Metaverse ESGDX for Climate Risk and Net-Zero Management

©2021 TechIPm, LLC All Rights Reserved www.techipm.com
Metaverse based
ESG Digital Transformation
for
Climate Risk & Net-Zero Management
November 24, 2021
Alex G. Lee

©2021 TechIPm, LLC All Rights Reserved
Speaker ESG/Sustainability Webinar
AI Blockchain IoT Convergence for ESG Digital
Transformation
Cybersecurity & Privacy in ESG Digital
Transformation
ESG Digital Transformation for Material
Sustainability Impact
Next Generation ESG DX Solutions for
Sustainability Innovation
ESG: Missing Piece of Digital Transformation
Revolution
Metaverse based ESG Digital Transformation for
Accelerating Sustainability
Metaverse based Sustainable Smart City
Development
Digital Twin Metaverse for Enterprise Digital
System Innovation
ESG Digital Transformation for Sustainability
Innovation
Recording:
https://www.youtube.com/channel/UCkERuP7J9
mW1tY1dgN_PttQ/videos
Presentation:
https://www.slideshare.net/alexglee

Part I. Metaverse Enterprise Platform for ESGDX
Metaverse Enterprise Platform
ESG + Digital Transformation (ESGDX) Business Model
Metaverse Enterprise Use Case - AI Innovation Platform
Metaverse Enterprise Use Case - Digital Twin Retail Store
Metaverse Enterprise Use Case - Global Supply Chain Risk Mitigation
Metaverse Enterprise Use Case – Energy Management Optimization
Immersive 3D Digital Twin Platform Demo
Part II. Climate Risk & Carbon Net-Zero Transition Management
Climate Change Sustainability Imperative
Climate Change Risks/Opportunities Management (TCFD)
Net-Zero Transition Target Setting Steps (SBTi)
Net-Zero Transition & Decarbonization Solutions
Digital Decarbonization
Digital Technology for Environmental Sustainability Innovation
Digital Twins for Dynamic Carbon Net-Zero Management
Digital Twins for Building Net-Zero Management
Climate Risk & Net-Zero Management Automation
Climate Risk & Net-Zero Management Digital Twins Under Development
Agenda

Metaverse Enterprise Platform for ESGDX

Metaverse Enterprise Platform
A metaverse enterprise is consist of a physical enterprise and virtual enterprise which is a realistic digital
representation (digital twin) of the physical enterprise. Physical and digital enterprises are connected
through real-time data.
The metaverse enterprise implements ESGDX.
Business resources can be used on demand without limitation in the digital enterprises (Dematerialization).
Based on actual real-time data in the physical enterprise, the virtual enterprise is created, and then, tested
using simulation models, big data analytics, and scenario analysis to obtain meaningful insights regarding
businesses of the physical enterprise. Digital world decisions based on the obtained meaningful insights are
transferred back towards the physical enterprise to improve businesses of the physical enterprise.
Digital twins combined with extended reality (XR) devices provide a feeling of a real physical presence in
virtual space for remote workers to operate digital twin models collaboratively as if remote workers feel that
they are at the same place (Realistic Immersion and Sense). Workforces can participate and collaborate as
digital humans (avatars) for creating genuine and trustworthy digital interactions.
Gamification is a process of using game mechanics, elements, and principles and applying them to non-
game contexts to engage people better. Gamification can be used in a metaverse enterprise for providing
remote collaborative working operations as a means to keep the workforce engaged and to drive
productivity.

ESG + Digital Transformation (ESGDX) Business Model
Embedding ESG sustainability into digitally enabled business models (Sustainable Digital Business Models)
can serve as a growth engine by improving a company’s operations/products/services and by creating new
market opportunities/revenue streams through the orchestration, optimization and dematerialization of
resources and assets.
Sustainable digital value proposition reshapes value proposition (products/services to target customers,
measurable environmental/social values (positive impacts) to stakeholders, trust, and resilient business)
using digital capabilities, customers/stakeholders relations, and marketing channels.
Sustainable digital value creation refers to produce/provide products/services to target customers and
environmental/social values to stakeholders using digital capabilities and business resources sustainablly.
Sustainable digital operating model refers to deliver the created values using digital business processes
(e.g., intelligent workflows/process automations), supply/value chain networks, and sustainable business
operation (e.g., integrate ESG considerations into business operational decision-making criteria).
Sustainable digital value capture refers to monetize the created value through profit formula (revenue/cost
structure). Profit formula includes revenue generation/cost reduction through efficiency/productivity
gain/new market opportunities by digital transformation and sustainability opportunities/risks management.

