Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

Abstract

The rise of stakeholder interest globally in sustainable business practices has resulted in a rise in demands from stakeholders that companies report on the environmental and social impacts of their business activities. In certain cases, however, companies have resorted to the practice of providing inaccurate disclosures regarding sustainability as part of their corporate communications and sustainability reporting—commonly referred to as “greenwashing”. Concurrently, technological improvements in artificial intelligence have presented the means to rapidly and accurately analyze large volumes of text-based information, such as that contained in sustainability reports. Despite the possible impacts of artificial intelligence and machine learning on the fields of greenwashing and sustainability reporting, no literature to date has comprehensively and holistically addressed the interrelationship between these three important topics. This paper contributes to the body of knowledge by using bibliometric and thematic analyses to systematically analyze the interrelationship between those fields. The analysis is also used to conjecture a conceptual and thematic framework for the use of artificial intelligence with machine learning in relation to greenwashing and company sustainability reporting. This paper finds that the use of artificial intelligence in relation to greenwashing, and greenwashing within sustainability reporting, is an underexplored research field.

Full text

Citation: Moodaley, W.; Telukdarie,
A. Greenwashing, Sustainability
Reporting, and Artificial Intelligence:
A Systematic Literature Review.
Sustainability 2023,15, 1481. https://
doi.org/10.3390/su15021481
Academic Editor: Evangelos
Katsamakas
Received: 22 November 2022
Revised: 6 January 2023
Accepted: 10 January 2023
Published: 12 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Review
Greenwashing, Sustainability Reporting, and Artificial
Intelligence: A Systematic Literature Review
Wayne Moodaley * and Arnesh Telukdarie
Johannesburg Business School, University of Johannesburg, Johannesburg 2092, South Africa
*Correspondence: wayne.moodaley@jbs.ac.za
Abstract: The rise of stakeholder interest globally in sustainable business practices has resulted in a
rise in demands from stakeholders that companies report on the environmental and social impacts
of their business activities. In certain cases, however, companies have resorted to the practice of
providing inaccurate disclosures regarding sustainability as part of their corporate communications
and sustainability reporting—commonly referred to as “greenwashing”. Concurrently, technological
improvements in artificial intelligence have presented the means to rapidly and accurately analyze
large volumes of text-based information, such as that contained in sustainability reports. Despite
the possible impacts of artificial intelligence and machine learning on the fields of greenwashing
and sustainability reporting, no literature to date has comprehensively and holistically addressed
the interrelationship between these three important topics. This paper contributes to the body of
knowledge by using bibliometric and thematic analyses to systematically analyze the interrelationship
between those fields. The analysis is also used to conjecture a conceptual and thematic framework for
the use of artificial intelligence with machine learning in relation to greenwashing and company sus-
tainability reporting. This paper finds that the use of artificial intelligence in relation to greenwashing,
and greenwashing within sustainability reporting, is an underexplored research field.
Keywords:
greenwashing; sustainability reporting; artificial intelligence; machine learning; sustainability
1. Introduction
Sustainability as both a practice and concept is complex, particularly in business. En-
vironmental sustainability implies the stewardship of resources by companies for current
and future generations, which in turn involves appropriate measurement and evaluation
of such impacts [
1
]. Social sustainability incorporates considerations regarding people,
communities, and broader society. While clear consensus on the definition of social sus-
tainability is not present, in the context of business it is frequently defined in relation
to minimizing the negative, and maximizing the positive impacts of a company’s opera-
tions [
2
]. Economic sustainability, in turn, reflects economic progress which is not at the
expense of the environment, people, or society [
3
]. Jurisdictions such as the European
Union have recognized the importance of these elements of sustainability and adopted
initiatives such as the EU Circular Economy Action Plan [4].
The practice of sustainability reporting reflects increasing public awareness regarding
the importance of sustainability, and an increase in stakeholder expectations and pressure
for companies to disclose the impact of their operations on the environment and society. In
that context, sustainability reporting has become increasingly important for companies to
signal their commitment to stakeholders regarding environmental, social, and governance
(ESG) factors [5].
The Global Reporting Initiative (GRI) states that the “foundation of sustainability
reporting is for an organization to identify and prioritize its impacts on the economy,
environment, and people to be transparent about their impacts” [
6
]. Many companies now
choose to report on ESG factors in sustainability reports [7].
Sustainability 2023,15, 1481. https://doi.org/10.3390/su15021481 https://www.mdpi.com/journal/sustainability

Sustainability 2023,15, 1481 2 of 25
Within that context, the use of artificial intelligence (AI) and machine learning (ML)
has fundamentally changed the landscape of business and society in recent years [
8
]. The
progression in the field of sustainability reporting has coincided with the rise in interest in,
and use of, artificial intelligence—both within business and society, as well as academic
literature. The term “Artificial Intelligence” stems from the 1956 Dartmouth summer
research project proposal on artificial intelligence, in which the authors, John McCarthy,
Marvin Minsky, Nathaniel Rochester, and Claude Shannon, proposed a study on “the basis
of the conjecture that every aspect of learning or any other feature of intelligence can in
principle be so precisely described that a machine can be made to simulate it” [9].
Since then, the field has experienced a dramatic evolution, and the last decade has seen
significant improvements and breakthroughs relating to data, computing technology, and
algorithms [
10
]. Goodell et al. [
8
] reference this evolution and indicate that the evolution
relates to technology that is able to tackle tasks at the level of difficulty at which humans
are able to operate. A steady increase in academic literature relating to AI has accompanied
this evolution [11].
With the increased interest in sustainability reporting and the impact of company
actions on ESG factors, has come an increase in the prevalence of misleading information re-
garding such impact—commonly referred to as greenwashing [
5
]. In and Schumacher [
12
]
contend that “companies have misaligned incentives to deliberately or selectively com-
municate information not matched with actual environmental impacts or make largely
unsubstantiated promises around future ambitions”.
The term greenwashing was coined by Jay Westerveld in 1986 [
13
]. Westerveld first
used the term in relation to hotel signs requesting that guests hang up used towels for
environmental reasons, and posited that these requests related more to the hotels’ desire
to reduce the costs of laundry, rather than reducing the impact on the environment. He
christened this misleading form of communication “greenwashing” [14].
Large bodies of academic literature are devoted to sustainability reporting, greenwash-
ing, and AI with ML. Certain recent studies have sought to link two of these respective
fields to each other, while others link one of these fields to other disciplines. None, however,
consider the intersection of all three within a systematic or bibliometric literature review.
This paper considers extant literature reviews relating to each intersection below.
Goodell et al. [
8
] conduct a bibliometric study of the application of artificial intelli-
gence and machine learning in the field of finance. Lombardi and Secundo [
15
] conduct
a systematic literature review to analyze the intersection of reporting, and digital and
smart technologies.
Beltrami et al. [
16
] conduct a systematic literature review to evaluate the link between
Industry 4.0 and sustainability, and develop a framework that highlights relationships be-
tween those fields—both of which are significantly broader than the fields of sustainability
reporting and AI and ML.
Crucially, none of the abovementioned reviews specifically or explicitly study the link
between sustainability reporting and AI with ML.
While there is a lack of literature reviews relating to the intersection of sustainability
reporting and AI with ML, it is worthwhile noting that recent advancements in ML and
natural language processing (NLP) have highlighted the ability of ML and NLP tools
in assessing large volumes of complex textual information, such as that contained in
sustainability reports [
17
,
18
]. Kotzian [
19
] proposes methods for applying AI with ML to
the Corporate Social Responsibility (CSR) domain to detect CSR non-compliance.
Other studies relating to sustainability reporting or greenwashing have incorporated
AI with ML as methodological tools. Examples include the use of AI with ML in analyzing
sustainability reporting using NLP [
20
,
21
], applying machine learning algorithms for
topic modeling to analyze firm sustainability reporting disclosures available online [
22
],
measuring the readability of sustainability reports [
23
], and measuring corporate alignment
with the UN Sustainable Development Goals (SDGs) [24].

