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American Journal of  Economics and 
Business Innovation (AJEBI)

The Intersection of  Green AI, Digital Advertising, and Corporate Sustainability: 
A Systematic Review

Ogechukwu T. Ibeama1, Rebecca O. Alabi2, Lydia A. Dampare Addo1, Levi Ijebor1, Arinze E. Anaege3* 

Volume 4 Issue 2, Year 2025
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v4i2.5373
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: April 02, 2025

Accepted: May 08, 2025

Published: July 26, 2025

The growing intersection of  digital advertising, corporate sustainability, and artificial 
intelligence (AI) is reshaping how organizations engage with consumers and communicate 
their environmental and social commitments. This study conducts a PRISMA-guided 
systematic review of  peer-reviewed literature published between 2015 and 2025, with the 
objective of  understanding how AI, particularly sustainable or “Green AI”, is influencing 
digital advertising strategies aligned with environmental, social, and governance (ESG) goals. 
A total of  19 articles were included based on clear inclusion criteria: English-language, peer-
reviewed empirical or conceptual studies addressing AI, sustainability, and digital advertising. 
Articles were excluded if  they lacked relevance to the intersection of  these domains or did 
not meet minimum methodological standards. Five key themes emerged: AI’s transformative 
role in green marketing, the integration of  sustainability into advertising strategy, risks such 
as greenwashing and ethical concerns, personalization’s influence on consumer trust and 
behavior, and the theoretical frameworks shaping the field. While AI enhances targeting 
and sustainability messaging, it also introduces challenges, such as energy consumption, 
ethical trade-offs, and strategic misalignment. Drawing on Stakeholder Theory and the 
Triple Bottom Line, this review provides a structured lens for understanding these tensions. 
Limitations include the novelty of  the topic, limited geographic diversity, and a concentration 
of  studies in consumer-facing sectors. Practical implications include the need for firms to 
align AI use with authentic sustainability commitments and for policymakers to strengthen 
digital ESG accountability frameworks.

Keywords
Corporate Sustainability, Digital 
Advertising, Green AI, Green 
Marketing, Stakeholder Theory, 
Triple Bottom Line

1 D’Amore-McKim School of  Business, Northeastern University, Boston, MA, United States
2  Department of  Advertising, Public Relations & Social Media, Suffolk University, Boston, MA, United States
3  Department of  Accounting, Kingsley Ozumba Mbadiwe University, Ideato, Nigeria,
* Corresponding author’s e-mail: arinze.anaege@komu.edu.ng

INTRODUCTION
The growth in artificial intelligence (AI) has revolutionized 
the way businesses operate, communicate, and compete 
(Haleem et al., 2022). Aldoseri et al. (2023) noted that 
technologies in AI, including machine learning, natural 
language processing, and predictive analytics, are widely 
implemented in industries such as marketing, finance, and 
healthcare, among others. This growth, however, has not 
been without pitfalls. Among the greatest concerns is the 
power consumption and environmental footprint created 
by large-scale AI models. For instance, training large 
language models can generate multiple tons of  carbon 
footprint, and this has raised questions surrounding the 
sustainability of  AI innovation (Iqbal et al., 2025; Schwartz 
et al., 2020). Sustainability has emerged as a business’s 
fundamental issue, for governments, as well as for 
consumers. Younas et al. (2023) revealed that companies 
are increasingly being called upon to conduct business 
in a manner that is environmentally friendly, socially 
inclusive, and economically viable. These demands paved 
the path toward corporate sustainability strategy, which 
seeks to balance profit with the demands of  society and 
the environment (Barbosa et al., 2023). Incorporating 
sustainability within business processes is no longer a 
choice. It is currently a strategic imperative for long-term 
success and trust from stakeholders.
Digital advertising has become a part of  modern business 
strategy. Using digital media including social media, search 

engines, and websites, help companies generate visibility 
in a global audience with targeted and data-based content 
(Dwivedi et al., 2021). The digital advertising industry is 
projected to grow to over $600 billion by 2027 (Statista, 
2025), reflecting the prominence of  digital advertising in 
corporate communications. Pärssinen et al. (2018) also 
observed, however, that the digital advertising sector is 
also energy-intensive, notably in data storage facilities, 
tracking mechanisms, and real-time bidding software. 
With businesses utilizing AI-powered ad-targeting and 
optimization technology, the environmental impact of  
digital advertising is mounting (Le Poidevin, 2025). This 
developing contradiction between digital innovation and 
sustainability has led to the concept of  “Green AI.” Green 
AI is the application and creation of  AI technologies that 
are both efficient and eco-friendly. The aim is to decrease 
the environmental footprint of  AI systems without 
compromising or even enhancing effectiveness (Bolón-
Canedo et al., 2024). Green AI is particularly applicable in 
digital advertising, where energy-hogging AI algorithms 
help gather, analyze, and act on consumer information 
(Schwartz et al., 2020). Integrating Green AI in advertising 
strategy can assist companies in reducing environmental 
degradation, achieving sustainability targets, and evading 
reputational hazards like greenwashing.
Despite increasing demand for Green AI and 
sustainability, scholarly research that bridges the two 
with digital advertising is, as yet, largely underexplored. 