Reference: Present and Future of Metaverse Infographics
Metaverse = Physical + Virtual Converged Augmented World
Virtually Augmented
Physical World
Physically Augmented Virtual World = Interoperable Interconnected (To Be) Virtual 3D Spaces
Constructed on the Internet; Physically Persistence (To Be)
Me
Digital Me (Avatar)
You Digital You
Digital
People/Objects/Environments
Mixed Reality
Virtual Reality
Reality Technology Innovation
Wearable Devices HCI Digital Senses
Augmented Reality
Extended Reality
AI Digital Human Innovation
People can interact freely in a variety of ways
and forms with one another, digital objects
and environments for a variety of virtual
experiences.
Physical Earth:
Space/time/resource
limitations
Digital Twin Earth:
Space/time/resource
limitations
ICT/Digital+ Digital Twin Innovation
IoT Sensors
AI Predictions
Digital Twins
Metaverse is a collection of fully
connected interoperable physically
augmented digital worlds with
physical persistence that are
converged with the virtually
augmented physical world in which
people and digital representations of
people (digital people) can fully
interact with one another and digital
objects/environments (including
digital twins) with full reality.
Metaverse is the converged world of
augmented physical world and
virtual world that is hyper-
decentralized, hyper-connected,
hyper-visualized, hyper-interacted,
and hyper-reality enabled.
Metaverse can create economic
values without space/time/resource
limitations
Metaverse NFT Projects AT A Glance:
https://www.slideshare.net/alexglee/metaverse
-nft-projects-at-a-glance
NFT

Reference: Metaverse - Gartner Emerging Technology Mapping

Reference: Metaverse Enterprise for Sustainable Smart City
Source. Smart Cities Technologies and applications : BIM-GIS- IOT-AI@udemy
BIM (Building Information Modeling for 3D digital
representations of infrastructure to get proper design,
construction, operation, and maintenance
GIS (Geographic Information System) to map and
analyze geographic features
IoT (Internet of Things) monitoring for real-time
data sensing of smart city components
Smart City Digital Twins
Metaverse Project Management Platform
Design Team Construction Team Management Team
City of the future
Metaverse City Living Lab
metaverse platform for
public–private–people partnerships
for city-wide circular economy
development
public–private–people collaboration
for sustainable city management and
development
co-creation of eco-friendly city
environment
test-bed for innovation
commercialization of new business
ideas
Metaverse City Service Portal
metaverse platform for city
government services: call center;
citizen portal; utility management;
waste management; license/permit
management; asset/land
management; public security and
health management; mobility and
parking management; job search
service
Metaverse Commerce Portal
metaverse platform for digital
billboards/shops/showroom where users
can access products/services from
companies based on brand reputations and
try them virtually; market place for direct
transaction among people; active marketing
campaigns; real/virtual property
renting/sales; land sales
Metaverse IR/PR Portal
metaverse platform for
linking investors and
investees; virtual fund raising
events; virtual public-private
investment partnerships
platform; virtual PR event
with tour of cities for global
investors and tourist
Metaverse NFT
based DeFi
Financing
DeFi Platform
Design and
Development:
https://www.yout
ube.com/watch?v
=onsUBhWBrfU&
t=1629s
Metaverse
Building
Metaverse
Home
Metaverse
Office
Metaverse Store

Reference: On Leading Digital Transformation (HBR Press) One Page Book Summary
2. The Transformative
Business Model
3. Digital Doesn’t Have to
Be Disruptive
4. What’s Your
Data Strategy?
1. Discovery-Driven Digital
Transformation
5. Competing in the Age of AI 6. Building the AI-Powered
Organization
7. How Smart, Connected Products Are
Transforming Companies
8. The Age of Continuous
Connection
9. The Problem with Legacy
Ecosystems
10. Your Workforce Is
More Adaptable Than You
Think
The essential part of digital-transformation strategy is using data and digital capabilities to
create new value for customers. A key part of discovery-driven digital transformation is
identifying organizational problems that can be addressed with digital technology, the desired
improvement for each, and a metric for assessing progress toward it.
Business model describes how a company creates
and captures value. Business model defines the
customer value proposition and the pricing
mechanism, indicate how the company will organize
itself and whom it will partner with to produce value,
and specify how it will structure its supply chain.
Coherent dynamic strategy for organizing, governing, analyzing,
and deploying an organization’s information assets is needed.
Companies need a coherent strategy that strikes
the proper balance between two types of data
management: defensive, such as security and
governance, and offensive, such as increasing
revenue, profitability, and customer satisfaction.
To scale up AI, companies must make three shifts:
(1) Develop cross-functional teams with a mix of
skills and perspectives for interdisciplinary
collaboration ; (2) Develop data-driven decision
making procedure at the front line; (3) Develop
agile, experimental, and adaptable mindset
To launch successful AI, leaders should devote
early attention to several tasks:
(1) Explaining why AI is important to the business
and how they’ll fit into a new, AI-oriented culture; (2)
Anticipating unique barriers to change; (3)
Budgeting as much for integration and adoption as
for technology ; 4) Balancing feasibility, time
investment, and value
Through what we call connected
strategies such that companies are
addressing customers’ needs the
moment they arise nd sometimes even
earlier, customers get a dramatically
improved experience, and companies
boost operational efficiencies and lower
costs.
Identifies a problem, describes what a
solution would achieve, and proposes a
way to measure progress on that
solution
Digital
transformative
business model
can link a new
digital technology
to an emerging
market need.
Smart, connected products require (1)
companies to build and support an
entirely new technology infrastructure;
(2) a fundamental rethinking of product
development and manufacturing; (3) a
new organizational structure.
Smart, connected products create (1) new
production requirements and
opportunities; (2) new services.
Identify metrics that are more closely
linked to the specific improvements you
hope digital transformation initiatives
will bring about
Transformative business model
features: (1) personalization, (2)
a closed-loop process, (3) asset
sharing, (4) usage-based pricing,
(5) a collaborative ecosystem,
and (6) an agile and adaptive
organization
Digital transformation means using digital tools to
better serve the known customer.
Digital transformation often enables the elimination of
inefficient intermediaries and costly physical
infrastructure. But that doesn’t mean the physical
goes away entirely.
Digital transformation requires slow replacing of
legacy systems in a modular, agile fashion.
Balancing offense and defense requires
balancing data control and flexibility.
A company’s data architecture describes how
data is collected, stored, transformed,
distributed, and consumed.
A robust a single source of truth (SSOT) for
control and multiple versions of the truth
(MVOTs) data architecture for flexibility are
need.
The SSOT works at the data level; MVOTs
support the management of information.
Competing in the Age of AI
One Page Book Summary
Companies need to make continuous
connection a fundamental part of their
business models. They can do so with
four strategies: respond to desire,
curated offering, coach behavior, and
automatic execution.
11. How Apple Is
Organized for Innovation
Bonus. Digital
Transformation Comes
Down to Talent in Four
Key Areas
They must function
well together.
To build effective new business models that
take advantage of digital technology, older
companies need to agree on the way forward,
adopt new performance metrics, and rebuild
their supplier, distributor, and partner
networks.
Companies need to start thinking of their
employees as a reserve of talent and energy
that can be tapped by providing smart on-the-
job skills training and career development.
The Apple Model: The company
is organized around functions,
and expertise aligns with
decision rights. Leaders are
cross-functionally collaborative
and deeply knowledgeable about
details.
Technology is the engine of digital
transformation, data is the fuel, process is the
guidance system, and organizational change
capability is the landing gear.

