ References/Annexes
APPENDIX A. Countries included in SSP regions
OECD
Includes the OECD 90 and EU member states and candidates.
Albania, Australia, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Guam, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands,
New Zealand, Norway, Poland, Portugal, Puerto Rico, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, The former Yugoslav
Republic of Macedonia, Turkey, United Kingdom, United States of America
REF
Countries from the Reforming Economies of Eastern Europe and the Former Soviet Union.
Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan,
Ukraine, Uzbekistan
ASIA
The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.
Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China (incl. Hong Kong and Macao, excl. Taiwan) Democratic People's
Republic of Korea, Fiji, French Polynesia, India, Indonesia, Lao People's Democratic Republic, Malaysia, Maldives, Micronesia (Fed. Statesof), Mongolia, Myanmar, Nepal, New Caledonia, Pakistan, Papua New Guinea, Philippines, Republic of Korea, Samoa, Singapore, Solomon
Islands, Sri Lanka, Taiwan, Thailand, Timor-Leste, Vanuatu, Viet Nam
MAF
This region includes the countries of the Middle East and Africa.

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Algeria, Angola, Bahrain, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo,
Côte d`Ivoire, Democratic Republic of the Congo, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea,
Guinea-Bissau,“Iran (Islamic Republic of), Iraq, Israel, Jordan, Kenya, Kuwait, Lebanon, Lesotho, Liberia, Libyan Arab Jamahiriya, Madagascar, Malawi,
Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Occupied Palestinian Territory, Oman, Qatar,
Rwanda, Réunion, Saudi Arabia, Senegal, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Syrian Arab Republic, Togo,
Tunisia, Uganda, United Arab Emirates, United Republic of Tanzania, Western Sahara, Yemen, Zambia, Zimbabwe
LAM
This region includes the countries of Latin America and the Caribbean.
Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic,
Ecuador, El Salvador, French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Nicaragua,
Panama, Paraguay, Peru, Suriname, Trinidad and Tobago, United States Virgin Islands, Uruguay, Venezuela (Bolivarian Republic of).
APPENDIX B. Tables
Table 3: Climanomics® 2024 Release Changes
Climanomics (2023)

Climanomics (1H2024)

Underlying climate data: CMIP6

Underlying climate data: CMIP6

HazardName

Hazard Metric

Modelling Approach

Spatialresolution

Hazard Metric

Model
Changes/Enhancements

Spatialresolution

CoastalFlood

Annual Frequencyof historical 100year flood

Enhanced‘bathtub’modelling incorporating
a flood-water pathfinding algorithm todetermine the interior
points that are subject toflooding in response todifferent coastal waterlevels.

~30m
(US);
~90m
(rest ofworld)No Change

No Change

No
ChangeWildfire

Annual probabilityofexceedinghistorical
90th
percentile wildfire
conditions using
the FWI index,
relativetoahistorical baseline

Preprocessed
methodusing a land use mask tomodify wildfire risk
results, calculated on thehazard metric

25km for
FWI
index;
300mn
for landcovermodeling

No Change

No Change

No
ChangeTropicalCycloneAnnual frequency
of a category 3 orhigher (Cat3+) TC
passingwithin50km of a location

The updated model’s
globalcoverageencompasses
theremaining key basins
with hazard projections
extending to 2040

25km

No Change

No Change

No
ChangeProprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form requires
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21

Pluvial
Flooding

Projected
frequency of thehistorical base-line
100-year
flood
depth

Uses daily precipitation
data from an ensembleof
NEX-GDDPdownscaled
CMIP6models(25-kmresolution worldwide). A
statistical model ofGeneralized
ExtremeValue analysis is appliedtodeterminetheIntensity of extreme rare
events25km

No Change

No Change

No
ChangeFluvialFlooding

Projected
frequency of thehistorical base-line
100-yearflood
depth, adjusted forflood extent

Basin scale projectedflood frequencies areoverlaid with projectedflood extent at 1x1km
resolution. Pixels outsideof the projected flood
extent are assigned ahazard value of zero.

1km

No Change

No Change

No
ChangeExtreme
Heat

Tx95p

25km

No Change

No Change

No
ChangeTx95pAbsChng
Tx50pAbsChg

CMIP6
temperature
projections
averaged
across models and years
per decade

Annual frequencyof days with theAnnual frequency of days
with the25km

No Change

No Change

No
ChangeStandardized
Precipitation andEvapotranspiration
Index (SPEI) lessthan orself-calibrating
Droughtequal
tohistoricalpercentile

the
10th

equal to the historical
10th percentile

Projected future
ratio of water

Water stress projections
adapted from the World
Resources
Institute
Aqueduct dataset, inwhich projections are
made through the 2040s.
Scenarios SSP2-4.5 and
SSP5-8.5 are directlymodeled, whereas SSP12.6 and SSP3-7.0 are
interpolated.