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Research tends to address either AI and sustainability or 
AI in advertising, but not the triad of  Green AI, digital 
advertising, and corporate sustainability. This gap in 
the extant literature hinders understanding by scholars 
and practitioners of  opportunities, challenges, and best 
practices for this intersection point. It also constrains 
companies from formulating clear-cut strategies that 
can leverage digital innovation with environmental and 
social responsibility. A systematic review can help bridge 
the gap. A systematic review is a scientific approach for 

collating, summarizing, and synthesizing evidence on a 
targeted topic (Avenali et al., 2023).

Objectives of  the Study
1. To examine how Green AI is being adopted in digital 

advertising for corporate sustainability.
2. To determine the benefits and risks of  adopting 

Green AI for marketing efforts.
3. To explore theoretical and practical models that 

promote sustainable digital advertising.

Table 1: Summary of  Key Terms and Definitions
Term Definition Source / Notes
Artificial Intelligence 
(AI)

The simulation of  human intelligence processes by machines, 
particularly computer systems, including learning (machine 
learning), reasoning, and self-correction.

Maguire & White (2025)

Green AI A subfield of  AI that emphasizes energy-efficient, 
environmentally responsible approaches to AI model training 
and deployment, balancing performance with ecological 
impact.

Yigitcanlar et al. (2021)

Digital Advertising The use of  digital channels (e.g., search engines, social media, 
email, and websites) to promote products and services through 
targeted, data-driven campaigns.

Dwivedi et al. (2021)

Sustainability Meeting the needs of  the present without compromising 
the ability of  future generations to meet their own needs, 
encompassing environmental, social, and economic 
dimensions.

Kuhlman and Farrington 
(2010)

Sustainable
Marketing

Marketing that not only meets organizational goals but also 
promotes environmental and social well-being, aligning brand 
and product strategies with sustainability principles.

Jia et al. (2023)

Greenwashing The act of  misleading consumers regarding the environmental 
practices of  a company or the environmental benefits of  a 
product or service.

Dalhoum et al. (2024)

Theoretical Framework
This review is guided by two theoretical frameworks: The 
Triple Bottom Line (TBL) and Stakeholder Theory. These 
provide a perspective on how organizations can make 
sustainability central to digital and AI-based business 
activities without compromising on responsibility to 
multiple stakeholders.

Triple Bottom Line (TBL)
The Triple Bottom Line concept, developed by Elkington 
(1997), suggests that companies measure not just 
economic returns, but also environmental and social 
performance. These dimensions (people, planet, and 
profit) are the basis for sustainable business practices 
(Nogueira et al., 2025). In digital advertising and the use 
of  AI, TBL considers whether the application of  cutting-
edge technologies supports or negates sustainability. For 
instance, advertising applications that use AI can maximize 
the efficiency of  marketing and customer access (profit), 
but must also be assessed for climate impact (planet) 
and proper use of  personal data (people). Some studies 
covered in this review indicate that companies are using 
AI for promoting green products and raising awareness 

for sustainability (e.g., Salehzadeh et al., 2024; Bashynska, 
2023). Others identify worries relating to over-reliance on 
customer data, transparency, and the energy consumption 
of  machine learning models (Rathore, 2018; Hammami, 
2025). The system encourages companies to implement 
Green AI that reduces environmental degradation 
alongside fulfilling economic and social objectives. It also 
encourages digital marketers to prioritize longer-term 
strategy sustainability, and not just performance in the 
short term.

