AI-Driven Sustainable Architectures in Modern Information Systems: Aligning Technological Infrastructure with Financial Risk Management and Green Consumer Dynamics
Jul 2026· Journal of Computer Science and Information Technology· Vol 3, pp. 93-103· 0 citations· 45 references
TL;DR
The study examines the potential of AI technologies to improve the design optimization, resource efficiency, and life cycle sustainability in the fields of architecture and construction and explores the potential of smart systems, the Internet of Things (IoT) and digital twin technologies in the further development of sustainable construction methods.
Abstract
AI based sustainable architectures is an emerging interdisciplinary approach that combines sustainable design principles, modern information systems, financial risk management and artificial intelligence, solving modern environmental and economic challenges. The study examines the potential of AI technologies to improve the design optimization, resource efficiency, and life cycle sustainability in the fields of architecture and construction. AI helps in informed decision-making by leveraging data-driven modeling, predictive analytics, and intelligent automation, which minimizes environmental effects and enhances building performance in areas such as material selection, energy management, and structural planning. The study also explores the potential of smart systems, the Internet of Things (IoT) and digital twin technologies in the further development of sustainable construction methods. These technologies enable monitoring, predictive maintenance and adaptive control of building systems in real-time, which leads to lower operating costs and better energy efficiency. Furthermore, the study emphasizes that lifecycle aspects such as maintenance, deconstruction, and material re-use are crucial, contributing to the principles of circular economy in the built environment. In addition, the conversation includes topics of financial risk management and green consumerism. AI can improve risk assessments by analyzing credit, market, and operational risks in real-time, as well as integrating environmental, social, and governance (ESG) considerations into financial decision-making. Moreover, AI has the potential to shape consumer behavior by facilitating personalized sustainability communication and improving green marketing efforts, which foster sustainable consumption. Additionally, AI plays a role in influencing consumer behavior by enabling personalized sustainability communication and enhancing green marketing strategies, promoting sustainable consumption.
Artificial Intelligence (AI) is transforming the global business landscape by enabling organizations to improve efficiency, innovation, and sustainability performance. In recent years, businesses have increasingly integrated AI technologies into their operational and strategic activities to strengthen Environmental, Social, and Governance (ESG) practices and contribute toward achieving the United Nations Sustainable Development Goals (SDGs). This paper examines the role of AI in enabling sustainable business transformation and enhancing ESG performance.The study is conceptual in nature and is based on secondary data collected from journal articles, sustainability reports, industry publications, and global case examples. The research highlights how AI-driven technologies such as machine learning, predictive analytics, intelligent automation, and data analytics support organizations in resource optimization, carbon emission reduction, supply chain resilience, ethical governance, and stakeholder engagement. The findings indicate that AI can significantly improve sustainability performance by enabling data-driven decision-making, operational transparency, and efficient resource management. AI applications in smart energy systems, waste reduction, workforce management, and ESG reporting are helping organizations align business goals with sustainability objectives. Companies such as Microsoft, Tesla, and Unilever demonstrate how AI can be strategically integrated into sustainable business models to create long-term economic, environmental, and social value.However, the study also identifies key challenges including data privacy concerns, algorithmic bias, ethical risks, implementation costs, and workforce displacement. Therefore, organizations must adopt responsible AI governance frameworks and ensure transparency, accountability, and inclusiveness in AI adoption.
S. R, Neetha Veronica A, Arpita Sastri et al.· International journal of com...· 0 citations
This study explores the role of artificial intelligence driven smart energy management system as a tool for
addressing rising urban energy demand and promoting sustainable development in developing countries, with specific
focus on Nigeria. The study revealed that rapid urbanization has placed significant strain on conventional power
infrastructure, resulting in inefficiencies, high operational costs, frequent outages, and increased carbon emissions. To
respond to these challenges, the paper examined the design and application of an AI-powered platform that integrates
data from IoT sensors, smart meters, and weather stations to support real time energy monitoring and decision making.
The study analyzed existing utility operations and technical frameworks to identify key system requirements and data
workflows that inform the development of an intelligent energy management solution. Using Objection-Oriented Analysis
and Design Methodology (OOADM) and Unified Modeling Language (UML), the study proposed modular and scalable
system architecture capable of demand forecasting, grid anomaly detection, predictive maintenance, and optimized
integration of renewable energy sources such as solar and wind. The findings indicated that the adoption of AI-driven
energy management systems can substantially reduce peak energy load, minimize unplanned outages, lower maintenance
costs, and cut carbon emissions, while providing grid operators and policymakers with accurate, real time insights. The
paper concludes that intelligent energy management solutions are critical for improving efficiency, strengthening energy
security, and supporting sustainable national development in Nigeria and similar developing economies.
Stella Ebere Edeh, C. Ituma, Maduabuchi Ignatius Edeh et al.· International Journal of Inn...· 0 citations
The use of AI is transforming supply chain management, moving from a reactive to a proactive, adaptive, and data-driven approach. This research explores the impact of AI in the supply chain on enhancing operational efficiency and fostering sustainability in today's business landscape. The focus of the research is in the application of machine learning, predictive analysis, smart automation, Internet of Things (IoT) connected systems, demand forecasting, inventory optimization, route planning and real-time tracking of the supply chain network. AI can help with more accurate demand forecasting, curbing overstocking, transportation inefficiencies, optimizing resource usage and enhancing responsiveness to market changes. Concurrently, AI-powered supply chain systems can play a role in sustainability by minimizing material waste, optimizing energy usage, curbing unnecessary transportation, and promoting smart utilization of natural resources. The benefits of AI are also reliant on the quality of data, technology, organization, employee skills, cybersecurity, and responsible data management practices. Part particular special attention is given to the connection between technical efficiency and environmental concern – excessive dependence on computational technologies can create energy and resource problems. The paper, therefore, argues that AI is a strategic enabler, rather than a solution on its own to the problem of supply chain. A combination of intelligent technologies and sustainable business practices adds to the resilience of supply chains, improves economic outcomes and helps an organisation better meet evolving customer and environmental needs. In conclusion, the study highlights the potential of AI to revolutionize supply chain management practices, enabling businesses to build efficient, sustainable, resilient, and transparent supply chains.
