# AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply
Chain Carbon Accounting and Emissions Liability Management

Olamide Oladeji1,2, Seyed Shahabeddin Mousavi1,2, Marc Roston2

###### Abstract

> While carbon accounting plays a fundamental role in our fight against
> climate change, it is not without its challenges. We begin the paper with a
> critique of the conventional carbon accounting practices, after which we
> proceed to introduce the E-liability carbon accounting methodology and
> Emissions Liability Management (ELM) originally proposed by Kaplan and
> Ramanna in (Kaplan and Ramanna 2021), highlighting their strengths.
> Recognizing the immense value of this novel approach for real-world carbon
> accounting improvement, we introduce a novel data-driven integrative
> framework that leverages AI and computation - the E-Liability Knowledge
> Graph framework - to achieve real-world implementation of the E-liability
> carbon accounting methodology. In addition to providing a path-to-
> implementation, our proposed framework brings clarity to the complex
> environmental interactions within supply chains, thus enabling better
> informed and more responsible decision-making. We analyze the implementation
> aspects of this framework and conclude with a discourse on the role of this
> AI-aided knowledge graph in ensuring the transparency and decarbonization of
> global supply chains.

## Introduction

Robust carbon accounting is very vital to the goal of cutting carbon emissions
by providing a clearer picture of the sources and magnitude of emissions.
Among other uses, rigorous carbon accounting can enable us to identify the
most effective areas for climate change mitigation and also aid in the
monitoring of progress of global mitigation efforts. Despite the increasing
adoption of various carbon accounting methodology such as the GHG Protocol’s
Corporate Accounting Standards, by organizations and governments around the
world, a number of issues have necessitated that we rethink modern carbon
accounting methodology in order to realize its true potential. We highlight
below, some of the limitations of current carbon accounting practices:

  * •

Complexity of emissions sources: Carbon accounting can be very complex due to
the vast range of emission sources that exit. Such emitting sources can
include everything from large-scale industrial processes to the burning of
fossil fuels for electricity and transportation, deforestation, and even
natural events such wildfires. With each source having distinct
characteristics and requiring different methodologies to accurately measure
its emissions, robustly accounting for emissions may sometimes be a daunting
challenge. Another aspect that affects this is the interwoven nature of most
supply chains(Jiang et al. 2019). Manufacturing a single product or providing
a service will typically require complex interactions and exchange of goods
and services, all guided by complex technological processes. Robust carbon
accounting methodology must sufficiently capture these complexities however
they present in the supply chain.

  * •

Disparate, unavailable data: Another major limitation associated with current
carbon accounting practices is that the data necessary for carbon accounting
is often scattered across various repositories, or in some cases, not
available. This necessitates significant efforts to gather, verify, and
standardize the data. Beyond data gathering, data often needs to be updated
regularly to accurately reflect evolving realities. Several research studies
have described this data limitation challenge. For example, the authors of
(Ducoulombier 2021) review the data limitations associated with carbon
accounting in significant detail.

  * •

High uncertainty: One of the biggest challenges is the inherent uncertainty in
the measurement of greenhouse gas emissions. It is often not possible to
directly measure emissions, especially at large scales. Instead, researchers,
organizations and policymakers often have to rely on estimates and indirect
measurements which are subject to varying degrees of uncertainty. Such
uncertainty is an inherent part of carbon accounting and needs to be
adequately managed to ensure the reliability of the results. Examining the
literature, we can see that many criticisms have been made on current
practices not properly addressing uncertainty issues (Brander et al. 2021).
For example, Williamson et. al in (Williamson and Gattuso 2022) criticize the
integration of uncertainty in carbon accounting for coastal ecosystems. (He et
al. 2022) also presents a systematic review of corporate carbon accounting
research, highlighting insufficient management of uncertainty as a significant
issue.

  * •

Limited, duplicative counting methodology: The most popular framework for
accounting and reporting carbon emissions at the organizational level is the
GreenHouse Gas Protocol Corporate Standard. The Greenhouse Gas Protocol (GHG
Protocol) was established as an initiative of the World Resource Institute and
the World Business Council for Sustainable Development (WBCSD) in 1998. It
published its first version of Corporate Standards categorizing corporate
emissions reporting into Scope 1, Scope 2, and Scope 3, setting the stage for
what has become the de facto standard for carbon accounting at the corporate
and national level(Wbcsd 2004). While it has encouraged carbon emissions
reporting and provided direction where there was none, the delineation into
Scope 1-2-3 emissions by the GHG protocol has inadvertently led duplicative
counting of emissions, among other issues (Williamson and Gattuso 2022).