Reference: Competing in the Age of AI (HBR Press) One Page Book Summary
2. Rethinking the Firm
3. The AI Factory
4. Rearchitecting
the Firm
1. The Age of AI
5. Becoming an AI Company 6. Strategy for a New Age
7. Strategic Collisions
8. The Ethics of Digital Scale,
Scope, and Learning
9. The New Meta
10. A Leadership Mandate
a. AI is transforming the very nature of companies—how they operate and how they compete.
b. AI is restructuring the economy.
Understanding the new
opportunities and challenges
has become essential, and
many time-honored
assumptions about strategy
and leadership no longer
apply.
Emergence of firms that are
designed and architected to
release the full potential of
digital networks, data,
algorithms, and AI (digital
operating models).
Case studies of three digital unicorns: Ant Financial,
Ocado, and Peloton
Firms need a fundamentally different operating architecture
to remove constraints on firm scale, growth, and learning
exploiting the full power of digital networks and AI.
Operating architecture for an AI-powered firm: a
common foundation of data inputs, software
technology and algorithms that are provided by an
AI factory, easily accessible (but carefully designed
and secure) interfaces that agile teams developing
individual applications can use.
Digital firms enable and require a
new approach to strategy exploiting
the digital network and learning
effects.
Leaders should be aware of how their newly
deployed digital capabilities can be misused
in ways they never intended-or possibly even
imagined.
The age of AI is defining a new
set of challenges for leaders.
Amazon's digital operating model
illustrates the advantages of digital
scale, scope, and learning. Its digital
systems scale more easily and
continue to improve despite the size
and complexity of its operation.
Digital unicorns’ operating model (delivers value) enables
striking capacity to drive scale, scope, and learning and their
business model enables creation and capture of value without
operational limitation.
The AI factory is the scalable decision engine that enable
data-driven and AI-driven automation, analysis, and
insights and powers the digital operating model of the
twenty-first-century firm.
AI factory components: the AI algorithms that make predictions
and influence decisions, the data pipeline that feeds them, and
the software, connectivity, and infrastructure that power them.
To become an AI-enabled firm that can leverage the power of
data, networks, and AI requires the transformation journey
of deploying a digital operating model.
Five guiding principles that characterize an
effective transformation process:
1. Development of strategic clarity and commitment
2. Development of a clear operating architecture
3. Development of a product-focused agile
organization
4. Development of a deep foundation of capability in
software, data sciences, and advanced analytics
5. Development of a clear multidisciplinary
governance
The stronger the network and
learning effects, the sharper the
increase in value with scale:
a. The most important value
creation dynamic of a digital
operating model is its network
effects.
b. Learning effects can either add
value to existing network effects or
generate value in their own right.
A new generation of digital operating
models transform the economics and
nature of service delivery, and thus,
competition.
As collisions between digital firms and
traditional firms multiply across the
economy, different industries become
increasingly connected to each other
coalescing around a small number of
digital superpowers (hub firms).
Ethical challenges created by the combination of
digital networks and AI: digital amplification,
algorithmic bias, data security and privacy, platform
control, fairness and equity
The age of AI is changing the rules of the game. The new rules are
defining the new age, shaping key arenas, and transforming our
collective future.
Rule 1: The age of AI is driven by a relentless and systemic driver
of change.
Rule 2: AI-driven world has more to do with a universal and
horizontal capabilities.
Rule 3: Traditional industry boundaries are disappearing.
Rule 4: As digital operating models continue to displace traditional
industrial processes, they also remove traditional operating
constraints.
Rule 5: Concentration and inequality will likely get worse.
Enterprise transformation
Entrepreneurial opportunity
Regulation
Community

