Solution

Solution: Linux Foundation OS-Climate (OS-C)

Apply the Community-Based Open Source approach that has enabled breakthroughs in Life Sciences & Tech to solve data & analytics challenges required for investment to achieve Paris Climate Accord goals.

OPEN SOURCE COMMUNITY

Governance, licensing, and collaboration structures enabling stakeholders to share cost, intellectual property, and effort.

Joint projects for new data, modeling, standards, and supporting technology.

GLOBAL DATA COMMONS

Curated library of public and private sources, for both transition and physical risk/ opportunity.

More accurate corporate historical and forwardlooking climate & ESG metrics as a public good.

SCENARIO-BASED PREDICTIVE ANALYTICS

Top-down and bottom-up modeling to integrate climate-related risk and opportunity into decisions by investors, financial institutions, regulators, etc.

Multiple climate scenarios and transition pathways.

OS-Climate Focus Users & Use Cases in 2021

Asset Owners, Asset Managers, Banks & Regulators

Scenario Analysis (Risk & Opportunity

Alignment (Portfolios, Investments/Loans)

Stress testing

Stress testing

Asset allocation

Portfolio construction

Research (investment, banking, etc.)

Manager selection

Analysis of securities & loans

Design and execution of benchmarks, strategies & products

Disclosure & reporting

Engagement with companies & financial institutions

The OS-C Platform also will help Corporates efficiently disclose climate & ESG data while building a library of trustworthy data, available to the public at no cost.

OS-Climate Initial Platform Components

Data Commons

Tools for
Scenario Analysis & Alignment

Functional Schematic of Data Commons / Tools Linkage

The Platform components are shown here and on the following page are already fully tested and in use by institutional investors:

Top Down Model from Ortec Finance/Cambridge Econometrics.

Bottom Up Model by Entelligent.

Physical Risk Extreme Event Data by Jupiter Intelligence (depicted in next schematic).

Data Commons – Focus on “Material” ESG Factors

V1.0 will focus on factors identified by asset owners as top priorities from among SASB, TCFD, CDSB, GRI, and CDP “highly material” climate-related and other ESG factors. For example (not exhaustive):

Physical Risk Data from Jupiter Intelligence

Physical Risk Data from Jupiter Intelligence

Top-Down Climate Scenario Analysis Component of the Platform:
Ortec Finance – Cambridge Econometrics ClimateMAPS Model

Top-Down Climate Scenario Analysis Component of the Platform

Users Will Be Able To Dial Up or Down
Material Policy and Technology Factors

Material Policy and Technology Factors

Global Warming Pathways Modelled

Global Warming Pathways Modelled

For Paris Orderly, we assume that markets will gradually price in from 2020 – 2024

For Paris Disorderly, we assume that pricing in will take place instantaneously in one year in 2024.

For Failed Transition, we assume that pricing in of future physical impacts will take place in 2025 – 2029. Global shock: -15% (preliminary, indicative).
For Singapore (preliminary, indicative): Paris aligned -2%, Failed Transition -12%

Methodology at a glance:
Integrating climate risk into financial scenarios

Methodology at a glance

Output example: Total portfolio impacts

Quantified Return Impact for Total Fund Value
Cumulative investment return ratio 2019-2059
  • Different climate pathways are expected to impact economic and financial risk drivers in their own way, per horizon and per region.
  • This also translates to differentiated impacts across all asset classes, regions and sectors in turn.
  • Are expected returns for different climate paths still aligned with required return?

Output Example: Risk-Return Insights across asset classes

From climate-uninformed to climate risk-aware

Output Example - Risk-Return Insights across asset classes

Output example: Region – Sector climate risk heat maps

Cumulative return (diff. to baseline) heat map – Public equities – 5 and 10 years

Region - Sector climate risk heat maps

Output example: Disaggregation to climate risk factors

Disaggregation of total climate risk impact to individual climate factors: example for MSCI World Equity benchmark
(available for each modelled macro-economic and financial variable)

Disaggregation to climate risk factors

Component 1: illustrative use case – Country/Industry Insight

illustrative use case - country industry insight

For example, users can vary policy and technology variables to see how they affect deployment of renewable energy in India.

Insights on sectoral dimension

Sectorial Dimension

Component 1: illustrative use case II – climate risk portfolio analysis

How robust is your portfolio for different climate paths?

Example: model-based risk-return projections

illustrative use case ii - climate risk portfolio analysis

For example, users can vary policy and technology variables to see how these affect the relative exposure of asset classes & regions to the different global warming pathways

Note: these are results based on a fictive demo set-up, results will vary for each specific investor.

Bottom-up contributed scenario – Entelligent (cont’d)

Security Level Return Projections
(For multiple energy transition and global warming pathways)

Security Level Return Projections for multiple energy transitions

Component 7: ‘User Journey’ for climate scenario analysis in v1.0

User Journey for climate scenario analysis in v1.0

Example Third Party Tools Enabled by Platform

Managing Physical Risk

— Wireframe of planned interface

CLIMATE-RELATED RISK AND OPPORTUNITY FOR COMPANIES IS DRIVEN BY MANY FACTORS

EACH FACTOR “BUCKET” INCLUDES MANY VARIABLES THAT MATERIALLY IMPACT PERFORMANCE AND PRICES OF COMPANIES, ASSETS, AND PROJECTS

EXAMPLE OPPORTUNITY INDUSTRIES & TECHNOLOGIES

OPPORTUNITY – TRANSPORT VALUE CHAIN EXAMPLE

Stress testing