Stakeholder Theory
Stakeholder Theory, as established by Freeman (1984), 
highlights that organizations owe a responsibility to all 
parties who are affected by their actions. These include 
shareholders, customers, workers, society, and the 
environment (Awa et al., 2024). The theory particularly 
comes into relevance in sustainability-oriented digital 
marketing, where transparency, trust, and interaction 
are paramount. Research has documented how digital 
advertisements can help connect to customers with a 
sense of  ethical and environmental cause (e.g., Winarto 
& Wisesa, 2024; Gündüzyeli, 2024). AI facilitates targeted 



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messaging that can directly address such concerns. To 
illustrate, personalized ads showing cruelty-free, low-
carbon, or recyclable goods prove particularly influential 
with Gen Z and value-oriented audiences.
Stakeholder Theory explains such trends through the lens 
of  inclusivity as well as accountability. It lends support 
to the notion that advertising with digital media not only 
sell goods but also represents and respects stakeholder 
expectations. This theory also supports the desirability 
of  organizational alignment, so that internal actions as 
well as external communication match. If  a firm tells a 
story of  sustainability in advertisements but engages 
in questionable AI methods or conceals excessive 
emissions, it stands to misplace stakeholder trust. In 
addition, Stakeholder Theory underpins ethical debates 
surrounding the use of  AI. It highlights concerns such 
as data privacy, algorithmic fairness, and the digital divide 
(Miller, 2022; Radanliev, 2025). These concerns become 
even more critical as business increases the use of  AI to 
collect insight as well as automate advertising functions.
These two theories lend a robust conceptual basis to 
the evaluation in the studies reviewed. TBL informs 
the evaluation of  environmental and social impacts, 
and Stakeholder Theory ensures that the interests and 
rights of  interested groups are taken into account. These 
frameworks enable researchers and practitioners not only 
to examine if  AI-based digital advertising is successful, 
but also whether it is sustainable, ethical, and inclusive. As 
demonstrated by this review, the intersection of  Green AI, 
digital advertising, and sustainability is in a developmental 
stage. These theories offer both a diagnostic tool as well 
as a strategic guide, allowing organizations to match 
digital innovations with appropriate business approaches.

MATERIALS AND METHODS
The current study applied a systematic review in 
accordance with the Preferred Reporting Items for 
Systematic Reviews and Meta-Analyses (PRISMA) 
guidelines (Page et al., 2021). PRISMA is a system that 
provides a structured protocol for executing systematic 
reviews with comprehensive coverage and methodologic 
rigor. It guides the systematic search, analysis, and 
synthesis of  the literature, providing a transparent and 
clear approach for summarizing earlier studies (Moher et 
al., 2010).

Systematic Review Protocol
The PRISMA protocol was followed in the systematic 
review, involving the following four steps:
• Identification: Searching relevant academic databases 

using predefined keywords.
• Screening: Removing duplicate records and screening 

titles and abstracts.
• Eligibility: Assessing full-text articles against inclusion 

and exclusion criteria.
• Inclusion: Selecting final studies that matched all 

criteria for detailed analysis.
The results section is accompanied by a PRISMA 2020 

flow diagram, summarizing the process.

Databases searched
The databases selected for the search for this study 
included the well-known and highly cited databases 
(Google Scholar, Scopus, and Web of  Science). These 
databases were selected given that they comprehensively 
cover peer-reviewed scholarly articles in a broad array of  
disciplines (Martín-Martín et al., 2018).

Inclusion and Exclusion Criteria
To ensure the quality and relevance of  selected studies, 
the following criteria were applied:

Inclusion Criteria
• Published between 2015 and 2025
• Written in English
• Peer-reviewed articles, conference papers, or book 

chapters
• Focused on at least two of  the following: Green AI, 

digital advertising, corporate sustainability
• Empirical or conceptual studies with clear applicability 

to sustainable marketing practices

Exclusion Criteria
• Non-English publications
• Studies unrelated to AI, sustainability, or digital 

advertising
• Articles lacking abstract or full text
• Editorials, opinion pieces, or non-academic sources
• Studies focused purely on technical AI performance 

with no sustainability or marketing implications

Search Strategy and Keywords
A systematic keyword strategy was developed and applied 
across all three databases. Boolean operators (AND, OR) 
and truncation techniques were used to capture variations 
in terminology.

Search strings used included
• “Green AI” AND “digital marketing” AND 

“sustainability”
• “Artificial Intelligence” AND “corporate sustainability” 

AND “advertising”
• “Energy-efficient AI” OR “low-carbon AI” AND 

“marketing strategy”
• “Sustainable advertising” AND “AI tools”
• “Responsible AI” AND “brand communication”

Keywords were adapted slightly based on the syntax and 
advanced search functions available in each database. 
Filters were applied to limit results to the publication 
period of  2015 to 2025 and to English-language 
publications.

Screening Process
A total of  324 records were initially identified from three 
academic databases: Scopus (n = 103), Web of  Science (n 
= 87), and Google Scholar (n = 134). After removing 47 



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duplicate entries and 4 non-English papers, 273 records 
remained for title and abstract screening. From these, 197 
were excluded due to irrelevance. Of  the remaining 76 
reports sought for retrieval, 5 could not be accessed. The 
remaining 71 full-text articles were assessed for eligibility, 
out of  which 52 were excluded. Finally, 19 studies were 
included in the final synthesis. The study selection process 
is summarized in Figure 1.