Metaverse Enterprise Use Case - AI Innovation Platform
US20210004076 illustrates an AI innovation platform that provides a virtual AI
development and testing environment. In the metaverse virtual AI development
processes, AI model training data, system validation, system deployment, and system
testing can be performed within a real-time 3D virtual environment incorporating objects,
camera systems, sensors and digital human avatars representing the collaborating
developers (who can interact using AR/VR/MR devices). The virtual 3D spatial
environment can be networked with external computer resources and their digital twins
to simulate the end-use environment of the AI system. This environment can include
various sub-systems that feed data into the AI system. This data can be captured and
utilized to train and validate the AI system, which can then itself be deployed into the
same real-time virtual environment. Finally, real-time motion capture techniques and
human actors can be used to drive humanoid avatars within the virtual environment.

Metaverse Enterprise Use Case - Digital Twin Retail Store
A simple use case of retail store digital twin that is checking if a store is in compliance
with guidelines from its headquarters, by checking if the shelves in the real store look
like the shelves in the real store. Also, store layout can be evaluated before rearranging
shelves in the store. For example, by using an VR-Headset and going through the digital
twin, store managers can get a feeling if a layout works out, or to check visibility of
articles, shelves and departments. Another visualization use case requires tracking of
customer movement through the store. This allows the creation of heat-maps to
visualize areas that are highly frequented by customers, and in general to see how
customers walk through a store to do their shopping. Store managers can gain valuable
insights from such visualizations, that aid them in their work and to optimize the store
layout and operation. But also use cases like simulating customer flow through a store
and how different layouts and article placements affect customer flow can be
investigated. Of course, these simulations require realistic customer models using
avatars to be representative.
Source. https://medium.com/sap-cic/building-a-digital-twin-for-a-retail-store-d0fcfb5598ec

Metaverse Enterprise Use Case - Global Supply Chain Risk Mitigation
Mareana’s Global Supply Chain, Risk Mitigation tool can take in data from business
locations, transportation, warehouse paths, climate and so on - and uses public data
from a diverse range of data sources to produce immediate visibility dashboards. It
allows you to recognize risks and changes quickly, understand the impact and size, and
make informed and faster decisions about the next best actions using adaptable digital
twins of the supply chain: transform the supply chain into a system of execution that can
dynamically respond to continuous changes. Source. https://mareana.com/

Metaverse Enterprise Use Case – Energy Management Optimization
UrsaLeo’s Gemini offers an immersive 3D platform that combines digital twin technology
with real-time and historical data from IoT streams, document management,
maintenance, and other enterprise systems. Built using the Unity gaming engine, Gemini
delivers a holistic view of industrial assets and facilities including complete picture of
energy usage so users can monitor and manage everything from anywhere at any time.
Gemini ingests data from many sources, from static sensor data to moving sensors on
objects or machinery, and even connects to other systems for maintenance records,
documentation, and more. Using rich collaboration feature, team members can inspect
visualizations, review information, and interact simultaneously no matter where they are
in the world. Through the use of Augmented Reality (AR), users go beyond basic on-
screen visualization and reporting to enhance training, assembly, and diagnostic
activities. Source. https://ursaleo.com/

Immersive 3D Digital Twin Platform Demo

Reference: Next Move of Epic Games
Source. https://www.dezeen.com/2021/07/23/digital-twins-metaverse-david-weir-mccall-epic-games/

Climate Risk & Carbon Net-Zero Transition
Management

Climate Change Sustainability Imperative
Source. https://www.metoffice.gov.uk/weather/climate-change/effects-of-climate-change

Climate Change Risks/Opportunities Management (TCFD)
1. GOAL AND
SCOPE
DEFINITION
2. ANALYZE
EXTERNAL
AND INTERNAL
ENVIRONMENT
3. SCENARIO
BUILDING
4.
ASSESSMENTS
5. STRATEGIC
RESPONSES
•Identify core problem
(leadership/stakeholders)
•Define scope/boundary and level of
analysis
•Set time horizon
The objective of the scenario analysis is to evaluate the impacts of climate change-related risks and
opportunities on business and examine the resilience of current climate change-related strategy.
The scenario analysis is used as an essential tool for organization’s strategic plans or risk management
processes.
The scope of analysis can
include value/supply chain
beyond direct operations, to
achieve a more comprehensive
understanding of the possible
indirect impacts of change-
related risks and opportunities
on business. After familiar with
the qualitative scenario
analysis, a data-driven
quantitative analysis can be
added to further develop the
possible pathways to the
scenarios. The scope of impact
can extend to the impact of a
company’s activities to climate
change (financial v. ES
materiality).
The identified time frames are short (2030),
medium (2040) and long term (2050).
•Identify the key forces and uncertainties
•Identify material risks
•Identify opportunities
Acquire information to predict trends/uncertainties in
environmental and socio-economic conditions such as changes
in policies/regulations, market/technology/investment shifts,
energy supply/demand, changes in population/ GDP.
Material risks include climate-related risks (transition and physical risks).
Specifically, transition risks include climate change-related policy and regulation,
technology development, and reputation. Physical risks include chronic longer-term
climate shifts, such as sustained higher temperatures, sea level rise and acute
event-driven extreme weather, such as heat waves, water stress, wild fires, floods,
drought, and hurricanes.
Climate change-related opportunities include resource efficiency,
energy source, new products and services, markets, revenue
generation, and resilience.
•Select the right public scenarios
•Develop scenarios through internal modeling
under key uncertainties
Commonly used reference scenarios are the physical scenarios, such as the
Intergovernmental Panel on Climate Change (IPCC) scenarios and the transition
scenarios, such as the International Energy Agency (IEA) scenarios. Other
publicly available climate change scenarios include the International Renewable
Energy Agency (IRENA) scenarios and the Network for Greening the Financial
Systems’ (NGFS) Transition scenarios.
Climate change scenarios are can be developed using
projections of what can happen by creating plausible,
coherent and internally consistent descriptions of possible
climate change pathways towards certain goals such as
the Paris Agreement targets of 2°C (or less) of global
temperature warming.
•Evaluate impacts of each
identified risk/opportunity on
business for each scenario
per defined time flame
(scenario analysis outcomes)
To assess a risk/opportunity until a
time frame, a selection of relevant
external variables can be used to
simulate a scenario. These variables
can include carbon pricing and
sectoral carbon intensities.
For example, for a policy transition
risk, carbon price of around 2$ per ton
by 2025 with low financial impacts
was expected.
•Implement net-zero strategy
•Derive action plan
•Monitor developments
•Sustainability reporting
Specify plan to decrease greenhouse gas
(GHG) emission (e.g., halve GHG
emissions by 2030 for Scope 1/2/3
emission) and implement the net zero
roadmap to achieve net zero by 2050.