In an attempt to remedy these and many other issues, the concept of
E-liability based Carbon Accounting was introduced by Kaplan and Ramanna in
(Kaplan and Ramanna 2021). In (Kaplan and Ramanna 2021), the authors propose
the E-liability accounting system to address the duplicative counting and
transparency issues associated with the existing GHG Protocol Scope 1-2-3
carbon accounting framework. Under this framework, where emissions are
measured using a combination of chemistry and engineering, and the principles
of cost accounting are applied to assign the emissions to individual outputs.
The authors provide a detailed method for assigning E-liabilities across an
entire value chain, using the example of a car-door manufacturer whose
furthest-removed supplier is a mining company, which transfers its products to
a shipping company, which transports them to a steel company, and so on until
the car reaches the end customer. The E-liability accounting allocates carbon
emissions in an approach that is similar to how cost accounting distributes
different expenses across the stages of production. It does this by tracking
the costs in terms of carbon emissions, quantified in tons of CO2 thus serving
as a way to quantify the ‘carbon cost’ of a product or service. Under this
model, all inputs into a product, whether raw materials or intermediary goods,
are deemed to carry an E-liability. This e-liability refers to the estimated
amount of carbon emissions associated with that input’s production and supply.
As a firm goes about its production process, it adds these inherited
E-liabilities to its own direct (Scope 1) emissions. Thus, the inherited
E-liabilities and the firm’s own direct emissions together give a more
complete picture of the carbon cost of production for the firm for that
product. Having described E-liabilities, we now discuss how they might be
implemented in real-world supply chains.

## Leveraging Knowledge Graphs and AI for E-Liability Carbon Accounting in
Supply Chains

### Introduction to Knowledge Graphs

Knowledge graphs (KGs) are emerging as a powerful tool for integrating,
organizing, and analyzing complex, multi-relational data. First popularized by
Google’s Knowledge Graph which extracts and integrates information from a
variety of sources to enhance its search results (Fensel et al. 2020), they
have since been adopted in diverse fields such as bioinformatics (Mohamed,
Nováček, and Nounu 2020), e-commerce (Xu et al. 2020), and social network
analysis (Qian et al. 2017). Fundamentally, a knowledge graph can be defined
as a graph-based data model where nodes represent entities and edges denote
the relationships or properties between these entities. Such a data model
facilitates the encoding of rich semantic relationships between diverse
entities and thus going beyond the capabilities of traditional relational
databases. In addition to these capabilities, knowledge graphs can also
capture the attributes of these entities and relationships, leading to a
highly detailed and context-aware data representation (Fensel et al. 2020).
One of the key advantages of knowledge graphs is their ability to handle
heterogeneous and multi-modal data, which is often the case in real-world
scenarios. They can accommodate various data types and structures, providing
much-needed flexibility in data representation and integration. This
flexibility, coupled with their semantic richness makes them well-suited for
complex analytical tasks, such as anomaly detection, entity resolution, among
others (Fensel et al. 2020).

Despite these advantages, the development and maintenance of KGs are not
without challenges. For example, data quality must be ensured when working
with these graphs, as is the management of uncertainty. In addition,
considering the fact that knowledge graphs must enable efficient querying and
manipulation, while preserving privacy and security, this can be a complicated
effort. However, with the rapid advancements in graph databases and machine
learning techniques, many of these challenges are being actively addressed
(Peng et al. 2023).

Having introduced these graphs, we now discuss their potential role in the
context of E-liability tracking and emissions liability management.

### The Need for Knowledge Graphs in Carbon Accounting

A crucial challenge in carbon accounting, especially in the context of
E-liabilities and ELM, is the inherent complexity and heterogeneity of the
data involved. Emissions data span multiple dimensions – they can be
associated with various activities (such as manufacturing or transportation),
be tied to specific products or services, and occur at different stages of the
supply chain. Moreover, the data come from diverse sources, which can range
from company reports and governmental databases to sensor readings and
satellite images (Wiedmann and Lenzen 2018). As we have previously mentioned,
currently existing accounting protocols, such as those disparately counting
Scope 1, 2, and 3 emissions, often lead to duplicative counting or
misrepresentation of total emissions due to their limitations in handling this
complex data landscape(Monyei and Jenkins 2018)(Brander, Gillenwater, and
Ascui 2018). For example, as Brander et al. point out in (Brander,
Gillenwater, and Ascui 2018), some current market-based practices whereby an
organization purchases contractually, the right to claim emissions factors
associated with a renewable project, and then uses that for its Scope 2
generation reporting purposes is very misleading. We have previously
introduced the E-liabilities carbon accounting methodology proposed by Robert
Kaplan and Karthik Ramanna as an effective approach to mitigate many of these
issues. However, from an implementation standpoint, such a method will require
the integration and management of heterogenous data types from various sources
and a robust computational framework to track and evaluate a firm’s current
E-liabilities. This need for a robust and flexible data framework becomes
paramount due to the multifaceted nature of supply chains and the dynamic
aspects of E-liabilities. The absence of such a framework could mean that the
emissions liability management proposals such as those by Roston et. al in
(Roston et al. 2022) remain theoretical and academic exercises. The
heterogeneity of supply chain data —spanning from raw materials to finished
products, involving multiple entities such as suppliers, manufacturers,
distributors and end consumers, and containing diverse data types like
quantities, costs, emissions, and more— demands an associated data model that
can capture these complexities.