Sustainability 2023,15, 1481 3 of 25
Similar to the intersection for sustainability reporting and AI with ML, limited system-
atic literature or bibliometric reviews have been conducted to study the interplay between
greenwashing and sustainability reporting. Velte [
25
] conducts a systematic literature
review of research on integrated reporting in relation to the “business case for integrated
reporting” and creates a differentiation within the corpus reviewed which the author
indicates “is crucial, to stress the challenges of greenwashing policies and information
overload”. The study is, however, focused on integrated reporting and does not specifically
review literature on the intersection between sustainability reporting and greenwashing.
Systematic literature reviews or bibliometric reviews relating to the intersection of
greenwashing and AI with ML have not been found.
Given the paucity of comprehensive systematic or bibliometric reviews of literature
described above, it is clear that comprehensive analysis of the intersections between green-
washing, sustainability reporting, and AI with ML in academic literature is not present.
As described above, the previous literature relating to sustainability reporting, green-
washing, and AI with ML, have linked one or the other to adjacent fields, or dealt with
subsets of each field. Other literature reviews have a much wider purview and different
aims, such as literature dealing with the broader topic of Industry 4.0, of which AI with ML
are subsets.
The aim of this paper is to infer and develop a clear thematic and conceptual frame-
work relating to the intersection of AI with ML, greenwashing, and sustainability reporting,
and specifically, for the use of AI with ML in relation to greenwashing and sustainability
reporting. This paper fills the gap in the academic literature by applying bibliometric and
thematic analysis to analyze the interrelationship between these fields within that context
and identify specific thematic trends.
The remainder of the paper is structured as follows. Section two sets out an overview
of each of these fields. Section three details the research methodology, while section four
presents a bibliometric and thematic analysis and results. Lastly, section five sets out the
conclusions, implications of the research, and limitations of the study.
2. Overview
2.1. Sustainability Reporting
Sustainability reporting is synonymous with a number of different types of reporting,
including, inter alia, corporate social responsibility (CSR) reporting, greenhouse gas emis-
sions (GHG) reporting, and UN Sustainable Development Goals (SDG) reporting [
26
]. In
academic literature and practice, the term is also often used interchangeably with corporate
social responsibility (CSR) reporting [27], and triple-bottom-line (TBL) reporting [28].
Various bodies globally have responded to stakeholder calls for reliable sustainability
reporting standards by developing a plethora of guidance documents and frameworks
on disclosures relating to ESG and sustainability. These have included initiatives and
guidelines published by, inter alia, the Global Reporting Initiative (GRI), the Interna-
tional Financial Reporting Standards (IFRS) Foundation, the European Financial Reporting
Advisory Group (EFRAG), the Sustainability Accounting Standards Board (SASB), the
International Integrated Reporting Council (IIRC), and the European Commission [
29
,
30
].
Other initiatives include those by the Task Force on Climate Related Financial Disclosure
(TFCD), the G20, G7, and World Economic Forum [31].
The diversity in frameworks, standards, and practices relating to sustainability re-
porting reflects the complexity of sustainability itself, which involves multiple actors,
stakeholders, and interests [
2
]. However, at the heart of sustainability reporting is the goal
of holding companies accountable for the environmental, social, and economic impact of
their operations [2].
2.2. Greenwashing
Numerous approaches have been followed in an attempt to define what greenwashing
is. De Freitas Netto et al. [
5
] present three forms in terms of the approach followed in

Sustainability 2023,15, 1481 4 of 25
academic literature to define greenwashing. The first, greenwashing as selective disclo-
sure, refers to retaining “the disclosure of negative information related to the company’s
environmental performance and expose positive information regarding its environmental
performance” [
5
]. The second, “greenwashing as decoupling”, refers to the decoupling of
the reality of company behavior on sustainability issues from its communications through
the use of, inter alia, “symbolic actions” [
5
]. The third identifies the use of legitimacy theory
and signaling in relation to the practice of greenwashing [5].
Tateishi [
32
] defines greenwashing in relation to “communication that misleads peo-
ple (e.g., consumers and stakeholders) regarding environmental performance/benefits”.
Lyon and Maxwell [
33
] define greenwashing more broadly to include social elements,
i.e., “selective
disclosure of positive information about a company’s environmental or so-
cial performance, without full disclosure of negative information on these dimensions, so
as to create an overly positive corporate image”.
Greenwashing is therefore, “an umbrella term for a variety of misleading commu-
nications and practices that intentionally or not, induce false positive perceptions of an
organization’s environmental performance” [34].
2.2.1. Impact of Greenwashing
Testa et al. [
35
] state that greenwashing undermines “corporate accountability toward
stakeholders and the credibility of environmental initiatives”. Various industry and regulatory
bodies have acknowledged the negative impact of greenwashing on sustainability efforts.
The European Commission [
4
] states that greenwashing “misleads market actors
and does not give due advantage to those companies that are making the effort to green
their products and activities. It ultimately leads to a less green economy”. The former
Commissioner of the USA’s Securities and Exchange Commission (SEC), Allison Herron Lee,
states that “Greenwashing can mislead investors as to the true risks, rewards, and pricing
of investment assets” [
36
]. The United Kingdom’s Financial Conduct Authority (FCA)
states that “Greenwashing misleads consumers and erodes trust in all ESG products” [
37
].
One of the more prominent greenwashing examples relates to Volkswagen publishing
misleading and incorrect information relating to its vehicles’ emissions, which led to signifi-
cant reputational damage, as well as financial losses, and a drastic decline in the company’s
share price [
38
]. Greenwashing may also lead to a decline in consumer confidence in a
company’s brand and products [39].
In response, the EU has proposed changes to the Unfair Commercial Practices Directive
to combat greenwashing and misleading green claims [
4
]. Similarly, the U.S. Securities
and Exchange Commission has proposed rules to enhance and standardize climate related
disclosures to investors [
40
]. In her support of the proposal, former SEC Commissioner
Allison Herren Lee states the need for “consistent, comparable, and reliable information—
information to help protect investors from “greenwashing” or exaggerated or false claims
about ESG practices” [40].
In the context of sustainability reporting, greenwashing represents a threat to the
accuracy, reliability, and transparency of such reporting, because at the heart of green-
washing is the difference between what a company chooses to disclose (and signal) to
stakeholders regarding its performance on ESG and climate-related factors, and its actions
in relating to such factors. Steiner et al. [
41
] express this as the “incongruence between
the reputational intention and the actual, real sustainability performance of the company”.
Greenwashing is therefore detrimental to the interests of multiple stakeholders, including
investors, consumers, and other market actors.
2.2.2. Increase in Greenwashing
Delmas and Burbano [
42
] reported an increase in the practice of greenwashing by
firms. Lyon and Montgomery [
43
] conduct a review of the greenwashing literature and
indicate that both green claims and “the incidence of greenwash” have increased rapidly in
recent years. Nemes et al. [
44
], in their more recent study state the issue more bluntly when

