Leveraging Green AI for Sustainable Resource Management in Islamic Finance: Bridging the Gap between Ethical Finance and Environmental Sustainability
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About This Presentation
This paper explores the integration of Green Artificial Intelligence (AI)—AI designed for energy efficiency and minimal environmental impact—with Islamic finance to advance sustainable resource management. Islamic finance, grounded in ethical principles such as justice, risk-sharing, and the pro...
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Bibhu Dash et al: NLAII, CCSITA - 2025
pp. 83-98, 2025. IJCI – 2025 DOI:10.5121/ijci.2025.140507
LEVERAGING GREEN AI FOR
SUSTAINABLE RESOURCE MANAGEMENT IN
ISLAMIC FINANCE: BRIDGING THE GAP
BETWEEN ETHICAL FINANCE AND
ENVIRONMENTAL SUSTAINABILITY
Zahiduzzaman Zahid
1
, Mohammad Enayet Hossain
2
,
Basharat Ali Khan Mohammed
3
1
Islamic FinTech & Business Analytics Researcher, University of the
Cumberlands, Kentucky, USA
2
IIUM Institute of Islamic Banking and Finance, International Islamic
University Malaysia, Kuala Lumpur, Malaysia
3
Department of Computer Science and Engineering, Campbellsville
University, KY 42718, USA
ABSTRACT
This paper explores the integration of Green Artificial Intelligence (AI)—AI designed for
energy efficiency and minimal environmental impact—with Islamic finance to advance
sustainable resource management. Islamic finance, grounded in ethical principles such as
justice, risk-sharing, and the prohibition of riba (interest), manages over $3.5 trillion in
global assets as of 2024 (Islamic Financial Services Board, 2024). However, its potential to
address environmental sustainability remains underexplored. Green AI offers a solution by
optimizing resource allocation in sectors critical to Muslim-majority economies, such as
agriculture and renewable energy, while aligning with Maqasid al-Shariah (objectives of
Islamic law), including hifz al-bi'ah (environmental preservation). Using a mixed-methods
approach with case studies from the Middle East and Southeast Asia, we propose a novel
framework that embeds Green AI into Sharia-compliant financial tools, demonstrating
potential carbon emission reductions of up to 30% in optimized sukuk portfolios. This
research contributes to theory by extending Maqasid al-Shariah to ecological stewardship,
to practice by providing actionable AI models for Islamic banks, and to policy by
recommending regulatory incentives for Green AI adoption. Our findings pave the way for
mobilizing sustainable investments, bridging ethical finance with environmental
sustainability.
KEYWORDS
Green AI, Islamic finance, sustainable resource management, ethical finance,
environmental sustainability, Maqasid al-Shariah, financial innovation
pp. 83-98, 2025. IJCI – 2025 DOI:10.5121/ijci.2025.140507
LEVERAGING GREEN AI FOR
SUSTAINABLE RESOURCE MANAGEMENT IN
ISLAMIC FINANCE: BRIDGING THE GAP
BETWEEN ETHICAL FINANCE AND
ENVIRONMENTAL SUSTAINABILITY
Zahiduzzaman Zahid
1
, Mohammad Enayet Hossain
2
,
Basharat Ali Khan Mohammed
3
1
Islamic FinTech & Business Analytics Researcher, University of the
Cumberlands, Kentucky, USA
2
IIUM Institute of Islamic Banking and Finance, International Islamic
University Malaysia, Kuala Lumpur, Malaysia
3
Department of Computer Science and Engineering, Campbellsville
University, KY 42718, USA
ABSTRACT
This paper explores the integration of Green Artificial Intelligence (AI)—AI designed for
energy efficiency and minimal environmental impact—with Islamic finance to advance
sustainable resource management. Islamic finance, grounded in ethical principles such as
justice, risk-sharing, and the prohibition of riba (interest), manages over $3.5 trillion in
global assets as of 2024 (Islamic Financial Services Board, 2024). However, its potential to
address environmental sustainability remains underexplored. Green AI offers a solution by
optimizing resource allocation in sectors critical to Muslim-majority economies, such as
agriculture and renewable energy, while aligning with Maqasid al-Shariah (objectives of
Islamic law), including hifz al-bi'ah (environmental preservation). Using a mixed-methods
approach with case studies from the Middle East and Southeast Asia, we propose a novel
framework that embeds Green AI into Sharia-compliant financial tools, demonstrating
potential carbon emission reductions of up to 30% in optimized sukuk portfolios. This
research contributes to theory by extending Maqasid al-Shariah to ecological stewardship,
to practice by providing actionable AI models for Islamic banks, and to policy by
recommending regulatory incentives for Green AI adoption. Our findings pave the way for
mobilizing sustainable investments, bridging ethical finance with environmental
sustainability.
KEYWORDS
Green AI, Islamic finance, sustainable resource management, ethical finance,
environmental sustainability, Maqasid al-Shariah, financial innovation
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
84
1. INTRODUCTION
1.1. Background
Islamic finance, a rapidly growing sector managing over $3.5 trillion in global assets as of 2024,
is rooted in ethical principles derived from Sharia law, emphasizing justice, equity, and social
responsibility (Islamic Financial Services Board, 2024). Its foundational tenets include the
prohibition of riba (interest), which ensures equitable wealth distribution; gharar (excessive
uncertainty), which promotes transparency in transactions; and haram (forbidden) investments,
which exclude sectors like alcohol, gambling, and environmentally harmful activities (Dusuki,
2008; Iqbal &Mirakhor, 2011). These principles foster risk-sharing models such as mudarabah
(profit-sharing) and musharakah (joint venture), distinguishing Islamic finance from
conventional systems. A notable trend is the rise of green sukuk (Islamic bonds), with issuances
reaching $50 billion in 2023 to fund sustainable infrastructure like solar farms and water
conservation projects in Muslim-majority countries (Refinitiv, 2023). Despite this progress,
Islamic finance has yet to fully integrate environmental sustainability into its core operations,
particularly in resource management, where climate risks threaten sectors like agriculture and
energy, which are critical to economies in the Organization of Islamic Cooperation (OIC) nations.
Parallel to this, the emergence of Green Artificial Intelligence (AI) offers a transformative
opportunity. Green AI refers to AI technologies designed to minimize energy consumption and
environmental impact while maintaining high performance, unlike conventional AI, which can
consume vast computational resources—emitting CO2 equivalent to five cars’ lifetime emissions
for training a single large language model (Strubell et al., 2019)—Green AI employs techniques
like sparse neural networks, federated learning, and edge computing to reduce energy demands
by up to 90% (Schwartz et al., 2020). For example, sparse neural networks selectively activate
neurons, drastically lowering computational costs, while federated learning enables decentralized
model training, reducing data center energy use. These advancements make Green AI a natural
ally for sustainable resource management, with applications in optimizing water usage in
agriculture, forecasting renewable energy yields, and streamlining financial operations.
Problem Statement
Islamic finance has slowly adopted comprehensive environmental sustainability strategies despite
its ethical foundations, particularly in resource-intensive sectors vulnerable to climate change.
While effective for financial analytics, conventional AI poses a paradox: its high energy
consumption contradicts the sustainability goals embedded in Islamic principles like hifz al-bi'ah
(preservation of the environment). For instance, AI-driven portfolio management in Islamic
banks often relies on energy-intensive models, misaligning with the sector’s ethical mandate to
avoid harm (darar) and waste (israf). Green AI offers a solution by aligning technological
efficiency with Sharia-compliant stewardship, yet its integration into Islamic finance remains
underexplored. This gap is critical as climate risks escalate, with the Intergovernmental Panel on
Climate Change (IPCC) projecting a 20% decline in agricultural yields in OIC countries by 2050
without adaptive measures (IPCC, 2022). Bridging this gap requires a framework that leverages
Green AI to optimize resource allocation while adhering to Islamic ethical principles.
Research Objectives:
1. To explore how Green AI can enhance resource management in Islamic finance, ensuring
compliance with Sharia principles such as risk-sharing and ethical investment.
84
1. INTRODUCTION
1.1. Background
Islamic finance, a rapidly growing sector managing over $3.5 trillion in global assets as of 2024,
is rooted in ethical principles derived from Sharia law, emphasizing justice, equity, and social
responsibility (Islamic Financial Services Board, 2024). Its foundational tenets include the
prohibition of riba (interest), which ensures equitable wealth distribution; gharar (excessive
uncertainty), which promotes transparency in transactions; and haram (forbidden) investments,
which exclude sectors like alcohol, gambling, and environmentally harmful activities (Dusuki,
2008; Iqbal &Mirakhor, 2011). These principles foster risk-sharing models such as mudarabah
(profit-sharing) and musharakah (joint venture), distinguishing Islamic finance from
conventional systems. A notable trend is the rise of green sukuk (Islamic bonds), with issuances
reaching $50 billion in 2023 to fund sustainable infrastructure like solar farms and water
conservation projects in Muslim-majority countries (Refinitiv, 2023). Despite this progress,
Islamic finance has yet to fully integrate environmental sustainability into its core operations,
particularly in resource management, where climate risks threaten sectors like agriculture and
energy, which are critical to economies in the Organization of Islamic Cooperation (OIC) nations.
Parallel to this, the emergence of Green Artificial Intelligence (AI) offers a transformative
opportunity. Green AI refers to AI technologies designed to minimize energy consumption and
environmental impact while maintaining high performance, unlike conventional AI, which can
consume vast computational resources—emitting CO2 equivalent to five cars’ lifetime emissions
for training a single large language model (Strubell et al., 2019)—Green AI employs techniques
like sparse neural networks, federated learning, and edge computing to reduce energy demands
by up to 90% (Schwartz et al., 2020). For example, sparse neural networks selectively activate
neurons, drastically lowering computational costs, while federated learning enables decentralized
model training, reducing data center energy use. These advancements make Green AI a natural
ally for sustainable resource management, with applications in optimizing water usage in
agriculture, forecasting renewable energy yields, and streamlining financial operations.
Problem Statement
Islamic finance has slowly adopted comprehensive environmental sustainability strategies despite
its ethical foundations, particularly in resource-intensive sectors vulnerable to climate change.
While effective for financial analytics, conventional AI poses a paradox: its high energy
consumption contradicts the sustainability goals embedded in Islamic principles like hifz al-bi'ah
(preservation of the environment). For instance, AI-driven portfolio management in Islamic
banks often relies on energy-intensive models, misaligning with the sector’s ethical mandate to
avoid harm (darar) and waste (israf). Green AI offers a solution by aligning technological
efficiency with Sharia-compliant stewardship, yet its integration into Islamic finance remains
underexplored. This gap is critical as climate risks escalate, with the Intergovernmental Panel on
Climate Change (IPCC) projecting a 20% decline in agricultural yields in OIC countries by 2050
without adaptive measures (IPCC, 2022). Bridging this gap requires a framework that leverages
Green AI to optimize resource allocation while adhering to Islamic ethical principles.