This is where knowledge graphs shine. Knowledge graphs, given their ability to
handle multi-relational and multi-modal data, are well suited to meet these
requirements. Their graph-based structure allows for the representation of the
intricate dependencies between different entities in the supply chain and the
various dimensions of emissions data. As we have previously mentioned, they
provide a highly adaptable and semantically rich representation of data,
accommodating the multi-dimensional attributes of the supply chain. In
addition, knowledge graphs can also support a more granular and accurate
tracking of E-liabilities across supply chain processes because they are
designed to handle the ‘inheritance’ and accumulation of E-liabilities from
suppliers to products and to buyers, offering a holistic view of a product’s
carbon footprint. Lastly, the semantic richness of KGs enables capturing the
nuances and contexts in emissions data, thereby enabling a more accurate and
comprehensive carbon accounting (Heath and Bizer 2011).
The rich semantics of knowledge graphs also have the potential to improve
decision-making processes for Emissions Liability Management. Under an
E-liability accounting framework, various stakeholders need to make important
decisions to manage emissions and mitigate climate change. Beyond facilitating
how each firm can easily and transparently compute its emission, E-liability
based knowledge graphs can also help with policymaking and climate mitigation
research efforts. For example, policymakers may need to introduce new
regulations informed by learning from E-liability based carbon accounting
practices. An E-liability knowledge graph of an industry could facilitate
insights on how E-liabilities are distributed across different parts of the
supply chain, which suppliers or products have the highest E-liabilities, and
how changes in one part of the supply chain might impact the overall
E-liabilities of firms in the industry. There are other potential uses of any
such knowledge graph which is able to capture the emissions and interactions
at a detailed yet large-enough scale. Some of these other uses are enumerated
below:

  * •

Enabling independent verification of carbon emissions.

  * •

Facilitating climate and macroeconomic research and technology development.

  * •

Informing standard-setting.

Overall, we can see that the potential of knowledge graphs in ELM is
considerable and its integration for real-world decision-making warrants
further exploration. However, developing a KG-based carbon accounting
framework requires careful consideration of several aspects. These include
identifying suitable data sources, developing effective methods for data
extraction and integration, defining the structure and semantics of the KG,
and ensuring data quality, security, and privacy. In subsequent subsections,
we discuss these aspects in detail and propose a roadmap for developing a KG-
based carbon accounting framework.

### Designing the AI-driven E-Liability Knowledge Graph

Here, we propose an approach to design the conceptual E-liability Knowledge
Graph. Designing a knowledge graph to support emissions liability management
involves several considerations. We need to decide what entities and
relationships to represent, as well as what data to store about each entity
and relationship. The core this graph would be entities that play a part in
the lifecycle of products and services, namely, the organizations, products,
and services themselves. Each organization entity would represent a unique
company or institution, storing data about its location, industry, size, and
carbon emissions data if available. On the other hand, product entities would
represent distinct goods or services. These entities would carry data about
the product’s life cycle emissions which would encompass Scope 1, Scope 2, and
Scope 3 emissions. Other relevant product attributes such as product category,
raw material inputs, and information about the production process could also
be included. This is crucial since different products have vastly different
carbon footprints even within the same company. Service entities would store
similar data to products but with customization that allows us to cater for
services rather than physical products. For example, they would need to
include data about the nature of the service, and its associated carbon
footprint derived from the energy used to deliver the service and the
resources utilized in the process.

The edges in this graph are relationships and they would encode the
transactions between organizations (such as sales and purchases), the
production of products by organizations, and the provision of services. These
relationships are very important as they allow us to trace the flow of
products and services along the supply chain, while also capturing the
transfer of E-liabilities. The data stored about each transaction might
include the quantity of the product or service transacted, the associated
carbon footprint, as well as the timing of the transaction.

The design of this knowledge graph is dictated by the need to manage the
aggregated E-liabilities via Emissions Liability Management practices such as
those discussed in (Roston et al. 2022) and (Kaplan, Ramanna, and Roston
2023). To accurately track E-liabilities, we need comprehensive data about the
carbon footprints of all products and services, and we need to trace the flow
of these goods and services through the economy. By representing
organizations, products, services, and transactions as entities and
relationships in a knowledge graph, we can create a flexible, scalable model
that accommodates this complexity. Furthermore, the semantic nature of
knowledge graphs enables capturing nuances in relationships, critical for
accurate carbon accounting. Altogether, these then inform emissions liability
management decisions such as the investments in removal offsets.

It is very important to mention that the design of such a knowledge graph
should be iterative. As we learn more about the carbon emissions associated
with different industries, products, and services, we would need to refine the
data stored about each entity and relationship. Similarly, as we understand
more about how carbon emissions are transferred through the economy, we can
refine the relationships represented in the graph. The fact that Knowledge
Graphs are by nature designed to be dynamic means that this should not be a
significant issue.

#### Entity and Relationship Representation

The proposed approach to create a granular, interconnected model of the supply
chain would involve creating distinct nodes for organizations, products, and
services, and encoding additional information within these nodes as
attributes. In order to set it up to handle the accumulation and attribution
of E-liabilities at different levels, we can define the following for the
e-liability knowledge graph:

  * •

Organization Nodes: Each organization involved in the supply chain would be
represented as a node. The attributes for these nodes could include the
organization’s name, location, industry sector, and any other relevant
information. An important attribute would be the organization’s total
E-liabilities, which can be updated as new information on E-liabilities it
inherits comes in.