Quality Assessment
A quality appraisal was carried out for all 19 included 
studies to ensure academic rigor and relevance. The 
appraisal followed a structured checklist based on criteria 

adapted from Tranfield et al. (2003) and the Joanna 
Briggs Institute (JBI). To ensure the academic rigor and 
relevance of  the included studies, a structured quality 
assessment was conducted using five criteria: clarity of  
research aim, appropriateness of  methodology, relevance 
to the review themes, transparency in data collection and 
analysis, and theoretical or practical contribution. Out of  
the 19 studies, 15 had a rating as high quality, and 4 had 
a rating as moderate quality. This step ensured that the 
reliability of  the findings from the review was reinforced 
and that only conceptually and methodologically sound 
studies guided the thematic synthesis.
Data Extraction and Synthesis

Table 2: Quality Assessment of  Included Studies
No. Study Clear

Research Aim
Appropriate
Methodology

Relevance Transparency Contribution Overall
Quality

1 Akshita et al. 
(2024)

✔ ✔ ✔ ✔ ✔ High

2 Kumar et al. 
(2025)

✔ ✔ ✔ ✔ ✔ High

3 Baruno & 
Indrasari (2025)

✔ ✔ ✔ ✔ ✔ High

4 Nianko & 
Andrushkevych 
(2025)

✔ ✔ ✔ ✔ ✔ Moderate

5 Emon & Khan 
(2025)

✔ ✔ ✔ ✔ ✔ High

6 Saadi & 
Azdimousa 
(2024)

✔ ✔ ✔ ✔ ✔ Moderate

7 Dalhoum et al. 
(2024)

✔ ✔ ✔ ✔ ✔ High

8 Hammami 
(2025)

✔ ✔ ✔ ✔ ✔ High

9 Salehzadeh et al. 
(2024)

✔ ✔ ✔ ✔ ✔ High

10 Keke (2023) ✔ ✔ ✔ ✔ ✔ Moderate

11 Ahn (2025) ✔ ✔ ✔ ✔ ✔ High

12 Hamamah et al. 
(2024)

✔ ✔ ✔ ✔ ✔ High

13 Gündüzyeli 
(2024)

✔ ✔ ✔ ✔ ✔ High

14 Alkhatib et al. 
(2023)

✔ ✔ ✔ ✔ ✔ High

15 Boza et al. (2025) ✔ ✔ ✔ ✔ ✔ High

16 Rai & Pandey 
(2025)

✔ ✔ ✔ ✔ ✔ High

17 Rathore (2018) ✔ ✔ ✔ ✔ ✔ Moderate

18 Bashynska 
(2023)

✔ ✔ ✔ ✔ ✔ High

19 Winarto & 
Wisesa (2024)

✔ ✔ ✔ ✔ ✔ High



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The data from the included studies were systematically 
extracted using a standard data extraction form. For every 
article, essential details were captured, including the year, 
author(s), study title, journal or source, objectives and 
questions stated, approach taken, and main findings as 
presented in Table 3. Particular attention was given to the 
study’s relevance to the three core domains of  this review. 
This extracted information was organized into a structured 
matrix to enable effective cross-study comparison and 
thematic grouping. The synthesis process followed an 
inductive approach, allowing themes to emerge naturally 
from the data as patterns and relationships across the 
studies were identified.

Thematic Synthesis Approach
This thematic structure was developed iteratively as 
patterns emerged from the extracted data. An inductive 

thematic analysis was used to categorize the studies into 
five major themes:
• AI-Driven Transformation of  Green Marketing
• Strategic Integration of  Sustainability into Digital 
Advertising
• Challenges and Risks in AI-Supported Sustainable 
Marketing
• Consumer Engagement, Brand Trust, and 
Personalization
• Theoretical Models and Frameworks Guiding the Field

Ethical Considerations
The study is solely based on published secondary data. 
Personal data and participants were not included. All the 
sources used are referenced appropriately. Hence, no 
ethical approval was necessary.
Findings and Thematic Synthesis

Figure 1: PRISMA Flow Diagram
Source: Page et al. (2021)

Table 3: Summary of  Included Studies
Author(s) Year Methodology Focus Area Key Findings / Contribution
Akshita et al. 2024 Review & conceptual 

model
Green marketing & 
digital branding

Highlights the importance of  integrating 
green values in brand positioning and 
digital marketing strategies.

