Citation: Saxena, A.; Singh, R.;
Gehlot, A.; Akram, S.V.; Twala, B.;
Singh, A.; Montero, E.C.; Priyadarshi,
N. Technologies Empowered
Environmental, Social, and
Governance (ESG): An Industry 4.0
Landscape. Sustainability 2023, 15,
309. https://doi.org/10.3390/
su15010309
Academic Editors: Wen-Hsien Tsai,
Luís Miguel Pacheco and Mónica
Azevedo
Received: 24 November 2022
Revised: 18 December 2022
Accepted: 22 December 2022
Published: 25 December 2022
Copyright:
© 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Technologies Empowered Environmental, Social, and
Governance (ESG): An Industry 4.0 Landscape
Archana Saxena 1, Rajesh Singh 2,3, Anita Gehlot 2,3
, Shaik Vaseem Akram 2,4
, Bhekisipho Twala 5,*
,
Aman Singh 6,7, Elisabeth Caro Montero 6,8 and Neeraj Priyadarshi 9,*
1
Uttaranchal Institute of Management, Uttaranchal University, Dehradun 248007, Uttarakhand, India
2
Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
3
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico
4
Law College Dehradun, Uttaranchal University, Dehradun 248007, Uttarakhand, India
5
Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd., Pretoria West,
Pretoria 0183, South Africa
6
Higher Polytechnic School, Universidad Europea del Atlantico, C/Isabel Torres 21, 39011 Santander, Spain
7
Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
8
Department of Project Management, Universidade Internacional do Cuanza, Estrada Nacional 250,
Bairro Kaluapanda, Cuito EN250, Bié, Angola
9
Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
*
Correspondence: twalab@tut.ac.za (B.T.); neerajrjd@gmail.com (N.P.)
Abstract: Currently, sustainability is a vital aspect for every nation and organization to accomplish
Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG)
metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53%
of respondents in the BlackRock survey are concerned about the availability of low ESG data, which
is critical for determining the organization’s sustainability level. This obstacle can be overcome by
implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction,
transparency, authentication, and structured data. Based on the review of previous studies, it was
determined that only a few studies discussed the implementation of Industry 4.0 technologies for
ESG data and evaluation. The objective of the study is to discuss the signiﬁcance of ESG data and
report, which is used for the evaluation of the sustainability of an organization. In this regard,
the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artiﬁcial intelligence (AI),
blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the
progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes
that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG
data and suggest that these digital technologies have a crucial role in framing an accurate ESG report.
After analysis a few vital conclusions are drawn such as ESG investment has beneﬁted from AI
capabilities, which previously relied on self-disclosed, annualized company information that was
susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations
that can be implemented for future work.
Keywords: ESG; sustainability; SDGs; Industry 4.0; IoT; artiﬁcial intelligence; big data; ESG reporting
1. Introduction
The United Nations laid out the SDGs in 2015 to achieve global sustainability by
2030 [1]. States, individuals, and investors are increasingly aware that ﬁrms can play a
signiﬁcant role in addressing societal issues, and the world still is on track to meet the UN
SDGs for 2030 [2]. ESG factors are progressively being infused into investment and business
management because of their potential to develop superior productivity and longer-term
proﬁt growth [3]. The performance of any organization related to its contribution and
progress towards sustainability in terms of environmental, social, and governance can
Sustainability 2023, 15, 309. https://doi.org/10.3390/su15010309
https://www.mdpi.com/journal/sustainability

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be evaluated with the assistance of the ESG approach [4]. The author claimed that the
traditional Fama-French three-factor model, which establishes market, size, and value
components to describe average returns, is the foundation upon which the role of ESG is
built [5,6]
Figure 1 shows the ESG framework which consists of stakeholders and standards. The
ESG strategies and risk management will deploy change management through which value
creation monitoring so that the organization can identify and assess ESG risks, it will help
in futureprooﬁng and scenario planning [7,8]. Three main tensions are examined in the
study such as control and creativity, standardization and variation, and individual and
group [9].
 