  * •

Product/Service Nodes: Each distinct product or service would be a node, with
attributes including its name, the organization that produces it, and its
individual E-liabilities.

  * •

Process Nodes: These nodes represent processes within an organization, such as
manufacturing or distribution, that generate emissions. These nodes could
store information such as the process name, related organization, the
emissions associated with this process (which might change based on the
product or service being created), and other relevant data.

The next representation category to be considered are the edges. As we have
described previously, edges represent relationships between nodes. We can
identify the following types of edges:

  * •

Production Edges: These would connect an organization node to the
product/service nodes it produces. The edges may carry attributes like the
quantity of product/service produced and the amount of CO2 emissions per unit
of product/service. The latter can be used to update the E-liabilities of the
product/service nodes and in turn, the organization node.

  * •

Supply Edges: These edges would connect an organization node to the
product/service nodes it supplies to other organizations while also being able
to carry similar attributes to the production edges.

  * •

Process Edges: These would connect an organization node to its internal
process nodes or from internal process nodes to product/service. They can
carry information on the quantity of product/service each process produces and
the associated CO2 emissions. Edges from an organization to a process would
indicate that the organization carries out this process. The edges could
contain attributes like the quantity of product or service created by this
process. Edges from a process to a product/service would indicate that the
process creates this product/service, with the edge attributes detailing the
associated emissions.

#### E-liability Attribution in this Computational Framework

Attribution here refers to how E-liabilities is assigned and inherited across
the supply chain and with respect to he E-liability KG, how we can represent
this transfer and inheritance. Having defined the entity and relationship
aspects of this knowledge graph, we describe the E-liability attribution under
this framework as follows:

  * •

Product/Service Level: The E-liabilities of each product/service would be the
sum of the CO2 emissions associated with its production and supply, as
indicated by the attributes of the connecting edges.

  * •

Process Level: The E-liabilities of a process would be the sum of the CO2
emissions associated with that process, as indicated by the attributes of the
connecting edges.

  * •

Organization Level: The E-liabilities of an organization would be the sum of
the E-liabilities of all the product/service nodes it produces, or the sum of
the E-liabilities of its process nodes, depending on how granular we want the
analysis to be. This setup allows us to update the E-liabilities of different
entities in the graph as new data comes in. For example, if new data reveals
increased CO2 emissions in a certain process, we can update the E-liabilities
of the corresponding process node, and then propagate this change up to the
organization node and down to the product/service nodes.

This approach that we have presented is a generalized one that would need to
be tailored to a firm’s specific needs and the available data. In order to
fully deploy it in the real world, several additional considerations would be
necessary. For example, we would need to address data security and privacy
issues when dealing with potentially sensitive information on organizations
and their operations. There are also other considerations that must be taken
into account. We describe some of these next.

#### Additional Considerations for the AI-driven E-Liability Knowledge Graph

To ensure our E-Liability Knowledge Graph provides the most accurate and
granular account of E-liabilities across a supply chain, there are a number of
additional considerations and aspects we must address.

  * •

Handling of Circular Supply Chains: It is important to understand that while
some supply chains may be circular in nature, particularly in the context of a
circular economy where materials are reused and reintroduced into the system,
they still have a linear progression from a temporal and process perspective.
Each step in a supply chain, irrespective of material reuse, is distinct and
takes place at a unique point in time, generating new emissions with each
step. These steps can thus be represented as new process nodes in our
E-Liability Knowledge Graph, each with their own accumulated E-liabilities.
This detailed tracking and accounting of emissions is fundamental to enable
effective management of E-liabilities and ultimately the transition towards
net-zero emissions. To illustrate this, consider a scenario where a car
manufacturer, AutoCorp, produces vehicles, then reuses or recycles parts when
the vehicle reaches the end of its life. Despite the recycling loop, each
step— from receiving raw materials, to production, sale, return, recycling,
and reuse— is unique, occurs at a different time, and results in new
emissions. As a result, each step forms a distinct process node on the
E-Liability Knowledge Graph, with E-liabilities accumulated at each node due
to the emissions generated at that step. This level of granularity in tracking
allows AutoCorp to pinpoint the emission-intensive steps in their supply chain
and strategize targeted carbon reduction or offset efforts.

  * •

Balancing Granularity and Complexity: While we seek to create a comprehensive
and detailed representation of E-liabilities in a supply chain, we must also
be mindful of the complexity this could introduce into the E-Liability
Knowledge Graph. It is crucial that we find the right balance between
granularity and complexity to ensure the graph remains manageable and
practical. We have previously proposed a framework that incorporating
organization, product, and process nodes, with processes also forming part of
the edge data. This level of granularity may be allow us to gain visibility at
the organization and product level while maintaining a manageable complexity.
On the other hand, a deeper granularity than this representation may be too
difficult to computationally manage. It is also important that the data used
to populate the knowledge graph is reliable and verifiable. We must give
careful consideration to the source of this data, and the methods used for its
acquisition and verification. This will be discussed further in the next
section on sourcing and verifying data.

In conclusion, the design of the E-Liability Knowledge Graph must be
comprehensive yet practical, allowing us to accurately track the accumulation
of E-liabilities at each step in the supply chain, even in the context of
circular supply chains and other complexities. However, even with a robust
structural framework like the one we have proposed, certain issues in data
aquisition and processing may still arise. In the next section, we will look
at the challenges and opportunities involved in sourcing data to populate this
graph.

