Integrate Climate Risks into Credit and Financial Risk with
Climate Credit Analytics
Copyright © 2023 S&P Global Market Intelligence.
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Agenda:
1.
Overview of the Solution
2.
Methodology Deep Dive
3.
Implementation and Support
2
Agenda:
1.
Overview of the Solution
2.
Methodology Deep Dive
3.
Implementation and Support
3
Introducing Climate Credit Analytics
Our approach
• Climate Credit Analytics combines S&P
Global’s best-in-class data and credit
analytics with Oliver Wyman's industryleading climate scenario and stress testing
expertise
A climate scenario analysis solution
that captures transition and
physical risk in a rigorous and
transparent manner
• Our solution is designed for banks, insurers,
asset owners and managers, and corporates
that want to assess impacts of climate
scenarios on the financial performance of
public and private companies
Use cases
• Risk management
• Regulatory climate scenario exercises and
stress tests
• Disclosures & TCFD implementation
• Climate risk appetite
• ICAAP
• Business strategy and opportunities
• Portfolio steering and alignment
• Client engagement
• Capital solutions and opportunity
identification
• Corporate development (M&A and
divestitures)
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4
Road-tested by Financial Institutions and Widely-cited by Supervisors
Enhanced climate stress-tests and
scenario analysis exercises
•
•
•
Industry research
•
S&P Global and Oliver Wyman's Climate
Credit Analytics (CCA) is being used by three
of the six major FIs participating in the
Federal Reserve Board’s 2023 pilot Climate
Scenario Analysis exercise
CCA supported the International Monetary
Fund’s (IMF) assessment of transition risks,
with results included in the following paper:
• Systemic stress, and climate-related financial
risk: implications for balance sheet
resilience, 2022 (link)
CCA is used by 20+ financial institutions
across North America, Europe, Middle East,
and Asia, and the underlying methodology is
used by 50+ institutions globally, supporting
the build out climate stress testing and
scenario analysis capabilities1
•
CCA supported research led by Morgan
Stanley and Oliver Wyman on the risks facing
global wholesale banks, as quoted in the
following paper:
•
CCA has been onboarded and validated by
Model Risk Management at multiple
regional and global financial institutions
Climate Crypto, and Competing in This
Cycle, 2022 (link)
Supervisory publications
•
CCA’s underlying methodology is widely cited in
publications from the UNEP-FI, Network for
Greening the Financial System (NGFS) and
global regulators, including:1
• Bank of Canada: Using Scenario Analysis to
Assess Climate Transition Risk, 2022 (link)
• Bank for International Settlements:
Climate-related risk drivers and their
transmission channels, 2021 (link)
• Federal Reserve Bank: The Role of
Financial Institutions in Tackling the
Challenges of Climate Change, 2021 (link)
• UNEPFI: Climate Risk Tools Landscape,
2021 (link)
(link)
• NGFS: Integrating climate-related and
environmental risks into prudential
supervision, 2020 (link)
• Commodities Futures Trading
Commission: Managing climate risk
in the US Financial System, 2020 (link)
Developing climate scenario analysis capabilities
Helping to solve complex challenges
Helping drive the global financial services industry response to climate
change
1. CCA’s underlying methodology was collaboratively developed by Oliver Wyman, the UNEP Finance Initiative (UNEP FI) and thirty-nine global financial institutions as part of UNEP FI’s TCFD pilot program (link). CCA is also comprehensively cited in the UNEP FI’s 2023 Climate Risk Landscape report (link)
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5
Well-Recognized and Widely Accepted
Published Case Studies
Awards and Recognition
2023
• Most Innovative Regulatory
Solution
Inside Market Data & Inside
Reference Data Awards 2023
• Best Climate Risk Management
Solution
Hong Kong Monetary Authority’s
Green Fintech Competition
2024
• Best ESG Climate Risk Solution
ESG Insights Awards 2024
• Best Credit Risk Management
Solution and Climate Advisory
Firm Asia Risk Awards
• Best Climate Risk Management
Solution
Regulation Asia Awards for
Excellence
Banks
Asset Managers
A Global Bank Assesses Its Resilience to
Climate Risks
The bank’s risk team translates climate
scenarios into financial and credit risk drivers
A Global Asset Manager Enhances
Investment Strategies with Comprehensive
Risk Assessment Solutions
The investment management risk department
of a large European asset management firm
integrated climate risk quantification into their
investment framework
A Globally Significant Financial Institution
Improves its Capabilities to Assess ClimateRelated Credit Risks
The climate team at this GSIFI compare the
outcome of internal analysis of the bank’s loan
portfolios with an independent challenger model
Private Credit Funds
A Global Private Credit Firm Strengthens
Risk Assessment with Advanced Climate
Analytics
The investment team of one of the largest
private credit fund globally use Climate Credit
Analytics to enhance their portfolio of energy
transition financing
Sovereign Wealth Funds
A Sovereign Wealth Fund’s Path to
Sustainability: Key Benefits of Climate
Credit Risk Assessment
The sustainable investment team of a
sovereign wealth fund evaluates the credit
impact of climate scenarios to their portfolio.