Kumar et al. 2025 Systematic literature 
review + topic 
modeling (LDA)

Green AI, 
sustainable 
marketing

Identifies key themes in sustainable 
digital marketing and proposes a TCM 
framework for future research.

Baruno & 
Indrasari

2025 Case study AI-driven 
advertising

Shows how AI improves advertising 
effectiveness and contributes to green 
strategy in consumer campaigns.



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Nianko & 
Andrushkevych

2025 Conceptual analysis AI tools in 
marketing

Discusses the role of  Green AI tools in 
achieving environmental goals through 
data efficiency.

Emon & Khan 2025 Systematic review Sustainability + 
AI in marketing 
intelligence

Explores the dual role of  AI in 
enhancing marketing efficiency and 
addressing ethical sustainability goals.

Saadi & 
Azdimousa

2024 Case-based study Digital advertising 
and green branding

Discusses the role of  AI-driven content 
in promoting sustainable brand images.

Dalhoum et al. 2024 Mixed methods Green digital 
marketing

Evaluates how digital marketing 
campaigns influence consumer behavior 
toward green consumption.

Hammami 2025 Theoretical 
discussion

Green marketing 
+ organizational 
change

Emphasizes the need for holistic 
integration of  sustainability in digital 
strategies to avoid greenwashing.

Salehzadeh et al. 2024 Survey (Quantitative) Sustainable brand 
equity

Demonstrates that digital sustainability 
initiatives strengthen brand equity among 
environmentally conscious consumers.

Keke 2023 Qualitative analysis Ethical advertising 
& environmental 
impact

Discusses the relationship between 
ethical marketing strategies and 
environmental awareness.

Ahn 2025 Conceptual 
framework

AI & consumer 
engagement

Proposes a digital engagement model 
incorporating sustainability values 
through personalized AI marketing.

Hamamah et al. 2024 Empirical analysis Smart city 
sustainability & 
digital tools

Examines smart technologies in digital 
urban marketing and their role in public 
sustainability communication.

Gündüzyeli 2024 Mixed methods ESG & green 
digital policy

Evaluates how digital tools support 
corporate ESG communication and 
performance monitoring.

Alkhatib et al. 2023 Conceptual + 
content analysis

Digital and green 
marketing synergy

Highlights the emerging convergence of  
green and digital marketing as a strategic 
business opportunity.

Boza et al. 2025 Systematic review 
(PRISMA)

Sustainability & 
digital strategy

Analyzes how digital marketing aligns (or 
fails to align) with corporate sustainability 
and organizational culture.

Rai & Pandey 2025 Conceptual AI in sustainable 
ad campaigns

Discusses the use of  AI for targeting 
sustainability-focused consumer 
segments.

Rathore 2018 Theoretical AI, sustainability & 
metaverse

Explores the convergence of  AI and 
green marketing in the metaverse as a 
future business frontier.

Bashynska 2023 Mixed methods AI personalization 
& circular economy

Evaluates AI-driven personalization in 
advertising and its link to sustainable 
consumer behavior.

Winarto & 
Wisesa

2024 Survey (Quantitative) Gen Z, AI & 
sustainability

Assesses how AI and eco-practices 
affect Gen Z's purchase intention in the 
cosmetics sector.

This section offers a synthesis of  the identified themes 
that emerge from the intersection of  Green AI, digital 
advertising, and corporate sustainability. The thematic 
synthesis reveals five main themes: AI-powered green 
marketing transformation, strategic integration of  
sustainability in digital advertising, challenges and pitfalls 
in AI-assisted sustainable marketing, consumer trust 
and brand involvement, and developing theoretical 

and strategic frameworks. These themes reflect the 
promise and challenges in relating AI technologies to 
environmental and ethical marketing goals.