### E-Liability Knowledge Graph Data Acquisition and Processing

A critical step in constructing an E-Liability Knowledge Graph involves the
sourcing, extraction, processing, and integration of multi-dimensional and
multi-source data. We have previously discussed the heterogeneous and complex
nature of this data. In order to integrate them, an E-liability Knowledge
Graph will have to require the application of sophisticated data acquisition
and processing techniques, and advanced computational tools. We discuss how
this might occur as follows:

#### Data Acquisition

Data Acquisition is the preliminary stage where various data sources relevant
to the E-liability KG framework are identified. This is where AI-based data
extraction methods is particularly valuable, especially when applied to
alternative, non-traditional data sources. These sources are typically multi-
dimensional, ranging from structured data like corporate environmental
reports, shipping logs, and operational records, to unstructured data such as
news reports, social media posts related to environmental issues, and more.
Leveraging such a broad data spectrum ensures a comprehensive understanding of
an organization’s E-liabilities. For example, data integration from news
reports and environmental reports could provide insightful information about
an organization’s environmental impact(Tsalis et al. 2020). If the E-liability
Knowledge Graph is being constructed by the firm for its internal analyses
then the data acquisition process is significantly straightforward and
directly obtainable in a structured form. However, if knowledge graph is
constructed to map out E-liability transfer and inheritance across firms in an
industry, e.g. by a policymaker, researcher or independent carbon emissions
auditor, then alternative data sources such as shipping logs, news reports are
particularly more important.

#### Data Extraction

Post data acquisition, extraction becomes a crucial process, particularly when
handling unstructured data. To this end, advanced AI techniques such as
Natural Language Processing (NLP) can be employed. Named Entity Recognition
(NER) algorithms, as we have previously introduced, are a subset NLP that can
parse entities such as organization names, product identifiers, locations,
etc., from textual data.

#### Data Processing

Post extraction, the data should undergo processing to fit into the framework
of the Knowledge Graph. Tasks such as data cleaning, resolving
inconsistencies, and standardizing formats can be undertaken during this
stage. Semantic technologies like RDF (Resource Description Framework) (Assi,
Mcheick, and Dhifli 2020) and OWL (Web Ontology Language) (Chen, Jia, and
Xiang 2020) may also help in facilitating this process as they structure the
data into a format that is machine-readable.

#### Data Integration

The final phase of this process involves integrating the refined data into the
Knowledge Graph. The nodes and edges of the graph are populated based on the
relationships inferred from the processed data. Advanced graph algorithms can
be utilized to ensure the graph remains dynamic and accurately mirrors the
real-world emissions liability structure. The use of sophisticated
computational tools like Apache Hadoop for distributed data storage as Do. et
al. describe for their knowledge graph in (Do et al. 2022), Elasticsearch for
data indexing, and Apache TinkerPop for creating and querying graph databases
can facilitate this entire process, making it scalable and efficient (Zamfir
et al. 2019). Considerations while choosing these tools include scalability,
cost, ease of integration with other systems, and support for advanced
analytics.

#### Computational Tools for Knowledge Graph Management

The power of computational tools can be harnessed at every stage of the
knowledge graph development and management process, ultimately helping to
improve efficiency and ensure scalability. Graph databases like Neo4j can be
employed for their exceptional capabilities in storing and retrieving complex
data relationships in the form of a graph (Needham and Hodler 2019). Neo4j has
already been used in applications such as healthcare data management with
tremendous success - an example being the work described in (Tuck 2022) about
its use in modeling and supporting graph analytics on lung cancer properties.
Other data processing tools like Pandas(McKinney 2010) and PySpark (Armbrust
et al. 2015) allow efficient handling of large datasets, facilitating tasks
like data cleaning, transformations, and aggregations. Apache TinkerPop, a
graph computing framework, is useful in creating and querying the knowledge
graph, making the graph data easily navigable and analyzable. For large-scale
data storage and processing, distributed systems like Apache Hadoop and Apache
Spark prove invaluable, providing scalability, resilience, and enhanced
processing capabilities. Additionally, Elasticsearch helps with efficient
indexing and search capabilities for quick data retrieval (Zamfir et al.
2019).

### Data Verification and Security

The importance of data accuracy and protecting any potentially sensitivity
data cannot be included in the E-Liability Knowledge Graph cannot under-
emphasized. Data elements need to be classified as confidential or otherwise
and data verification and and security measures must be put be place after
this. We describe additional considerations as follows:

#### Data Verification

Ensuring data accuracy is integral to the reliability of the E-Liability
Knowledge Graph. Verification can be undertaken by cross-verification with
other data sources, randomly examining data samples if the volumes are large,
checksum validations, and by leveraging machine learning models for anomaly
detection.

#### Data Integrity

In order to maintain data integrity, the accuracy and consistency of data
throughout its lifecycle must be preserved. There are a few techniques which
can help ensure the consistency of data and prevent unauthorized data
modifications. Some of them include: audit trails of logs, backup and recovery
mechanisms, and checksum validations (Kumar and Goyal 2019).