Figure 1. ESG Framework.
Meanwhile, worldwide involvement in ESG investments is intensifying. Traditional
investment paradigms are gradually shifting to investments that emphasize environmental,
social, and corporate responsibility [10]. Currently, the ﬁeld of digital transformation is
limited in its analysis of sustainability factors. Along with this, there are several issues
with the contemporary approach to gathering and analyzing environmental data, which
is inadequate and irregular [11–13]. In addition, the diversity and complexity of envi-
ronmental controls can have a signiﬁcant impact on data accuracy, dependability, and
impartiality. Industry 4.0 has the potency to be a substantial possibility for assimilating
SDGs with advanced technology digital transformation and it can evaluate the ESG metrics
of an organization [14]. These industrial 4.0 technologies enable real-time energy man-
agement, waste management, automatic carbon footprint calculation, improving human
well-being, supply chain resiliency, real-time operational performance of production, and
so on [15]. According to the statistics, 53% of respondents to the BlackRock survey are
concerned about the availability of low-quality ESG data, which is impeding the adoption
of sustainable investing [16]. Based on this motivation, this study examined the different
previous studies that have addressed the role of Industry 4.0 for ESG. Ref [14] developed
a conceptual model of impact factors that integrates the notion of “Industry 4.0” to the
Morgan Stanley Capital ESG to examine vital difﬁculties in the energy sector. Ref [17]
applied computer-aided textual analysis was used to evaluate the implications of corporate
transformation toward Industry 4.0 (CTTI4.0) data on ﬁnancial effectiveness and observed

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that companies with stronger ESG performance are quite involved in CTTI4.0 disclosure
and have better ﬁnancial at the same time. Ref [18] developed an innovative approach to
empower Chinese energy companies will obtain real-time environmental data to improve
the efﬁciency of ESG reporting. According to the ﬁndings of these studies, there are a
limited number of studies that discuss the implementation of Industry 4.0 technologies
such as IoT, AI, blockchain, big data, and digital twins for ESG data. The study is ﬁrst to
discuss the signiﬁcance of Industry 4.0 enabling technologies for framing the appropriate
and accurate ESG data is the novelty of this study. This study has discussed future research
directions based on previous studies that focused on the ESG data and report.
The study has applied different keywords such as ESG, sustainability, sustainable
development goals, Industry 4.0, Internet of Things for ESG, AI for ESG, blockchain for
ESG, and big data for ESG, in the Scopus database by applying the Boolean operations
“AND” and “OR”. The articles which are obtained through the above methods are utilized
to carry out the study for presenting the signiﬁcance of Industry 4.0 for ESG in different
aspects. The contribution of the study is as follows:
•
In this study, the signiﬁcance of the ESG data and reporting for sustainability is
detailed and presented with concepts and terminology.
•
The integration of IoT, AI, blockchain, and big data for ESG are discussed with ar-
chitecture to provide an opportunity to investors for effective planning before the
investment.
•
The article discusses the vital recommendations that can be implemented for future
work.
The structure of the article is as follows: Section 2 covers the overview of ESG and
sustainability in detail; Section 3 presents the technology integration for ESG to obtain real-
time data, and enhance data transparency, and authentication with prediction; Section 4
addresses future recommendations.
2. Overview of ESG and Sustainability
In this section, we will discuss the signiﬁcance and different elements involved in the
environment, social, and governance towards sustainability. Figure 2 illustrates different
metrics, in which ESG data are collected for the evaluation of sustainability. The discussion
of these parameters is detailed discussed in below:
2.1. Terminology of ESG
Environment: Several interconnected environmental problems, such as soil, water
pollution, climate change, air pollution, biodiversity loss, and overuse of natural resources,
have gotten worse as technology has advanced. Since the term “sustainable development”
was coined, a large number of market players, including institutional and private investors,
have expressed a desire to take environmental sustainability into account when making
investment decisions [19]. Social: Worldwide awareness of ESG risks like those related to
the environment, society, and governance has grown over the past several years, and non-
ﬁnancial investments are now taken into account when making investment decisions [20].
The focus is on the environment, and investments in bio and health are becoming more
popular as a result of recent technological advancements, while new investments and
decisions are being made in the direction of the sequential termination or withdrawal of
existing ﬁrms [21]. Human behavior in the social domain is obtained in real-time using data
from social networks and smart cities [22]. These increase the intelligence of the ecosystem
and create an intelligent space to sense our activities or actions [23]. Based upon this
signiﬁcance there is an increased number of managers, who are prioritizing sustainability
and combating climate change in their investment portfolios.