AI-Driven Transformation of  Green Marketing
Artificial Intelligence (AI) is revolutionizing the practice 
of  green advertising through increased automation, 
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green messaging with unparalleled accuracy and scale, as 
well as enhancing campaign performance. AI technologies 
like machine learning, natural language processing, and 
predictive analytics allow for the segmentation of  green-
oriented customers in real-time. This enable companies 
to customize messaging by environmental values and 
attitudes (Baruno & Indrasari, 2025). AI functions aid 
in optimizing resources by eliminating ad waste and 
guaranteeing best targets hit, aligning performance with 
sustainability objectives.
The literature points to AI as a central enabler for 
green content personalization for sustainability-seeking 
consumers. Saadi and Azdimousa (2024), for example, 
illustrated how content facilitated by AI can help 
enable green positioning for a brand by reflecting green 
values in promotional content as well as in customer 
experience. Bashynska (2023) also illustrated that AI 
personalization helps in sustainability not through 
improved customer engagement, but also by facilitating 
consumption behavior in conformity with the circular 
approach to the economy. AI-based systems can leverage 
behavior data to determine eco-aware consumers and 
provide them with goods or services that help in waste 
reduction, emission reduction, or reduced consumption. 
In addition, Kumar et al. (2025) pointed out that AI 
facilitates large-scale marketer insight through green 
consumer trend identification. Using 2,061 publications 
on a topic modeling application, they illustrated that AI-
based marketing is moving toward a central enabling role 
for corporate sustainability strategies. Environmental, 
social, and corporate governance (ESG) data processing 
in real time also enables marketers to directly incorporate 
sustainability metrics in digital campaign planning. This 
opens doors for increased visibility and data-informed 
sustainability narratives that increase credibility and trust 
with stakeholders.
The role of  AI in green marketing also goes beyond the 
optimization of  campaigns. Algorithmic adjustments in 
digital advertising placement occur in real-time. This can 
be user feedback, device power consumption, or local 
environmental regulations. For example, Rathore (2018) 
suggested that prescriptive AI can customize promotional 
content in emerging digital spaces such as the metaverse 
in a manner that sustainability is a primary element even 
in simulated environments. This point indicates that green 
marketing transformation by AI is not merely technical but 
strategic. It contributes to companies moving away from 
symbolic green messaging to measurable environmental 
contributions. Adding intelligence to environmental 
practice integrates awareness into sustainability, allowing 
organizations to translate digital performance into 
ecological responsibility. This is value for environmental, 
economic, and consumer stakeholders.

Integrating Sustainability Strategically into Online 
Advertising
The embedding of  sustainability in digital advertising 
requires a long-term cultural transformation in the 

organization, a buy-in by the leadership, and policy 
alignment. Boza et al. (2025) believe that digital 
sustainability needs to be incorporated into the central 
strategy of  a business. They established that numerous 
businesses acknowledge the value of  sustainability, but in 
practice, the integration is usually shallow. Inconsistent 
messaging and greenwashing could result from a lack of  
alignment between digital advertising practices and wider 
corporate sustainability objectives. Organisational culture, 
especially leadership direction and staff  involvement, 
should focus on making digital sustainability a lived 
experience and not a branding initiative.
Akshita et al. (2024) validate this viewpoint with 
supporting evidence that brand identity is being shaped 
by digital marketing practices centered on environmental 
responsibility. The study finds that sustainability must 
be woven into brand narratives through intentional 
and repetitive efforts with a long-term strategic vision. 
Sustainable values can be propagated through digital 
channels, but they need to be integrated into the business 
framework, not done for the sake of  consumer acceptance. 
Hammami (2025) also explores how internal reform can 
help make digital changes sustainable. The study believes 
that integrating sustainability into digital strategy is a 
multi-level approach. This involves picturing a different 
infrastructure, investing in low-carbon technologies, 
and cultivating a mindset change among marketers. The 
study underlines that sustainability integration cannot 
be done from outside; it needs to be part of  a grander 
vision with leadership and cross-functional coordination. 
In another similar discovery, Gündüzyeli (2024) explores 
the realization and communication of  corporate ESG 
strategy through digital media. The study finds that various 
organizations make use of  dashboards, social media, and 
reporting websites to align digital marketing with ESG 
disclosures. It also finds that in the absence of  policy 
coordination and a system for internal responsibility, the 
digital media can become mere signaling devices.

AI-Supported Sustainable Marketing: Challenges 
and Risks
Despite AI being a promising instrument for encouraging 
sustainability through digital advertising, there are several 
risks confronting it. Using AI for greenwashing is one 
such risk. Dalhoum et al. (2024) discuss that as digital 
advertising goes on to become even bigger, so does the 
risk of  making false statements about sustainability. These 
authors mention in a study that when organizations take 
up AI-powered campaigns that promise a green image 
without supporting it with real practice, consumer trust is 
lost. Discrepancy between promise and practice can cause 
loss of  reputation as well as a backlash from regulations, 
undermining actual sustainability efforts.
Gündüzyeli (2024) continues by spelling out the ethical 
dilemmas in ESG policy driven by digitalization. The 
article highlights that there are companies that apply 
AI-based solutions such as dashboards and reporting 
systems that leverage the use of  automation to present 