#### Data Confidentiality

Given the sensitivity of certain data, there is also a need to maintain data
confidentiality. Data confidentiality measures such as encryption, access
control mechanisms, and anonymization techniques can be used protect sensitive
data in these knowledge graphs (Kumar and Goyal 2019) (Yang, Xiong, and Ren
2020). In addition, the General Data Protection Regulation (GDPR) promulgated
by the European Union (gdp 2016) and the California Consumer Privacy Act
(CCPA) (Legislature 2018) are foundational privacy regulations that must be
adhered to when dealing with data that may be in these knowledge graphs.
Adherence to these and other regional or national privacy regulations is
important.

Thus far, we have described how we might build an E-liability Knowledge Graph,
discussing the various elements and additional consideration around data,
tools, storage that must be considered for real-world adoption of the
framework. We can see that with meticulously acquiring, processing, verifying,
and securing data, the E-Liability Knowledge Graph can be a potent tool for
providing actionable insights into the attribution of E-liabilities across
supply chains and ultimately how they can be managed. With the proposal of
this integration pathway already provided, we now highlight limitations and
challenges that might affect the real-world implementation of the framework.

### Potential Limitations and Challenges

The implementation of the E-Liability Knowledge Graph, despite its potential
benefits, does pose several challenges and limitations that should be
addressed. These can broadly be divided into issues related to data,
computational aspects, and policy considerations. We summarize three types of
challenges, data-related, computational challenges and policy-related.

#### Data-related Challenges

  * •

Data Availability and Quality: The effectiveness of E-Liability Knowledge
Graph depends on access to reliable and comprehensive data. However, the
availability and quality of data related to emissions can vary widely. Even
when an organization maintains good internal data, it is still subject to
uncertainty or unreliable data on E-liabilities from upstream. Some
organizations also might not report their emissions data completely or
accurately, due to lack of regulatory mandates or incentives. Additionally,
the data may be inconsistent across different sources, creating discrepancies
in the knowledge graph.

  * •

Data Integration: Even when adequate data is available, integrating disparate
data sources into a unified graph can be complex. Different data sources might
use different formats, standards, or terminologies, requiring significant pre-
processing before integration.

#### Computational Challenges

  * •

Complexity and Scalability: The creation and management of a detailed
knowledge graph, especially one that needs to represent a complex system like
global emissions liabilities, can be computationally intensive. As the number
of entities and relationships in any such graph increases, so does the
complexity of managing and querying the graph. This has significant
implications in the computational resources required to analyze emissions at
industrial, national or global levels.

  * •

Security and Privacy: As we have previously mentioned, the E-liability
knowledge graph may contain potentially sensitive information about
organizations’ emissions and related data. Ensuring the security of this data
and maintaining privacy where required can be a critical challenge.
Appropriate encryption, access control mechanisms, and compliance with data
protection regulations will be necessary and at large-scale data volumes,
ensuring 24/7 compliance may be challenging.

#### Policy-related Challenges

  * •

Regulatory Compliance: Regulations related to emissions reporting vary
globally. Ensuring that the E-Liability Knowledge Graph aligns with the
varying regulations and standards in different regions will be a challenge.

  * •

Stakeholder Acceptance: Finally, achieving widespread acceptance and adoption
of the knowledge graph among all stakeholders (organizations, regulators,
consumers, etc.) might be a challenge. Each stakeholder might have different
priorities and concerns that need to be addressed.

In spite of these challenges, we believe that the potential benefits of an
E-Liability Knowledge Graph makes it a worthwhile pursuit. With a considered
approach that addresses these issues, this framework can provide a powerful
tool for carbon accounting and the management of carbon emissions. To
demonstrate how this might work at a local firm level, we introduce a toy
example in the appendix.

### E-Liability Knowledge Graph Framework: Conclusion and Future Directions

Concluding this paper, we acknowledge the E-Liability Knowledge Graph as a
pioneering computational and data framework for the practical implementation
of the E-liabilities accounting method conceptualized by Kaplan and Rammana in
(Kaplan and Ramanna 2021). This computational and data framework serves the
important function of tracking, managing, and evaluating E-liabilities
throughout the supply chain. It provides a structure that enables the
capturing of environmental impact data at an unprecedented level of detail,
offering unparalleled visibility into the ecological footprint of entities
across the supply chain. It not only streamlines emissions liability tracking
but also fundamentally redefines our capacity to answer key research questions
in business and policy-making contexts. In particular, sustainability decision
makers can make more informed decisions around the following:

  * •

Transparency in Supply Chains: The E-Liability Knowledge Graph framework
illuminates the hidden environmental costs inherent within supply chains. The
framework facilitates insights on how E-liabilities are distributed across
different parts of the supply chain. It can identify which suppliers or
products have the highest E-liabilities, and how changes in one part of the
supply chain might impact the overall E-liabilities. This unprecedented
transparency fosters accountability, drives policy intervention, and
encourages environmentally conscious business practices.

  * •

Product Life Cycle Analysis: This comprehensive data structure enables a
detailed life cycle analysis of products. We can trace the cumulative
environmental impact of a product from raw material extraction to disposal.
This level of granularity aids sustainable product design, influencing
decisions about material selection, manufacturing techniques, and end-of-life
disposal.