Net-Zero Transition Target Setting Steps (SBTi)
1. SELECT A
BASE YEAR
2. CALCULATE
YOUR
COMPANY’S
EMISSIONS
3. SET TARGET
BOUNDARIES
4. CHOOSE A
TARGET YEAR
5. CALCULATE
TARGETS
Companies need to establish a base year to track emissions performance consistently
and meaningfully over the target period. Companies that have already set near-term
science-based targets must use the same base year for their long-term science-based
target.
Key elements of the Net-Zero Standard
Companies are required to have a thorough
emissions inventory that covers at least 95%
of company-wide scope 1 and 2 GHG
emissions and a complete scope 3 screening.
Companies are required to calculate
emissions that are reported separately from
the GHG inventory.
• Scope 1, 2, and 3 emissions data should be accurate and verifiable.
• Base year emissions should be representative of a company’s typical GHG profile. Companies may use a multi-year average base year approach, as
described in Chapter 5 of the Greenhouse Gas Protocol Corporate Standard
• The base year should be chosen such that targets have sufficient forward-looking ambition.
• The base year must be no earlier than 2015.
Companies must set near-term targets with
a minimum ambition of 1.5°C for scopes 1
and 2 and a minimum ambition of well-below
2°C for scope 3. Long-term targets must
have a minimum ambition of 1.5°C across
scopes.
• Ensure the target boundary is aligned with the GHG Inventory boundary
• Determine how to treat subsidiaries
• Exclude the use of carbon credits
• Exclude avoided emissions
• Include all mandatory scope 3 emissions
• Determine how to treat indirect use-phase emissions
• Review any sector-specific guidance
Near-term science-based targets must cover at least
95% of company-wide scope 1 and 2 emissions.
Long-term science-based targets must cover at least
95% of company-wide scope 1 and 2 emissions and
90% of scope 3 emissions.
Consider additional required science-based target
coverage.
Companies often set several targets that collectively
meet the boundary requirements.
Near-term targets must have a target year 5-
10 years from the date of submission to the
SBTi, while long-term targets must have a
target year of 2050 or sooner (2040 for
companies in the power sector).
Target methods are used to calculate near-
term and long-term targets based on a
mitigation pathway and company inputs.
Companies may choose from the following
science-based target methods to calculate
their targets.
Ambition ranges for target classification of
near-term science-based targets
Calculation Methods
Eligible for scopes 1+2 (both near-term and long-term science-based targets)
• Absolute contraction
• Physical intensity convergence
• Renewable electricity (scope 2 only)
Eligible for scope 3 (both near-term and long-term targets)
• Physical intensity contraction
• Economic intensity
Eligible for near-term scope 3 targets only
• Engagement targets (scope 3 near-term targets only)

Reference: GHG Inventory Example – Moody’s 2020 TCFD Report

Net-Zero Transition & Decarbonization Solutions: Energy Supply Sector
Source.
https://drawdown.org/sites/default/files/pdfs/TheDrawdownReview%E2%80%932020%E2%80%93Dow
nload.pdf
(Billions of tones of CO2e, baseline year 2020 to 2030)
Total 9.4
Source. https://exponentialroadmap.org/wp-
content/uploads/2020/03/ExponentialRoadmap_1.5.1_216x279_08_AW_Download_Singles_Small.pdf
Minimum Maximum

Net-Zero Transition & Decarbonization Solutions: Transportation & Building
Source.
nload.pdf
Minimum Maximum
Total 4.4
Total 5.3

Net-Zero Transition & Decarbonization Solutions: Food & Agriculture & Industry
Source.
nload.pdf
Minimum Maximum
Total 8.5