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ESG performance, yet with weak internal controls in 
place, such systems may be able to cover up performance 
vulnerabilities. The digital divide, where some companies 
may be able to leverage powerful AI solutions but others 
cannot, is also a concern for equity in sustainability 
reporting. The study also identifies a lack of  transparency 
in algorithmic logic and procurement as a barrier to 
ethical communication.
Another central problem is energy consumption. 
Rathore (2018) raises the environmental costs imposed 
by adopting AI for green marketing. The article cites 
that the solutions may streamline message delivery 
and personalization. This will support hardware such 
as cloud servers and GPU-based processes, which 
commonly consume a lot of  power. This is a paradox as 
the solutions that promote sustainability end up having 
a large carbon footprint, particularly when applied in 
immersive environments such as the metaverse. In 
addition to environmental and ethical difficulties, cultural 
or functional resistance is another strategic hindrance. 
Hammami (2025) observes that organizations often make 
green AI solutions without addressing underlying cultural 
or functional inertia. Without buy-in from top leadership 
and from the employees, AI deployment can be cosmetic 
and ineffective in generating meaningful sustainability 
outcomes.

Consumer Engagement, Brand Trust, and 
Personalization
AI-powered digital advertising is a prime mechanism 
for consumer engagement and building brand equity in 
sustainability-oriented markets. Various studies examine 
and describe the impact of  AI and sustainability practices 
on consumer attitudes, behavior, and long-term trust. 
Winarto and Wisesa (2024), in a case study among Gen Z 
consumers in Indonesia’s cosmetics industry, established 
that AI-powered technologies substantially augment 
hedonic and utilitarian value with personalized, green 
experiences. These results validate that sustainability-
oriented AI applications not only enable purchase intent 
but also enhance brand credibility and loyalty among 
young, sustainability-oriented consumers.
Bashynska (2023) also points to AI personalization as 
a catalyst for consumer sustainability. The results prove 
that personalization at scale through advertisements 
driven by AI can align product supplies with those of  
a circular economy. By showcasing content that aligns 
with consumer values (e.g., less-wasteful packaging, 
ethical sourcing), brands can gain a higher emotional 
connection and position themselves as credible actors for 
sustainability. This personalization is not even limited to 
convenience but includes educational as well as advocacy 
components, allowing consumers to make well-informed, 
responsible shopping decisions.
Salehzadeh et al. (2024) also state further that digital 
sustainability strengthens brand equity. Through their 
empirical evidence, they illustrate that with companies 
including environmental responsibility in ad content 

backed by transparent, AI-derived metrics, consumers 
see such brands as more reputable and trustworthy. This 
trust further promotes emotional bonds and advocacy 
behaviors. Ahn (2025) offers a theoretical approach, 
suggesting a model that coordinates AI-facilitated digital 
experience with consumer identity and eco-brand fit. 
The argument is that by integrating sustainability into 
the tailored digital experience, it becomes a part of  the 
lifestyle of  the consumer, strengthening both short-term 
conversions and long-term loyalty.

Theoretical and Strategic Frameworks Guiding the 
Field
There has emerged an increasing volume of  scholarship 
that defines theoretical and strategic models to explain 
and facilitate the integration of  Green AI, ESG goals, 
and digital advertising. Among these is the effort by 
Kumar et al. (2025), who leveraged Latent Dirichlet 
Allocation (LDA) topic model over 2,061 papers to 
create a Thematic-Consolidated Model (TCM). This 
model categorizes the emerging discipline of  green digital 
marketing into thematic pillars such as green innovation, 
ethical application of  AI, consumer ethics, and corporate 
transparency. This framework offers a guide for scholars 
and practitioners to map AI-powered advertising practices 
to long-term sustainability goals.
This is expanded upon by Ahn (2025) through a proposed 
conceptual framework linking digital consumer interaction 
directly with AI personalization and sustainability values. 
In the model, it is suggested that when AI technology 
is applied not just for commercial personalization, but 
also for advancing eco-friendly decision making, they 
form a “green engagement loop.” This incorporates 
brand messaging with personal consumer identities, 
building long-term brand sustainability and consumer 
loyalty alignment. Here, personalization is not merely 
a promotional instrument, but also a behavior change 
medium for promoting green consumption.
Emon and Khan (2025) take a wider strategic perspective 
in their systematic review of  sustainability and AI in 
marketing intelligence. They offer no specific named 
model, but in synthesis they highlight the need for the 
application of  AI to be aligned with SDGs and ethical 
governance standards. They advocate a far more cohesive, 
cross-functional approach that incorporates data ethics, 
energy efficiency, consumer trust, and long-term brand 
strategy in one sustainability-oriented AI paradigm.
These studies form the basis for theory-informed 
knowledge on sustainable digital marketing. They provide 
frameworks that consolidate disparate activities in 
sustainability, AI, and marketing, steering future corporate 
and scholarly action toward coherent, quantifiable 
objectives.