Sustainability 2023,15, 1481 5 of 25
they contend that despite “growing awareness” regarding greenwashing, the phenomenon
is still “widespread”.
Regulators and industry bodies have affirmed this trend. A screening by the European
Commission focusing on greenwashing analyzed online claims [
45
]. The findings indicate
that “authorities had reason to believe that in 42% of cases the claims were exaggerated,
false or deceptive and could potentially qualify as unfair commercial practices under EU
rules”, and stated that greenwashing “has increased as consumers increasingly seek to
buy environmentally sound products” [
45
]. Varying definitions of greenwashing make the
identification of greenwashing more complex [46].
2.2.3. Increase in Greenwashing Academic Literature
Besides the increasing trend in the practice of greenwashing, recent years have seen
a significant increase in the academic literature relating to the topic of greenwashing.
Montero-Nevarro et al. [
47
] analyze academic literature on greenwashing in the agriculture,
food, and retail industries, and identify a significant increase in greenwashing literature
from 2016. They identify three distinct periods for this trend, with the last being a period
of significant growth between 2016 and 2020. The increase reflects growing interest in
greenwashing as a topic of research [47].
Lyon and Montgomery [
43
], in their review of the greenwashing literature, identify
a “sharp increase in articles since 2011”. Lu Zhang et al. [
48
] state that “references to
greenwashing in the literature has increased rapidly and the types and consequences of
greenwashing have become a research hotspot”.
This trend is supported by the work of Yang et al. [
49
] who identify an increasing
trend in academic literature relating to greenwashing from 2010, and Andreoli et al. [
39
]
in their bibliometric analysis of the field. De Freitas Netto et al. [
5
], based on a systematic
review of the literature, identify a rise in academic interest in greenwashing. Pope and
Wæraas [
50
] contend that that the growth in academic discourse relating to greenwashing
has been significant.
2.3. Artificial Intelligence and Machine Learning
AI presents multiple opportunities and possibilities to change society, whether in
the production of goods or services, in broader business, or in shaping and changing
approaches to environmental and social issues [
11
,
51
]. The field is becoming increasingly
relevant in terms of both its impacts on, and transformations of, various fields [
11
,
52
].
Within the context of sustainability, AI may have both positive and negative impacts on
societal, economic, and environmental outcomes [
53
] which reflects its importance in
relation to the field of sustainability, and concomitantly, sustainability reporting.
A subfield of AI that has emerged is machine learning (ML). ML uses algorithms to
recognize patterns, make decisions, and imitate the way that humans learn and solve prob-
lems [
7
,
8
,
52
]. ML, like AI, has become increasingly prominent in the academic literature, as
reflected by increases in the occurrences of the term as well as its evolution toward being a
term that is sometimes viewed as being distinct or autonomous from AI within academic
literature [11].
Natural language processing (NLP), itself a subset of AI and ML, uses algorithms and
ML to analyze text, and allows analysis of large quantities of data much faster than manual
analysis and review [
54
]. Recent bibliometric studies identify NLP as an emerging topic
requiring more research in future [
11
], while scholarly tools, such as VosViewer, employ
NLP to conduct bibliometric analyses [55].
Text mining is related to NLP as it “is the process of transforming unstructured text
into structured data for easy analysis”, and “uses natural language processing tools to
interpret the human language and process text documents automatically” [56].

Sustainability 2023,15, 1481 6 of 25
3. Materials and Methods
The methodology applied is designed to ensure transparency and replicability of
the study. To achieve those aims, this paper follows a bibliometric analysis approach,
combined with thematic analysis. Bibliometric analysis as a research method has become
more prominent in academic research and allows for the analysis of large quantities of
bibliometric information to identify themes and trends [
8
,
26
]. Thematic analysis is the
process of “identifying, analyzing, and reporting patterns (themes) within data” [57].
A five-step process was followed for the bibliometric analysis, presented graphically
in Figure 1below:
Sustainability 2023, 15, x FOR PEER REVIEW 6 of 27
Natural language processing (NLP), itself a subset of AI and ML, uses algorithms and
ML to analyze text, and allows analysis of large quantities of data much faster than man-
ual analysis and review [54]. Recent bibliometric studies identify NLP as an emerging
topic requiring more research in future [11], while scholarly tools, such as VosViewer,
employ NLP to conduct bibliometric analyses [55].
Text mining is related to NLP as it “is the process of transforming unstructured text
into structured data for easy analysis”, and “uses natural language processing tools to
interpret the human language and process text documents automatically” [56].
3. Materials and Methods
The methodology applied is designed to ensure transparency and replicability of the
study. To achieve those aims, this paper follows a bibliometric analysis approach, com-
bined with thematic analysis. Bibliometric analysis as a research method has become more
prominent in academic research and allows for the analysis of large quantities of biblio-
metric information to identify themes and trends [8,26]. Thematic analysis is the process
of “identifying, analyzing, and reporting patterns (themes) within data” [57].
A five-step process was followed for the bibliometric analysis, presented graphically
in Figure 1 below:
Figure 1. Analysis process.
The bibliometric process incorporates an approach recommended by Donthu et al.
[58] for bibliometric analysis, which begins with the definition of the research aim. This
paper applies RStudio and Biblioshiny, which are powerful bibliometric tools for analyz-
ing bibliographic data to identify themes and core relationships [59].
3.1. Research Aim Definition
As a first step, the aim of the review is defined. The aim of the review is to use bibli-
ometric mapping and analysis techniques, as well as thematic analysis, to infer and de-
velop a clear thematic and conceptual framework relating to the intersection of AI with
ML, greenwashing, and sustainability reporting, and specifically, for the use of AI with
ML in relation to greenwashing and sustainability reporting.
The scope of the bibliometric review is sufficiently large [58] when taking into ac-
count the impact of AI with ML as fields within the literature and practice, the importance
of sustainability and sustainability reporting, and the potential negative impact of green-
washing on both sustainability and sustainability reporting.
Thematic analysis is used to generate additional insights for the corpora analyzed.
Similarly, where the results of a defined query return a literature corpus that is too small
for bibliometric analysis, thematic analysis is applied.
Figure 1. Analysis process.
The bibliometric process incorporates an approach recommended by Donthu et al. [
58
]
for bibliometric analysis, which begins with the definition of the research aim. This paper
applies RStudio and Biblioshiny, which are powerful bibliometric tools for analyzing
bibliographic data to identify themes and core relationships [59].
3.1. Research Aim Definition
As a first step, the aim of the review is defined. The aim of the review is to use
bibliometric mapping and analysis techniques, as well as thematic analysis, to infer and
develop a clear thematic and conceptual framework relating to the intersection of AI with
ML, greenwashing, and sustainability reporting, and specifically, for the use of AI with ML
in relation to greenwashing and sustainability reporting.
The scope of the bibliometric review is sufficiently large [
58
] when taking into account
the impact of AI with ML as fields within the literature and practice, the importance of sus-
tainability and sustainability reporting, and the potential negative impact of greenwashing
on both sustainability and sustainability reporting.
Thematic analysis is used to generate additional insights for the corpora analyzed.
Similarly, where the results of a defined query return a literature corpus that is too small
for bibliometric analysis, thematic analysis is applied.
3.2. Initial Review of Literature for Keyword Identification
As a second step, in August 2022, a review of papers was conducted relating to:
•sustainability reporting;
•AI with ML;
•greenwashing.
This was done to gain a better understanding and overview of each of these fields
within the research topic context, with the aim of constructing a robust research
search query.
To identify keywords used in search queries in academic literature databases, ex-
tant literature often relies on author perceptions, author brainstorming [
8
], or limited
initial investigations. Such analyses also typically do not provide information on how
initial literature was selected, providing a lack of transparency on the identification of