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6
Why S&P?
Robust methodology
Best-in-class Data &
Analytics
•Distinct approach to each
asset class – Corp, FIs, Real
estate, Sovereigns; sectorspecific methodology for
corps – 145+ nonfinancial
corporate sectors
•S&P`s extensive and
proprietary datasets Company, industry, and
climate data – emissions,
physical risk, transition
targets
•Bottom-up modeling –
climate risk -> drivers of
financial impact ->financials>credit metrics
•S&P`s proprietary credit
models - Trained on S&P’s
rated universe and default
data
Flexibility and
Adaptability
•Multiple climate scenarios including the ones from
NGFS, ECB, MAS, Fed Res
etc. with the ability to run
user defined scenarios
•Automated with company
specific information – Allows
for running over 2.2 million
companies on the fly
•Aligned to UNEP-FI*
Ease in Implementation
•Efficient - run a large # of
exposures through API,
desktop or excel
•Secure – Designed with
restricted access and layers
of data protection
•Dashboards – produces
portfolio level visuals,
metrics, heatmaps for
actionable insights
Client-friendly post-sale
service model
•Workshops and tailored
trainings
•Product updates –
Methodology refinements,
new regulatory requirements
and incorporation of future
evolution in climate
scenarios
•Validation - Regulatory
query support and internal
risk management
•Validated by model risk
management teams at
GSIFIs
* United Nations Environment Program – Financial Institutions. Note: This refers to the methodology developed for Climate Credit Analytics.
7
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Agenda:
1.
Overview of the Solution
2.
Methodology Deep Dive
3.
Implementation and Support
8
Built on a robust foundation
2. Multifacted scenarios to support risk management,
sensitivity analysis and transition financing
•
•
1. Powerful datasets and analytics embedded to
support assessment of risk & feed internal models
•
•
•
Company financials, asset level information,
environmental data, company-specific pledges to
transition
Credit Analytics modeling suite that credit scores and
PDs, alongside full-fledged forecasted financials
Network for Greening the Financial System (NGFS),
Regulatory stress testing scenarios (including FRB, ECB,
MAS),
User-defined scenarios
Scenarios
3. Bottom-up, analytical approach with
an in-depth, flexible model suite:
Climate
Credit
Analytics
Data
•
•
•
•
Firm-level and portfolios
Public and private
Scoring on-the-fly
Asset class and sector-specific models
Modelling
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1. Powerful Datasets and Analytics
Transition Risks
•
•
Reported and estimated
Scope 1, Scope 2, and
Scope 3 carbon emissions
for over 15,000 companies
globally since 2005
Transition plans of
companies are incorporated
from S&P Global’s
Sustainable1 dataset ‘Net
Zero Tracker,’ CDP, and
company filings
Physical Risks
Financial Information
•
Over 4 million asset
locations for 270+ asset
types
•
Financial impact from 8
physical hazards.
•
Modeled for 4 IPCC climate
scenarios and harmonized
with NGFS scenarios.
•
•
Financial statements and
other fundamental
information on companies
and assets from S&P
Global’s Capital IQ
database.
Coverage across regions
and industries, both public
and private, enabling “onthe-fly” analysis for millions
of companies.