Basin
Level

No Change

Water stress is now
based on CMIP6 data,
due to upgrading to WRI
Aqueduct
4.0.
Projections incorporate
an expanded scope andare modeled throughthe 2080s. Scenarios
SSP1-2.6, SSP3-7.0, and
SSP5-8.5
arenow
directly modeled, with
only SSP2-4.5 being
interpolated. The water
stress index is acontinuous
variable

No
ChangeDrought

Water
Stresswithdrawals
tototal
renewable
water
supply in a given
area.

PalmerSeverity Index (scPDSI)
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22

capped at a range of 0 to
1.
The change to the
Water
Stressmethodology produceschanges in the water
stress index of +/- 0.2(on a scale of 0-1) for
75% of basins, and +/0.1 for 50% of basins.

Landslide

NA

NA

NA

Antecedent
Rainfall
Index
(ARI)
which
representsaweighted
average of themost recent 7
days of rainfall.
Landslidesusceptibility
maps
which
representconditions suchaselevation,accessibility toroads etc thatmakealandscapesusceptible
tolandslide.

Preprocessed methodusingalandslidesusceptibility as a mask
to modify landslide risk
results, calculated on
the hazard metric

8 km

Table 4: Climate Change Hazard Modelling Changes - Legacy Climanomics® vs Climanomics® 2023 Release
Climanomics (1H 2023)

Climanomics (2H2023)

Underlying climate data: CMIP6

Underlying climate data: CMIP6

HazardName

Hazard Metric

Modelling
Approach

Spatialresolution

Hazard Metric

Model
Changes/Enhancements

Spatialresolution

CoastalFlood

AnnualFrequencyofhistorical 100year flood‘Bathtub’approachtomodelling
coastalinundation due
to sea level rise

Variable,
determinedby GoogleEarthelevation
sources.

No Change

Enhanced‘bathtub’modelling incorporating aflood-water
path-finding
algorithm to determine theinterior points that are
subject to flooding in
responsetodifferent
coastal water levels.

~30m (US);~90m (restof world)

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Wildfire

Annualprobabilityofexceedinghistorical 90th
percentile
wildfireconditions using
the Palmer Zindex, relative toahistoricalbaseline

Preprocessed
method using aland use mask tomodify wildfire
risk
results,
calculatedonthehazardmetric25km for Zindex; 300m
forlandcovermodeling

Annual probability ofexceeding historical
90th
percentile
wildfireconditions
using the FWI index,
relative to a historicalbaseline

Preprocessed method using
a land use mask to modify
wildfirerisk
results,
calculated on the hazard
metric25km for
FWI index;
300mn forland-covermodeling

TropicalCycloneAnnualfrequency of acategory 3 orhigher (Cat3+)
TC
passingwithin 50km of alocationThe
model’s
coveragewaslimited to assets
in the North
Atlantic
(NA)
and
the
Northwest
Pacific(NW)
basinswithhazardprojections
extendingto2030 for NA and
2040 for NW.

25km

No Change

The updated model’s global
coverage encompasses theremaining key basins withhazardprojections
extending to 2040.

25km

PluvialFlooding

NA

NA

NA

Projected frequencyof the historical baseline 100-year flood
depth

NA

25km

FluvialFlooding

Projected
frequency of thehistorical baseline
100-yearflood depth

The 10-yr and100-yr returnperiod (RP) riverdischarges are
relatedstatistically tosevencovariates, fourof which are
topographic innature and threeareclimatological.
For
futuredecades,theclimatecovariates arederived from thedownscaled
CMIP6 dataset.

HydroAtlas
Level
12
Basin
(approx.
25km)

Projected frequencyof the historical baseline 100-year flood
depth, adjusted forflood extent

Basin scale projected flood
frequencies are overlaid
with projected flood extent
at 1x1km resolution. Pixels
outside of the projectedflood extent are assigned ahazard value of zero.