Sustainability 2023,15, 1481 7 of 25
keywords, as well as whether search terms, which directly affect bibliometric analysis
results, are comprehensive.
The goal is to conduct an initial review in a transparent and replicable manner that
provides a high probability of identifying the correct corpus of literature to be analyzed
as part of the bibliometric analysis. This approached is referred to as ‘purposeful’: the
review and search methodology is a purposeful review, the selection of keywords and
sample selection is a purposeful selection, and the review of the results of the bibliometric
procedures focuses on the research aim. This is because the goal is purposeful navigation
of literature to ensure the research aim is met.
To begin the purposeful review, bibliometric review literature was identified relating
to each field separately to ensure a more global overview of the existing literature in each
field. Data were obtained from Scopus, considered to be the most comprehensive database
of peer-reviewed literature in these fields [60].
The following initial search queries were constructed and run in the Scopus database,
using the operator “TITLE-ABS-KEY” to identify the search terms in the titles, abstracts, or
keywords of documents in the database:
The titles and abstracts in the search results returned by the search strings in Table 1
below were then reviewed to select a sample of appropriate and relevant bibliometric or
systematic literature reviews in each field. The focus of the initial review was on the most
recent literature reviews to ensure an up-to-date view of each field, as well as on reviews
that were more global, encapsulating a broad range of bibliometric data, rather than reviews
that focused on a specialized discipline or niche (e.g., AI in the field of oncology). The
literature selected for review consists of nine papers, and is presented in Table 2below.
Table 1. Initial search queries.
Topic Search String
Artificial intelligence
TITLE-ABS-KEY ((“artificial intelligence” AND
“machine learning”) AND (“bibliometric”))
Sustainability reporting
TITLE-ABS-KEY ((“sustainability report*” OR
“csr report*”) AND (“literature review”
OR “bibliometric”))
Greenwashing
TITLE-ABS-KEY ((“greenwashing*” OR
“greenwash*” OR “green claim “) AND
(“literature review” OR “bibliometric”))
Table 2. Initial literature selection.
Bibliometric Literature:
Artificial Intelligence and ML
(Title, Reference)
Bibliometric Literature:
Sustainability Reporting
(Title, Reference)
Bibliometric Literature:
Greenwashing
(Title, Reference)
Global bibliometric mapping of the
frontier of knowledge in the field of
artificial intelligence for the period
1990–2019, [11]
Providing a Roadmap for Future Research
Agenda: A Bibliometric Literature Review
of Sustainability Performance Reporting
(SPR), [2]
Concepts and forms of greenwashing: a
systematic review, [5]
Artificial intelligence and machine
learning in finance: Identifying
foundations, themes, and research
clusters from bibliometric analysis, [8]
Mapping the literature on sustainability
reporting: A bibliometric analysis
grounded in Scopus and web of science
core collection, [61]
What has been (short) written about
greenwashing: A bibliometric research
and a critical analysis of the articles
found regarding this theme, [39]
Approaching Artificial Intelligence in
business and economics research: a
bibliometric panorama (1966–2020), [
62
]
Non-financial reporting research and
practice: Lessons from the last decade, [26]
Greenwashing behaviours: Causes,
taxonomy and consequences based on a
systematic literature review, [49]
Each of these literature reviews were read to identify keywords commonly used and
relevant to each field and the aim of the study. Tables 3–5below illustrate the keywords

Sustainability 2023,15, 1481 8 of 25
identified for each field, with the letter “X” used to indicate keywords identified for a
specific paper within the selected literature for each field:
Table 3. Identified keywords: AI with ML literature.
Reference Artificial
Intelligence Big Data Machine
Learning
Natural
Language
Processing
[11] X X X X
[8] X X X X
[62] X X X
Table 4. Identified keywords: Sustainability reporting literature.
Reference CSR Reporting Sustainability
Reporting
Non-Financial
Reporting ESG Reporting
[2] X X
[61] X
[26] X X X X
Table 5. Identified keywords: Greenwashing literature.
Reference Greenwashing Green Claim
[5] X X
[39] X
[49] X
The identified keywords then informed the construction of three combined intermedi-
ate Scopus search queries to return literature for the intersections of each of the relevant
fields i.e., the intersections of:
1. Sustainability reporting and AI with ML
2. Sustainability reporting and greenwashing
3. Greenwashing and AI with ML
These intersections are depicted graphically in Figure 2below:
Queries were defined for these intersections to better understand the interplay of
literature relating to each one.
3.2.1. Intersection 1: Sustainability Reporting and AI with ML
The search for this intersection was limited to results from 2018–2022, given the rapidly
evolving nature of each field, and to ensure identification of the most up-to-date keywords.
The search was also limited to articles, conference papers, and reviews in English. The
intermediate query applied in Scopus for the intersection of sustainability reporting and
AI with ML is as follows, and is presented in the format proposed by Goodell et al. [
8
] in
Table 6below:

Sustainability 2023,15, 1481 9 of 25
Sustainability 2023, 15, x FOR PEER REVIEW 9 of 27
3. Greenwashing and AI with ML
These intersections are depicted graphically in Figure 2 below:
Intersection 1: Sustainability reporting and AI
and ML
Intersection 2: Sustainability reporting and
greenwashing
Intersection 3: AI and ML and greenwashing
Figure 2. Graphical depiction of field intersections.
Queries were defined for these intersections to better understand the interplay of lit-
erature relating to each one.
3.2.1. Intersection 1: Sustainability Reporting and AI with ML
The search for this intersection was limited to results from 2018–2022, given the rap-
idly evolving nature of each field, and to ensure identification of the most up-to-date key-
words. The search was also limited to articles, conference papers, and reviews in English.
The intermediate query applied in Scopus for the intersection of sustainability reporting
and AI with ML is as follows, and is presented in the format proposed by Goodell et al.
[8] in Table 6 below:
Table 6. Intermediate search criteria: Intersection 1.
Filtering and Search Criteria
Database: Scopus
Search date: 14 October 2022
Search term:
TITLE-ABS-KEY ((“machine learning” OR “AI” OR “artificial intelligence”) AND (“sus-
tainability report*” OR “non-financial report*” OR “ESG report*” OR “CSR report”))
Year: 2018–2022
Document type: ‘‘Articles’’, “Conference paper”, “Review”
Language screening: Only English language documents included
Relevance screening: Articles selected for inclusion only where ‘‘Titles, abstracts, and
keywords’’ are relevant to review aim (i.e., sustainability reporting and AI)
Number of items returned by query: 20
Figure 2. Graphical depiction of field intersections.
Table 6. Intermediate search criteria: Intersection 1.
Filtering and Search Criteria
Database: Scopus
Search date: 14 October 2022
Search term:
TITLE-ABS-KEY ((“machine learning” OR “AI” OR “artificial intelligence”) AND (“sustainability
report*” OR “non-financial report*” OR “ESG report*” OR “CSR report”))
Year: 2018–2022
Document type: “Articles”, “Conference paper”, “Review”
Language screening: Only English language documents included
Relevance screening: Articles selected for inclusion only where “Titles, abstracts, and keywords”
are relevant to review aim (i.e., sustainability reporting and AI)
Number of items returned by query: 20
Number of items selected for review: 14
Of the twenty results returned by the query, a sample of fourteen was selected based
on a review of each title and abstract for relevance. Each of these fourteen items were
then read in order to identify additional relevant keywords relating to the review aim.
Table 7below reflects the relevant keywords identified in those fourteen items relating to
the intersection of the fields of sustainability reporting and AI. The letter “X” within the
table denotes keywords identified for a particular title.

Sustainability 2023,15, 1481 10 of 25
Table 7. Intermediate search keywords identified: Intersection 1.
Title
Artificial Intelligence
Big Data
Machine Learning
Natural Language Processing
Text Mining
Deep Learning
CSR Reporting
Sustainability Reporting
Non-Financial Reporting
ESG Reporting
1
Performance Evaluation of the
Implementation of the 2013/34/EU Directive
in Romania on the Basis of Corporate Social
Responsibility Reports
X X
2
Past, present, and future of sustainable
finance: insights from big data analytics
through machine learning of
scholarly research
X X X X
3A framework for evaluating and disclosing
the ESG related impacts of AI with the SDGs
X X X X X X
4Society 5.0 as a Contribution to the
Sustainable Development Report X X X X
5
Artificial intelligence activities and ethical
approaches in leading listed companies in
the European Union
X X X X X X X
6
Who Are the Intended Users of CSR Reports?
Insights from a Data-Driven Approach XXXXX XX
7
Identifying Corporate Sustainability Issues
by Analyzing Shareholder Resolutions: A
Machine-Learning Text Analytics Approach
X X X X X
8
Interrelation between the climate-related
sustainability and the financial reporting
disclosures of the European
automotive industry
X X X
9Fundamental ratios as predictors of ESG
scores: a machine learning approach X X X
10 Sentiment analysis of CSR disclosures in
annual reports of EU companies X X X
11
Natural Language Processing Methods for
Scoring Sustainability Reports—A Study of
Nordic Listed Companies
X X X X X X
12
Incorporating ESG in Decision Making for
Responsible and Sustainable Investments
using Machine Learning
X X X X X X
13
Develop CSR Themes using Text-Mining and
Topic Modelling Techniques X X X X X
14
Classification of CSR Using Latent Dirichlet
Allocation and Analysis of the Relationship
Between CSR and Corporate Value
X X X
3.2.2. Intersection 2: Sustainability Reporting and Greenwashing
The process was then repeated for each of the other intersections. That systematic
process is summarized in Tables 8–10 below:

Sustainability 2023,15, 1481 11 of 25
Table 8. Intermediate search criteria: Intersection 2.
Filtering and Search Criteria
Database: Scopus
Search date: 14 October 2022
Search term:
(TITLE-ABS-KEY((“sustainability report*” OR “ESG report*” OR “CSR report” OR “non-financial
report*”) AND (“greenwash*” OR “green claim”))
Year: 2018–2022
Document type: “Articles”, “Conference paper”, “Review”
Language screening: Only English language documents included
Relevance screening: Articles selected for inclusion only where “Titles, abstracts, and keywords”
are relevant to review aim (i.e., sustainability reporting and greenwashing)
Number of items returned by query: 39
Number of items selected for review: 15
Table 9. Intermediate search keywords identified: Intersection 2.
Item Title Sustainability
Disclosure
ESG
Disclosure
1CSR Performance and the Readability of CSR
Reports: Too Good to be True?
2Greenwashing in environmental, social and
governance disclosures X X
3
Is corporate social responsibility reporting a tool
of signaling or greenwashing? Evidence from the
worldwide logistics sector
X
4Green brand of companies and greenwashing
under sustainable development goals
5
CSR achievement, reporting, and assurance in the
energy sector: Does economic
development matter?
X
6Corporate social responsibility disclosure level,
external assurance and cost of equity capital X
7
Past, present, and future of sustainable finance:
insights from big data analytics through machine
learning of scholarly research
X
8
The relationship between non-financial reporting,
environmental strategies and financial
performance. Empirical evidence from Milano
stock exchange
9The greenwashing triangle: adapting tools from
fraud to improve CSR reporting
10
Is Femvertising the New Greenwashing?
Examining Corporate Commitment to
Gender Equality
11 Mapping corporate climate change ethics:
Responses among three Danish energy firms
12 Does internal control contribute to a firm’s green
information disclosure? Evidence from China X
13
Green, blue or black, but washing–What company
characteristics determine greenwashing? X
14
Representative account or greenwashing?
Voluntary sustainability reports in Australia’s
mining/metals and financial services industries
X
15
Sustainable grocery retailing: Myth or reality?—A
content analysis X

Sustainability 2023,15, 1481 12 of 25
Table 10. Intermediate search criteria: Intersection 3.
Filtering and Search Criteria
Database: Scopus
Search date: 14 October 2022
Search string:
TITLE-ABS-KEY (“greenwash*” OR “green claim”) AND (“machine learning” OR “AI” OR
“artificial intelligence”)
Year: ALL
Document type: “Articles”, “Conference paper”, “Review”
Language screening: Only English language documents included
Relevance screening: Articles selected for inclusion only where “Titles, abstracts, and keywords”
are relevant to review aim (i.e., greenwashing and AI)
Number of items returned by query: 9
Number of items selected for review: 9
Of the thirty-nine results returned by the query, a sample of fifteen was selected
based on a review of each title and abstract for relevance. Each of these fifteen items were
then read in order to identify additional relevant keywords relating to the review aim.
Table 9below reflects the relevant keywords identified in those fifteen items relating to
the intersection of the fields of sustainability reporting and greenwashing. It is important
to note that the table illustrates only additional keywords identified in the fifteen items
selected for review. Where keywords were previously identified for example, from the
initial search queries, or the intermediate search query relating to sustainability reporting
and AI described above, these are not duplicated here. The letter “X” within the table
denotes keywords identified for a particular title.
3.2.3. Intersection 3: Greenwashing and AI with ML
Of the nine results returned by the query shown in Table 10, nine were selected based
on a review of each title and abstract for relevance. Each of these nine items were then
read in order to identify additional relevant keywords relating to the review aim. No new
keywords were identified within the review.
3.3. Scopus Search Query Definition
The third step involved defining the final search queries to be used within the bib-
liometric analysis, for each field intersection. Based on the initial literature review and
keyword analysis a list of keywords was created.
The identified list of keywords was further enriched by analyzing the queries used in
the following bibliometric literature identified in the initial review:
•Turzo et al. [26] relating to non-financial and sustainability reporting;
•Goodell et al. [8] relating to artificial intelligence and machine learning;
•De Freitas Netto et al. [5] relating to greenwashing.
Each query was optimized based on Scopus guidelines to consider the impact of
Boolean operators, punctuation, wildcards, and braces in arriving at the final search query.
The query for each binary intersection was defined sufficiently broadly, based on the
initial review, to make provision for differences in terminology, synonyms, and definitions
used and identified in the initial literature review. Consistent with the research aim, a final
query for the intersection of all three fields was also defined. The defined search query for
each intersection is shown in Table 11 below:

Sustainability 2023,15, 1481 13 of 25
Table 11. Final search queries.
# Intersection Description Intersection Defined Query
1 Sustainability reporting and AI with ML
(“non financ* report*” OR “sustainab* disclo*” OR “environment* report*” OR
“CSR report*” OR “CSR disclo*” OR “corporate social responsibility report*”
OR “corporate social responsibility disclo*” OR “sustainab* report*” OR
“responsib* report*” OR “ESG report*” OR “TBL report*” OR “triple* report*”
OR “GHG report*” OR “greenhouse gas report*” OR “integr* report*” OR
“corporate citizenship report*” OR “SDG* report*” OR “sustainable
development goal* report*” OR “carbon report*” OR “social report*”) AND
(“machine learning” OR “AI” OR “artificial intelligence” OR “natural
language processing” OR “text mining” OR “algorithm” OR “soft computing”
OR “data mining” OR “big data” OR “robot” OR “automation” OR “analytics”
OR “deep learning”)
2Sustainability reporting
and greenwashing
(“non financ* report*” OR “sustainab* disclo*” OR “environment* report*” OR
“CSR report*” OR “CSR disclo*” OR “corporate social responsibility report*”
OR “corporate social responsibility disclo*” OR “sustainab* report*” OR
“responsib* report*” OR “ESG report*” OR “TBL report*” OR “triple* report*”
OR “GHG report*” OR “greenhouse gas report*” OR “integr* report*” OR
“corporate citizenship report*” OR “SDG* report*” OR “sustainable
development goal* report*” OR “carbon report*” OR “social report*”) AND
(“greenwashing*” OR “greenwash*” OR “green claim”)
3AI with ML
and greenwashing
(“machine learning” OR “AI” OR “artificial intelligence” OR “natural language
processing” OR “text mining” OR “algorithm” OR “soft computing” OR “data
mining” OR “big data” OR “robot” OR “automation” OR “analytics” OR
“deep learning”) AND (“greenwashing*” OR “greenwash*” OR “green claim”)
4Greenwashing, sustainability reporting,
and AI with ML
(“greenwashing*” OR “greenwash*” OR “green claim”) AND (“non financ*
report*” OR “sustainab* disclo*” OR “environment* report*” OR “CSR report*”
OR “CSR disclo*” OR “corporate social responsibility report*” OR “corporate
social responsibility disclo*” OR “sustainab* report*” OR “responsib* report*”
OR “ESG report*” OR “TBL report*” OR “triple* report*” OR “GHG report*”
OR “greenhouse gas report*” OR “integr* report*” OR “corporate citizenship
report*” OR “SDG* report*” OR “sustainable development goal* report*” OR
“carbon report*” OR “social report*”)
3.4. Bibliographic Data Collection
For the fourth step, the final defined query for each intersection was run in the
Scopus database.
Intersection 1: Sustainability Reporting and AI with ML
Table 12 illustrates the structured procedure followed to select the population of
documents for each intersection.
Table 12. Document selection process per intersection.
Intersection. 1 2 3 4
Filtering and Search
Criteria Reject Accept Reject Accept Reject Accept Reject Accept
Database: Scopus
Search date:
14 October 2022
Search string results: 296 89 17 2
Year: 2003–2023 10 286 1 88 0 17 0 2
Document type:
“Articles”, “Conference
paper”, “Review”
27 259 12 76 1 16 0 2