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Figure 2. Metrics involved in ESG.
Governance: Corporate governance refers to the “rules and processes by which organi-
zations are controlled or run.” Corporate governance has its roots in a variety of ideologies.
The agency hypothesis is one of the fundamental theories. The deﬁnition of an agency
relationship given by Jensen is “a contract whereby one or more persons engage another
person to perform some service on their behalf and whereby the principal delegate some
decision-making authority to the agent [24].
2.2. ESG Investing
ESG investing is the practice of prioritizing the best ESG aspects or outcomes. It is also
known as socially responsible investing, impact investing, and sustainable investing [25].
ESG investing is frequently referred to as a technique of “sustainably” investing, wherein

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investments are made while taking the economy, the environment, and human welfare
into account [26]. We put forth a theory in which the ESG score of each stock inﬂuences
investor preferences in addition to providing information on the ﬁrm’s fundamentals. An
ESG-efﬁcient frontier, which displays the maximum possible Sharpe ratio for each ESG, is
the deﬁning feature of the solution to the investor’s portfolio problem.
ESG or Corporate Social Responsibility (CSR) is a common term used to describe
corporate behavior in this area [27]. ESG demonstrates the ﬁnancial markets’ commitment
to considering ESG factors when making investment decisions [28]. The Governance &
Accountability Institute reported in 2018 that 86 percent of S&P 500 corporations submitted
sustainability or corporate responsibility reports compared to just under 20 percent in 2011.
This indicates interest in ESG/CSR from a corporate standpoint [29]. It is based on the
growing premise that environmental and social problems are that had a great effect on an
organization’s ﬁnancial productivity [30]. Institutional frameworks that promote spending
on government initiatives that improve social equality and natural capital as a means of
accelerating the transition to ESG.
Many businesses are looking for ways to enhance their ESG standards in light of
the rise of stakeholder capitalism and responsible investment. Business leaders in this
new era need to focus more intently on factors other than their company’s short-term
operations and ﬁnancial results to better understand how underlying social, economic, po-
litical, environmental, and technological conditions are changing and potentially affecting
their ﬁrm’s operations and prospects [30]. A ﬁrm is more than just a wealth-producing
economic entity. As a component of the larger social system, it satisﬁes human and societal
objectives. Performance must be evaluated based on how well an organization fulﬁlls its
environmental, social, and good governance goals in addition to its return to shareholders.
Stakeholder responsibility should be reﬂected in executive compensation [31].
Responsible Investing—A strategy for investing that takes into account. ESG consid-
erations as well as the market’s overall long-term stability and health are referred to as
“responsible investing.” It acknowledges that sound social, environmental, and economic
systems are necessary for long-term, sustainable results. In most parts of the world, in-
stitutional and individual investors are becoming more interested in socially responsible
investing [14]. Though the notion of sustainable investing is complicated and frequently
misinterpreted, three primary strands may be found. Initially, it was known as Socially
Responsible Investment (SRI). The Quaker movement and other religious movements of
the 20th century served as the foundation for SRI and provided it with an ethical orienta-
tion [32].
The study takes into account a network of equity mutual funds that exhibit varying
degrees of adherence to ESG factors. They assess the effects of portfolio liquidation under
stress on several ESG-rated funds [33]. The study shows fundamental reforms in govern-
ment reporting and accountability. The current government reporting structure cannot
keep up with the public’s growing need for accountability in light of the accessibility of
Big Data and the development of technologies. The requirement for the government to
update its reporting model and suggest possible routes to analytics-based, data-driven,
immediate, and proactive reporting. They visualize a continuous monitoring and reporting
environment that is real-time and includes non-ﬁnancial data such as ESG and infrastruc-
ture. This updated reporting paradigm emphasizes the purpose of government reporting,
which is to hold ofﬁcials accountable to the people [34]. In response to growing investor
demand for non-ﬁnancial information from corporations, a multitude of environmental
accounting frameworks have surfaced to advertise standardized disclosure of ESG informa-
tion. These frameworks have improved the consistency, accessibility, and readability of the
data that investors can use to evaluate the sustainability implications of capital allocation
decisions [35]