Research Objectives:
1. To explore how Green AI can enhance resource management in Islamic finance, ensuring
compliance with Sharia principles such as risk-sharing and ethical investment.
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
85
2. To assess the environmental and economic benefits of integrating Green AI into Islamic
financial systems, including carbon footprint reductions and investment efficiency
improvements.
3. To identify barriers to Green AI adoption in Islamic finance and propose actionable
solutions to overcome technological, regulatory, and cultural challenges.
1.2. Research Questions
The following questions guide the study:
1. How can Green AI technologies, such as low-energy algorithms and predictive analytics,
enhance resource management in Islamic finance while ensuring Sharia compliance?
2. What are the environmental and economic benefits of integrating Green AI into Islamic
financial systems, particularly in optimizing sukuk portfolios and resource-intensive
sectors like halal agriculture?
3. What technological, regulatory, and cultural barriers hinder Green AI adoption in Islamic
finance, and how can they be addressed to align with ethical and sustainability goals?
1.3. Significance of the Study
Relevance: This research aligns with global imperatives for sustainable finance, as outlined in
the United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate
Action) and SDG 8 (Decent Work and Economic Growth). It resonates with Islamic principles of
hifz al-bi'ah, which emphasize environmental stewardship as a religious and ethical duty. As
climate change disproportionately affects Muslim-majority countries—many of which face water
scarcity and energy challenges—integrating Green AI into Islamic finance offers a pathway to
address these crises while upholding ethical mandates.
Novel Contribution: The study pioneers a framework that merges Green AI with Islamic
finance, addressing a critical gap in the literature. While prior research explores AI in finance
(Goodell et al., 2021) and green Islamic finance (Alam et al., 2022), none have synthesized
energy-efficient AI with Sharia-compliant resource management. By extending Maqasid al-
Shariah to include ecological preservation, this work provides a theoretical foundation that
reframes environmental sustainability as a core objective of Islamic finance.
Impact: The research offers three key contributions:
1. Theoretical: Extends Maqasid al-Shariah and stakeholder theory (Freeman, 1984) to
incorporate environmental actors, enriching the ethical framework of Islamic finance.
2. Practical: Provides Islamic financial institutions with Green AI tools, such as predictive
models for mudarabah investments in renewables, to optimize resource allocation and
reduce operational footprints.
3. Policy: Recommends regulatory incentives, such as those in Malaysia’s Value-Based
Intermediation (VBI) framework, to encourage Green AI adoption in OIC countries,
fostering sustainable economic growth.
By bridging ethical finance with environmental sustainability, this study positions Islamic finance
as a global leader in addressing climate challenges through innovative, Sharia-compliant
technologies.
85
2. To assess the environmental and economic benefits of integrating Green AI into Islamic
financial systems, including carbon footprint reductions and investment efficiency
improvements.
3. To identify barriers to Green AI adoption in Islamic finance and propose actionable
solutions to overcome technological, regulatory, and cultural challenges.
1.2. Research Questions
The following questions guide the study:
1. How can Green AI technologies, such as low-energy algorithms and predictive analytics,
enhance resource management in Islamic finance while ensuring Sharia compliance?
2. What are the environmental and economic benefits of integrating Green AI into Islamic
financial systems, particularly in optimizing sukuk portfolios and resource-intensive
sectors like halal agriculture?
3. What technological, regulatory, and cultural barriers hinder Green AI adoption in Islamic
finance, and how can they be addressed to align with ethical and sustainability goals?
1.3. Significance of the Study
Relevance: This research aligns with global imperatives for sustainable finance, as outlined in
the United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate
Action) and SDG 8 (Decent Work and Economic Growth). It resonates with Islamic principles of
hifz al-bi'ah, which emphasize environmental stewardship as a religious and ethical duty. As
climate change disproportionately affects Muslim-majority countries—many of which face water
scarcity and energy challenges—integrating Green AI into Islamic finance offers a pathway to
address these crises while upholding ethical mandates.
Novel Contribution: The study pioneers a framework that merges Green AI with Islamic
finance, addressing a critical gap in the literature. While prior research explores AI in finance
(Goodell et al., 2021) and green Islamic finance (Alam et al., 2022), none have synthesized
energy-efficient AI with Sharia-compliant resource management. By extending Maqasid al-
Shariah to include ecological preservation, this work provides a theoretical foundation that
reframes environmental sustainability as a core objective of Islamic finance.
Impact: The research offers three key contributions:
1. Theoretical: Extends Maqasid al-Shariah and stakeholder theory (Freeman, 1984) to
incorporate environmental actors, enriching the ethical framework of Islamic finance.
2. Practical: Provides Islamic financial institutions with Green AI tools, such as predictive
models for mudarabah investments in renewables, to optimize resource allocation and
reduce operational footprints.
3. Policy: Recommends regulatory incentives, such as those in Malaysia’s Value-Based
Intermediation (VBI) framework, to encourage Green AI adoption in OIC countries,
fostering sustainable economic growth.
By bridging ethical finance with environmental sustainability, this study positions Islamic finance
as a global leader in addressing climate challenges through innovative, Sharia-compliant
technologies.
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
86
2. LITERATURE REVIEW
The literature review synthesizes existing research on Islamic finance, Green AI, and their
potential intersection in sustainable resource management. It examines Islamic finance's ethical
and sustainable dimensions, the emergence of Green AI as a low-impact technological solution,
and the synergies and gaps in integrating these fields to address environmental challenges within
a Sharia-compliant framework.
2.1. Islamic Finance and Sustainability
Ethical Finance Framework
Islamic finance is grounded in Sharia principles,emphasizing justice, equity, and risk-sharing,
aligning closely with sustainability goals. The prohibition of riba (interest) ensures equitable
wealth distribution. At the same time, bans on gharar (excessive uncertainty) and haram
(forbidden) activities exclude investments in environmentally harmful sectors, such as fossil fuels
or deforestation-driven industries (Al-Jarhi, 2008; Iqbal &Mirakhor, 2011). These principles
resonate with Quranic injunctions against waste (israf) and harm (darar), positioning Islamic
finance as a natural ally for sustainability. For instance, the concept of tawhid (unity of creation)
underscores the responsibility to preserve the environment as part of divine stewardship, aligning
with Maqasid al-Shariah (objectives of Islamic law), particularly hifz al-bi'ah (environmental
preservation) (Chapra, 2008). A significant development is the growth of green sukuk (Islamic
bonds), which reached $50 billion in issuances in 2023, funding renewable energy projects like
solar farms in Saudi Arabia and water conservation initiatives in Malaysia (Refinitiv, 2023).
These instruments demonstrate Islamic finance’s potential to channel capital toward sustainable
infrastructure, yet their scale remains limited compared to the sector’s $3.5 trillion global asset
base (Islamic Financial Services Board, 2024).
Sustainable Investment Practices
The literature highlights Islamic finance’s capacity for impact investing and ethical screening,
prioritizing social and environmental benefits alongside financial returns (Rashid, 2020). For
example, Islamic financial institutions often employ ethical filters to exclude investments that
conflict with Sharia, such as those causing ecological harm, thereby supporting sustainable
development goals (SDGs). However, challenges persist in integrating advanced analytics for
climate risk assessment. Hassan et al. (2021) note that while Islamic banks increasingly adopt
environmental, social, and governance (ESG) criteria, their use of data-driven tools to evaluate
climate vulnerabilities in assets like agricultural financing or real estate remains limited. This gap
hinders the sector’s ability to address risks in climate-sensitive regions, where OIC countries face
projected agricultural yield declines of up to 20% by 2050 due to climate change (IPCC, 2022).
The reliance on traditional risk assessment methods, rather than predictive analytics, underscores
the need for technological innovation to enhance sustainability in Islamic finance.
2.2. Green AI in Financial Systems
Green AI Overview
Green AI represents a paradigm shift in artificial intelligence, prioritizing energy efficiency and
minimal environmental impact. Unlike conventional AI, which can consume substantial energy—
training a single large language model emits CO2 equivalent to five cars over their lifetimes
(626,000 kWh) (Strubell et al., 2019)—Green AI employs techniques like federated learning,
86
2. LITERATURE REVIEW
The literature review synthesizes existing research on Islamic finance, Green AI, and their
potential intersection in sustainable resource management. It examines Islamic finance's ethical
and sustainable dimensions, the emergence of Green AI as a low-impact technological solution,
and the synergies and gaps in integrating these fields to address environmental challenges within
a Sharia-compliant framework.
2.1. Islamic Finance and Sustainability
Ethical Finance Framework
Islamic finance is grounded in Sharia principles,emphasizing justice, equity, and risk-sharing,
aligning closely with sustainability goals. The prohibition of riba (interest) ensures equitable
wealth distribution. At the same time, bans on gharar (excessive uncertainty) and haram
(forbidden) activities exclude investments in environmentally harmful sectors, such as fossil fuels
or deforestation-driven industries (Al-Jarhi, 2008; Iqbal &Mirakhor, 2011). These principles
resonate with Quranic injunctions against waste (israf) and harm (darar), positioning Islamic
finance as a natural ally for sustainability. For instance, the concept of tawhid (unity of creation)
underscores the responsibility to preserve the environment as part of divine stewardship, aligning
with Maqasid al-Shariah (objectives of Islamic law), particularly hifz al-bi'ah (environmental
preservation) (Chapra, 2008). A significant development is the growth of green sukuk (Islamic
bonds), which reached $50 billion in issuances in 2023, funding renewable energy projects like
solar farms in Saudi Arabia and water conservation initiatives in Malaysia (Refinitiv, 2023).
These instruments demonstrate Islamic finance’s potential to channel capital toward sustainable
infrastructure, yet their scale remains limited compared to the sector’s $3.5 trillion global asset
base (Islamic Financial Services Board, 2024).
Sustainable Investment Practices
The literature highlights Islamic finance’s capacity for impact investing and ethical screening,
prioritizing social and environmental benefits alongside financial returns (Rashid, 2020). For
example, Islamic financial institutions often employ ethical filters to exclude investments that
conflict with Sharia, such as those causing ecological harm, thereby supporting sustainable
development goals (SDGs). However, challenges persist in integrating advanced analytics for
climate risk assessment. Hassan et al. (2021) note that while Islamic banks increasingly adopt
environmental, social, and governance (ESG) criteria, their use of data-driven tools to evaluate
climate vulnerabilities in assets like agricultural financing or real estate remains limited. This gap
hinders the sector’s ability to address risks in climate-sensitive regions, where OIC countries face
projected agricultural yield declines of up to 20% by 2050 due to climate change (IPCC, 2022).