  * •

Efficacy of Sustainability Interventions: The E-Liability Knowledge Graph
framework enables us to quantify and evaluate the effectiveness of
sustainability initiatives. Due to more rigorous measuring the reduction in
E-liabilities, decision makers can assess the real-world impact of these
interventions and pinpoint areas for further improvement.

  * •

Policy Impact Analysis: From a policy perspective, the E-Liability Knowledge
Graph framework provides a tool to measure the impact of environmental
regulations on E-liabilities. This can serve as an invaluable feedback
mechanism for policy measures, guiding future policy formulation.

  * •

Risk Assessment: Additionally, the framework equips us with a more nuanced
understanding of environmental risks associated with specific activities,
locations, or business partners. Such risk assessments can inform business
strategy and contingency planning.

By addressing these crucial research questions, the E-Liability Knowledge
Graph framework brings clarity to the complex environmental interactions
within supply chains, empowering more informed, responsible, and sustainable
decision-making. This novel approach marks a significant advancement in the
practical implementation of E-liabilities based carbon accounting,
underscoring the transformative potential of data-driven insights for
promoting environmental sustainability. By continually refining and adopting
such a methodology, we can progress towards a future where carbon emissions
are not just properly accounted for, but also actively managed. One key
feature of this framework is its ability to incorporate advanced computational
techniques such as machine learning and large-volumes of data, which has been
a major focus of this study.

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## Appendix

### Toy Example: Tracking E-Liabilities in the AutoFab Supply Chain

In the context of E-Liability carbon accounting, a detailed mapping of the
automobile manufacturing supply chain using a knowledge graph can be a nuanced
task, considering the multitude of product nodes, process nodes, organization
nodes, and their various relationships. To illustrate how this works, in this
section we present a hypothetical case study featuring the operations of a
hypothetical car manufacturer, AutoFab, and its associated supply chain from
raw material sourcing to final product consumption.

In this case study, we take a deep dive into the supply chain of AutoFab to
showcase the granular representation of E-liabilities using the E-Liabilities
Knowledge Graph Framework. This comprehensive network encapsulates a diverse
set of nodes, each representing different organizations, products, services,
and processes. Additionally, the intricate network of relationships between
these nodes enables us to track and accumulate E-liabilities at every stage.

#### Organization Nodes in the AutoFab Supply Chain

The Organization Nodes in this supply chain would be as follows:

  * •

AutoFab: The automobile manufacturer located in Detroit, MI.

  * •

SteelCo: AutoFab’s steel supplier based in Pittsburgh, PA.

  * •

IronMine Corp: The source of iron ore for SteelCo, situated in Minnesota.

  * •

RubberInc: The tire supplier of AutoFab, located in Akron, OH.

  * •

RubberPlantations Ltd: The producer of natural rubber for RubberInc, based in
Thailand.

  * •

EnergyCorp: The electricity provider for AutoFab and SteelCo.

  * •

AutoDealer LLC: The distributors of AutoFab cars.

  * •

Consumer Doreen: Buys an AutoFab 2023 Car

#### Defining Inputs, Processes, and Outputs

1 IronMine Corp

  * •

Raw material: Iron ore - product node

  * •

Processes: Mining, Transport to SteelCo

  * •

Output: Iron supplied to SteelCo

2 SteelCo

  * •

Inputs: Iron from IronMine Corp, Electricity (from the grid)

  * •

Processes: Smelting and steel production, Transport to AutoFab

  * •

Output: Steel supplied to AutoFab

3 RubberPlantations Ltd

  * •

Raw material: Rubber - product node

  * •

Processes: Rubber extraction, Processing, Transport to RubberInc

  * •

Output: Processed rubber supplied to RubberInc

4 RubberInc

  * •

Inputs: Processed rubber from RubberPlantations Ltd, Electricity (self-
generated)

  * •

Processes: Electricity generation, Tire production, Transport to AutoFab,

  * •

Output: Tires supplied to AutoFab, Excess electricity (if any)

5 AutoFab

  * •

Inputs: Steel from SteelCo, Tires from RubberInc, Electricity (from the grid)

  * •

Processes: Automobile assembly, Transport to Dealerships

  * •

Output: Cars supplied to various dealerships

6 AutoDealer LLC

  * •

Inputs: Cars from AutoFab, Electricity (from the grid)

  * •

Processes: facilities and operations, vehicle servicing, vehicle
transportation

  * •

Output: Cars sold to consumers

7 EnergyCorp

  * •

Inputs: Electricity

  * •

Processes: Voltage Transformation, Electricity Operations (Dispatch),
Facilities Management.

  * •

Output:Electricity

#### Product Nodes

  * •

Iron Ore at IronMine Corp: The iron ore when it is at the mining company.

  * •

Iron Ore at SteelCo: The Iron when it is still with SteelCo.

  * •

Steel at SteelCo: The Steel at SteelCo.

  * •

Steel at AutoFab: The steel when it reaches AutoFab, with a distinct node to
account for its transition across the supply chain.

  * •

Electricity at AutoFab: Electricity from the grid purchased via EnergyCorp as
received at AutoFab.