Digital Decarbonization
Digital Twins CO2e emissions reductions/Cost savings
between 2020 and 2030
Buildings/Construction 6.9Gt/$288B
Transportation 229Mt/$690B
Consumer Packaged Goods 281Mt/$137B
Life Sciences 61Mt/$106B
Electrical and Electronics 36Mt/$73B
Total 7.5Gt/$1.29T

Reference: Digital Twins
A product digital twin is created when a new product is designed. The product digital twin
enables the functionality validation of the new product.
A production process digital twin is a virtual model of the production process. The
production process digital twin can be used to predict the behavior and to optimize the
performance of the production process.
A digital reality of a human is called a digital human, and can be created by a 3D avatar
integrating with artificial intelligent (AI).
A digital twin of an organization (DTO)–developed by Gartner–is a realistic digital
representation of any organization‘s operation of its business model
Source. https://ieeexplore.ieee.org/document/9359733

Digital Technology for Environmental Sustainability Innovation

AI Blockchain IoT for ESG Digital Transformation AT A Glance

Digital Twins for Dynamic Carbon Net-Zero Management
A digital twin of a carbon emitting/energy intensive physical asset which can be used to
understand, predict and optimize the performance of the asset. The digital twin can
establish a ‘control loop’ enabling the monitoring, reduction and optimization of
greenhouse gas emissions of the physical asset. Such digital control loops can be
defined by sensors collecting data, feeding into a data-driven system, and generating
feedback which will be used to inform decisions and optimize of the physical asset.
Source. https://royalsociety.org/topics-
policy/projects/digital-technology-and-the-planet/

Digital Twins Use Case: A Pulp/Paper Company in S. Korea
Pulp and paper is considered to be the fourth most energy-intensive industry worldwide.
Pulp refiner take 1/3 of the electricity consumed by the entire raw material processing in
a paper company.
Current practice:
- 20+ variables involved in the refiner control and a group of very experienced (2~30
years) engineers manually controls the facility purely based on their experience
- Refiner data are logged every 10 seconds for 2+ years, but not utilized at all
- Refiner throughput is gradually changing due to aging, and each engineer with different
level of experience has different ways to control the facility (thus, not optimal)
Task: Maximizing energy efficiency of the pulp refiner without hurting the refiner
throughput and the quality of resulting pulp product
Method: Use data-driven deep learning model as a digital twin (surrogate, simulator, or
model) and use search engine to safely and efficiently try various scenarios with the
trained model to find the optimally efficient control parameters
Result: 36+% energy saving (wattage), and thus, a lot of carbon emission reduction!
Source. http://tilda.co.kr/

Digital Twins for Building Net-Zero Management
3D digital twins of commercial buildings can show real-time energy generation and
consumption at a granular level. Monitor and manage commercial buildings through 3D
digital twins reduce building’s carbon impact on a room-by-room basis. A 3D digital twin
of a building with real-time data accessible enables (i) collaborate to share the digital
twin’s information with as many users as needed, located anywhere in the world (ii)
perform equipment maintenance using collaboration, augmented reality, and live video
(iii) track people, equipment, lighting, and temperature inside the building for safety and
energy efficiency (iv) generate visual reports of building status for various periods of
time

Reference: ESG Knowledge Map

Reference: ESG Strategic Planning and Program Management
Step 1.
Identify ESG
Issues &
Conduct
Materiality
Assessment
Step 2.
Assess As-Is
Current State
Step 3. Set
To-Be Goals
& Develop
As-Is to To-
Be Roadmap
Step 4. Set
ESG
Strategic
Framework
& Action
Plans
Step 5.
Execute
Step 6.
Review &
Improve
Program
Identify the most relevant ESG issues
dynamically considering double materiality
•Identify ESG issues that can create risks and opportunities
•Identify ESG risks and opportunities that can impact on business (financial materiality) and impact on people and the environment (impact materiality)
•Monitor and update the change of material ESG risks/opportunities/impacts over time
Diagnosis of current program management state
and maturity regarding material ESG issues
•Set ESG program management priorities
•Set specific ways to ESG integration into business
•Set specific ways to implement roadmap
•Set specific ways to work systematically
•Set specific ways to measure material ESG metrics/KPIs
•Set specific ways to leverage technology and expertise
ESG
Program
Management
•Measure the level of governance over ESG program management
•Measure the level of ESG integration into business objectives,
strategy, risk management, and corporate culture
•Measure the performance of ESG program management expected
from stakeholders
•Measure the quality of ESG disclosure and communication
Establish clear objectives/goals & develop
roadmap to achieve the goals
•Define purpose of ESG program management and target goals to
achieve
•Identify measurable outcomes and material ESG metrics/KPIs for
performance measurement
•Identify gap between current state and target state
•Set the short-term, mid-term, long-term time frames
•Specify specific to-do key actions along the roadmap time lines
Set clear framework and actions for ESG
program execution
Implement management program and monitor
& evaluate progress and performance
•Integrate ESG into business practices and processes
•Monitor constantly ESG program progress
•Measure material ESG metrics/KPIs
•Evaluate ESG program management performance
Evaluate outcomes and revise any needed
part of program
•Compare outcomes to target goals
•Disclose and communicate result of outcome analysis
•Identify needed changes and specific was to improve program
•Revise ESG program management