Industry Information
•
Niche datasets from S&P
Global Market Intelligence,
S&P Global Commodity
Insights, and S&P Global
Mobility
•
Tailored approaches for over
145+ sectors that
incorporate specific
characteristics and evolution
in industry dynamics
Credit Models
•
Powerful credit scoring
analytics trained on S&P
Global’s credit ratings’
and default flags from a
proprietary database of
defaulted companies
globally
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2. Multifaceted Scenarios
NGFS scenarios: Phase IV
Userdefined
scenarios
Regulatory
Scenarios
(ECB, FRB*,
MAS*,
S&P**)
Flexibility to
override with
user inputs
Run and analyze
multiple scenarios
simultaneously
NGFS
Scenarios
Physical
Risk
Scenarios
(IPCC,
RCP)
Short-term
Carbon Tax
scenarios
European Central Bank, (ECB), Federal Reserve Board (FRB), Monetary Authority of Singapore (MAS)
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11
Overview of Methodology
Climate scenarios
•
Key outputs
Climate Credit Analytics methodology ©
Input data
Bottom-up module
Driver drill downs
SECTOR SPECIFIC VIEW
Network for Greening the Financial System (NGFS) regulatory, and custom
climate scenario variables (e.g., ECB, FRB, MAS) with flexibility available for
prop input.
POWERED BY CREDIT ANALYTICS
Company/Scenario Selection
Select Company
Selected Scenario
Financial statements
(scenario-adjusted)
Key drivers
Financial metrics
(scenario-adjusted)
Select Variable
42,000
40,000
38,000
34,000
32,000
Data provided by
Estimated financial impact % based on ~3.1M asset locations linked to
c.150,000 corporate entities, sourced from S&P Global Sustainable1 with
flexibility available for prop input.
Transition plans
•
~1,000 company emission transition plans, preloaded from third-party
sources with flexibility available for proprietary user inputs.
Industry-specificities
•
Industry data from S&P Global (e.g., OEM industry forecasts and R&D from
S&P Global Mobility, oil and gas reserves from S&P Global
Commodity Insights.
Top-down module
•
2021
Privileged Top-down module user data supplements S&P Global sources
(e.g., Identifier, PD, EAD, industry information).
% Change from Baseline
0
Price
Physical Risk
Scope 1, 2, and 3 emissions data for over 20,000 public companies, sourced
from S&P Global TruCost with flexibility available for prop input.
Physical financial impact
•
Company Industry
Transition Risk
•
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2020
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default
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2030
2035
2040
2045
2050
Company Emissions breakdown
Financials and industry segment-specific data from S&P Global Market
Intelligence with flexibility available for prop input.
Emissions
Mt CO2/yr
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36,000
2015
•
Em issions|CO2|Energy
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44,000
30,000
Company financials
NGFS (Phase 3) REMIND - Net Zero 2050
Collapse by default
Scenario variables
Credit Scores1
Income Statement
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
Technology Hardware, Storage and Peripherals
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by default
2025
2030
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Scope 1 by default
Scope 2
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2035
Scope 3 upstream
2040
2045
2050
Scope 3 downstream
Financial statements
Volume
Unit Cost
Cash Flow Statement
Bond Valuation
Balance Sheet
Equity Valuation
Capital
Expenditure
Asset Value
Financial metrics (e.g.,credit score)
Link scenario variables to
drivers of performance based
each company’s characteristics
Produce annual financial
statements for each company
up to 2050
Assess company’s scenarioadjusted credit scores or
valuation
For each company covered (e.g., 2.2M companies)
Extrapolation
Based on company characteristics and bottom-up population (for companies where data is not sufficient to run
bottom-up and in order to ensure comprehensive coverage of portfolios)
1. S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit model scores from the credit ratings issued by S&P Global Ratings.