1km

Extreme
Heat

Tx95p

CMIP6
temperature
projections

25km

No Change

NA

25km

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24

Drought

Water
StressTx95pAbsChng
Tx50pAbsChg

averaged acrossmodels
and
years
per
decadeAnnualfrequencyofdays with theAnnualfrequencyofdays with theself-calibrating
Palmer Drought

self-calibrating
Palmer Drought

Severity(scPDSI)
than orSeverity(scPDSI)
than or

Index
less

equal to thehistorical 10th
percentile

Projected future
ratio of water

Water
stressprojections
adapted from
the
World
Resources
Institute
Aqueductdataset, in which
projections are
made throughthe2040s.
Scenarios SSP24.5 and SSP5-8.5
aredirectlymodeled,whereas SSP12.6 and SSP3-7.0
are interpolated.

supply in a given
area.

Annual frequency ofdays with the

NA

25km

NA

Basin Level

Standardized
Precipitation
andEvapotranspiration
Index (SPEI) less than
or

Index
less

equal to thehistorical 10th
percentile

withdrawals tototal renewable
water

25km

equal to the historical
10th percentile

Basin Level

No Change

Table 5: Climanomics® Asset Type Coverage
Asset Type

Asset Structure

Ownership

Amusement Park - Owner/Operator

Point

Owner-Operator

Condominium - Owner/Occupier (Ground Floor)

Point

Owner-Occupier

Condominium - Owner/Occupier (Upper Story)

Point

Owner-Occupier

Data Center - Investor

Point

Owner-Investor

Data Center - Owner/Occupier

Point

Owner-Occupier

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Data Center - Tenant

Point

TenantDistribution Center - Owner/Occupier

Point

Owner-Occupier

Executive Suite - Tenant

Point

Tenant

Health Facility - Owner/Occupier

Point

Owner-Occupier

Hospital - Investor

Point

Owner-Investor

Hospital - Owner/Occupier

Point

Owner-Occupier

Hotel - Investor

Point

Owner-Investor

Hotel - Owner/Occupier

Point

Owner-Occupier

Hotel - Tenant

Point

TenantIndustrial - Investor

Point

Owner-Investor

Industrial and Logistics - Owner/Occupier

Point

Owner-Occupier

Leisure Center - Investor

Point

Owner-Investor

Manufactured Housing - Investor

Point

Owner-Investor

Mixed Facility - Owner/Operator

Point

Owner-Occupier

Mixed Use - Investor

Point

Owner-Investor

Multifamily Housing - Investor (Low-Rise or Garden Style)

Point

Owner-Investor

Multifamily Housing - Investor (Mid or High Rise)

Point

Owner-Investor

Multifamily Housing - Owner/Occupier (Low-Rise or Garden Style)

Point

Owner-Occupier

Office - Owner/Occupier

Point

Owner-Occupier

Office - Tenant

Point

TenantOffice (Low-Rise) - Investor

Point

Owner-Investor

Office (Mid & High-Rise) - Investor

Point

Owner-Investor

Parking Garage - Investor

Point

Owner-Investor

Parking Garage - Owner-Occupier

Point

Owner-Occupier

R and D - Owner/Occupier

Point

Owner-Occupier

Research Laboratory (General) - Owner/Occupier

Point

Owner-Occupier

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Resort - Investor

Point

Owner-Investor

Retail - Investor

Point

Owner-Investor

Retail - Tenant

Point

TenantRetail - Owner/Occupier

Point

Owner-Occupier

Shopping Center - Investor

Point

Owner-Investor

Single Family Housing - Owner/Occupier

Point

Owner-Occupier

Training Center - Owner/Occupier

Point

Owner-Occupier

Warehouse - Investor

Point

Owner-Investor

Warehouse - Owner/Occupier

Point

Owner-Occupier

Warehouse - Tenant

Point

TenantCut and Sew Facility - Manufacturer

Point

Manufacturer

Equipment (General) - Owner/Operator

Point

Owner-Operator

Footwear Manufacturing Facility - Manufacturer

Point

Manufacturer

General Manufacturing - Investor

Point

Owner-Investor

General Manufacturing - Owner/Occupier (TIV)

Point

Owner-Occupier

Light Manufacturing - Investor

Point

Owner-Investor

Light Manufacturing - Owner/Occupier

Point

Owner-Occupier

Light Manufacturing - Tenant

Point

TenantManufacturing (Clean Room) - Owner/Operator

Point

Owner-Occupier

Manufacturing (Heavy) - Owner/Operator

Point

Owner-Occupier

General Manufacturing - Owner/Occupier (Book Value)

Point

Owner-Occupier

Paper Product Mills

Point

Owner-OccupierRepair Facility - Tenant

Point

TenantRepair Facility - Owner/Occupier

Point

Owner-OccupierTannery - Manufacturer

Point

Manufacturer

Textile Dying and Finishing Facility - Manufacturer

Point

Manufacturer

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Cement Manufacturing/Municipal Water Usage - Owner/Occupier