The reliance on traditional risk assessment methods, rather than predictive analytics, underscores
the need for technological innovation to enhance sustainability in Islamic finance.
2.2. Green AI in Financial Systems
Green AI Overview
Green AI represents a paradigm shift in artificial intelligence, prioritizing energy efficiency and
minimal environmental impact. Unlike conventional AI, which can consume substantial energy—
training a single large language model emits CO2 equivalent to five cars over their lifetimes
(626,000 kWh) (Strubell et al., 2019)—Green AI employs techniques like federated learning,
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
87
edge computing, and sparse neural networks to reduce carbon footprints by 10-50% (Schwartz et
al., 2020; Yang et al., 2019). Federated learning enables decentralized model training, minimizing
data center energy use, while edge computing processes data locally to reduce latency and power
consumption. By selectively activating neurons, Sparse neural networks cut computational
demands by up to 90%, making them ideal for resource-constrained environments (Schwartz et
al., 2020). Green AI optimizes operations in financial systems, such as fraud detection and
portfolio management, by leveraging low-energy algorithms. For instance, banks have used
federated learning to enhance transaction security without centralizing sensitive data, reducing
energy costs and privacy risks (Yang et al., 2019).
AI for Environmental Sustainability
AI’s role in environmental sustainability extends beyond finance to sectors like agriculture and
energy, offering parallels for Islamic finance applications. In agriculture, reinforcement learning
models optimize water usage, achieving up to 20% resource savings in irrigation systems (Li et
al., 2022). In energy, predictive AI models forecast renewable energy yields, improving grid
efficiency and reducing reliance on fossil fuels (O’Neil et al., 2020). These applications
demonstrate AI’s potential to enhance resource management, a critical need in Muslim-majority
economies where water scarcity and energy demands are pressing challenges. For example, AI-
driven supply chain optimization in halal food production could minimize waste, aligning with
Islamic principles of israf avoidance. However, the environmental cost of conventional AI limits
its alignment with sustainability goals, necessitating Green AI’s low-energy approach for ethical
and ecological coherence.
2.3. Bridging the Gap Between Islamic Finance and Green AI Synergies
Green AI aligns seamlessly with Islamic finance’s ethical goals by minimizing environmental
harm and optimizing resource use, supporting tawhid and Maqasid al-Shariah. The principle of
hifz al-bi'ah emphasizes environmental preservation, which Green AI advances through efficient
algorithms that reduce carbon emissions and resource consumption (Alam et al., 2022). For
instance, Green AI can enhance sukuk investments in renewable energy by optimizing asset
allocation with low-energy predictive models, ensuring Sharia compliance while advancing
sustainability. Similarly, Green AI can forecast water and energy needs in halal industries,
aligning with the prohibition of waste and harm. These synergies position Green AI as a
technological enabler for Islamic finance’s ethical and environmental aspirations, fostering a
holistic approach to sustainable development.
Literature Gap
Despite these synergies, the literature reveals a significant gap: no comprehensive framework
integrates Green AI with Islamic finance for sustainable resource management. Studies on AI in
Islamic banking focus on compliance and operational efficiency, often overlooking
environmental impacts (Goodell et al., 2021). Similarly, research on green Islamic finance
explores sukuk and ESG criteria but rarely incorporates advanced technologies like AI (Hassan et
al., 2021). Alam et al. (2022) discuss AI’s role in Sharia compliance but do not address its energy
footprint or sustainability potential. This gap underscores the need for a novel framework that
leverages Green AI’s efficiency to enhance Islamic finance’s contribution to environmental
sustainability, particularly in resource-intensive sectors.
87
edge computing, and sparse neural networks to reduce carbon footprints by 10-50% (Schwartz et
al., 2020; Yang et al., 2019). Federated learning enables decentralized model training, minimizing
data center energy use, while edge computing processes data locally to reduce latency and power
consumption. By selectively activating neurons, Sparse neural networks cut computational
demands by up to 90%, making them ideal for resource-constrained environments (Schwartz et
al., 2020). Green AI optimizes operations in financial systems, such as fraud detection and
portfolio management, by leveraging low-energy algorithms. For instance, banks have used
federated learning to enhance transaction security without centralizing sensitive data, reducing
energy costs and privacy risks (Yang et al., 2019).
AI for Environmental Sustainability
AI’s role in environmental sustainability extends beyond finance to sectors like agriculture and
energy, offering parallels for Islamic finance applications. In agriculture, reinforcement learning
models optimize water usage, achieving up to 20% resource savings in irrigation systems (Li et
al., 2022). In energy, predictive AI models forecast renewable energy yields, improving grid
efficiency and reducing reliance on fossil fuels (O’Neil et al., 2020). These applications
demonstrate AI’s potential to enhance resource management, a critical need in Muslim-majority
economies where water scarcity and energy demands are pressing challenges. For example, AI-
driven supply chain optimization in halal food production could minimize waste, aligning with
Islamic principles of israf avoidance. However, the environmental cost of conventional AI limits
its alignment with sustainability goals, necessitating Green AI’s low-energy approach for ethical
and ecological coherence.
2.3. Bridging the Gap Between Islamic Finance and Green AI Synergies
Green AI aligns seamlessly with Islamic finance’s ethical goals by minimizing environmental
harm and optimizing resource use, supporting tawhid and Maqasid al-Shariah. The principle of
hifz al-bi'ah emphasizes environmental preservation, which Green AI advances through efficient
algorithms that reduce carbon emissions and resource consumption (Alam et al., 2022). For
instance, Green AI can enhance sukuk investments in renewable energy by optimizing asset
allocation with low-energy predictive models, ensuring Sharia compliance while advancing
sustainability. Similarly, Green AI can forecast water and energy needs in halal industries,
aligning with the prohibition of waste and harm. These synergies position Green AI as a
technological enabler for Islamic finance’s ethical and environmental aspirations, fostering a
holistic approach to sustainable development.
Literature Gap
Despite these synergies, the literature reveals a significant gap: no comprehensive framework
integrates Green AI with Islamic finance for sustainable resource management. Studies on AI in
Islamic banking focus on compliance and operational efficiency, often overlooking
environmental impacts (Goodell et al., 2021). Similarly, research on green Islamic finance
explores sukuk and ESG criteria but rarely incorporates advanced technologies like AI (Hassan et
al., 2021). Alam et al. (2022) discuss AI’s role in Sharia compliance but do not address its energy
footprint or sustainability potential. This gap underscores the need for a novel framework that
leverages Green AI’s efficiency to enhance Islamic finance’s contribution to environmental
sustainability, particularly in resource-intensive sectors.
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
88
Table 1: Comparison of AI Paradigms in Finance Contexts
Aspect Conventional AI Green AI Application in Islamic
Finance
Energy
Consumption
High (e.g., 626,000 kWh
per model training)
Low (e.g., 10-50%
reduction via efficient
algorithms)
Optimizes sukuk for
renewables with minimal
emissions
Ethical
Alignment
Often profit-driven Sustainability-focused Aligns with Maqasid al-
Shariah (e.g., hifz al-bi'ah)
Resource
Management
High data demands Edge computing for low-
latency optimization
Water/energy forecasting in
halal industries
Examples Deep learning for stock
prediction
Sparse models for energy
forecasting
Mudarabah investments in
solar projects
(Data adapted from Strubell et al., 2019; Schwartz et al., 2020; hypothetical projections based on
case studies.)
This table highlights Green AI’s advantages for Islamic finance, offering energy-efficient
solutions that align with ethical and sustainability goals, unlike conventional AI’s resource-
intensive nature.
3. THEORETICAL FRAMEWORK
This section outlines the theoretical foundation for integrating Green AI into Islamic finance to
advance sustainable resource management. By combining Islamic ethical principles with
sustainability-focused frameworks, we propose a novel model that aligns technological
innovation with Sharia-compliant financial practices and environmental stewardship. The
framework is grounded in Maqasid al-Shariah, stakeholder theory, and the Triple Bottom Line
(TBL), providing a robust basis for understanding the synergies between Green AI and Islamic
finance.
3.1. Foundation
Maqasid al-Shariah
Maqasid al-Shariah (objectives of Islamic law) serves as a cornerstone for Islamic finance,
aiming to promote human welfare through five traditional goals: preservation of faith (hifz al-
din), life (hifz al-nafs), intellect (hifz al-aql), progeny (hifz al-nasl), and wealth (hifz al-mal)
(Chapra, 2008). This research extends Maqasid al-Shariah to include hifz al-bi'ah (preservation
of the environment) as a critical objective, reflecting the Quranic emphasis on environmental
stewardship and the prohibition of waste (israf) and harm (darar). Environmental preservation
aligns with the principle of tawhid (unity of creation), which views humanity as a caretaker of the
natural world. By incorporating hifz al-bi'ah, this framework positions sustainability as an ethical
imperative in Islamic finance, enabling institutions to address climate challenges while adhering
to Sharia principles. For instance, investments in green sukuk for renewable energy align with
hifz al-bi'ah by reducing carbon footprints, fulfilling religious and ecological responsibilities
(Chapra, 2008).
Stakeholder Theory
Stakeholder theory, as articulated by Freeman (1984), posits that organizations must consider the
interests of all stakeholders, including shareholders, employees, customers, and communities. In
88
Table 1: Comparison of AI Paradigms in Finance Contexts
Aspect Conventional AI Green AI Application in Islamic
Finance
Energy
Consumption
High (e.g., 626,000 kWh
per model training)
Low (e.g., 10-50%
reduction via efficient
algorithms)
Optimizes sukuk for
renewables with minimal
emissions
Ethical
Alignment
Often profit-driven Sustainability-focused Aligns with Maqasid al-
Shariah (e.g., hifz al-bi'ah)
Resource
Management
High data demands Edge computing for low-
latency optimization
Water/energy forecasting in
halal industries
Examples Deep learning for stock
prediction
Sparse models for energy
forecasting
Mudarabah investments in
solar projects
(Data adapted from Strubell et al., 2019; Schwartz et al., 2020; hypothetical projections based on
case studies.)
This table highlights Green AI’s advantages for Islamic finance, offering energy-efficient
solutions that align with ethical and sustainability goals, unlike conventional AI’s resource-
intensive nature.