Sustainability 2023,15, 1481 14 of 25
Table 12. Cont.
Intersection. 1 2 3 4
Filtering and Search
Criteria Reject Accept Reject Accept Reject Accept Reject Accept
Language screening: Only
English language
documents included
4 255 0 76 0 16 0 2
Relevance screening:
Articles selected for
inclusion only where
“Titles, abstracts, and
keywords” are relevant to
intersection and
review aim
94 160 0 76 0 14 0 2
4. Results and Discussion
4.1. Summary Information
Summary information provides an overview of the literature analyzed, which is an
important element in understanding the corpus. The basic statistics returned relating to
the distinct literature items analyzed, for each of the three intersections, are presented in
Table 13 below.
Table 13. Summary statistics per intersection.
Description
Intersection 1
Sustainability Reporting
and AI and ML
Intersection 2
Greenwashing and
Sustainability Reporting
Intersection 3
Greenwashing and AI
and ML
Intersection 4
Greenwashing,
Sustainability Reporting
and AI and ML
MAIN INFORMATION
ABOUT DATA
Timespan 2004:2022 2003:2022 2016:2022 2022:2022
Sources (Journals,
Books, etc.) 121 61 15 2
Documents 160 76 16 2
Annual Growth Rate % 14.25 14.45 38.31 0.00
Document Average Age 4.28 3.51 1.25 0.00
Average citations per doc 11.66 31.97 8.06 8
References 8098 5691 1127 228
DOCUMENT CONTENTS
Keywords Plus (ID) 876 156 143 17
Author’s Keywords (DE) 528 278 90 19
AUTHORS
Authors 417 179 45 10
Authors of
single-authored docs 16 18 4 0
AUTHORS
COLLABORATION
Single-authored docs 16 18 4 0
Co-Authors per Doc 2.96 2.54 2.94 5.00
International
co-authorships % 19.38 28.95 18.75 50.00
DOCUMENT TYPES
article 100 72 10 2
conference paper 54 2 4 0
review 6 2 2 0
The literature identified relating to Intersection 4 is too small a corpus for bibliometric
analysis, and is therefore analyzed separately in 4.5 below using thematic analysis.

Sustainability 2023,15, 1481 15 of 25
4.2. Evolution—Publications
Figure 3below illustrates a summary graph of the publication trends for the three
intersections:
Sustainability 2023, 15, x FOR PEER REVIEW 17 of 27
Figure 3. Publication trend per intersection.
4.2.1. Intersection 3
The highest growth rate is found in the literature for Intersection 3, the greenwashing
and AI with ML intersection, with a 38.31% annual average growth rate. This corpus co-
vers a shorter time period, however, with publications for this intersection only occurring
from 2016, and is a smaller corpus, with only 16 total articles during that timespan. The
relative size and timespan of the literature in this corpus reflects the emerging and nascent
nature of research pertaining to artificial intelligence and ML within the field of green-
washing.
4.2.2. Intersection 2
Within Intersection 2, the intersection of greenwashing and sustainability reporting,
an upward trend is also observed, with some fluctuations during the time period ana-
lyzed. This intersection reflects an annual average growth rate of 14.45%, over a much
longer timespan than the greenwashing and AI intersection, from 2003 to 2022.
The number of publications relating to this research field intersection has increased
consistently since the beginning of the analysis period. This upward trend may be under-
stood with reference to three distinct periods.
The first, for the period 2003 to 2016, saw a limited increase in the literature in the
research field, with only 17 publications within 14 years, an average of only 1.21 publica-
tions per year. The second period, from 2017 to 2019 saw faster growth, with 13 publica-
tions in 3 years at an average of 4.33 publications per year, 3.5 times more than the prior
period. The third period has seen the most rapid growth, with 46 publications from 2020
to October 2022. The highest number of publications in a specific period to date occurs in
2022.
Comparing the number of publications from the first to the second period illustrates
that the average number of publications per year was 1.21 in the first period, 4.33 in the
second, and 15.33 in the last. When considering the upward trend from 2017 onward, this
may be understood with reference to the Paris Agreement on Climate Change, which con-
cluded in late 2015, with the increase in academic literature relating to this intersection
then trending upward from 2017, as academic literature relating to both sustainability re-
porting and greenwashing was published.
Figure 3. Publication trend per intersection.
4.2.1. Intersection 3
The highest growth rate is found in the literature for Intersection 3, the greenwashing
and AI with ML intersection, with a 38.31% annual average growth rate. This corpus covers
a shorter time period, however, with publications for this intersection only occurring from
2016, and is a smaller corpus, with only 16 total articles during that timespan. The relative
size and timespan of the literature in this corpus reflects the emerging and nascent nature
of research pertaining to artificial intelligence and ML within the field of greenwashing.
4.2.2. Intersection 2
Within Intersection 2, the intersection of greenwashing and sustainability reporting,
an upward trend is also observed, with some fluctuations during the time period analyzed.
This intersection reflects an annual average growth rate of 14.45%, over a much longer
timespan than the greenwashing and AI intersection, from 2003 to 2022.
The number of publications relating to this research field intersection has increased
consistently since the beginning of the analysis period. This upward trend may be under-
stood with reference to three distinct periods.
The first, for the period 2003 to 2016, saw a limited increase in the literature in the
research field, with only 17 publications within 14 years, an average of only 1.21 publications
per year. The second period, from 2017 to 2019 saw faster growth, with 13 publications in
3 years at an average of 4.33 publications per year, 3.5 times more than the prior period.
The third period has seen the most rapid growth, with 46 publications from 2020 to October
2022. The highest number of publications in a specific period to date occurs in 2022.
Comparing the number of publications from the first to the second period illustrates
that the average number of publications per year was 1.21 in the first period, 4.33 in the
second, and 15.33 in the last. When considering the upward trend from 2017 onward,

Sustainability 2023,15, 1481 16 of 25
this may be understood with reference to the Paris Agreement on Climate Change, which
concluded in late 2015, with the increase in academic literature relating to this intersection
then trending upward from 2017, as academic literature relating to both sustainability
reporting and greenwashing was published.
4.2.3. Intersection 1
The Intersection 1 publication trend illustrates a high average annual growth rate of
14.25%, close to that of Intersection 2 (14.45%). This trend is for the period from 2004 to
2022, whereas the trend for Intersection 2 is for slightly longer, from 2003 to 2022.
Though Intersection 1
0
s growth rate is marginally lower than that of Intersection 2,
it relates to a much larger corpus of academic literature than both Intersection 2 and
Intersection 3, with 160 documents published during that time period. This upward trend
may be also understood with reference to three distinct periods.
The first, for the period 2004 to 2016, saw a limited increase in literature in the research
field, with only 47 of the total 160 publications occurring within those 13 years, an average
of only 3.62 publications per year. The second period, from 2017 to 2019 saw faster growth,
with 40 publications in only 3 years. The third period has seen the most rapid growth, with
73 publications from 2020 to October 2022. The highest number of publications in a specific
period to date occurs in 2021.
Comparing the number of publications from the first to the second period illustrates
that the average number of publications per year was 3.62 in the first period, 13.33 in the
second, and 24.33 in the last.
The key publication trend statistics across each intersection are summarized in
Table 14 below.
Table 14. Publication trend summary statistics.
# Intersection Average Annual
Growth Rate
Size of Corpus
(# of Documents) Timespan Document
Average Age
1Sustainability reporting
and AI with ML 14.25% 160 2003:2022 4.28
2Greenwashing and
sustainability reporting 14.45% 76 2003:2022 3.51
3Greenwashing and AI
with ML 38.31% 16 2016:2022 1.25
The relative timespan for literature for each intersection reflects that research within
Intersection 3 is an emerging field, with research in this field only being published since
2016. The larger corpus of literature relating to Intersection 1, for the timespan from 2004 to
2022, reflects that research within the intersection is more mature and established than that
both Intersections 2 and 3. This is supported by Intersection 1 having the highest average
document age amongst the three intersections.
The above analysis reflects the relative maturity of the academic literature relating to
Intersections 1 and 2 in relation to Intersection 3. It also reveals that literature relating to
the use of AI with ML within the field of greenwashing, Intersection 3, has only emerged
recently and is in its nascent stages both in terms of age and volume of publications.
4.3. Evolution—Journal Publications
Figures 4–6below show top journal sources for Intersections 1 to 3:

Sustainability 2023,15, 1481 17 of 25
Sustainability 2023, 15, x FOR PEER REVIEW 19 of 27
Figure 4. Top sources: Sustainability reporting and AI with ML.
Figure 5. Top sources: Sustainability reporting and greenwashing.
Figure 6. Top sources: Greenwashing and AI with ML.
Figure 4. Top sources: Sustainability reporting and AI with ML.
Sustainability 2023, 15, x FOR PEER REVIEW 19 of 27
Figure 4. Top sources: Sustainability reporting and AI with ML.
Figure 5. Top sources: Sustainability reporting and greenwashing.
Figure 6. Top sources: Greenwashing and AI with ML.
Figure 5. Top sources: Sustainability reporting and greenwashing.
Sustainability 2023, 15, x FOR PEER REVIEW 19 of 27
Figure 4. Top sources: Sustainability reporting and AI with ML.
Figure 5. Top sources: Sustainability reporting and greenwashing.
Figure 6. Top sources: Greenwashing and AI with ML.
Figure 6. Top sources: Greenwashing and AI with ML.
Across all of the intersections, the journal “Sustainability (Switzerland)” is the top
source journal for documents.