Point

Owner-Occupier

Cement Manufacturing/No Municipal Water Usage - Owner/Occupier

Point

Owner-Occupier

Light Manufacturing - Manufacturer

Point

Manufacturer

Clean Room Manufacturing - Manufacturer

Point

Manufacturer

Mining/Extraction - Owner/Operator

Point; Spatial

Owner-Operator

Underground Coal Mine Site - Owner/Operator

Point

Owner-Operator

Underground Copper Mine Site - Owner/Operator

Point

Owner-Operator

Underground Iron Ore Mine Site - Owner/Operator

Point

Owner-Operator

Underground Gold Mine Site - Owner/Operator

Point

Owner-Operator

Mining - Owner/Operator

Point; Spatial

Owner-Operator

Semiconductor Manufacturing - Owner/Occupier

Point

Owner-Operator

Aboveground Pipeline - Owner/Operator

Point;Linear

Owner-Operator

Gas Production Facility - Owner/Operator

Point

Owner-Operator

Offshore Oil and Gas Platform - Owner/Operator

Point

Owner-Operator

Oil Refinery - Investor

Point

Owner-Investor

Oil Refinery - Owner/Operator

Point

Owner-Operator

Oil Refinery - Supplier

Point;Linear

Manufacturer

Product Terminal - Owner/Operator

Point

Owner-Operator

Water Networks - Owner/Operator

Point;Linear

Owner-Operator

Electricity Transmission Tower (Regulated) - Owner/Operator

Point;Linear

Owner-Operator

Electricity Transmission Tower (Deregulated) - Owner/Operator

Point;Linear

Owner-Operator

Electricity Transmission Tower (Regulated) - Investor

Point;Linear

Owner-Investor

Electricity Transmission Tower (Deregulated) - Investor

Point;Linear

Owner-Investor

Solar Battery Storage - Investor

Point;Linear

Owner-Investor

Wastewater Treatment Plant - Owner/Operator

Point

Owner-Operator

Water Pumping Site (Aboveground) - Owner/Operator

Point

Owner-Operator

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Water Pumping Site (Underground) - Owner/Operator