RESULTS AND DISCUSSION
The results from this review indicate that, whilst the use 
of  AI in sustainable digital marketing is increasing, efforts 
has been inconsistent and evolving. The two theoretical 



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perspectives: Triple Bottom Line (TBL) and Stakeholder 
Theory provide a framework for understanding how 
companies are navigating the intersection between 
Green AI, advertising, and sustainability. In line with the 
TBL approach, AI enhances economic efficiency (e.g., 
campaign optimization), but environmental gains are 
less evident due to the carbon footprint of  AI systems 
(Rathore, 2018). Social performance, exemplified by 
AI-driven personalization in sync with environmentally 
aware consumer values, is promising (Bashynska, 2023; 
Winarto & Wisesa, 2024), but raises questions around 
privacy (Dalhoum et al., 2024). Stakeholder Theory 
highlights that organizations leverage AI to connect with 
a greener consumer base, but they fail to track internal 
misalignments between sustainability messaging and 
business practice (Boza et al., 2025).
Compared to previous literature, this review provides a 
more targeted focus on Green AI and its link to digital 
advertising. Earlier reviews, such as those by Dangelico 
and Vocalelli (2017) and Tuz and Sertyeşilışık (2022) 
on green branding, rarely consider AI’s strategic role. 
Likewise, the broader field of  AI ethics often omits 
digital marketing’s environmental dimension. This review 
bridges that gap by focusing on how AI tools enable, and 
sometimes undermine, credible sustainability messaging. 
Practically, the findings suggest that digital marketers 
must do more than adopt sustainable messaging. They 
must embed sustainability into data strategies, platform 
selection, and content personalization. Firms should 
apply green computing principles and invest in energy-
efficient AI models. Policymakers must regulate digital 
green claims to avoid misleading practices and incentivize 
low-carbon digital technologies.
However, organizations face trade-offs. AI offers 
marketing efficiency but may increase emissions unless 
properly managed. Highly personalized sustainability 
content may boost engagement but raise privacy risks 
if  not governed by transparent data ethics policies 
(Gündüzyeli, 2024). Balancing these goals requires cross-
functional leadership and investment in infrastructure that 
supports both marketing effectiveness and environmental 
responsibility.

Gaps in Literature and Future Research Directions
The reviewed literature highlights several important gaps 
that limit current understanding of  AI-driven sustainable 
advertising. First, methodological diversity is limited. 
Many studies are conceptual or based on single-case 
analyses, with few using longitudinal or multi-country 
datasets (Winarto & Wisesa, 2024). Future research 
should use mixed methods and longitudinal designs to 
assess how consumer trust and brand loyalty evolve in 
response to sustained AI-led sustainability strategies.
Secondly, most research is concentrated in specific sectors 
like cosmetics (Winarto & Wisesa, 2024) or consumer 
goods. There is minimal exploration of  AI-led sustainable 
advertising in B2B sectors, public institutions, or energy-
intensive industries like manufacturing or logistics, where 

sustainability messaging is complex but equally essential.
Thirdly, very few studies examine how emerging AI 
technologies such as generative AI (e.g., ChatGPT), digital 
twins, or blockchain-based advertising—intersect with 
sustainability communication. These technologies are 
already shaping digital engagement and content creation 
but have yet to be studied for their environmental or 
ethical impacts in advertising.
Another noticeable gap is regional imbalance. While 
some papers examine Southeast Asia (e.g., Winarto 
& Wisesa, 2024), most insights come from Western or 
global contexts. There is a lack of  research on Africa, 
South America, or the Middle East, despite growing AI 
adoption and pressing sustainability challenges in these 
regions.
Addressing these gaps will not only improve academic 
robustness but also help companies design sustainable AI 
strategies that are regionally relevant, ethically grounded, 
and scalable.

CONCLUSION
This systematic review examined the intersection of  
Green AI, digital advertising, and corporate sustainability 
using 19 scholarly articles. The findings show that AI 
has the potential to optimize sustainable marketing by 
enabling personalization, efficiency, and data-driven 
insights. However, challenges such as greenwashing, 
energy consumption, and internal misalignment 
hinder its impact. The review identified five key 
themes: AI’s transformative role in green marketing, 
strategic integration of  sustainability, risks in AI-
supported messaging, consumer engagement through 
personalization, and emerging theoretical frameworks. 
Theoretically, the study contributes by applying the Triple 
Bottom Line and Stakeholder Theory to interpret the 
complexities of  AI-driven sustainability communication. 
Practically, it urges firms to embed sustainability not just 
in messaging but in operations, data ethics, and platform 
design. For policymakers, it highlights the need for stricter 
regulation of  digital green claims and greater support for 
low-carbon AI infrastructure.
 
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