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2.3. ESG for Sustainability
ESG methodologies should beneﬁt sustainability in all three ways. If every conse-
quence suggested by the ESG approaches is undeniably true, we could assume that a
synergistic effect manifests and effectively enhances our economic and social systems. We
must ensure that each effect is understood [12,36]. ESG methodologies ﬁrst attempt to
calculate welfare losses in terms of numbers and values. The outcome is anticipated to
shield the world’s communities from serious disasters. Second, ESG schemes unleash the
potential for rivalry and collaboration in local and international societies. In other words,
the ESG framework could direct local and international communities toward sustainability.
Thirdly, social innovation is sparked by ESG methodologies.
The incorporation of some stakeholders’ evaluations in the articulations could increase
the corporation’s receptivity to risks and innovations. ESG initiatives could signiﬁcantly
advance the future fourth industrial revolution and sharing economies. A variety of
investments for social requirements should be cultivated during the development process
using the ESG scheme [37]. Sustainability reporting is the disclosure of information about
the environment, society, and corporate governance. Its goal, as with all disclosures, is
to bring attention to a company’s ESG initiatives, enhance investor transparency, and
encourage other businesses to follow suit. Reporting is a powerful tool for proving that
your ESG efforts are real and not just lip service or greenwashing and that you are on track
to reach your objectives.
3. Technology Intervention for ESG
In this section, we discuss the impact of current digital technology in the implementa-
tion of projects for ESG/sustainable development. ESG conversations often center on topics
like pollution management, biodiversity, health and safety, corporate ethics, and boardroom
diversity in the majority of businesses. Consumers concerned about ESG have become
increasingly with companies that make inaccurate, or even completely false, statements
about their ESG policies. Fact-checking is made simpler by the internet, and social media
allows for quick denunciation. Investors will increasingly demand deﬁnite, veriﬁable
proof that ESG factors are explicitly included in a manager’s investment process, as well
as evidence to back up such assertions through stock selection, reporting, and portfolio
structure [38].
3.1. IoT for ESG
A study proposes an architecture of a smart ESG reporting platform leveraging
blockchain technology and IoT to enable corporate crowd-sensing for environmental data
and improve the transparency, security, and creditability of the ESG reporting proce-
dure [39]. Multidimensional environmental data are being produced in ever-increasing
amounts by weather forecast models and high-resolution climate as well as regional and
worldwide sensor networks. To be useful, the information must be kept, managed, and
made accessible to a global group of academics, policymakers, and others [40].
Figure 3 illustrates an architecture that is implemented for ESG based on IoT. IoT is
based on the networking of things where smart devices communicate with each other by
sending and receiving data. Fundamentally the IoT comprises ﬁve layers such as perception
layer, network layer, and application layer. Data acquisition through wireless sensors with
the computing unit is available in the perception layer. Oxygen, Catalytic gas sensors,
PM2.5, PM10, ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), sulphur dioxide
(SO2), nitrogen dioxide (NO2), Formaldehyde (HCHO), Total volatile organic compounds
(TVOCs), temperature, noise, humidity sensors, and ultraviolet radiation index, etc. are
used to measure carbon footprint and air quality (Indoor/outdoor) of any premises and
location [41–43]. These sensors assist to obtain the environmental data in real-time and
it concludes its impact on the sustainability parameter. The quality environment boosts
the standard of living in terms of health can be achieved. Airﬂow, Pressure, Oxygen,
Temperature, Humidity, Magnetic, Thermistor, Barcode, Force, Pulse, heart rate (HR),

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electromyography (EMG), Electrocardiogram (ECG), Electroencephalography (EEG), blood
pressure (BP), Pulsoxymetrie, Photoplethysmography (PPG), Glucose, Gyroscope, motion
tracker sensor is integrated with IoT devices to obtain the real-time data for the one of
parameter social in ESG [44].
Figure 3. IoT framework for ESG.
These sensors assist to enhance health monitoring and maintain the health of a person
by alerting frequently. Vision cameras, motion sensors, radio frequency identiﬁcation
(RFID), barcodes, a global positioning system (GPS), ultrasonic sensor, proximity sensors,
infrared (IR) sensors, pressure sensors, touch sensors, vibration sensors, piezo sensors,
are other sensors and identiﬁcation technologies that have boosted the monitoring of an
organization in its supply chain and operational functions through real-time data (social
reporting). This sensor data is processed and communicated to the application layer
through the network layer. The network layer establishes the networking between the
perception layer and application layer through different communication protocols such
as Bluetooth low energy (BLE), Bluetooth 5, Zigbee, Long range (LoRa), wireless ﬁdelity
(Wi-Fi), Narrowband-IoT(NB-IoT), Sigfox, Advanced Message Queuing Protocol (AMQP),
Data Distribution Service (DDS), Message Queue Telemetry Transport (MQTT), etc. [45].
3.2. Blockchain for ESG
Blockchain has completely changed the ﬁnancial industry with its distributed ledger
technology [46,47]. Asset management and peer-to-peer networks for the open interchange
of data are promising applications of blockchain [48]. These new developments in ﬁnancial
technology will be particularly advantageous for international climate ﬁnance [49]. Cur-
rently, there is a lack of a standardized reporting framework to empower the information
to be scalable and comparable [50]. Due to the market’s large variety of ESG rating models,
reporting practices have constructed the ESG reporting system as obvious, discontinuous,
inconstant, and complicated to compare data disclosures [51]. By developing agile, trans-