Reference: SDG-SASB-GRI-WEF ESG Metrics Mapping

Reference: Digital Solutions for ESG Sustainability Investing/Management
modular based flexible component solutions
development
-data management solution
-data analysis solution
-performance management solution
- ...
data governance solution for data
availability/visibility/transparency and quality control
-end to end data flow management with advanced
search capabilities
assessment solution for ESG sustainability-related
risks, opportunities, real-world impacts
-development of new metrics that can represent the
processes and systems governing outcomes and
impacts
solution for maturity evaluation
-forward-looking analytic tools for assessing likely
future performance
solution for tracking the ROI of ESG sustainability
management efforts across enterprise
AI & big data analytics-driven automatic real-
time data capture, processing, and storing
standardized analytically rigorous metrics
measurement ways to ensure consistency and
comparability
capture business/industry/jurisdiction-specific
dynamic and time-sensitive material ESG
sustainability considerations based on double
materiality approach
provide a reliable foundation for cross-company
comparisons
use of right framework to evaluate ESG
sustainability-related information critically and
appropriately
capture matters that potentially significant,
monetary implications over time
link between a company’s management in ESG
sustainability-related risks, opportunities and
impacts and its financial performance over time
Management Platform Assessment Platform
Linking Solution
blockchain
based
secure and
credible data
sharing
Integrated Platform

Reference: Balancing Green (MIT Press) One Page Book Summary
1. The Growing Pressures
3. Impact Assessment
4. Making with Less Taking
5. The Sorcery of
Sustainable Sourcing
2. The Structure of Supply Chains
6. Moving More, Emitting Less
7. All’s Well That Ends Well 8. Green by Design
9. Talking the Walk:
Communicating Sustainability
10. Managing Sustainability
11. Creating Deep Sustainability
12. The Travails of Scale
13. A Road to Sustainable Growth
Sustainability-focused
stakeholders' forces
regarding environmental
impacts create economic
incentives for corporate
environmental initiatives.
Examination of sustainability risks, opportunities, and impacts in a
company and across a supply chain is crucial because the vast majority
of environmental (and the associated potential improvements) issues
take place outside the four walls of most companies.
a. Life cycle assessment (LCA) and Greenhouse Gas Protocol (GGP) are
example methodologies for estimating a product's total environmental
impact.
b. Materiality assessment plays a crucial role in allowing companies to make
sound business decisions about which impacts to tackle.
Carbon footprint
evaluation of the supply
chain of bananas sold in
the United States.
BASF environmental
materiality assessment
Case studies of sustainability improvements
regarding carbon footprint, water consumption, and
toxin emissions
Upstream supply chain sustainability case studies: IKEA
and Starbucks cases for reducing negative environmental
impacts and the risk of reputational damage
Examples that show how companies can make significant
reductions in GHG emissions through transportation and
distribution management
Sustainability improvements at the end-of-life of the product
and beyond (circular economy)
Product design and engineering changes that can markedly
affect environmental impact across the full product life cycle
including packaging design and design for recycling
Chapter 16: Circularity for Design
-Redesigning Design
-Recognizing the Problems Designers
-Face Creating a Framework for Circular Design
Sustainability-related labeling, annual corporate social
responsibility reporting, and other marketing
communications
In-depth case studies of three “deep green”
companies: Dr. Bronner's Magic Soaps,
Patagonia, and Seventh Generation
Management issues regarding sustainability
initiatives across an organization
Fundamental challenges of replicating deep green
practices on a larger scale
Trade-off between financial and
environmental performance
Companies can achieve both of environmental and financial goals within the grayed region.
This is the case with initiatives motivated by eco-efficiency, eco-risk management, and/ or
eco-segmentation considerations. However, once companies reach the Pareto frontier,
without radical change, they will face unavoidable trade-offs between environmental and
financial performance.
The set of options
available for creating
new opportunities to
achieve both economic
growth and a reduction
in environmental
impacts.