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12
Comprehensive Analysis Across the Portfolio
140+ industries covered with automated analysis for 2.2 million companies
• Depending on the characteristics of the sector and the
availability of information, we have different approaches that
can be applied:
Oil and Gas
Airlines
Metals &
Mining
Rail
Trucking
Power
Generation
• Product-specific: typically suited for high-emitting sectors
(e.g., oil and gas, airlines, metals and mining, power
generation, et cetera)
• Product-specific methodologies reflect different business
activities, transition adaptation pathways, dynamics,
and financial drivers particular to a given sector
Semiconductors
Automotive
Manufacturing
Forest
Products
Pharmaceuticals
Hotels, Resorts
& Cruise Lines
Manufacturing
• Emissions-based: relies on company-specific emissions,
industry-specific elasticities, and scenario-dependent
emission pathways
• This approach assesses how prospective companyspecific Scope 1, 2, and 3 emissions and
macroeconomic variables will affect revenue, costs,
and CapEx
•
Chemicals
Agriculture
Construction
Materials/ Cement
Steel
Shipping
+ All others
A top-down approach that enables extrapolation from the
population of bottom-up results to the remainder of the
portfolios where information is unavailable (whether readily
available and imputed by S&P Global or via internal data)
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13
Incorporates Transition Plans Enabling Rigorous Bottom-up Analysis
Emissions
Fuel Mix
EVs
Operational emissions transition plans
Capacity mix transition plans
Product mix forecasts
•
Individual company emissions targets (for Scope 1,
2, and 3) are considered in CCA’s emissions-based
approach and in product-specific approaches for certain
sectors (e.g., airlines, metals and mining, oil and gas)
•
Preloaded emissions transition plans are sourced from
the CDP Targets database
•
All transition plan metrics can be adjusted or added
manually via user input
•
•
For power generation and oil and gas, companyspecific capacity mix targets are considered by
including the following metrics:
•
For automotive manufacturing, electric vehicle
transition forecasts are incorporated via product mix by
propulsion type (e.g., EV and ICE production)
•
Power generation: planned capacity (in MW) by fuel
type
•
•
Oil and gas: planned renewable capacity (in GW for
wind and solar)
Forecast data for original equipment manufacturers are
sourced from S&P Global Mobility/IHS Automotive,
which covers 50+ countries and represents 96% of
global light vehicle production
Preloaded targets are sourced from sustainability and
annual reports, press releases, and environmental
pledges
Capacity Transition Plans (MW)
Capacity Transition Plans (Current)
Coal
Oil
Natural gas
Nuclear
Hydro
Biomass
Wind
Solar
Geothermal
Capacity Transition Plans (User Input)
2025
2030
2035
2040
2045
2050
329.6
12,605.0
32,210.4
24.3
3,611.7
3,386.2
-
33,962.3
24.6
7,858.6
10,030.3
-
35,464.8
24.7
10,491.4
12,115.7
-
36,146.2
25.9
12,569.3
13,346.7
-
40,726.2
25.6
14,667.1
14,022.3
-
39,501.2
25.3
16,698.0
14,220.8
-
2025
2030
2035
2040
2045
2050
Coal
Oil
Natural gas
Nuclear
Hydro
Biomass
Wind
Solar
Geothermal
All transition plan metrics can be entered manually. CCA includes various debt-to-equity and cash flow checks to ensure transition plans remain credible.
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14
Incorporates financial impact from physical risk for each company using assetlevel data and impact functions
Physical Risk Data
Physical Risk Methodology
Key Drivers and outputs
Asset-level data
• ~4M asset locations linked to c.150,000
corporate entities representing 20,000 public
companies (98% of global market capitalization),
their corporate subsidiaries, operating
companies and others, sourced from S&P
Global Market Intelligence, S&P Global
Sustainable1, IHS Markit, Government Asset
Registries, and more
Hazard
exposure
A
Scenarios
Impact functions
Financial impacts
Aggregated as “Operating
Expenses” in current version
B
Asset Type
D
F
~1,280 Impact
Functions
Asset Location
Operational
Expenses
Key drivers
Asset Exposure
E
Exposure and impact mapping
• S&P Global Sustainable1 climate hazard
modeling covers eight individual climate
hazards across four future climate scenarios
based on IPCC pathways
Financial
Impact (AssetLevel)
Asset Value
Capital Expenses
Financial statements
Productivity
Impacts
C
Financial
Impact
(CompanyLevel)
Hazard Data
• Sustainable1 physical risk model quantifies the
financial consequences of changes in physical risk
exposure using a library of Impact Functions
developed by S&P Global
SECTOR SPECIFIC VIEW
POWERED BY CREDIT ANALYTICS
Company/Scenario Selection
Select Company
Insurance Costs
Selected Scenario
NGFS (Phase 3) REMIND - Net Zero 2050
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Scenario variables
Select Variable
Em issions|CO2|Energy
Mt CO2/yr
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44,000