Irshadullah Asim Mohammed, Sunakshi Verma, J. Srinivasan et al.· International journal of com...· 0 citations
The rapid advancement of digital technologies has fundamentally transformed how small and medium-sized enterprises (SMEs) make strategic decisions and expand into international markets. This review synthesizes current evidence on the role of artificial intelligence (AI), machine learning (ML), information systems, engineering applications, Industry 4.0 technologies, and big data analytics in supporting smart decision-making for SME internationalization. The article examines the theoretical foundations of SME internationalization, the evolution of AI-enabled decision support, digital transformation strategies, intelligent manufacturing, supply chain digitalization, and integrated decision-making frameworks that enhance organizational agility and competitiveness. It further evaluates the organizational factors influencing technology adoption, including AI readiness, data quality, knowledge management, and dynamic capabilities, while discussing key implementation challenges related to governance, resource constraints, and technological integration. A comparative synthesis of recent studies highlights how integrated digital capabilities improve strategic planning, market intelligence, operational efficiency, risk management, and innovation performance. The review concludes that smart decision-making frameworks provide SMEs with a comprehensive foundation for sustainable digital transformation and international growth by combining intelligent technologies with organizational capabilities. The findings offer a consolidated perspective for researchers, practitioners, and policymakers seeking to strengthen evidence-based managerial decision-making and enhance the global competitiveness of SMEs.
Sevdie Alshiqi, Mehdi Safaei, Juan de Dios Aguilar Sánchez et al.· Journal of Intelligent Decis...· 0 citations
The global energy sector is in the process of making a dual transition towards decarbonization and digitalization. This paper discusses how the fundamental digital technologies, Internet of Things (IoT), Artificial Intelligence (AI), machine learning, digital twins, edge computing, and blockchain facilitate environmental objectives in the energy sector by enhancing efficiency, integrating variable renewables, lowering emissions, and enhancing system resilience. Studies are reviewed to evaluate five pillars of applications, (i) integration of renewable energy through high‑fidelity forecasting, smart inverters, and AI‑aided dispatch, (ii) smart grids that employ ubiquitous sensing, automation, and edge intelligence for real‑time stability, demand response, and losses reduction, (iii) end‑use energy efficiency in buildings and industry through data‑driven controls and digital twin-based optimization, (iv) predictive maintenance of generation and network assets via condition monitoring and fault‑prediction models to reduce downtime and resource waste, and (v) emissions tracking and carbon management through IoT‑enabled monitoring, AI analytics, and blockchain‑backed certificates and markets. Observed benefits encompass double digit gains in energy efficiency, increased renewable penetration without reliability loss, quantifiable decreases in curtailment and peaking demand, and enhanced transparency in carbon accounting. Constrains such as cybersecurity, data privacy, interoperability, worker skills, and the energy profile of digital infrastructure are assessed with mitigation techniques such as privacy-preserving analytics, standard adoption, edge processing, and low-energy consensus mechanisms. Looking ahead, development in AI, IoT, digital-twin grids, 5G/6G‑facilitated edge coordination, sector coupling, and trusted decentralized markets will bring increasingly autonomous, adaptive, and verifiably low-carbon energy systems. Digitalization thereby presents itself as a catalyst and control layer for realizing scalable, equitable, and resilient decarbonization.
Sanjana Santra, B. Kumar· Environment, Social and Gove...· 1 citation
The growing demand for environmentally sustainable logistics has increased the need for cutting-edge solutions that enhance operational efficiency while reducing environmental impacts. This paper examines how artificial intelligence (AI) can contribute to environmental sustainability and green logistics and proposes a policy framework to accelerate sustainable logistics development in Nigeria. A qualitative review approach was adopted, drawing on contemporary academic literature, international sustainability reports, and industry case studies on AI-enabled logistics. The literature was systematically synthesized using thematic analysis to identify recurring themes, applications, benefits, challenges, and policy requirements associated with AI adoption in green logistics. The findings indicate that AI technologies, including machine learning, predictive analytics, the Internet of Things (IoT), big data analytics, digital twins, robotics, autonomous vehicles, computer vision, and natural language processing, can significantly improve logistics performance through predictive maintenance, route optimization, demand forecasting, inventory and warehouse management, reverse logistics, and continuous carbon-emission monitoring. These applications enhance supply chain efficiency and resilience while reducing fuel consumption, greenhouse gas emissions, operating costs, and waste. However, major barriers to AI adoption include inadequate digital infrastructure, high implementation costs, cybersecurity risks, limited skilled human resources, and weak regulatory frameworks. In response, the study proposes a conceptual policy framework centred on investment in digital infrastructure, AI governance, public-private partnerships, research and innovation, and capacity building. The framework aligns with the United Nations Sustainable Development Goals (SDGs) 9, 11, 12, 13, and 17. The paper concludes that AI represents a strategic enabler of green logistics and offers practical policy recommendations for researchers, industry practitioners, and governments seeking to accelerate the transition towards sustainable logistics in Nigeria and other emerging economies.
Unknown authors· Journal of Science Innovatio...· 0 citations
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