Point

Owner-Operator

District Heating Network - Owner/Operator

Point;Linear

Owner-Operator

Waste To Energy Plant - Owner-Operator

Point

Owner-Operator

Anaerobic Digester - Owner-Operator

Point

Owner-Operator

Coal and Oil-Fired Power Plant - Investor

Point

Owner-Investor

Coal and Oil-Fired Power Plant - Owner/Operator

Point

Owner-Operator

Combined Heat and Power Plant - Investor

Point

Owner-Investor

Combined Heat and Power Plant - Owner/Operator

Point

Owner-Operator

Geothermal Power Plant - Investor

Point

Owner-Investor

Geothermal Power Plant - Owner/Operator

Point

Owner-Operator

Hydropower Plant - Investor

Point

Owner-Investor

Hydropower Plant - Owner/Operator

Point

Owner-Occupier

Natural Gas-Fired Power Plant - Investor

Point

Owner-Investor

Natural Gas-Fired Power Plant - Owner/Operator

Point

Owner-Occupier

Nuclear Power Plant - Investor

Point

Owner-Investor

Nuclear Power Plant - Owner/Operator

Point

Owner-Operator

Power Generation (General) - Investor

Point

Owner-Investor

Power Generation (General) - Owner/Operator

Point

Owner-Operator

Solar Farm - Investor

Point;Spatial

Owner-Investor

Solar Farm - Owner/Operator

Point;Spatial

Owner-Operator

Wind Farm - Investor

Point;Spatial

Owner-Investor

Wind Farm - Owner/Operator

Point;Spatial

Owner-Operator

Offshore Wind Farm - Investor

Point

Owner-Investor

Thermal Power Plant - Owner/Operator

Point

Owner-Investor

Airport - Investor

Point

Owner-Investor

Airport Kiosk - Tenant

Point

Tenant

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29

Airport Lounge - Tenant

Point

TenantAirport Terminal - Tenant

Point

TenantAir Transportation - Supply Chain

Point

Supply Chain

Air Transportation - Tenant

Point

TenantAircraft - Owner/Operator

Point

Owner-Operator

Cargo Terminal - Tenant

Point

TenantHangar - Tenant

Point

TenantHighway - Investor

Point;Linear

Owner-Investor

Highway Transportation - Supply Chain

Point;Linear

Supply Chain

Marine Transportation - Supply Chain

Point

Supply Chain

Motor Vehicle - Owner/Operator

Point

Owner-Operator

Sea Port - Investor

Point

Owner-Investor

River Port - Investor

Point

Owner-Investor

Sea Port - Owner/Operator

Point

Owner-Operator

River Port - Owner/Operator

Point

Owner-Operator

Port - Investor

Point

Owner-Investor

Railroad - Investor

Point;Linear

Owner-Investor

Railroad Transportation - Supply Chain

Point;Linear

Supply Chain

Ship - Owner/Operator

Point

Owner-Operator

Train - Owner/Operator

Point

Owner-Operator

Hangar - Owner/Occupier

Point

Owner-Operator

Communications Network - Investor

Point;Linear

Owner-Investor

Cell Tower Site - Investor

Point;Linear

Owner-Investor

Cell Tower Site - Tenant

Point;Linear

TenantCell Tower Site - Owner/Operator

Point;Linear

Owner-Operator

Communications Network - Owner/Operator

Point;Linear

Owner-Operator

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Underground Fiber Optic Cable - Investor

Point;Linear

Owner-Investor

Underground Fiber Optic Cable - Owner/Operator

Point;Linear

Owner-Operator

Agriculture (General) - Owner/Occupier

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Alfalfa (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Almonds

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierApple (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Biofuel Crop Farms (Sugarcane) - Owner/Operator

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCassava (only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

CattlePoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Chickpeas (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCitrus (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Coconut (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCoffee (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCorn (Temperate) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCorn (Tropical) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Cotton (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Grapes-Raisins

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Table Grapes

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

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31

Macadamia

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Milk

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Oats (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Oil Palm (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Olives (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierPecansPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Pistachios

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Plantain (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Potato (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Rapeseed (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Rice (Temperate) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Rice (Tropical) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Row Crops (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Soybeans (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sugar Beet (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sugarcane (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sunflower (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sweet Potato (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

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Tomatoes (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Tree Nuts

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Walnuts

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wheat (Temperate) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wheat (Tropical) (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wine Grapes

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Avocados

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sorghum (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Cherries

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierLemonsPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

BarleyPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

LupinsPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Table Grapes (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Cherries (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierLemons (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Barley (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Lupins (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wine Grapes (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

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33

Pistachios (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Walnuts (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Almonds (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Avocados (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierApple

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCitrusPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCorn (Temperate)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCorn (Tropical)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

PotatoPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Rapeseed

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Soybeans

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sugar Beet

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sugarcane

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Sunflower

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wheat (Temperate)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Wheat (Tropical)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Timber (Gross Asset Value)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Timber (Revenue)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Proprietary and Confidential: Intended for Recipient only. Further distribution or publication of the content in any form requires
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34

Cocoa (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Aquaculture (Marine)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Aquaculture (Freshwater Lake)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Aquaculture (River)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Blueberry

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierGlasshouse Strawberry

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierGlasshouse Tomato

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierGlasshouse Cucumber

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCoffee - Arabica

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCoffee - Arabica (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCoffee - Robusta

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierCoffee - Robusta (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Pine Timber (Revenue)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Eucalyptus Timber (Revenue)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Douglas fir Timber (Revenue)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Teak Timber (Revenue)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Tea Leaves

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Tea Leaves (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

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

35

Sheep (Meat)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Chicken (Egg) (Climate Controlled)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Chicken (Egg) (Non-Climate Controlled)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Grapefruit

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Grapefruit (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Hops

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierHops (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierRye (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierSesamePoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierSesame (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Agave (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierBlack Currant

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;SupplierBlack Currant (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Rubber (Only Rainfed)

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Row Crops

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

CottonPoint;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Forest - Owner/Operator

Point;Spatial

Owner-Occupier;OwnerInvestor;Tenant;Supplier

Non-Human Primates

Point;Spatial

Investor;OwnerOccupier;Tenant;Supplier

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

36

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

37

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independence and integrity of Content is embedded in its culture and at the core of everything S&P Global does. Accordingly, S&P Global
has developed measures to identify, eliminate and/or minimize potential conflicts of interest for Sustainable1 as an organization and forindividual employees. Such measures include, without limitation, establishing a clear separation between the activities and interactions of
its analytical teams and non-analytical teams; email surveillance by compliance teams; and policy role designations. In addition, S&P Global
employees are subject to mandatory annual training and attestations and must adhere to the Sustainable1 Independence and Objectivity
Policy, the Sustainable1 Code of Conduct, the S&P Global Code of Business Ethics and any other related policies.
See additional Disclaimers at https://www.spglobal.com/en/terms-of-use
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