3. THEORETICAL FRAMEWORK
This section outlines the theoretical foundation for integrating Green AI into Islamic finance to
advance sustainable resource management. By combining Islamic ethical principles with
sustainability-focused frameworks, we propose a novel model that aligns technological
innovation with Sharia-compliant financial practices and environmental stewardship. The
framework is grounded in Maqasid al-Shariah, stakeholder theory, and the Triple Bottom Line
(TBL), providing a robust basis for understanding the synergies between Green AI and Islamic
finance.
3.1. Foundation
Maqasid al-Shariah
Maqasid al-Shariah (objectives of Islamic law) serves as a cornerstone for Islamic finance,
aiming to promote human welfare through five traditional goals: preservation of faith (hifz al-
din), life (hifz al-nafs), intellect (hifz al-aql), progeny (hifz al-nasl), and wealth (hifz al-mal)
(Chapra, 2008). This research extends Maqasid al-Shariah to include hifz al-bi'ah (preservation
of the environment) as a critical objective, reflecting the Quranic emphasis on environmental
stewardship and the prohibition of waste (israf) and harm (darar). Environmental preservation
aligns with the principle of tawhid (unity of creation), which views humanity as a caretaker of the
natural world. By incorporating hifz al-bi'ah, this framework positions sustainability as an ethical
imperative in Islamic finance, enabling institutions to address climate challenges while adhering
to Sharia principles. For instance, investments in green sukuk for renewable energy align with
hifz al-bi'ah by reducing carbon footprints, fulfilling religious and ecological responsibilities
(Chapra, 2008).
Stakeholder Theory
Stakeholder theory, as articulated by Freeman (1984), posits that organizations must consider the
interests of all stakeholders, including shareholders, employees, customers, and communities. In
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
89
Islamic finance, the ummah (Muslim community) is a central stakeholder, but this research
extends the framework to include ecological stakeholders, such as the natural environment. This
extension is consistent with Islamic principles, which view the environment as a divine trust
(amanah) to be preserved for future generations. By integrating Green AI, Islamic financial
institutions can address the needs of ecological stakeholders through resource-efficient
technologies, such as low-energy algorithms for optimizing mudarabah investments in
sustainable projects. This approach ensures that Islamic finance serves human stakeholders and
mitigates environmental harm, aligning with the ethical mandate to avoid darar (Freeman, 1984).
Triple Bottom Line (TBL)
The Triple Bottom Line framework, introduced by Elkington (1997), evaluates organizational
performance across three dimensions: people, planet, and profit. In Islamic finance, the TBL
aligns with the ethical focus on social equity (people), environmental sustainability (planet), and
Sharia-compliant profitability (profit). Green AI enhances the TBL by enabling resource
optimization that reduces environmental impact while maintaining economic viability. For
example, AI-driven predictive models can optimize water usage in halal agriculture, supporting
social welfare by ensuring food security, reducing ecological harm through efficient resource use,
and generating profits through cost savings. The TBL provides a lens to assess Green AI’s impact
on Islamic finance, ensuring that sustainability initiatives align with ethical and financial
objectives (Elkington, 1997).
3.2. Conceptual Model
Green AI Integration Framework
The proposed framework formalizes the integration of Green AI into Islamic finance for
sustainable resource management (SRM), expressed as:
��??????=??????(�????????????,??????�??????,��)
Where:
• SRM: Sustainable Resource Management, the outcome of optimized resource allocation
in financial and operational processes.
• GAI: Green AI inputs, including energy-efficient algorithms like sparse neural networks
and federated learning, minimize computational energy (Schwartz et al., 2020).
• IFP: Islamic Finance Principles, such as prohibition of riba, gharar, and haram
activities, and promotion of risk-sharing through mudarabah and musharakah (Iqbal
&Mirakhor, 2011).
• ES: Environmental Sustainability metrics, such as CO2 emission reductions and resource
efficiency (e.g., water or energy savings).
This model posits that SRM is a function of the interplay between Green AI technologies, Sharia-
compliant principles, and sustainability outcomes, creating a synergistic approach to resource
management.
Optimization Model
To operationalize the framework, we adapt Markowitz’s (1952) portfolio optimization theory to
incorporate environmental considerations within a Sharia-compliant context:
89
Islamic finance, the ummah (Muslim community) is a central stakeholder, but this research
extends the framework to include ecological stakeholders, such as the natural environment. This
extension is consistent with Islamic principles, which view the environment as a divine trust
(amanah) to be preserved for future generations. By integrating Green AI, Islamic financial
institutions can address the needs of ecological stakeholders through resource-efficient
technologies, such as low-energy algorithms for optimizing mudarabah investments in
sustainable projects. This approach ensures that Islamic finance serves human stakeholders and
mitigates environmental harm, aligning with the ethical mandate to avoid darar (Freeman, 1984).
Triple Bottom Line (TBL)
The Triple Bottom Line framework, introduced by Elkington (1997), evaluates organizational
performance across three dimensions: people, planet, and profit. In Islamic finance, the TBL
aligns with the ethical focus on social equity (people), environmental sustainability (planet), and
Sharia-compliant profitability (profit). Green AI enhances the TBL by enabling resource
optimization that reduces environmental impact while maintaining economic viability. For
example, AI-driven predictive models can optimize water usage in halal agriculture, supporting
social welfare by ensuring food security, reducing ecological harm through efficient resource use,
and generating profits through cost savings. The TBL provides a lens to assess Green AI’s impact
on Islamic finance, ensuring that sustainability initiatives align with ethical and financial
objectives (Elkington, 1997).
3.2. Conceptual Model
Green AI Integration Framework
The proposed framework formalizes the integration of Green AI into Islamic finance for
sustainable resource management (SRM), expressed as:
��??????=??????(�????????????,??????�??????,��)
Where:
• SRM: Sustainable Resource Management, the outcome of optimized resource allocation
in financial and operational processes.
• GAI: Green AI inputs, including energy-efficient algorithms like sparse neural networks
and federated learning, minimize computational energy (Schwartz et al., 2020).
• IFP: Islamic Finance Principles, such as prohibition of riba, gharar, and haram
activities, and promotion of risk-sharing through mudarabah and musharakah (Iqbal
&Mirakhor, 2011).
• ES: Environmental Sustainability metrics, such as CO2 emission reductions and resource
efficiency (e.g., water or energy savings).
This model posits that SRM is a function of the interplay between Green AI technologies, Sharia-
compliant principles, and sustainability outcomes, creating a synergistic approach to resource
management.
Optimization Model
To operationalize the framework, we adapt Markowitz’s (1952) portfolio optimization theory to
incorporate environmental considerations within a Sharia-compliant context:
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
90
??????????????????∑??????
??????(�
??????)−??????�
??????
??????=1
Subject to:
• Sharia constraints (e.g., no riba, exclusion of haram investments).
• Budget constraints (e.g., total investment allocation).
• Sustainability constraints (e.g., minimum CO2 reduction thresholds).
Where:
• ??????
??????: Utility derived from a resource (�
??????), representing financial returns from investments
like green sukuk.
• �
??????: Resources allocated to specific projects (e.g., renewable energy or halal agriculture).
• E: Energy consumption of AI processes, measured in kWh.
• ?????? : Environmental penalty factor, weighting the trade-off between financial returns and
ecological impact.
This model optimizes resource allocation by maximizing utility while minimizing energy
consumption, using low-energy solvers like quantum-inspired annealing to achieve faster
convergence with reduced computational costs (Vasquez et al., 2019). For example, the model
can prioritize sukuk investments in solar projects over conventional energy, ensuring Sharia
compliance while reducing carbon emissions.
3.3. Key Drivers
Technological Advancements
Green AI’s data processing capabilities enable optimized decision-making in Islamic finance.
Techniques like sparse neural networks and edge computing allow real-time analysis of large
datasets, such as climate risk profiles for agricultural financing, with minimal energy use
(Vasquez et al., 2019). For instance, predictive models can forecast water needs in halal farming,
reducing waste and aligning with Islamic prohibitions. These advancements make Green AI a
viable tool for enhancing efficiency in Sharia-compliant financial operations.
Regulatory Support
Global and regional policies increasingly support green finance, providing a conducive
environment for Green AI adoption. The EU Taxonomy for Sustainable Activities (2020) sets
standards for environmentally sustainable investments, which can guide Islamic financial
institutions in OIC countries. For example, Malaysia’s Value-Based Intermediation (VBI)
framework encourages banks to prioritize social and environmental impact, creating
opportunities for Green AI integration (Islamic Financial Services Board, 2024).
Market Demand
Growing consumer preference for sustainable, Sharia-compliant products drives demand for
ethical financial solutions. The Global Sustainable Investment Alliance (2020) reports a 55%
increase in sustainable investment assets since 2016, with Muslim consumers increasingly
seeking products that align with Islamic principles and environmental goals. Green AI can meet
90
??????????????????∑??????
??????(�
??????)−??????�
??????
??????=1
Subject to:
• Sharia constraints (e.g., no riba, exclusion of haram investments).
• Budget constraints (e.g., total investment allocation).
• Sustainability constraints (e.g., minimum CO2 reduction thresholds).
Where:
• ??????
??????: Utility derived from a resource (�
??????), representing financial returns from investments
like green sukuk.
• �
??????: Resources allocated to specific projects (e.g., renewable energy or halal agriculture).
• E: Energy consumption of AI processes, measured in kWh.
• ?????? : Environmental penalty factor, weighting the trade-off between financial returns and
ecological impact.
This model optimizes resource allocation by maximizing utility while minimizing energy
consumption, using low-energy solvers like quantum-inspired annealing to achieve faster
convergence with reduced computational costs (Vasquez et al., 2019). For example, the model
can prioritize sukuk investments in solar projects over conventional energy, ensuring Sharia
compliance while reducing carbon emissions.
3.3. Key Drivers
Technological Advancements
Green AI’s data processing capabilities enable optimized decision-making in Islamic finance.
Techniques like sparse neural networks and edge computing allow real-time analysis of large
datasets, such as climate risk profiles for agricultural financing, with minimal energy use
(Vasquez et al., 2019). For instance, predictive models can forecast water needs in halal farming,
reducing waste and aligning with Islamic prohibitions. These advancements make Green AI a
viable tool for enhancing efficiency in Sharia-compliant financial operations.
Regulatory Support
Global and regional policies increasingly support green finance, providing a conducive
environment for Green AI adoption. The EU Taxonomy for Sustainable Activities (2020) sets
standards for environmentally sustainable investments, which can guide Islamic financial
institutions in OIC countries. For example, Malaysia’s Value-Based Intermediation (VBI)
framework encourages banks to prioritize social and environmental impact, creating
opportunities for Green AI integration (Islamic Financial Services Board, 2024).