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parent, and automated data collection processes, blockchain can streamline the processing
and packaging of ESG reports.
Figure 4 illustrates an architecture that is used to implement the blockchain for ESG
reports and evaluation of the sustainability in a ﬁrm. ESG data from the different IoT devices
are collected and transmitted to the blockchain gateway through a wireless communication
protocol. Blockchain-based IoT edge gateway empowers to overcome the authentication
concerns in the IoT data. This gateway acts as a node to transmit the data of IoT devices to
the blockchain network, which satisﬁes both transparency and privacy. The smart contract
notion automates the implementation of a contract to ensure that all participants can be
sure of the results instantly, without the participation of a middleman or time failure [52].
Furthermore, smart contracts are used to assess the continuity between the raw data and
the ﬁnal ESG report. In the ﬁnal, sustainability measurements obtained through the smart
contract are used for evaluation and behaviors of ﬁrms.
 
Figure 4. Blockchain for ESG reporting.
Table 1 illustrates the studies that focused on implementing blockchain technology
for ESG in different aspects. In the majority of the studies, blockchain is implemented
for enhancing transparency, security, credibility, and data authentication. The majority
of studies have proposed token and coin-based mechanisms for the assessment of ESG
reports. For the authenticity of data, blockchain technology is also leveraged with IoT and
AI, where obtained data is veriﬁed and cleaned for further assessment. It is also observed
from the studies that the blockchain has been implemented majorly for energy trading,
to monitor carbon emissions transparently. Few studies have conducted a pilot study to
evaluate the signiﬁcance of blockchain for automatic data collection.

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Table 1. Studies of blockchain technology implementation for ESG.
Ref
Objective
Purpose of Blockchain
Sector
[50]
Proposed blockchain-based framework and
token-based mechanism to aid in the ESG-based
sustainability assessment of companies.
Transparency, data
authentication & consistency
Supply chain
[39]
Leveraging the IoT and blockchain technologies
to address the greenwashing in ﬁrms and
actualize intelligent and trustable ESG reporting.
Security, transparency, and
creditability
Apparel industry
[53]
Cross-validating ESG disclosures from
businesses along the whole value chain using a
blockchain-based Life Cycle Assessment system
Life Cycle Assessment
Tesla’s electric vehicles
[54]
An innovative system for assimilating ESG with
ﬁnancial data and reporting in real-time using
blockchain, as well as automating assurance
service utilizing smart contracts
Credibility, transparency, and
traceability
Peer-2-Peer Energy Trading
[55]
A pilot study is carried out to conclude the
signiﬁcance of distributed ledger technology
Agile, transparent, and
automated data collection
Asset Management Firms
[56]
Carboncoin is a blockchain investment that
tokenizes energy producers’ privilege to
generate carbon.
On-chain assets
carbon trading
[57]
A framework of the environmental smart
reporting system is being established relying on
blockchain and IoT technologies to automate the
acquisition of environmental data and create
reporting quite credibly
The authenticity of the data
NA
3.3. AI for ESG
Alan Turing ﬁrst advocated for AI research in the 1950s, and John McCarthy ﬁrst used
the term “artiﬁcial intelligence” in 1955. The inﬂuence of AI has been signiﬁcant in several
industries, including marketing, ﬁnance, engineering, healthcare, and education. AI creates
objects that extract, analyze, and react instinctively like the human mind, and does so with
greater accuracy, speed, and correctness [58]. ESG analyses as resources for AI auditing
that is ethical- It has become clear that AI-based systems need to be effectively controlled
to prevent, or at least limit, possible dangers and harms as the usage of AI increases
throughout sectors and communities [59]. The inclusion of AI within ESG assessments
may be warranted given that it is acknowledged as a potential material concern for many
different industries and businesses. Standardized measures for AI accountability are
required, and it’s also important to address asymmetric knowledge relations and crucial
bottlenecks [60].
Both portfolio managers and individual investors are looking for effective asset alloca-
tion models that express investor behavioral biases as well as ESG factors. Despite investor
biases for ESG sustainability, loss aversion, and cognitive dissonance. The ﬁve different
experiments in this paper’s integrated AI approach to the ESG datasets are suggested [61].
The study also concentrates on using NLP algorithms to analyze governance and social
statistics and offers a simple way of forecasting a particular ﬁrm’s ESG rankings [62].
To evaluate the signiﬁcance of ESG criteria for investment decisions and how they
affect ﬁnancial performance, a methodology is implemented for conducting statistical
analysis and applying machine learning techniques. Companies with the strongest ESG
ratings outperformed the others in terms of return on equity [63]. The governance and
auditing of AI systems hold the promise of bridging the gap between the ethical principles
guiding AI and the ethical application of AI systems, but they also call for evaluation
tools and metrics [64]. Organizations can try to go above legal obligations by proactively
analyzing the risks inherent in their AI systems. Effective AI governance is not just about
complying with the law. This also identiﬁes three broad categories of ﬁnance scholarship
that are broadly equal for both types of study, including sentiment inference, forecasting,
and planning, valuation, portfolio construction, investor behavior, ﬁnancial fraud, and