  * •

Natural Rubber at RubberPlantations Ltd: The harvested rubber when it is still
at the plantation.

  * •

Processed Rubber at RubberPlantations Ltd: The processed rubber after under
going rubber production at RubberPlantations Ltd.

  * •

Processed Rubber at RubberInc: The processed rubber after being transported to
RubberInc.

  * •

Tires at RubberInc: The tires when they are manufactured by RubberInc.

  * •

Tires at AutoFab: The tires when they reach AutoFab.

  * •

Automobile at AutoFab: The finished automobile produced by AutoFab.

  * •

Automobile at Consumer Doreen: The automobile after being delivered to
Consumer Doreen

#### Building Relationships and Accumulating E-liabilities

Upon defining the nodes, we can begin to link these entities with edges while
accounting for the flow of products, services and associated E-liabilities.

  * •

IronMine Corp - SteelCo Relationship

An edge between the ‘Iron Ore’ product node of IronMine Corp and the
organization node of SteelCo represents the transaction of iron ore. This edge
carries e-liability information, such as the CO2 emissions related to mining
and transport. When SteelCo receives the iron ore, a new product node for
‘Iron Ore’ is created under SteelCo, and the E-liability from IronMine Corp’s
‘Iron Ore’ node is added to it.

  * •

SteelCo - AutoFab Relationship

SteelCo’s ‘Steel’ product node carries the cumulative E-liability from its
‘Iron Ore’ node, the process of steel production, and transport. When AutoFab
receives the steel, a new product node for ‘Steel’ is created under AutoFab
with SteelCo’s ‘Steel’ node’s E-liability.

  * •

EnergyCorp - AutoFab Relationship A similar process is repeated as with
SteelCo to capture AutoFab’s electricity usage from the grid. As AutoFab
purchases electricity from EnergyCorp, it inherits the E-liabilities
associated with the amount of electricity purchased. An edge is used to
describe this purchase process with electricity nodes on either end.

  * •

EnergyCorp - SteelCo Relationship As SteelCo purchases electricity from
EnergyCorp, it also inherits the E-liabilities associated with the amount of
electricity purchased. We use an edgeto describe this purchase process with
electricity nodes on either end.

  * •

EnergyCorp - IronMine Relationship Similarly to SteelCo, as Ironmine purchases
electricity from EnergyCorp, it also inherits the E-liabilities associated
with the amount of electricity purchased. An edge is also used to describe
this purchase process with electricity nodes on either end.

  * •

RubberPlantations Ltd - RubberInc - AutoFab Relationships

RubberInc’s ‘Tires’ product node accumulates E-liabilities from the ‘Processed
Rubber’ node, tire production, and transport. AutoFab’s ’Tires’ product node,
in turn, inherits these E-liabilities as it receives the ‘Tires’.

  * •

AutoFab - Dealership - Consumer Relationships

AutoFab’s ’Car’ product node holds the cumulative E-liabilities from its
’Steel’ and ’Tires’ nodes, the assembly process, and transport. When a
dealership sells a car to a consumer, the accumulated E-liabilities are passed
on to the consumer.

Figure 1: Simplified visualization of Knowledge Graph for AutoFab’s Supply
Chain showing E-liability Accumulation

Figure 1 illustrates the various entities and relationships between them in
this knowledge graph.

#### Tracking E-Liabilities

This framework enables the precise tracking of E-liabilities throughout the
supply chain. For instance, if the process of creating steel at SteelCo and
transporting that steel to AutoFab results in an emission of 2 tons of CO2 per
ton of steel, and 1 ton of steel is used per car by AutoFab, we can attribute
2 tons of CO2 e-liability to each car inherited due to steel. Similarly, we
can also calculate E-liabilities associated with AutoFab’s other processes
that go into manufacturing its cars such as its electricity usage, and include
them in as E-liabilities inherited into the product node of the car produced
by AutoFab. This process continues for the other nodes in the supply chain,
each accumulating E-liabilities as materials and products are transferred,
processed, and finally sold to the consumer. This resulting in a comprehensive
and granular mapping of E-liabilities. It is crucial to note that these are
illustrative numbers; the exact quantities would depend on the specific
details of the processes involved and the emissions associated with those
processes.

By designing our knowledge graph in this manner, we can efficiently track,
aggregate, and assign E-liabilities at each step of the supply chain, from raw
material extraction to the end consumer. Ultimately, an E-Liability Knowledge
Graph framework allows us to have a visual, structured, and scalable
representation of E-liabilities as they move through the supply chain. It
provides a granular view of each step of the process, offering insights into
which processes or nodes contribute most to the total emissions of a product.
By capturing these details, the E-Liability Knowledge Graph framework serves
as a tool for understanding and managing the environmental footprint of an
organization’s supply chain. Businesses can make informed decisions, using the
insights gained from this framework, to optimize their operations, reducing
their environmental impact and aligning their strategies with sustainability
goals. Finally, it is important to recognize that the process of establishing
and managing an E-liabilities Knowledge Graph requires rigorous data
collection and processing, which can be enhanced with AI/NLP techniques for
extracting and interpreting information from diverse data sources.