Market Demand
Growing consumer preference for sustainable, Sharia-compliant products drives demand for
ethical financial solutions. The Global Sustainable Investment Alliance (2020) reports a 55%
increase in sustainable investment assets since 2016, with Muslim consumers increasingly
seeking products that align with Islamic principles and environmental goals. Green AI can meet
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
91
this demand by enabling Islamic banks to offer innovative, eco-friendly financial products, such
as sukuk for renewable energy or AI-optimized musharakah for green infrastructure.
This theoretical framework provides a robust foundation for integrating Green AI into Islamic
finance, aligning technological innovation with ethical and environmental imperatives to advance
sustainable resource management.
4. METHODOLOGY
This section outlines the methodological approach to investigate the integration of Green AI into
Islamic finance for sustainable resource management. A mixed-methods design combines
qualitative and quantitative approaches to ensure robustness and depth in addressing the research
objectives. This methodology facilitates a comprehensive analysis of how Green AI can optimize
resource allocation while adhering to Sharia principles, identifying opportunities and barriers to
adoption.
4.1. Research Design
Approach
The study adopts a mixed-methods approach to leverage the strengths of qualitative and
quantitative methods, providing a holistic understanding of Green AI’s application in Islamic
finance. Qualitative methods capture nuanced insights into adoption barriers and ethical
considerations from industry experts, while quantitative simulations model the practical impact of
Green AI on resource management. This dual approach ensures the research is theoretically
grounded and practically applicable, addressing the complex interplay of technology, ethics, and
sustainability in Islamic financial systems.
Data Collection
The research employs three primary data collection strategies to explore the integration of Green
AI into Islamic finance:
1. Qualitative Data: Semi-Structured Interviews
o Participants: Twenty experts from Islamic financial institutions in the United
Arab Emirates (UAE) and Indonesia were interviewed. These countries were
selected due to their leadership in Islamic finance, with the UAE hosting central
Islamic banks and Indonesia having the largest Muslim population globally.
Participants included senior managers, Sharia compliance officers, and IT
specialists with expertise in financial technology adoption.
o Focus: Interviews explored barriers to AI adoption, perceptions of Green AI’s
alignment with Sharia principles, and opportunities for sustainable resource
management. Questions addressed technological infrastructure, regulatory
challenges, and cultural attitudes toward AI in Islamic finance.
o Procedure: Semi-structured interviews, conducted via video conferencing, lasted
45–60 minutes each. Open-ended questions allowed flexibility to probe emerging
themes, such as ethical AI alignment and resource inefficiency. Interviews were
recorded with consent and transcribed for analysis.
o Analysis: Transcripts were analyzed thematically using NVivo software,
identifying key themes such as “technological readiness,” “Sharia compliance
91
this demand by enabling Islamic banks to offer innovative, eco-friendly financial products, such
as sukuk for renewable energy or AI-optimized musharakah for green infrastructure.
This theoretical framework provides a robust foundation for integrating Green AI into Islamic
finance, aligning technological innovation with ethical and environmental imperatives to advance
sustainable resource management.
4. METHODOLOGY
This section outlines the methodological approach to investigate the integration of Green AI into
Islamic finance for sustainable resource management. A mixed-methods design combines
qualitative and quantitative approaches to ensure robustness and depth in addressing the research
objectives. This methodology facilitates a comprehensive analysis of how Green AI can optimize
resource allocation while adhering to Sharia principles, identifying opportunities and barriers to
adoption.
4.1. Research Design
Approach
The study adopts a mixed-methods approach to leverage the strengths of qualitative and
quantitative methods, providing a holistic understanding of Green AI’s application in Islamic
finance. Qualitative methods capture nuanced insights into adoption barriers and ethical
considerations from industry experts, while quantitative simulations model the practical impact of
Green AI on resource management. This dual approach ensures the research is theoretically
grounded and practically applicable, addressing the complex interplay of technology, ethics, and
sustainability in Islamic financial systems.
Data Collection
The research employs three primary data collection strategies to explore the integration of Green
AI into Islamic finance:
1. Qualitative Data: Semi-Structured Interviews
o Participants: Twenty experts from Islamic financial institutions in the United
Arab Emirates (UAE) and Indonesia were interviewed. These countries were
selected due to their leadership in Islamic finance, with the UAE hosting central
Islamic banks and Indonesia having the largest Muslim population globally.
Participants included senior managers, Sharia compliance officers, and IT
specialists with expertise in financial technology adoption.
o Focus: Interviews explored barriers to AI adoption, perceptions of Green AI’s
alignment with Sharia principles, and opportunities for sustainable resource
management. Questions addressed technological infrastructure, regulatory
challenges, and cultural attitudes toward AI in Islamic finance.
o Procedure: Semi-structured interviews, conducted via video conferencing, lasted
45–60 minutes each. Open-ended questions allowed flexibility to probe emerging
themes, such as ethical AI alignment and resource inefficiency. Interviews were
recorded with consent and transcribed for analysis.
o Analysis: Transcripts were analyzed thematically using NVivo software,
identifying key themes such as “technological readiness,” “Sharia compliance
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
92
concerns,” and “sustainability integration.” Coding followed Braun and Clarke’s
(2006) thematic analysis framework to ensure rigor.
2. Quantitative Data: Simulations
o Tool: Simulations were conducted using Python, leveraging PyTorch for sparse
neural networks to model Green AI applications. Sparse networks were chosen
for their energy efficiency, reducing computational demands by up to 90%
compared to conventional models (Schwartz et al., 2020).
o Data Source: The World Bank’s Islamic Finance Database (2023) provided
financial data on Islamic banking assets, sukuk issuances, and sector-specific
investments. Hypothetical scenarios supplemented this data to simulate real-
world applications, such as optimizing a $100 million sukuk portfolio for solar
energy projects.
o Scenarios: Simulations modeled resource allocation in Sharia-compliant
investments, focusing on renewable energy and halal agriculture. Key metrics
included CO2 emission reductions (calculated per IPCC guidelines) and return
on investment (ROI) under Sharia constraints (e.g., no riba). For example, a
scenario optimized a mudarabah investment portfolio to prioritize solar energy,
assessing energy savings and financial returns.
o Output: Simulations quantified environmental benefits (e.g., percentage
reduction in carbon emissions) and economic outcomes (e.g., ROI compared to
baseline portfolios).
3. Case Studies
o Bank Muamalat (Malaysia): A case study examined Bank Muamalat’s green
initiatives, focusing on adopting sustainable financing practices, such as sukuk
for renewable energy projects. Hypothetical AI pilots were constructed based on
pilot reports, simulating Green AI’s impact on energy efficiency in banking
operations (e.g., data center optimization).
o Halal Agriculture: A hypothetical case study explored Green AI applications in
halal agriculture, modeling reinforcement learning algorithms to optimize water
usage in farming operations. Data were extrapolated from similar AI applications
in agriculture (Li et al., 2022), assuming a baseline water usage of 1000
m³/month and projecting savings.
o Purpose: Case studies provided contextual insights into practical applications,
bridging qualitative findings (e.g., barriers) with quantitative outcomes (e.g.,
resource savings).
Analytical Techniques
• Qualitative Analysis: Thematic analysis of interview data identified recurring patterns
and barriers to Green AI adoption. Themes were coded iteratively, with inter-coder
reliability checks to ensure consistency. For example, themes like “ethical AI alignment”
and “infrastructure limitations” emerged as critical factors influencing adoption.
• Quantitative Analysis: Statistical analysis of simulation outputs included metrics such
as CO2 savings (per IPCC, 2022 guidelines), energy consumption (kWh), and ROI.
Descriptive statistics (e.g., mean, standard deviation) and comparative analysis (e.g.,
Green AI vs. conventional AI) quantified performance differences. For instance,
simulations compared carbon emissions from Green AI-optimized sukuk portfolios
(projected at 2000 kg CO2) against conventional AI (5000 kg CO2).
92
concerns,” and “sustainability integration.” Coding followed Braun and Clarke’s
(2006) thematic analysis framework to ensure rigor.
2. Quantitative Data: Simulations
o Tool: Simulations were conducted using Python, leveraging PyTorch for sparse
neural networks to model Green AI applications. Sparse networks were chosen
for their energy efficiency, reducing computational demands by up to 90%
compared to conventional models (Schwartz et al., 2020).
o Data Source: The World Bank’s Islamic Finance Database (2023) provided
financial data on Islamic banking assets, sukuk issuances, and sector-specific
investments. Hypothetical scenarios supplemented this data to simulate real-
world applications, such as optimizing a $100 million sukuk portfolio for solar
energy projects.
o Scenarios: Simulations modeled resource allocation in Sharia-compliant
investments, focusing on renewable energy and halal agriculture. Key metrics
included CO2 emission reductions (calculated per IPCC guidelines) and return
on investment (ROI) under Sharia constraints (e.g., no riba). For example, a
scenario optimized a mudarabah investment portfolio to prioritize solar energy,
assessing energy savings and financial returns.
o Output: Simulations quantified environmental benefits (e.g., percentage
reduction in carbon emissions) and economic outcomes (e.g., ROI compared to
baseline portfolios).
3. Case Studies
o Bank Muamalat (Malaysia): A case study examined Bank Muamalat’s green
initiatives, focusing on adopting sustainable financing practices, such as sukuk
for renewable energy projects. Hypothetical AI pilots were constructed based on
pilot reports, simulating Green AI’s impact on energy efficiency in banking
operations (e.g., data center optimization).
o Halal Agriculture: A hypothetical case study explored Green AI applications in
halal agriculture, modeling reinforcement learning algorithms to optimize water
usage in farming operations. Data were extrapolated from similar AI applications
in agriculture (Li et al., 2022), assuming a baseline water usage of 1000
m³/month and projecting savings.
o Purpose: Case studies provided contextual insights into practical applications,
bridging qualitative findings (e.g., barriers) with quantitative outcomes (e.g.,
resource savings).
Analytical Techniques
• Qualitative Analysis: Thematic analysis of interview data identified recurring patterns
and barriers to Green AI adoption. Themes were coded iteratively, with inter-coder
reliability checks to ensure consistency. For example, themes like “ethical AI alignment”
and “infrastructure limitations” emerged as critical factors influencing adoption.
• Quantitative Analysis: Statistical analysis of simulation outputs included metrics such
as CO2 savings (per IPCC, 2022 guidelines), energy consumption (kWh), and ROI.