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distress. The author also highlights trends and future research topics for ML and AI
in ﬁnance research using co-occurrence and conﬂuence studies [65]. ESG investment,
which frequently relied on self-disclosed, annualized company information susceptible to
inherent data issues and biases, has proven to beneﬁt from AI capabilities. The pressure on
investment managers to incorporate ESG factors into their portfolios is growing. AI can
offer a solution in this case by acting as the trigger for scaled sustainable investing through
analysis technologies that ﬁlter important data [66].
Figure 5 illustrates the AI implementation for ESG, where the environmental, social,
and governance data are combined to form a dataset. The architecture shows the ﬂow of
IoT data for generating a dataset. IoT data is obtained from the devices through different
sensors, communication protocols, and actuators. These data are transferred to the database
through Web API. Once data is received from the IoT devices undergoes for preprocessing
stage, where the data is ﬁltered and cleaned. Pre-processing of data enables standardized
data, i.e., structured data that is feasible to form a dataset and easy to apply to the program.
Once the dataset is formed, the AI/ML model will be selected and trained with previous
data with different learning algorithms for identifying the best algorithm for the dataset.
After training, the dataset is tested with a selected learning algorithm for identifying the
unhidden patterns and predicting the opportunities for the future. These AI framework
act as a catalyst for humans to evaluate the necessary data in the ESG metrics so that the
investors can have an idea of which organizations they have invested in. In sentiment
analysis, and natural language processing, the AI algorithms apply and analyze the context,
texture, and tone and identify the patterns at which top executives follow strategies in
implementing the ESG in their organization. The same techniques can be applied by other
organizations in effectively implementing ESG criteria.
Figure 5. ML implementation for ESG.
Table 2 presents the studies that have implemented AI in ESG reporting. The majority
of the studies have studied and implemented AI for evaluating ESG’s impact on the ﬁrm’s
performance. In addition to this, for the prediction of ESG rating the following three models
such as feedforward neural networks, gradient-boosted trees (xgboost), and categorical
gradient-boosted trees(catboost) are implemented on the fundamental data. Few studies
have implemented AI for risk assessment and valuation.