Sustainability 2023,15, 1481 18 of 25
4.4. Research Trends and Themes
In order to identify research trends, Biblioshiny’s document keyword functionality is
used, based on “Keywords Plus”. Keywords Plus are “words or phrases that frequently
appear in the titles of the article’s references and not necessarily in the article’s title or
as Author Keywords” [
60
]. Zhang et al. [
63
] suggest a number of advantages to using
Keywords Plus and recommend that Keywords Plus be used for bibliometric analyses.
4.4.1. Highest Occurrence Keywords
Intersection 1: Sustainability Reporting and AI with ML
Figure 7below illustrates the top 10 keywords, based on Keywords Plus and ranked
according to frequency of occurrence for Intersection 1. The high number of occurrences of
the terms ‘data mining’ (32), ‘text mining’ (17), and ‘big data’ (11) reflects the relevance of
these methods within both sustainable development and sustainability as broader themes,
as well as within sustainability reporting and corporate social responsibility, which are also
keywords identified within the ranking.
Sustainability 2023, 15, x FOR PEER REVIEW 20 of 27
Across all of the intersections, the journal “Sustainability (Switzerland)” is the top
source journal for documents.
4.4. Research Trends and Themes
In order to identify research trends, Biblioshiny’s document keyword functionality is
used, based on “Keywords Plus”. Keywords Plus are “words or phrases that frequently
appear in the titles of the article’s references and not necessarily in the article’s title or as
Author Keywords” [60]. Zhang et al. [63] suggest a number of advantages to using Key-
words Plus and recommend that Keywords Plus be used for bibliometric analyses.
4.4.1. Highest Occurrence Keywords
Intersection 1: Sustainability Reporting and AI with ML
Figure 7 below illustrates the top 10 keywords, based on Keywords Plus and ranked
according to frequency of occurrence for Intersection 1. The high number of occurrences
of the terms ‘data mining’ (32), ‘text mining’ (17), and ‘big data’ (11) reflects the relevance
of these methods within both sustainable development and sustainability as broader
themes, as well as within sustainability reporting and corporate social responsibility,
which are also keywords identified within the ranking.
Figure 7. Intersection 1 keyword ranking based on Keywords Plus and number of occurrences.
Intersection 2: Greenwashing and Sustainability Reporting
The keyword ranking for Intersection 2 shown in Figure 8 below provides similar
insights, showing within the smaller corpus the highest occurrence of terms relates to sus-
tainability, sustainable development, and CSR, with greenwashing also identified within
the ranking.
Figure 7. Intersection 1 keyword ranking based on Keywords Plus and number of occurrences.
Intersection 2: Greenwashing and Sustainability Reporting
The keyword ranking for Intersection 2 shown in Figure 8below provides similar
insights, showing within the smaller corpus the highest occurrence of terms relates to
sustainability, sustainable development, and CSR, with greenwashing also identified within
the ranking.
Intersection 3: Greenwashing and AI with ML
Within Intersection 3, ‘data mining’, ‘regression analysis’, and ‘artificial intelligence’
are highly-ranked keywords, as are ‘greenwashing’, ‘environmental monitoring’, and
related terms such as ‘air pollution’ and ‘anomaly detection’, as shown in Figure 9below.
4.5. Thematic Analysis
As the research aims relates to understanding the use of AI with ML in relation to
sustainability reporting and greenwashing, a thematic analysis was conducted to consider
the use of AI with ML within the literature identified. This was done by reviewing the titles
and abstracts within each intersection corpus to identify documents which indicate the use
of AI or ML tools within their research methodologies.

Sustainability 2023,15, 1481 19 of 25
Sustainability 2023, 15, x FOR PEER REVIEW 21 of 27
Figure 8. Intersection 2 keyword ranking based on Keywords Plus and number of occurrences.
Intersection 3: Greenwashing and AI with ML
Within Intersection 3, ‘data mining’, ‘regression analysis’, and ‘artificial intelligence’
are highly-ranked keywords, as are ‘greenwashing’, ‘environmental monitoring’, and re-
lated terms such as ‘air pollution’ and ‘anomaly detection’, as shown in Figure 9 below.
Figure 9. Intersection 3 keyword ranking based on Keywords Plus and number of occurrences.
4.5. Thematic Analysis
As the research aims relates to understanding the use of AI with ML in relation to
sustainability reporting and greenwashing, a thematic analysis was conducted to consider
the use of AI with ML within the literature identified. This was done by reviewing the
titles and abstracts within each intersection corpus to identify documents which indicate
the use of AI or ML tools within their research methodologies.
4.5.1. Intersection 1: Sustainability Reporting and AI with ML
The thematic analysis of this intersection reveals that 82 of the 160 documents
(51.25%) within this corpus use AI or ML as a methodological tool. From this result, it may
be inferred that a significant amount of research within this field applies AI with ML as a
Figure 8. Intersection 2 keyword ranking based on Keywords Plus and number of occurrences.
Sustainability 2023, 15, x FOR PEER REVIEW 21 of 27
Figure 8. Intersection 2 keyword ranking based on Keywords Plus and number of occurrences.
Intersection 3: Greenwashing and AI with ML
Within Intersection 3, ‘data mining’, ‘regression analysis’, and ‘artificial intelligence’
are highly-ranked keywords, as are ‘greenwashing’, ‘environmental monitoring’, and re-
lated terms such as ‘air pollution’ and ‘anomaly detection’, as shown in Figure 9 below.
Figure 9. Intersection 3 keyword ranking based on Keywords Plus and number of occurrences.
4.5. Thematic Analysis
As the research aims relates to understanding the use of AI with ML in relation to
sustainability reporting and greenwashing, a thematic analysis was conducted to consider
the use of AI with ML within the literature identified. This was done by reviewing the
titles and abstracts within each intersection corpus to identify documents which indicate
the use of AI or ML tools within their research methodologies.
4.5.1. Intersection 1: Sustainability Reporting and AI with ML
The thematic analysis of this intersection reveals that 82 of the 160 documents
(51.25%) within this corpus use AI or ML as a methodological tool. From this result, it may
be inferred that a significant amount of research within this field applies AI with ML as a
Figure 9. Intersection 3 keyword ranking based on Keywords Plus and number of occurrences.
4.5.1. Intersection 1: Sustainability Reporting and AI with ML
The thematic analysis of this intersection reveals that 82 of the 160 documents (51.25%)
within this corpus use AI or ML as a methodological tool. From this result, it may be
inferred that a significant amount of research within this field applies AI with ML as a
methodological tool. Below is a list of AI with ML methodological tool descriptions, as
identified within those 82 documents:
•Algorithms;
•Artificial intelligence;
•Automated content analysis;
•Bibliometric analysis;
•Big data analysis;
•Data mining;
•Machine Learning;
•Machine Learning clustering algorithm;
•Machine learning using Multivariate Discriminant Analysis (MDA);
•Natural Language Processing;
•Natural Language Processing using Latent Dirichlet Allocation (LDA);