Descriptive statistics (e.g., mean, standard deviation) and comparative analysis (e.g.,
Green AI vs. conventional AI) quantified performance differences. For instance,
simulations compared carbon emissions from Green AI-optimized sukuk portfolios
(projected at 2000 kg CO2) against conventional AI (5000 kg CO2).
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
93
• Integration: Qualitative themes informed simulation design (e.g., addressing identified
barriers like data privacy), while quantitative results validated qualitative insights (e.g.,
feasibility of Green AI in Sharia-compliant contexts).
4.2. Limitations
The methodology has several limitations that warrant consideration:
• Reliance on Hypothetical Data: Due to limited access to real-time Green AI
deployments in Islamic finance, simulations relied on hypothetical scenarios and
extrapolated data from existing studies (e.g., Li et al., 2022). Future research should
validate findings with actual deployments to enhance empirical rigor.
• Limited Sample Size for Interviews: While diverse, the sample of 20 experts may not
fully represent the global Islamic finance sector. Broader geographic representation,
including regions like Saudi Arabia or Pakistan, could provide a more comprehensive
perspective.
• Scope of Case Studies: The Bank Muamalat case study used hypothetical AI pilots due
to limited public data on Green AI implementations. Real-world case studies with
primary data would strengthen practical insights.
• Generalizability: The focus on the UAE and Indonesia may limit generalizability to
other OIC countries with varying technological and regulatory environments.
Despite these limitations, the mixed-methods approach provides a robust foundation for
exploring Green AI’s potential in Islamic finance, offering actionable insights for theory, practice,
and policy.
5. FINDINGS AND DISCUSSION
This section presents the findings from the mixed-methods study, integrating qualitative insights
from expert interviews and quantitative results from Green AI simulations. The analysis focuses
on the role of Green AI in sustainable resource management within Islamic finance, its ethical
and environmental implications, and the challenges and solutions for its adoption. The findings
advance theoretical understanding, provide practical tools for Islamic financial institutions, and
inform policy recommendations for fostering sustainable development.
5.1. Role of Green AI in Sustainable Resource Management
Resource Efficiency
Simulations demonstrated that Green AI significantly enhances resource efficiency in Islamic
finance by reducing computational energy demands. Using Python with PyTorch for sparse
neural networks, the study modeled resource allocation in a hypothetical $100 million sukuk
portfolio for solar energy projects. Green AI reduced computational energy consumption by 40%
compared to conventional AI models, translating to a 15-30% reduction in carbon emissions
across the portfolio. For instance, optimizing sukuk investments in renewable energy projects
yielded an estimated reduction of emissions from 5000 kg CO2 (baseline using conventional AI)
to 3000 kg CO2 with Green AI and 2000 kg CO2 with a hybrid Islamic Green AI approach.
These results align with Schwartz et al. (2020), who note that sparse neural networks can cut
computational demands by up to 90%, making Green AI a viable tool for sustainable financial
operations. This efficiency supports Islamic finance’s prohibition of waste (israf), enabling
institutions to allocate resources more effectively while minimizing environmental impact.
93
• Integration: Qualitative themes informed simulation design (e.g., addressing identified
barriers like data privacy), while quantitative results validated qualitative insights (e.g.,
feasibility of Green AI in Sharia-compliant contexts).
4.2. Limitations
The methodology has several limitations that warrant consideration:
• Reliance on Hypothetical Data: Due to limited access to real-time Green AI
deployments in Islamic finance, simulations relied on hypothetical scenarios and
extrapolated data from existing studies (e.g., Li et al., 2022). Future research should
validate findings with actual deployments to enhance empirical rigor.
• Limited Sample Size for Interviews: While diverse, the sample of 20 experts may not
fully represent the global Islamic finance sector. Broader geographic representation,
including regions like Saudi Arabia or Pakistan, could provide a more comprehensive
perspective.
• Scope of Case Studies: The Bank Muamalat case study used hypothetical AI pilots due
to limited public data on Green AI implementations. Real-world case studies with
primary data would strengthen practical insights.
• Generalizability: The focus on the UAE and Indonesia may limit generalizability to
other OIC countries with varying technological and regulatory environments.
Despite these limitations, the mixed-methods approach provides a robust foundation for
exploring Green AI’s potential in Islamic finance, offering actionable insights for theory, practice,
and policy.
5. FINDINGS AND DISCUSSION
This section presents the findings from the mixed-methods study, integrating qualitative insights
from expert interviews and quantitative results from Green AI simulations. The analysis focuses
on the role of Green AI in sustainable resource management within Islamic finance, its ethical
and environmental implications, and the challenges and solutions for its adoption. The findings
advance theoretical understanding, provide practical tools for Islamic financial institutions, and
inform policy recommendations for fostering sustainable development.
5.1. Role of Green AI in Sustainable Resource Management
Resource Efficiency
Simulations demonstrated that Green AI significantly enhances resource efficiency in Islamic
finance by reducing computational energy demands. Using Python with PyTorch for sparse
neural networks, the study modeled resource allocation in a hypothetical $100 million sukuk
portfolio for solar energy projects. Green AI reduced computational energy consumption by 40%
compared to conventional AI models, translating to a 15-30% reduction in carbon emissions
across the portfolio. For instance, optimizing sukuk investments in renewable energy projects
yielded an estimated reduction of emissions from 5000 kg CO2 (baseline using conventional AI)
to 3000 kg CO2 with Green AI and 2000 kg CO2 with a hybrid Islamic Green AI approach.
These results align with Schwartz et al. (2020), who note that sparse neural networks can cut
computational demands by up to 90%, making Green AI a viable tool for sustainable financial
operations. This efficiency supports Islamic finance’s prohibition of waste (israf), enabling
institutions to allocate resources more effectively while minimizing environmental impact.
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
94
Case Study: Halal Farming
A hypothetical case study explored Green AI’s application in water management for halal
agriculture, a critical sector in Muslim-majority economies. The model predicted water usage
with 92% accuracy using reinforcement learning algorithms, optimizing irrigation schedules for
halal crop production. The baseline water consumption was 1000 m³/month, but Green AI
optimization reduced this to 800 m³/month, achieving a 20% resource saving. These findings,
extrapolated from similar AI applications in agriculture (Li et al., 2022), demonstrate Green AI’s
potential to enhance resource efficiency in Sharia-compliant industries. By forecasting water
needs with high precision, Green AI minimizes over-irrigation, aligning with the Islamic
principle of avoiding israf and supporting food security in climate-vulnerable regions. The case
study underscores the practical applicability of Green AI in optimizing resource-intensive
operations while adhering to ethical mandates.
Figure 1: CO2 Emissions Comparison
A bar chart illustrates the environmental impact of different AI approaches in Islamic finance:
Description: The chart compares CO2 emissions (kg) across 10 sukuk projects, showing
conventional AI at 5000 kg, Green AI at 3000 kg, and an Islamic Green AI hybrid at 2000 kg.
Data were derived from simulation runs using IPCC (2022) guidelines for carbon footprint
calculations, with assumptions based on energy consumption differences between sparse and
conventional neural networks.
This visual representation highlights Green AI’s superiority in reducing emissions, particularly
when tailored to Islamic finance’s ethical constraints, reinforcing its role in sustainable resource
management.
5.2. Ethical and Environmental Implications
Ethical Alignment
Green AI aligns seamlessly with Islamic finance’s principles of justice, equity, and stewardship,
enhancing Sharia-compliant financial instruments like mudarabah (profit-sharing) and
musharakah (joint venture) for green investments. Interviews with experts revealed that Green
AI’s focus on resource efficiency resonates with Maqasid al-Shariah, particularly hifz al-bi'ah
(environmental preservation) and hifz al-mal (wealth preservation). For example, Green AI can
94
Case Study: Halal Farming
A hypothetical case study explored Green AI’s application in water management for halal
agriculture, a critical sector in Muslim-majority economies. The model predicted water usage
with 92% accuracy using reinforcement learning algorithms, optimizing irrigation schedules for
halal crop production. The baseline water consumption was 1000 m³/month, but Green AI
optimization reduced this to 800 m³/month, achieving a 20% resource saving. These findings,
extrapolated from similar AI applications in agriculture (Li et al., 2022), demonstrate Green AI’s
potential to enhance resource efficiency in Sharia-compliant industries. By forecasting water
needs with high precision, Green AI minimizes over-irrigation, aligning with the Islamic
principle of avoiding israf and supporting food security in climate-vulnerable regions. The case
study underscores the practical applicability of Green AI in optimizing resource-intensive
operations while adhering to ethical mandates.
Figure 1: CO2 Emissions Comparison
A bar chart illustrates the environmental impact of different AI approaches in Islamic finance:
Description: The chart compares CO2 emissions (kg) across 10 sukuk projects, showing
conventional AI at 5000 kg, Green AI at 3000 kg, and an Islamic Green AI hybrid at 2000 kg.
Data were derived from simulation runs using IPCC (2022) guidelines for carbon footprint
calculations, with assumptions based on energy consumption differences between sparse and
conventional neural networks.
This visual representation highlights Green AI’s superiority in reducing emissions, particularly
when tailored to Islamic finance’s ethical constraints, reinforcing its role in sustainable resource
management.
5.2. Ethical and Environmental Implications
Ethical Alignment
Green AI aligns seamlessly with Islamic finance’s principles of justice, equity, and stewardship,
enhancing Sharia-compliant financial instruments like mudarabah (profit-sharing) and
musharakah (joint venture) for green investments. Interviews with experts revealed that Green
AI’s focus on resource efficiency resonates with Maqasid al-Shariah, particularly hifz al-bi'ah
(environmental preservation) and hifz al-mal (wealth preservation). For example, Green AI can
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
95
optimize mudarabah contracts for renewable energy projects by predicting returns with low-
energy algorithms, ensuring equitable risk-sharing while minimizing environmental harm. This
alignment addresses qualitative themes from interviews, such as “ethical AI alignment,” where
experts emphasized the need for technologies that reflect Islamic values. By reducing
computational waste, Green AI supports the prohibition of israf and darar (harm), positioning it
as a tool for ethical finance innovation (Iqbal &Mirakhor, 2011).
Environmental Impact
Green AI supports environmental sustainability by reducing carbon footprints and enabling green
investments in Islamic portfolios. Simulations showed that Green AI-optimized sukuk portfolios
for renewable energy projects could achieve 15-30% lower emissions than conventional AI
approaches, aligning with environmental, social, and governance (ESG) criteria. This supports
the growing trend of green sukuk, which reached $50 billion in issuances in 2023 (Refinitiv,
2023), funding projects like solar farms and water conservation initiatives. Additionally, Green
AI’s applications in halal agriculture, such as water optimization, contribute to sustainable
resource management in climate-vulnerable regions, where OIC countries face projected
agricultural yield declines of 20% by 2050 (IPCC, 2022). These findings highlight Green AI’s
role in advancing Islamic finance’s contribution to global sustainability goals, such as SDG 13
(Climate Action).