Reference: ESG Investing For Dummies (Wiley) One Page Book Summary
1. Entering the World of ESG
2. Evolution and Growth of ESG Investing
7. Approaches to
ESG Investing
8. Equity-Based Instruments
3. Environmental Component
15. Elaborating the ESG Endgame
12. Creating Value through ESG
10. Derivative and Alternative Instruments
11. Geographical Differences
14. ESG Performance and Reporting
ESG Investing Consideration Core Factors: industry
sectors, ESG strategies, material indicators
Impact investing is an
approach to investing in
initiatives, organizations, and
funds that pursue the
development of both financial
returns and quantifiable social
and environmental impact.
Benefits of integrating ESG
factors into organization’s
management, strategy, and
goal and effective ESG
practices: generating
enhanced returns;
attracting more customers;
reducing costs; increasing
productivity and attracting
talent
Greenwashing appears to have become more prevalent,
but it’s difficult to prove, given the lack of a common
definition for what constitutes good corporate behavior.
Part 1: Getting to Know ESG
13 Devising an ESG Policy
4. Social Component
5. Governance Component
6. Greenwashing
Understanding Why ESG Is
Important:
global sustainability challenges;
interest of millennial investors in
ESG; producing more systematic,
quantitative, impartial, and
financially applicable approaches to
highlight the core ESG factors.
The ‘E’ in ESG considers the company’s use of
natural resources and the effect its operations have
on the environment, in terms of direct operations
and throughout its supply chains.
climate change: rapid reduction in GHG emissions + offsetting
(e.g., carbon credits) to reach net carbon zero; energy efficiency:
decrease hydrocarbon-sourced energy consumption by displacing
it with clean energy sources, or to integrate systems to improve
energy usage; global water crisis: water access, pollution, and
scarcity; air and water pollution: pollutant emissions ; biodiversity
and deforestation crisis; waste management: circular economy,
waste-to-energy solutions Qualitative nature of social performance and the
wide range of related issues make ‘S’ to be the most
difficult to analyze, measure, and integrate into
investment strategies.
Social performance indicators: customer satisfaction, data security and privacy, gender and
ethnic equality, diversity and inclusion, employee engagement, community relations, , human
rights, labor standards, health, safety, and wellbeing
Corporate governance principally describes the systems a company uses to balance
the competing demands of its diverse stakeholders,
Evaluating governance: board responsibilities
and composition, audit committee structure,
bribery and corruption, executive compensation,
lobbying, political contribution, whistleblower
schemes
Part 2: Investing in ESG
Socially Responsible
Investing SRI);
Impact Investing;
Green Investing;
Faith-based
Investing
Integrating ESG Strategies into equity-based investment
funds are either actively managed (the portfolio manager
decides where and what to invest in) or passively
managed.
Positive screening strategies based on ESG scores
can raise the ESG quality of both passive and active
traditional and smart beta portfolios, without
reducing risk-adjusted returns. However, instead of
focusing on maximizing financial performance from
ESG criteria, many investors are now concentrating
on maximizing ESG performance subject to risk-
return constraints.
9. Fixed Income Instruments
Using ESG factors to inform fixed income investment (Bond) decision-
making is primarily about identifying risks and opportunities that might
otherwise be overlooked.
Integration of ESG factors into
fixed income portfolio
strategies: positive/negative
screening, best-in-class,
engagement, thematic
ESG indexes are becoming
fundamental building
blocks for asset allocation.
Derivative and alternative
instruments can achieve
passive returns using ESG
indexes.
ESG investing is expected
to top US$50 trillion in the
next 20 years confirms that
it has moved from niche to
mainstream.
Part 3: Applying ESG Philosophy
Investors' key
considerations to
develop an ESG
framework:
material ESG
metrics, internal
governance
structure, peer
reviews
a. criteria and metrics for material
ESG factors for performance
measurement b. frameworks for
related reporting of the data behind
material ESG factors
A materiality analysis is a method used to
pinpoint and prioritize the issues that are
most important to an organization’s value
chain and its stakeholders. ESG ratings
grade companies against given ESG
standards, evaluating their performance
on a sustainability scale.
Part 4: The Part of Tens
16. Ten Frequently Asked Questions
17. Ten Issues Surrounding ESG Portfolio
Construction
18. Ten Factors Influencing the Growth of ESG Investing Climate Change
ESG strategies have mostly outperformed
the most popular conventional passive
ETFs.
ESG evolved to mainstream strategy

Climate Risk & Net-Zero Management Automation
Internal/External Environment Analysis: AI/big data analytics can search, access, capture, process,
transform, classify, extract context automatically – Mareana Manufacturing Data Hub.
Climate Scenario Building: AI in collaboration with domain experts can help to generate diverse alternate
(“what if”) scenarios of the future – IBM Scenario Planning Advisor.
Risk Management: Business/financial digital twins can help for risk/opportunity quantification and financial
impact assessment/response (mitigation) automation - Risilience Quantitative Risk Management Solution.
GHG Inventory Management: AI can automate data integration and tracking, calculation of carbon
footprint (metrics) based on GHG Protocol standards within organization and across value/supply chain.
Blockchain can provide transparent and secure data repository - Persefoni Carbon Accounting Platform.
Decarbonization/Net-Zero Transition Management: Industrial digital twins can monitor, measure, control,
and report for carbon emission/energy use reduction – Mareana Adaptive Digital Twin Solution; DTO can
manage decarbonization processes - BusinessOptix DTO/BPM Solution.

Climate Risk & Net-Zero Management Digital Twins Under Development
Digital Twins for Climate Change Risks Quantification & Financial Impact Analysis:
- DTO based on business process workflow oriented risk management system
- DTO based on process mining for quantitative risk assessments based on company
organizational structure, historical data, and business operation context
- DTO based on the financial model for carbon accounting and net-zero transition
- DTO for “what-if” climate scenario
analysis regarding forward-looking
quantitative risk management
- DTO for providing carbon metrics
and real-time view of KPIs
- DTO for climate risk management
process automation
Digital Twins for Decarbonization Management in Manufacturing/Supply Chain
- AI based automatic data captures and context extraction
- Automatic building of adaptive digital twins for business operations/processes from the
extracted data context
-Sustainable outcomes based on deep performance insights (optimization/risk assessment etc.)
using the digital twins in manufacturing/supply chain management/facility management etc.
- Immersive 3D digital twin collaboration platform for decarbonization management
Climate Financial Risk
Forum Guide 2020 - Scenario
Analysis Process

Thank you!

Metaverse ESGDX for Climate Risk and Net-Zero Management

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