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Table 2. Studies of AI implementation for ESG.
Ref
Objective
Purpose of AI
AI Model
[57]
An unsupervised network is
implemented for anomaly detection
and indexing data with an authenticity
rate.
Data authenticity
Artiﬁcial neural network
[67]
AI ESG protocol is implemented for
evaluating and disclosing the
sustainability impact
Valuation and risk
assessments
NA
[68]
A framework for evaluating and
disclosing ESG
Sustainability-related impacts
of AI
NA
[69]
Using machine learning to provide
deep insight into the impact of ESG on
ﬁrm performance
Firm’s operational
Machine Learning techniques
[70]
Heterogeneous ensemble model using
fundamental data to anticipate ESG
ratings
ESG rating
Feedforward neural networks,
Gradient
boosted trees (XGBoost) & Categorical
gradient boosted trees
(CatBoost).
[71]
Assessing structural balance sheet to
check its effect on ESG score
Traded stocks
Random Forest algorithm
3.4. Big Data for ESG
Figure 6 illustrates the basic big data architecture that can be implemented for ESG
reporting. The architecture comprises data sources, data collection, data storage & analytics,
and data visualization. Generally, in ESG reporting, the data sources are primarily from
remote sensing (geospatial) data, end devices of IoT, operational systems, and social
media. The amount of data generated from these sources is huge. The identiﬁcation of
hidden patterns, unidentiﬁed correlations, uncovered trends, and customer preferences.
Environmental data comprise air quality, weather data, forest data, water resources data,
waste management, etc. Remote sensing data make up a major percentage of big data, and
its volume is expanding quickly—at least by 20% annually [49]. The researcher examines
the beneﬁts and difﬁculties that geospatial big data has offered us in this study. Several
case examples are advantages of geographic big data analytics, including time and fuel
savings, revenue growth, urban planning, and health care. Then, they introduce brand-new
technologies that are now in development for distributing amassed geospatial big data and
monitoring human motion using mobile devices [72].
Figure 6. Big data architecture.

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Social data comprises health, diversity, freedom of expression, human rights, etc. [73].
The governance data comprises data transparency, taxes, policies of the administration,
etc. Data collection is obtained from the data source through IoT gateway, web application
programming interface, and Node.js (it is javascript that allows running on the server). In
big data, the main issue with the data ingestion process ﬂow is keeping data in the correct
location based on usage. Relational databases that we have been successful at storing our
data lack the consistency in data ingestion for being relational. However, the polygot persis-
tence method can separate the data across multiple databases by leveraging the combined
power. The data analytics layer is key in big data, where different analytical models can
be used not only for identifying current patterns but also to predict the future. Some of
the techniques are data mining, predictive analytics, and deep learning. Data mining is
applied to detect relationships and patterns through anomalies and data clusters. Predictive
analytics works on previous data to realize predictions and upcoming opportunities. Deep
learning utilizes AI and machine learning to layer algorithms for identifying patterns in
complex and abstract data. In the visualization, the results are visualized in the form of
analytics and reports.
4. Discussion and Recommendations
ESG became a signiﬁcant element to evaluate the sustainability level of an organization.
Sustainability measurements are evaluated based on ESG data obtained by an organiza-
tion in its process ﬂow. The integration of Industry 4.0 technology is highly valuable in
achieving the best ESG reports. Figure 7 illustrates a framework in which all the Industry
4.0 enabling technologies are amalgamated to obtain the ESG data and also presents an
intelligent platform for an evaluation of ESG reports and metrics. Based on the above
analysis, the below we have addressed the few limitations and recommendations that can
be implemented as a part of the research work.
 
Figure 7. Emerging ESG technology phase.