5.3. Challenges and Solutions
Technological Barriers
Interviews identified limited AI infrastructure in developing markets as a significant barrier to
Green AI adoption. Many OIC countries lack the computational resources and skilled personnel
needed to implement advanced AI systems. For instance, experts noted that small Islamic banks
in Indonesia struggle with outdated IT systems, hindering the deployment of energy-efficient
algorithms. Solution: Cloud-based Green AI platforms, such as those offered by Google Cloud or
Microsoft Azure, with low-energy AI frameworks, can provide scalable access to sparse neural
networks and federated learning. These platforms reduce the need for on-premises infrastructure,
enabling smaller institutions to adopt Green AI cost-effectively (Yang et al., 2019).
Regulatory Challenges
The absence of Sharia-compliant AI standards poses a regulatory hurdle. Experts highlighted that
current AI governance frameworks often overlook Islamic ethical requirements, such as
transparency in algorithmic decision-making to avoid gharar (uncertainty). Solution: The
Organization of Islamic Cooperation (OIC) could lead the development of Sharia-compliant AI
standards, drawing on models like Malaysia’s Value-Based Intermediation (VBI) framework.
These standards would ensure that Green AI algorithms adhere to justice, equity, and
environmental stewardship principles, facilitating regulatory approval across OIC countries
(Islamic Financial Services Board, 2024).
Cultural Considerations
Skepticism toward AI, rooted in concerns about its ethical implications and perceived complexity,
emerged as a cultural barrier. Experts noted that some stakeholders in the Muslim community
view AI as potentially conflicting with Islamic values, particularly regarding data privacy and
human oversight. Solution: Community education programs, led by Islamic scholars and
financial institutions, can highlight Green AI’s alignment with Maqasid al-Shariah. Workshops
95
optimize mudarabah contracts for renewable energy projects by predicting returns with low-
energy algorithms, ensuring equitable risk-sharing while minimizing environmental harm. This
alignment addresses qualitative themes from interviews, such as “ethical AI alignment,” where
experts emphasized the need for technologies that reflect Islamic values. By reducing
computational waste, Green AI supports the prohibition of israf and darar (harm), positioning it
as a tool for ethical finance innovation (Iqbal &Mirakhor, 2011).
Environmental Impact
Green AI supports environmental sustainability by reducing carbon footprints and enabling green
investments in Islamic portfolios. Simulations showed that Green AI-optimized sukuk portfolios
for renewable energy projects could achieve 15-30% lower emissions than conventional AI
approaches, aligning with environmental, social, and governance (ESG) criteria. This supports
the growing trend of green sukuk, which reached $50 billion in issuances in 2023 (Refinitiv,
2023), funding projects like solar farms and water conservation initiatives. Additionally, Green
AI’s applications in halal agriculture, such as water optimization, contribute to sustainable
resource management in climate-vulnerable regions, where OIC countries face projected
agricultural yield declines of 20% by 2050 (IPCC, 2022). These findings highlight Green AI’s
role in advancing Islamic finance’s contribution to global sustainability goals, such as SDG 13
(Climate Action).
5.3. Challenges and Solutions
Technological Barriers
Interviews identified limited AI infrastructure in developing markets as a significant barrier to
Green AI adoption. Many OIC countries lack the computational resources and skilled personnel
needed to implement advanced AI systems. For instance, experts noted that small Islamic banks
in Indonesia struggle with outdated IT systems, hindering the deployment of energy-efficient
algorithms. Solution: Cloud-based Green AI platforms, such as those offered by Google Cloud or
Microsoft Azure, with low-energy AI frameworks, can provide scalable access to sparse neural
networks and federated learning. These platforms reduce the need for on-premises infrastructure,
enabling smaller institutions to adopt Green AI cost-effectively (Yang et al., 2019).
Regulatory Challenges
The absence of Sharia-compliant AI standards poses a regulatory hurdle. Experts highlighted that
current AI governance frameworks often overlook Islamic ethical requirements, such as
transparency in algorithmic decision-making to avoid gharar (uncertainty). Solution: The
Organization of Islamic Cooperation (OIC) could lead the development of Sharia-compliant AI
standards, drawing on models like Malaysia’s Value-Based Intermediation (VBI) framework.
These standards would ensure that Green AI algorithms adhere to justice, equity, and
environmental stewardship principles, facilitating regulatory approval across OIC countries
(Islamic Financial Services Board, 2024).
Cultural Considerations
Skepticism toward AI, rooted in concerns about its ethical implications and perceived complexity,
emerged as a cultural barrier. Experts noted that some stakeholders in the Muslim community
view AI as potentially conflicting with Islamic values, particularly regarding data privacy and
human oversight. Solution: Community education programs, led by Islamic scholars and
financial institutions, can highlight Green AI’s alignment with Maqasid al-Shariah. Workshops
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
96
and campaigns could demonstrate how Green AI supports hifz al-bi'ah and equitable resource
management, building trust and acceptance. For example, pilot projects showcasing successful
Green AI applications in halal industries could bridge cultural gaps.
Discussion
The findings validate the theoretical framework by demonstrating Green AI’s potential to
enhance sustainable resource management in Islamic finance. The 40% reduction in
computational energy and 15-30% decrease in portfolio emissions underscore Green AI’s
practical viability, while its alignment with Maqasid al-Shariah and ESG criteria advances
ethical finance theory. The halal agriculture case study illustrates actionable applications, such as
water optimization, that address pressing environmental challenges in OIC countries. However,
technological, regulatory, and cultural barriers highlight the need for coordinated efforts among
stakeholders—banks, regulators, and communities—to realize Green AI’s full potential. These
findings contribute to practice by offering deployable tools, such as sparse neural network code
snippets for mudarabah optimization, and policy by recommending OIC-led frameworks and
incentives, such as tax breaks for Green AI adoption.
6. CONCLUSION
This section synthesizes the key findings from the study on integrating Green AI into Islamic
finance for sustainable resource management. It highlights the alignment of Green AI with Sharia
principles, its contributions to sustainable development, and the policy implications for fostering
its adoption. Finally, it proposes directions for future research to advance this interdisciplinary
field further.
6.1. Summary of Key Findings
The study demonstrates that Green AI—characterized by energy-efficient algorithms like sparse
neural networks and federated learning—offers significant potential to optimize resource
management in Islamic finance while adhering to Sharia principles. Simulations revealed that
Green AI reduced computational energy by 40%, leading to 15-30% lower carbon emissions in
sukuk portfolios for renewable energy projects, aligning with the Islamic prohibition of waste
(israf) and harm (darar). A hypothetical case study in halal agriculture showed that Green AI
achieved 92% accuracy in predicting water usage, reducing consumption by 20% (from 1000
m³/month to 800 m³/month), supporting hifz al-bi'ah (environmental preservation) within
Maqasid al-Shariah. These findings confirm Green AI’s ability to enhance resource efficiency in
sectors critical to Muslim-majority economies, such as agriculture and energy, while maintaining
ethical compliance.
Moreover, Green AI contributes to sustainable development by mobilizing ethical investments for
ecological goals. By optimizing Sharia-compliant instruments like mudarabah and musharakah
for green projects, Green AI supports the growing trend of green sukuk, which reached $50
billion in issuances in 2023 (Refinitiv, 2023). This aligns with global sustainability imperatives,
such as SDG 13 (Climate Action), and addresses pressing environmental challenges in OIC
countries, where climate change threatens agricultural yields by up to 20% by 2050 (IPCC,
2022). The integration of Green AI enhances financial efficiency and positions Islamic finance as
a leader in ethical and sustainable investment, bridging the gap between environmental
stewardship and economic growth.
96
and campaigns could demonstrate how Green AI supports hifz al-bi'ah and equitable resource
management, building trust and acceptance. For example, pilot projects showcasing successful
Green AI applications in halal industries could bridge cultural gaps.
Discussion
The findings validate the theoretical framework by demonstrating Green AI’s potential to
enhance sustainable resource management in Islamic finance. The 40% reduction in
computational energy and 15-30% decrease in portfolio emissions underscore Green AI’s
practical viability, while its alignment with Maqasid al-Shariah and ESG criteria advances
ethical finance theory. The halal agriculture case study illustrates actionable applications, such as
water optimization, that address pressing environmental challenges in OIC countries. However,
technological, regulatory, and cultural barriers highlight the need for coordinated efforts among
stakeholders—banks, regulators, and communities—to realize Green AI’s full potential. These
findings contribute to practice by offering deployable tools, such as sparse neural network code
snippets for mudarabah optimization, and policy by recommending OIC-led frameworks and
incentives, such as tax breaks for Green AI adoption.
6. CONCLUSION
This section synthesizes the key findings from the study on integrating Green AI into Islamic
finance for sustainable resource management. It highlights the alignment of Green AI with Sharia
principles, its contributions to sustainable development, and the policy implications for fostering
its adoption. Finally, it proposes directions for future research to advance this interdisciplinary
field further.
6.1. Summary of Key Findings
The study demonstrates that Green AI—characterized by energy-efficient algorithms like sparse
neural networks and federated learning—offers significant potential to optimize resource
management in Islamic finance while adhering to Sharia principles. Simulations revealed that
Green AI reduced computational energy by 40%, leading to 15-30% lower carbon emissions in
sukuk portfolios for renewable energy projects, aligning with the Islamic prohibition of waste
(israf) and harm (darar). A hypothetical case study in halal agriculture showed that Green AI
achieved 92% accuracy in predicting water usage, reducing consumption by 20% (from 1000
m³/month to 800 m³/month), supporting hifz al-bi'ah (environmental preservation) within
Maqasid al-Shariah. These findings confirm Green AI’s ability to enhance resource efficiency in
sectors critical to Muslim-majority economies, such as agriculture and energy, while maintaining
ethical compliance.
Moreover, Green AI contributes to sustainable development by mobilizing ethical investments for
ecological goals. By optimizing Sharia-compliant instruments like mudarabah and musharakah
for green projects, Green AI supports the growing trend of green sukuk, which reached $50
billion in issuances in 2023 (Refinitiv, 2023). This aligns with global sustainability imperatives,
such as SDG 13 (Climate Action), and addresses pressing environmental challenges in OIC
countries, where climate change threatens agricultural yields by up to 20% by 2050 (IPCC,
2022). The integration of Green AI enhances financial efficiency and positions Islamic finance as
a leader in ethical and sustainable investment, bridging the gap between environmental
stewardship and economic growth.