42,000
40,000
38,000
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36,000
34,000
32,000
30,000
2015
Collapse by default
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2020
Collapse by 2025
default
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2030
2035
2040
2045
2050
Company Emissions breakdown
Company Industry
2021
% Change from Baseline
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
Data
Intermediate
7
Hazards
Included
Output to
Drivers
Technology Hardware, Storage and Peripherals
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by default
2025
2030
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Scope 1 by default
Scope 2
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2035
Scope 3 upstream
2040
2045
2050
Scope 3 downstream
Data flow
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15
Power Generation Example 1 of 2
Climate scenarios are expected to impact all drivers of performance for power generation companies
Input data
Financials
Key drivers
Company-specific
• Company financials
• Fuel mix
• Emissions profile
• Volume of production and cost profile
• Transition plans
• Capital expenditure
• (Un)regulated capacity
• Asset exposure to physical risk
(damage, downtime)
Current fuel mix (illustrative)
•
Price
•
•
Volume evolves following the company’s transition plans, CapEx spend,
and scenario demand for a given fuel in a given geography
•
Physical risk creates additional downtime1
•
Cost of production depends on the company’s energy mix and the
carbon price from the scenario
•
Heightened physical risk leads to increased insurance costs
•
Investments driven by company transition plan, scenario demand,
resilience spendings, and ability to invest (through debt or cash)
•
Impairments booked when assets are retired before the end of their
useful life (“stranded assets”)
•
Damage to assets from physical risk effects 1
Volume
Unit Cost
Higher price due to increased demand, costs, and investment in
scenarios
Depends on (un)regulated capacity for a given company
Financial Statements
Financial metrics
(e.g., change in credit score)
Coal
Natural Gas
Nuclear
Capital Expenditure
Hydro
Scenario data
• Network for Greening the Financial
System (NGFS)
• Regulatory scenarios
• Customized scenarios
Asset Value
1. Target state. Physical risk impacts are aggregated in Summer 2023 release.
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16
Power Generation Example 2 of 2
In this sector, the energy mix is the key vector of disparities in the rating of companies
Electricity generation by fuel source
Network for Greening the Financial System (NGFS) Net
Zero 2050 Scenario
Credit Impact
2
2030: 95 GW of commitment in renewables
Large
renewables
producer
Comments
Credit score notch change vs. baseline year (e.g., 2020)
•
1
•
2020
2030
2035
2040
2045
2050
•
2030: 60 GW
commitment
in renewables
Large
nuclear
producer
2025
1
•
•
Stable nuclear capacity
2020
2025
2030
2035
2040
2045
2050
2020
2025
2030
2035
2040
2045
2050
•
•
Carbon
heavy
producer
•
-2
Default
•
2020 2025 2030 2035 2040 2045 2050
Scale is company-specific
Coal
Natural Gas
Nuclear
Hydro
Pursues its current transition to renewables, following
ambitious commitments (Gigawatt (GW) by 2030)
Company manages to capture market demand thus
allowing to improve in the rating
Wind
Nuclear (a large majority of current production) stays
stable in the scenario
Minimal exposure to gas and coal
Development of renewables capacity following the target
of 60 GW of wind and solar by 2030
Maintains a stable rating due to stable volume and
investment in renewables
High carbon energy mix (coal and gas): targets zero coal
in 2040 and gas gets hit severely
Low target for wind and solar and no stated target for
hydro
Growing debt and insufficient cash flows limit the ability to
invest in renewables, no compensation for coal exit
assumed
Solar
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17
Agenda:
1.
Overview of the Solution
2.
Methodology Deep Dive
3.
Implementation and Support
18
Varied Delivery Options
For both individual and enterprise needs
1.
Fully automated
with data plug-ins
Comprehensive
single entity
analysis
Analyze up to 500
company IDs in
one run
19
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Varied Delivery Options
For both individual and enterprise needs
2.
Intuitive user
interface that
allows
customization
and flexibility
Efficient
portfolio
analysis with
Interactive
visuals and
dashboards
Integrated
Modeling
Environment
20
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Varied Delivery Options
For both individual and enterprise needs
3.
Lightweight integration that meets
audit and compliance requirements
Efficient implementation and analysis
for up to 5,000 company IDs
Ideal for on-demand company scoring
with proprietary data
•
Detailed documentation facilitating stakeholder engagement
200+
Pages
22
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•
•
•
•
•
•
•
23
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