Sustainability 2023, 15, 309
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•
According to previous research, there is a lack of consistency in ESG, and the standards
of various dimensions are measured by various database metrics for an organiza-
tion [74]. This is due to the majority of evaluation systems being based on expert
scoring, there is some subjectivity. Simultaneously, the database lacks applicability
and feasibility due to differences in industry backgrounds and national systems [75].
A consistent and developed ESG evaluation framework provides the premise for the
investigation of corporate sustainable development [76,77]. The integration of AI
and big data technologies can empower to implementation effective ESG evaluation
system that maintains uniformity in its evaluation due to its intelligent algorithms and
data analysis [78,79].
•
Blockchain technology has proved its capability in terms of transparency, immutability,
and security based on hash cryptographic algorithms. Generally, during the processing
of supply chain tracking energy trading, monitoring of greenhouse gas emissions,
ﬁnancial transactions, etc., the data generated by the respective devices need to be
protected by blockchain for obtaining standard data concerning the environment of
the particular nation. The distributed ledger and different consensus mechanisms in
the blockchain empower allow the parties to visualize the data and standards that
followed during the collection of data for ESG evaluation [80,81]. A recent study
proposed a blockchain-enabled design for obtaining generic ESG data in the ﬁeld
of energy, in which a carboncoin is assigned to tokenize energy producers’ right
to emit carbon [56]. The study also concluded that using ESG data and on-chain
assets, blockchain-based carbon markets can be built more comprehensively, but
at the expense of lower performance. Along with this study, another study [39]
leveraged the IoT, and blockchain technologies to enable corporate crowdsourcing for
environmental data and improve the security, credibility, and transparency of the ESG
reporting process.
•
There are many issues around ESG data, as three dimensions such as environment,
social, and governance are merely different due to their nature [82]. In the scenario of
environmental data, they are more quantitative with better standardized. However,
natural calamities and pandemics are unpredictable. The social and governance data
are unstandardized and qualitative as they emphasize more on the social sciences.
Social and governance parameters are different for every nation [83,84]. The implemen-
tation of the same kind of approach for ESG data collection merely causes a challenge
in terms of accuracy [85,86]. To overcome these challenges, deep learning and natural
language processing can be adopted to minimize noise during obtaining the unstruc-
tured data present in public companies such as regulatory ﬁling, government studies,
and industry publications [87,88]. A study evaluated governance and social datasets
utilizing NLP algorithms to introduce a simple method for predicting a specialized
ﬁrm’s ESG rankings [89].
•
Currently, IoT is generating a large amount of data, in which the majority of the
data is stored in a cloud server [62,90]. The storage of large amounts of data indeed
increases the burning of carbon per year, and it affects the climate. However, the
evolution of the digital twin has provided an opportunity to use IoT data for mini-
mizing carbon emissions and simultaneously strengthening the management of an
organization. Moreover, the digital twins identify which data need to be collected,
stored, and applied for the organization to form ESG data [91,92]. Along with this, it
also identiﬁes which IoT data is missing and suggests the appropriate sensors which
can be deployed sustainably. The implementation of a digital twin with IoT is costly
because it necessitates highly skilled employees such as data scientists and software
engineers.
5. Conclusions
The signiﬁcance of sustainability and ESG are increasing in a wide range so that
the organization meets SDGs effectively. The lack of high-accuracy ESG data has caused

Sustainability 2023, 15, 309
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challenges to evaluate the ESG report. However, Industry 4.0 technologies are capable of
overcoming the challenges in ESG data and reports. These facts have inspired this study
to present the signiﬁcance of ESG for sustainability and implementation of Industry 4.0
technologies for obtaining the high accurate ESG data. automatic data collection. The
objective of the study is to discuss the signiﬁcance of ESG data and report, which is used for
evaluation of sustainability of an organization. The ﬁndings of the study are AI framework
serves as a driving force for humans to evaluate the necessary data in ESG metrics so
that investors can understand which organisations they have decided to invest in; In any
case, AI can provide a solution by acting as a catalytic for scaled sustainable investing via
analysis technologies that ﬁlter important data; data mining is used to explore relationships
and patterns in data by using anomalies and data clusters. Predictive analytics utilises
historical data to make predictions and identify new opportunities; deep learning layers
algorithms for identifying patterns in complex and abstract data using AI and machine
learning. Currently, there is a lack of a standardized reporting framework to empower
the information to be scalable and comparable. Due to the market’s large variety of
ESG rating models, reporting practices have constructed the ESG reporting system as
obvious, dis-continuous, inconstant, and complicated to compare data disclosures. By
developing agile, transparent, and automated data collection processes, blockchain can
streamline the processing and packaging of ESG reports. Based upon the analysis this study
identiﬁed valuable ﬁndings such as there is a lack of consistency in the overall ESG, and
the standards of various dimensions measured by various databases, blockchain allows to
track of sustainable efforts carried out by the organization with an aim responsible to the
sustainability, and implementation of the same kind of approach for ESG data collection
merely cause a challenge in terms of accuracy. In future work, the study likes to evaluate
the other Industry 4.0 technologies such as robotic, digital twin, edge computing for ESG.
Author Contributions: Conceptualization, A.G.; methodology, R.S.; validation, A.S. (Aman Singh).,
and E.C.M., formal analysis, N.P.; investigation, A.S. (Aman Singh); resources, B.T.; data curation, A.S.
(Archana Saxena); writing—original draft preparation, A.S. (Archana Saxena); writing—review and
editing, A.G; visualization, S.V.A.; supervision, R.S.; project administration, B.T.; funding acquisition,
B.T. All authors have read and agreed to the published version of the manuscript.
Funding: The APC was funded by Tshwane University of Technology, South Africa.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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