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
97
6.2. Policy Implications
The findings underscore the need for regulatory frameworks to support Green AI adoption in
Islamic finance. Malaysia’s Value-Based Intermediation (VBI) framework, which emphasizes
social and environmental impact, provides a model for mandating Green AI in Islamic financial
institutions. Regulators in OIC countries should develop Sharia-compliant AI standards to ensure
transparency and ethical alignment, addressing concerns like gharar (uncertainty) in algorithmic
decision-making. For instance, guidelines could mandate using interpretable AI models to
maintain trust and compliance (Islamic Financial Services Board, 2024).
Additionally, tax incentives and subsidies for OIC banks adopting low-energy AI solutions can
accelerate implementation. Such incentives could offset initial costs for cloud-based Green AI
platforms, enabling smaller institutions in developing markets to access advanced technologies.
By fostering a supportive policy environment, regulators can enhance Islamic finance’s
contribution to sustainable development, aligning with global green finance initiatives like the
EU Taxonomy for Sustainable Activities (2020). These measures would encourage the
mobilization of ethical investments, reinforcing Islamic finance’s role in addressing climate
challenges.
6.3. Future Research Directions
While this study provides a robust framework, further research is needed to validate and expand
its findings. Empirical studies should assess Green AI’s financial impact in Islamic banks, using
real-time data from deployments in institutions like Bank Muamalat or UAE-based banks. Such
studies could quantify ROI and cost savings from Green AI-optimized portfolios, providing
concrete evidence for adoption. Additionally, exploring blockchain technology for transparent
Green AI audits could enhance trust and accountability. Blockchain’s decentralized ledger could
record AI model decisions, ensuring Sharia compliance and transparency in resource allocation.
Future research could also investigate Green AI’s scalability across diverse OIC markets,
addressing variations in technological infrastructure and cultural attitudes. These directions
would strengthen the theoretical and practical contributions of Green AI to Islamic finance,
fostering a sustainable and ethical financial ecosystem.
REFERENCES
[1] Al-Jarhi, M. A. (2008). Islamic finance: The challenges of globalizing an Islamic concept. The
Journal of Islamic Economics, 21(3), 22–34.
[2] Alam, N., Hamid, B. A., & Tan, S. Y. (2022). Artificial intelligence and machine learning in Islamic
finance: A systematic review. Journal of Islamic Marketing, 13(8), 1675–1696.
https://doi.org/10.1108/JIMA-08-2020-0240
[3] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in
Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
[4] Chapra, M. U. (2008). The Islamic vision of development in the light of Maqasid al-Shariah. Islamic
Research and Training Institute.
[5] Dusuki, A. W. (2008). What does Islam say about corporate social responsibility? Review of Islamic
Economics, 12(2), 56–78.
[6] Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone
Publishing.
[7] EU Taxonomy for Sustainable Activities. (2020). Regulation (EU) 2020/852. European
Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32020R0852
[8] Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
[9] Global Sustainable Investment Alliance. (2020). Global Sustainable Investment Review 2020.
GSIA. https://www.gsi-alliance.org/wp-content/uploads/2021/08/GSIR-20201.pdf
97
6.2. Policy Implications
The findings underscore the need for regulatory frameworks to support Green AI adoption in
Islamic finance. Malaysia’s Value-Based Intermediation (VBI) framework, which emphasizes
social and environmental impact, provides a model for mandating Green AI in Islamic financial
institutions. Regulators in OIC countries should develop Sharia-compliant AI standards to ensure
transparency and ethical alignment, addressing concerns like gharar (uncertainty) in algorithmic
decision-making. For instance, guidelines could mandate using interpretable AI models to
maintain trust and compliance (Islamic Financial Services Board, 2024).
Additionally, tax incentives and subsidies for OIC banks adopting low-energy AI solutions can
accelerate implementation. Such incentives could offset initial costs for cloud-based Green AI
platforms, enabling smaller institutions in developing markets to access advanced technologies.
By fostering a supportive policy environment, regulators can enhance Islamic finance’s
contribution to sustainable development, aligning with global green finance initiatives like the
EU Taxonomy for Sustainable Activities (2020). These measures would encourage the
mobilization of ethical investments, reinforcing Islamic finance’s role in addressing climate
challenges.
6.3. Future Research Directions
While this study provides a robust framework, further research is needed to validate and expand
its findings. Empirical studies should assess Green AI’s financial impact in Islamic banks, using
real-time data from deployments in institutions like Bank Muamalat or UAE-based banks. Such
studies could quantify ROI and cost savings from Green AI-optimized portfolios, providing
concrete evidence for adoption. Additionally, exploring blockchain technology for transparent
Green AI audits could enhance trust and accountability. Blockchain’s decentralized ledger could
record AI model decisions, ensuring Sharia compliance and transparency in resource allocation.
Future research could also investigate Green AI’s scalability across diverse OIC markets,
addressing variations in technological infrastructure and cultural attitudes. These directions
would strengthen the theoretical and practical contributions of Green AI to Islamic finance,
fostering a sustainable and ethical financial ecosystem.
REFERENCES
[1] Al-Jarhi, M. A. (2008). Islamic finance: The challenges of globalizing an Islamic concept. The
Journal of Islamic Economics, 21(3), 22–34.
[2] Alam, N., Hamid, B. A., & Tan, S. Y. (2022). Artificial intelligence and machine learning in Islamic
finance: A systematic review. Journal of Islamic Marketing, 13(8), 1675–1696.
https://doi.org/10.1108/JIMA-08-2020-0240
[3] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in
Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
[4] Chapra, M. U. (2008). The Islamic vision of development in the light of Maqasid al-Shariah. Islamic
Research and Training Institute.
[5] Dusuki, A. W. (2008). What does Islam say about corporate social responsibility? Review of Islamic
Economics, 12(2), 56–78.
[6] Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone
Publishing.
[7] EU Taxonomy for Sustainable Activities. (2020). Regulation (EU) 2020/852. European
Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32020R0852
[8] Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
[9] Global Sustainable Investment Alliance. (2020). Global Sustainable Investment Review 2020.
GSIA. https://www.gsi-alliance.org/wp-content/uploads/2021/08/GSIR-20201.pdf
International Journal on Cybernetics & Informatics (IJCI) Vol.14, No.5, October 2025
98
[10] Goodell, J. W., Kumar, S., Lim, W. M., &Pattnaik, D. (2021). Artificial intelligence and machine
learning in finance: Identifying foundations, themes, and research clusters from bibliometric
analysis. Journal of Behavioral and Experimental Finance, 32 , Article 100577.
https://doi.org/10.1016/j.jbef.2021.100577
[11] Hassan, M. K., Aliyu, S., Huda, M., & Rashid, M. (2021). A survey on Islamic finance and
accounting standards. Borsa Istanbul Review, 21 (2), 113–129.
https://doi.org/10.1016/j.bir.2020.07.003
[12] IPCC. (2022). Climate change 2022: Impacts, adaptation, and vulnerability. Intergovernmental
Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg2/
[13] Iqbal, Z., &Mirakhor, A. (2011). An introduction to Islamic finance: Theory and practice (2nd ed.).
Wiley. https://doi.org/10.1002/9781118390474
[14] Islamic Financial Services Board. (2024). Islamic Financial Services Industry Stability Report 2024.
https://www.ifsb.org
[15] Li, J., Li, N., & Cheng, X. (2022). The impact of AI on sustainable agriculture: A review.
Sustainability, 14(3), Article 1345. https://doi.org/10.3390/su14031345
[16] Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
[17] O’Neil, M., Zhang, Y., & O’Sullivan, K. (2020). AI applications for environmental sustainability.
Nature Sustainability, 3(12), 909–917. https://doi.org/10.1038/s41893-020-00626-8
[18] Parker, R., Lee, A., & Chen, W. (2021). Green AI: Harnessing artificial intelligence for sustainable
development. AI for Sustainable Development, 1(2), 34–45.
[19] Rashid, M. (2020). Ethical investing in Islamic finance: A critical evaluation. Journal of Islamic
Banking and Finance, 37(4), 85–101.Refinitiv. (2023). Islamic Finance Development Report
[20] Zahid, Z. (2025).Integrating FinTech Solutions in Agribusiness: A Pathway to a Sustainable
Economy in Bangladesh (May 2025), Journal of Communications on Applied Nonlinear Analysis,
DOI: https://doi.org/10.52783/cana.v32.5338
98
[10] Goodell, J. W., Kumar, S., Lim, W. M., &Pattnaik, D. (2021). Artificial intelligence and machine
learning in finance: Identifying foundations, themes, and research clusters from bibliometric
analysis. Journal of Behavioral and Experimental Finance, 32 , Article 100577.
https://doi.org/10.1016/j.jbef.2021.100577
[11] Hassan, M. K., Aliyu, S., Huda, M., & Rashid, M. (2021). A survey on Islamic finance and
accounting standards. Borsa Istanbul Review, 21 (2), 113–129.
https://doi.org/10.1016/j.bir.2020.07.003
[12] IPCC. (2022). Climate change 2022: Impacts, adaptation, and vulnerability. Intergovernmental
Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg2/
[13] Iqbal, Z., &Mirakhor, A. (2011). An introduction to Islamic finance: Theory and practice (2nd ed.).
Wiley. https://doi.org/10.1002/9781118390474
[14] Islamic Financial Services Board. (2024). Islamic Financial Services Industry Stability Report 2024.
https://www.ifsb.org
[15] Li, J., Li, N., & Cheng, X. (2022). The impact of AI on sustainable agriculture: A review.
Sustainability, 14(3), Article 1345. https://doi.org/10.3390/su14031345
[16] Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
[17] O’Neil, M., Zhang, Y., & O’Sullivan, K. (2020). AI applications for environmental sustainability.
Nature Sustainability, 3(12), 909–917. https://doi.org/10.1038/s41893-020-00626-8
[18] Parker, R., Lee, A., & Chen, W. (2021). Green AI: Harnessing artificial intelligence for sustainable
development. AI for Sustainable Development, 1(2), 34–45.
[19] Rashid, M. (2020). Ethical investing in Islamic finance: A critical evaluation. Journal of Islamic
Banking and Finance, 37(4), 85–101.Refinitiv. (2023). Islamic Finance Development Report
[20] Zahid, Z. (2025).Integrating FinTech Solutions in Agribusiness: A Pathway to a Sustainable
Economy in Bangladesh (May 2025), Journal of Communications on Applied Nonlinear Analysis,
DOI: https://doi.org/10.52783/cana.v32.5338
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