Sustainability 2023,15, 1481 20 of 25
•Random forest;
•Topic modeling;
•Text mining.
Note that within the list, bibliometric analysis is considered to be an AI with ML tool,
as this type of analysis relates to the use of machine learning within research [64].
These identified methodologies predominantly relate to the use of NLP in the analysis
of textual information relating to sustainability reporting. This reflects the maturity in the
use of NLP techniques in the analysis of sustainability reporting information.
4.5.2. Intersection 2: Greenwashing and Sustainability Reporting
The thematic analysis of this intersection reveals one article that uses AI or ML within
this corpus. However, this article is an article common to Intersections 1, 2, and 3. This
exemplifies the limited use of AI with ML, and NLP, as a methodological tool relating to
the research on greenwashing within the field of sustainability reporting. The identified
article is shown in Table 15 below:
Table 15. Intersection 2 common literature.
Title Year
Past, present, and future of sustainable finance:
insights from big data analytics through
machine learning of scholarly research
2022
4.5.3. Intersection 3: Greenwashing and AI with ML
Similar to Intersection 2, only one article that uses AI or ML as a methodological tool
is found within this corpus, and is the same article common to common to Intersections 1,
2, and 3. This further exemplifies the limited use of AI with ML as a methodological tool in
relating to the research on greenwashing. The identified article is shown in Table 16 below:
Table 16. Intersection 2 common literature.
Title Year
Past, present, and future of sustainable finance:
insights from big data analytics through
machine learning of scholarly research
2022
The thematic analysis above illustrates the relative maturity of the use of AI with ML as
methodological tools within the field of sustainability reporting (Intersection 1). However,
when looking at the corpus of the field of greenwashing within sustainability reporting
(Intersection 2), or AI with ML in relation to greenwashing (Intersection 3), limited use of
AI with ML as a methodological tool is found.
4.5.4. Intersection 4: Greenwashing, Sustainability Reporting and AI with ML
Intersection 4 reflects the intersection of greenwashing, sustainability reporting, and
AI with ML. In order to conduct the thematic analysis, the literature within this corpus
which consisted of two documents, shown in Table 17 below, was read.

Sustainability 2023,15, 1481 21 of 25
Table 17. Intersection 4 common literature.
Item # Title Year
1
Past, present, and future of
sustainable finance: insights
from big data analytics
through machine learning of
scholarly research
2022
2
Unsupervised neural
network-enabled
spatial-temporal analytics for
data authenticity under
environmental smart
reporting system
2022
Item 1 above is found within the corpora of Intersections 1 to 4. Item 1 shows the use
of AI with ML as a methodological tool. The aim of this item is a major review to provide
an overview of the field of sustainable finance by using machine learning [
64
]. This item
therefore, when considering the research aim, does not relate to the use of AI with ML in
relation to greenwashing or sustainability reporting.
Item 2, besides presenting a reporting system, also evaluates “the authenticity of
the data collected from IoT devices, considering human-made counterfeits on measuring
instruments for greenwashing” [
65
]. This article does therefore relate to the use of AI with
ML in relation to greenwashing or sustainability reporting.
The use of AI with ML in relation to greenwashing and sustainability reporting is
found in one article within Intersection 4. While that article accounts for half of this corpus
of two documents, identifying only a single instance of the use of AI with ML in relation
to greenwashing within sustainability reporting reflects that such use is underexplored in
the literature.
5. Conclusions
In this paper, a purposeful review of the literature was conducted using bibliometric
and thematic analysis of the intersections of the fields of greenwashing, sustainability
reporting, and AI with ML, respectively. The foundation of the review is bibliometric
analyses of the binary combinations of these fields, which we combine with a thematic
analysis of each of those intersections and of the intersection of all three fields. We introduce
additional insights by a thematic analysis of the methodological tools applied.
This paper presents two foundational contributions. The first is the application of
bibliometric and thematic analysis to comprehensively and holistically address the interre-
lationship between greenwashing, sustainability reporting, and AI with ML, an analysis
which is not present within extant literature. The second is the conjecture of a concep-
tual and thematic framework for the use of artificial intelligence and machine learning in
relation to greenwashing and company sustainability reporting.
The systematic methodological process followed for the analysis is designed to ensure
transparency and replicability, in a manner that provides scope for future use of a similar
process when analyzing the interrelationships between multiple fields.
Through the purposeful review, a number of trends and themes are identified.
First, the review identifies the emerging and nascent nature of the research relating to
the use of AI with ML within the field of greenwashing. This is apparent when considering
multiple maturity measures, such as the number of documents that relate to the intersection
of those two fields, as well as the average age and timespan of the documents within the
corpus. These maturity measures are in stark contrast to those for the literature analyzed
for the intersections of AI with ML and sustainability reporting, and greenwashing and
sustainability reporting, respectively. Both of those intersections reflect a body of research
that is significantly more mature for each maturity measure namely, corpus size, document

Sustainability 2023,15, 1481 22 of 25
average age, and timespan. The corpus for the intersection of AI with ML and sustainability
reporting is both the most mature and largest amongst the intersections.
Second, from a thematic perspective, clear keyword trends are identified relating to
foundational themes such as sustainability and sustainable development.
Third, important trends are observed relating to the high keyword occurrences of AI
with ML-related tools and techniques, such as data mining, text mining, big data, and
artificial intelligence. Those trends reflect the significance of AI with ML within the fields
of greenwashing and sustainability reporting, respectively.
Fourth, the significance of the use of AI with ML in the field of sustainability reporting
is supported by the thematic analysis which identifies maturity in the use of AI with ML
techniques in that field. The wide application of AI with ML tools such as NLP within the
field of sustainability reporting reflects its perceived usefulness by researchers.
Last, the mature or wide application of AI with ML techniques within the other
intersections is not found, and it may therefore be inferred that the use of AI with ML in
relation to greenwashing, and in relation to greenwashing within sustainability reporting,
is an underexplored research field. This finding is significant given the negative impacts of
greenwashing on sustainability and sustainable reporting, and the potential of AI and ML
to ameliorate such impacts.
5.1. Implications for Future Research
This paper provides a number of implications for future research.
Given the significance of the business and societal impact of greenwashing, future
research could explore the use of ML and NLP tools and techniques in relation to green-
washing generally or greenwashing within sustainability reporting specifically. The use of
such tools presents significant opportunities for identifying or combating greenwashing.
Such a study would likely contribute significant business and societal value in terms of
environmental, social, and economic impacts.
Future research may also relate to how insights from other fields that are more mature
may contribute to greenwashing research, given the emergent nature of research into the
intersection of AI and greenwashing. Such research could consider to what extent theories,
practices, themes, tools, taxonomies, or analyses from the fields of AI with ML or sus-
tainability reporting may have relevance to the study of greenwashing and greenwashing
behaviors. These may include, for example, research into more data-driven approaches to
sourcing and reporting on environmental information, or data authentication relating to
green claims and reported sustainability information.
5.2. Limitations
The study reflects a number of inherent limitations. We limit the study to academic
databases and academic literature. Practice-based literature may reflect different themes,
practices, or conclusions. Given the nature of the three fields analyzed, practice-based
literature may either lag or be ahead of academic literature in inferring a thematic or
conceptual structure for these intersections. We also note the rapidly evolving nature of
each of the three fields studied, which may affect the currency of the findings
Author Contributions:
W.M.: Conceptualization; Methodology; Validation; Formal analysis; Investi-
gation; Data Curation; Writing—Original Draft; Writing—Review and Editing; Visualization; Project
administration. A.T.: Conceptualization; Methodology; Validation; Formal analysis; Investigation;
Data Curation; Writing—Original Draft; Writing—Review and Editing; Visualization; Supervision;
Project administration. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.

Sustainability 2023,15, 1481 23 of 25
Data Availability Statement:
The data presented in this study are available upon request from the
corresponding author.
Acknowledgments:
The authors would like to acknowledge the professional support of the Univer-
sity of Johannesburg’s Johannesburg Business School.
Conflicts of Interest: The authors declare no conflict of interest.
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